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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert GIT checkpoints from the original repository.
URL: https://github.com/microsoft/GenerativeImage2Text/tree/main"""
import argparse
from pathlib import Path
import av
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
AutoTokenizer,
CLIPImageProcessor,
GitConfig,
GitForCausalLM,
GitProcessor,
GitVisionConfig,
VideoMAEImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_git_config(model_name):
if "base" in model_name and "vqa" in model_name:
image_size = 480
elif "large" in model_name and "vqa" in model_name:
image_size = 420
else:
image_size = 224
vision_config = GitVisionConfig(image_size=image_size)
if "large" in model_name:
vision_config.patch_size = 14
vision_config.hidden_size = 1024
vision_config.intermediate_size = 4096
vision_config.num_hidden_layers = 24
vision_config.num_attention_heads = 16
is_video = "vatex" in model_name or "msrvtt" in model_name
num_image_with_embedding = 6 if is_video else None
config = GitConfig(vision_config=vision_config.to_dict(), num_image_with_embedding=num_image_with_embedding)
return config, image_size, is_video
# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config, prefix=""):
rename_keys = []
# image encoder
# ftm: off
rename_keys.append(
(f"{prefix}image_encoder.class_embedding", "git.image_encoder.vision_model.embeddings.class_embedding")
)
rename_keys.append(
(
f"{prefix}image_encoder.positional_embedding",
"git.image_encoder.vision_model.embeddings.position_embedding.weight",
)
)
rename_keys.append(
(f"{prefix}image_encoder.conv1.weight", "git.image_encoder.vision_model.embeddings.patch_embedding.weight")
)
rename_keys.append((f"{prefix}image_encoder.ln_pre.weight", "git.image_encoder.vision_model.pre_layrnorm.weight"))
rename_keys.append((f"{prefix}image_encoder.ln_pre.bias", "git.image_encoder.vision_model.pre_layrnorm.bias"))
rename_keys.append(
(f"{prefix}image_encoder.ln_post.weight", "git.image_encoder.vision_model.post_layernorm.weight")
)
rename_keys.append((f"{prefix}image_encoder.ln_post.bias", "git.image_encoder.vision_model.post_layernorm.bias"))
# fmt: on
rename_keys.append((f"{prefix}image_encoder.proj", "git.image_encoder.visual_projection.weight"))
# fmt: off
for i in range(config.vision_config.num_hidden_layers):
# image encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.attn.out_proj.weight", f"git.image_encoder.vision_model.encoder.layers.{i}.self_attn.out_proj.weight"))
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.attn.out_proj.bias", f"git.image_encoder.vision_model.encoder.layers.{i}.self_attn.out_proj.bias"))
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.ln_1.weight", f"git.image_encoder.vision_model.encoder.layers.{i}.layer_norm1.weight"))
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.ln_1.bias", f"git.image_encoder.vision_model.encoder.layers.{i}.layer_norm1.bias"))
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.mlp.c_fc.weight", f"git.image_encoder.vision_model.encoder.layers.{i}.mlp.fc1.weight"))
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.mlp.c_fc.bias", f"git.image_encoder.vision_model.encoder.layers.{i}.mlp.fc1.bias"))
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.mlp.c_proj.weight", f"git.image_encoder.vision_model.encoder.layers.{i}.mlp.fc2.weight"))
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.mlp.c_proj.bias", f"git.image_encoder.vision_model.encoder.layers.{i}.mlp.fc2.bias"))
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.ln_2.weight", f"git.image_encoder.vision_model.encoder.layers.{i}.layer_norm2.weight"))
rename_keys.append((f"{prefix}image_encoder.transformer.resblocks.{i}.ln_2.bias", f"git.image_encoder.vision_model.encoder.layers.{i}.layer_norm2.bias"))
# fmt: on
# text decoder
# fmt: off
rename_keys.append((f"{prefix}textual.embedding.words.weight", "git.embeddings.word_embeddings.weight"))
rename_keys.append((f"{prefix}textual.embedding.positions.weight", "git.embeddings.position_embeddings.weight"))
rename_keys.append((f"{prefix}textual.visual_projection.0.weight", "git.visual_projection.visual_projection.0.weight"))
rename_keys.append((f"{prefix}textual.visual_projection.0.bias", "git.visual_projection.visual_projection.0.bias"))
rename_keys.append((f"{prefix}textual.visual_projection.1.weight", "git.visual_projection.visual_projection.1.weight"))
rename_keys.append((f"{prefix}textual.visual_projection.1.bias", "git.visual_projection.visual_projection.1.bias"))
rename_keys.append((f"{prefix}textual.embedding.layer_norm.weight", "git.embeddings.LayerNorm.weight"))
rename_keys.append((f"{prefix}textual.embedding.layer_norm.bias", "git.embeddings.LayerNorm.bias"))
rename_keys.append((f"{prefix}textual.output.weight", "output.weight"))
rename_keys.append((f"{prefix}textual.output.bias", "output.bias"))
for i in range(config.num_hidden_layers):
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.self.query.weight", f"git.encoder.layer.{i}.attention.self.query.weight"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.self.query.bias", f"git.encoder.layer.{i}.attention.self.query.bias"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.self.key.weight", f"git.encoder.layer.{i}.attention.self.key.weight"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.self.key.bias", f"git.encoder.layer.{i}.attention.self.key.bias"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.self.value.weight", f"git.encoder.layer.{i}.attention.self.value.weight"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.self.value.bias", f"git.encoder.layer.{i}.attention.self.value.bias"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.output.dense.weight", f"git.encoder.layer.{i}.attention.output.dense.weight"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.output.dense.bias", f"git.encoder.layer.{i}.attention.output.dense.bias"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.output.LayerNorm.weight", f"git.encoder.layer.{i}.attention.output.LayerNorm.weight"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.attention.output.LayerNorm.bias", f"git.encoder.layer.{i}.attention.output.LayerNorm.bias"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.intermediate.dense.weight", f"git.encoder.layer.{i}.intermediate.dense.weight"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.intermediate.dense.bias", f"git.encoder.layer.{i}.intermediate.dense.bias"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.output.dense.weight", f"git.encoder.layer.{i}.output.dense.weight"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.output.dense.bias", f"git.encoder.layer.{i}.output.dense.bias"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.output.LayerNorm.weight", f"git.encoder.layer.{i}.output.LayerNorm.weight"))
rename_keys.append((f"{prefix}textual.transformer.encoder.layer.{i}.output.LayerNorm.bias", f"git.encoder.layer.{i}.output.LayerNorm.bias"))
# fmt: on
if config.num_image_with_embedding is not None:
rename_keys.append(("img_temperal_embedding.0", "git.img_temperal_embedding.0"))
rename_keys.append(("img_temperal_embedding.1", "git.img_temperal_embedding.1"))
rename_keys.append(("img_temperal_embedding.2", "git.img_temperal_embedding.2"))
rename_keys.append(("img_temperal_embedding.3", "git.img_temperal_embedding.3"))
rename_keys.append(("img_temperal_embedding.4", "git.img_temperal_embedding.4"))
rename_keys.append(("img_temperal_embedding.5", "git.img_temperal_embedding.5"))
return rename_keys
def rename_key(dct, old, new):
val = dct.pop(old)
dct[new] = val.T if "image_encoder.visual_projection" in new else val
# we split up the matrix of each CLIP encoder layer into queries, keys and values
def read_in_q_k_v(state_dict, config, prefix=""):
dim = config.vision_config.hidden_size
for i in range(config.vision_config.num_hidden_layers):
# read in weights + bias of input projection layer (in the original implementation, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"{prefix}image_encoder.transformer.resblocks.{i}.attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"{prefix}image_encoder.transformer.resblocks.{i}.attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"git.image_encoder.vision_model.encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[
:dim, :
]
state_dict[f"git.image_encoder.vision_model.encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:dim]
state_dict[f"git.image_encoder.vision_model.encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[
dim : dim * 2, :
]
state_dict[f"git.image_encoder.vision_model.encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[
dim : dim * 2
]
state_dict[f"git.image_encoder.vision_model.encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[
-dim:, :
]
state_dict[f"git.image_encoder.vision_model.encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-dim:]
# We will verify our results on an image
def prepare_img(model_name):
if "textvqa" in model_name:
filepath = hf_hub_download(repo_id="nielsr/textvqa-sample", filename="bus.png", repo_type="dataset")
image = Image.open(filepath).convert("RGB")
else:
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
return image
def prepare_video():
def read_video_pyav(container, indices):
"""
Decode the video with PyAV decoder.
Args:
container (`av.container.input.InputContainer`): PyAV container.
indices (`list[int]`): List of frame indices to decode.
Returns:
result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
"""
frames = []
container.seek(0)
start_index = indices[0]
end_index = indices[-1]
for i, frame in enumerate(container.decode(video=0)):
if i > end_index:
break
if i >= start_index and i in indices:
frames.append(frame)
return np.stack([x.to_ndarray(format="rgb24") for x in frames])
def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
"""
Sample a given number of frame indices from the video.
Args:
clip_len (`int`): Total number of frames to sample.
frame_sample_rate (`int`): Sample every n-th frame.
seg_len (`int`): Maximum allowed index of sample's last frame.
Returns:
indices (`list[int]`): List of sampled frame indices
"""
converted_len = int(clip_len * frame_sample_rate)
end_idx = np.random.randint(converted_len, seg_len)
start_idx = end_idx - converted_len
indices = np.linspace(start_idx, end_idx, num=clip_len)
indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
return indices
# set seed for reproducibility
np.random.seed(0)
file_path = hf_hub_download(repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset")
with av.open(file_path) as container:
# sample 6 frames
num_frames = 6
indices = sample_frame_indices(
clip_len=num_frames, frame_sample_rate=4, seg_len=container.streams.video[0].frames
)
frames = read_video_pyav(container, indices)
return frames
@torch.no_grad()
def convert_git_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=False):
"""
Copy/paste/tweak model's weights to our GIT structure.
"""
model_name_to_url = {
"git-base": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE/snapshot/model.pt",
"git-base-coco": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_COCO/snapshot/model.pt",
"git-base-textcaps": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_TEXTCAPS/snapshot/model.pt",
"git-base-vqav2": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_VQAv2/snapshot/model.pt",
"git-base-textvqa": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_TEXTVQA/snapshot/model.pt", # todo
"git-base-vatex": "https://publicgit.blob.core.windows.net/data/output/GIT_BASE_VATEX/snapshot/model.pt",
"git-base-msrvtt-qa": (
"https://publicgit.blob.core.windows.net/data/output/GIT_BASE_MSRVTT_QA/snapshot/model.pt"
),
"git-large": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE/snapshot/model.pt",
"git-large-coco": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_COCO/snapshot/model.pt",
"git-large-textcaps": (
"https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_TEXTCAPS/snapshot/model.pt"
),
"git-large-vqav2": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_VQAv2/snapshot/model.pt",
"git-large-textvqa": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_TEXTVQA/snapshot/model.pt",
"git-large-vatex": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_VATEX/snapshot/model.pt",
"git-large-msrvtt-qa": (
"https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_MSRVTT_QA/snapshot/model.pt"
),
"git-large-r": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_R/snapshot/model.pt",
"git-large-r-coco": "https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_R_COCO/snapshot/model.pt",
"git-large-r-textcaps": (
"https://publicgit.blob.core.windows.net/data/output/GIT_LARGE_R_TEXTCAPS/snapshot/model.pt"
),
}
model_name_to_path = {
"git-large": "/Users/nielsrogge/Documents/GIT/git_large_model.pt",
"git-large-coco": "/Users/nielsrogge/Documents/GIT/git_large_coco_model.pt",
"git-large-textcaps": "/Users/nielsrogge/Documents/GIT/git_large_textcaps_model.pt",
"git-large-vqav2": "/Users/nielsrogge/Documents/GIT/git_large_vqav2_model.pt",
"git-large-textvqa": "/Users/nielsrogge/Documents/GIT/git_large_textvqa_model.pt",
}
# define GIT configuration based on model name
config, image_size, is_video = get_git_config(model_name)
if "large" in model_name and not is_video and "large-r" not in model_name:
# large checkpoints take way too long to download
checkpoint_path = model_name_to_path[model_name]
state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=True)["model"]
else:
checkpoint_url = model_name_to_url[model_name]
state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu", file_name=model_name)[
"model"
]
# rename keys
prefix = "module." if model_name == "git-base" else ""
rename_keys = create_rename_keys(config, prefix=prefix)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_q_k_v(state_dict, config, prefix=prefix)
# load HuggingFace model
model = GitForCausalLM(config)
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
model.eval()
print("Missing keys:", missing_keys)
print("Unexpected keys:", unexpected_keys)
assert missing_keys == ["git.embeddings.position_ids", "git.image_encoder.vision_model.embeddings.position_ids"]
assert unexpected_keys == ["git.image_encoder.visual_projection.weight"]
# verify results
image_processor = (
VideoMAEImageProcessor(
size={"shortest_edge": image_size}, crop_size={"height": image_size, "width": image_size}
)
if is_video
else CLIPImageProcessor(
size={"shortest_edge": image_size}, crop_size={"height": image_size, "width": image_size}
)
)
tokenizer = AutoTokenizer.from_pretrained(
"google-bert/bert-base-uncased", model_input_names=["input_ids", "attention_mask"]
)
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
if is_video:
video = prepare_video()
pixel_values = processor(images=list(video), return_tensors="pt").pixel_values
else:
image = prepare_img(model_name)
image_transforms = Compose(
[
Resize(image_size, interpolation=Image.BICUBIC),
CenterCrop(image_size),
ToTensor(),
Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
]
)
original_pixel_values = image_transforms(image).unsqueeze(0)
pixel_values = processor(images=image, return_tensors="pt").pixel_values
assert torch.allclose(pixel_values, original_pixel_values)
input_ids = torch.tensor([[101]])
outputs = model(input_ids, pixel_values=pixel_values)
logits = outputs.logits
print("Logits:", logits[0, -1, :3])
if model_name == "git-base":
expected_slice_logits = torch.tensor([-1.2832, -1.2835, -1.2840])
elif model_name == "git-base-coco":
expected_slice_logits = torch.tensor([-0.9925, -0.9930, -0.9935])
elif model_name == "git-base-textcaps":
expected_slice_logits = torch.tensor([-1.2980, -1.2983, -1.2985])
elif model_name == "git-base-vqav2":
expected_slice_logits = torch.tensor([-0.8570, -0.8568, -0.8561])
elif model_name == "git-base-textvqa":
expected_slice_logits = torch.tensor([-1.4085, -1.4083, -1.4082])
elif model_name == "git-base-vatex":
expected_slice_logits = torch.tensor([-1.3451, -1.3447, -1.3447])
elif model_name == "git-base-msrvtt-qa":
expected_slice_logits = torch.tensor([-0.8554, -0.8550, -0.8540])
elif model_name == "git-large":
expected_slice_logits = torch.tensor([-1.1708, -1.1707, -1.1705])
elif model_name == "git-large-coco":
expected_slice_logits = torch.tensor([-1.0425, -1.0423, -1.0422])
elif model_name == "git-large-textcaps":
expected_slice_logits = torch.tensor([-1.2705, -1.2708, -1.2706])
elif model_name == "git-large-vqav2":
expected_slice_logits = torch.tensor([-0.7042, -0.7043, -0.7043])
elif model_name == "git-large-textvqa":
expected_slice_logits = torch.tensor([-0.8590, -0.8592, -0.8590])
elif model_name == "git-large-vatex":
expected_slice_logits = torch.tensor([-1.0113, -1.0114, -1.0113])
elif model_name == "git-large-msrvtt-qa":
expected_slice_logits = torch.tensor([0.0130, 0.0134, 0.0131])
elif model_name == "git-large-r":
expected_slice_logits = torch.tensor([-1.1283, -1.1285, -1.1286])
elif model_name == "git-large-r-coco":
expected_slice_logits = torch.tensor([-0.9641, -0.9641, -0.9641])
elif model_name == "git-large-r-textcaps":
expected_slice_logits = torch.tensor([-1.1121, -1.1120, -1.1124])
assert torch.allclose(logits[0, -1, :3], expected_slice_logits, atol=1e-4)
print("Looks ok!")
prompt = ""
if "textvqa" in model_name:
prompt = "what does the front of the bus say at the top?"
elif "msrvtt-qa" in model_name:
prompt = "what does the woman eat?"
elif "vqa" in model_name:
prompt = "what are the cats doing?"
input_ids = tokenizer(prompt, add_special_tokens=False).input_ids
input_ids = [processor.tokenizer.cls_token_id] + input_ids
input_ids = torch.tensor(input_ids).unsqueeze(0)
print("Generating caption...")
generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
print("Generated caption:", processor.batch_decode(generated_ids, skip_special_tokens=True))
if pytorch_dump_folder_path is not None:
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
processor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
print(f"Pushing model and processor of {model_name} to the hub...")
model.push_to_hub(f"microsoft/{model_name}")
processor.push_to_hub(f"microsoft/{model_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="git-base",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model to the hub.",
)
args = parser.parse_args()
convert_git_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| transformers/src/transformers/models/git/convert_git_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/git/convert_git_to_pytorch.py",
"repo_id": "transformers",
"token_count": 9860
} | 451 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert GLPN checkpoints."""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def rename_keys(state_dict):
new_state_dict = OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder"):
key = key.replace("module.encoder", "glpn.encoder")
if key.startswith("module.decoder"):
key = key.replace("module.decoder", "decoder.stages")
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
idx = key[key.find("patch_embed") + len("patch_embed")]
key = key.replace(f"patch_embed{idx}", f"patch_embeddings.{int(idx) - 1}")
if "norm" in key:
key = key.replace("norm", "layer_norm")
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
idx = key[key.find("glpn.encoder.layer_norm") + len("glpn.encoder.layer_norm")]
key = key.replace(f"layer_norm{idx}", f"layer_norm.{int(idx) - 1}")
if "layer_norm1" in key:
key = key.replace("layer_norm1", "layer_norm_1")
if "layer_norm2" in key:
key = key.replace("layer_norm2", "layer_norm_2")
if "block" in key:
# replace for example block1 by block.0
idx = key[key.find("block") + len("block")]
key = key.replace(f"block{idx}", f"block.{int(idx) - 1}")
if "attn.q" in key:
key = key.replace("attn.q", "attention.self.query")
if "attn.proj" in key:
key = key.replace("attn.proj", "attention.output.dense")
if "attn" in key:
key = key.replace("attn", "attention.self")
if "fc1" in key:
key = key.replace("fc1", "dense1")
if "fc2" in key:
key = key.replace("fc2", "dense2")
if "linear_pred" in key:
key = key.replace("linear_pred", "classifier")
if "linear_fuse" in key:
key = key.replace("linear_fuse.conv", "linear_fuse")
key = key.replace("linear_fuse.bn", "batch_norm")
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
idx = key[key.find("linear_c") + len("linear_c")]
key = key.replace(f"linear_c{idx}", f"linear_c.{int(idx) - 1}")
if "bot_conv" in key:
key = key.replace("bot_conv", "0.convolution")
if "skip_conv1" in key:
key = key.replace("skip_conv1", "1.convolution")
if "skip_conv2" in key:
key = key.replace("skip_conv2", "2.convolution")
if "fusion1" in key:
key = key.replace("fusion1", "1.fusion")
if "fusion2" in key:
key = key.replace("fusion2", "2.fusion")
if "fusion3" in key:
key = key.replace("fusion3", "3.fusion")
if "fusion" in key and "conv" in key:
key = key.replace("conv", "convolutional_layer")
if key.startswith("module.last_layer_depth"):
key = key.replace("module.last_layer_depth", "head.head")
new_state_dict[key] = value
return new_state_dict
def read_in_k_v(state_dict, config):
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks):
for j in range(config.depths[i]):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
kv_weight = state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.weight")
kv_bias = state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.bias")
# next, add keys and values (in that order) to the state dict
state_dict[f"glpn.encoder.block.{i}.{j}.attention.self.key.weight"] = kv_weight[
: config.hidden_sizes[i], :
]
state_dict[f"glpn.encoder.block.{i}.{j}.attention.self.key.bias"] = kv_bias[: config.hidden_sizes[i]]
state_dict[f"glpn.encoder.block.{i}.{j}.attention.self.value.weight"] = kv_weight[
config.hidden_sizes[i] :, :
]
state_dict[f"glpn.encoder.block.{i}.{j}.attention.self.value.bias"] = kv_bias[config.hidden_sizes[i] :]
# We will verify our results on a COCO image
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
return image
@torch.no_grad()
def convert_glpn_checkpoint(checkpoint_path, pytorch_dump_folder_path, push_to_hub=False, model_name=None):
"""
Copy/paste/tweak model's weights to our GLPN structure.
"""
# load GLPN configuration (Segformer-B4 size)
config = GLPNConfig(hidden_sizes=[64, 128, 320, 512], decoder_hidden_size=64, depths=[3, 8, 27, 3])
# load image processor (only resize + rescale)
image_processor = GLPNImageProcessor()
# prepare image
image = prepare_img()
pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
logger.info("Converting model...")
# load original state dict
state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"), weights_only=True)
# rename keys
state_dict = rename_keys(state_dict)
# key and value matrices need special treatment
read_in_k_v(state_dict, config)
# create HuggingFace model and load state dict
model = GLPNForDepthEstimation(config)
model.load_state_dict(state_dict)
model.eval()
# forward pass
outputs = model(pixel_values)
predicted_depth = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
expected_slice = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]
)
elif "kitti" in model_name:
expected_slice = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]
)
else:
raise ValueError(f"Unknown model name: {model_name}")
expected_shape = torch.Size([1, 480, 640])
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3], expected_slice, atol=1e-4)
print("Looks ok!")
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the hub...")
model.push_to_hub(
repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
organization="nielsr",
commit_message="Add model",
use_temp_dir=True,
)
image_processor.push_to_hub(
repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
organization="nielsr",
commit_message="Add image processor",
use_temp_dir=True,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
default=None,
type=str,
help="Path to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
parser.add_argument(
"--model_name",
default="glpn-kitti",
type=str,
help="Name of the model in case you're pushing to the hub.",
)
args = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| transformers/src/transformers/models/glpn/convert_glpn_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/glpn/convert_glpn_to_pytorch.py",
"repo_id": "transformers",
"token_count": 3807
} | 452 |
# coding=utf-8
# Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Optional
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from ...modeling_flax_outputs import (
FlaxBaseModelOutputWithPastAndCrossAttentions,
FlaxCausalLMOutputWithCrossAttentions,
)
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_gpt2 import GPT2Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openai-community/gpt2"
_CONFIG_FOR_DOC = "GPT2Config"
GPT2_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`GPT2Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
GPT2_INPUTS_DOCSTRING = r"""
Args:
input_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`):
`input_ids_length` = `sequence_length`. Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
past_key_values (`dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class FlaxConv1D(nn.Module):
features: int
use_bias: bool = True
dtype: Any = jnp.float32
precision: Any = None
@nn.compact
def __call__(self, inputs):
inputs = jnp.asarray(inputs, self.dtype)
kernel = self.param("kernel", jax.nn.initializers.normal(stddev=0.02), (self.features, inputs.shape[-1]))
kernel = jnp.asarray(kernel.transpose(), self.dtype)
y = lax.dot_general(inputs, kernel, (((inputs.ndim - 1,), (0,)), ((), ())), precision=self.precision)
if self.use_bias:
bias = self.param("bias", jax.nn.initializers.zeros, (self.features,))
bias = jnp.asarray(bias, self.dtype)
y = y + bias
return y
class FlaxGPT2Attention(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
causal: bool = True
is_cross_attention: bool = False
def setup(self):
config = self.config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.is_cross_attention:
self.c_attn = FlaxConv1D(2 * self.embed_dim, dtype=self.dtype)
self.q_attn = FlaxConv1D(self.embed_dim, dtype=self.dtype)
else:
self.c_attn = FlaxConv1D(3 * self.embed_dim, dtype=self.dtype)
self.c_proj = FlaxConv1D(self.embed_dim, dtype=self.dtype)
self.resid_dropout = nn.Dropout(rate=config.resid_pdrop)
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, config.max_position_embeddings), dtype="bool"), dtype="bool"
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slightly adapted from the official Flax repository:
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
"""
# detect if we're initializing by absence of existing cache data.
is_initialized = self.has_variable("cache", "cached_key")
cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update_slice(cached_key.value, key, indices)
value = lax.dynamic_update_slice(cached_value.value, value, indices)
cached_key.value = key
cached_value.value = value
num_updated_cache_vectors = query.shape[1]
cache_index.value = cache_index.value + num_updated_cache_vectors
# causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
pad_mask = jnp.broadcast_to(
jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask
def __call__(
self,
hidden_states,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask=None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
):
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
batch_size = hidden_states.shape[0]
if not is_cross_attention:
qkv_out = self.c_attn(hidden_states)
query, key, value = jnp.split(qkv_out, 3, axis=2)
else:
q_out = self.q_attn(hidden_states)
(query,) = jnp.split(q_out, 1, axis=2)
kv_out = self.c_attn(key_value_states)
key, value = jnp.split(kv_out, 2, axis=2)
query = self._split_heads(query)
key = self._split_heads(key)
value = self._split_heads(value)
query_length, key_length = query.shape[1], key.shape[1]
if self.causal:
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_mask = lax.dynamic_slice(
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
)
else:
causal_mask = self.causal_mask[:, :, :query_length, :key_length]
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask = causal_mask
elif attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
dropout_rng = None
if not deterministic and self.config.attn_pdrop > 0.0:
dropout_rng = self.make_rng("dropout")
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
key, value, attention_mask = self._concatenate_to_cache(key, value, query, attention_mask)
# transform boolean mask into float mask
if attention_mask is not None:
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
else:
attention_bias = None
# usual dot product attention
attn_weights = dot_product_attention_weights(
query,
key,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.config.attn_pdrop,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value)
attn_output = self._merge_heads(attn_output)
attn_output = self.c_proj(attn_output)
attn_output = self.resid_dropout(attn_output, deterministic=deterministic)
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
return outputs
class FlaxGPT2MLP(nn.Module):
config: GPT2Config
intermediate_size: int
dtype: jnp.dtype = jnp.float32
def setup(self):
embed_dim = self.config.hidden_size
self.c_fc = FlaxConv1D(self.intermediate_size, dtype=self.dtype)
self.c_proj = FlaxConv1D(embed_dim, dtype=self.dtype)
self.act = ACT2FN[self.config.activation_function]
self.dropout = nn.Dropout(rate=self.config.resid_pdrop)
def __call__(self, hidden_states, deterministic: bool = True):
hidden_states = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
return hidden_states
class FlaxGPT2Block(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
hidden_size = self.config.hidden_size
inner_dim = self.config.n_inner if self.config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)
self.attn = FlaxGPT2Attention(self.config, dtype=self.dtype)
self.ln_2 = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)
if self.config.add_cross_attention:
self.crossattention = FlaxGPT2Attention(
config=self.config, dtype=self.dtype, causal=False, is_cross_attention=True
)
self.ln_cross_attn = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)
self.mlp = FlaxGPT2MLP(self.config, inner_dim, dtype=self.dtype)
def __call__(
self,
hidden_states,
attention_mask=None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
):
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
attention_mask=attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
)
# residual connection
attn_output = attn_outputs[0] # output_attn: a, (attentions)
outputs = attn_outputs[1:]
# residual connection
hidden_states = attn_output + residual
# Cross-Attention Block
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
"cross-attention layers by setting `config.add_cross_attention=True`"
)
residual = hidden_states
hidden_states = self.ln_cross_attn(hidden_states)
cross_attn_outputs = self.crossattention(
hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
deterministic=deterministic,
output_attentions=output_attentions,
)
attn_output = cross_attn_outputs[0]
# residual connection
hidden_states = residual + attn_output
outputs = outputs + cross_attn_outputs[1:] # add cross attentions if we output attention weights
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states, deterministic=deterministic)
# residual connection
hidden_states = residual + feed_forward_hidden_states
outputs = (hidden_states,) + outputs
return outputs
class FlaxGPT2PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
base_model_prefix = "transformer"
module_class: nn.Module = None
def __init__(
self,
config: GPT2Config,
input_shape: tuple = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
attention_mask = jnp.ones_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
if self.config.add_cross_attention:
encoder_hidden_states = jnp.zeros(input_shape + (self.config.n_embd,))
encoder_attention_mask = attention_mask
module_init_outputs = self.module.init(
rngs,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states,
encoder_attention_mask,
return_dict=False,
)
else:
module_init_outputs = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False)
random_params = module_init_outputs["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def init_cache(self, batch_size, max_length):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
"""
# init input variables to retrieve cache
input_ids = jnp.ones((batch_size, max_length))
attention_mask = jnp.ones_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
init_variables = self.module.init(
jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
)
return unfreeze(init_variables["cache"])
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
def __call__(
self,
input_ids,
attention_mask=None,
position_ids=None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
params: Optional[dict] = None,
past_key_values: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
if encoder_hidden_states is not None and encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = input_ids.shape
if position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
if attention_mask is None:
attention_mask = jnp.ones((batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxGPT2Attention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
outputs = self.module.apply(
inputs,
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
encoder_hidden_states,
encoder_attention_mask,
not train,
False,
output_attentions,
output_hidden_states,
return_dict,
rngs=rngs,
mutable=mutable,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past_key_values = outputs
outputs["past_key_values"] = unfreeze(past_key_values["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past_key_values = outputs
outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
return outputs
class FlaxGPT2BlockCollection(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.blocks = [
FlaxGPT2Block(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_states,
attention_mask=None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
for block in self.blocks:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
hidden_states,
attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# this contains possible `None` values - `FlaxGPT2Module` will filter them out
outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions)
return outputs
class FlaxGPT2Module(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embed_dim = self.config.hidden_size
self.wte = nn.Embed(
self.config.vocab_size,
self.embed_dim,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
dtype=self.dtype,
)
self.wpe = nn.Embed(
self.config.max_position_embeddings,
self.embed_dim,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
dtype=self.dtype,
)
self.dropout = nn.Dropout(rate=self.config.embd_pdrop)
self.h = FlaxGPT2BlockCollection(self.config, dtype=self.dtype)
self.ln_f = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic=True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
input_embeds = self.wte(input_ids.astype("i4"))
position_embeds = self.wpe(position_ids.astype("i4"))
hidden_states = input_embeds + position_embeds
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
outputs = self.h(
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
hidden_states = self.ln_f(hidden_states)
if output_hidden_states:
all_hidden_states = outputs[1] + (hidden_states,)
outputs = (hidden_states, all_hidden_states) + outputs[2:]
else:
outputs = (hidden_states,) + outputs[1:]
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=outputs[1],
attentions=outputs[2],
cross_attentions=outputs[3],
)
@add_start_docstrings(
"The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
GPT2_START_DOCSTRING,
)
class FlaxGPT2Model(FlaxGPT2PreTrainedModel):
module_class = FlaxGPT2Module
append_call_sample_docstring(
FlaxGPT2Model,
_CHECKPOINT_FOR_DOC,
FlaxBaseModelOutputWithPastAndCrossAttentions,
_CONFIG_FOR_DOC,
)
class FlaxGPT2LMHeadModule(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.transformer = FlaxGPT2Module(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
outputs = self.transformer(
input_ids,
attention_mask,
position_ids,
encoder_hidden_states,
encoder_attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_kernel = self.transformer.variables["params"]["wte"]["embedding"].T
lm_logits = self.lm_head.apply({"params": {"kernel": shared_kernel}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
if not return_dict:
return (lm_logits,) + outputs[1:]
return FlaxCausalLMOutputWithCrossAttentions(
logits=lm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
@add_start_docstrings(
"""
The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
GPT2_START_DOCSTRING,
)
class FlaxGPT2LMHeadModel(FlaxGPT2PreTrainedModel):
module_class = FlaxGPT2LMHeadModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values = self.init_cache(batch_size, max_length)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since GPT2 uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if attention_mask is not None:
position_ids = attention_mask.cumsum(axis=-1) - 1
extended_attention_mask = lax.dynamic_update_slice(
extended_attention_mask, attention_mask.astype("i4"), (0, 0)
)
else:
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
return {
"past_key_values": past_key_values,
"attention_mask": extended_attention_mask,
"position_ids": position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
return model_kwargs
append_call_sample_docstring(
FlaxGPT2LMHeadModel,
_CHECKPOINT_FOR_DOC,
FlaxCausalLMOutputWithCrossAttentions,
_CONFIG_FOR_DOC,
)
__all__ = ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]
| transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py/0 | {
"file_path": "transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py",
"repo_id": "transformers",
"token_count": 14172
} | 453 |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/gpt_neox/modular_gpt_neox.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_gpt_neox.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
from typing import Callable, Optional, Union
import torch
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...integrations import use_kernel_forward_from_hub
from ...masking_utils import create_causal_mask
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
from ...utils.deprecation import deprecate_kwarg
from ...utils.generic import check_model_inputs
from .configuration_gpt_neox import GPTNeoXConfig
logger = logging.get_logger(__name__)
class GPTNeoXMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size)
self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size)
self.act = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
hidden_states = self.dense_h_to_4h(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dense_4h_to_h(hidden_states)
return hidden_states
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
# Keep half or full tensor for later concatenation
rotary_dim = cos.shape[-1]
q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
# Apply rotary embeddings on the first half or full tensor
q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
# Concatenate back to full shape
q_embed = torch.cat([q_embed, q_pass], dim=-1)
k_embed = torch.cat([k_embed, k_pass], dim=-1)
return q_embed, k_embed
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: torch.Tensor,
scaling: float,
dropout: float = 0.0,
head_mask: Optional[torch.Tensor] = None,
**kwargs,
):
attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value)
# Reshape outputs
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class GPTNeoXAttention(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
self.config = config
self.head_size = config.hidden_size // config.num_attention_heads
self.attention_dropout = config.attention_dropout
self.rotary_ndims = int(self.head_size * config.rotary_pct)
self.scaling = self.head_size**-0.5
self.is_causal = True
self.layer_idx = layer_idx
self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=config.attention_bias)
self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
head_mask: Optional[torch.FloatTensor] = None,
layer_past: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[FlashAttentionKwargs],
):
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, 3 * self.head_size)
qkv = self.query_key_value(hidden_states).view(hidden_shape).transpose(1, 2)
query_states, key_states, value_states = qkv.chunk(3, dim=-1)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
# Cache QKV values
if layer_past is not None:
cache_kwargs = {
"sin": sin,
"cos": cos,
"partial_rotation_size": self.rotary_ndims,
"cache_position": cache_position,
}
key_states, value_states = layer_past.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
# Compute attention
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
scaling=self.scaling,
dropout=0.0 if not self.training else self.attention_dropout,
head_mask=head_mask,
**kwargs,
)
# Reshape outputs and final projection
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.dense(attn_output)
return attn_output, attn_weights
class GPTNeoXLayer(GradientCheckpointingLayer):
def __init__(self, config, layer_idx):
super().__init__()
self.use_parallel_residual = config.use_parallel_residual
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_attention_dropout = nn.Dropout(config.hidden_dropout)
self.post_mlp_dropout = nn.Dropout(config.hidden_dropout)
self.attention = GPTNeoXAttention(config, layer_idx)
self.mlp = GPTNeoXMLP(config)
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
layer_past: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[FlashAttentionKwargs],
):
attn_output, attn_weights = self.attention(
self.input_layernorm(hidden_states),
attention_mask=attention_mask,
position_ids=position_ids,
layer_past=layer_past,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
attn_output = self.post_attention_dropout(attn_output)
if self.use_parallel_residual:
# pseudocode:
# x = x + attn(ln1(x)) + mlp(ln2(x))
mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
mlp_output = self.post_mlp_dropout(mlp_output)
hidden_states = mlp_output + attn_output + hidden_states
else:
# pseudocode:
# x = x + attn(ln1(x))
# x = x + mlp(ln2(x))
attn_output = attn_output + hidden_states
mlp_output = self.mlp(self.post_attention_layernorm(attn_output))
mlp_output = self.post_mlp_dropout(mlp_output)
hidden_states = mlp_output + attn_output
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class GPTNeoXRotaryEmbedding(nn.Module):
inv_freq: torch.Tensor # fix linting for `register_buffer`
def __init__(self, config: GPTNeoXConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
@use_kernel_forward_from_hub("RMSNorm")
class GPTNeoXRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
GPTNeoXRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class GPTNeoXDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: GPTNeoXConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GPTNeoXAttention(config=config, layer_idx=layer_idx)
self.mlp = GPTNeoXMLP(config)
self.input_layernorm = GPTNeoXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = GPTNeoXRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[TransformersKwargs],
) -> torch.Tensor:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states
@auto_docstring
class GPTNeoXPreTrainedModel(PreTrainedModel):
config: GPTNeoXConfig
base_model_prefix = "gpt_neox"
supports_gradient_checkpointing = True
_no_split_modules = ["GPTNeoXLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn = True
_supports_sdpa = True
_supports_flex_attn = True
_can_compile_fullgraph = True
_supports_attention_backend = True
_can_record_outputs = {
"hidden_states": GPTNeoXDecoderLayer,
"attentions": GPTNeoXAttention,
}
_keys_to_ignore_on_load_unexpected = [r"attention.bias", r"attention.masked_bias"]
@auto_docstring
class GPTNeoXModel(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
self.emb_dropout = nn.Dropout(config.hidden_dropout)
self.layers = nn.ModuleList([GPTNeoXLayer(config, i) for i in range(config.num_hidden_layers)])
self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.rotary_emb = GPTNeoXRotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
@check_model_inputs
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Cache] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_in(input_ids)
# TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
if not isinstance(past_key_values, (type(None), Cache)):
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = create_causal_mask(
config=self.config,
input_embeds=inputs_embeds,
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=past_key_values,
position_ids=position_ids,
)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
converted_head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
# Flex Attention converts it to a separate mask
if head_mask is not None:
converted_head_mask = ~converted_head_mask.bool() * torch.finfo(inputs_embeds.dtype).min
converted_head_mask = converted_head_mask.to(dtype=self.dtype, device=self.device)
head_mask = converted_head_mask
hidden_states = self.emb_dropout(inputs_embeds)
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
head_mask=head_mask[i],
layer_past=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = outputs[0]
if output_attentions:
all_attentions = all_attentions + (outputs[1],)
hidden_states = self.final_layer_norm(hidden_states)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
def get_input_embeddings(self):
return self.embed_in
def set_input_embeddings(self, value):
self.embed_in = value
@auto_docstring(
custom_intro="""
GPTNeoX Model with a `language modeling` head on top for CLM fine-tuning.
"""
)
class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["embed_out.weight"]
_tp_plan = {"embed_out": "colwise_rep"}
_pp_plan = {"embed_out": (["hidden_states"], ["logits"])}
def __init__(self, config):
super().__init__(config)
self.gpt_neox = GPTNeoXModel(config)
self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.embed_out
def set_output_embeddings(self, new_embeddings):
self.embed_out = new_embeddings
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Union[Cache, tuple[tuple[torch.FloatTensor]]]] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[TransformersKwargs],
) -> Union[tuple, CausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
>>> config = GPTNeoXConfig.from_pretrained("EleutherAI/gpt-neox-20b")
>>> config.is_decoder = True
>>> model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
```"""
outputs: BaseModelOutputWithPast = self.gpt_neox(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.embed_out(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@auto_docstring(
custom_intro="""
The GPTNeoX Model transformer with a sequence classification head on top (linear layer).
[`GPTNeoXForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
"""
)
class GPTNeoXForSequenceClassification(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.gpt_neox = GPTNeoXModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Union[Cache, tuple[tuple[torch.FloatTensor]]]] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> SequenceClassifierOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
outputs: BaseModelOutputWithPast = self.gpt_neox(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
hidden_states = outputs.last_hidden_state
logits = self.score(hidden_states)
batch_size = logits.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
else:
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class GPTNeoXForTokenClassification(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.gpt_neox = GPTNeoXModel(config)
self.dropout = nn.Dropout(config.classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, tuple[tuple[torch.Tensor]]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> TokenClassifierOutput:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
outputs: BaseModelOutputWithPast = self.gpt_neox(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
hidden_states = outputs.last_hidden_state
hidden_states = self.dropout(hidden_states)
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
loss = self.loss_function(logits, labels, self.config)
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@auto_docstring
class GPTNeoXForQuestionAnswering(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.gpt_neox = GPTNeoXModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply final processing
self.post_init()
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> QuestionAnsweringModelOutput:
outputs: BaseModelOutputWithPast = self.gpt_neox(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
sequence_output = outputs.last_hidden_state
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
loss = None
if start_positions is not None and end_positions is not None:
loss = self.loss_function(start_logits, end_logits, start_positions, end_positions)
return QuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = [
"GPTNeoXForCausalLM",
"GPTNeoXForQuestionAnswering",
"GPTNeoXForSequenceClassification",
"GPTNeoXForTokenClassification",
"GPTNeoXLayer",
"GPTNeoXModel",
"GPTNeoXPreTrainedModel",
]
| transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py/0 | {
"file_path": "transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py",
"repo_id": "transformers",
"token_count": 15323
} | 454 |
# coding=utf-8
# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""GPT-J model configuration"""
from collections import OrderedDict
from collections.abc import Mapping
from typing import Any, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
logger = logging.get_logger(__name__)
class GPTJConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the GPT-J
[EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) architecture. Configuration objects inherit from
[`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`]
for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50400):
Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GPTJModel`].
n_positions (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 4096):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
rotary_dim (`int`, *optional*, defaults to 64):
Number of dimensions in the embedding that Rotary Position Embedding is applied to.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"gelu_new"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
Example:
```python
>>> from transformers import GPTJModel, GPTJConfig
>>> # Initializing a GPT-J 6B configuration
>>> configuration = GPTJConfig()
>>> # Initializing a model from the configuration
>>> model = GPTJModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "gptj"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=50400,
n_positions=2048,
n_embd=4096,
n_layer=28,
n_head=16,
rotary_dim=64,
n_inner=None,
activation_function="gelu_new",
resid_pdrop=0.0,
embd_pdrop=0.0,
attn_pdrop=0.0,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
tie_word_embeddings=False,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.rotary_dim = rotary_dim
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(
bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
)
# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
class GPTJOnnxConfig(OnnxConfigWithPast):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: Optional[list[PatchingSpec]] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
if not getattr(self._config, "pad_token_id", None):
# TODO: how to do that better?
self._config.pad_token_id = 0
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
else:
common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
return common_inputs
@property
def num_layers(self) -> int:
return self._config.n_layer
@property
def num_attention_heads(self) -> int:
return self._config.n_head
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
# We need to order the input in the way they appears in the forward()
ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, seqlen = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
past_key_values_length = seqlen + 2
past_shape = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
ordered_inputs["past_key_values"] = [
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
]
ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
if self.use_past:
mask_dtype = ordered_inputs["attention_mask"].dtype
ordered_inputs["attention_mask"] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
)
return ordered_inputs
@property
def default_onnx_opset(self) -> int:
return 13
__all__ = ["GPTJConfig", "GPTJOnnxConfig"]
| transformers/src/transformers/models/gptj/configuration_gptj.py/0 | {
"file_path": "transformers/src/transformers/models/gptj/configuration_gptj.py",
"repo_id": "transformers",
"token_count": 3699
} | 455 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_hunyuan_v1_moe import *
from .modeling_hunyuan import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
| transformers/src/transformers/models/hunyuan_v1_moe/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/hunyuan_v1_moe/__init__.py",
"repo_id": "transformers",
"token_count": 141
} | 456 |
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch IdeficsVision model: a copy of CLIPVisionModel using a simpler config object"""
import math
from dataclasses import dataclass
from typing import Callable, Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...utils import (
ModelOutput,
can_return_tuple,
logging,
)
from .configuration_idefics import IdeficsVisionConfig
logger = logging.get_logger(__name__)
@dataclass
class IdeficsVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
The image embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
image_embeds: Optional[torch.FloatTensor] = None
last_hidden_state: Optional[torch.FloatTensor] = None
hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
attentions: Optional[tuple[torch.FloatTensor, ...]] = None
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings
class IdeficsVisionEmbeddings(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
# Heavily inspired from https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/vit/modeling_vit.py#L82
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
num_patches = embeddings.shape[1] - 1
pos_embed = self.position_embedding(self.position_ids)
num_positions = pos_embed.shape[1] - 1
if num_patches == num_positions and height == width:
return pos_embed
class_pos_embed = pos_embed[:, 0]
patch_pos_embed = pos_embed[:, 1:]
embed_dim = embeddings.shape[-1]
num_h_patches = height // self.config.patch_size
num_w_patches = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1
sqrt_num_positions = math.sqrt(num_positions)
patch_pos_embed = patch_pos_embed.reshape(1, int(sqrt_num_positions), int(sqrt_num_positions), embed_dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
fp32_upcasting = patch_pos_embed.dtype == torch.bfloat16
if fp32_upcasting:
logger.warning_once(
"Upcasting patch_pos_embed to fp32 for interpolation since `upsample_bicubic2d_out_frame` in nn.functional.interpolate "
"is not implemented for 'torch.bfloat16' dtype. This will result in a slight overhead."
)
patch_pos_embed = patch_pos_embed.to(torch.float)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
scale_factor=(num_h_patches / sqrt_num_positions, num_w_patches / sqrt_num_positions),
mode="bicubic",
align_corners=False,
)
if fp32_upcasting:
patch_pos_embed = patch_pos_embed.to(torch.bfloat16)
if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]:
raise ValueError(
f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the "
f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})"
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, embed_dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if not interpolate_pos_encoding:
if height != self.image_size or width != self.image_size:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size}*{self.image_size}). You should try to set `interpolate_pos_encoding=True`"
)
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
# Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
):
attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class IdeficsVisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.is_causal = False
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time x Channel"""
batch_size, seq_length, embed_dim = hidden_states.shape
queries = self.q_proj(hidden_states)
keys = self.k_proj(hidden_states)
values = self.v_proj(hidden_states)
queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
# CLIP text model uses both `causal_attention_mask` and `attention_mask`
# in case FA2 kernel is called, `is_causal` should be inferred from `causal_attention_mask`
if self.config._attn_implementation != "flash_attention_2":
if attention_mask is not None and causal_attention_mask is not None:
attention_mask = attention_mask + causal_attention_mask
elif causal_attention_mask is not None:
attention_mask = causal_attention_mask
else:
self.is_causal = causal_attention_mask is not None
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and output_attentions:
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
queries,
keys,
values,
attention_mask,
is_causal=self.is_causal,
scaling=self.scale,
dropout=0.0 if not self.training else self.dropout,
)
attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
attn_output = self.out_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->IdeficsVision
class IdeficsVisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoderLayer with AltCLIP->IdeficsVision
class IdeficsVisionEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = IdeficsVisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = IdeficsVisionMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->IdeficsVision
class IdeficsVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`IdeficsVisionEncoderLayer`].
Args:
config: IdeficsVisionConfig
"""
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([IdeficsVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
@can_return_tuple
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer
class IdeficsVisionTransformer(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = IdeficsVisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.encoder = IdeficsVisionEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
| transformers/src/transformers/models/idefics/vision.py/0 | {
"file_path": "transformers/src/transformers/models/idefics/vision.py",
"repo_id": "transformers",
"token_count": 9072
} | 457 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""InstructBLIP model configuration"""
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig
logger = logging.get_logger(__name__)
class InstructBlipVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipVisionModel`]. It is used to
instantiate a InstructBLIP vision encoder according to the specified arguments, defining the model architecture.
Instantiating a configuration defaults will yield a similar configuration to that of the InstructBLIP
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 1408):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 6144):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 39):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported. to 1e-5): The epsilon used by the layer
normalization layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 1e-10):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries and values in the self-attention layers.
Example:
```python
>>> from transformers import InstructBlipVisionConfig, InstructBlipVisionModel
>>> # Initializing a InstructBlipVisionConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVisionConfig()
>>> # Initializing a InstructBlipVisionModel (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "instructblip_vision_model"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=1408,
intermediate_size=6144,
num_hidden_layers=39,
num_attention_heads=16,
image_size=224,
patch_size=14,
hidden_act="gelu",
layer_norm_eps=1e-6,
attention_dropout=0.0,
initializer_range=1e-10,
qkv_bias=True,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.qkv_bias = qkv_bias
class InstructBlipQFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipQFormerModel`]. It is used to
instantiate a InstructBLIP Querying Transformer (Q-Former) model according to the specified arguments, defining the
model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the InstructBLIP [Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5)
architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs.
Read the documentation from [`PretrainedConfig`] for more information.
Note that [`InstructBlipQFormerModel`] is very similar to [`BertLMHeadModel`] with interleaved cross-attention.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling the model.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Token id used for padding sequences.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://huggingface.co/papers/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://huggingface.co/papers/2009.13658).
cross_attention_frequency (`int`, *optional*, defaults to 2):
The frequency of adding cross-attention to the Transformer layers.
encoder_hidden_size (`int`, *optional*, defaults to 1408):
The hidden size of the hidden states for cross-attention.
Examples:
```python
>>> from transformers import InstructBlipQFormerConfig, InstructBlipQFormerModel
>>> # Initializing a InstructBLIP Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipQFormerConfig()
>>> # Initializing a model (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipQFormerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "instructblip_qformer"
base_config_key = "qformer_config"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
cross_attention_frequency=2,
encoder_hidden_size=1408,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.cross_attention_frequency = cross_attention_frequency
self.encoder_hidden_size = encoder_hidden_size
class InstructBlipConfig(PretrainedConfig):
r"""
[`InstructBlipConfig`] is the configuration class to store the configuration of a
[`InstructBlipForConditionalGeneration`]. It is used to instantiate a InstructBLIP model according to the specified
arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configuration with
the defaults will yield a similar configuration to that of the InstructBLIP
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipVisionConfig`].
qformer_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipQFormerConfig`].
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize any [`PretrainedConfig`].
num_query_tokens (`int`, *optional*, defaults to 32):
The number of query tokens passed through the Transformer.
image_token_index (`int`, *optional*):
Token index of special image token.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import (
... InstructBlipVisionConfig,
... InstructBlipQFormerConfig,
... OPTConfig,
... InstructBlipConfig,
... InstructBlipForConditionalGeneration,
... )
>>> # Initializing a InstructBlipConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipConfig()
>>> # Initializing a InstructBlipForConditionalGeneration (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a InstructBlipConfig from a InstructBlipVisionConfig, InstructBlipQFormerConfig and any PretrainedConfig
>>> # Initializing InstructBLIP vision, InstructBLIP Q-Former and language model configurations
>>> vision_config = InstructBlipVisionConfig()
>>> qformer_config = InstructBlipQFormerConfig()
>>> text_config = OPTConfig()
>>> config = InstructBlipConfig.from_text_vision_configs(vision_config, qformer_config, text_config)
```"""
model_type = "instructblip"
attribute_map = {
"image_token_id": "image_token_index",
}
sub_configs = {
"text_config": AutoConfig,
"qformer_config": InstructBlipQFormerConfig,
"vision_config": InstructBlipVisionConfig,
}
def __init__(
self,
vision_config=None,
qformer_config=None,
text_config=None,
num_query_tokens=32,
image_token_index=None,
**kwargs,
):
super().__init__(**kwargs)
if vision_config is None:
vision_config = {}
logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values.")
if qformer_config is None:
qformer_config = {}
logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.")
if text_config is None:
text_config = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).")
self.vision_config = InstructBlipVisionConfig(**vision_config)
self.qformer_config = InstructBlipQFormerConfig(**qformer_config)
text_model_type = text_config.get("model_type", "opt")
self.text_config = CONFIG_MAPPING[text_model_type](**text_config)
self.num_query_tokens = num_query_tokens
self.image_token_index = image_token_index
self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size
self.use_decoder_only_language_model = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
self.initializer_factor = 1.0
self.initializer_range = 0.02
@classmethod
def from_vision_qformer_text_configs(
cls,
vision_config: InstructBlipVisionConfig,
qformer_config: InstructBlipQFormerConfig,
text_config: PretrainedConfig,
**kwargs,
):
r"""
Instantiate a [`InstructBlipConfig`] (or a derived class) from a InstructBLIP vision model, Q-Former and
language model configurations.
Returns:
[`InstructBlipConfig`]: An instance of a configuration object
"""
return cls(
vision_config=vision_config.to_dict(),
qformer_config=qformer_config.to_dict(),
text_config=text_config.to_dict(),
**kwargs,
)
__all__ = ["InstructBlipConfig", "InstructBlipQFormerConfig", "InstructBlipVisionConfig"]
| transformers/src/transformers/models/instructblip/configuration_instructblip.py/0 | {
"file_path": "transformers/src/transformers/models/instructblip/configuration_instructblip.py",
"repo_id": "transformers",
"token_count": 5800
} | 458 |
# coding=utf-8
# Copyright 2025 HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections.abc
from dataclasses import dataclass
from typing import Callable, Optional, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import auto_docstring, can_return_tuple, logging, torch_int
from ..clip.modeling_clip import CLIPMLP
from ..janus.modeling_janus import JanusVisionAttention
from ..llama.modeling_llama import LlamaRMSNorm
from ..llava.modeling_llava import (
LlavaCausalLMOutputWithPast,
LlavaForConditionalGeneration,
LlavaModel,
LlavaModelOutputWithPast,
LlavaPreTrainedModel,
)
from .configuration_internvl import InternVLConfig, InternVLVisionConfig
logger = logging.get_logger(__name__)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
):
key_states = key
value_states = value
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# No upcasting of the attention weights to float32 in this implementation
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class InternVLVisionRMSNorm(LlamaRMSNorm):
pass
class InternVLVisionAttention(JanusVisionAttention):
def __init__(self, config: InternVLVisionConfig):
super().__init__()
del self.num_key_value_groups
# Needed for flash attention
self.is_causal = False
qk_norm = config.use_qk_norm
self.q_norm = InternVLVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity()
self.k_norm = InternVLVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[torch.Tensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
):
batch_size, seq_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self.q_norm(query_states)
key_states = self.k_norm(key_states)
query_states = query_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scale,
is_causal=False,
**kwargs,
)
attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim)
output = self.projection_layer(attn_output)
output = self.projection_dropout(output)
outputs = (output, attn_weights) if output_attentions else (output, None)
return outputs
@auto_docstring
class InternVLVisionPreTrainedModel(PreTrainedModel):
config: InternVLVisionConfig
base_model_prefix = "internvl_vision"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
_no_split_modules = ["InternVLVisionLayer"]
_supports_sdpa = True
_supports_flash_attn = True
_supports_flex_attn = True
_supports_attention_backend = True
def _init_weights(self, module):
"""Initialize the weights"""
super()._init_weights(module)
if isinstance(module, InternVLVisionEmbeddings):
module.cls_token.data.zero_()
if module.mask_token is not None:
module.mask_token.data.zero_()
if module.position_embeddings is not None:
module.position_embeddings.data.zero_()
elif isinstance(module, InternVLVisionLayer):
module.lambda_1.data.fill_(self.config.layer_scale_init_value)
module.lambda_2.data.fill_(self.config.layer_scale_init_value)
@dataclass
@auto_docstring(
custom_intro="""
Class for outputs of [`InternVLVisionModel`].
"""
)
class InternVLVisionModelOutputWithPooling(BaseModelOutputWithPooling):
r"""
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if
*config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token
will be returned.
"""
class InternVLVisionPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.num_patches = num_patches
self.patch_shape = patch_shape
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embeddings = self.projection(pixel_values)
patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
embeddings = embeddings.flatten(2).transpose(1, 2)
return embeddings, (patch_height, patch_width)
# Based on timm implementation, which can be found here:
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
class InternVLVisionEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: InternVLVisionConfig) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
if config.use_mask_token:
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
else:
self.mask_token = None
self.patch_embeddings = InternVLVisionPatchEmbeddings(config)
self.patch_size = config.patch_size
self.image_size = (
config.image_size
if isinstance(config.image_size, collections.abc.Iterable)
else (config.image_size, config.image_size)
)
num_patches = self.patch_embeddings.num_patches
if config.use_absolute_position_embeddings:
self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
else:
self.position_embeddings = None
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embeddings.shape[1] - 1
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, :1]
patch_pos_embed = self.position_embeddings[:, 1:]
dim = embeddings.shape[-1]
new_height = height // self.patch_size[0]
new_width = width // self.patch_size[1]
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
) -> torch.Tensor:
_, _, height, width = pixel_values.shape
embeddings, (patch_height, patch_width) = self.patch_embeddings(pixel_values)
batch_size, seq_len, _ = embeddings.size()
if bool_masked_pos is not None:
mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
# replace the masked visual tokens by mask_tokens
w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
embeddings = embeddings * (1 - w) + mask_tokens * w
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
if self.position_embeddings is not None:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
embeddings = self.dropout(embeddings)
return embeddings, (patch_height, patch_width)
class InternVLVisionMLP(CLIPMLP):
pass
NORM2FN = {"layer_norm": nn.LayerNorm, "rms_norm": InternVLVisionRMSNorm}
class InternVLVisionLayer(GradientCheckpointingLayer):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: InternVLVisionConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = InternVLVisionAttention(config)
self.mlp = InternVLVisionMLP(config)
# InternVL uses different layernorm implementations for different models
self.layernorm_before = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)
self.layernorm_after = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)
init_values = config.layer_scale_init_value
self.lambda_1 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
self.lambda_2 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: bool = False,
) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
attention_output, attention_weights = self.attention(
self.layernorm_before(hidden_states), # in InternVLVision, layernorm is applied before self-attention
output_attentions=output_attentions,
)
attention_output = self.lambda_1 * attention_output
# first residual connection
hidden_states = attention_output + hidden_states
# in InternVLVision, layernorm is also applied after self-attention
layer_output = self.layernorm_after(hidden_states)
layer_output = self.mlp(layer_output)
layer_output = self.dropout(layer_output)
if self.lambda_2 is not None:
layer_output = self.lambda_2 * layer_output
# second residual connection
layer_output = layer_output + hidden_states
return layer_output, attention_weights
class InternVLVisionEncoder(nn.Module):
def __init__(self, config: InternVLVisionConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([InternVLVisionLayer(config) for i in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
@can_return_tuple
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: bool = False,
output_hidden_states: bool = False,
) -> Union[tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
@auto_docstring
class InternVLVisionModel(InternVLVisionPreTrainedModel):
def __init__(self, config: InternVLVisionConfig) -> None:
super().__init__(config)
self.config = config
self.embeddings = InternVLVisionEmbeddings(config)
self.encoder = InternVLVisionEncoder(config)
self.layernorm = (
nn.Identity() if config.use_mean_pooling else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
@can_return_tuple
@auto_docstring
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> Union[tuple, InternVLVisionModelOutputWithPooling]:
r"""
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
embedding_output, _ = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
encoder_outputs = self.encoder(
embedding_output,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
return InternVLVisionModelOutputWithPooling(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class InternVLPreTrainedModel(LlavaPreTrainedModel):
pass
INTERNVL_INPUTS_DOCSTRING = None
class InternVLMultiModalProjector(nn.Module):
def __init__(self, config: InternVLConfig):
super().__init__()
self.layer_norm = nn.LayerNorm(config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2)
self.linear_1 = nn.Linear(
config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2, config.text_config.hidden_size
)
self.act = ACT2FN[config.projector_hidden_act]
self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size)
def forward(self, image_features):
hidden_states = self.layer_norm(image_features)
hidden_states = self.linear_1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states
class InternVLModelOutputWithPast(LlavaModelOutputWithPast):
pass
class InternVLModel(LlavaModel):
def pixel_shuffle(self, vision_features: torch.Tensor, scale_factor: float = 0.5):
"""Perform pixel shuffle downsampling on vision features.
Args:
vision_features (`torch.Tensor`):
Input tensor of shape (batch_size, width, height, channels).
scale_factor (`float`, *optional*, defaults to `0.5`):
Factor by which to downsample. Default is 0.5, which halves the dimensions.
Returns:
vision_features (`torch.Tensor`):
Downsampled tensor of shape (batch_size, height*scale_factor, width*scale_factor, channels/(scale_factor^2)).
"""
batch_size, width, height, channels = vision_features.size()
if height % scale_factor != 0 or width % scale_factor != 0:
raise ValueError("Height and width must be divisible by scale_factor for proper downsampling.")
# Reshape to allow downsampling
vision_features = vision_features.view(
batch_size, width, int(height * scale_factor), int(channels / scale_factor)
)
# Permute dimensions to align downsampled axis correctly
vision_features = vision_features.permute(0, 2, 1, 3).contiguous()
# Reshape to achieve final downsampled dimensions
vision_features = vision_features.view(
batch_size, int(height * scale_factor), int(width * scale_factor), int(channels / (scale_factor**2))
)
# Swap height and width back for proper orientation
vision_features = vision_features.permute(0, 2, 1, 3).contiguous()
return vision_features
def get_image_features(
self,
pixel_values: torch.FloatTensor,
vision_feature_layer: Optional[Union[int, list[int]]] = None,
vision_feature_select_strategy: Optional[str] = None,
**kwargs,
):
"""
Obtains image last hidden states from the vision tower and apply multimodal projection.
Args:
pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
The tensors corresponding to the input images.
vision_feature_layer (`int` or `list[int]`):
Layer index or list of layer indices to extract features from.
Returns:
vision_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`.
"""
vision_feature_layer = (
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
)
vision_feature_select_strategy = (
vision_feature_select_strategy
if vision_feature_select_strategy is not None
else self.config.vision_feature_select_strategy
)
pixel_values = pixel_values.to(dtype=self.dtype) # fp16 compatibility
downsample_ratio = self.config.downsample_ratio
if vision_feature_layer == -1:
vision_features = self.vision_tower(pixel_values=pixel_values).last_hidden_state
else:
vision_features = self.vision_model(pixel_values=pixel_values).hidden_states[vision_feature_layer]
if vision_feature_select_strategy == "default":
vision_features = vision_features[:, 1:, :]
# Calculate dimensions based on vision features
channels = vision_features.shape[1]
feature_size = int(channels**0.5)
batch_size = vision_features.shape[0]
# Reshape tensor to spatial dimensions
vision_features = vision_features.reshape(batch_size, feature_size, feature_size, -1)
# Apply downsampling using pixel shuffle
vision_features = self.pixel_shuffle(vision_features, scale_factor=downsample_ratio)
# Reshape tensor to prepare for projection
vision_features = vision_features.reshape(batch_size, -1, vision_features.shape[-1])
# Project features through multi-modal projector
vision_features = self.multi_modal_projector(vision_features)
return vision_features
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
vision_feature_layer: Optional[Union[int, list[int]]] = None,
vision_feature_select_strategy: Optional[str] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> Union[tuple, InternVLModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_feature_layer = (
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
)
vision_feature_select_strategy = (
vision_feature_select_strategy
if vision_feature_select_strategy is not None
else self.config.vision_feature_select_strategy
)
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
if pixel_values is not None:
image_features = self.get_image_features(
pixel_values=pixel_values,
vision_feature_layer=vision_feature_layer,
vision_feature_select_strategy=vision_feature_select_strategy,
)
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
special_image_mask = self.get_placeholder_mask(
input_ids, inputs_embeds=inputs_embeds, image_features=image_features
)
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
outputs = self.language_model(
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
cache_position=cache_position,
**kwargs,
)
return InternVLModelOutputWithPast(
last_hidden_state=outputs.last_hidden_state,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=image_features if pixel_values is not None else None,
)
class InternVLCausalLMOutputWithPast(LlavaCausalLMOutputWithPast):
pass
class InternVLForConditionalGeneration(LlavaForConditionalGeneration):
def forward(**super_kwargs):
r"""
Example:
```python
>>> import torch
>>> from transformers import AutoProcessor, AutoModelForImageTextToText
>>> torch_device = "cuda"
>>> processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL3-1B-hf")
>>> model = AutoModelForImageTextToText.from_pretrained(
... "OpenGVLab/InternVL3-1B-hf", dtype=torch.bfloat16, device_map=torch_device
... )
>>> messages = [
... {
... "role": "user",
... "content": [
... {
... "type": "image",
... "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
... },
... {
... "type": "image",
... "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
... },
... {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
... ],
... },
... ]
>>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(torch_device)
>>> generate_ids = model.generate(**inputs, max_new_tokens=200)
>>> print(processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))
The images depict the Statue of Liberty and the Golden Gate Bridge.
```"""
super().forward(**super_kwargs)
__all__ = [
"InternVLVisionPreTrainedModel",
"InternVLVisionModel",
"InternVLPreTrainedModel",
"InternVLModel",
"InternVLForConditionalGeneration",
]
| transformers/src/transformers/models/internvl/modular_internvl.py/0 | {
"file_path": "transformers/src/transformers/models/internvl/modular_internvl.py",
"repo_id": "transformers",
"token_count": 11649
} | 459 |
# coding=utf-8
# Copyright 2024 JetMoe AI and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch JetMoe model."""
import math
from typing import Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
from ...modeling_layers import (
GenericForSequenceClassification,
GradientCheckpointingLayer,
)
from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
from ...utils.deprecation import deprecate_kwarg
from .configuration_jetmoe import JetMoeConfig
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import BlockMask
from ...integrations.flex_attention import make_flex_block_causal_mask
if is_flash_attn_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
# Copied from transformers.models.qwen2_moe.modeling_qwen2_moe.load_balancing_loss_func
def load_balancing_loss_func(
gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
num_experts: Optional[int] = None,
top_k=2,
attention_mask: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, int]:
r"""
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.
Args:
gate_logits:
Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
shape [batch_size X sequence_length, num_experts].
num_experts:
Number of experts
top_k:
The number of experts to route per-token, can be also interpreted as the `top-k` routing
parameter.
attention_mask (`torch.Tensor`, *optional*):
The attention_mask used in forward function
shape [batch_size X sequence_length] if not None.
Returns:
The auxiliary loss.
"""
if gate_logits is None or not isinstance(gate_logits, tuple):
return 0
if isinstance(gate_logits, tuple):
compute_device = gate_logits[0].device
concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
if attention_mask is None:
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.mean(routing_weights, dim=0)
else:
batch_size, sequence_length = attention_mask.shape
num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
expert_attention_mask = (
attention_mask[None, :, :, None, None]
.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
.reshape(-1, top_k, num_experts)
.to(compute_device)
)
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
expert_attention_mask, dim=0
)
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
router_per_expert_attention_mask = (
attention_mask[None, :, :, None]
.expand((num_hidden_layers, batch_size, sequence_length, routing_weights.shape[1]))
.reshape(-1, routing_weights.shape[1])
.to(compute_device)
)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
router_per_expert_attention_mask, dim=0
)
device_index = routing_weights.device.index if routing_weights.device.index is not None else 0
rank = routing_weights.shape[1] * int(device_index)
overall_loss = torch.sum(
tokens_per_expert[:, rank : rank + routing_weights.shape[1]] * router_prob_per_expert.unsqueeze(0)
)
return overall_loss * num_experts
class JetMoeParallelExperts(nn.Module):
def __init__(self, num_experts: int, input_size: int, output_size: int) -> None:
"""
Initialize the JetMoeParallelExperts module.
The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's compatible with
many MoE libraries, such as [Megablock](https://github.com/databricks/megablocks) and
[ScatterMoE](https://github.com/shawntan/scattermoe), as well as the
[MoE kernel](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py)
used in vllm.
Args:
num_experts (int):
Number of experts.
input_size (int):
Size of the input.
output_size (int):
Size of the output.
"""
super().__init__()
self.weight = nn.Parameter(torch.empty(num_experts, output_size, input_size))
self.num_experts = num_experts
self.input_size = input_size
self.output_size = output_size
def forward(self, inputs, expert_size):
"""
Forward pass of the JetMoeParallelExperts module.
Args:
inputs (Tensor):
Input tensor.
expert_size:
Expert size information.
Returns:
Tensor: Output tensor.
"""
input_list = inputs.split(expert_size, dim=0)
output_list = []
for i in range(self.num_experts):
output_list.append(F.linear(input_list[i], self.weight[i]))
results = torch.cat(output_list, dim=0)
return results
class JetMoeTopKGating(nn.Module):
def __init__(self, input_size: int, num_experts: int, top_k: int):
"""
Initialize the top-k gating mechanism.
Args:
input_size (`int`):
Size of the input.
num_experts (`int`):
Number of experts.
top_k (`int`):
Number of top experts to select.
"""
super().__init__()
self.num_experts = num_experts
self.input_size = input_size
self.top_k = top_k
self.layer = nn.Linear(input_size, num_experts, bias=False)
def forward(self, hidden_states):
# compute the top_k routing decision
logits = self.layer(hidden_states).float() # [batch_size x seq_len, num_experts]
top_k_logits, top_k_indices = logits.topk(self.top_k, dim=1) # [num_tokens, top_k]
top_k_gates = torch.softmax(top_k_logits, dim=1).type_as(hidden_states) # [num_tokens, top_k]
# compute number of input given to each expert
zeros = torch.zeros(
[top_k_gates.size(0), self.num_experts], dtype=top_k_gates.dtype, device=top_k_gates.device
) # [num_tokens, num_experts]
gates = zeros.scatter(1, top_k_indices, 1) # [num_tokens, num_experts]
expert_size = gates.long().sum(0) # [num_experts,]
# (This cause torch.compile to fail with `torch._dynamo.exc.Unsupported: Backend compiler failed with a fake tensor exception at`)
# (and `DataDependentOutputException`)
expert_size = expert_size.tolist()
# sort and group input tokens according to expert assignment
top_k_experts = top_k_indices.flatten() # [num_tokens * top_k]
_, index_sorted_experts = top_k_experts.sort(0) # [num_tokens * top_k]
batch_index = index_sorted_experts.div(self.top_k, rounding_mode="trunc") # [num_tokens * top_k]
# gather the gate values for grouped input tokens
top_k_gates = top_k_gates.flatten() # [num_tokens * top_k]
batch_gates = top_k_gates[index_sorted_experts] # [num_tokens * top_k]
return index_sorted_experts, batch_index, batch_gates, expert_size, logits
class JetMoeMoE(nn.Module):
"""
A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: JetMoeConfig):
super().__init__()
self.input_size = config.hidden_size
self.hidden_size = config.intermediate_size
self.activation = ACT2FN[config.activation_function]
self.bias = torch.nn.Parameter(torch.empty(self.input_size))
self.input_linear = JetMoeParallelExperts(config.num_local_experts, self.input_size, self.hidden_size * 2)
self.output_linear = JetMoeParallelExperts(config.num_local_experts, self.hidden_size, self.input_size)
self.router = JetMoeTopKGating(
input_size=self.input_size,
num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
)
def forward(self, layer_input):
"""
Forward pass of the mixture of experts layer.
Args:
layer_input (Tensor):
Input tensor.
Returns:
Tensor:
Output tensor.
Tensor:
Router logits.
"""
bsz, length, emb_size = layer_input.size()
layer_input = layer_input.reshape(-1, emb_size)
_, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
expert_inputs = layer_input[batch_index]
hidden_states = self.input_linear(expert_inputs, expert_size)
chunked_hidden_states = hidden_states.chunk(2, dim=-1)
hidden_states = self.activation(chunked_hidden_states[0]) * chunked_hidden_states[1]
expert_outputs = self.output_linear(hidden_states, expert_size)
expert_outputs = expert_outputs * batch_gates[:, None]
zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
layer_output = zeros.index_add(0, batch_index, expert_outputs)
layer_output = layer_output.view(bsz, length, self.input_size)
layer_output = layer_output + self.bias
return layer_output, router_logits
class JetMoeMoA(nn.Module):
"""
A Sparsely gated mixture of attention layer with pairs of query- and output-projections as experts.
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: JetMoeConfig):
super().__init__()
self.num_experts = config.num_local_experts
self.input_size = config.hidden_size
self.hidden_size = config.kv_channels * config.num_key_value_heads
self.top_k = config.num_experts_per_tok
self.bias = torch.nn.Parameter(torch.empty(self.input_size))
self.input_linear = JetMoeParallelExperts(self.num_experts, self.input_size, self.hidden_size)
self.output_linear = JetMoeParallelExperts(self.num_experts, self.hidden_size, self.input_size)
self.router = JetMoeTopKGating(
input_size=self.input_size,
num_experts=self.num_experts,
top_k=self.top_k,
)
def map(self, layer_input):
"""
Map inputs to attention experts according to routing decision and compute query projection inside each experts.
"""
# Compute gating topology
bsz, length, emb_size = layer_input.size()
layer_input = layer_input.reshape(-1, emb_size) # [bsz * length, emb_size]
index_sorted_experts, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
topo_info = (index_sorted_experts, batch_index, batch_gates, expert_size)
# Group inputs according to topology and compute query projection
expert_inputs = layer_input[batch_index] # [bsz * length * top_k, emb_size]
expert_outputs = self.input_linear(expert_inputs, expert_size) # [bsz * length * top_k, hidden_size]
# Ungroup queries back to original order
zeros = torch.zeros(
(bsz * length * self.top_k, self.hidden_size), dtype=expert_outputs.dtype, device=expert_outputs.device
)
layer_output = zeros.index_add(0, index_sorted_experts, expert_outputs)
layer_output = layer_output.view(bsz, length, self.top_k, -1) # [bsz, length, top_k, hidden_size]
return layer_output, router_logits, topo_info
def reduce(self, layer_input, topo_info):
"""
Compute output projection inside each attention experts and merge the outputs of different experts.
"""
bsz, length, k, hidden_size = layer_input.size()
layer_input = layer_input.reshape(-1, hidden_size) # [bsz * length * k, hidden_size]
index_sorted_experts, batch_index, batch_gates, expert_size = topo_info
# Group inputs according to topology and compute output projection
expert_inputs = layer_input[index_sorted_experts] # [bsz * length * top_k, hidden_size]
expert_outputs = self.output_linear(expert_inputs, expert_size) # [bsz * length * top_k, emb_size]
# Apply gates to attention expert outputs
expert_outputs = expert_outputs * batch_gates[:, None]
# Ungroup and merge outputs to original order
zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
layer_output = zeros.index_add(0, batch_index, expert_outputs)
layer_output = layer_output.view(bsz, length, self.input_size)
layer_output = layer_output + self.bias
return layer_output
def forward(self, layer_input):
raise NotImplementedError("This module doesn't support call and forward.")
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->JetMoe
class JetMoeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
JetMoeRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with Gemma->JetMoe
class JetMoeRotaryEmbedding(nn.Module):
inv_freq: torch.Tensor # fix linting for `register_buffer`
def __init__(self, config: JetMoeConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
# Copied from transformers.models.llama.modeling_llama.rotate_half
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class JetMoeAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper.
"""
def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
"""
Initialize the JetMoeAttention module.
Args:
config:
Configuration object with model hyperparameters.
layer_idx:
Index of the layer in the model.
"""
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.is_causal = True
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.top_k = config.num_experts_per_tok
self.attention_dropout = config.attention_dropout
self.kv_projection_size = config.kv_channels * config.num_key_value_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_heads = config.num_attention_heads
self.head_dim = config.kv_channels
self.experts = JetMoeMoA(config)
self.kv_proj = torch.nn.Linear(config.hidden_size, self.kv_projection_size * 2, bias=False)
self.rotary_emb = JetMoeRotaryEmbedding(config)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states, router_logits, topo_info = self.experts.map(hidden_states)
key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_values is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
# repeat k/v heads for top-k attention experts
key_states = key_states.repeat(1, self.top_k, 1, 1)
value_states = value_states.repeat(1, self.top_k, 1, 1)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
attn_output = self.experts.reduce(attn_output, topo_info)
attn_output = attn_output.view(bsz, q_len, -1)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, router_logits
class JetMoeSdpaAttention(JetMoeAttention):
"""
JetMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`JetMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from JetMoeAttention.forward
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]], Optional[torch.Tensor]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"JetMoeModel is using JetMoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
bsz, q_len, _ = hidden_states.size()
query_states, router_logits, topo_info = self.experts.map(hidden_states)
key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_values is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
# repeat k/v heads for top-k attention experts
key_states = key_states.repeat(1, self.top_k, 1, 1)
value_states = value_states.repeat(1, self.top_k, 1, 1)
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and causal_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
is_causal = causal_mask is None and q_len > 1
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
attn_output = self.experts.reduce(attn_output, topo_info)
attn_output = attn_output.view(bsz, q_len, -1)
return attn_output, None, router_logits
class JetMoeFlashAttention2(JetMoeAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[
tuple[torch.Tensor, tuple[torch.Tensor]],
Optional[tuple[torch.Tensor, tuple[torch.Tensor], tuple[torch.Tensor, ...]]],
]:
"""
Forward pass of the JetMoeAttention module.
Args:
hidden_states (Optional[torch.FloatTensor]): Input hidden states.
attention_mask (Optional[torch.FloatTensor]): Attention mask.
layer_past (Optional[tuple[torch.Tensor]]): Past layer state.
use_cache (Optional[bool]): Whether to use cached states.
output_attentions (Optional[bool]): Whether to output attention weights.
cache_position (Optional[torch.LongTensor]): Position of the cache.
Returns:
Union[tuple[torch.Tensor, tuple[torch.Tensor]], Optional[tuple[...]]]: Tuple containing outputs.
"""
output_attentions = False
bsz, q_len, hidden_size = hidden_states.size()
# calculate query, key, values
query_states, router_logits, topo_info = self.experts.map(hidden_states)
key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_values is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
# repeat k/v heads for top-k attention experts
key_states = key_states.repeat(1, self.top_k, 1, 1)
value_states = value_states.repeat(1, self.top_k, 1, 1)
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = (
torch.get_autocast_dtype(device_type)
if hasattr(torch, "get_autocast_dtype")
else torch.get_autocast_gpu_dtype()
)
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.kv_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=dropout_rate,
use_top_left_mask=self._flash_attn_uses_top_left_mask,
is_causal=self.is_causal,
).to(input_dtype)
# output projection
attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
attn_output = self.experts.reduce(attn_output, topo_info)
attn_output = attn_output.view(bsz, q_len, hidden_size) # re-assemble all head outputs side by side
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, router_logits
JETMOE_ATTENTION_CLASSES = {
"eager": JetMoeAttention,
"flash_attention_2": JetMoeFlashAttention2,
"sdpa": JetMoeSdpaAttention,
}
class JetMoeBlock(GradientCheckpointingLayer):
def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
"""
Initialize the JetMoeBlock module.
Args:
config:
Configuration object with model hyperparameters.
"""
super().__init__()
self.input_layernorm = JetMoeRMSNorm(config.hidden_size)
self.self_attention = JETMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
self.post_attention_layernorm = JetMoeRMSNorm(config.hidden_size)
self.mlp = JetMoeMoE(config)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[tuple[torch.Tensor], Optional[tuple[torch.Tensor, tuple[torch.FloatTensor, ...]]]]:
# Self Attention
attn_output, self_attn_weights, attn_router_logits = self.self_attention(
hidden_states=self.input_layernorm(hidden_states),
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = hidden_states + attn_output
x_mlp, mlp_router_logits = self.mlp(self.post_attention_layernorm(hidden_states))
hidden_states = hidden_states + x_mlp
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if output_router_logits:
outputs += attn_router_logits, mlp_router_logits
return outputs
@auto_docstring
class JetMoePreTrainedModel(PreTrainedModel):
config: JetMoeConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = False
_no_split_modules = ["JetMoeBlock"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn = True
_supports_sdpa = True
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear,)):
# Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, JetMoeRMSNorm):
module.weight.data.fill_(1.0)
elif isinstance(module, JetMoeParallelExperts):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, (JetMoeMoA, JetMoeMoE)):
module.bias.data.zero_()
@auto_docstring
class JetMoeModel(JetMoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JetMoeBlock`]
Args:
config:
JetMoeConfig
"""
def __init__(self, config: JetMoeConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList([JetMoeBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
self._attn_implementation = config._attn_implementation
self.norm = JetMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> MoeModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_router_logits = (
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
if not isinstance(past_key_values, (type(None), Cache)):
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
batch_size = inputs_embeds.shape[0]
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of JetMoe. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_router_logits = () if output_router_logits else None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
output_router_logits=output_router_logits,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if output_router_logits:
all_router_logits += (layer_outputs[-2], layer_outputs[-1])
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
router_logits=all_router_logits,
)
# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: Union[torch.Tensor, "BlockMask"],
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool = False,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
return attention_mask
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
is_training=self.training,
):
return None
dtype = input_tensor.dtype
sequence_length = input_tensor.shape[1]
if using_compilable_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu", "npu"]
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
class JetMoeForCausalLM(JetMoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = JetMoeModel(config)
self.vocab_size = config.vocab_size
self.aux_loss_coef = config.aux_loss_coef
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.tie_word_embeddings = config.tie_word_embeddings
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
def set_decoder(self, decoder):
self.model = decoder
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
def get_decoder(self):
return self.model
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs,
) -> MoeCausalLMOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs: MoeModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(
logits,
labels,
vocab_size=self.config.vocab_size,
**kwargs,
)
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits,
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not None:
loss += self.aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
return MoeCausalLMOutputWithPast(
loss=loss,
aux_loss=aux_loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
router_logits=outputs.router_logits,
)
class JetMoeForSequenceClassification(GenericForSequenceClassification, JetMoePreTrainedModel): ...
__all__ = ["JetMoeForCausalLM", "JetMoeModel", "JetMoePreTrainedModel", "JetMoeForSequenceClassification"]
| transformers/src/transformers/models/jetmoe/modeling_jetmoe.py/0 | {
"file_path": "transformers/src/transformers/models/jetmoe/modeling_jetmoe.py",
"repo_id": "transformers",
"token_count": 23640
} | 460 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
"""Tokenization classes for LayoutXLM model."""
import os
from shutil import copyfile
from typing import Optional, Union
from ...tokenization_utils import AddedToken
from ...tokenization_utils_base import (
BatchEncoding,
EncodedInput,
PreTokenizedInput,
TextInput,
TextInputPair,
TruncationStrategy,
)
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, TensorType, add_end_docstrings, is_sentencepiece_available, logging
from ..xlm_roberta.tokenization_xlm_roberta_fast import (
VOCAB_FILES_NAMES,
)
if is_sentencepiece_available():
from .tokenization_layoutxlm import LayoutXLMTokenizer
else:
LayoutXLMTokenizer = None
logger = logging.get_logger(__name__)
LAYOUTXLM_ENCODE_KWARGS_DOCSTRING = r"""
add_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to encode the sequences with the special tokens relative to their model.
padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (`int`, *optional*, defaults to 0):
If set to a number along with `max_length`, the overflowing tokens returned when
`return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
return_token_type_ids (`bool`, *optional*):
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer's default, defined by the `return_outputs` attribute.
[What are token type IDs?](../glossary#token-type-ids)
return_attention_mask (`bool`, *optional*):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
of returning overflowing tokens.
return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
Whether or not to return special tokens mask information.
return_offsets_mapping (`bool`, *optional*, defaults to `False`):
Whether or not to return `(char_start, char_end)` for each token.
This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
Python's tokenizer, this method will raise `NotImplementedError`.
return_length (`bool`, *optional*, defaults to `False`):
Whether or not to return the lengths of the encoded inputs.
verbose (`bool`, *optional*, defaults to `True`):
Whether or not to print more information and warnings.
**kwargs: passed to the `self.tokenize()` method
Return:
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model.
[What are input IDs?](../glossary#input-ids)
- **bbox** -- List of bounding boxes to be fed to a model.
- **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
if *"token_type_ids"* is in `self.model_input_names`).
[What are token type IDs?](../glossary#token-type-ids)
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
[What are attention masks?](../glossary#attention-mask)
- **labels** -- List of labels to be fed to a model. (when `word_labels` is specified).
- **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
`return_overflowing_tokens=True`).
- **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
`return_overflowing_tokens=True`).
- **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
- **length** -- The length of the inputs (when `return_length=True`).
"""
class LayoutXLMTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" LayoutXLM tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
cls_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
The bounding box to use for the special [CLS] token.
sep_token_box (`list[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`):
The bounding box to use for the special [SEP] token.
pad_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
The bounding box to use for the special [PAD] token.
pad_token_label (`int`, *optional*, defaults to -100):
The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
CrossEntropyLoss.
only_label_first_subword (`bool`, *optional*, defaults to `True`):
Whether or not to only label the first subword, in case word labels are provided.
additional_special_tokens (`list[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
Additional special tokens used by the tokenizer.
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = LayoutXLMTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
cls_token_box=[0, 0, 0, 0],
sep_token_box=[1000, 1000, 1000, 1000],
pad_token_box=[0, 0, 0, 0],
pad_token_label=-100,
only_label_first_subword=True,
**kwargs,
):
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
mask_token=mask_token,
cls_token_box=cls_token_box,
sep_token_box=sep_token_box,
pad_token_box=pad_token_box,
pad_token_label=pad_token_label,
only_label_first_subword=only_label_first_subword,
**kwargs,
)
self.vocab_file = vocab_file
# additional properties
self.cls_token_box = cls_token_box
self.sep_token_box = sep_token_box
self.pad_token_box = pad_token_box
self.pad_token_label = pad_token_label
self.only_label_first_subword = only_label_first_subword
@add_end_docstrings(LAYOUTXLM_ENCODE_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
word_labels: Optional[Union[list[int], list[list[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with word-level normalized bounding boxes and optional labels.
Args:
text (`str`, `list[str]`, `list[list[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
words).
text_pair (`list[str]`, `list[list[str]]`):
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
(pretokenized string).
boxes (`list[list[int]]`, `list[list[list[int]]]`):
Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
word_labels (`list[int]`, `list[list[int]]`, *optional*):
Word-level integer labels (for token classification tasks such as FUNSD, CORD).
"""
# Input type checking for clearer error
def _is_valid_text_input(t):
if isinstance(t, str):
# Strings are fine
return True
elif isinstance(t, (list, tuple)):
# List are fine as long as they are...
if len(t) == 0:
# ... empty
return True
elif isinstance(t[0], str):
# ... list of strings
return True
elif isinstance(t[0], (list, tuple)):
# ... list with an empty list or with a list of strings
return len(t[0]) == 0 or isinstance(t[0][0], str)
else:
return False
else:
return False
if text_pair is not None:
# in case text + text_pair are provided, text = questions, text_pair = words
if not _is_valid_text_input(text):
raise ValueError("text input must of type `str` (single example) or `list[str]` (batch of examples). ")
if not isinstance(text_pair, (list, tuple)):
raise ValueError(
"words must of type `list[str]` (single pretokenized example), "
"or `list[list[str]]` (batch of pretokenized examples)."
)
else:
# in case only text is provided => must be words
if not isinstance(text, (list, tuple)):
raise ValueError(
"Words must of type `list[str]` (single pretokenized example), "
"or `list[list[str]]` (batch of pretokenized examples)."
)
if text_pair is not None:
is_batched = isinstance(text, (list, tuple))
else:
is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
words = text if text_pair is None else text_pair
if boxes is None:
raise ValueError("You must provide corresponding bounding boxes")
if is_batched:
if len(words) != len(boxes):
raise ValueError("You must provide words and boxes for an equal amount of examples")
for words_example, boxes_example in zip(words, boxes):
if len(words_example) != len(boxes_example):
raise ValueError("You must provide as many words as there are bounding boxes")
else:
if len(words) != len(boxes):
raise ValueError("You must provide as many words as there are bounding boxes")
if is_batched:
if text_pair is not None and len(text) != len(text_pair):
raise ValueError(
f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
f" {len(text_pair)}."
)
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
is_pair = bool(text_pair is not None)
return self.batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
text=text,
text_pair=text_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> list[str]:
batched_input = [(text, pair)] if pair else [text]
self._tokenizer.encode_special_tokens = kwargs.pop(
"split_special_tokens", self._tokenizer.encode_special_tokens
)
encodings = self._tokenizer.encode_batch(
batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
)
return encodings[0].tokens
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
list[TextInput],
list[TextInputPair],
list[PreTokenizedInput],
],
is_pair: Optional[bool] = None,
boxes: Optional[list[list[list[int]]]] = None,
word_labels: Optional[list[list[int]]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
if not isinstance(batch_text_or_text_pairs, list):
raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
# Set the truncation and padding strategy and restore the initial configuration
self.set_truncation_and_padding(
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
)
if is_pair:
batch_text_or_text_pairs = [(text.split(), text_pair) for text, text_pair in batch_text_or_text_pairs]
encodings = self._tokenizer.encode_batch(
batch_text_or_text_pairs,
add_special_tokens=add_special_tokens,
is_pretokenized=True, # we set this to True as LayoutLMv2 always expects pretokenized inputs
)
# Convert encoding to dict
# `Tokens` has type: tuple[
# list[dict[str, list[list[int]]]] or list[dict[str, 2D-Tensor]],
# list[EncodingFast]
# ]
# with nested dimensions corresponding to batch, overflows, sequence length
tokens_and_encodings = [
self._convert_encoding(
encoding=encoding,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=True
if word_labels is not None
else return_offsets_mapping, # we use offsets to create the labels
return_length=return_length,
verbose=verbose,
)
for encoding in encodings
]
# Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
# From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
# (we say ~ because the number of overflow varies with the example in the batch)
#
# To match each overflowing sample with the original sample in the batch
# we add an overflow_to_sample_mapping array (see below)
sanitized_tokens = {}
for key in tokens_and_encodings[0][0]:
stack = [e for item, _ in tokens_and_encodings for e in item[key]]
sanitized_tokens[key] = stack
sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
# If returning overflowing tokens, we need to return a mapping
# from the batch idx to the original sample
if return_overflowing_tokens:
overflow_to_sample_mapping = []
for i, (toks, _) in enumerate(tokens_and_encodings):
overflow_to_sample_mapping += [i] * len(toks["input_ids"])
sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
for input_ids in sanitized_tokens["input_ids"]:
self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
# create the token boxes
token_boxes = []
for batch_index in range(len(sanitized_tokens["input_ids"])):
if return_overflowing_tokens:
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
else:
original_index = batch_index
token_boxes_example = []
for id, sequence_id, word_id in zip(
sanitized_tokens["input_ids"][batch_index],
sanitized_encodings[batch_index].sequence_ids,
sanitized_encodings[batch_index].word_ids,
):
if word_id is not None:
if is_pair and sequence_id == 0:
token_boxes_example.append(self.pad_token_box)
else:
token_boxes_example.append(boxes[original_index][word_id])
else:
if id == self.cls_token_id:
token_boxes_example.append(self.cls_token_box)
elif id == self.sep_token_id:
token_boxes_example.append(self.sep_token_box)
elif id == self.pad_token_id:
token_boxes_example.append(self.pad_token_box)
else:
raise ValueError("Id not recognized")
token_boxes.append(token_boxes_example)
sanitized_tokens["bbox"] = token_boxes
# optionally, create the labels
if word_labels is not None:
labels = []
for batch_index in range(len(sanitized_tokens["input_ids"])):
if return_overflowing_tokens:
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
else:
original_index = batch_index
labels_example = []
for id, offset, word_id in zip(
sanitized_tokens["input_ids"][batch_index],
sanitized_tokens["offset_mapping"][batch_index],
sanitized_encodings[batch_index].word_ids,
):
if word_id is not None:
if self.only_label_first_subword:
if offset[0] == 0:
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
labels_example.append(word_labels[original_index][word_id])
else:
labels_example.append(self.pad_token_label)
else:
labels_example.append(word_labels[original_index][word_id])
else:
labels_example.append(self.pad_token_label)
labels.append(labels_example)
sanitized_tokens["labels"] = labels
# finally, remove offsets if the user didn't want them
if not return_offsets_mapping:
del sanitized_tokens["offset_mapping"]
return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
boxes: Optional[list[list[int]]] = None,
word_labels: Optional[list[int]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_tensors: Optional[bool] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
# make it a batched input
# 2 options:
# 1) only text, in case text must be a list of str
# 2) text + text_pair, in which case text = str and text_pair a list of str
batched_input = [(text, text_pair)] if text_pair else [text]
batched_boxes = [boxes]
batched_word_labels = [word_labels] if word_labels is not None else None
batched_output = self._batch_encode_plus(
batched_input,
is_pair=bool(text_pair is not None),
boxes=batched_boxes,
word_labels=batched_word_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
# Return tensor is None, then we can remove the leading batch axis
# Overflowing tokens are returned as a batch of output so we keep them in this case
if return_tensors is None and not return_overflowing_tokens:
batched_output = BatchEncoding(
{
key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
for key, value in batched_output.items()
},
batched_output.encodings,
)
self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
return batched_output
def _pad(
self,
encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs:
Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
padding_side (`str`, *optional*):
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(required_input)
if needs_to_be_padded:
difference = max_length - len(required_input)
padding_side = padding_side if padding_side is not None else self.padding_side
if padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
)
if "bbox" in encoded_inputs:
encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
if "labels" in encoded_inputs:
encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
elif padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if "bbox" in encoded_inputs:
encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
if "labels" in encoded_inputs:
encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
else:
raise ValueError("Invalid padding strategy:" + str(padding_side))
return encoded_inputs
def build_inputs_with_special_tokens(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
) -> list[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLM-RoBERTa sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`list[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def create_token_type_ids_from_sequences(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
) -> list[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
)
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory.")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
__all__ = ["LayoutXLMTokenizerFast"]
| transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm_fast.py/0 | {
"file_path": "transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm_fast.py",
"repo_id": "transformers",
"token_count": 18119
} | 461 |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/lfm2/modular_lfm2.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_lfm2.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ...cache_utils import Cache
from ...generation import GenerationMixin
from ...integrations import use_kernel_forward_from_hub
from ...masking_utils import create_causal_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.deprecation import deprecate_kwarg
from ...utils.generic import check_model_inputs
from ...utils.import_utils import is_causal_conv1d_available
from .configuration_lfm2 import Lfm2Config
if is_causal_conv1d_available():
from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
else:
causal_conv1d_fn, causal_conv1d_update = None, None
@use_kernel_forward_from_hub("RMSNorm")
class Lfm2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Lfm2RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class Lfm2RotaryEmbedding(nn.Module):
inv_freq: torch.Tensor # fix linting for `register_buffer`
def __init__(self, config: Lfm2Config, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
class Lfm2MLP(nn.Module):
def __init__(self, config: Lfm2Config):
super().__init__()
intermediate_size = config.intermediate_size
if config.block_auto_adjust_ff_dim:
intermediate_size = int(2 * intermediate_size / 3)
# custom dim factor multiplier
if config.block_ffn_dim_multiplier is not None:
intermediate_size = int(config.block_ffn_dim_multiplier * intermediate_size)
intermediate_size = config.block_multiple_of * (
(intermediate_size + config.block_multiple_of - 1) // config.block_multiple_of
)
self.w1 = nn.Linear(config.hidden_size, intermediate_size, bias=False)
self.w3 = nn.Linear(config.hidden_size, intermediate_size, bias=False)
self.w2 = nn.Linear(intermediate_size, config.hidden_size, bias=False)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class Lfm2HybridConvCache:
"""
Attention and conv cache for Lfm2.
It stores the Key and Value states as a list of tensors, one for each layer.
Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
Conv layer cache shape: `[batch_size, hidden_size, L_cache-1]`.
"""
# Override @property existing in Cache
max_batch_size = None
is_compileable = False
key_cache = None
value_cache = None
def __init__(
self,
config: Lfm2Config,
max_batch_size: int,
dtype: torch.dtype = torch.float32,
device: Union[torch.device, str, None] = None,
):
self.key_cache = []
self.value_cache = []
self.max_batch_size = max_batch_size
self.layer_types = config.layer_types
self.first_attention_layer = self.layer_types.index("full_attention")
self.conv_L_cache = config.conv_L_cache
self._dtype = dtype
self.conv_cache: list[torch.Tensor] = []
device = torch.device(device) if device is not None else None
for _ in range(config.num_hidden_layers):
conv_state = torch.zeros(
self.max_batch_size,
config.hidden_size,
self.conv_L_cache,
dtype=self._dtype,
device=device,
)
torch._dynamo.mark_static_address(conv_state)
self.conv_cache.append(conv_state)
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: int,
cache_kwargs: Optional[dict[str, Any]] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
Parameters:
key_states (`torch.Tensor`):
The new key states to cache.
value_states (`torch.Tensor`):
The new value states to cache.
layer_idx (`int`):
The index of the layer to cache the states for.
cache_kwargs (`Dict[str, Any]`, `optional`):
Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
Return:
A tuple containing the updated key and value states.
"""
# Update the cache
if key_states is not None:
if len(self.key_cache) <= layer_idx:
# There may be skipped layers, fill them with empty lists
for _ in range(len(self.key_cache), layer_idx):
self.key_cache.append(torch.tensor([]))
self.value_cache.append(torch.tensor([]))
self.key_cache.append(key_states)
self.value_cache.append(value_states)
elif (
not self.key_cache[layer_idx].numel() # prefers not t.numel() to len(t) == 0 to export the model
): # fills previously skipped layers; checking for tensor causes errors
self.key_cache[layer_idx] = key_states
self.value_cache[layer_idx] = value_states
else:
self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
return self.key_cache[layer_idx], self.value_cache[layer_idx]
def reorder_cache(self, beam_idx: torch.LongTensor):
"""Reorders the cache for beam search, given the selected beam indices."""
for layer_idx in range(len(self.key_cache)):
device = self.key_cache[layer_idx].device
self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
device = self.value_cache[layer_idx].device
self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
device = self.conv_cache[layer_idx].device
self.conv_cache[layer_idx] = self.conv_cache[layer_idx].index_select(0, beam_idx.to(device))
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
"""Returns the sequence length of the cached states. A layer index can be optionally passed."""
# take any layer that contains cache and not empty tensor
layer_idx = self.first_attention_layer if self.layer_types[layer_idx] != "full_attention" else layer_idx
if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
return 0
return self.key_cache[layer_idx].shape[-2]
def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
"""
Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
the given layer at `layer_idx`.
The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns (i.e. sliding_window, chunk_size),
for each layer.
"""
full_mask_kv_offset = 0
query_length = cache_position.shape[0]
past_seen_tokens = self.get_seq_length()
kv_length = query_length + past_seen_tokens
return kv_length, full_mask_kv_offset
def crop(self, max_length: int):
"""Crop the cache to the given length"""
if max_length < 0:
max_length = self.get_seq_length() - abs(max_length)
if self.get_seq_length() <= max_length:
return
for idx in range(len(self.key_cache)):
if self.key_cache[idx].numel():
self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
def __len__(self) -> int:
return len(self.key_cache)
def __getitem__(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
return self.key_cache[layer_idx], self.value_cache[layer_idx]
def reset(self):
for layer_idx in range(len(self.conv_cache)):
# In-place ops prevent breaking the static address
self.conv_cache[layer_idx].zero_()
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs: Unpack[TransformersKwargs],
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class Lfm2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Lfm2Config, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.is_causal = True
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
self.out_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
self.q_layernorm = Lfm2RMSNorm(self.head_dim, eps=config.norm_eps)
self.k_layernorm = Lfm2RMSNorm(self.head_dim, eps=config.norm_eps)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_values: Optional[Lfm2HybridConvCache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_layernorm(self.q_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
key_states = self.k_layernorm(self.k_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(*hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_values is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
output = self.out_proj(attn_output)
return output, attn_weights
def apply_mask_to_padding_states(hidden_states, attention_mask):
"""
Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
"""
if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
dtype = hidden_states.dtype
hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
return hidden_states
kernel_modules = (causal_conv1d_fn, causal_conv1d_update)
is_fast_path_available = all(kernel_modules)
class Lfm2ShortConv(nn.Module):
def __init__(
self,
config: Lfm2Config,
layer_idx: int,
):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.L_cache = config.conv_L_cache
self.bias = config.conv_bias
self.conv = nn.Conv1d(
in_channels=config.hidden_size,
out_channels=config.hidden_size,
kernel_size=self.L_cache,
groups=config.hidden_size,
bias=self.bias,
padding=self.L_cache - 1,
)
self.in_proj = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=self.bias)
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=self.bias)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def cuda_kernels_forward(
self,
x: torch.Tensor,
past_key_values: Optional[Lfm2HybridConvCache] = None,
cache_position: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
):
x = apply_mask_to_padding_states(x, attention_mask)
BCx = self.in_proj(x).transpose(-1, -2)
B, C, x = BCx.chunk(3, dim=-2)
Bx = B * x
conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
if past_key_values is not None and cache_position[0] > 0:
conv_out = causal_conv1d_update(
Bx.squeeze(-1),
past_key_values.conv_cache[self.layer_idx],
conv_weights,
self.conv.bias,
None,
)
conv_out = conv_out.unsqueeze(-1)
else:
if past_key_values is not None:
conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
past_key_values.conv_cache[self.layer_idx].copy_(conv_state)
conv_out = causal_conv1d_fn(Bx, conv_weights, self.conv.bias, activation=None)
y = C * conv_out
y = self.out_proj(y.transpose(-1, -2).contiguous())
return y
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def slow_forward(
self,
x: torch.Tensor,
past_key_values: Optional[Lfm2HybridConvCache] = None,
cache_position: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
):
seqlen = x.shape[1]
x = apply_mask_to_padding_states(x, attention_mask)
BCx = self.in_proj(x).transpose(-1, -2)
B, C, x = BCx.chunk(3, dim=-2)
Bx = B * x
if past_key_values is not None and cache_position[0] > 0:
conv_state = past_key_values.conv_cache[self.layer_idx]
cache_position = cache_position.clamp(0, self.L_cache - 1)
conv_state = conv_state.roll(shifts=-1, dims=-1)
conv_state[:, :, cache_position] = Bx.to(device=conv_state.device, dtype=conv_state.dtype)
past_key_values.conv_cache[self.layer_idx].copy_(conv_state)
conv_out = torch.sum(conv_state.to(Bx.device) * self.conv.weight[:, 0, :], dim=-1)
if self.bias:
conv_out += self.conv.bias
conv_out = conv_out.unsqueeze(-1)
else:
if past_key_values is not None:
conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
past_key_values.conv_cache[self.layer_idx].copy_(conv_state)
conv_out = self.conv(Bx)[..., :seqlen]
y = C * conv_out
y = y.transpose(-1, -2).contiguous()
y = self.out_proj(y)
return y
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
past_key_values: Optional[Lfm2HybridConvCache] = None,
cache_position: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
):
if is_fast_path_available and "cuda" in hidden_states.device.type and not torch._dynamo.is_compiling():
return self.cuda_kernels_forward(hidden_states, past_key_values, cache_position, attention_mask)
return self.slow_forward(hidden_states, past_key_values, cache_position, attention_mask)
class Lfm2DecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: Lfm2Config, layer_idx: int):
super().__init__()
self.is_attention_layer = config.layer_types[layer_idx] == "full_attention"
if self.is_attention_layer:
self.self_attn = Lfm2Attention(config, layer_idx)
else:
self.conv = Lfm2ShortConv(config, layer_idx)
self.feed_forward = Lfm2MLP(config)
self.operator_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
self.ffn_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[tuple[torch.Tensor]] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> torch.Tensor:
residual = hidden_states
if self.is_attention_layer:
hidden_states, _ = self.self_attn(
hidden_states=self.operator_norm(hidden_states),
position_embeddings=position_embeddings,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
cache_position=cache_position,
**kwargs,
)
else:
hidden_states = self.conv(
hidden_states=self.operator_norm(hidden_states),
past_key_values=past_key_values,
cache_position=cache_position,
attention_mask=attention_mask,
)
hidden_states = hidden_states + residual
hidden_states = hidden_states + self.feed_forward(self.ffn_norm(hidden_states))
return hidden_states
@auto_docstring
class Lfm2PreTrainedModel(PreTrainedModel):
config: Lfm2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Lfm2DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn = True
_supports_sdpa = True
_supports_flex_attn = True
_can_compile_fullgraph = False
_supports_attention_backend = True
_can_record_outputs = {
"hidden_states": Lfm2DecoderLayer,
"attentions": Lfm2Attention,
}
@auto_docstring
class Lfm2Model(Lfm2PreTrainedModel):
def __init__(self, config: Lfm2Config):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[Lfm2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.rotary_emb = Lfm2RotaryEmbedding(config=config)
self.gradient_checkpointing = False
self.pos_emb = Lfm2RotaryEmbedding(config)
self.embedding_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
# Initialize weights and apply final processing
self.post_init()
@check_model_inputs
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Lfm2HybridConvCache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutputWithPast:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if use_cache and past_key_values is None:
batch_size = inputs_embeds.shape[0]
past_key_values = Lfm2HybridConvCache(
config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = create_causal_mask(
config=self.config,
input_embeds=inputs_embeds,
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=past_key_values,
position_ids=position_ids,
)
hidden_states = inputs_embeds
position_embeddings = self.pos_emb(hidden_states, position_ids)
# decoder layers
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
hidden_states = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_values=past_key_values,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = self.embedding_norm(hidden_states)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
)
@auto_docstring
class Lfm2ForCausalLM(Lfm2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
def __init__(self, config):
super().__init__(config)
self.model = Lfm2Model(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[TransformersKwargs],
) -> CausalLMOutputWithPast:
r"""
Example:
```python
>>> from transformers import AutoTokenizer, Lfm2ForCausalLM
>>> model = Lfm2ForCausalLM.from_pretrained("meta-lfm2/Lfm2-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-lfm2/Lfm2-2-7b-hf")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
outputs: BaseModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = ["Lfm2ForCausalLM", "Lfm2Model", "Lfm2PreTrainedModel"]
| transformers/src/transformers/models/lfm2/modeling_lfm2.py/0 | {
"file_path": "transformers/src/transformers/models/lfm2/modeling_lfm2.py",
"repo_id": "transformers",
"token_count": 14489
} | 462 |
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for LLaMA."""
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Optional
import sentencepiece as spm
from ...convert_slow_tokenizer import import_protobuf
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
from ...utils.import_utils import requires
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
SPIECE_UNDERLINE = "▁"
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
correct. If you don't know the answer to a question, please don't share false information.""" # fmt: skip
@requires(backends=("sentencepiece",))
class LlamaTokenizer(PreTrainedTokenizer):
"""
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model.
Args:
vocab_file (`str`):
Path to the vocabulary file.
unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
The end of sequence token.
pad_token (`str` or `tokenizers.AddedToken`, *optional*):
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation.
sp_model_kwargs (`dict[str, Any]`, `Optional`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
add_bos_token (`bool`, *optional*, defaults to `True`):
Whether or not to add an `bos_token` at the start of sequences.
add_eos_token (`bool`, *optional*, defaults to `False`):
Whether or not to add an `eos_token` at the end of sequences.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.
use_default_system_prompt (`bool`, *optional*, defaults to `False`):
Whether or not the default system prompt for Llama should be used.
spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to add spaces between special tokens.
legacy (`bool`, *optional*):
Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
and #25224 which includes fixes to properly handle tokens that appear after special tokens.
Make sure to also set `from_slow` to `True`.
A simple example:
- `legacy=True`:
```python
>>> from transformers import LlamaTokenizerFast
>>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=True, from_slow=True)
>>> tokenizer.encode("Hello <s>.") # 869 is '▁.'
[1, 15043, 29871, 1, 869]
```
- `legacy=False`:
```python
>>> from transformers import LlamaTokenizerFast
>>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=False, from_slow=True)
>>> tokenizer.encode("Hello <s>.") # 29889 is '.'
[1, 15043, 29871, 1, 29889]
```
Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
add_prefix_space (`bool`, *optional*, defaults to `True`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. Again, this should be set with `from_slow=True` to make sure it's taken into account.
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token=None,
sp_model_kwargs: Optional[dict[str, Any]] = None,
add_bos_token=True,
add_eos_token=False,
clean_up_tokenization_spaces=False,
use_default_system_prompt=False,
spaces_between_special_tokens=False,
legacy=None,
add_prefix_space=True,
**kwargs,
):
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
if legacy is None:
logger.warning_once(
f"You are using the default legacy behaviour of the {self.__class__}. This is"
" expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
" If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
" means, and thoroughly read the reason why this was added as explained in"
" https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file"
" you can ignore this message"
)
legacy = True
self.legacy = legacy
self.vocab_file = vocab_file
self.add_bos_token = add_bos_token
self.add_eos_token = add_eos_token
self.use_default_system_prompt = use_default_system_prompt
self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
self.add_prefix_space = add_prefix_space
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
add_bos_token=add_bos_token,
add_eos_token=add_eos_token,
sp_model_kwargs=self.sp_model_kwargs,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
use_default_system_prompt=use_default_system_prompt,
spaces_between_special_tokens=spaces_between_special_tokens,
legacy=legacy,
add_prefix_space=add_prefix_space,
**kwargs,
)
@property
def unk_token_length(self):
return len(self.sp_model.encode(str(self.unk_token)))
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
def get_spm_processor(self, from_slow=False):
tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
if self.legacy or from_slow: # no dependency on protobuf
tokenizer.Load(self.vocab_file)
return tokenizer
with open(self.vocab_file, "rb") as f:
sp_model = f.read()
model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
model = model_pb2.ModelProto.FromString(sp_model)
normalizer_spec = model_pb2.NormalizerSpec()
normalizer_spec.add_dummy_prefix = False
model.normalizer_spec.MergeFrom(normalizer_spec)
sp_model = model.SerializeToString()
tokenizer.LoadFromSerializedProto(sp_model)
return tokenizer
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
state["sp_model_proto"] = self.sp_model.serialized_model_proto()
return state
def __setstate__(self, d):
self.__dict__.update(d)
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
@property
def vocab_size(self):
"""Returns vocab size"""
return self.sp_model.get_piece_size()
def get_vocab(self):
"""Returns vocab as a dict"""
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
def tokenize(self, text: "TextInput", **kwargs) -> list[str]:
"""
Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
first token is special.
"""
if self.legacy or len(text) == 0:
return super().tokenize(text, **kwargs)
text = text.replace(SPIECE_UNDERLINE, " ")
if self.add_prefix_space:
text = SPIECE_UNDERLINE + text
tokens = super().tokenize(text, **kwargs)
if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
tokens = tokens[1:]
return tokens
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
def _tokenize(self, text, **kwargs):
"""
Returns a tokenized string.
We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
`['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
`unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
"""
if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
return self.sp_model.encode(text, out_type=str)
# 1. Encode string + prefix ex: "<unk> Hey"
tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = self.sp_model.IdToPiece(index)
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
# since we manually add the prefix space, we have to remove it when decoding
if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
tokens[0] = tokens[0][1:]
current_sub_tokens = []
out_string = ""
prev_is_special = False
for i, token in enumerate(tokens):
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special and i != 0 and self.legacy:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
if prev_is_special and i == 1 and self.add_prefix_space and not token.startswith(SPIECE_UNDERLINE):
out_string += " "
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
return out_string
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> tuple[str]:
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
output = bos_token_id + token_ids_0 + eos_token_id
if token_ids_1 is not None:
output = output + bos_token_id + token_ids_1 + eos_token_id
return output
def get_special_tokens_mask(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
) -> list[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
bos_token_id = [1] if self.add_bos_token else []
eos_token_id = [1] if self.add_eos_token else []
if token_ids_1 is None:
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
return (
bos_token_id
+ ([0] * len(token_ids_0))
+ eos_token_id
+ bos_token_id
+ ([0] * len(token_ids_1))
+ eos_token_id
)
def create_token_type_ids_from_sequences(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
) -> list[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
if token_ids_1 is None, only returns the first portion of the mask (0s).
Args:
token_ids_0 (`list[int]`):
List of ids.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
if token_ids_1 is not None:
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
return output
__all__ = ["LlamaTokenizer"]
| transformers/src/transformers/models/llama/tokenization_llama.py/0 | {
"file_path": "transformers/src/transformers/models/llama/tokenization_llama.py",
"repo_id": "transformers",
"token_count": 7924
} | 463 |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Llava-NeXT model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig
logger = logging.get_logger(__name__)
class LlavaNextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaNextForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf)
model.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
The config object or dictionary of the vision backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
The config object or dictionary of the text backbone.
image_token_index (`int`, *optional*, defaults to 32000):
The image token index to encode the image prompt.
projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
The activation function used by the multimodal projector.
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
The feature selection strategy used to select the vision feature from the vision backbone.
Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
If `"full"`, the full vision features are used.
vision_feature_layer (`Union[int, list[int]]`, *optional*, defaults to -2):
The index of the layer to select the vision feature. If multiple indices are provided,
the vision feature of the corresponding indices will be concatenated to form the
vision features.
image_grid_pinpoints (`List`, *optional*, defaults to `[[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]`):
A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list
of the form `(height, width)`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
image_seq_length (`int`, *optional*, defaults to 576):
Sequence length of one image embedding.
multimodal_projector_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the multimodal projector.
Example:
```python
>>> from transformers import LlavaNextForConditionalGeneration, LlavaNextConfig, CLIPVisionConfig, LlamaConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = CLIPVisionConfig()
>>> # Initializing a Llama config
>>> text_config = LlamaConfig()
>>> # Initializing a Llava-Next llava-hf/llava-v1.6-mistral-7b-hf style configuration
>>> configuration = LlavaNextConfig(vision_config, text_config)
>>> # Initializing a model from the llava-hf/llava-v1.6-mistral-7b-hf style configuration
>>> model = LlavaNextForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "llava_next"
attribute_map = {
"image_token_id": "image_token_index",
}
sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
def __init__(
self,
vision_config=None,
text_config=None,
image_token_index=32000,
projector_hidden_act="gelu",
vision_feature_select_strategy="default",
vision_feature_layer=-2,
image_grid_pinpoints=None,
tie_word_embeddings=False,
image_seq_length=576,
multimodal_projector_bias=True,
**kwargs,
):
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
self.image_seq_length = image_seq_length
self.multimodal_projector_bias = multimodal_projector_bias
if vision_feature_select_strategy not in ["default", "full"]:
raise ValueError(
"vision_feature_select_strategy should be one of 'default', 'full'."
f"Got: {vision_feature_select_strategy}"
)
self.vision_feature_select_strategy = vision_feature_select_strategy
self.vision_feature_layer = vision_feature_layer
image_grid_pinpoints = (
image_grid_pinpoints
if image_grid_pinpoints is not None
else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
)
self.image_grid_pinpoints = image_grid_pinpoints
if isinstance(vision_config, dict):
vision_config["model_type"] = vision_config.get("model_type", "clip_vision_model")
vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
elif vision_config is None:
vision_config = CONFIG_MAPPING["clip_vision_model"](
intermediate_size=4096,
hidden_size=1024,
patch_size=14,
image_size=336,
num_hidden_layers=24,
num_attention_heads=16,
vocab_size=32000,
projection_dim=768,
)
self.vision_config = vision_config
if isinstance(text_config, dict):
text_config["model_type"] = text_config.get("model_type", "llama")
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
text_config = CONFIG_MAPPING["llama"]()
self.text_config = text_config
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
__all__ = ["LlavaNextConfig"]
| transformers/src/transformers/models/llava_next/configuration_llava_next.py/0 | {
"file_path": "transformers/src/transformers/models/llava_next/configuration_llava_next.py",
"repo_id": "transformers",
"token_count": 2592
} | 464 |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert LLaVa-Onevision checkpoints from the original repository.
URL: https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main
"""
import argparse
import gc
import glob
import json
from pathlib import Path
import requests
import torch
from accelerate import init_empty_weights
from huggingface_hub import hf_hub_download, snapshot_download
from PIL import Image
from safetensors import safe_open
from transformers import (
AddedToken,
AutoConfig,
AutoTokenizer,
LlavaOnevisionConfig,
LlavaOnevisionForConditionalGeneration,
LlavaOnevisionImageProcessor,
LlavaOnevisionProcessor,
LlavaOnevisionVideoProcessor,
SiglipVisionConfig,
)
KEYS_TO_MODIFY_MAPPING = {
"model.vision_tower.": "",
"model.mm_projector": "multi_modal_projector",
"model": "model.model",
"vision_model.model": "vision_model",
"lm_head": "language_model.lm_head",
"model.model": "language_model.model",
"multi_modal_projector.0": "multi_modal_projector.linear_1",
"multi_modal_projector.2": "multi_modal_projector.linear_2",
"language_model.model.image_newline": "image_newline",
}
chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n'}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all video then #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '<video>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
def load_original_state_dict(model_id):
directory_path = snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"])
original_state_dict = {}
for path in glob.glob(f"{directory_path}/*"):
if path.endswith(".safetensors"):
with safe_open(path, framework="pt", device="cpu") as f:
for key in f.keys():
original_state_dict[key] = f.get_tensor(key)
# tied weights so lm.head is not saved. Let's clone to load state dict
if "lm_head.weight" not in original_state_dict:
original_state_dict["lm_head.weight"] = original_state_dict["model.embed_tokens.weight"].clone()
return original_state_dict
def convert_state_dict_to_hf(state_dict):
new_state_dict = {}
for key, value in state_dict.items():
if key.endswith(".inv_freq"):
continue
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
key = key.replace(key_to_modify, new_key)
new_state_dict[key] = value.to(torch.float16)
return new_state_dict
def load_image():
url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
image = Image.open(requests.get(url, stream=True).raw)
return image
def convert_llava_to_hf(model_id, pytorch_dump_folder_path, push_to_hub=False):
# load original config
filepath = hf_hub_download(repo_id=model_id, filename="config.json", repo_type="model")
# read json
with open(filepath) as f:
data = json.load(f)
print(data)
if model_id in ["lmms-lab/llava-onevision-qwen2-0.5b-ov", "lmms-lab/llava-onevision-qwen2-0.5b-si"]:
text_model_id = "Qwen/Qwen2-0.5B-Instruct"
elif model_id in [
"lmms-lab/llava-onevision-qwen2-7b-ov",
"lmms-lab/llava-onevision-qwen2-7b-si",
"lmms-lab/llava-onevision-qwen2-7b-ov-chat",
]:
text_model_id = "Qwen/Qwen2-7B-Instruct"
elif model_id in [
"lmms-lab/llava-onevision-qwen2-72b-ov",
"lmms-lab/llava-onevision-qwen2-72b-si",
"lmms-lab/llava-onevision-qwen2-72b-ov-chat",
]:
text_model_id = "Qwen/Qwen2-72B-Instruct"
vision_model_id = data["mm_vision_tower"]
torch.set_default_dtype(torch.float16)
text_config = AutoConfig.from_pretrained(text_model_id)
tokenizer = AutoTokenizer.from_pretrained(text_model_id, use_fast=True)
tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False), special_tokens=True)
tokenizer.add_tokens(AddedToken("<video>", special=True, normalized=False), special_tokens=True)
image_processor = LlavaOnevisionImageProcessor.from_pretrained(vision_model_id)
video_processor = LlavaOnevisionVideoProcessor.from_pretrained(vision_model_id)
processor = LlavaOnevisionProcessor(
tokenizer=tokenizer,
video_processor=video_processor,
image_processor=image_processor,
num_image_tokens=729,
vision_feature_select_strategy="full",
chat_template=chat_template,
)
vision_config = SiglipVisionConfig(
hidden_size=1152,
image_size=384,
intermediate_size=4304,
num_attention_heads=16,
num_hidden_layers=26, # drop the last layer
patch_size=14,
vision_use_head=False, # no head
).to_dict()
config = LlavaOnevisionConfig(
text_config=text_config.to_dict(),
vision_config=vision_config,
use_image_newline_parameter=True,
)
with init_empty_weights():
model = LlavaOnevisionForConditionalGeneration(config)
# load original state dict
state_dict = load_original_state_dict(model_id)
state_dict = convert_state_dict_to_hf(state_dict)
model.load_state_dict(state_dict, assign=True)
model.eval()
pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
mu = torch.mean(pre_expansion_embeddings, dim=0).float()
n = pre_expansion_embeddings.size()[0]
sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
# We add an image token so we resize the model
# Pad to 64 for performance reasons
# Qwen-based models have extra unused space in the vocab size already, so no need to resize
pad_shape = 64
vocab_size = config.text_config.vocab_size
num_tokens = vocab_size + 2
model.resize_token_embeddings(num_tokens, pad_to_multiple_of=pad_shape)
model.language_model.model.embed_tokens.weight.data[vocab_size:] = torch.stack(
tuple(dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[vocab_size:].shape[0])),
dim=0,
)
model.language_model.lm_head.weight.data[vocab_size:] = torch.stack(
tuple(dist.sample() for _ in range(model.language_model.lm_head.weight.data[vocab_size:].shape[0])),
dim=0,
)
print(f"Saving model and processor for {model_id} to {pytorch_dump_folder_path}")
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
processor.save_pretrained(pytorch_dump_folder_path)
# Make space so we can load the model properly now.
del state_dict
gc.collect()
# Load everything back for inference tests in float32 because prev script was written as that
# Though it's mostly loaded in fp16 as original weights are in fp16
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
pytorch_dump_folder_path, dtype="float16", device_map="auto"
)
processor = LlavaOnevisionProcessor.from_pretrained(pytorch_dump_folder_path)
device = model.device
# prepare inputs
image = load_image()
prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\nWhat is shown in this image?<|im_end|>\n<|im_start|>assistant\n"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch.float16)
# verify inputs
filepath = hf_hub_download(
repo_id="RaushanTurganbay/test-image", filename="llava_onevision_pixel_values.pt", repo_type="dataset"
)
original_pixel_values = torch.load(filepath, map_location="cpu", weights_only=True)
assert torch.allclose(original_pixel_values, inputs.pixel_values.half())
image_sizes = torch.tensor([[899, 1024]])
assert image_sizes[0].tolist() == inputs.image_sizes[0].tolist()
# verify single forward pass
print("Single forward pass")
with torch.inference_mode():
inputs = inputs.to(device)
outputs = model(**inputs)
print("Shape of logits:", outputs.logits.shape)
print("First values of logits:", outputs.logits[0, :3, :3])
if model_id == "lmms-lab/llava-onevision-qwen2-0.5b-si":
# Not yet checked against reference
expected_slice = torch.tensor(
[[-12.1953, -14.6797, -12.7891], [0.5840, -0.8467, 1.3799], [3.6055, 4.5430, 9.9062]],
dtype=torch.float32,
device=device,
)
elif model_id == "lmms-lab/llava-onevision-qwen2-0.5b-ov":
# Not yet checked against reference
expected_slice = torch.tensor(
[[-12.0234, -14.3828, -12.7500], [2.3594, 1.0000, 3.9336], [3.6582, 4.7148, 9.1172]],
dtype=torch.float32,
device=device,
)
elif model_id == "lmms-lab/llava-onevision-qwen2-7b-si":
# Not yet checked against reference
expected_slice = torch.tensor(
[[1.7656, 3.3418, 1.4033], [0.0757, 0.7427, 3.5098], [6.7109, 5.6797, 9.3828]],
dtype=torch.float32,
device=device,
)
elif model_id == "lmms-lab/llava-onevision-qwen2-7b-ov":
# Not yet checked against reference
expected_slice = torch.tensor(
[[1.8496, 3.4219, 1.3135], [3.0996, 3.0117, 3.1484], [4.2422, 4.7109, 9.9688]],
dtype=torch.float32,
device=device,
)
elif model_id == "lmms-lab/llava-onevision-qwen2-72b-si":
# Not yet checked against reference
expected_slice = torch.tensor(
[[4.1875, 4.4883, 2.7910], [1.2949, 5.1328, 3.1582], [0.9390, 6.4531, 8.4375]],
dtype=torch.float32,
device=device,
)
elif model_id == "lmms-lab/llava-onevision-qwen2-72b-ov":
# Not yet checked against reference
expected_slice = torch.tensor(
[[4.2930, 4.7305, 2.7363], [1.7529, 5.0742, 3.9590], [1.3936, 6.3438, 9.3984]],
dtype=torch.float32,
device=device,
)
elif model_id == "lmms-lab/llava-onevision-qwen2-7b-ov-chat":
# Not yet checked against reference
expected_slice = torch.tensor(
[[1.8662, 3.4316, 1.3174], [2.7109, 2.5488, 3.0117], [4.4648, 4.9648, 10.3359]],
dtype=torch.float32,
device=device,
)
elif model_id == "lmms-lab/llava-onevision-qwen2-72b-ov-chat":
# Not yet checked against reference
expected_slice = torch.tensor(
[[4.3086, 4.7344, 2.6953], [1.7090, 5.1719, 4.0234], [1.3057, 6.3438, 9.5469]],
dtype=torch.float32,
device=device,
)
else:
raise ValueError(f"Model {model_id} not supported")
assert torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4)
print("Logits are ok!")
# verify generation
output_ids = model.generate(
**inputs,
max_new_tokens=100,
use_cache=True,
)
generated_text = processor.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
print("Generated text:", repr(generated_text))
if model_id == "lmms-lab/llava-onevision-qwen2-0.5b-si":
expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image is a radar chart that shows the performance of different algorithms or models in a specific domain, such as image classification or natural language processing. The chart is color-coded to represent different algorithms, with each color corresponding to a specific algorithm. The algorithms are labeled as BLIP-2, InstructBLIP, Owen-VL-Chat, and LLaVA-1.5. The chart also includes a legend at the bottom that explains the color coding and the algorithms represented."
elif model_id == "lmms-lab/llava-onevision-qwen2-0.5b-ov":
expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into different categories, each represented by a different color and labeled with the name of the model or technique used. The models are evaluated based on their performance metrics, such as BLEU-2, InstructBLIP, Qwen-VL-Chat, and LLaVA-1.5. The radar chart helps to visualize the relative"
elif model_id == "lmms-lab/llava-onevision-qwen2-7b-si":
expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThis image is a radar chart that compares the performance of different models on various metrics. The models being compared are BLIP-2, InstructBLIP, and Qwen-VL-Chat. The metrics being compared are VQA, QA, GQA, VQA-av2, and VQA-av2. The chart shows that BLIP-2 performs the best on all metrics, followed by InstructBLIP and Qwen-VL-Chat."
elif model_id == "lmms-lab/llava-onevision-qwen2-7b-ov":
expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart, also known as a spider chart or a star chart, which is used to compare multiple quantitative variables. Each axis represents a different variable, and the chart is filled with data points that represent the performance or values of different entities across these variables.\n\nIn this particular radar chart, the variables are represented on the axes, and the performance of different models or systems is shown by the lines connecting the data points. The models or systems are labeled along the bottom of the chart,"
elif model_id == "lmms-lab/llava-onevision-qwen2-72b-si":
expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart, which is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. The chart is used to compare the performance of different models or systems across various benchmarks or metrics.\n\nIn this specific radar chart, there are multiple axes, each representing a different benchmark or metric, such as VQA2, GQA, TextVQA, and others. The chart includes several colored lines"
elif model_id == "lmms-lab/llava-onevision-qwen2-72b-ov":
expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image is a radar chart comparing the performance of different models on various multimodal benchmarks. The models compared are BLIP-2, InstructBLIP, POPE, QWen-VL-Chat, and LLava-1.5. The benchmarks include VQAv2, GQA, TextVQA, SQA-IMG, VizWiz, MM-IMDb, MM-VQA, MM-IMDb-CN, MM-IMDb-EN, MM-"
elif model_id == "lmms-lab/llava-onevision-qwen2-7b-ov-chat":
expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart, also known as a spider chart or a star chart, which is used to display multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. Each axis represents a different variable, and the values are plotted along these axes.\n\nIn this particular radar chart, there are multiple lines representing different models or systems, each distinguished by a different color and labeled with a name such as BLIP-2, In"
elif model_id == "lmms-lab/llava-onevision-qwen2-72b-ov-chat":
expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image is a radar chart comparing the performance of different models on various multimodal benchmarks. The models compared are BLIP-2, InstructBLIP, POPE, QWen-VL-Chat, and LLava-1.5. The benchmarks include VQAv2, GQA, TextVQA, SQA-IMG, VizWiz, MM-IMDb, MM-VQA, MM-IMDb-CN, MM-IMDb-EN, MM-"
else:
raise ValueError(f"Model {model_id} not supported")
assert generated_text == expected_text
print("Generated text is ok!")
# verify batched generation
print("Batched generation...")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
cats_image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(
images=[image, cats_image],
text=[prompt, prompt],
padding=True,
return_tensors="pt",
).to(device, torch.float16)
for k, v in inputs.items():
print(k, v.shape)
print("Image sizes:", inputs.image_sizes)
# make sure image_sizes are the same
# as otherwise batched generation doesn't work
inputs.image_sizes[1] = inputs.image_sizes[0]
print("Batched generation...")
output_ids = model.generate(
**inputs,
max_new_tokens=20,
use_cache=True,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
print(outputs)
if push_to_hub:
checkpoint_name = model_id.split("/")[-1]
print(f"Pushing to repo llava-hf/{checkpoint_name}-hf")
model.push_to_hub(f"llava-hf/{checkpoint_name}-hf")
processor.push_to_hub(f"llava-hf/{checkpoint_name}-hf")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_id",
help="Hub location of the model to convert",
default="lmms-lab/llava-onevision-qwen2-0.5b-ov",
choices=[
"lmms-lab/llava-onevision-qwen2-0.5b-ov",
"lmms-lab/llava-onevision-qwen2-0.5b-si",
"lmms-lab/llava-onevision-qwen2-7b-si",
"lmms-lab/llava-onevision-qwen2-7b-ov",
"lmms-lab/llava-onevision-qwen2-72b-si",
"lmms-lab/llava-onevision-qwen2-72b-ov",
"lmms-lab/llava-onevision-qwen2-7b-ov-chat",
"lmms-lab/llava-onevision-qwen2-72b-ov-chat",
],
required=False,
)
parser.add_argument(
"--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
args = parser.parse_args()
convert_llava_to_hf(args.model_id, args.pytorch_dump_folder_path, args.push_to_hub)
| transformers/src/transformers/models/llava_onevision/convert_llava_onevision_weights_to_hf.py/0 | {
"file_path": "transformers/src/transformers/models/llava_onevision/convert_llava_onevision_weights_to_hf.py",
"repo_id": "transformers",
"token_count": 8104
} | 465 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert T5/LongT5X checkpoints from the original repository to JAX/FLAX model. This script is an extension of
'src/transformers/models/t5/convert_t5x_checkpoint_to_flax.
"""
import argparse
from t5x import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM
def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path):
config = AutoConfig.from_pretrained(config_name)
flax_model = FlaxAutoModelForSeq2SeqLM.from_config(config=config)
t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path)
split_mlp_wi = "wi_0" in t5x_model["target"]["encoder"]["layers_0"]["mlp"]
if config.model_type == "t5":
encoder_attn_name = "SelfAttention"
if config.model_type == "longt5" and config.encoder_attention_type == "local":
encoder_attn_name = "LocalSelfAttention"
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
encoder_attn_name = "TransientGlobalSelfAttention"
else:
raise ValueError(
"Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
" attribute with a value from ['local', 'transient-global]."
)
# Encoder
for layer_index in range(config.num_layers):
layer_name = f"layers_{str(layer_index)}"
# Self-Attention
t5x_attention_key = t5x_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
t5x_attention_out = t5x_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
t5x_attention_query = t5x_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
t5x_attention_value = t5x_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
t5x_global_layer_norm = t5x_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]
# Layer Normalization
t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]
if split_mlp_wi:
t5x_mlp_wi_0 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
t5x_mlp_wi_1 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
else:
t5x_mlp_wi = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]
t5x_mlp_wo = t5x_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]
# Layer Normalization
t5x_mlp_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
# Assigning
flax_model_encoder_layer_block = flax_model.params["encoder"]["block"][str(layer_index)]["layer"]
flax_model_encoder_layer_block["0"][encoder_attn_name]["k"]["kernel"] = t5x_attention_key
flax_model_encoder_layer_block["0"][encoder_attn_name]["o"]["kernel"] = t5x_attention_out
flax_model_encoder_layer_block["0"][encoder_attn_name]["q"]["kernel"] = t5x_attention_query
flax_model_encoder_layer_block["0"][encoder_attn_name]["v"]["kernel"] = t5x_attention_value
flax_model_encoder_layer_block["0"]["layer_norm"]["weight"] = t5x_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
flax_model_encoder_layer_block["0"][encoder_attn_name]["global_input_layer_norm"]["weight"] = (
t5x_global_layer_norm
)
if split_mlp_wi:
flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0
flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1
else:
flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi
flax_model_encoder_layer_block["1"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo
flax_model_encoder_layer_block["1"]["layer_norm"]["weight"] = t5x_mlp_layer_norm
flax_model.params["encoder"]["block"][str(layer_index)]["layer"] = flax_model_encoder_layer_block
# Only for layer 0:
t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["relative_attention_bias"][
"embedding"
] = t5x_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
t5x_encoder_global_rel_embedding = t5x_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["global_relative_attention_bias"][
"embedding"
] = t5x_encoder_global_rel_embedding
# Assigning
t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"]
flax_model.params["encoder"]["final_layer_norm"]["weight"] = t5x_encoder_norm
# Decoder
for layer_index in range(config.num_layers):
layer_name = f"layers_{str(layer_index)}"
# Self-Attention
t5x_attention_key = t5x_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
t5x_attention_out = t5x_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
t5x_attention_query = t5x_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
t5x_attention_value = t5x_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]
# Layer Normalization
t5x_pre_attention_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
"scale"
]
# Encoder-Decoder-Attention
t5x_enc_dec_attention_module = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
t5x_enc_dec_attention_key = t5x_enc_dec_attention_module["key"]["kernel"]
t5x_enc_dec_attention_out = t5x_enc_dec_attention_module["out"]["kernel"]
t5x_enc_dec_attention_query = t5x_enc_dec_attention_module["query"]["kernel"]
t5x_enc_dec_attention_value = t5x_enc_dec_attention_module["value"]["kernel"]
# Layer Normalization
t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]
# MLP
if split_mlp_wi:
t5x_mlp_wi_0 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
t5x_mlp_wi_1 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
else:
t5x_mlp_wi = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]
t5x_mlp_wo = t5x_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]
# Layer Normalization
tx5_mlp_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
# Assigning
flax_model_decoder_layer_block = flax_model.params["decoder"]["block"][str(layer_index)]["layer"]
flax_model_decoder_layer_block["0"]["SelfAttention"]["k"]["kernel"] = t5x_attention_key
flax_model_decoder_layer_block["0"]["SelfAttention"]["o"]["kernel"] = t5x_attention_out
flax_model_decoder_layer_block["0"]["SelfAttention"]["q"]["kernel"] = t5x_attention_query
flax_model_decoder_layer_block["0"]["SelfAttention"]["v"]["kernel"] = t5x_attention_value
flax_model_decoder_layer_block["0"]["layer_norm"]["weight"] = t5x_pre_attention_layer_norm
flax_model_decoder_layer_block["1"]["EncDecAttention"]["k"]["kernel"] = t5x_enc_dec_attention_key
flax_model_decoder_layer_block["1"]["EncDecAttention"]["o"]["kernel"] = t5x_enc_dec_attention_out
flax_model_decoder_layer_block["1"]["EncDecAttention"]["q"]["kernel"] = t5x_enc_dec_attention_query
flax_model_decoder_layer_block["1"]["EncDecAttention"]["v"]["kernel"] = t5x_enc_dec_attention_value
flax_model_decoder_layer_block["1"]["layer_norm"]["weight"] = t5x_cross_layer_norm
if split_mlp_wi:
flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0
flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1
else:
flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi
flax_model_decoder_layer_block["2"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo
flax_model_decoder_layer_block["2"]["layer_norm"]["weight"] = tx5_mlp_layer_norm
flax_model.params["decoder"]["block"][str(layer_index)]["layer"] = flax_model_decoder_layer_block
# Decoder Normalization
tx5_decoder_norm = t5x_model["target"]["decoder"]["decoder_norm"]["scale"]
flax_model.params["decoder"]["final_layer_norm"]["weight"] = tx5_decoder_norm
# Only for layer 0:
t5x_decoder_rel_embedding = t5x_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][
"embedding"
] = t5x_decoder_rel_embedding
# Token Embeddings
tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"]
flax_model.params["shared"]["embedding"] = tx5_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in t5x_model["target"]["decoder"]:
flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"]
flax_model.save_pretrained(flax_dump_folder_path)
print("T5X Model was successfully converted!")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
)
parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
parser.add_argument(
"--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
)
args = parser.parse_args()
convert_t5x_checkpoint_to_flax(args.t5x_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| transformers/src/transformers/models/longt5/convert_longt5x_checkpoint_to_flax.py/0 | {
"file_path": "transformers/src/transformers/models/longt5/convert_longt5x_checkpoint_to_flax.py",
"repo_id": "transformers",
"token_count": 4984
} | 466 |
# coding=utf-8
# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""M2M100 model configuration"""
from collections import OrderedDict
from collections.abc import Mapping
from typing import Any, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
logger = logging.get_logger(__name__)
class M2M100Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an
M2M100 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the M2M100
[facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the M2M100 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`M2M100Model`] or
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
Example:
```python
>>> from transformers import M2M100Config, M2M100Model
>>> # Initializing a M2M100 facebook/m2m100_418M style configuration
>>> configuration = M2M100Config()
>>> # Initializing a model (with random weights) from the facebook/m2m100_418M style configuration
>>> model = M2M100Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "m2m_100"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=128112,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.05,
decoder_layerdrop=0.05,
use_cache=True,
is_encoder_decoder=True,
activation_function="relu",
d_model=1024,
dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
init_std=0.02,
decoder_start_token_id=2,
scale_embedding=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
**kwargs,
)
class M2M100OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
]
)
if self.use_past:
common_inputs["decoder_input_ids"] = {0: "batch"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
return common_inputs
# Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering
# A better name would be _generate_dummy_inputs_for_encoder_and_decoder because sequence classification and question
# answering are not supported for M2M100, but this name is preserved to be able to check that the copy matches what
# was done for BART so that it can be updated if need be.
def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
batch_size = compute_effective_axis_dimension(
batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
)
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
seq_length = compute_effective_axis_dimension(
seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
)
# Generate dummy inputs according to compute batch and sequence
dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
return common_inputs
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._generate_dummy_inputs_for_default_and_seq2seq_lm
def _generate_dummy_inputs_for_default_and_seq2seq_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size, seq_length, is_pair, framework
)
# Generate decoder inputs
decoder_seq_length = seq_length if not self.use_past else 1
decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size, decoder_seq_length, is_pair, framework
)
decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
common_inputs = dict(**encoder_inputs, **decoder_inputs)
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, encoder_seq_length = common_inputs["input_ids"].shape
decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
encoder_shape = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
decoder_past_length = decoder_seq_length + 3
decoder_shape = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
common_inputs["decoder_attention_mask"] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
)
common_inputs["past_key_values"] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
num_encoder_layers, num_decoder_layers = self.num_layers
min_num_layers = min(num_encoder_layers, num_decoder_layers)
max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(min_num_layers):
common_inputs["past_key_values"].append(
(
torch.zeros(decoder_shape),
torch.zeros(decoder_shape),
torch.zeros(encoder_shape),
torch.zeros(encoder_shape),
)
)
# TODO: test this.
shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(min_num_layers, max_num_layers):
common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))
return common_inputs
generate_dummy_inputs = _generate_dummy_inputs_for_default_and_seq2seq_lm
__all__ = ["M2M100Config", "M2M100OnnxConfig"]
| transformers/src/transformers/models/m2m_100/configuration_m2m_100.py/0 | {
"file_path": "transformers/src/transformers/models/m2m_100/configuration_m2m_100.py",
"repo_id": "transformers",
"token_count": 5595
} | 467 |
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Mask2Former model."""
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...file_utils import ModelOutput, is_scipy_available, requires_backends
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import compile_compatible_method_lru_cache
from ...utils import auto_docstring, is_accelerate_available, logging
from ...utils.backbone_utils import load_backbone
from .configuration_mask2former import Mask2FormerConfig
if is_scipy_available():
from scipy.optimize import linear_sum_assignment
if is_accelerate_available():
from accelerate import PartialState
from accelerate.utils import reduce
logger = logging.get_logger(__name__)
@dataclass
@auto_docstring(
custom_intro="""
Mask2Former's pixel decoder module output, practically a Multi-Scale Deformable Attention based decoder. It returns
the mask features and the multiscale features.
"""
)
class Mask2FormerPixelDecoderOutput(ModelOutput):
r"""
multi_scale_features (`tuple(torch.FloatTensor)`):
Tuple of multi-scale features of scales [1/8, 1/16, 1/32] and shape `(batch_size, num_channels, height,
width)`from the Multi-Scale Deformable Attenntion based Pixel Decoder.
mask_features (`torch.FloatTensor`):
Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel Decoder
Layer.
attentions (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights from pixel decoder. Returned when `output_attentions=True` is passed
or when `config.output_attentions=True`
"""
multi_scale_features: tuple[torch.FloatTensor] = None
mask_features: Optional[torch.FloatTensor] = None
attentions: Optional[tuple[torch.FloatTensor]] = None
@dataclass
@auto_docstring(
custom_intro="""
Base class for outputs of the Transformer decoder. This class adds two attributes to
BaseModelOutputWithCrossAttentions for mask predictions logits and a tuple of intermediate decoder activations,
i.e. the output of each decoder layer, each of them gone through a layernorm.
"""
)
class Mask2FormerMaskedAttentionDecoderOutput(BaseModelOutputWithCrossAttentions):
r"""
hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs. Returned when `output_hidden_states=True`.
attentions (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads. Returned when `output_attentions=True`.
masks_queries_logits (`tuple(torch.FloatTensor)` of shape `(batch_size, num_queries, height, width)`):
Tuple of mask predictions from all layers of the transformer decoder.
intermediate_hidden_states (`tuple(torch.FloatTensor)` of shape `(num_queries, 1, hidden_size)`):
Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
layernorm.
"""
last_hidden_state: Optional[torch.FloatTensor] = None
hidden_states: Optional[tuple[torch.FloatTensor]] = None
attentions: Optional[torch.FloatTensor] = None
masks_queries_logits: tuple[torch.FloatTensor] = None
intermediate_hidden_states: tuple[torch.FloatTensor] = None
@dataclass
@auto_docstring(
custom_intro="""
Mask2Former's pixel level module output. It returns the output of the encoder (optional) and all hidden states
(multi-scale features) from the `decoder`. By default, the `encoder` is a Swin Backbone and the `decoder` is a
Multi-Scale Deformable Attention based decoder.
The `decoder_last_hidden_state` are the **per-pixel embeddings** while `decoder_hidden_states` refer to multi-scale
feature maps produced using **multi-scaling strategy** defined in the paper.
"""
)
class Mask2FormerPixelLevelModuleOutput(ModelOutput):
r"""
encoder_last_hidden_state (`torch.FloatTensor`):
Last hidden states (final feature map of shape `(batch_size, num_channels, height, width)`) of the last
stage of the encoder.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`. Hidden states (also
called feature maps) of the model at the output of each stage. Returned if output_hidden_states is set to
True.
decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)):
1/4 scale features from the last Pixel Decoder Layer.
decoder_hidden_states (`tuple(torch.FloatTensor)`):
Tuple of `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`. Hidden states (also
called feature maps) of the model at the output of each stage.
"""
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
decoder_last_hidden_state: Optional[torch.FloatTensor] = None
decoder_hidden_states: tuple[torch.FloatTensor] = None
@dataclass
@auto_docstring(
custom_intro="""
Class for outputs of [`Mask2FormerModel`]. This class returns all the needed hidden states to compute the logits.
"""
)
class Mask2FormerModelOutput(ModelOutput):
r"""
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*):
Last hidden states (final feature map) of the last stage of the encoder model (backbone). Returned when
`output_hidden_states=True` is passed.
pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*):
Last hidden states (final feature map) of the last stage of the pixel decoder model.
transformer_decoder_last_hidden_state (`tuple(torch.FloatTensor)`):
Final output of the transformer decoder `(batch_size, sequence_length, hidden_size)`.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
model at the output of each stage. Returned when `output_hidden_states=True` is passed.
pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, , *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
decoder model at the output of each stage. Returned when `output_hidden_states=True` is passed.
transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
transformer decoder at the output of each stage. Returned when `output_hidden_states=True` is passed.
transformer_decoder_intermediate_states (`tuple(torch.FloatTensor)` of shape `(num_queries, 1, hidden_size)`):
Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
layernorm.
masks_queries_logits (`tuple(torch.FloatTensor)` of shape `(batch_size, num_queries, height, width)`)
Mask Predictions from each layer in the transformer decoder.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed):
Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Self attentions weights from transformer decoder.
"""
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
pixel_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
transformer_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
transformer_decoder_intermediate_states: tuple[torch.FloatTensor] = None
masks_queries_logits: tuple[torch.FloatTensor] = None
attentions: Optional[tuple[torch.FloatTensor]] = None
@dataclass
@auto_docstring(
custom_intro="""
Class for outputs of [`Mask2FormerForUniversalSegmentationOutput`].
This output can be directly passed to [`~Mask2FormerImageProcessor.post_process_semantic_segmentation`] or
[`~Mask2FormerImageProcessor.post_process_instance_segmentation`] or
[`~Mask2FormerImageProcessor.post_process_panoptic_segmentation`] to compute final segmentation maps. Please, see
[`~Mask2FormerImageProcessor] for details regarding usage.
"""
)
class Mask2FormerForUniversalSegmentationOutput(ModelOutput):
r"""
loss (`torch.Tensor`, *optional*):
The computed loss, returned when labels are present.
class_queries_logits (`torch.FloatTensor`):
A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each
query. Note the `+ 1` is needed because we incorporate the null class.
masks_queries_logits (`torch.FloatTensor`):
A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each
query.
auxiliary_logits (`list[Dict(str, torch.FloatTensor)]`, *optional*):
List of class and mask predictions from each layer of the transformer decoder.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Last hidden states (final feature map) of the last stage of the encoder model (backbone).
pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Last hidden states (final feature map) of the last stage of the pixel decoder model.
transformer_decoder_last_hidden_state (`tuple(torch.FloatTensor)`):
Final output of the transformer decoder `(batch_size, sequence_length, hidden_size)`.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
model at the output of each stage.
pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
decoder model at the output of each stage.
transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
transformer decoder at the output of each stage.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Self and Cross Attentions weights from transformer decoder.
"""
loss: Optional[torch.FloatTensor] = None
class_queries_logits: Optional[torch.FloatTensor] = None
masks_queries_logits: Optional[torch.FloatTensor] = None
auxiliary_logits: Optional[list[dict[str, torch.FloatTensor]]] = None
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
pixel_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
transformer_decoder_hidden_states: Optional[torch.FloatTensor] = None
attentions: Optional[tuple[torch.FloatTensor]] = None
# Adapted from https://github.com/facebookresearch/detectron2/blob/main/projects/PointRend/point_rend/point_features.py
def sample_point(
input_features: torch.Tensor, point_coordinates: torch.Tensor, add_dim=False, **kwargs
) -> torch.Tensor:
"""
A wrapper around `torch.nn.functional.grid_sample` to support 3D point_coordinates tensors.
Args:
input_features (`torch.Tensor` of shape (batch_size, channels, height, width)):
A tensor that contains features map on a height * width grid
point_coordinates (`torch.Tensor` of shape (batch_size, num_points, 2) or (batch_size, grid_height, grid_width,:
2)):
A tensor that contains [0, 1] * [0, 1] normalized point coordinates
add_dim (`bool`):
boolean value to keep track of added dimension
Returns:
point_features (`torch.Tensor` of shape (batch_size, channels, num_points) or (batch_size, channels,
height_grid, width_grid):
A tensor that contains features for points in `point_coordinates`.
"""
if point_coordinates.dim() == 3:
add_dim = True
point_coordinates = point_coordinates.unsqueeze(2)
# use nn.function.grid_sample to get features for points in `point_coordinates` via bilinear interpolation
point_features = torch.nn.functional.grid_sample(input_features, 2.0 * point_coordinates - 1.0, **kwargs)
if add_dim:
point_features = point_features.squeeze(3)
return point_features
# Copied from transformers.models.maskformer.modeling_maskformer.dice_loss
def dice_loss(inputs: Tensor, labels: Tensor, num_masks: int) -> Tensor:
r"""
Compute the DICE loss, similar to generalized IOU for masks as follows:
$$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$
In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow
$$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * y }{x + y + 1}} $$
Args:
inputs (`torch.Tensor`):
A tensor representing a mask.
labels (`torch.Tensor`):
A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
(0 for the negative class and 1 for the positive class).
num_masks (`int`):
The number of masks present in the current batch, used for normalization.
Returns:
`torch.Tensor`: The computed loss.
"""
probs = inputs.sigmoid().flatten(1)
numerator = 2 * (probs * labels).sum(-1)
denominator = probs.sum(-1) + labels.sum(-1)
loss = 1 - (numerator + 1) / (denominator + 1)
loss = loss.sum() / num_masks
return loss
def sigmoid_cross_entropy_loss(inputs: torch.Tensor, labels: torch.Tensor, num_masks: int) -> torch.Tensor:
r"""
Args:
inputs (`torch.Tensor`):
A float tensor of arbitrary shape.
labels (`torch.Tensor`):
A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
(0 for the negative class and 1 for the positive class).
Returns:
loss (`torch.Tensor`): The computed loss.
"""
criterion = nn.BCEWithLogitsLoss(reduction="none")
cross_entropy_loss = criterion(inputs, labels)
loss = cross_entropy_loss.mean(1).sum() / num_masks
return loss
# Copied from transformers.models.maskformer.modeling_maskformer.pair_wise_dice_loss
def pair_wise_dice_loss(inputs: Tensor, labels: Tensor) -> Tensor:
"""
A pair wise version of the dice loss, see `dice_loss` for usage.
Args:
inputs (`torch.Tensor`):
A tensor representing a mask
labels (`torch.Tensor`):
A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
(0 for the negative class and 1 for the positive class).
Returns:
`torch.Tensor`: The computed loss between each pairs.
"""
inputs = inputs.sigmoid().flatten(1)
numerator = 2 * torch.matmul(inputs, labels.T)
# using broadcasting to get a [num_queries, NUM_CLASSES] matrix
denominator = inputs.sum(-1)[:, None] + labels.sum(-1)[None, :]
loss = 1 - (numerator + 1) / (denominator + 1)
return loss
def pair_wise_sigmoid_cross_entropy_loss(inputs: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
r"""
A pair wise version of the cross entropy loss, see `sigmoid_cross_entropy_loss` for usage.
Args:
inputs (`torch.Tensor`):
A tensor representing a mask.
labels (`torch.Tensor`):
A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
(0 for the negative class and 1 for the positive class).
Returns:
loss (`torch.Tensor`): The computed loss between each pairs.
"""
height_and_width = inputs.shape[1]
criterion = nn.BCEWithLogitsLoss(reduction="none")
cross_entropy_loss_pos = criterion(inputs, torch.ones_like(inputs))
cross_entropy_loss_neg = criterion(inputs, torch.zeros_like(inputs))
loss_pos = torch.matmul(cross_entropy_loss_pos / height_and_width, labels.T)
loss_neg = torch.matmul(cross_entropy_loss_neg / height_and_width, (1 - labels).T)
loss = loss_pos + loss_neg
return loss
# Adapted from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/matcher.py
class Mask2FormerHungarianMatcher(nn.Module):
"""This class computes an assignment between the labels and the predictions of the network.
For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more
predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are
un-matched (and thus treated as non-objects).
"""
def __init__(
self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0, num_points: int = 12544
):
"""Creates the matcher
Params:
cost_class (`float`, *optional*, defaults to 1.0):
Relative weight of the classification error in the matching cost.
cost_mask (`float`, *optional*, defaults to 1.0):
This is the relative weight of the focal loss of the binary mask in the matching cost.
cost_dice (`float`, *optional*, defaults to 1.0):
This is the relative weight of the dice loss of the binary mask in the matching cost.
num_points (`int`, *optional*, defaults to 12544):
No. of points to sample on which the mask loss will be calculated. The same set of K points are
uniformly sampled for all prediction and ground truth masks to construct the cost matrix for bipartite
matching.
"""
super().__init__()
if cost_class == 0 and cost_mask == 0 and cost_dice == 0:
raise ValueError("All costs can't be 0")
self.num_points = num_points
self.cost_class = cost_class
self.cost_mask = cost_mask
self.cost_dice = cost_dice
@torch.no_grad()
def forward(
self,
masks_queries_logits: torch.Tensor,
class_queries_logits: torch.Tensor,
mask_labels: torch.Tensor,
class_labels: torch.Tensor,
) -> list[tuple[Tensor]]:
"""
Params:
masks_queries_logits (`torch.Tensor`):
A tensor of dim `batch_size, num_queries, num_labels` with the classification logits.
class_queries_logits (`torch.Tensor`):
A tensor of dim `batch_size, num_queries, height, width` with the predicted masks.
class_labels (`torch.Tensor`):
A tensor of dim `num_target_boxes` (where num_target_boxes is the number of ground-truth objects in the
target) containing the class labels.
mask_labels (`torch.Tensor`):
A tensor of dim `num_target_boxes, height, width` containing the target masks.
Returns:
matched_indices (`list[tuple[Tensor]]`): A list of size batch_size, containing tuples of (index_i, index_j)
where:
- index_i is the indices of the selected predictions (in order)
- index_j is the indices of the corresponding selected labels (in order)
For each batch element, it holds:
len(index_i) = len(index_j) = min(num_queries, num_target_boxes).
"""
indices: list[tuple[np.array]] = []
# iterate through batch size
batch_size = masks_queries_logits.shape[0]
for i in range(batch_size):
pred_probs = class_queries_logits[i].softmax(-1)
pred_mask = masks_queries_logits[i]
# Compute the classification cost. Contrary to the loss, we don't use the NLL, but approximate it in 1 - proba[target class]. The 1 is a constant that doesn't change the matching, it can be omitted.
cost_class = -pred_probs[:, class_labels[i]]
target_mask = mask_labels[i].to(pred_mask)
target_mask = target_mask[:, None]
pred_mask = pred_mask[:, None]
# Sample ground truth and predicted masks
point_coordinates = torch.rand(1, self.num_points, 2, device=pred_mask.device)
target_coordinates = point_coordinates.repeat(target_mask.shape[0], 1, 1)
target_mask = sample_point(target_mask, target_coordinates, align_corners=False).squeeze(1)
pred_coordinates = point_coordinates.repeat(pred_mask.shape[0], 1, 1)
pred_mask = sample_point(pred_mask, pred_coordinates, align_corners=False).squeeze(1)
# compute the cross entropy loss between each mask pairs -> shape (num_queries, num_labels)
cost_mask = pair_wise_sigmoid_cross_entropy_loss(pred_mask, target_mask)
# Compute the dice loss between each mask pairs -> shape (num_queries, num_labels)
cost_dice = pair_wise_dice_loss(pred_mask, target_mask)
# final cost matrix
cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice
# eliminate infinite values in cost_matrix to avoid the error ``ValueError: cost matrix is infeasible``
cost_matrix = torch.minimum(cost_matrix, torch.tensor(1e10))
cost_matrix = torch.maximum(cost_matrix, torch.tensor(-1e10))
cost_matrix = torch.nan_to_num(cost_matrix, 0)
# do the assignment using the hungarian algorithm in scipy
assigned_indices: tuple[np.array] = linear_sum_assignment(cost_matrix.cpu())
indices.append(assigned_indices)
# It could be stacked in one tensor
matched_indices = [
(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices
]
return matched_indices
# Adapted from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py
class Mask2FormerLoss(nn.Module):
def __init__(self, config: Mask2FormerConfig, weight_dict: dict[str, float]):
"""
The Mask2Former Loss. The loss is computed very similar to DETR. The process happens in two steps: 1) we
compute hungarian assignment between ground truth masks and the outputs of the model 2) we supervise each pair
of matched ground-truth / prediction (supervise class and mask)
Args:
config (`Mask2FormerConfig`):
The configuration for Mask2Former model also containing loss calculation specific parameters.
weight_dict (`dict[str, float]`):
A dictionary of weights to be applied to the different losses.
"""
super().__init__()
requires_backends(self, ["scipy"])
self.num_labels = config.num_labels
self.weight_dict = weight_dict
# Weight to apply to the null class
self.eos_coef = config.no_object_weight
empty_weight = torch.ones(self.num_labels + 1)
empty_weight[-1] = self.eos_coef
self.register_buffer("empty_weight", empty_weight)
# pointwise mask loss parameters
self.num_points = config.train_num_points
self.oversample_ratio = config.oversample_ratio
self.importance_sample_ratio = config.importance_sample_ratio
self.matcher = Mask2FormerHungarianMatcher(
cost_class=config.class_weight,
cost_dice=config.dice_weight,
cost_mask=config.mask_weight,
num_points=self.num_points,
)
def _max_by_axis(self, sizes: list[list[int]]) -> list[int]:
maxes = sizes[0]
for sublist in sizes[1:]:
for index, item in enumerate(sublist):
maxes[index] = max(maxes[index], item)
return maxes
# Adapted from nested_tensor_from_tensor_list() in original implementation
def _pad_images_to_max_in_batch(self, tensors: list[Tensor]) -> tuple[Tensor, Tensor]:
# get the maximum size in the batch
max_size = self._max_by_axis([list(tensor.shape) for tensor in tensors])
# compute final size
batch_shape = [len(tensors)] + max_size
batch_size, _, height, width = batch_shape
dtype = tensors[0].dtype
device = tensors[0].device
padded_tensors = torch.zeros(batch_shape, dtype=dtype, device=device)
padding_masks = torch.ones((batch_size, height, width), dtype=torch.bool, device=device)
# pad the tensors to the size of the biggest one
for tensor, padded_tensor, padding_mask in zip(tensors, padded_tensors, padding_masks):
padded_tensor[: tensor.shape[0], : tensor.shape[1], : tensor.shape[2]].copy_(tensor)
padding_mask[: tensor.shape[1], : tensor.shape[2]] = False
return padded_tensors, padding_masks
def loss_labels(
self, class_queries_logits: Tensor, class_labels: list[Tensor], indices: tuple[np.array]
) -> dict[str, Tensor]:
"""Compute the losses related to the labels using cross entropy.
Args:
class_queries_logits (`torch.Tensor`):
A tensor of shape `batch_size, num_queries, num_labels`
class_labels (`list[torch.Tensor]`):
List of class labels of shape `(labels)`.
indices (`tuple[np.array])`:
The indices computed by the Hungarian matcher.
Returns:
`dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key:
- **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
"""
pred_logits = class_queries_logits
batch_size, num_queries, _ = pred_logits.shape
criterion = nn.CrossEntropyLoss(weight=self.empty_weight)
idx = self._get_predictions_permutation_indices(indices) # shape of (batch_size, num_queries)
target_classes_o = torch.cat(
[target[j] for target, (_, j) in zip(class_labels, indices)]
) # shape of (batch_size, num_queries)
target_classes = torch.full(
(batch_size, num_queries), fill_value=self.num_labels, dtype=torch.int64, device=pred_logits.device
)
target_classes[idx] = target_classes_o
# Permute target_classes (batch_size, num_queries, num_labels) -> (batch_size, num_labels, num_queries)
pred_logits_transposed = pred_logits.transpose(1, 2)
loss_ce = criterion(pred_logits_transposed, target_classes)
losses = {"loss_cross_entropy": loss_ce}
return losses
def loss_masks(
self,
masks_queries_logits: torch.Tensor,
mask_labels: list[torch.Tensor],
indices: tuple[np.array],
num_masks: int,
) -> dict[str, torch.Tensor]:
"""Compute the losses related to the masks using sigmoid_cross_entropy_loss and dice loss.
Args:
masks_queries_logits (`torch.Tensor`):
A tensor of shape `(batch_size, num_queries, height, width)`.
mask_labels (`torch.Tensor`):
List of mask labels of shape `(labels, height, width)`.
indices (`tuple[np.array])`:
The indices computed by the Hungarian matcher.
num_masks (`int)`:
The number of masks, used for normalization.
Returns:
losses (`dict[str, Tensor]`): A dict of `torch.Tensor` containing two keys:
- **loss_mask** -- The loss computed using sigmoid cross entropy loss on the predicted and ground truth.
masks.
- **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth,
masks.
"""
src_idx = self._get_predictions_permutation_indices(indices)
tgt_idx = self._get_targets_permutation_indices(indices)
# shape (batch_size * num_queries, height, width)
pred_masks = masks_queries_logits[src_idx]
# shape (batch_size, num_queries, height, width)
# pad all and stack the targets to the num_labels dimension
target_masks, _ = self._pad_images_to_max_in_batch(mask_labels)
target_masks = target_masks[tgt_idx]
# No need to upsample predictions as we are using normalized coordinates
pred_masks = pred_masks[:, None]
target_masks = target_masks[:, None]
# Sample point coordinates
with torch.no_grad():
point_coordinates = self.sample_points_using_uncertainty(
pred_masks,
lambda logits: self.calculate_uncertainty(logits),
self.num_points,
self.oversample_ratio,
self.importance_sample_ratio,
)
point_labels = sample_point(target_masks, point_coordinates, align_corners=False).squeeze(1)
point_logits = sample_point(pred_masks, point_coordinates, align_corners=False).squeeze(1)
losses = {
"loss_mask": sigmoid_cross_entropy_loss(point_logits, point_labels, num_masks),
"loss_dice": dice_loss(point_logits, point_labels, num_masks),
}
del pred_masks
del target_masks
return losses
def _get_predictions_permutation_indices(self, indices):
# Permute predictions following indices
batch_indices = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
predictions_indices = torch.cat([src for (src, _) in indices])
return batch_indices, predictions_indices
def _get_targets_permutation_indices(self, indices):
# Permute labels following indices
batch_indices = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
target_indices = torch.cat([tgt for (_, tgt) in indices])
return batch_indices, target_indices
def calculate_uncertainty(self, logits: torch.Tensor) -> torch.Tensor:
"""
In Mask2Former paper, uncertainty is estimated as L1 distance between 0.0 and the logit prediction in 'logits'
for the foreground class in `classes`.
Args:
logits (`torch.Tensor`):
A tensor of shape (R, 1, ...) for class-specific or class-agnostic, where R is the total number of predicted masks in all images and C is:
the number of foreground classes. The values are logits.
Returns:
scores (`torch.Tensor`): A tensor of shape (R, 1, ...) that contains uncertainty scores with the most
uncertain locations having the highest uncertainty score.
"""
uncertainty_scores = -(torch.abs(logits))
return uncertainty_scores
def sample_points_using_uncertainty(
self,
logits: torch.Tensor,
uncertainty_function,
num_points: int,
oversample_ratio: int,
importance_sample_ratio: float,
) -> torch.Tensor:
"""
This function is meant for sampling points in [0, 1] * [0, 1] coordinate space based on their uncertainty. The
uncertainty is calculated for each point using the passed `uncertainty function` that takes points logit
prediction as input.
Args:
logits (`float`):
Logit predictions for P points.
uncertainty_function:
A function that takes logit predictions for P points and returns their uncertainties.
num_points (`int`):
The number of points P to sample.
oversample_ratio (`int`):
Oversampling parameter.
importance_sample_ratio (`float`):
Ratio of points that are sampled via importance sampling.
Returns:
point_coordinates (`torch.Tensor`):
Coordinates for P sampled points.
"""
num_boxes = logits.shape[0]
num_points_sampled = int(num_points * oversample_ratio)
# Get random point coordinates
point_coordinates = torch.rand(num_boxes, num_points_sampled, 2, device=logits.device)
# Get sampled prediction value for the point coordinates
point_logits = sample_point(logits, point_coordinates, align_corners=False)
# Calculate the uncertainties based on the sampled prediction values of the points
point_uncertainties = uncertainty_function(point_logits)
num_uncertain_points = int(importance_sample_ratio * num_points)
num_random_points = num_points - num_uncertain_points
idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
shift = num_points_sampled * torch.arange(num_boxes, dtype=torch.long, device=logits.device)
idx += shift[:, None]
point_coordinates = point_coordinates.view(-1, 2)[idx.view(-1), :].view(num_boxes, num_uncertain_points, 2)
if num_random_points > 0:
point_coordinates = torch.cat(
[point_coordinates, torch.rand(num_boxes, num_random_points, 2, device=logits.device)],
dim=1,
)
return point_coordinates
def forward(
self,
masks_queries_logits: torch.Tensor,
class_queries_logits: torch.Tensor,
mask_labels: list[torch.Tensor],
class_labels: list[torch.Tensor],
auxiliary_predictions: Optional[dict[str, torch.Tensor]] = None,
) -> dict[str, torch.Tensor]:
"""
This performs the loss computation.
Args:
masks_queries_logits (`torch.Tensor`):
A tensor of shape `(batch_size, num_queries, height, width)`.
class_queries_logits (`torch.Tensor`):
A tensor of shape `(batch_size, num_queries, num_labels)`.
mask_labels (`torch.Tensor`):
List of mask labels of shape `(labels, height, width)`.
class_labels (`list[torch.Tensor]`):
List of class labels of shape `(labels)`.
auxiliary_predictions (`dict[str, torch.Tensor]`, *optional*):
if `use_auxiliary_loss` was set to `true` in [`Mask2FormerConfig`], then it contains the logits from
the inner layers of the Mask2FormerMaskedAttentionDecoder.
Returns:
losses (`dict[str, Tensor]`): A dict of `torch.Tensor` containing three keys:
- **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
- **loss_mask** -- The loss computed using sigmoid cross_entropy loss on the predicted and ground truth
masks.
- **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth
masks.
if `use_auxiliary_loss` was set to `true` in [`Mask2FormerConfig`], the dictionary contains additional
losses for each auxiliary predictions.
"""
# retrieve the matching between the outputs of the last layer and the labels
indices = self.matcher(masks_queries_logits, class_queries_logits, mask_labels, class_labels)
# compute the average number of target masks for normalization purposes
num_masks = self.get_num_masks(class_labels, device=class_labels[0].device)
# get all the losses
losses: dict[str, Tensor] = {
**self.loss_masks(masks_queries_logits, mask_labels, indices, num_masks),
**self.loss_labels(class_queries_logits, class_labels, indices),
}
# in case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if auxiliary_predictions is not None:
for idx, aux_outputs in enumerate(auxiliary_predictions):
masks_queries_logits = aux_outputs["masks_queries_logits"]
class_queries_logits = aux_outputs["class_queries_logits"]
loss_dict = self.forward(masks_queries_logits, class_queries_logits, mask_labels, class_labels)
loss_dict = {f"{key}_{idx}": value for key, value in loss_dict.items()}
losses.update(loss_dict)
return losses
def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> torch.Tensor:
"""
Computes the average number of target masks across the batch, for normalization purposes.
"""
num_masks = sum([len(classes) for classes in class_labels])
num_masks = torch.as_tensor(num_masks, dtype=torch.float, device=device)
world_size = 1
if is_accelerate_available():
if PartialState._shared_state != {}:
num_masks = reduce(num_masks)
world_size = PartialState().num_processes
num_masks = torch.clamp(num_masks / world_size, min=1)
return num_masks
# Copied from transformers.models.oneformer.modeling_oneformer.multi_scale_deformable_attention
def multi_scale_deformable_attention(
value: Tensor,
value_spatial_shapes: Union[Tensor, list[tuple]],
sampling_locations: Tensor,
attention_weights: Tensor,
) -> Tensor:
batch_size, _, num_heads, hidden_dim = value.shape
_, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
value_list = value.split([height * width for height, width in value_spatial_shapes], dim=1)
sampling_grids = 2 * sampling_locations - 1
sampling_value_list = []
for level_id, (height, width) in enumerate(value_spatial_shapes):
# batch_size, height*width, num_heads, hidden_dim
# -> batch_size, height*width, num_heads*hidden_dim
# -> batch_size, num_heads*hidden_dim, height*width
# -> batch_size*num_heads, hidden_dim, height, width
value_l_ = (
value_list[level_id].flatten(2).transpose(1, 2).reshape(batch_size * num_heads, hidden_dim, height, width)
)
# batch_size, num_queries, num_heads, num_points, 2
# -> batch_size, num_heads, num_queries, num_points, 2
# -> batch_size*num_heads, num_queries, num_points, 2
sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
# batch_size*num_heads, hidden_dim, num_queries, num_points
sampling_value_l_ = nn.functional.grid_sample(
value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
)
sampling_value_list.append(sampling_value_l_)
# (batch_size, num_queries, num_heads, num_levels, num_points)
# -> (batch_size, num_heads, num_queries, num_levels, num_points)
# -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
attention_weights = attention_weights.transpose(1, 2).reshape(
batch_size * num_heads, 1, num_queries, num_levels * num_points
)
output = (
(torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
.sum(-1)
.view(batch_size, num_heads * hidden_dim, num_queries)
)
return output.transpose(1, 2).contiguous()
# Copied from transformers.models.maskformer.modeling_maskformer.MaskFormerSinePositionEmbedding with MaskFormer->Mask2Former
class Mask2FormerSinePositionEmbedding(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
need paper, generalized to work on images.
"""
def __init__(
self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: Optional[float] = None
):
super().__init__()
if scale is not None and normalize is False:
raise ValueError("normalize should be True if scale is passed")
self.num_pos_feats = num_pos_feats
self.temperature = temperature
self.normalize = normalize
self.scale = 2 * math.pi if scale is None else scale
@compile_compatible_method_lru_cache(maxsize=1)
def forward(
self,
shape: torch.Size,
device: Union[torch.device, str],
dtype: torch.dtype,
mask: Optional[Tensor] = None,
) -> Tensor:
if mask is None:
mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool)
not_mask = (~mask).to(dtype)
y_embed = not_mask.cumsum(1)
x_embed = not_mask.cumsum(2)
if self.normalize:
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype)
dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
return pos
# Modified from transformers.models.detr.modeling_deformable_detr.DeformableDetrMultiscaleDeformableAttention
class Mask2FormerPixelDecoderEncoderMultiscaleDeformableAttention(nn.Module):
"""
Multiscale deformable attention as proposed in Deformable DETR.
"""
def __init__(self, embed_dim: int, num_heads: int, n_levels: int, n_points: int):
super().__init__()
if embed_dim % num_heads != 0:
raise ValueError(
f"embed_dim (d_model) must be divisible by num_heads, but got {embed_dim} and {num_heads}"
)
dim_per_head = embed_dim // num_heads
# check if dim_per_head is power of 2
if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
warnings.warn(
"You'd better set embed_dim (d_model) in DeformableDetrMultiscaleDeformableAttention to make the"
" dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
" implementation."
)
self.im2col_step = 128
self.d_model = embed_dim
self.n_levels = n_levels
self.n_heads = num_heads
self.n_points = n_points
self.sampling_offsets = nn.Linear(embed_dim, num_heads * n_levels * n_points * 2)
self.attention_weights = nn.Linear(embed_dim, num_heads * n_levels * n_points)
self.value_proj = nn.Linear(embed_dim, embed_dim)
self.output_proj = nn.Linear(embed_dim, embed_dim)
def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
return tensor if position_embeddings is None else tensor + position_embeddings
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states=None,
encoder_attention_mask=None,
position_embeddings: Optional[torch.Tensor] = None,
reference_points=None,
spatial_shapes_list=None,
level_start_index=None,
output_attentions: bool = False,
):
# add position embeddings to the hidden states before projecting to queries and keys
if position_embeddings is not None:
hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
batch_size, num_queries, _ = hidden_states.shape
batch_size, sequence_length, _ = encoder_hidden_states.shape
total_elements = sum(height * width for height, width in spatial_shapes_list)
if total_elements != sequence_length:
raise ValueError(
"Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
)
value = self.value_proj(encoder_hidden_states)
if attention_mask is not None:
# we invert the attention_mask
value = value.masked_fill(attention_mask[..., None], float(0))
value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
sampling_offsets = self.sampling_offsets(hidden_states).view(
batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
)
attention_weights = self.attention_weights(hidden_states).view(
batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
)
attention_weights = nn.functional.softmax(attention_weights, -1).view(
batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
)
# batch_size, num_queries, n_heads, n_levels, n_points, 2
if reference_points.shape[-1] == 2:
offset_normalizer = torch.tensor(
[[shape[1], shape[0]] for shape in spatial_shapes_list],
dtype=torch.long,
device=reference_points.device,
)
sampling_locations = (
reference_points[:, :, None, :, None, :]
+ sampling_offsets / offset_normalizer[None, None, None, :, None, :]
)
elif reference_points.shape[-1] == 4:
sampling_locations = (
reference_points[:, :, None, :, None, :2]
+ sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
)
else:
raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
output = multi_scale_deformable_attention(value, spatial_shapes_list, sampling_locations, attention_weights)
output = self.output_proj(output)
return output, attention_weights
class Mask2FormerPixelDecoderEncoderLayer(nn.Module):
def __init__(self, config: Mask2FormerConfig):
super().__init__()
self.embed_dim = config.feature_size
self.self_attn = Mask2FormerPixelDecoderEncoderMultiscaleDeformableAttention(
embed_dim=self.embed_dim,
num_heads=config.num_attention_heads,
n_levels=3,
n_points=4,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = nn.functional.relu
self.activation_dropout = config.dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_feedforward_dim)
self.fc2 = nn.Linear(config.encoder_feedforward_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_embeddings: Optional[torch.Tensor] = None,
reference_points=None,
spatial_shapes_list=None,
level_start_index=None,
output_attentions: bool = False,
):
"""
Args:
hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Input to the layer.
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Attention mask.
position_embeddings (`torch.FloatTensor`, *optional*):
Position embeddings, to be added to `hidden_states`.
reference_points (`torch.FloatTensor`, *optional*):
Reference points.
spatial_shapes_list (`list` of `tuple`):
Spatial shapes of the backbone feature maps as a list of tuples.
level_start_index (`torch.LongTensor`, *optional*):
Level start index.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
# Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
position_embeddings=position_embeddings,
reference_points=reference_points,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
if self.training:
if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights.transpose(1, 0),)
return outputs
# Modified from from transformers.models.detr.modeling_deformable_detr.DeformableDetrEncoder with DeformableDetrEncoder->Mask2FormerPixelDecoderEncoderOnly
class Mask2FormerPixelDecoderEncoderOnly(nn.Module):
"""
Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
[`Mask2FormerPixelDecoderEncoderLayer`]. The encoder updates the flattened multi-scale feature maps through
multiple deformable attention layers.
Args:
config: Mask2FormerConfig
"""
def __init__(self, config: Mask2FormerConfig):
super().__init__()
self.config = config
self.dropout = config.dropout
self.layers = nn.ModuleList(
[Mask2FormerPixelDecoderEncoderLayer(config) for _ in range(config.encoder_layers)]
)
@staticmethod
def get_reference_points(spatial_shapes_list, valid_ratios, device):
"""
Get reference points for each feature map. Used in decoder.
Args:
spatial_shapes_list (`list` of `tuple`):
Spatial shapes of the backbone feature maps as a list of tuples.
valid_ratios (`torch.FloatTensor`):
Valid ratios of each feature map, has shape of `(batch_size, num_feature_levels, 2)`.
device (`torch.device`):
Device on which to create the tensors.
Returns:
`torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)`
"""
reference_points_list = []
for lvl, (height, width) in enumerate(spatial_shapes_list):
ref_y, ref_x = torch.meshgrid(
torch.linspace(0.5, height - 0.5, height, dtype=valid_ratios.dtype, device=device),
torch.linspace(0.5, width - 0.5, width, dtype=valid_ratios.dtype, device=device),
indexing="ij",
)
ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * height)
ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * width)
ref = torch.stack((ref_x, ref_y), -1)
reference_points_list.append(ref)
reference_points = torch.cat(reference_points_list, 1)
reference_points = reference_points[:, :, None] * valid_ratios[:, None]
return reference_points
def forward(
self,
inputs_embeds=None,
attention_mask=None,
position_embeddings=None,
spatial_shapes_list=None,
level_start_index=None,
valid_ratios=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
- 1 for pixel features that are real (i.e. **not masked**),
- 0 for pixel features that are padding (i.e. **masked**).
[What are attention masks?](../glossary#attention-mask)
position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Position embeddings that are added to the queries and keys in each self-attention layer.
spatial_shapes_list (`list` of `tuple`):
Spatial shapes of each feature map as a list of tuples.
level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`):
Starting index of each feature map.
valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
Ratio of valid area in each feature level.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
hidden_states = inputs_embeds
reference_points = self.get_reference_points(spatial_shapes_list, valid_ratios, device=inputs_embeds.device)
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, encoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states.transpose(1, 0),)
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
position_embeddings=position_embeddings,
reference_points=reference_points,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states.transpose(1, 0),)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
# Modified from from transformers.models.detr.modeling_deformable_detr.DeformableDetrModel with DeformableDetrModel->Mask2FormerPixelDecoder
class Mask2FormerPixelDecoder(nn.Module):
def __init__(self, config: Mask2FormerConfig, feature_channels):
super().__init__()
self.config = config
feature_dim = config.feature_size
mask_dim = config.mask_feature_size
num_pos_features = feature_dim // 2
self.position_embedding = Mask2FormerSinePositionEmbedding(num_pos_feats=num_pos_features, normalize=True)
self.num_feature_levels = 3
transformer_in_channels = feature_channels[-self.num_feature_levels :]
self.transformer_feature_strides = config.feature_strides[-self.num_feature_levels :]
self.feature_channels = feature_channels
self.level_embed = nn.Parameter(torch.Tensor(self.num_feature_levels, feature_dim))
# Create input projection layers
if self.num_feature_levels > 1:
input_projections_list = []
for in_channels in transformer_in_channels[::-1]:
input_projections_list.append(
nn.Sequential(
nn.Conv2d(in_channels, feature_dim, kernel_size=1),
nn.GroupNorm(32, feature_dim),
)
)
self.input_projections = nn.ModuleList(input_projections_list)
else:
self.input_projections = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(transformer_in_channels[-1], feature_dim, kernel_size=1),
nn.GroupNorm(32, feature_dim),
)
]
)
self.encoder = Mask2FormerPixelDecoderEncoderOnly(config)
self.mask_projection = nn.Conv2d(feature_dim, mask_dim, kernel_size=1, stride=1, padding=0)
# Extra FPN levels
stride = min(self.transformer_feature_strides)
self.common_stride = config.common_stride
self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))
lateral_convs = []
output_convs = []
for idx, in_channels in enumerate(self.feature_channels[: self.num_fpn_levels]):
lateral_conv = nn.Sequential(
nn.Conv2d(in_channels, feature_dim, kernel_size=1, bias=False),
nn.GroupNorm(32, feature_dim),
)
output_conv = nn.Sequential(
nn.Conv2d(feature_dim, feature_dim, kernel_size=3, stride=1, padding=1, bias=False),
nn.GroupNorm(32, feature_dim),
nn.ReLU(),
)
self.add_module(f"adapter_{idx + 1}", lateral_conv)
self.add_module(f"layer_{idx + 1}", output_conv)
lateral_convs.append(lateral_conv)
output_convs.append(output_conv)
# Order convolutional layers from low to high resolution
self.lateral_convolutions = lateral_convs[::-1]
self.output_convolutions = output_convs[::-1]
def get_valid_ratio(self, mask, dtype=torch.float32):
"""Get the valid ratio of all feature maps."""
_, height, width = mask.shape
valid_height = torch.sum(~mask[:, :, 0], 1)
valid_width = torch.sum(~mask[:, 0, :], 1)
valid_ratio_height = valid_height.to(dtype) / height
valid_ratio_width = valid_width.to(dtype) / width
valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
return valid_ratio
def forward(
self,
features,
encoder_outputs=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# Apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
input_embeds = []
position_embeddings = []
for level, x in enumerate(features[::-1][: self.num_feature_levels]):
input_embeds.append(self.input_projections[level](x))
position_embeddings.append(self.position_embedding(x.shape, x.device, x.dtype))
masks = [
torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in input_embeds
]
# Prepare encoder inputs (by flattening)
spatial_shapes_list = [(embed.shape[2], embed.shape[3]) for embed in input_embeds]
input_embeds_flat = torch.cat([embed.flatten(2).transpose(1, 2) for embed in input_embeds], 1)
spatial_shapes = torch.as_tensor(spatial_shapes_list, dtype=torch.long, device=input_embeds_flat.device)
masks_flat = torch.cat([mask.flatten(1) for mask in masks], 1)
position_embeddings = [embed.flatten(2).transpose(1, 2) for embed in position_embeddings]
level_pos_embed_flat = [x + self.level_embed[i].view(1, 1, -1) for i, x in enumerate(position_embeddings)]
level_pos_embed_flat = torch.cat(level_pos_embed_flat, 1)
level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
valid_ratios = torch.stack([self.get_valid_ratio(mask, dtype=input_embeds_flat.dtype) for mask in masks], 1)
# Send input_embeds_flat + masks_flat + level_pos_embed_flat (backbone + proj layer output) through encoder
if encoder_outputs is None:
encoder_outputs = self.encoder(
inputs_embeds=input_embeds_flat,
attention_mask=masks_flat,
position_embeddings=level_pos_embed_flat,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
valid_ratios=valid_ratios,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs.last_hidden_state
batch_size = last_hidden_state.shape[0]
# We compute level_start_index_list separately from the tensor version level_start_index
# to avoid iterating over a tensor which breaks torch.compile/export.
level_start_index_list = [0]
for height, width in spatial_shapes_list[:-1]:
level_start_index_list.append(level_start_index_list[-1] + height * width)
split_sizes = [None] * self.num_feature_levels
for i in range(self.num_feature_levels):
if i < self.num_feature_levels - 1:
split_sizes[i] = level_start_index_list[i + 1] - level_start_index_list[i]
else:
split_sizes[i] = last_hidden_state.shape[1] - level_start_index_list[i]
encoder_output = torch.split(last_hidden_state, split_sizes, dim=1)
# Compute final features
outputs = [
x.transpose(1, 2).view(batch_size, -1, spatial_shapes_list[i][0], spatial_shapes_list[i][1])
for i, x in enumerate(encoder_output)
]
# Append extra FPN levels to outputs, ordered from low to high resolution
for idx, feature in enumerate(features[: self.num_fpn_levels][::-1]):
lateral_conv = self.lateral_convolutions[idx]
output_conv = self.output_convolutions[idx]
current_fpn = lateral_conv(feature)
# Following FPN implementation, we use nearest upsampling here
out = current_fpn + nn.functional.interpolate(
outputs[-1], size=current_fpn.shape[-2:], mode="bilinear", align_corners=False
)
out = output_conv(out)
outputs.append(out)
num_cur_levels = 0
multi_scale_features = []
for out in outputs:
if num_cur_levels < self.num_feature_levels:
multi_scale_features.append(out)
num_cur_levels += 1
return Mask2FormerPixelDecoderOutput(
mask_features=self.mask_projection(outputs[-1]),
multi_scale_features=tuple(multi_scale_features),
attentions=encoder_outputs.attentions,
)
class Mask2FormerPixelLevelModule(nn.Module):
def __init__(self, config: Mask2FormerConfig):
"""
Pixel Level Module proposed in [Masked-attention Mask Transformer for Universal Image
Segmentation](https://huggingface.co/papers/2112.01527). It runs the input image through a backbone and a pixel
decoder, generating multi-scale feature maps and pixel embeddings.
Args:
config ([`Mask2FormerConfig`]):
The configuration used to instantiate this model.
"""
super().__init__()
self.encoder = load_backbone(config)
self.decoder = Mask2FormerPixelDecoder(config, feature_channels=self.encoder.channels)
def forward(self, pixel_values: Tensor, output_hidden_states: bool = False) -> Mask2FormerPixelLevelModuleOutput:
backbone_features = self.encoder(pixel_values).feature_maps
decoder_output = self.decoder(backbone_features, output_hidden_states=output_hidden_states)
return Mask2FormerPixelLevelModuleOutput(
encoder_last_hidden_state=backbone_features[-1],
encoder_hidden_states=tuple(backbone_features) if output_hidden_states else None,
decoder_last_hidden_state=decoder_output.mask_features,
decoder_hidden_states=decoder_output.multi_scale_features,
)
# Modified from transformers.models.detr.modeling_detr.DetrAttention with Detr->Mask2Former
class Mask2FormerAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Here, we add position embeddings to the queries and
keys (as explained in the DETR paper).
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if self.head_dim * num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
return tensor if position_embeddings is None else tensor + position_embeddings
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_embeddings: Optional[torch.Tensor] = None,
key_value_states: Optional[torch.Tensor] = None,
key_value_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
hidden_states = hidden_states.permute(1, 0, 2) if hidden_states is not None else None
position_embeddings = position_embeddings.permute(1, 0, 2) if position_embeddings is not None else None
key_value_states = key_value_states.permute(1, 0, 2) if key_value_states is not None else None
key_value_position_embeddings = (
key_value_position_embeddings.permute(1, 0, 2) if key_value_position_embeddings is not None else None
)
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
batch_size, target_len, embed_dim = hidden_states.size()
# add position embeddings to the hidden states before projecting to queries and keys
if position_embeddings is not None:
hidden_states_original = hidden_states
hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
# add key-value position embeddings to the key value states
if key_value_position_embeddings is not None:
key_value_states_original = key_value_states
key_value_states = self.with_pos_embed(key_value_states, key_value_position_embeddings)
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, batch_size)
value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)
proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
source_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
raise ValueError(
f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (batch_size * self.num_heads, target_len, source_len):
raise ValueError(
f"Attention mask should be of size {(target_len, batch_size * self.num_heads, source_len)}, but is"
f" {attention_mask.size()}"
)
attn_weights += attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(batch_size, target_len, embed_dim)
attn_output = self.out_proj(attn_output).permute(1, 0, 2)
return attn_output, attn_weights_reshaped
class Mask2FormerMaskedAttentionDecoderLayer(GradientCheckpointingLayer):
"""
The Mask2FormerMaskedAttentionDecoderLayer is made up of self-attention, cross (masked) attention as well as FFN
blocks. The cross attention block used as part of `Mask2FormerMaskedAttentionDecoderLayer` is actually a `masked
attention` block that restricts the attention to localized features centered around predicted segments which leads
to faster convergence and improved performance. The order of self and cross (i.e. masked) attention blocks have
also been swapped in Mask2FormerMaskedAttentionDecoder compared to a standard DetrDecoder as an optimization
improvement.
Args:
config (`Mask2FormerConfig`):
The configuration used to initialize the Mask2FormerMaskedAttentionDecoder.
"""
def __init__(self, config: Mask2FormerConfig):
super().__init__()
self.config = config
self.embed_dim = self.config.hidden_dim
self.pre_norm = self.config.pre_norm
self.self_attn = Mask2FormerAttention(
embed_dim=self.embed_dim,
num_heads=config.num_attention_heads,
dropout=config.dropout,
is_decoder=True,
)
self.dropout = self.config.dropout
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout = self.config.dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.cross_attn = nn.MultiheadAttention(self.embed_dim, self.config.num_attention_heads, self.config.dropout)
self.cross_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, self.config.dim_feedforward)
self.fc2 = nn.Linear(self.config.dim_feedforward, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(
self,
hidden_states: torch.Tensor,
level_index: Optional[int] = None,
attention_mask: Optional[torch.Tensor] = None,
position_embeddings: Optional[torch.Tensor] = None,
query_position_embeddings: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
):
# Masked(Cross)-Attention Block
cross_attn_weights = None
self_attn_weights = None
residual = hidden_states
hidden_states, cross_attn_weights = self.cross_attn(
query=self.with_pos_embed(hidden_states, query_position_embeddings),
key=self.with_pos_embed(encoder_hidden_states[level_index], position_embeddings[level_index]),
value=encoder_hidden_states[level_index],
attn_mask=encoder_attention_mask,
key_padding_mask=None,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.cross_attn_layer_norm(hidden_states)
# Self Attention Block
residual = hidden_states
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
position_embeddings=query_position_embeddings,
attention_mask=None,
output_attentions=True,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
def forward_pre(
self,
hidden_states: torch.Tensor,
level_index: Optional[int] = None,
attention_mask: Optional[torch.Tensor] = None,
position_embeddings: Optional[torch.Tensor] = None,
query_position_embeddings: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
):
# Masked(Cross)-Attention Block
cross_attn_weights = None
self_attn_weights = None
residual = hidden_states
hidden_states = self.cross_attn_layer_norm(hidden_states)
hidden_states, cross_attn_weights = self.cross_attn(
query=self.with_pos_embed(hidden_states, query_position_embeddings),
key=self.with_pos_embed(encoder_hidden_states[level_index], position_embeddings[level_index]),
value=encoder_hidden_states[level_index],
attn_mask=encoder_attention_mask,
key_padding_mask=None,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Self Attention Block
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
position_embeddings=query_position_embeddings,
attention_mask=None,
output_attentions=True,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
def forward(
self,
hidden_states: torch.Tensor,
level_index: Optional[int] = None,
attention_mask: Optional[torch.Tensor] = None,
position_embeddings: Optional[torch.Tensor] = None,
query_position_embeddings: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
):
"""
Args:
hidden_states (`torch.FloatTensor`):
Input to the layer of shape `(seq_len, batch, embed_dim)`.
attention_mask (`torch.FloatTensor`):
Attention mask of shape `(1, seq_len, tgt_len, src_len)`.
position_embeddings (`torch.FloatTensor`, *optional*):
Position embeddings that are added to the keys in the masked-attention layer.
query_position_embeddings (`torch.FloatTensor`, *optional*):
Position embeddings that are added to the queries and keys in the self-attention layer.
encoder_hidden_states (`torch.FloatTensor`):
Cross attention input to the layer of shape `(seq_len, batch, embed_dim)`.
encoder_attention_mask (`torch.FloatTensor`):
Encoder attention mask of size`(1, seq_len, tgt_len, src_len)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
if self.pre_norm:
outputs = self.forward_pre(
hidden_states=hidden_states,
level_index=level_index,
position_embeddings=position_embeddings,
query_position_embeddings=query_position_embeddings,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
)
else:
outputs = self.forward_post(
hidden_states=hidden_states,
level_index=level_index,
position_embeddings=position_embeddings,
query_position_embeddings=query_position_embeddings,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
)
return outputs
class Mask2FormerMaskedAttentionDecoder(nn.Module):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a
[`Mask2FormerMaskedAttentionDecoderLayer`]. The decoder updates the query embeddings through multiple cross
(masked) and self-attention layers. The decoder uses a new **masked attention** mechanism instead of the standard
cross-attention, which extracts localized features by constraining cross-attention to within the foreground region
of the predicted mask for each query, instead of attending to the full feature map.
Args:
config (`Mask2FormerConfig`):
Configuration used to instantiate Mask2FormerMaskedAttentionDecoder.
"""
def __init__(self, config: Mask2FormerConfig):
super().__init__()
self.config = config
self.mask_feature_size = config.mask_feature_size
self.dropout = config.dropout
self.layerdrop = config.dropout
self.num_feature_levels = 3 # level embedding (3 scales)
self.decoder_layers = config.decoder_layers - 1
self.layers = nn.ModuleList(
[Mask2FormerMaskedAttentionDecoderLayer(self.config) for _ in range(self.decoder_layers)]
)
self.layernorm = nn.LayerNorm(config.hidden_dim)
self.mask_predictor = Mask2FormerMaskPredictor(
hidden_size=config.hidden_dim,
num_heads=config.num_attention_heads,
mask_feature_size=self.mask_feature_size,
)
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds: Optional[torch.Tensor] = None,
multi_stage_positional_embeddings: Optional[torch.Tensor] = None,
pixel_embeddings: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
query_position_embeddings: Optional[torch.Tensor] = None,
feature_size_list: Optional[list] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
The query embeddings that are passed into the decoder.
multi_stage_positional_embeddings (`torch.FloatTensor` of shape `(height*width, batch_size, num_channels)`):
Position embeddings that are added to the keys in each cross(masked)-attention layer.
pixel_embeddings (`torch.FloatTensor`):
Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel
Decoder.
query_position_embeddings (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
, *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the
cross(masked)-attention of the decoder.
feature_size_list (`list[torch.Size]`):
This is a list containing shapes (height & width) of multi-scale features from the Pixel Decoder.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if inputs_embeds is not None:
hidden_states = inputs_embeds
# intermediate hidden states with layernorm applied - required for predicting class logits
intermediate = ()
# decoder layers
all_hidden_states = () if output_hidden_states else None
attentions = () if output_attentions else None
# intermediate mask predictions from transformer decoder layers
intermediate_mask_predictions = ()
intermediate_hidden_states = self.layernorm(inputs_embeds)
intermediate += (intermediate_hidden_states,)
predicted_mask, attention_mask = self.mask_predictor(
intermediate_hidden_states, pixel_embeddings, feature_size_list[0]
)
intermediate_mask_predictions += (predicted_mask,)
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = torch.rand([])
if self.training and (dropout_probability < self.layerdrop):
continue
level_index = idx % self.num_feature_levels
where = (attention_mask.sum(-1) != attention_mask.shape[-1]).to(attention_mask.dtype)
# Multiply the attention mask instead of indexing to avoid issue in torch.export.
attention_mask = attention_mask * where.unsqueeze(-1)
layer_outputs = decoder_layer(
hidden_states,
level_index,
None, # attention_mask
multi_stage_positional_embeddings,
query_position_embeddings,
encoder_hidden_states, # as a positional argument for gradient checkpointing
encoder_attention_mask=attention_mask,
output_attentions=output_attentions,
)
intermediate_hidden_states = self.layernorm(layer_outputs[0])
predicted_mask, attention_mask = self.mask_predictor(
intermediate_hidden_states,
pixel_embeddings,
feature_size_list[(idx + 1) % self.num_feature_levels],
)
intermediate_mask_predictions += (predicted_mask,)
# add intermediate hidden states with layer norm applied which will be used for predicting class logits
intermediate += (intermediate_hidden_states,)
hidden_states = layer_outputs[0]
if output_attentions:
attentions += (layer_outputs[1],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
hidden_states = hidden_states.transpose(1, 0)
if not return_dict:
outputs = [hidden_states, all_hidden_states, attentions, intermediate, intermediate_mask_predictions]
return tuple(v for v in outputs if v is not None)
return Mask2FormerMaskedAttentionDecoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=attentions,
intermediate_hidden_states=intermediate,
masks_queries_logits=intermediate_mask_predictions,
)
# Copied from transformers.models.maskformer.modeling_maskformer.PredictionBlock with MaskFormer->Mask2Former
class Mask2FormerPredictionBlock(nn.Module):
def __init__(self, in_dim: int, out_dim: int, activation: nn.Module) -> None:
super().__init__()
self.layers = [nn.Linear(in_dim, out_dim), activation]
# Maintain submodule indexing as if part of a Sequential block
for i, layer in enumerate(self.layers):
self.add_module(str(i), layer)
def forward(self, input: Tensor) -> Tensor:
hidden_state = input
for layer in self.layers:
hidden_state = layer(hidden_state)
return hidden_state
class Mask2FormerMLPPredictionHead(nn.Module):
def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):
"""
A classic Multi Layer Perceptron (MLP).
Args:
input_dim (`int`):
The input dimensions.
hidden_dim (`int`):
The hidden dimensions.
output_dim (`int`):
The output dimensions.
num_layers (int, *optional*, defaults to 3):
The number of layers.
"""
super().__init__()
in_dims = [input_dim] + [hidden_dim] * (num_layers - 1)
out_dims = [hidden_dim] * (num_layers - 1) + [output_dim]
self.layers = []
for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)):
activation = nn.ReLU() if i < num_layers - 1 else nn.Identity()
layer = Mask2FormerPredictionBlock(in_dim, out_dim, activation=activation)
self.layers.append(layer)
# Provide backwards compatibility from when the class inherited from nn.Sequential
# In nn.Sequential subclasses, the name given to the layer is its index in the sequence.
# In nn.Module subclasses they derived from the instance attribute they are assigned to e.g.
# self.my_layer_name = Layer()
# We can't give instance attributes integer names i.e. self.0 is not permitted and so need to register
# explicitly
self.add_module(str(i), layer)
def forward(self, input: Tensor) -> Tensor:
hidden_state = input
for layer in self.layers:
hidden_state = layer(hidden_state)
return hidden_state
class Mask2FormerMaskPredictor(nn.Module):
def __init__(self, hidden_size: int, num_heads: int, mask_feature_size: torch.Tensor):
"""
This class is used to get the predicted mask for a given Mask2FormerMaskedAttentionDecoder layer. It also
generates the binarized attention mask associated with the given predicted mask. The attention mask obtained
using predicted mask of the (l-1)th decoder layer is fed to the cross(masked)-attention block of the next
decoder layer as input.
Args:
hidden_size (`int`):
The feature dimension of the Mask2FormerMaskedAttentionDecoder
num_heads (`int`):
The number of heads used in the Mask2FormerMaskedAttentionDecoder
mask_feature_size (`torch.Tensor`):
one of the output dimensions of the predicted masks for each query
"""
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.mask_embedder = Mask2FormerMLPPredictionHead(self.hidden_size, self.hidden_size, mask_feature_size)
def forward(
self, outputs: torch.Tensor, pixel_embeddings: torch.Tensor, attention_mask_target_size: Optional[int] = None
):
mask_embeddings = self.mask_embedder(outputs.transpose(0, 1))
# Sum up over the channels
outputs_mask = torch.einsum("bqc, bchw -> bqhw", mask_embeddings, pixel_embeddings)
attention_mask = nn.functional.interpolate(
outputs_mask, size=attention_mask_target_size, mode="bilinear", align_corners=False
)
attention_mask = attention_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1)
attention_mask = (attention_mask.flatten(0, 1) < 0.5).bool()
attention_mask = attention_mask.detach()
return outputs_mask, attention_mask
class Mask2FormerTransformerModule(nn.Module):
"""
The Mask2Former's transformer module.
"""
def __init__(self, in_features: int, config: Mask2FormerConfig):
super().__init__()
hidden_dim = config.hidden_dim
self.num_feature_levels = 3
self.position_embedder = Mask2FormerSinePositionEmbedding(num_pos_feats=hidden_dim // 2, normalize=True)
self.queries_embedder = nn.Embedding(config.num_queries, hidden_dim)
self.queries_features = nn.Embedding(config.num_queries, hidden_dim)
self.input_projections = []
for _ in range(self.num_feature_levels):
if in_features != hidden_dim or config.enforce_input_projection:
self.input_projections.append(nn.Conv2d(in_features, hidden_dim, kernel_size=1))
else:
self.input_projections.append(nn.Sequential())
self.decoder = Mask2FormerMaskedAttentionDecoder(config=config)
self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)
def forward(
self,
multi_scale_features: list[Tensor],
mask_features: Tensor,
output_hidden_states: bool = False,
output_attentions: bool = False,
) -> Mask2FormerMaskedAttentionDecoderOutput:
multi_stage_features = []
multi_stage_positional_embeddings = []
size_list = []
for i in range(self.num_feature_levels):
size_list.append(multi_scale_features[i].shape[-2:])
multi_stage_positional_embeddings.append(
self.position_embedder(
multi_scale_features[i].shape, multi_scale_features[i].device, multi_scale_features[i].dtype, None
).flatten(2)
)
multi_stage_features.append(
self.input_projections[i](multi_scale_features[i]).flatten(2)
+ self.level_embed.weight[i][None, :, None]
)
# Flatten (batch_size, num_channels, height, width) -> (height*width, batch_size, num_channels)
multi_stage_positional_embeddings[-1] = multi_stage_positional_embeddings[-1].permute(2, 0, 1)
multi_stage_features[-1] = multi_stage_features[-1].permute(2, 0, 1)
_, batch_size, _ = multi_stage_features[0].shape
# [num_queries, batch_size, num_channels]
query_embeddings = self.queries_embedder.weight.unsqueeze(1).repeat(1, batch_size, 1)
query_features = self.queries_features.weight.unsqueeze(1).repeat(1, batch_size, 1)
decoder_output = self.decoder(
inputs_embeds=query_features,
multi_stage_positional_embeddings=multi_stage_positional_embeddings,
pixel_embeddings=mask_features,
encoder_hidden_states=multi_stage_features,
query_position_embeddings=query_embeddings,
feature_size_list=size_list,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
return_dict=True,
)
return decoder_output
@auto_docstring
class Mask2FormerPreTrainedModel(PreTrainedModel):
config: Mask2FormerConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
def _init_weights(self, module: nn.Module):
xavier_std = self.config.init_xavier_std
std = self.config.init_std
if isinstance(module, Mask2FormerTransformerModule):
if module.input_projections is not None:
for input_projection in module.input_projections:
if not isinstance(input_projection, nn.Sequential):
nn.init.xavier_uniform_(input_projection.weight, gain=xavier_std)
nn.init.constant_(input_projection.bias, 0)
elif isinstance(module, Mask2FormerPixelDecoderEncoderMultiscaleDeformableAttention):
nn.init.constant_(module.sampling_offsets.weight.data, 0.0)
thetas = torch.arange(module.n_heads, dtype=torch.int64).float() * (2.0 * math.pi / module.n_heads)
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
grid_init = (
(grid_init / grid_init.abs().max(-1, keepdim=True)[0])
.view(module.n_heads, 1, 1, 2)
.repeat(1, module.n_levels, module.n_points, 1)
)
for i in range(module.n_points):
grid_init[:, :, i, :] *= i + 1
with torch.no_grad():
module.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
nn.init.constant_(module.attention_weights.weight.data, 0.0)
nn.init.constant_(module.attention_weights.bias.data, 0.0)
nn.init.xavier_uniform_(module.value_proj.weight.data)
nn.init.constant_(module.value_proj.bias.data, 0.0)
nn.init.xavier_uniform_(module.output_proj.weight.data)
nn.init.constant_(module.output_proj.bias.data, 0.0)
elif isinstance(module, Mask2FormerMaskedAttentionDecoderLayer):
for p in module.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p, gain=xavier_std)
module.cross_attn.in_proj_bias.data.zero_()
elif isinstance(module, Mask2FormerPixelDecoder):
nn.init.normal_(module.level_embed, std=0)
elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
module.weight.data.fill_(1.0)
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
if hasattr(module, "reference_points"):
nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0)
nn.init.constant_(module.reference_points.bias.data, 0.0)
@auto_docstring
class Mask2FormerModel(Mask2FormerPreTrainedModel):
main_input_name = "pixel_values"
def __init__(self, config: Mask2FormerConfig):
super().__init__(config)
self.pixel_level_module = Mask2FormerPixelLevelModule(config)
self.transformer_module = Mask2FormerTransformerModule(in_features=config.feature_size, config=config)
self.post_init()
@auto_docstring
def forward(
self,
pixel_values: Tensor,
pixel_mask: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Mask2FormerModelOutput:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
batch_size, _, height, width = pixel_values.shape
if pixel_mask is None:
pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device)
pixel_level_module_output = self.pixel_level_module(
pixel_values=pixel_values, output_hidden_states=output_hidden_states
)
transformer_module_output = self.transformer_module(
multi_scale_features=pixel_level_module_output.decoder_hidden_states,
mask_features=pixel_level_module_output.decoder_last_hidden_state,
output_hidden_states=True,
output_attentions=output_attentions,
)
encoder_hidden_states = None
pixel_decoder_hidden_states = None
transformer_decoder_hidden_states = None
transformer_decoder_intermediate_states = None
if output_hidden_states:
encoder_hidden_states = pixel_level_module_output.encoder_hidden_states
pixel_decoder_hidden_states = pixel_level_module_output.decoder_hidden_states
transformer_decoder_hidden_states = transformer_module_output.hidden_states
transformer_decoder_intermediate_states = transformer_module_output.intermediate_hidden_states
output = Mask2FormerModelOutput(
encoder_last_hidden_state=pixel_level_module_output.encoder_last_hidden_state,
pixel_decoder_last_hidden_state=pixel_level_module_output.decoder_last_hidden_state,
transformer_decoder_last_hidden_state=transformer_module_output.last_hidden_state,
encoder_hidden_states=encoder_hidden_states,
pixel_decoder_hidden_states=pixel_decoder_hidden_states,
transformer_decoder_hidden_states=transformer_decoder_hidden_states,
transformer_decoder_intermediate_states=transformer_decoder_intermediate_states,
attentions=transformer_module_output.attentions,
masks_queries_logits=transformer_module_output.masks_queries_logits,
)
if not return_dict:
output = tuple(v for v in output.values() if v is not None)
return output
@auto_docstring(
custom_intro="""
The Mask2Former Model with heads on top for instance/semantic/panoptic segmentation.
"""
)
class Mask2FormerForUniversalSegmentation(Mask2FormerPreTrainedModel):
main_input_name = "pixel_values"
def __init__(self, config: Mask2FormerConfig):
super().__init__(config)
self.model = Mask2FormerModel(config)
self.weight_dict: dict[str, float] = {
"loss_cross_entropy": config.class_weight,
"loss_mask": config.mask_weight,
"loss_dice": config.dice_weight,
}
self.class_predictor = nn.Linear(config.hidden_dim, config.num_labels + 1)
self.criterion = Mask2FormerLoss(config=config, weight_dict=self.weight_dict)
self.post_init()
def get_loss_dict(
self,
masks_queries_logits: Tensor,
class_queries_logits: Tensor,
mask_labels: Tensor,
class_labels: Tensor,
auxiliary_predictions: dict[str, Tensor],
) -> dict[str, Tensor]:
loss_dict: dict[str, Tensor] = self.criterion(
masks_queries_logits=masks_queries_logits,
class_queries_logits=class_queries_logits,
mask_labels=mask_labels,
class_labels=class_labels,
auxiliary_predictions=auxiliary_predictions,
)
# weight each loss by `self.weight_dict[<LOSS_NAME>]` including auxiliary losses
for key, weight in self.weight_dict.items():
for loss_key, loss in loss_dict.items():
if key in loss_key:
loss *= weight
return loss_dict
def get_loss(self, loss_dict: dict[str, Tensor]) -> Tensor:
return sum(loss_dict.values())
def get_auxiliary_logits(self, classes: torch.Tensor, output_masks: torch.Tensor):
auxiliary_logits: list[dict[str, Tensor]] = []
for aux_binary_masks, aux_classes in zip(output_masks[:-1], classes[:-1]):
auxiliary_logits.append({"masks_queries_logits": aux_binary_masks, "class_queries_logits": aux_classes})
return auxiliary_logits
@auto_docstring
def forward(
self,
pixel_values: Tensor,
mask_labels: Optional[list[Tensor]] = None,
class_labels: Optional[list[Tensor]] = None,
pixel_mask: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_auxiliary_logits: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Mask2FormerForUniversalSegmentationOutput:
r"""
mask_labels (`list[torch.Tensor]`, *optional*):
List of mask labels of shape `(num_labels, height, width)` to be fed to a model
class_labels (`list[torch.LongTensor]`, *optional*):
list of target class labels of shape `(num_labels, height, width)` to be fed to a model. They identify the
labels of `mask_labels`, e.g. the label of `mask_labels[i][j]` if `class_labels[i][j]`.
output_auxiliary_logits (`bool`, *optional*):
Whether or not to output auxiliary logits.
Examples:
Instance segmentation example:
```python
>>> from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
>>> from PIL import Image
>>> import requests
>>> import torch
>>> # Load Mask2Former trained on COCO instance segmentation dataset
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-coco-instance")
>>> model = Mask2FormerForUniversalSegmentation.from_pretrained(
... "facebook/mask2former-swin-small-coco-instance"
... )
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = image_processor(image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> # Model predicts class_queries_logits of shape `(batch_size, num_queries)`
>>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
>>> class_queries_logits = outputs.class_queries_logits
>>> masks_queries_logits = outputs.masks_queries_logits
>>> # Perform post-processing to get instance segmentation map
>>> pred_instance_map = image_processor.post_process_instance_segmentation(
... outputs, target_sizes=[(image.height, image.width)]
... )[0]
>>> print(pred_instance_map.shape)
torch.Size([480, 640])
```
Semantic segmentation example:
```python
>>> from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
>>> from PIL import Image
>>> import requests
>>> import torch
>>> # Load Mask2Former trained on ADE20k semantic segmentation dataset
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-ade-semantic")
>>> model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-small-ade-semantic")
>>> url = (
... "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
... )
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = image_processor(image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> # Model predicts class_queries_logits of shape `(batch_size, num_queries)`
>>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
>>> class_queries_logits = outputs.class_queries_logits
>>> masks_queries_logits = outputs.masks_queries_logits
>>> # Perform post-processing to get semantic segmentation map
>>> pred_semantic_map = image_processor.post_process_semantic_segmentation(
... outputs, target_sizes=[(image.height, image.width)]
... )[0]
>>> print(pred_semantic_map.shape)
torch.Size([512, 683])
```
Panoptic segmentation example:
```python
>>> from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
>>> from PIL import Image
>>> import requests
>>> import torch
>>> # Load Mask2Former trained on CityScapes panoptic segmentation dataset
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-cityscapes-panoptic")
>>> model = Mask2FormerForUniversalSegmentation.from_pretrained(
... "facebook/mask2former-swin-small-cityscapes-panoptic"
... )
>>> url = "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = image_processor(image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> # Model predicts class_queries_logits of shape `(batch_size, num_queries)`
>>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
>>> class_queries_logits = outputs.class_queries_logits
>>> masks_queries_logits = outputs.masks_queries_logits
>>> # Perform post-processing to get panoptic segmentation map
>>> pred_panoptic_map = image_processor.post_process_panoptic_segmentation(
... outputs, target_sizes=[(image.height, image.width)]
... )[0]["segmentation"]
>>> print(pred_panoptic_map.shape)
torch.Size([338, 676])
```
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.model(
pixel_values=pixel_values,
pixel_mask=pixel_mask,
output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss,
output_attentions=output_attentions,
return_dict=True,
)
loss, loss_dict, auxiliary_logits = None, None, None
class_queries_logits = ()
for decoder_output in outputs.transformer_decoder_intermediate_states:
class_prediction = self.class_predictor(decoder_output.transpose(0, 1))
class_queries_logits += (class_prediction,)
masks_queries_logits = outputs.masks_queries_logits
auxiliary_logits = self.get_auxiliary_logits(class_queries_logits, masks_queries_logits)
if mask_labels is not None and class_labels is not None:
loss_dict = self.get_loss_dict(
masks_queries_logits=masks_queries_logits[-1],
class_queries_logits=class_queries_logits[-1],
mask_labels=mask_labels,
class_labels=class_labels,
auxiliary_predictions=auxiliary_logits,
)
loss = self.get_loss(loss_dict)
encoder_hidden_states = None
pixel_decoder_hidden_states = None
transformer_decoder_hidden_states = None
if output_hidden_states:
encoder_hidden_states = outputs.encoder_hidden_states
pixel_decoder_hidden_states = outputs.pixel_decoder_hidden_states
transformer_decoder_hidden_states = outputs.transformer_decoder_hidden_states
output_auxiliary_logits = (
self.config.output_auxiliary_logits if output_auxiliary_logits is None else output_auxiliary_logits
)
if not output_auxiliary_logits:
auxiliary_logits = None
output = Mask2FormerForUniversalSegmentationOutput(
loss=loss,
class_queries_logits=class_queries_logits[-1],
masks_queries_logits=masks_queries_logits[-1],
auxiliary_logits=auxiliary_logits,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
pixel_decoder_last_hidden_state=outputs.pixel_decoder_last_hidden_state,
transformer_decoder_last_hidden_state=outputs.transformer_decoder_last_hidden_state,
encoder_hidden_states=encoder_hidden_states,
pixel_decoder_hidden_states=pixel_decoder_hidden_states,
transformer_decoder_hidden_states=transformer_decoder_hidden_states,
attentions=outputs.attentions,
)
if not return_dict:
output = tuple(v for v in output.values() if v is not None)
if loss is not None:
output = (loss) + output
return output
__all__ = ["Mask2FormerForUniversalSegmentation", "Mask2FormerModel", "Mask2FormerPreTrainedModel"]
| transformers/src/transformers/models/mask2former/modeling_mask2former.py/0 | {
"file_path": "transformers/src/transformers/models/mask2former/modeling_mask2former.py",
"repo_id": "transformers",
"token_count": 48963
} | 468 |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/metaclip_2/modular_metaclip_2.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_metaclip_2.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class MetaClip2TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MetaClip2TextModel`]. It is used to instantiate a METACLIP_2
text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the text encoder of the METACLIP_2
[openai/metaclip_2-vit-base-patch32](https://huggingface.co/openai/metaclip_2-vit-base-patch32) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 49408):
Vocabulary size of the METACLIP_2 text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`MetaClip2Model`].
hidden_size (`int`, *optional*, defaults to 512):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 2048):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and vision projection layers.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (`int`, *optional*, defaults to 77):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
pad_token_id (`int`, *optional*, defaults to 1):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 49406):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 49407):
End of stream token id.
Example:
```python
>>> from transformers import MetaClip2TextConfig, MetaClip2TextModel
>>> # Initializing a MetaClip2TextConfig with openai/metaclip_2-vit-base-patch32 style configuration
>>> configuration = MetaClip2TextConfig()
>>> # Initializing a MetaClip2TextModel (with random weights) from the openai/metaclip_2-vit-base-patch32 style configuration
>>> model = MetaClip2TextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "metaclip_2_text_model"
base_config_key = "text_config"
def __init__(
self,
vocab_size=49408,
hidden_size=512,
intermediate_size=2048,
projection_dim=512,
num_hidden_layers=12,
num_attention_heads=8,
max_position_embeddings=77,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
# This differs from `MetaClip2Tokenizer`'s default and from openai/metaclip_2
# See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
pad_token_id=1,
bos_token_id=49406,
eos_token_id=49407,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.attention_dropout = attention_dropout
class MetaClip2VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MetaClip2VisionModel`]. It is used to instantiate a
METACLIP_2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the vision encoder of the METACLIP_2
[openai/metaclip_2-vit-base-patch32](https://huggingface.co/openai/metaclip_2-vit-base-patch32) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and vision projection layers.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 32):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:
```python
>>> from transformers import MetaClip2VisionConfig, MetaClip2VisionModel
>>> # Initializing a MetaClip2VisionConfig with openai/metaclip_2-vit-base-patch32 style configuration
>>> configuration = MetaClip2VisionConfig()
>>> # Initializing a MetaClip2VisionModel (with random weights) from the openai/metaclip_2-vit-base-patch32 style configuration
>>> model = MetaClip2VisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "metaclip_2_vision_model"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=768,
intermediate_size=3072,
projection_dim=512,
num_hidden_layers=12,
num_attention_heads=12,
num_channels=3,
image_size=224,
patch_size=32,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
class MetaClip2Config(PretrainedConfig):
r"""
[`MetaClip2Config`] is the configuration class to store the configuration of a [`MetaClip2Model`]. It is used to instantiate
a METACLIP_2 model according to the specified arguments, defining the text model and vision model configs. Instantiating
a configuration with the defaults will yield a similar configuration to that of the METACLIP_2
[openai/metaclip_2-vit-base-patch32](https://huggingface.co/openai/metaclip_2-vit-base-patch32) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`MetaClip2TextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`MetaClip2VisionConfig`].
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and vision projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The initial value of the *logit_scale* parameter. Default is used as per the original METACLIP_2 implementation.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import MetaClip2Config, MetaClip2Model
>>> # Initializing a MetaClip2Config with openai/metaclip_2-vit-base-patch32 style configuration
>>> configuration = MetaClip2Config()
>>> # Initializing a MetaClip2Model (with random weights) from the openai/metaclip_2-vit-base-patch32 style configuration
>>> model = MetaClip2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a MetaClip2Config from a MetaClip2TextConfig and a MetaClip2VisionConfig
>>> from transformers import MetaClip2TextConfig, MetaClip2VisionConfig
>>> # Initializing a MetaClip2Text and MetaClip2Vision configuration
>>> config_text = MetaClip2TextConfig()
>>> config_vision = MetaClip2VisionConfig()
>>> config = MetaClip2Config.from_text_vision_configs(config_text, config_vision)
```"""
model_type = "metaclip_2"
sub_configs = {"text_config": MetaClip2TextConfig, "vision_config": MetaClip2VisionConfig}
def __init__(
self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs
):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
text_config_dict = kwargs.pop("text_config_dict", None)
vision_config_dict = kwargs.pop("vision_config_dict", None)
super().__init__(**kwargs)
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
text_config = {}
# This is the complete result when using `text_config_dict`.
_text_config_dict = MetaClip2TextConfig(**text_config_dict).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
message = (
f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
f'The value `text_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
message = (
f"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The "
f'value `text_config["{key}"]` will be overridden.'
)
logger.info(message)
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict)
if vision_config_dict is not None:
if vision_config is None:
vision_config = {}
# This is the complete result when using `vision_config_dict`.
_vision_config_dict = MetaClip2VisionConfig(**vision_config_dict).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
_vision_config_dict["id2label"] = {
str(key): value for key, value in _vision_config_dict["id2label"].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
message = (
f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
f'values. The value `vision_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
message = (
f"`vision_config_dict` is provided which will be used to initialize `CLIPVisionConfig`. "
f'The value `vision_config["{key}"]` will be overridden.'
)
logger.info(message)
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict)
if text_config is None:
text_config = {}
logger.info("`text_config` is `None`. Initializing the `MetaClip2TextConfig` with default values.")
if vision_config is None:
vision_config = {}
logger.info("`vision_config` is `None`. initializing the `MetaClip2VisionConfig` with default values.")
self.text_config = MetaClip2TextConfig(**text_config)
self.vision_config = MetaClip2VisionConfig(**vision_config)
self.projection_dim = projection_dim
self.logit_scale_init_value = logit_scale_init_value
self.initializer_factor = 1.0
__all__ = ["MetaClip2Config", "MetaClip2TextConfig", "MetaClip2VisionConfig"]
| transformers/src/transformers/models/metaclip_2/configuration_metaclip_2.py/0 | {
"file_path": "transformers/src/transformers/models/metaclip_2/configuration_metaclip_2.py",
"repo_id": "transformers",
"token_count": 7142
} | 469 |
# coding=utf-8
# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch MiniMax model."""
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...configuration_utils import layer_type_validation
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import MoeModelOutputWithPast
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, logging
from ...utils.deprecation import deprecate_kwarg
from ...utils.generic import OutputRecorder
from ..mixtral.configuration_mixtral import MixtralConfig
from ..mixtral.modeling_mixtral import (
MixtralAttention,
MixtralDecoderLayer,
MixtralForCausalLM,
MixtralForQuestionAnswering,
MixtralForSequenceClassification,
MixtralForTokenClassification,
MixtralModel,
MixtralPreTrainedModel,
MixtralRMSNorm,
MixtralSparseMoeBlock,
)
logger = logging.get_logger(__name__)
class MiniMaxConfig(MixtralConfig):
r"""
This is the configuration class to store the configuration of a [`MiniMaxModel`]. It is used to instantiate an
MiniMax model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the MiniMax.
[MiniMaxAI/MiniMax-Text-01-hf](https://huggingface.co/MiniMaxAI/MiniMax-Text-01-hf)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the MiniMax model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MiniMaxModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 14336):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details, check out [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
The attention head dimension.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
The maximum sequence length that this model might ever be used with. MiniMax's sliding window attention
allows sequence of up to 4096*32 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
sliding_window (`int`, *optional*):
Sliding window attention window size. If not specified, will default to `4096`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
num_experts_per_tok (`int`, *optional*, defaults to 2):
The number of experts to route per-token, can be also interpreted as the `top-k` routing
parameter
num_local_experts (`int`, *optional*, defaults to 8):
Number of experts per Sparse MLP layer.
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabeling this will also
allow the model to output the auxiliary loss. See [here]() for more details
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The aux loss factor for the total loss.
router_jitter_noise (`float`, *optional*, defaults to 0.0):
Amount of noise to add to the router.
layer_types (`list`, *optional*):
Attention pattern for each layer.
block_size (`int`, *optional*, defaults to 256):
The length of each attention block, determining how queries, keys, and values
are grouped and processed for intra- and inter-block attention.
full_attn_alpha_factor (`float`, *optional*, defaults to 1):
Weight for residual value in residual connection after normal attention.
full_attn_beta_factor (`float`, *optional*, defaults to 1):
Weight for hidden state value in residual connection after normal attention.
linear_attn_alpha_factor (`float`, *optional*, defaults to 1):
Weight for residual value in residual connection after lightning attention.
linear_attn_beta_factor (`float`, *optional*, defaults to 1):
Weight for hidden state value in residual connection after lightning attention.
mlp_alpha_factor (`float`, *optional*, defaults to 1):
Weight for residual value in residual connection after MLP.
mlp_beta_factor (`float`, *optional*, defaults to 1):
Weight for hidden state value in residual connection after MLP.
```python
>>> from transformers import MiniMaxModel, MiniMaxConfig
>>> # Initializing a MiniMax style configuration
>>> configuration = MiniMaxConfig()
>>> # Initializing a model from the MiniMax style configuration
>>> model = MiniMaxModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
def __init__(
self,
layer_types=None,
block_size=256,
full_attn_alpha_factor=1,
full_attn_beta_factor=1,
linear_attn_alpha_factor=1,
linear_attn_beta_factor=1,
mlp_alpha_factor=1,
mlp_beta_factor=1,
**super_kwargs,
):
super().__init__(**super_kwargs)
self.layer_types = layer_types
self.block_size = block_size
self.full_attn_alpha_factor = full_attn_alpha_factor
self.full_attn_beta_factor = full_attn_beta_factor
self.linear_attn_alpha_factor = linear_attn_alpha_factor
self.linear_attn_beta_factor = linear_attn_beta_factor
self.mlp_alpha_factor = mlp_alpha_factor
self.mlp_beta_factor = mlp_beta_factor
if self.layer_types is None:
self.layer_types = [
"full_attention" if bool((i + 1) % 2) else "linear_attention" for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types)
class MiniMaxRMSNorm(MixtralRMSNorm):
pass
class MiniMaxCache(DynamicCache):
def __init__(self):
super().__init__()
self.linear_cache: list[torch.Tensor] = []
def set_linear_cache(self, layer_idx, linear_cache):
# There may be skipped layers, fill them with empty lists
for _ in range(len(self.linear_cache), layer_idx + 1):
self.linear_cache.append([])
self.linear_cache[layer_idx] = linear_cache
def get_linear_cache(self, layer_idx: int):
if layer_idx < len(self):
return self.linear_cache[layer_idx]
return None
def __len__(self):
return max(super().__len__(), len(self.linear_cache))
def __getitem__(self, layer_idx: int):
if layer_idx < len(self.linear_cache) and self.linear_cache[layer_idx] != []:
return (self.linear_cache[layer_idx],)
return super().__getitem__(layer_idx)
def __iter__(self):
for layer_idx in range(len(self)):
yield self[layer_idx]
def batch_repeat_interleave(self, repeats: int):
for layer_idx in range(len(self)):
if self.linear_cache[layer_idx] != []:
self.linear_cache[layer_idx] = self.linear_cache[layer_idx].repeat_interleave(repeats, dim=0)
else:
self.layers[layer_idx].batch_repeat_interleave(repeats)
def batch_select_indices(self, indices: torch.Tensor):
for layer_idx in range(len(self)):
if self.linear_cache[layer_idx] != []:
self.linear_cache[layer_idx] = self.linear_cache[layer_idx][indices, ...]
else:
self.layers[layer_idx].batch_select_indices(indices)
def crop(self, max_length: int):
raise RuntimeError("MiniMaxCache doesnot support `crop` method")
class MiniMaxLightningAttention(nn.Module):
def __init__(self, config: MiniMaxConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
self.num_attention_heads = config.num_attention_heads
self.num_hidden_layers = config.num_hidden_layers
self.block_size = config.block_size
self.act_fn = ACT2FN[config.hidden_act]
self.norm = MiniMaxRMSNorm(self.head_dim * self.num_attention_heads)
self.qkv_proj = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim * 3, bias=False)
self.out_proj = nn.Linear(self.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
self.output_gate = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
slope_rate = self.get_slope_rate()
query_decay, key_decay, diagonal_decay = self.decay_factors(slope_rate)
self.register_buffer("slope_rate", slope_rate)
self.register_buffer("query_decay", query_decay)
self.register_buffer("key_decay", key_decay)
self.register_buffer("diagonal_decay", diagonal_decay)
def get_slope_rate(self):
base = 1 / (2 ** (8 / self.num_attention_heads))
exponent = torch.arange(self.num_attention_heads) + 1
factor = 1 - self.layer_idx / (self.num_hidden_layers - 1 + 1e-5) + 1e-5
rate = base**exponent
rate = rate * factor
rate = rate[:, None, None]
return rate
def decay_factors(self, slope_rate):
block_size_range = torch.arange(self.block_size) + 1
query_decay = torch.exp(-slope_rate * block_size_range[:, None])
key_decay = torch.exp(-slope_rate * (self.block_size - block_size_range[:, None]))
diagonal_decay = block_size_range[:, None] - block_size_range[None, :]
diagonal_decay = diagonal_decay[None, None, :, :]
diagonal_decay = slope_rate * diagonal_decay
diagonal_decay = torch.where(diagonal_decay >= 0, -diagonal_decay, float("-inf"))
diagonal_decay = torch.exp(diagonal_decay)
return query_decay, key_decay, diagonal_decay
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_values: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
batch_size, seq_len, hidden_size = hidden_states.shape
num_blocks = (seq_len + self.block_size - 1) // self.block_size
qkv_states = self.act_fn(self.qkv_proj(hidden_states))
qkv_states = qkv_states.reshape(batch_size, seq_len, self.num_attention_heads, 3 * self.head_dim)
query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=3)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
# calculated (K.T @ V) and saved as cache
attn_weights_inter = None
if past_key_values is not None:
attn_weights_inter = past_key_values.get_linear_cache(self.layer_idx)
if attn_weights_inter is None:
attn_weights_inter = torch.zeros(batch_size, self.num_attention_heads, self.head_dim, self.head_dim).to(
value_states
)
# apply attention_mask
if attention_mask is not None:
attention_mask = attention_mask.to(dtype=torch.bool) # Ensure it's a boolean tensor
value_states = value_states.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(-1), 0)
attn_output = []
for i in range(num_blocks):
start_idx = i * self.block_size
end_idx = min(start_idx + self.block_size, seq_len)
current_block_size = end_idx - start_idx
current_query_states = query_states[:, :, start_idx:end_idx]
current_key_states = key_states[:, :, start_idx:end_idx]
current_value_states = value_states[:, :, start_idx:end_idx]
current_query_decay = self.query_decay[:, :current_block_size]
current_key_decay = self.key_decay[:, -current_block_size:]
current_diagonal_decay = self.diagonal_decay[:, :, :current_block_size, :current_block_size]
block_decay = torch.exp(-self.slope_rate * current_block_size)
# intra: ( Q @ K.T ) @ V -> QK * V
attn_weights_intra = torch.matmul(current_query_states, current_key_states.transpose(-1, -2))
attn_output_intra = torch.matmul(attn_weights_intra * current_diagonal_decay, current_value_states)
# inter: Q @ ( K.T @ V ) -> Q * KV
attn_output_inter = torch.matmul(current_query_states * current_query_decay, attn_weights_inter)
# final attention output
current_attn_output = attn_output_inter + attn_output_intra
attn_output.append(current_attn_output)
# cacluate attn_weights_inter for next block or cache
next_attn_weights_inter = torch.matmul(
(current_key_states * current_key_decay).transpose(-1, -2), current_value_states
)
attn_weights_inter = attn_weights_inter * block_decay + next_attn_weights_inter
else:
ratio = torch.exp(-self.slope_rate)
attn_output = []
for i in range(seq_len):
current_query_states = query_states[:, :, i : i + 1]
current_key_states = key_states[:, :, i : i + 1]
current_value_states = value_states[:, :, i : i + 1]
current_attn_weights_inter = torch.matmul(current_key_states.transpose(-1, -2), current_value_states)
attn_weights_inter = ratio * attn_weights_inter + current_attn_weights_inter
current_attn_output = torch.matmul(current_query_states, attn_weights_inter)
attn_output.append(current_attn_output)
# concatenate attention outputs over all blocks
attn_output = torch.cat(attn_output, dim=-2)
# final output projection
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(batch_size, seq_len, self.num_attention_heads * self.head_dim)
attn_output = self.norm(attn_output)
attn_output = F.sigmoid(self.output_gate(hidden_states)) * attn_output
attn_output = self.out_proj(attn_output)
# update cache
if past_key_values is not None:
past_key_values.set_linear_cache(self.layer_idx, attn_weights_inter)
return attn_output, attn_weights_inter
class MiniMaxAttention(MixtralAttention):
pass
class MiniMaxSparseMoeBlock(MixtralSparseMoeBlock):
pass
class MiniMaxDecoderLayer(MixtralDecoderLayer, GradientCheckpointingLayer):
def __init__(self, config: MiniMaxConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.layer_idx = layer_idx
self.layer_type = config.layer_types[layer_idx]
self.mlp_alpha_factor = config.mlp_alpha_factor
self.mlp_beta_factor = config.mlp_beta_factor
if self.layer_type == "linear_attention":
self.self_attn = MiniMaxLightningAttention(config, layer_idx)
self.attn_alpha_factor = config.linear_attn_alpha_factor
self.attn_beta_factor = config.linear_attn_beta_factor
else:
self.self_attn = MiniMaxAttention(config, layer_idx)
self.attn_alpha_factor = config.full_attn_alpha_factor
self.attn_beta_factor = config.full_attn_beta_factor
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
attention_mask (`torch.Tensor`, *optional*): attention mask of size
`(batch, sequence_length)` where padding elements are indicated by 0.
past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence.
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
hidden_states = self.input_layernorm(hidden_states)
residual = hidden_states
# Self Attention
hidden_states, _ = self.self_attn(
hidden_states=hidden_states,
position_embeddings=position_embeddings,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = residual * self.attn_alpha_factor + hidden_states * self.attn_beta_factor
# Fully Connected
hidden_states = self.post_attention_layernorm(hidden_states)
residual = hidden_states
hidden_states, _ = self.block_sparse_moe(hidden_states)
hidden_states = residual * self.mlp_alpha_factor + hidden_states * self.mlp_beta_factor
return hidden_states
class MiniMaxPreTrainedModel(MixtralPreTrainedModel):
_can_compile_fullgraph = False
_can_record_outputs = {
"router_logits": OutputRecorder(MiniMaxSparseMoeBlock, index=1),
"hidden_states": MiniMaxDecoderLayer,
"attentions": [MiniMaxAttention, MiniMaxLightningAttention],
}
class MiniMaxModel(MixtralModel):
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[MiniMaxCache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> MoeModelOutputWithPast:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if use_cache and past_key_values is None:
past_key_values = MiniMaxCache()
elif use_cache and not isinstance(past_key_values, MiniMaxCache):
raise ValueError(
f"MiniMax uses cache of its own and is not compatible with `past_key_values` of type {type(past_key_values)}."
)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
causal_mask = mask_function(
config=self.config,
input_embeds=inputs_embeds,
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=past_key_values,
position_ids=position_ids,
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
for decoder_layer in self.layers:
if decoder_layer.layer_type == "full_attention":
input_attention_mask = causal_mask
else:
# lightning attention uses original attention_mask, and uses it only for the first step
input_attention_mask = attention_mask
hidden_states = decoder_layer(
hidden_states,
position_embeddings=position_embeddings,
attention_mask=input_attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = self.norm(hidden_states)
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
)
class MiniMaxForCausalLM(MixtralForCausalLM):
def forward(self, **super_kwargs):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, MiniMaxForCausalLM
>>> model = MiniMaxForCausalLM.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
return super().forward(**super_kwargs)
class MiniMaxForSequenceClassification(MixtralForSequenceClassification):
pass
class MiniMaxForTokenClassification(MixtralForTokenClassification):
pass
class MiniMaxForQuestionAnswering(MixtralForQuestionAnswering):
pass
__all__ = [
"MiniMaxConfig",
"MiniMaxPreTrainedModel",
"MiniMaxModel",
"MiniMaxForCausalLM",
"MiniMaxForSequenceClassification",
"MiniMaxForTokenClassification",
"MiniMaxForQuestionAnswering",
]
| transformers/src/transformers/models/minimax/modular_minimax.py/0 | {
"file_path": "transformers/src/transformers/models/minimax/modular_minimax.py",
"repo_id": "transformers",
"token_count": 11466
} | 470 |
# coding=utf-8
# Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE
"""PyTorch MobileViT model."""
import math
from typing import Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
SemanticSegmenterOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import auto_docstring, logging, torch_int
from .configuration_mobilevit import MobileViTConfig
logger = logging.get_logger(__name__)
def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
"""
Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
original TensorFlow repo. It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
"""
if min_value is None:
min_value = divisor
new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_value < 0.9 * value:
new_value += divisor
return int(new_value)
class MobileViTConvLayer(nn.Module):
def __init__(
self,
config: MobileViTConfig,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
groups: int = 1,
bias: bool = False,
dilation: int = 1,
use_normalization: bool = True,
use_activation: Union[bool, str] = True,
) -> None:
super().__init__()
padding = int((kernel_size - 1) / 2) * dilation
if in_channels % groups != 0:
raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
if out_channels % groups != 0:
raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
self.convolution = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode="zeros",
)
if use_normalization:
self.normalization = nn.BatchNorm2d(
num_features=out_channels,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True,
)
else:
self.normalization = None
if use_activation:
if isinstance(use_activation, str):
self.activation = ACT2FN[use_activation]
elif isinstance(config.hidden_act, str):
self.activation = ACT2FN[config.hidden_act]
else:
self.activation = config.hidden_act
else:
self.activation = None
def forward(self, features: torch.Tensor) -> torch.Tensor:
features = self.convolution(features)
if self.normalization is not None:
features = self.normalization(features)
if self.activation is not None:
features = self.activation(features)
return features
class MobileViTInvertedResidual(nn.Module):
"""
Inverted residual block (MobileNetv2): https://huggingface.co/papers/1801.04381
"""
def __init__(
self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1
) -> None:
super().__init__()
expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8)
if stride not in [1, 2]:
raise ValueError(f"Invalid stride {stride}.")
self.use_residual = (stride == 1) and (in_channels == out_channels)
self.expand_1x1 = MobileViTConvLayer(
config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
)
self.conv_3x3 = MobileViTConvLayer(
config,
in_channels=expanded_channels,
out_channels=expanded_channels,
kernel_size=3,
stride=stride,
groups=expanded_channels,
dilation=dilation,
)
self.reduce_1x1 = MobileViTConvLayer(
config,
in_channels=expanded_channels,
out_channels=out_channels,
kernel_size=1,
use_activation=False,
)
def forward(self, features: torch.Tensor) -> torch.Tensor:
residual = features
features = self.expand_1x1(features)
features = self.conv_3x3(features)
features = self.reduce_1x1(features)
return residual + features if self.use_residual else features
class MobileViTMobileNetLayer(nn.Module):
def __init__(
self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1
) -> None:
super().__init__()
self.layer = nn.ModuleList()
for i in range(num_stages):
layer = MobileViTInvertedResidual(
config,
in_channels=in_channels,
out_channels=out_channels,
stride=stride if i == 0 else 1,
)
self.layer.append(layer)
in_channels = out_channels
def forward(self, features: torch.Tensor) -> torch.Tensor:
for layer_module in self.layer:
features = layer_module(features)
return features
class MobileViTSelfAttention(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
super().__init__()
if hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size {hidden_size} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}."
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
batch_size, seq_length, _ = hidden_states.shape
query_layer = (
self.query(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
key_layer = (
self.key(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
value_layer = (
self.value(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer
class MobileViTSelfOutput(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class MobileViTAttention(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
super().__init__()
self.attention = MobileViTSelfAttention(config, hidden_size)
self.output = MobileViTSelfOutput(config, hidden_size)
self.pruned_heads = set()
def prune_heads(self, heads: set[int]) -> None:
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.attention.query = prune_linear_layer(self.attention.query, index)
self.attention.key = prune_linear_layer(self.attention.key, index)
self.attention.value = prune_linear_layer(self.attention.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
self_outputs = self.attention(hidden_states)
attention_output = self.output(self_outputs)
return attention_output
class MobileViTIntermediate(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
super().__init__()
self.dense = nn.Linear(hidden_size, intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class MobileViTOutput(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
super().__init__()
self.dense = nn.Linear(intermediate_size, hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
class MobileViTTransformerLayer(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
super().__init__()
self.attention = MobileViTAttention(config, hidden_size)
self.intermediate = MobileViTIntermediate(config, hidden_size, intermediate_size)
self.output = MobileViTOutput(config, hidden_size, intermediate_size)
self.layernorm_before = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
self.layernorm_after = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
attention_output = self.attention(self.layernorm_before(hidden_states))
hidden_states = attention_output + hidden_states
layer_output = self.layernorm_after(hidden_states)
layer_output = self.intermediate(layer_output)
layer_output = self.output(layer_output, hidden_states)
return layer_output
class MobileViTTransformer(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int) -> None:
super().__init__()
self.layer = nn.ModuleList()
for _ in range(num_stages):
transformer_layer = MobileViTTransformerLayer(
config,
hidden_size=hidden_size,
intermediate_size=int(hidden_size * config.mlp_ratio),
)
self.layer.append(transformer_layer)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
for layer_module in self.layer:
hidden_states = layer_module(hidden_states)
return hidden_states
class MobileViTLayer(GradientCheckpointingLayer):
"""
MobileViT block: https://huggingface.co/papers/2110.02178
"""
def __init__(
self,
config: MobileViTConfig,
in_channels: int,
out_channels: int,
stride: int,
hidden_size: int,
num_stages: int,
dilation: int = 1,
) -> None:
super().__init__()
self.patch_width = config.patch_size
self.patch_height = config.patch_size
if stride == 2:
self.downsampling_layer = MobileViTInvertedResidual(
config,
in_channels=in_channels,
out_channels=out_channels,
stride=stride if dilation == 1 else 1,
dilation=dilation // 2 if dilation > 1 else 1,
)
in_channels = out_channels
else:
self.downsampling_layer = None
self.conv_kxk = MobileViTConvLayer(
config,
in_channels=in_channels,
out_channels=in_channels,
kernel_size=config.conv_kernel_size,
)
self.conv_1x1 = MobileViTConvLayer(
config,
in_channels=in_channels,
out_channels=hidden_size,
kernel_size=1,
use_normalization=False,
use_activation=False,
)
self.transformer = MobileViTTransformer(
config,
hidden_size=hidden_size,
num_stages=num_stages,
)
self.layernorm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
self.conv_projection = MobileViTConvLayer(
config, in_channels=hidden_size, out_channels=in_channels, kernel_size=1
)
self.fusion = MobileViTConvLayer(
config, in_channels=2 * in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size
)
def unfolding(self, features: torch.Tensor) -> tuple[torch.Tensor, dict]:
patch_width, patch_height = self.patch_width, self.patch_height
patch_area = int(patch_width * patch_height)
batch_size, channels, orig_height, orig_width = features.shape
new_height = (
torch_int(torch.ceil(orig_height / patch_height) * patch_height)
if torch.jit.is_tracing()
else int(math.ceil(orig_height / patch_height) * patch_height)
)
new_width = (
torch_int(torch.ceil(orig_width / patch_width) * patch_width)
if torch.jit.is_tracing()
else int(math.ceil(orig_width / patch_width) * patch_width)
)
interpolate = False
if new_width != orig_width or new_height != orig_height:
# Note: Padding can be done, but then it needs to be handled in attention function.
features = nn.functional.interpolate(
features, size=(new_height, new_width), mode="bilinear", align_corners=False
)
interpolate = True
# number of patches along width and height
num_patch_width = new_width // patch_width
num_patch_height = new_height // patch_height
num_patches = num_patch_height * num_patch_width
# convert from shape (batch_size, channels, orig_height, orig_width)
# to the shape (batch_size * patch_area, num_patches, channels)
patches = features.reshape(
batch_size * channels * num_patch_height, patch_height, num_patch_width, patch_width
)
patches = patches.transpose(1, 2)
patches = patches.reshape(batch_size, channels, num_patches, patch_area)
patches = patches.transpose(1, 3)
patches = patches.reshape(batch_size * patch_area, num_patches, -1)
info_dict = {
"orig_size": (orig_height, orig_width),
"batch_size": batch_size,
"channels": channels,
"interpolate": interpolate,
"num_patches": num_patches,
"num_patches_width": num_patch_width,
"num_patches_height": num_patch_height,
}
return patches, info_dict
def folding(self, patches: torch.Tensor, info_dict: dict) -> torch.Tensor:
patch_width, patch_height = self.patch_width, self.patch_height
patch_area = int(patch_width * patch_height)
batch_size = info_dict["batch_size"]
channels = info_dict["channels"]
num_patches = info_dict["num_patches"]
num_patch_height = info_dict["num_patches_height"]
num_patch_width = info_dict["num_patches_width"]
# convert from shape (batch_size * patch_area, num_patches, channels)
# back to shape (batch_size, channels, orig_height, orig_width)
features = patches.contiguous().view(batch_size, patch_area, num_patches, -1)
features = features.transpose(1, 3)
features = features.reshape(
batch_size * channels * num_patch_height, num_patch_width, patch_height, patch_width
)
features = features.transpose(1, 2)
features = features.reshape(
batch_size, channels, num_patch_height * patch_height, num_patch_width * patch_width
)
if info_dict["interpolate"]:
features = nn.functional.interpolate(
features, size=info_dict["orig_size"], mode="bilinear", align_corners=False
)
return features
def forward(self, features: torch.Tensor) -> torch.Tensor:
# reduce spatial dimensions if needed
if self.downsampling_layer:
features = self.downsampling_layer(features)
residual = features
# local representation
features = self.conv_kxk(features)
features = self.conv_1x1(features)
# convert feature map to patches
patches, info_dict = self.unfolding(features)
# learn global representations
patches = self.transformer(patches)
patches = self.layernorm(patches)
# convert patches back to feature maps
features = self.folding(patches, info_dict)
features = self.conv_projection(features)
features = self.fusion(torch.cat((residual, features), dim=1))
return features
class MobileViTEncoder(nn.Module):
def __init__(self, config: MobileViTConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList()
self.gradient_checkpointing = False
# segmentation architectures like DeepLab and PSPNet modify the strides
# of the classification backbones
dilate_layer_4 = dilate_layer_5 = False
if config.output_stride == 8:
dilate_layer_4 = True
dilate_layer_5 = True
elif config.output_stride == 16:
dilate_layer_5 = True
dilation = 1
layer_1 = MobileViTMobileNetLayer(
config,
in_channels=config.neck_hidden_sizes[0],
out_channels=config.neck_hidden_sizes[1],
stride=1,
num_stages=1,
)
self.layer.append(layer_1)
layer_2 = MobileViTMobileNetLayer(
config,
in_channels=config.neck_hidden_sizes[1],
out_channels=config.neck_hidden_sizes[2],
stride=2,
num_stages=3,
)
self.layer.append(layer_2)
layer_3 = MobileViTLayer(
config,
in_channels=config.neck_hidden_sizes[2],
out_channels=config.neck_hidden_sizes[3],
stride=2,
hidden_size=config.hidden_sizes[0],
num_stages=2,
)
self.layer.append(layer_3)
if dilate_layer_4:
dilation *= 2
layer_4 = MobileViTLayer(
config,
in_channels=config.neck_hidden_sizes[3],
out_channels=config.neck_hidden_sizes[4],
stride=2,
hidden_size=config.hidden_sizes[1],
num_stages=4,
dilation=dilation,
)
self.layer.append(layer_4)
if dilate_layer_5:
dilation *= 2
layer_5 = MobileViTLayer(
config,
in_channels=config.neck_hidden_sizes[4],
out_channels=config.neck_hidden_sizes[5],
stride=2,
hidden_size=config.hidden_sizes[2],
num_stages=3,
dilation=dilation,
)
self.layer.append(layer_5)
def forward(
self,
hidden_states: torch.Tensor,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[tuple, BaseModelOutputWithNoAttention]:
all_hidden_states = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
hidden_states = layer_module(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
@auto_docstring
class MobileViTPreTrainedModel(PreTrainedModel):
config: MobileViTConfig
base_model_prefix = "mobilevit"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
_no_split_modules = ["MobileViTLayer"]
def _init_weights(self, module: nn.Module) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@auto_docstring
class MobileViTModel(MobileViTPreTrainedModel):
def __init__(self, config: MobileViTConfig, expand_output: bool = True):
r"""
expand_output (`bool`, *optional*, defaults to `True`):
Whether to expand the output of the model using a 1x1 convolution. If `True`, the model will apply an additional
1x1 convolution to expand the output channels from `config.neck_hidden_sizes[5]` to `config.neck_hidden_sizes[6]`.
"""
super().__init__(config)
self.config = config
self.expand_output = expand_output
self.conv_stem = MobileViTConvLayer(
config,
in_channels=config.num_channels,
out_channels=config.neck_hidden_sizes[0],
kernel_size=3,
stride=2,
)
self.encoder = MobileViTEncoder(config)
if self.expand_output:
self.conv_1x1_exp = MobileViTConvLayer(
config,
in_channels=config.neck_hidden_sizes[5],
out_channels=config.neck_hidden_sizes[6],
kernel_size=1,
)
# Initialize weights and apply final processing
self.post_init()
def _prune_heads(self, heads_to_prune):
"""Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel
"""
for layer_index, heads in heads_to_prune.items():
mobilevit_layer = self.encoder.layer[layer_index]
if isinstance(mobilevit_layer, MobileViTLayer):
for transformer_layer in mobilevit_layer.transformer.layer:
transformer_layer.attention.prune_heads(heads)
@auto_docstring
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
embedding_output = self.conv_stem(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if self.expand_output:
last_hidden_state = self.conv_1x1_exp(encoder_outputs[0])
# global average pooling: (batch_size, channels, height, width) -> (batch_size, channels)
pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False)
else:
last_hidden_state = encoder_outputs[0]
pooled_output = None
if not return_dict:
output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,)
return output + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@auto_docstring(
custom_intro="""
MobileViT model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
"""
)
class MobileViTForImageClassification(MobileViTPreTrainedModel):
def __init__(self, config: MobileViTConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilevit = MobileViTModel(config)
# Classifier head
self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True)
self.classifier = (
nn.Linear(config.neck_hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@auto_docstring
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mobilevit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(self.dropout(pooled_output))
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
)
class MobileViTASPPPooling(nn.Module):
def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int) -> None:
super().__init__()
self.global_pool = nn.AdaptiveAvgPool2d(output_size=1)
self.conv_1x1 = MobileViTConvLayer(
config,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
use_normalization=True,
use_activation="relu",
)
def forward(self, features: torch.Tensor) -> torch.Tensor:
spatial_size = features.shape[-2:]
features = self.global_pool(features)
features = self.conv_1x1(features)
features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False)
return features
class MobileViTASPP(nn.Module):
"""
ASPP module defined in DeepLab papers: https://huggingface.co/papers/1606.00915, https://huggingface.co/papers/1706.05587
"""
def __init__(self, config: MobileViTConfig) -> None:
super().__init__()
in_channels = config.neck_hidden_sizes[-2]
out_channels = config.aspp_out_channels
if len(config.atrous_rates) != 3:
raise ValueError("Expected 3 values for atrous_rates")
self.convs = nn.ModuleList()
in_projection = MobileViTConvLayer(
config,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
use_activation="relu",
)
self.convs.append(in_projection)
self.convs.extend(
[
MobileViTConvLayer(
config,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
dilation=rate,
use_activation="relu",
)
for rate in config.atrous_rates
]
)
pool_layer = MobileViTASPPPooling(config, in_channels, out_channels)
self.convs.append(pool_layer)
self.project = MobileViTConvLayer(
config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu"
)
self.dropout = nn.Dropout(p=config.aspp_dropout_prob)
def forward(self, features: torch.Tensor) -> torch.Tensor:
pyramid = []
for conv in self.convs:
pyramid.append(conv(features))
pyramid = torch.cat(pyramid, dim=1)
pooled_features = self.project(pyramid)
pooled_features = self.dropout(pooled_features)
return pooled_features
class MobileViTDeepLabV3(nn.Module):
"""
DeepLabv3 architecture: https://huggingface.co/papers/1706.05587
"""
def __init__(self, config: MobileViTConfig) -> None:
super().__init__()
self.aspp = MobileViTASPP(config)
self.dropout = nn.Dropout2d(config.classifier_dropout_prob)
self.classifier = MobileViTConvLayer(
config,
in_channels=config.aspp_out_channels,
out_channels=config.num_labels,
kernel_size=1,
use_normalization=False,
use_activation=False,
bias=True,
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
features = self.aspp(hidden_states[-1])
features = self.dropout(features)
features = self.classifier(features)
return features
@auto_docstring(
custom_intro="""
MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC.
"""
)
class MobileViTForSemanticSegmentation(MobileViTPreTrainedModel):
def __init__(self, config: MobileViTConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilevit = MobileViTModel(config, expand_output=False)
self.segmentation_head = MobileViTDeepLabV3(config)
# Initialize weights and apply final processing
self.post_init()
@auto_docstring
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, SemanticSegmenterOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
Examples:
```python
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-small")
>>> model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> # logits are of shape (batch_size, num_labels, height, width)
>>> logits = outputs.logits
```"""
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None and self.config.num_labels == 1:
raise ValueError("The number of labels should be greater than one")
outputs = self.mobilevit(
pixel_values,
output_hidden_states=True, # we need the intermediate hidden states
return_dict=return_dict,
)
encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
logits = self.segmentation_head(encoder_hidden_states)
loss = None
if labels is not None:
# upsample logits to the images' original size
upsampled_logits = nn.functional.interpolate(
logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
)
loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
loss = loss_fct(upsampled_logits, labels)
if not return_dict:
if output_hidden_states:
output = (logits,) + outputs[1:]
else:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=None,
)
__all__ = [
"MobileViTForImageClassification",
"MobileViTForSemanticSegmentation",
"MobileViTModel",
"MobileViTPreTrainedModel",
]
| transformers/src/transformers/models/mobilevit/modeling_mobilevit.py/0 | {
"file_path": "transformers/src/transformers/models/mobilevit/modeling_mobilevit.py",
"repo_id": "transformers",
"token_count": 16836
} | 471 |
# Copyright 2025 Useful Sensors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import re
import h5py
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers.models.moonshine.modeling_moonshine import MoonshineConfig, MoonshineForConditionalGeneration
# Copied from https://github.com/usefulsensors/moonshine/blob/a1d77cc573b0471ac4602b86f67b3f48d67df1a9/moonshine/model.py
def _get_weights(model_name):
repo = "UsefulSensors/moonshine"
return (
hf_hub_download(repo, f"{x}.weights.h5", subfolder=model_name) for x in ("preprocessor", "encoder", "decoder")
)
def _read_h5_weights(group, current_key="", weights={}):
for key in group:
full_key = f"{current_key}.{key}" if current_key else key
if isinstance(group[key], h5py.Dataset):
w = np.array(group[key])
w = torch.from_numpy(w)
if len(w.shape) > 1:
if len(w.shape) == 3:
hidden_size = max(list(w.shape))
try:
w = w.reshape(hidden_size, hidden_size)
except RuntimeError:
# meaning its a conv layers
pass
w = w.transpose(0, -1)
weights[full_key] = w
else:
_read_h5_weights(group[key], full_key, weights)
return weights
def _convert_layer_names(name, gated_mlp=False):
name = re.sub(
r"layers\.functional(?:_(\d+))?\.layers",
lambda m: f"layers.{m.group(1) if m.group(1) else '0'}",
name,
count=1,
)
if gated_mlp:
name = re.sub(r"functional\.layers\.dense\.", "mlp.fc1.", name)
name = re.sub(r"functional\.layers\.dense_1\.", "mlp.fc2.", name)
else:
name = re.sub(r"functional\.layers\.sequential\.layers\.dense\.", "mlp.fc1.", name)
name = re.sub(r"functional\.layers\.sequential\.layers\.dense_1\.", "mlp.fc2.", name)
name = re.sub(r"layers\.sequential\.layers\.conv1d\.", "conv1.", name)
name = re.sub(r"layers\.sequential\.layers\.conv1d_1\.", "conv2.", name)
name = re.sub(r"layers\.sequential\.layers\.conv1d_2\.", "conv3.", name)
name = re.sub(r"layers\.sequential\.layers\.group_normalization\.", "groupnorm.", name)
name = re.sub(r"mha_with_rope\.key_dense", "self_attn.k_proj", name)
name = re.sub(r"mha_with_rope\.query_dense", "self_attn.q_proj", name)
name = re.sub(r"mha_with_rope\.value_dense", "self_attn.v_proj", name)
name = re.sub(r"mha_with_rope\.output_dense", "self_attn.o_proj", name)
name = re.sub(r"mha_precomputed_kv\.key_dense", "encoder_attn.k_proj", name)
name = re.sub(r"mha_precomputed_kv\.query_dense", "encoder_attn.q_proj", name)
name = re.sub(r"mha_precomputed_kv\.value_dense", "encoder_attn.v_proj", name)
name = re.sub(r"mha_precomputed_kv\.output_dense", "encoder_attn.o_proj", name)
name = re.sub(r"mha_causal_with_rope\.key_dense", "self_attn.k_proj", name)
name = re.sub(r"mha_causal_with_rope\.query_dense", "self_attn.q_proj", name)
name = re.sub(r"mha_causal_with_rope\.value_dense", "self_attn.v_proj", name)
name = re.sub(r"mha_causal_with_rope\.output_dense", "self_attn.o_proj", name)
name = re.sub(r"layer_normalization\.", "input_layernorm.", name)
name = re.sub(r"layer_normalization_1\.", "post_attention_layernorm.", name)
name = re.sub(r"layer_normalization_2\.", "final_layernorm.", name)
name = re.sub(r"vars\.0", "weight", name)
name = re.sub(r"vars\.1", "bias", name)
name = re.sub(r"layers\.reversible_embedding", "embed_tokens", name)
return name
def _convert_weights(weights, encoder=True):
if "layers.rotary_embedding.vars.0" in weights:
weights.pop("layers.rotary_embedding.vars.0")
converted_weights = {}
if encoder:
converted_weights["layer_norm.weight"] = weights.pop("layers.layer_normalization.vars.0")
else:
converted_weights["norm.weight"] = weights.pop("layers.layer_normalization.vars.0")
for name, w in weights.items():
if encoder:
new_name = _convert_layer_names(name)
else:
new_name = _convert_layer_names(name, gated_mlp=True)
converted_weights[new_name] = w
return converted_weights
def convert_usefulsensors_moonshine_to_hf(model_name, pytorch_dump_folder_path):
preprocessor_weights_path, encoder_weights_path, decoder_weights_path = _get_weights(model_name)
with h5py.File(preprocessor_weights_path, "r") as f:
loaded_preprocessor_weights = _read_h5_weights(f, weights={})
with h5py.File(encoder_weights_path, "r") as f:
loaded_encoder_weights = _read_h5_weights(f, weights={})
with h5py.File(decoder_weights_path, "r") as f:
loaded_decoder_weights = _read_h5_weights(f, weights={})
encoder_state_dict = {**loaded_encoder_weights, **loaded_preprocessor_weights}
converted_encoder_state_dict = _convert_weights(encoder_state_dict)
converted_decoder_state_dict = _convert_weights(loaded_decoder_weights, encoder=False)
converted_decoder_state_dict["embed_tokens.weight"] = converted_decoder_state_dict["embed_tokens.weight"].T
final_weights = {}
for k, v in converted_encoder_state_dict.items():
final_weights[f"model.encoder.{k}"] = v
for k, v in converted_decoder_state_dict.items():
final_weights[f"model.decoder.{k}"] = v
if model_name == "tiny":
config = MoonshineConfig()
elif model_name == "base":
config = MoonshineConfig(
hidden_size=416,
intermediate_size=1664,
encoder_num_hidden_layers=8,
decoder_num_hidden_layers=8,
encoder_num_attention_heads=8,
decoder_num_attention_heads=8,
partial_rotary_factor=0.62,
)
else:
raise ValueError(f"Unknown model name {model_name}")
final_weights["proj_out.weight"] = converted_decoder_state_dict["embed_tokens.weight"]
model = MoonshineForConditionalGeneration(config)
model.load_state_dict(final_weights)
model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--model_name", type=str, help="Path to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
args = parser.parse_args()
convert_usefulsensors_moonshine_to_hf(args.model_name, args.pytorch_dump_folder_path)
| transformers/src/transformers/models/moonshine/convert_usefulsensors_to_hf.py/0 | {
"file_path": "transformers/src/transformers/models/moonshine/convert_usefulsensors_to_hf.py",
"repo_id": "transformers",
"token_count": 3113
} | 472 |
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
from argparse import ArgumentParser
from collections import OrderedDict
import torch
from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer
from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
from nemo.utils import logging
from pytorch_lightning import Trainer
from transformers import LlamaTokenizer, PreTrainedTokenizerFast
from transformers.convert_slow_tokenizer import LlamaConverter
"""
Script to convert a nemotron checkpoint in nemo (mcore path) into a HuggingFace checkpoint.
This script can be used to 1) generate only the HF weights, or 2) generate an entire HF model folder.
1) Generate only HF weights from a nemo file:
python convert_nemotron_nemo_to_hf.py \
--input_name_or_path /path/to/file.nemo or /path/to/extracted_folder \
--output_path /path/to/pytorch_model.bin
2) Generate the full HF model folder
python convert_nemotron_nemo_to_hf.py \
--input_name_or_path /path/to/file.nemo or /path/to/extracted_folder \
--hf_input_path /path/to/input_hf_folder \
--hf_output_path /path/to/output_hf_folder \
Use the --cpu-only flag if the model cannot fit in the GPU (e.g. Nemotron4 340b).
However this option makes the conversion script significantly slower.
"""
def get_args():
parser = ArgumentParser()
parser.add_argument(
"--input_name_or_path",
type=str,
default=None,
required=True,
help="Path to .nemo file or extracted folder",
)
parser.add_argument("--output_path", type=str, default=None, required=False, help="Path to HF .bin file")
parser.add_argument(
"--hf_input_path",
type=str,
default=None,
help="A HF model path, e.g. a folder containing https://huggingface.co/nvidia/Minitron-8B-Base",
)
parser.add_argument(
"--hf_output_path",
type=str,
default=None,
help="Output HF model path, with the same format as above but user's own weights",
)
parser.add_argument(
"--precision",
type=str,
default=None,
help="Precision of output weights."
"Defaults to precision of the input nemo weights (model.cfg.trainer.precision)",
)
parser.add_argument(
"--cpu-only",
action="store_true",
help="Load model in cpu only. Useful if the model cannot fit in GPU memory, "
"but this option makes the conversion script significantly slower.",
)
args = parser.parse_args()
return args
def convert_hf_config(nemo_config, tokenizer, vocab_size, dtype, hf_output_path, hf_url="nvidia/Minitron-8B-Base"):
"""
Convert NeMo config to HF config
"""
NEMO_ACT2HF = {
"squared-relu": "relu2",
"fast-swiglu": "silu",
}
DTYPE2HF = {
torch.bfloat16: "bfloat16",
torch.float16: "float16",
torch.float32: "float32",
}
hf_config = {
"_name_or_path": hf_url,
"architectures": ["NemotronForCausalLM"],
"bos_token_id": tokenizer.bos_id,
"eos_token_id": tokenizer.eos_id,
"hidden_act": NEMO_ACT2HF[nemo_config.activation],
"hidden_size": nemo_config.hidden_size,
"initializer_range": nemo_config.init_method_std,
"intermediate_size": nemo_config.ffn_hidden_size,
"max_position_embeddings": nemo_config.max_position_embeddings,
"model_type": "nemotron",
"num_attention_heads": nemo_config.num_attention_heads,
"num_hidden_layers": nemo_config.num_layers,
"num_key_value_heads": nemo_config.get("num_query_groups", nemo_config.num_attention_heads),
"norm_eps": nemo_config.layernorm_epsilon,
"rope_theta": nemo_config.get("rotary_base", 10000),
"partial_rotary_factor": nemo_config.get("rotary_percentage", 1.0),
"tie_word_embeddings": False,
"dtype": DTYPE2HF[dtype],
"transformers_version": "4.32.0.dev0", # TODO
"use_cache": True,
"vocab_size": vocab_size,
}
if nemo_config.kv_channels is not None:
hf_config["kv_channels"] = nemo_config.kv_channels
json.dump(hf_config, open(f"{hf_output_path}/config.json", "w"), indent=2)
def convert(input_nemo_file, output_hf_file, precision=None, cpu_only=False) -> None:
"""
Convert NeMo weights to HF weights
"""
dummy_trainer = Trainer(devices=1, accelerator="cpu", strategy=NLPDDPStrategy())
model_config = MegatronGPTModel.restore_from(input_nemo_file, trainer=dummy_trainer, return_config=True)
model_config.tensor_model_parallel_size = 1
model_config.pipeline_model_parallel_size = 1
model_config.sequence_parallel = False
model_config.transformer_engine = True
if cpu_only:
map_location = torch.device("cpu")
model_config.use_cpu_initialization = True
model_config.dist_ckpt_load_on_device = False
else:
map_location = None
if cpu_only:
logging.info("******** Loading model on CPU. This will take a significant amount of time.")
model = MegatronGPTModel.restore_from(
input_nemo_file, trainer=dummy_trainer, override_config_path=model_config, map_location=map_location
)
vocab_size = model.padded_vocab_size
if precision is None:
precision = model.cfg.precision
if precision in [32, "32"]:
dtype = torch.float32
elif precision in [16, "16", "16-mixed"]:
dtype = torch.float16
elif precision in ["bf16", "bf16-mixed"]:
dtype = torch.bfloat16
else:
logging.warning(f"Precision string {precision} is not recognized, falling back to fp32")
dtype = torch.float32 # fallback
logging.info(f"Using precision {dtype}")
def param_to_weights(param):
return param.to(dtype)
checkpoint = OrderedDict()
hidden_size = model.cfg.hidden_size
head_num = model.cfg.num_attention_heads
num_layers = model.cfg.num_layers
ffn_hidden_size = model.cfg.ffn_hidden_size
num_query_groups = model.cfg.get("num_query_groups", head_num) # different num_query_groups for 70B
if num_query_groups is None:
num_query_groups = head_num
heads_per_group = head_num // num_query_groups
qkv_total_dim = head_num + 2 * num_query_groups
# Embedding
embed_weight = model.state_dict()["model.embedding.word_embeddings.weight"]
embed_weights_base_name = "model.embed_tokens.weight"
checkpoint[embed_weights_base_name] = param_to_weights(embed_weight)
for l in range(int(num_layers)):
print(f"converting layer {l}")
qkv_weights = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_qkv.weight"]
qkv_weights = qkv_weights.reshape([qkv_total_dim, -1, hidden_size])
q_slice = torch.cat(
[
torch.arange((heads_per_group + 2) * i, (heads_per_group + 2) * i + heads_per_group)
for i in range(num_query_groups)
]
)
k_slice = torch.arange(heads_per_group, qkv_total_dim, (heads_per_group + 2))
v_slice = torch.arange(heads_per_group + 1, qkv_total_dim, (heads_per_group + 2))
## Example of slices
## (without GQA): num_query_groups = head_num = 32,
## q_slice = [0, 3, 6, 9 , ... 90, 93]
## k_slice = [1, 4, 7, 10, ... 91, 94]
## v_slice = [2, 5, 8, 11, ... 92, 95]
## (with GQA): num_query_groups = 8, head_num = 64
## q_slice = [0, 1, .. 6, 7, 10, 11, .. 16, 17, 20, 21, .. 67, 70, ... 76, 77]
## k_slice = [8, 18, 28, ... 68, 78]
## v_slice = [9, 19, 29, ... 69, 79]
q_weights_base_name = f"model.layers.{l}.self_attn.q_proj.weight"
k_weights_base_name = f"model.layers.{l}.self_attn.k_proj.weight"
v_weights_base_name = f"model.layers.{l}.self_attn.v_proj.weight"
checkpoint[q_weights_base_name] = param_to_weights(qkv_weights[q_slice].reshape(-1, hidden_size))
checkpoint[k_weights_base_name] = param_to_weights(qkv_weights[k_slice].reshape(-1, hidden_size))
checkpoint[v_weights_base_name] = param_to_weights(qkv_weights[v_slice].reshape(-1, hidden_size))
# attention dense
o_weight = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_proj.weight"]
o_weight_base_name = f"model.layers.{l}.self_attn.o_proj.weight"
checkpoint[o_weight_base_name] = param_to_weights(o_weight)
# mlp
mlp_weights = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc1.weight"]
mlp_up_proj_weight = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc2.weight"]
if mlp_weights.shape[0] != mlp_up_proj_weight.shape[1]:
# Has projection (used for swi-glu)
logging.warning(
"Gated projection layers detected in NeMo checkpoint. Currently Nemotron HF does not support gated MLP."
)
assert mlp_weights.shape[0] == 2 * mlp_up_proj_weight.shape[1]
mlp_down_proj_weight = mlp_weights[:ffn_hidden_size, :]
mlp_gate_proj_weight = mlp_weights[ffn_hidden_size:, :]
mlp_down_proj_base_name = f"model.layers.{l}.mlp.gate_proj.weight"
mlp_gate_proj_base_name = f"model.layers.{l}.mlp.up_proj.weight"
checkpoint[mlp_down_proj_base_name] = param_to_weights(mlp_down_proj_weight)
checkpoint[mlp_gate_proj_base_name] = param_to_weights(mlp_gate_proj_weight)
else:
mlp_down_proj_weight = mlp_weights
mlp_down_proj_base_name = f"model.layers.{l}.mlp.up_proj.weight"
checkpoint[mlp_down_proj_base_name] = param_to_weights(mlp_down_proj_weight)
mlp_up_proj_base_name = f"model.layers.{l}.mlp.down_proj.weight"
checkpoint[mlp_up_proj_base_name] = param_to_weights(mlp_up_proj_weight)
# layernorm
input_ln_weight = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_weight"]
input_ln_base_name = f"model.layers.{l}.input_layernorm.weight"
checkpoint[input_ln_base_name] = param_to_weights(input_ln_weight)
if (
model.state_dict().get(f"model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_bias", None)
is not None
):
input_ln_bias = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_bias"]
input_ln_bias_name = f"model.layers.{l}.input_layernorm.bias"
checkpoint[input_ln_bias_name] = param_to_weights(input_ln_bias)
post_attn_ln_weight = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_weight"]
post_attn_ln_base_name = f"model.layers.{l}.post_attention_layernorm.weight"
checkpoint[post_attn_ln_base_name] = param_to_weights(post_attn_ln_weight)
if model.state_dict().get(f"model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_bias", None) is not None:
post_attn_ln_bias = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_bias"]
post_attn_ln_bias_name = f"model.layers.{l}.post_attention_layernorm.bias"
checkpoint[post_attn_ln_bias_name] = param_to_weights(post_attn_ln_bias)
print(f"done layer {l}")
final_ln_weight = model.state_dict()["model.decoder.final_layernorm.weight"]
final_ln_base_name = "model.norm.weight"
checkpoint[final_ln_base_name] = param_to_weights(final_ln_weight)
if model.state_dict().get("model.decoder.final_layernorm.bias", None) is not None:
final_ln_bias = model.state_dict()["model.decoder.final_layernorm.bias"]
final_ln_bias_name = "model.norm.bias"
checkpoint[final_ln_bias_name] = param_to_weights(final_ln_bias)
output_layer_weight = model.state_dict()["model.output_layer.weight"]
output_layer_base_name = "lm_head.weight"
checkpoint[output_layer_base_name] = param_to_weights(output_layer_weight)
os.makedirs(os.path.dirname(output_hf_file), exist_ok=True)
torch.save(checkpoint, output_hf_file)
logging.info(f"Weights saved to {output_hf_file}")
return model_config, model.tokenizer, dtype, vocab_size
def extract_nemotron_tokenizer(nemo_file, model_config, output_hf_path, nemo_tokenizer):
tokenizer_cfg = model_config.tokenizer
if tokenizer_cfg.library == "sentencepiece":
# For sentencepiece tokenizer, we are wrapping with HF's LlamaTokenizer
# and convert it to a PreTrainedTokenizerFast
tokenizer_fn = tokenizer_cfg.model[5:]
output_tokenizer = f"{output_hf_path}/tokenizer.model"
if nemo_file.endswith(".nemo"):
import tarfile
archive = tarfile.open(nemo_file, "r")
tokenizer_filename = "./" + tokenizer_fn # exclude 'nemo:' prefix
archive.extract(tokenizer_filename, output_hf_path)
archive.close()
os.rename(f"{output_hf_path}/{tokenizer_fn}", output_tokenizer)
elif os.path.isdir(nemo_file):
shutil.copy(f"{nemo_file}/{tokenizer_fn}", output_tokenizer)
# We use LlamaTokenizer for sentencepiece based tokenizer
tokenizer = LlamaTokenizer.from_pretrained(output_hf_path, legacy=False)
# Convert the LlamaTokenizer to a PreTrainedTokenizerFast instance
tokenizer = PreTrainedTokenizerFast(
tokenizer_object=LlamaConverter(tokenizer).converted(), model_input_names=["input_ids", "token_type_ids"]
)
tokenizer.save_pretrained(output_hf_path)
logging.info(f"Setencepiece tokenizer has been saved to {output_tokenizer}")
elif isinstance(nemo_tokenizer, AutoTokenizer):
nemo_tokenizer.tokenizer.save_pretrained(output_hf_path)
logging.info(f"HF AutoTokenizer has been saved to {output_hf_path}")
else:
raise ValueError(f"Unsupported tokenizer type: library: {tokenizer_cfg.library}, type: {tokenizer_cfg.type}")
if __name__ == "__main__":
args = get_args()
if not args.hf_output_path:
assert args.output_path is not None, "Need to provide either output_path or hf_output_path"
else:
args.output_path = f"{args.hf_output_path}/pytorch_model.bin"
logging.info(f"weight will be saved to {args.output_path}")
nemo_config, nemo_tokenizer, dtype, vocab_size = convert(
args.input_name_or_path, args.output_path, precision=args.precision, cpu_only=args.cpu_only
)
if args.hf_input_path and args.hf_output_path:
convert_hf_config(nemo_config, nemo_tokenizer, vocab_size, dtype, args.hf_output_path, args.hf_input_path)
extract_nemotron_tokenizer(args.input_name_or_path, nemo_config, args.hf_output_path, nemo_tokenizer)
else:
logging.info("`hf_input_path` and/or `hf_output_path` not provided, not generating full HF model.")
logging.info(f".bin file is saved to {args.output_path}")
| transformers/src/transformers/models/nemotron/convert_nemotron_nemo_to_hf.py/0 | {
"file_path": "transformers/src/transformers/models/nemotron/convert_nemotron_nemo_to_hf.py",
"repo_id": "transformers",
"token_count": 6731
} | 473 |
# coding=utf-8
# Copyright 2022 UW-Madison and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Nystromformer model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class NystromformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NystromformerModel`]. It is used to instantiate
an Nystromformer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Nystromformer
[uw-madison/nystromformer-512](https://huggingface.co/uw-madison/nystromformer-512) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30000):
Vocabulary size of the Nystromformer model. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`NystromformerModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`NystromformerModel`].
segment_means_seq_len (`int`, *optional*, defaults to 64):
Sequence length used in segment-means.
num_landmarks (`int`, *optional*, defaults to 64):
The number of landmark (or Nystrom) points to use in Nystrom approximation of the softmax self-attention
matrix.
conv_kernel_size (`int`, *optional*, defaults to 65):
The kernel size of depthwise convolution used in Nystrom approximation.
inv_coeff_init_option (`bool`, *optional*, defaults to `False`):
Whether or not to use exact coefficient computation for the initial values for the iterative method of
calculating the Moore-Penrose inverse of a matrix.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
Example:
```python
>>> from transformers import NystromformerModel, NystromformerConfig
>>> # Initializing a Nystromformer uw-madison/nystromformer-512 style configuration
>>> configuration = NystromformerConfig()
>>> # Initializing a model from the uw-madison/nystromformer-512 style configuration
>>> model = NystromformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "nystromformer"
def __init__(
self,
vocab_size=30000,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu_new",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=510,
type_vocab_size=2,
segment_means_seq_len=64,
num_landmarks=64,
conv_kernel_size=65,
inv_coeff_init_option=False,
initializer_range=0.02,
layer_norm_eps=1e-5,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.type_vocab_size = type_vocab_size
self.segment_means_seq_len = segment_means_seq_len
self.num_landmarks = num_landmarks
self.conv_kernel_size = conv_kernel_size
self.inv_coeff_init_option = inv_coeff_init_option
self.layer_norm_eps = layer_norm_eps
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
__all__ = ["NystromformerConfig"]
| transformers/src/transformers/models/nystromformer/configuration_nystromformer.py/0 | {
"file_path": "transformers/src/transformers/models/nystromformer/configuration_nystromformer.py",
"repo_id": "transformers",
"token_count": 2357
} | 474 |
# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch OpenAI GPT model."""
import json
import math
import os
from dataclasses import dataclass
from typing import Any, Callable, Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import gelu_new, get_activation, silu
from ...generation import GenerationMixin
from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
from ...utils import (
ModelOutput,
auto_docstring,
logging,
)
from .configuration_openai import OpenAIGPTConfig
logger = logging.get_logger(__name__)
def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
"""Load tf pre-trained weights in a pytorch model (from NumPy arrays here)"""
import re
import numpy as np
if ".ckpt" in openai_checkpoint_folder_path:
openai_checkpoint_folder_path = os.path.dirname(openai_checkpoint_folder_path)
logger.info(f"Loading weights from {openai_checkpoint_folder_path}")
with open(openai_checkpoint_folder_path + "/parameters_names.json", "r", encoding="utf-8") as names_handle:
names = json.load(names_handle)
with open(openai_checkpoint_folder_path + "/params_shapes.json", "r", encoding="utf-8") as shapes_handle:
shapes = json.load(shapes_handle)
offsets = np.cumsum([np.prod(shape) for shape in shapes])
init_params = [np.load(openai_checkpoint_folder_path + f"/params_{n}.npy") for n in range(10)]
init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
# This was used when we had a single embedding matrix for positions and tokens
# init_params[0] = np.concatenate([init_params[1], init_params[0]], 0)
# del init_params[1]
init_params = [arr.squeeze() for arr in init_params]
# Check that the token and position embeddings weight dimensions map those of the init parameters.
if model.tokens_embed.weight.shape != init_params[1].shape:
raise ValueError(
f"tokens_embed.weight.shape: {model.tokens_embed.weight.shape} does not match init_param[1].shape:"
f" {init_params[1].shape}"
)
if model.positions_embed.weight.shape != init_params[0].shape:
raise ValueError(
f"positions_embed.weight.shape: {model.positions_embed.weight.shape} does not match init_param[0].shape:"
f" {init_params[0].shape}"
)
model.tokens_embed.weight.data = torch.from_numpy(init_params[1])
model.positions_embed.weight.data = torch.from_numpy(init_params[0])
names.pop(0)
# Pop position and token embedding arrays
init_params.pop(0)
init_params.pop(0)
for name, array in zip(names, init_params): # names[1:n_transfer], init_params[1:n_transfer]):
name = name[6:] # skip "model/"
if name[-2:] != ":0":
raise ValueError(f"Layer {name} does not end with :0")
name = name[:-2]
name = name.split("/")
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+\d+", m_name):
scope_names = re.split(r"(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "g":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "b":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "w":
pointer = getattr(pointer, "weight")
else:
pointer = getattr(pointer, scope_names[0])
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
# Ensure that the pointer and array have compatible shapes.
if pointer.shape != array.shape:
raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
ACT_FNS = {"relu": nn.ReLU(), "silu": silu, "gelu": gelu_new, "swish": silu}
class Attention(nn.Module):
def __init__(self, nx, n_positions, config, scale=False):
super().__init__()
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implementation]
if n_state % config.n_head != 0:
raise ValueError(f"Attention n_state shape: {n_state} must be divisible by config.n_head {config.n_head}")
self.register_buffer(
"bias",
torch.tril(torch.ones(n_positions, n_positions)).view(1, 1, n_positions, n_positions),
persistent=False,
)
self.n_head = config.n_head
self.split_size = n_state
self.scale = scale
self.c_attn = Conv1D(n_state * 3, nx)
self.c_proj = Conv1D(n_state, nx)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.n_head, self.split_size // self.n_head, self.pruned_heads
)
index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
# Prune conv1d layers
self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
# Update hyper params
self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
self.n_head = self.n_head - len(heads)
self.pruned_heads = self.pruned_heads.union(heads)
def _attn(self, q, k, v, attention_mask=None, head_mask=None, output_attentions=False):
w = torch.matmul(q, k)
if self.scale:
w = w / math.sqrt(v.size(-1))
# w = w * self.bias + -1e9 * (1 - self.bias) # TF implementation method: mask_attn_weights
# XD: self.b may be larger than w, so we need to crop it
b = self.bias[:, :, : w.size(-2), : w.size(-1)]
w = w * b + -1e4 * (1 - b)
if attention_mask is not None:
# Apply the attention mask
w = w + attention_mask
w = nn.functional.softmax(w, dim=-1)
w = self.attn_dropout(w)
# Mask heads if we want to
if head_mask is not None:
w = w * head_mask
outputs = [torch.matmul(w, v)]
if output_attentions:
outputs.append(w)
return outputs
def merge_heads(self, x):
x = x.permute(0, 2, 1, 3).contiguous()
new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
return x.view(*new_x_shape) # in Tensorflow implementation: fct merge_states
def split_heads(self, x, k=False):
new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
x = x.view(*new_x_shape) # in Tensorflow implementation: fct split_states
if k:
return x.permute(0, 2, 3, 1)
else:
return x.permute(0, 2, 1, 3)
def forward(self, x, attention_mask=None, head_mask=None, output_attentions=False):
x = self.c_attn(x)
query, key, value = x.split(self.split_size, dim=2)
query = self.split_heads(query)
key = self.split_heads(key, k=True)
value = self.split_heads(value)
attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions)
a = attn_outputs[0]
a = self.merge_heads(a)
a = self.c_proj(a)
a = self.resid_dropout(a)
outputs = [a] + attn_outputs[1:]
return outputs # a, (attentions)
class MLP(nn.Module):
def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
super().__init__()
nx = config.n_embd
self.c_fc = Conv1D(n_state, nx)
self.c_proj = Conv1D(nx, n_state)
self.act = ACT_FNS[config.afn]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, x):
h = self.act(self.c_fc(x))
h2 = self.c_proj(h)
return self.dropout(h2)
class Block(nn.Module):
def __init__(self, n_positions, config, scale=False):
super().__init__()
nx = config.n_embd
self.attn = Attention(nx, n_positions, config, scale)
self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
self.mlp = MLP(4 * nx, config)
self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
def forward(self, x, attention_mask=None, head_mask=None, output_attentions=False):
attn_outputs = self.attn(
x,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
)
a = attn_outputs[0]
n = self.ln_1(x + a)
m = self.mlp(n)
h = self.ln_2(n + m)
outputs = [h] + attn_outputs[1:]
return outputs
# Copied from transformers.models.xlm.modeling_xlm.XLMSequenceSummary with XLM->OpenAIGPT
class OpenAIGPTSequenceSummary(nn.Module):
r"""
Compute a single vector summary of a sequence hidden states.
Args:
config ([`OpenAIGPTConfig`]):
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
config class of your model for the default values it uses):
- **summary_type** (`str`) -- The method to use to make this summary. Accepted values are:
- `"last"` -- Take the last token hidden state (like XLNet)
- `"first"` -- Take the first token hidden state (like Bert)
- `"mean"` -- Take the mean of all tokens hidden states
- `"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2)
- `"attn"` -- Not implemented now, use multi-head attention
- **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
- **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
(otherwise to `config.hidden_size`).
- **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output,
another string or `None` will add no activation.
- **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation.
- **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation.
"""
def __init__(self, config: OpenAIGPTConfig):
super().__init__()
self.summary_type = getattr(config, "summary_type", "last")
if self.summary_type == "attn":
# We should use a standard multi-head attention module with absolute positional embedding for that.
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
# We can probably just use the multi-head attention module of PyTorch >=1.1.0
raise NotImplementedError
self.summary = nn.Identity()
if hasattr(config, "summary_use_proj") and config.summary_use_proj:
if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
num_classes = config.num_labels
else:
num_classes = config.hidden_size
self.summary = nn.Linear(config.hidden_size, num_classes)
activation_string = getattr(config, "summary_activation", None)
self.activation: Callable = get_activation(activation_string) if activation_string else nn.Identity()
self.first_dropout = nn.Identity()
if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
self.first_dropout = nn.Dropout(config.summary_first_dropout)
self.last_dropout = nn.Identity()
if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
self.last_dropout = nn.Dropout(config.summary_last_dropout)
def forward(
self, hidden_states: torch.FloatTensor, cls_index: Optional[torch.LongTensor] = None
) -> torch.FloatTensor:
"""
Compute a single vector summary of a sequence hidden states.
Args:
hidden_states (`torch.FloatTensor` of shape `[batch_size, seq_len, hidden_size]`):
The hidden states of the last layer.
cls_index (`torch.LongTensor` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*):
Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token.
Returns:
`torch.FloatTensor`: The summary of the sequence hidden states.
"""
if self.summary_type == "last":
output = hidden_states[:, -1]
elif self.summary_type == "first":
output = hidden_states[:, 0]
elif self.summary_type == "mean":
output = hidden_states.mean(dim=1)
elif self.summary_type == "cls_index":
if cls_index is None:
cls_index = torch.full_like(
hidden_states[..., :1, :],
hidden_states.shape[-2] - 1,
dtype=torch.long,
)
else:
cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size)
elif self.summary_type == "attn":
raise NotImplementedError
output = self.first_dropout(output)
output = self.summary(output)
output = self.activation(output)
output = self.last_dropout(output)
return output
@auto_docstring
class OpenAIGPTPreTrainedModel(PreTrainedModel):
config: OpenAIGPTConfig
load_tf_weights = load_tf_weights_in_openai_gpt
base_model_prefix = "transformer"
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@dataclass
@auto_docstring(
custom_intro="""
Base class for outputs of models predicting if two sentences are consecutive or not.
"""
)
class OpenAIGPTDoubleHeadsModelOutput(ModelOutput):
r"""
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
mc_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mc_labels` is provided):
Multiple choice classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mc_logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
"""
loss: Optional[torch.FloatTensor] = None
mc_loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
mc_logits: Optional[torch.FloatTensor] = None
hidden_states: Optional[tuple[torch.FloatTensor]] = None
attentions: Optional[tuple[torch.FloatTensor]] = None
@auto_docstring
class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd)
self.positions_embed = nn.Embedding(config.n_positions, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([Block(config.n_positions, config, scale=True) for _ in range(config.n_layer)])
self.register_buffer("position_ids", torch.arange(config.n_positions), persistent=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.tokens_embed
def set_input_embeddings(self, new_embeddings):
self.tokens_embed = new_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple[torch.Tensor], BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if position_ids is None:
# Code is different from when we had a single embedding matrix from position and token embeddings
position_ids = self.position_ids[None, : input_shape[-1]]
# Attention mask.
if attention_mask is not None:
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if inputs_embeds is None:
inputs_embeds = self.tokens_embed(input_ids)
position_embeds = self.positions_embed(position_ids)
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
token_type_embeds = self.tokens_embed(token_type_ids)
else:
token_type_embeds = 0
hidden_states = inputs_embeds + position_embeds + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, block in enumerate(self.h):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = block(hidden_states, attention_mask, head_mask[i], output_attentions=output_attentions)
hidden_states = outputs[0]
if output_attentions:
all_attentions = all_attentions + (outputs[1],)
hidden_states = hidden_states.view(*output_shape)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
@auto_docstring(
custom_intro="""
OpenAI GPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
"""
)
class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = OpenAIGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[tuple[torch.Tensor], CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Flatten the tokens
loss = self.loss_function(
lm_logits,
labels,
vocab_size=self.config.vocab_size,
**kwargs,
)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutput(
loss=loss,
logits=lm_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def prepare_inputs_for_generation(self, input_ids: torch.LongTensor, **kwargs) -> dict[str, Any]:
# Overwritten -- old model with reduced inputs
return {"input_ids": input_ids}
@auto_docstring(
custom_intro="""
OpenAI GPT Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
input embeddings, the classification head takes as input the input of a specified classification token index in the
input sequence).
"""
)
class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 1
self.transformer = OpenAIGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.multiple_choice_head = OpenAIGPTSequenceSummary(config)
# Initialize weights and apply final processing
self.post_init()
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
mc_token_ids: Optional[torch.LongTensor] = None,
labels: Optional[torch.LongTensor] = None,
mc_labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple[torch.Tensor], OpenAIGPTDoubleHeadsModelOutput]:
r"""
mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
1]`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-1, 0, ..., config.vocab_size]` All labels set to `-100` are
ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above)
Examples:
```python
>>> from transformers import AutoTokenizer, OpenAIGPTDoubleHeadsModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/openai-gpt")
>>> model = OpenAIGPTDoubleHeadsModel.from_pretrained("openai-community/openai-gpt")
>>> tokenizer.add_special_tokens(
... {"cls_token": "[CLS]"}
... ) # Add a [CLS] to the vocabulary (we should train it also!)
>>> model.resize_token_embeddings(len(tokenizer))
>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
>>> input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
>>> mc_token_ids = torch.tensor([input_ids.size(-1) - 1, input_ids.size(-1) - 1]).unsqueeze(0) # Batch size 1
>>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
>>> lm_logits = outputs.logits
>>> mc_logits = outputs.mc_logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
lm_loss, mc_loss = None, None
if mc_labels is not None:
loss_fct = CrossEntropyLoss()
mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
if labels is not None:
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (lm_logits, mc_logits) + transformer_outputs[1:]
if mc_loss is not None:
output = (mc_loss,) + output
return ((lm_loss,) + output) if lm_loss is not None else output
return OpenAIGPTDoubleHeadsModelOutput(
loss=lm_loss,
mc_loss=mc_loss,
logits=lm_logits,
mc_logits=mc_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@auto_docstring(
custom_intro="""
The Original OpenAI GPT Model transformer with a sequence classification head on top (linear layer).
[`OpenAIGPTForSequenceClassification`] uses the last token in order to do the classification, as other causal
models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the
last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding
token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since
it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take
the last value in each row of the batch).
"""
)
class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = OpenAIGPTModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size, sequence_length = input_ids.shape[:2]
else:
batch_size, sequence_length = inputs_embeds.shape[:2]
# Ensure the batch size is > 1 if there is no padding.
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
else:
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=pooled_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
__all__ = [
"OpenAIGPTDoubleHeadsModel",
"OpenAIGPTForSequenceClassification",
"OpenAIGPTLMHeadModel",
"OpenAIGPTModel",
"OpenAIGPTPreTrainedModel",
"load_tf_weights_in_openai_gpt",
]
| transformers/src/transformers/models/openai/modeling_openai.py/0 | {
"file_path": "transformers/src/transformers/models/openai/modeling_openai.py",
"repo_id": "transformers",
"token_count": 16193
} | 475 |
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Optional, Union
import torch
from torch import nn
from ...generation import GenerationMixin
from ...modeling_outputs import BaseModelOutput
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, can_return_tuple
from ..aimv2.modeling_aimv2 import Aimv2Attention, Aimv2EncoderLayer
from ..auto import AutoModel
from ..llama.modeling_llama import LlamaMLP, LlamaRMSNorm
from ..llava.modeling_llava import LlavaForConditionalGeneration, LlavaModel
from ..llava_next.modeling_llava_next import LlavaNextCausalLMOutputWithPast, LlavaNextModelOutputWithPast
from ..siglip.modeling_siglip import SiglipEncoder, SiglipVisionEmbeddings
from .configuration_ovis2 import Ovis2Config, Ovis2VisionConfig
def hard_softmax(logits: torch.Tensor, dim: int):
y_soft = logits.softmax(dim)
# Straight through.
index = y_soft.max(dim, keepdim=True)[1]
y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
ret = y_hard - y_soft.detach() + y_soft
return ret
class Ovis2ModelOutputWithPast(LlavaNextModelOutputWithPast):
pass
class Ovis2CausalLMOutputWithPast(LlavaNextCausalLMOutputWithPast):
pass
class Ovis2RMSNorm(LlamaRMSNorm):
pass
class Ovis2VisionMLP(LlamaMLP):
pass
class Ovis2VisionEmbeddings(SiglipVisionEmbeddings):
def __init__(self, config: Ovis2VisionConfig):
super().__init__()
self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
def interpolate_pos_encoding(self):
raise NotImplementedError("Not needed for Ovis2")
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
embeddings = patch_embeds.flatten(2).transpose(1, 2)
embeddings = self.rms_norm(embeddings)
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
class Ovis2VisionAttention(Aimv2Attention):
pass
class Ovis2VisionEncoderLayer(Aimv2EncoderLayer):
pass
class Ovis2VisionEncoder(SiglipEncoder):
def __init__(self, config: Ovis2VisionConfig):
super().__init__()
self.layers = nn.ModuleList([Ovis2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
class Ovis2VisionTransformer(nn.Module):
def __init__(self, config: Ovis2VisionConfig):
super().__init__()
self.config = config
self.embeddings = Ovis2VisionEmbeddings(config)
self.encoder = Ovis2VisionEncoder(config)
self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
self.gradient_checkpointing = False
@can_return_tuple
def forward(
self,
pixel_values,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
hidden_states = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.rms_norm(last_hidden_state)
return BaseModelOutput(
last_hidden_state=last_hidden_state,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class Ovis2VisualEmbeddingTable(nn.Embedding):
def forward(self, visual_tokens: torch.Tensor) -> torch.Tensor:
if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]:
return super().forward(visual_tokens)
return torch.matmul(visual_tokens, self.weight)
class Ovis2PreTrainedModel(PreTrainedModel):
config: Ovis2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Ovis2VisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_supports_flash_attn = True
_supports_flex_attn = True
_supports_sdpa = True
_can_compile_fullgraph = True
_supports_attention_backend = True
class Ovis2VisionModel(Ovis2PreTrainedModel):
config: Ovis2VisionConfig
def __init__(self, config: Ovis2VisionConfig):
super().__init__(config)
self.config = config
self.transformer = Ovis2VisionTransformer(config)
self.num_visual_indicator_tokens = config.num_visual_indicator_tokens
self.vocab_size = config.vocab_size
self.head_linear = nn.Linear(
config.hidden_size * config.hidden_stride * config.hidden_stride,
self.vocab_size - self.num_visual_indicator_tokens,
bias=False,
)
self.head_norm = nn.LayerNorm(self.vocab_size - self.num_visual_indicator_tokens)
def forward(self, pixel_values: torch.FloatTensor) -> tuple[torch.Tensor, torch.Tensor]:
outputs = self.transformer(pixel_values)
last_hidden_state = outputs.last_hidden_state
if self.config.hidden_stride > 1:
num_images, seq_len, hidden_dim = last_hidden_state.shape
hidden_stride = self.config.hidden_stride
sqrt_l = int(math.sqrt(seq_len))
if sqrt_l * sqrt_l != seq_len:
raise ValueError("Token sequence length must be a perfect square")
pad_size = (hidden_stride - (sqrt_l % hidden_stride)) % hidden_stride
last_hidden_state = nn.functional.pad(last_hidden_state, (0, 0, 0, pad_size, 0, pad_size), "constant", 0)
sqrt_l += pad_size
last_hidden_state = last_hidden_state.reshape(
num_images, sqrt_l // hidden_stride, hidden_stride, sqrt_l // hidden_stride, hidden_stride, hidden_dim
)
last_hidden_state = last_hidden_state.permute(0, 1, 3, 2, 4, 5)
last_hidden_state = last_hidden_state.reshape(
num_images, -1, hidden_stride * hidden_stride * hidden_dim
) # (n, (sqrt_l//hs)^2, hs^2*d)
logits = self.head_linear(last_hidden_state)
logits = self.head_norm(logits)
if self.config.tokenize_function == "gumbel_argmax":
prob_token = nn.functional.gumbel_softmax(logits, dim=-1, hard=True)
elif self.config.tokenize_function == "st_argmax":
prob_token = hard_softmax(logits, dim=-1)
elif self.config.tokenize_function == "softmax":
prob_token = nn.functional.softmax(logits, dim=-1)
return prob_token
class Ovis2Model(LlavaModel):
_checkpoint_conversion_mapping = {}
def __init__(self, config: Ovis2Config):
super().__init__(config)
self.vision_tower = Ovis2VisionModel(config.vision_config)
self.visual_embeddings_table = Ovis2VisualEmbeddingTable(config.vision_config.vocab_size, config.hidden_size)
self.visual_vocab_size = config.vision_config.vocab_size
self.vocab_size = config.vocab_size
self.visual_indicator_token_ids = config.visual_indicator_token_ids
self.language_model = AutoModel.from_config(config.text_config)
del self.multi_modal_projector
def get_image_features(
self,
pixel_values: torch.FloatTensor,
) -> torch.FloatTensor:
image_features = self.vision_tower(pixel_values)
batch_size, img_seq_len, _ = image_features.shape
padding_tensor = torch.zeros(
(batch_size, img_seq_len, self.vision_tower.num_visual_indicator_tokens),
dtype=image_features.dtype,
device=image_features.device,
requires_grad=False,
layout=image_features.layout,
)
image_features = torch.cat([image_features, padding_tensor], dim=2)
image_features = self.visual_embeddings_table(image_features)
visual_indicator = torch.arange(
self.visual_vocab_size - self.vision_tower.num_visual_indicator_tokens,
self.visual_vocab_size,
dtype=torch.long,
).to(image_features.device)
visual_indicator_features = self.visual_embeddings_table(visual_indicator)
return image_features, visual_indicator_features
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[list[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs,
) -> Union[tuple, Ovis2ModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
if pixel_values is not None:
image_features, visual_indicator_features = self.get_image_features(pixel_values=pixel_values)
special_image_mask = self.get_placeholder_mask(
input_ids,
inputs_embeds=inputs_embeds,
image_features=image_features,
)
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
for i, visual_indicator_id in enumerate(self.visual_indicator_token_ids):
if input_ids is None:
mask = inputs_embeds == self.get_input_embeddings()(
torch.tensor(visual_indicator_id, dtype=torch.long, device=inputs_embeds.device)
)
mask = mask.all(-1)
else:
mask = (input_ids == visual_indicator_id).to(inputs_embeds.device)
if mask.any():
inputs_embeds[mask] = (
visual_indicator_features[i]
.expand_as(inputs_embeds[mask])
.to(inputs_embeds.device, inputs_embeds.dtype)
)
outputs = self.language_model(
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
cache_position=cache_position,
logits_to_keep=logits_to_keep,
**kwargs,
)
return Ovis2ModelOutputWithPast(
last_hidden_state=outputs.last_hidden_state,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=image_features if pixel_values is not None else None,
)
@auto_docstring
class Ovis2ForConditionalGeneration(LlavaForConditionalGeneration, GenerationMixin):
_checkpoint_conversion_mapping = {}
def __init__(self, config: Ovis2Config):
super().__init__(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
@property
def multi_modal_projector(self):
raise AttributeError("Not needed for Ovis2")
def get_image_features(self, pixel_values: torch.FloatTensor):
return self.model.get_image_features(pixel_values=pixel_values)
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[list[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs,
) -> Union[tuple, Ovis2CausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Ovis2ForConditionalGeneration
>>> model = Ovis2ForConditionalGeneration.from_pretrained("thisisiron/Ovis2-2B-hf")
>>> processor = AutoProcessor.from_pretrained("thisisiron/Ovis2-2B-hf")
>>> prompt = "<|im_start|>user\n<image>\nDescribe the image.<|im_end|>\n<|im_start|>assistant\n"
>>> url = "http://images.cocodataset.org/val2014/COCO_val2014_000000537955.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(**inputs, max_new_tokens=15)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0]
"user\n\nDescribe the image.\nassistant\nThe image features a brown dog standing on a wooden floor, looking up with"
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
outputs = self.model(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs[0]
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(
logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
)
return Ovis2CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=outputs.image_hidden_states,
)
__all__ = ["Ovis2PreTrainedModel", "Ovis2Model", "Ovis2ForConditionalGeneration"]
| transformers/src/transformers/models/ovis2/modular_ovis2.py/0 | {
"file_path": "transformers/src/transformers/models/ovis2/modular_ovis2.py",
"repo_id": "transformers",
"token_count": 7448
} | 476 |
# coding=utf-8
# Copyright 2020 Google and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from pathlib import Path
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
PATTERNS = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def rename_state_dict_key(k):
for pegasus_name, hf_name in PATTERNS:
k = k.replace(pegasus_name, hf_name)
return k
# See appendix C of paper for all hyperparams
def convert_pegasus(tf_weights: dict, cfg_updates: dict) -> PegasusForConditionalGeneration:
cfg_kwargs = DEFAULTS.copy()
cfg_kwargs.update(cfg_updates)
cfg = PegasusConfig(**cfg_kwargs)
torch_model = PegasusForConditionalGeneration(cfg)
sd = torch_model.model.state_dict()
mapping = {}
for k, v in tf_weights.items():
new_k = rename_state_dict_key(k)
if new_k not in sd:
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
if "dense" in k or "proj" in new_k:
v = v.T
mapping[new_k] = torch.tensor(v, dtype=sd[new_k].dtype)
assert v.shape == sd[new_k].shape, f"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
# make sure embedding.padding_idx is respected
mapping["shared.weight"][cfg.pad_token_id] = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1])
mapping["encoder.embed_tokens.weight"] = mapping["shared.weight"]
mapping["decoder.embed_tokens.weight"] = mapping["shared.weight"]
empty_biases = {k: torch.zeros_like(v) for k, v in sd.items() if k.endswith("bias") and k not in mapping}
mapping.update(**empty_biases)
missing, extra = torch_model.model.load_state_dict(mapping, strict=False)
unexpected_missing = [
k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
]
assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
assert extra == [], f"no matches found for the following tf keys {extra}"
return torch_model
def get_tf_weights_as_numpy(path="./ckpt/aeslc/model.ckpt-32000") -> dict:
init_vars = tf.train.list_variables(path)
tf_weights = {}
ignore_name = ["Adafactor", "global_step"]
for name, shape in tqdm(init_vars, desc="converting tf checkpoint to dict"):
skip_key = any(pat in name for pat in ignore_name)
if skip_key:
continue
array = tf.train.load_variable(path, name)
tf_weights[name] = array
return tf_weights
def convert_pegasus_ckpt_to_pytorch(ckpt_path: str, save_dir: str):
# save tokenizer first
dataset = Path(ckpt_path).parent.name
desired_max_model_length = task_specific_params[f"summarization_{dataset}"]["max_position_embeddings"]
tok = PegasusTokenizer.from_pretrained("sshleifer/pegasus", model_max_length=desired_max_model_length)
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(save_dir)
# convert model
tf_weights = get_tf_weights_as_numpy(ckpt_path)
cfg_updates = task_specific_params[f"summarization_{dataset}"]
if dataset == "large":
cfg_updates["task_specific_params"] = task_specific_params
torch_model = convert_pegasus(tf_weights, cfg_updates)
torch_model.save_pretrained(save_dir)
sd = torch_model.state_dict()
sd.pop("model.decoder.embed_positions.weight")
sd.pop("model.encoder.embed_positions.weight")
torch.save(sd, Path(save_dir) / "pytorch_model.bin")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
args = parser.parse_args()
if args.save_dir is None:
dataset = Path(args.tf_ckpt_path).parent.name
args.save_dir = os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| transformers/src/transformers/models/pegasus/convert_pegasus_tf_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/pegasus/convert_pegasus_tf_to_pytorch.py",
"repo_id": "transformers",
"token_count": 2019
} | 477 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization class for Perceiver."""
from typing import Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
class PerceiverTokenizer(PreTrainedTokenizer):
"""
Construct a Perceiver tokenizer. The Perceiver simply uses raw bytes utf-8 encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
bos_token (`str`, *optional*, defaults to `"[BOS]"`):
The BOS token (reserved in the vocab, but not actually used).
eos_token (`str`, *optional*, defaults to `"[EOS]"`):
The end of sequence token (reserved in the vocab, but not actually used).
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The MASK token, useful for masked language modeling.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The CLS token (reserved in the vocab, but not actually used).
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from two sequences.
"""
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
pad_token="[PAD]",
bos_token="[BOS]",
eos_token="[EOS]",
mask_token="[MASK]",
cls_token="[CLS]",
sep_token="[SEP]",
model_max_length=2048,
**kwargs,
) -> None:
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
mask_token = AddedToken(mask_token, lstrip=False, rstrip=False) if isinstance(mask_token, str) else mask_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
self._utf_vocab_size = 2**8 # utf is 8 bits
# Since these tokens are not part of the vocabulary, we manually add them
self._added_tokens_decoder: dict[str, int] = {
0: pad_token,
1: bos_token,
2: eos_token,
3: mask_token,
4: cls_token,
5: sep_token,
}
self._num_special_tokens = len(self._added_tokens_decoder)
super().__init__(
pad_token=pad_token,
bos_token=bos_token,
eos_token=eos_token,
mask_token=mask_token,
cls_token=cls_token,
sep_token=sep_token,
model_max_length=model_max_length,
**kwargs,
)
def get_vocab(self) -> dict[str, int]:
vocab = {}
for i in range(self._utf_vocab_size):
token = chr(i)
vocab[token] = i + self._num_special_tokens
vocab.update(self.added_tokens_encoder)
return vocab
@property
def vocab_size(self):
return self._utf_vocab_size
def get_special_tokens_mask(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
) -> list[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
# normal case: some special tokens
if token_ids_1 is None:
return [1] + [0] * len(token_ids_0) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def build_inputs_with_special_tokens(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
) -> list[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks. A sequence has the
following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`list[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
else:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1 + [self.sep_token_id]
def _tokenize(self, text: str) -> list[str]:
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
tokens = [chr(i) for i in text.encode("utf-8")]
return tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if len(token) != 1:
token_id = self.unk_token_id
else:
token_id = ord(token) + self._num_special_tokens
return token_id
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = chr(index - self._num_special_tokens)
return token
# TODO @ArthurZ refactor this as well....
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
bstring = b""
for token in tokens:
if token in self.added_tokens_encoder:
tok_string = str(token).encode("utf-8")
else:
tok_string = bytes([ord(token)])
bstring += tok_string
string = bstring.decode("utf-8", errors="replace")
return string
# PerceiverTokenizer has no vocab file
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
return ()
__all__ = ["PerceiverTokenizer"]
| transformers/src/transformers/models/perceiver/tokenization_perceiver.py/0 | {
"file_path": "transformers/src/transformers/models/perceiver/tokenization_perceiver.py",
"repo_id": "transformers",
"token_count": 3438
} | 478 |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/phi/modular_phi.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_phi.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
from typing import Callable, Optional, Union
import torch
import torch.nn as nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...masking_utils import create_causal_mask
from ...modeling_layers import (
GenericForSequenceClassification,
GenericForTokenClassification,
GradientCheckpointingLayer,
)
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
from ...utils.deprecation import deprecate_kwarg
from ...utils.generic import check_model_inputs
from .configuration_phi import PhiConfig
logger = logging.get_logger(__name__)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs: Unpack[TransformersKwargs],
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class PhiAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.attention_dropout = config.attention_dropout
self.is_causal = True
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
self.dense = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=True)
self.rotary_ndims = int(self.head_dim * config.partial_rotary_factor)
self.qk_layernorm = config.qk_layernorm
if self.qk_layernorm:
self.q_layernorm = nn.LayerNorm(
config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True
)
self.k_layernorm = nn.LayerNorm(
config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True
)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_values: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
if self.qk_layernorm:
query_states = self.q_layernorm(query_states)
key_states = self.k_layernorm(key_states)
cos, sin = position_embeddings
# Partial rotary embedding
query_rot, query_pass = (
query_states[..., : self.rotary_ndims],
query_states[..., self.rotary_ndims :],
)
key_rot, key_pass = (
key_states[..., : self.rotary_ndims],
key_states[..., self.rotary_ndims :],
)
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin)
# [batch_size, seq_length, num_heads, head_dim]
query_states = torch.cat((query_rot, query_pass), dim=-1)
key_states = torch.cat((key_rot, key_pass), dim=-1)
if past_key_values is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.dense(attn_output)
return attn_output, attn_weights
class PhiMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class PhiDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__()
self.self_attn = PhiAttention(config, layer_idx=layer_idx)
self.mlp = PhiMLP(config)
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
attn_outputs, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
attn_outputs = self.resid_dropout(attn_outputs)
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
hidden_states = attn_outputs + feed_forward_hidden_states + residual
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class PhiRotaryEmbedding(nn.Module):
inv_freq: torch.Tensor # fix linting for `register_buffer`
def __init__(self, config: PhiConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
@auto_docstring
class PhiPreTrainedModel(PreTrainedModel):
config: PhiConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PhiDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn = True
_supports_sdpa = True
_supports_flex_attn = True
_can_compile_fullgraph = True
_supports_attention_backend = True
_can_record_outputs = {
"hidden_states": PhiDecoderLayer,
"attentions": PhiAttention,
}
@auto_docstring
class PhiModel(PhiPreTrainedModel):
def __init__(self, config: PhiConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.rotary_emb = PhiRotaryEmbedding(config=config)
self.gradient_checkpointing = False
self.embed_dropout = nn.Dropout(config.embd_pdrop)
self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# Initialize weights and apply final processing
self.post_init()
@check_model_inputs
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = create_causal_mask(
config=self.config,
input_embeds=inputs_embeds,
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=past_key_values,
position_ids=position_ids,
)
inputs_embeds = self.embed_dropout(inputs_embeds) # diff with Llama
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.final_layernorm(hidden_states) # diff with Llama
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
@auto_docstring
class PhiForCausalLM(PhiPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
def __init__(self, config):
super().__init__(config)
self.model = PhiModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
# Initialize weights and apply final processing
self.post_init()
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[TransformersKwargs],
) -> CausalLMOutputWithPast:
r"""
Example:
```python
>>> from transformers import AutoTokenizer, PhiForCausalLM
>>> model = PhiForCausalLM.from_pretrained("meta-phi/Phi-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-phi/Phi-2-7b-hf")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
outputs: BaseModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class PhiForSequenceClassification(GenericForSequenceClassification, PhiPreTrainedModel):
pass
class PhiForTokenClassification(GenericForTokenClassification, PhiPreTrainedModel):
pass
__all__ = [
"PhiPreTrainedModel",
"PhiModel",
"PhiForCausalLM",
"PhiForSequenceClassification",
"PhiForTokenClassification",
]
| transformers/src/transformers/models/phi/modeling_phi.py/0 | {
"file_path": "transformers/src/transformers/models/phi/modeling_phi.py",
"repo_id": "transformers",
"token_count": 9721
} | 479 |
# coding=utf-8
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Phimoe model."""
import math
from typing import Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_causal_attention_mask
from ...modeling_flash_attention_utils import is_flash_attn_available
from ...modeling_layers import (
GenericForSequenceClassification,
GradientCheckpointingLayer,
)
from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
from ...utils.deprecation import deprecate_kwarg
from .configuration_phimoe import PhimoeConfig
if is_flash_attn_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import BlockMask
from ...integrations.flex_attention import make_flex_block_causal_mask
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
# It means that the function will not be traced through and simply appear as a node in the graph.
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
logger = logging.get_logger(__name__)
# Copied from transformers.models.qwen2_moe.modeling_qwen2_moe.load_balancing_loss_func
def load_balancing_loss_func(
gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
num_experts: Optional[int] = None,
top_k=2,
attention_mask: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, int]:
r"""
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.
Args:
gate_logits:
Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
shape [batch_size X sequence_length, num_experts].
num_experts:
Number of experts
top_k:
The number of experts to route per-token, can be also interpreted as the `top-k` routing
parameter.
attention_mask (`torch.Tensor`, *optional*):
The attention_mask used in forward function
shape [batch_size X sequence_length] if not None.
Returns:
The auxiliary loss.
"""
if gate_logits is None or not isinstance(gate_logits, tuple):
return 0
if isinstance(gate_logits, tuple):
compute_device = gate_logits[0].device
concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
if attention_mask is None:
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.mean(routing_weights, dim=0)
else:
batch_size, sequence_length = attention_mask.shape
num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
expert_attention_mask = (
attention_mask[None, :, :, None, None]
.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
.reshape(-1, top_k, num_experts)
.to(compute_device)
)
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
expert_attention_mask, dim=0
)
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
router_per_expert_attention_mask = (
attention_mask[None, :, :, None]
.expand((num_hidden_layers, batch_size, sequence_length, routing_weights.shape[1]))
.reshape(-1, routing_weights.shape[1])
.to(compute_device)
)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
router_per_expert_attention_mask, dim=0
)
device_index = routing_weights.device.index if routing_weights.device.index is not None else 0
rank = routing_weights.shape[1] * int(device_index)
overall_loss = torch.sum(
tokens_per_expert[:, rank : rank + routing_weights.shape[1]] * router_prob_per_expert.unsqueeze(0)
)
return overall_loss * num_experts
class PhimoeRotaryEmbedding(nn.Module):
def __init__(
self,
config: Optional[PhimoeConfig] = None,
):
super().__init__()
self.config = config
if config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
self.short_mscale = config.rope_scaling.get("short_mscale")
self.long_mscale = config.rope_scaling.get("long_mscale")
else:
self.rope_type = "default"
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
def forward(self, x, seq_len=None):
mscale = None
if self.config.rope_scaling and seq_len:
mscale = (
self.long_mscale
if seq_len > self.config.rope_scaling["original_max_position_embeddings"]
else self.short_mscale
)
inv_freq, attention_scaling = self.rope_init_fn(self.config, x.device, seq_len)
mscale = attention_scaling if mscale is None else mscale
t = torch.arange(seq_len, device=x.device, dtype=torch.float32)
freqs = torch.outer(t, inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
return (emb.cos() * mscale).to(x.dtype), (emb.sin() * mscale).to(x.dtype)
# Copied from transformers.models.llama.modeling_llama.rotate_half
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`):
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
used to pass offsetted position ids when working with a KV-cache.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
# Copied from transformers.models.llama.modeling_llama.repeat_kv
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class PhimoeAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
and "Generating Long Sequences with Sparse Transformers".
"""
def __init__(self, config: PhimoeConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
self.attention_dropout = config.attention_dropout
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=self.config.attention_bias)
self.k_proj = nn.Linear(
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=self.config.attention_bias
)
self.v_proj = nn.Linear(
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=self.config.attention_bias
)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=self.config.attention_bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_values is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights
class PhimoeFlashAttention2(PhimoeAttention):
"""
Phimoe flash attention module. This module inherits from `PhimoeAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
):
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_values is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
dropout_rate = 0.0 if not self.training else self.attention_dropout
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in float16 just to be sure everything works as expected.
input_dtype = query_states.dtype
device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = (
torch.get_autocast_dtype(device_type)
if hasattr(torch, "get_autocast_dtype")
else torch.get_autocast_gpu_dtype()
)
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
# Reashape to the expected shape for Flash Attention
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
position_ids=position_ids,
dropout=dropout_rate,
sliding_window=getattr(self.config, "sliding_window", None),
is_causal=self.is_causal,
)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights
class PhimoeSdpaAttention(PhimoeAttention):
"""
Phimoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`PhimoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from PhimoeAttention.forward
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"PhimoeModel is using PhimoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
position_embeddings=position_embeddings,
)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_values is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
causal_mask = attention_mask
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and attention_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
is_causal = causal_mask is None and q_len > 1
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output, None
PHIMOE_ATTENTION_CLASSES = {
"eager": PhimoeAttention,
"flash_attention_2": PhimoeFlashAttention2,
"sdpa": PhimoeSdpaAttention,
}
# Copied from transformers.models.mixtral.modeling_mixtral.MixtralBlockSparseTop2MLP with Mixtral->Phimoe
class PhimoeBlockSparseTop2MLP(nn.Module):
def __init__(self, config: PhimoeConfig):
super().__init__()
self.ffn_dim = config.intermediate_size
self.hidden_dim = config.hidden_size
self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
current_hidden_states = self.w2(current_hidden_states)
return current_hidden_states
class MultiplierProcessor(torch.autograd.Function):
@staticmethod
def forward(
ctx,
scores: torch.Tensor,
multiplier: torch.Tensor,
selected_experts: torch.Tensor,
masked_gates: torch.Tensor,
mask_for_one: torch.Tensor,
):
"""
Forward pass for the custom autograd function.
Args:
ctx: Context object to save information for backward computation.
scores (torch.Tensor): Input scores tensor.
multiplier (torch.Tensor): Multiplier tensor.
selected_experts (torch.Tensor): Tensor of selected experts.
masked_gates (torch.Tensor): Masked gates tensor.
mask_for_one (torch.Tensor): Mask for one tensor.
Returns:
torch.Tensor: Result of the forward pass.
"""
ctx.save_for_backward(multiplier, selected_experts, masked_gates)
return multiplier * mask_for_one
@staticmethod
def backward(
ctx,
grad_at_output: torch.Tensor,
):
"""
Backward pass for the custom autograd function.
Args:
ctx: Context object with saved tensors from the forward pass.
grad_at_output (torch.Tensor): Gradient at the output.
Returns:
tuple[torch.Tensor, None, None, None, None]: Gradients for the inputs.
"""
multiplier, selected_experts, masked_gates = ctx.saved_tensors
grad_at_output = grad_at_output * multiplier
grad_at_scores_expanded = masked_gates * grad_at_output.mul(-1)
grad_at_scores_expanded.scatter_add_(
dim=-1,
index=selected_experts,
src=grad_at_output,
)
return (
grad_at_scores_expanded,
None,
None,
None,
None,
)
def sparsemixer(scores, jitter_eps, training, top_k=2):
"""
Sparse mixer function to select top-k experts and compute multipliers.
Based on the paper: https://huggingface.co/papers/2409.12136
We first replace the TopK(·) function as random sampling of discrete variables
in model training. Then, following Liu et al. (2023a) and Liu et al. (2023b), we apply Heun's
third order method to approximate the expert routing gradient and construct a modified
back-propagation to give a mathematically sound gradient estimation for expert routing.
Args:
scores (torch.Tensor): Input scores tensor.
jitter_eps (float): Jitter epsilon for numerical stability.
training (bool): Flag indicating if the model is in training mode.
top_k (int): Number of top experts to select.
Returns:
tuple[torch.Tensor, torch.Tensor]: Multiplier and selected experts tensors.
"""
if top_k != 2:
raise ValueError("top_k must be equal to 2")
# first expert
with torch.no_grad():
# Compute mask for sparsity
mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True)
factor = scores.abs().clamp(min=mask_logits_threshold)
mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps)
# Apply mask
masked_gates = scores.masked_fill(mask_logits_threshold, float("-inf"))
if training:
selected_experts = (
(
masked_gates
- torch.empty_like(masked_gates, memory_format=torch.legacy_contiguous_format).exponential_().log()
)
.max(dim=-1)[1]
.unsqueeze(-1)
) # Gumbel sampling, more robust than the multinomial method
else:
selected_experts = max_ind
# Compute scores for gradients
masked_gates = torch.softmax(masked_gates, dim=-1)
multiplier_o = masked_gates.gather(dim=-1, index=selected_experts)
if training:
# Compute midpoint mask
max_scores, max_ind = masked_gates.max(dim=-1, keepdim=True)
mask_for_one = torch.logical_or(
selected_experts == max_ind,
torch.rand_like(max_scores) > 0.75, # Heun's third-order method
)
# 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5
mask_for_one = torch.add(0.3333, mask_for_one, alpha=0.6667).type_as(masked_gates)
multiplier = MultiplierProcessor.apply(
scores,
multiplier_o,
selected_experts,
masked_gates,
mask_for_one,
)
else:
multiplier = multiplier_o
# Masked out first expert
masked_scores = torch.scatter(
scores,
-1,
selected_experts,
float("-inf"),
)
with torch.no_grad():
# Compute mask for sparsity
mask_logits_threshold, max_ind = masked_scores.max(dim=-1, keepdim=True)
factor = scores.abs().clamp(min=mask_logits_threshold)
mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps)
# Apply mask
masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold, float("-inf"))
if training:
selected_experts_top2 = (
(
masked_gates_top2
- torch.empty_like(masked_gates_top2, memory_format=torch.legacy_contiguous_format)
.exponential_()
.log()
)
.max(dim=-1)[1]
.unsqueeze(-1)
) # Gumbel sampling, more robust than the multinomial method
else:
selected_experts_top2 = max_ind
# Compute scores for gradients
masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1)
multiplier_top2_o = masked_gates_top2.gather(dim=-1, index=selected_experts_top2)
if training:
# Compute midpoint mask
max_scores, max_ind = masked_gates_top2.max(dim=-1, keepdim=True)
mask_for_one_top2 = torch.logical_or(
selected_experts_top2 == max_ind,
torch.rand_like(max_scores).uniform_() > 0.75, # Heun's third-order method
)
# 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5
mask_for_one_top2 = torch.add(0.3333, mask_for_one_top2, alpha=0.6667).type_as(masked_gates_top2)
multiplier_top2 = MultiplierProcessor.apply(
scores,
multiplier_top2_o,
selected_experts_top2,
masked_gates_top2,
mask_for_one_top2,
)
else:
multiplier_top2 = multiplier_top2_o
multiplier = torch.concat((multiplier, multiplier_top2), dim=-1)
selected_experts = torch.concat((selected_experts, selected_experts_top2), dim=-1)
return (
multiplier,
selected_experts,
)
class PhimoeSparseMoeBlock(nn.Module):
"""
This implementation is
strictly equivalent to standard MoE with full capacity (no
dropped tokens). It's faster since it formulates MoE operations
in terms of block-sparse operations to accommodate imbalanced
assignments of tokens to experts, whereas standard MoE either
(1) drop tokens at the cost of reduced performance or (2) set
capacity factor to number of experts and thus waste computation
and memory on padding.
"""
def __init__(self, config):
super().__init__()
self.hidden_dim = config.hidden_size
self.ffn_dim = config.intermediate_size
self.num_experts = config.num_local_experts
self.top_k = config.num_experts_per_tok
# gating
self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
self.experts = nn.ModuleList([PhimoeBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
# Jitter parameters
self.router_jitter_noise = config.router_jitter_noise
self.input_jitter_noise = config.input_jitter_noise
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
""" """
batch_size, sequence_length, hidden_dim = hidden_states.shape
if self.training and self.input_jitter_noise > 0:
hidden_states *= torch.empty_like(hidden_states).uniform_(
1.0 - self.input_jitter_noise, 1.0 + self.input_jitter_noise
)
hidden_states = hidden_states.view(-1, hidden_dim)
router_logits = self.gate(hidden_states)
routing_weights, selected_experts = sparsemixer(
router_logits,
jitter_eps=self.router_jitter_noise,
training=self.training,
)
final_hidden_states = torch.zeros(
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
)
# One hot encode the selected experts to create an expert mask
# this will be used to easily index which expert is going to be sollicitated
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
# Loop over all available experts in the model and perform the computation on each expert
for expert_idx in range(self.num_experts):
expert_layer = self.experts[expert_idx]
idx, top_x = torch.where(expert_mask[expert_idx])
if top_x.shape[0] == 0:
continue
# Index the correct hidden states and compute the expert hidden state for
# the current expert. We need to make sure to multiply the output hidden
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
# However `index_add_` only support torch tensors for indexing so we'll use
# the `top_x` tensor here.
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
return final_hidden_states, router_logits
class PhimoeDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: PhimoeConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = PHIMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
self.block_sparse_moe = PhimoeSparseMoeBlock(config)
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True)
self.post_attention_layernorm = nn.LayerNorm(
config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True
)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
**kwargs,
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, sequence_length)` where padding elements are indicated by 0.
past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence.
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states, router_logits = self.block_sparse_moe(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if output_router_logits:
outputs += (router_logits,)
return outputs
@auto_docstring
class PhimoePreTrainedModel(PreTrainedModel):
config: PhimoeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PhimoeDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn = True
_supports_sdpa = True
_can_compile_fullgraph = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@auto_docstring
class PhimoeModel(PhimoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhimoeDecoderLayer`]
Args:
config: PhimoeConfig
"""
def __init__(self, config: PhimoeConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[PhimoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self._attn_implementation = config._attn_implementation
self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True)
self.rotary_emb = PhimoeRotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> MoeModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_router_logits = (
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
if not isinstance(past_key_values, (type(None), Cache)):
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
if use_cache and past_key_values is None:
past_key_values = DynamicCache(config=self.config)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
position_embeddings = self.rotary_emb(hidden_states, seq_len=cache_position[-1] + 1)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_router_logits = () if output_router_logits else None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=output_attentions,
output_router_logits=output_router_logits,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if output_router_logits:
all_router_logits += (layer_outputs[-1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
router_logits=all_router_logits,
)
def _update_causal_mask(
self,
attention_mask: Union[torch.Tensor, "BlockMask"],
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool = False,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and past_key_values is not None:
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Phimoe. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
return attention_mask
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
sliding_window=self.config.sliding_window,
is_training=self.training,
):
return None
dtype = input_tensor.dtype
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
# StaticCache
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
# DynamicCache or no cache
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
config=self.config,
past_key_values=past_key_values,
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu", "npu"]
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
cache_position: torch.Tensor,
batch_size: int,
config: PhimoeConfig,
past_key_values: Cache,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
config (`PhimoeConfig`):
The model's configuration class
past_key_values (`Cache`):
The cache class that is being used currently to generate
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
)
diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
-1, 1
)
text_config = config.get_text_config()
if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None:
# if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
# the check is needed to verify is current checkpoint was trained with sliding window or not
is_static_sliding_cache = isinstance(past_key_values, StaticCache) and all(past_key_values.is_sliding)
if not is_static_sliding_cache or sequence_length > target_length:
sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
cache_position.reshape(-1, 1) - text_config.sliding_window
)
diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
causal_mask *= diagonal_attend_mask
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
if attention_mask.shape[-1] > target_length:
attention_mask = attention_mask[:, :target_length]
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
class PhimoeForCausalLM(PhimoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = PhimoeModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=self.config.lm_head_bias)
self.router_aux_loss_coef = config.router_aux_loss_coef
self.num_experts = config.num_local_experts
self.num_experts_per_tok = config.num_experts_per_tok
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
def set_decoder(self, decoder):
self.model = decoder
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
def get_decoder(self):
return self.model
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs,
) -> MoeCausalLMOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, PhimoeForCausalLM
>>> model = PhimoeForCausalLM.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
if (
use_cache
and self.config.rope_scaling
and cache_position is not None
and cache_position[0] == self.config.original_max_position_embeddings
):
logger.warning(
f"If you are not using the generate method, you may encounter nonsensical outputs after the {self.config.original_max_position_embeddings}th token, as the KV cache needs to be recomputed."
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_router_logits = (
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs: MoeModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
cache_position=cache_position,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits,
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not None:
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
return MoeCausalLMOutputWithPast(
loss=loss,
aux_loss=aux_loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
router_logits=outputs.router_logits,
)
# Copied from transformers.models.phi3.modeling_phi3.Phi3ForCausalLM.prepare_inputs_for_generation
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
position_ids=None,
use_cache=True,
logits_to_keep=None,
**kwargs,
):
# Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the
# process
# When the first time input length reached long and short factor switching point, enforce re-compute cache
# It will cause downside of slower at this single token position, however, better than current failure.
if (
past_key_values
and self.config.rope_scaling
and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1
):
past_length = cache_position[0]
if past_length <= self.config.original_max_position_embeddings:
past_key_values = None
model_inputs = super().prepare_inputs_for_generation(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
cache_position=cache_position,
position_ids=position_ids,
use_cache=use_cache,
logits_to_keep=logits_to_keep,
**kwargs,
)
return model_inputs
class PhimoeForSequenceClassification(GenericForSequenceClassification, PhimoePreTrainedModel): ...
__all__ = [
"PhimoePreTrainedModel",
"PhimoeModel",
"PhimoeForCausalLM",
"PhimoeForSequenceClassification",
]
| transformers/src/transformers/models/phimoe/modeling_phimoe.py/0 | {
"file_path": "transformers/src/transformers/models/phimoe/modeling_phimoe.py",
"repo_id": "transformers",
"token_count": 26099
} | 480 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for Pop2Piano"""
import warnings
from typing import Optional, Union
import numpy
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import (
TensorType,
is_essentia_available,
is_librosa_available,
is_scipy_available,
logging,
requires_backends,
)
from ...utils.import_utils import requires
if is_essentia_available():
import essentia
import essentia.standard
if is_librosa_available():
import librosa
if is_scipy_available():
import scipy
logger = logging.get_logger(__name__)
@requires(backends=("essentia", "librosa", "scipy", "torch"))
class Pop2PianoFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Pop2Piano feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information regarding those methods.
This class extracts rhythm and preprocesses the audio before it is passed to the model. First the audio is passed
to `RhythmExtractor2013` algorithm which extracts the beat_times, beat positions and estimates their confidence as
well as tempo in bpm, then beat_times is interpolated and to get beatsteps. Later we calculate
extrapolated_beatsteps from it to be used in tokenizer. On the other hand audio is resampled to self.sampling_rate
and preprocessed and then log mel spectogram is computed from that to be used in our transformer model.
Args:
sampling_rate (`int`, *optional*, defaults to 22050):
Target Sampling rate of audio signal. It's the sampling rate that we forward to the model.
padding_value (`int`, *optional*, defaults to 0):
Padding value used to pad the audio. Should correspond to silences.
window_size (`int`, *optional*, defaults to 4096):
Length of the window in samples to which the Fourier transform is applied.
hop_length (`int`, *optional*, defaults to 1024):
Step size between each window of the waveform, in samples.
min_frequency (`float`, *optional*, defaults to 10.0):
Lowest frequency that will be used in the log-mel spectrogram.
feature_size (`int`, *optional*, defaults to 512):
The feature dimension of the extracted features.
num_bars (`int`, *optional*, defaults to 2):
Determines interval between each sequence.
"""
model_input_names = ["input_features", "beatsteps", "extrapolated_beatstep"]
def __init__(
self,
sampling_rate: int = 22050,
padding_value: int = 0,
window_size: int = 4096,
hop_length: int = 1024,
min_frequency: float = 10.0,
feature_size: int = 512,
num_bars: int = 2,
**kwargs,
):
super().__init__(
feature_size=feature_size,
sampling_rate=sampling_rate,
padding_value=padding_value,
**kwargs,
)
self.sampling_rate = sampling_rate
self.padding_value = padding_value
self.window_size = window_size
self.hop_length = hop_length
self.min_frequency = min_frequency
self.feature_size = feature_size
self.num_bars = num_bars
self.mel_filters = mel_filter_bank(
num_frequency_bins=(self.window_size // 2) + 1,
num_mel_filters=self.feature_size,
min_frequency=self.min_frequency,
max_frequency=float(self.sampling_rate // 2),
sampling_rate=self.sampling_rate,
norm=None,
mel_scale="htk",
)
def mel_spectrogram(self, sequence: np.ndarray):
"""
Generates MelSpectrogram.
Args:
sequence (`numpy.ndarray`):
The sequence of which the mel-spectrogram will be computed.
"""
mel_specs = []
for seq in sequence:
window = np.hanning(self.window_size + 1)[:-1]
mel_specs.append(
spectrogram(
waveform=seq,
window=window,
frame_length=self.window_size,
hop_length=self.hop_length,
power=2.0,
mel_filters=self.mel_filters,
)
)
mel_specs = np.array(mel_specs)
return mel_specs
def extract_rhythm(self, audio: np.ndarray):
"""
This algorithm(`RhythmExtractor2013`) extracts the beat positions and estimates their confidence as well as
tempo in bpm for an audio signal. For more information please visit
https://essentia.upf.edu/reference/std_RhythmExtractor2013.html .
Args:
audio(`numpy.ndarray`):
raw audio waveform which is passed to the Rhythm Extractor.
"""
requires_backends(self, ["essentia"])
essentia_tracker = essentia.standard.RhythmExtractor2013(method="multifeature")
bpm, beat_times, confidence, estimates, essentia_beat_intervals = essentia_tracker(audio)
return bpm, beat_times, confidence, estimates, essentia_beat_intervals
def interpolate_beat_times(
self, beat_times: numpy.ndarray, steps_per_beat: numpy.ndarray, n_extend: numpy.ndarray
):
"""
This method takes beat_times and then interpolates that using `scipy.interpolate.interp1d` and the output is
then used to convert raw audio to log-mel-spectrogram.
Args:
beat_times (`numpy.ndarray`):
beat_times is passed into `scipy.interpolate.interp1d` for processing.
steps_per_beat (`int`):
used as an parameter to control the interpolation.
n_extend (`int`):
used as an parameter to control the interpolation.
"""
requires_backends(self, ["scipy"])
beat_times_function = scipy.interpolate.interp1d(
np.arange(beat_times.size),
beat_times,
bounds_error=False,
fill_value="extrapolate",
)
ext_beats = beat_times_function(
np.linspace(0, beat_times.size + n_extend - 1, beat_times.size * steps_per_beat + n_extend)
)
return ext_beats
def preprocess_mel(self, audio: np.ndarray, beatstep: np.ndarray):
"""
Preprocessing for log-mel-spectrogram
Args:
audio (`numpy.ndarray` of shape `(audio_length, )` ):
Raw audio waveform to be processed.
beatstep (`numpy.ndarray`):
Interpolated values of the raw audio. If beatstep[0] is greater than 0.0, then it will be shifted by
the value at beatstep[0].
"""
if audio is not None and len(audio.shape) != 1:
raise ValueError(
f"Expected `audio` to be a single channel audio input of shape `(n, )` but found shape {audio.shape}."
)
if beatstep[0] > 0.0:
beatstep = beatstep - beatstep[0]
num_steps = self.num_bars * 4
num_target_steps = len(beatstep)
extrapolated_beatstep = self.interpolate_beat_times(
beat_times=beatstep, steps_per_beat=1, n_extend=(self.num_bars + 1) * 4 + 1
)
sample_indices = []
max_feature_length = 0
for i in range(0, num_target_steps, num_steps):
start_idx = i
end_idx = min(i + num_steps, num_target_steps)
start_sample = int(extrapolated_beatstep[start_idx] * self.sampling_rate)
end_sample = int(extrapolated_beatstep[end_idx] * self.sampling_rate)
sample_indices.append((start_sample, end_sample))
max_feature_length = max(max_feature_length, end_sample - start_sample)
padded_batch = []
for start_sample, end_sample in sample_indices:
feature = audio[start_sample:end_sample]
padded_feature = np.pad(
feature,
((0, max_feature_length - feature.shape[0]),),
"constant",
constant_values=0,
)
padded_batch.append(padded_feature)
padded_batch = np.asarray(padded_batch)
return padded_batch, extrapolated_beatstep
def _pad(self, features: np.ndarray, add_zero_line=True):
features_shapes = [each_feature.shape for each_feature in features]
attention_masks, padded_features = [], []
for i, each_feature in enumerate(features):
# To pad "input_features".
if len(each_feature.shape) == 3:
features_pad_value = max([*zip(*features_shapes)][1]) - features_shapes[i][1]
attention_mask = np.ones(features_shapes[i][:2], dtype=np.int64)
feature_padding = ((0, 0), (0, features_pad_value), (0, 0))
attention_mask_padding = (feature_padding[0], feature_padding[1])
# To pad "beatsteps" and "extrapolated_beatstep".
else:
each_feature = each_feature.reshape(1, -1)
features_pad_value = max([*zip(*features_shapes)][0]) - features_shapes[i][0]
attention_mask = np.ones(features_shapes[i], dtype=np.int64).reshape(1, -1)
feature_padding = attention_mask_padding = ((0, 0), (0, features_pad_value))
each_padded_feature = np.pad(each_feature, feature_padding, "constant", constant_values=self.padding_value)
attention_mask = np.pad(
attention_mask, attention_mask_padding, "constant", constant_values=self.padding_value
)
if add_zero_line:
# if it is batched then we separate each examples using zero array
zero_array_len = max([*zip(*features_shapes)][1])
# we concatenate the zero array line here
each_padded_feature = np.concatenate(
[each_padded_feature, np.zeros([1, zero_array_len, self.feature_size])], axis=0
)
attention_mask = np.concatenate(
[attention_mask, np.zeros([1, zero_array_len], dtype=attention_mask.dtype)], axis=0
)
padded_features.append(each_padded_feature)
attention_masks.append(attention_mask)
padded_features = np.concatenate(padded_features, axis=0).astype(np.float32)
attention_masks = np.concatenate(attention_masks, axis=0).astype(np.int64)
return padded_features, attention_masks
def pad(
self,
inputs: BatchFeature,
is_batched: bool,
return_attention_mask: bool,
return_tensors: Optional[Union[str, TensorType]] = None,
):
"""
Pads the inputs to same length and returns attention_mask.
Args:
inputs (`BatchFeature`):
Processed audio features.
is_batched (`bool`):
Whether inputs are batched or not.
return_attention_mask (`bool`):
Whether to return attention mask or not.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
If nothing is specified, it will return list of `np.ndarray` arrays.
Return:
`BatchFeature` with attention_mask, attention_mask_beatsteps and attention_mask_extrapolated_beatstep added
to it:
- **attention_mask** numpy.ndarray of shape `(batch_size, max_input_features_seq_length)` --
Example :
1, 1, 1, 0, 0 (audio 1, also here it is padded to max length of 5 that's why there are 2 zeros at
the end indicating they are padded)
0, 0, 0, 0, 0 (zero pad to separate audio 1 and 2)
1, 1, 1, 1, 1 (audio 2)
0, 0, 0, 0, 0 (zero pad to separate audio 2 and 3)
1, 1, 1, 1, 1 (audio 3)
- **attention_mask_beatsteps** numpy.ndarray of shape `(batch_size, max_beatsteps_seq_length)`
- **attention_mask_extrapolated_beatstep** numpy.ndarray of shape `(batch_size,
max_extrapolated_beatstep_seq_length)`
"""
processed_features_dict = {}
for feature_name, feature_value in inputs.items():
if feature_name == "input_features":
padded_feature_values, attention_mask = self._pad(feature_value, add_zero_line=True)
processed_features_dict[feature_name] = padded_feature_values
if return_attention_mask:
processed_features_dict["attention_mask"] = attention_mask
else:
padded_feature_values, attention_mask = self._pad(feature_value, add_zero_line=False)
processed_features_dict[feature_name] = padded_feature_values
if return_attention_mask:
processed_features_dict[f"attention_mask_{feature_name}"] = attention_mask
# If we are processing only one example, we should remove the zero array line since we don't need it to
# separate examples from each other.
if not is_batched and not return_attention_mask:
processed_features_dict["input_features"] = processed_features_dict["input_features"][:-1, ...]
outputs = BatchFeature(processed_features_dict, tensor_type=return_tensors)
return outputs
def __call__(
self,
audio: Union[np.ndarray, list[float], list[np.ndarray], list[list[float]]],
sampling_rate: Union[int, list[int]],
steps_per_beat: int = 2,
resample: Optional[bool] = True,
return_attention_mask: Optional[bool] = False,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchFeature:
"""
Main method to featurize and prepare for the model.
Args:
audio (`np.ndarray`, `List`):
The audio or batch of audio to be processed. Each audio can be a numpy array, a list of float values, a
list of numpy arrays or a list of list of float values.
sampling_rate (`int`):
The sampling rate at which the `audio` input was sampled. It is strongly recommended to pass
`sampling_rate` at the forward call to prevent silent errors.
steps_per_beat (`int`, *optional*, defaults to 2):
This is used in interpolating `beat_times`.
resample (`bool`, *optional*, defaults to `True`):
Determines whether to resample the audio to `sampling_rate` or not before processing. Must be True
during inference.
return_attention_mask (`bool` *optional*, defaults to `False`):
Denotes if attention_mask for input_features, beatsteps and extrapolated_beatstep will be given as
output or not. Automatically set to True for batched inputs.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
If nothing is specified, it will return list of `np.ndarray` arrays.
"""
requires_backends(self, ["librosa"])
is_batched = isinstance(audio, (list, tuple)) and isinstance(audio[0], (np.ndarray, tuple, list))
if is_batched:
# This enables the user to process files of different sampling_rate at same time
if not isinstance(sampling_rate, list):
raise ValueError(
"Please give sampling_rate of each audio separately when you are passing multiple raw_audios at the same time. "
f"Received {sampling_rate}, expected [audio_1_sr, ..., audio_n_sr]."
)
return_attention_mask = True if return_attention_mask is None else return_attention_mask
else:
audio = [audio]
sampling_rate = [sampling_rate]
return_attention_mask = False if return_attention_mask is None else return_attention_mask
batch_input_features, batch_beatsteps, batch_ext_beatstep = [], [], []
for single_raw_audio, single_sampling_rate in zip(audio, sampling_rate):
bpm, beat_times, confidence, estimates, essentia_beat_intervals = self.extract_rhythm(
audio=single_raw_audio
)
beatsteps = self.interpolate_beat_times(beat_times=beat_times, steps_per_beat=steps_per_beat, n_extend=1)
if self.sampling_rate != single_sampling_rate and self.sampling_rate is not None:
if resample:
# Change sampling_rate to self.sampling_rate
single_raw_audio = librosa.core.resample(
single_raw_audio,
orig_sr=single_sampling_rate,
target_sr=self.sampling_rate,
res_type="kaiser_best",
)
else:
warnings.warn(
f"The sampling_rate of the provided audio is different from the target sampling_rate "
f"of the Feature Extractor, {self.sampling_rate} vs {single_sampling_rate}. "
f"In these cases it is recommended to use `resample=True` in the `__call__` method to "
f"get the optimal behaviour."
)
single_sampling_rate = self.sampling_rate
start_sample = int(beatsteps[0] * single_sampling_rate)
end_sample = int(beatsteps[-1] * single_sampling_rate)
input_features, extrapolated_beatstep = self.preprocess_mel(
single_raw_audio[start_sample:end_sample], beatsteps - beatsteps[0]
)
mel_specs = self.mel_spectrogram(input_features.astype(np.float32))
# apply np.log to get log mel-spectrograms
log_mel_specs = np.log(np.clip(mel_specs, a_min=1e-6, a_max=None))
input_features = np.transpose(log_mel_specs, (0, -1, -2))
batch_input_features.append(input_features)
batch_beatsteps.append(beatsteps)
batch_ext_beatstep.append(extrapolated_beatstep)
output = BatchFeature(
{
"input_features": batch_input_features,
"beatsteps": batch_beatsteps,
"extrapolated_beatstep": batch_ext_beatstep,
}
)
output = self.pad(
output,
is_batched=is_batched,
return_attention_mask=return_attention_mask,
return_tensors=return_tensors,
)
return output
__all__ = ["Pop2PianoFeatureExtractor"]
| transformers/src/transformers/models/pop2piano/feature_extraction_pop2piano.py/0 | {
"file_path": "transformers/src/transformers/models/pop2piano/feature_extraction_pop2piano.py",
"repo_id": "transformers",
"token_count": 8871
} | 481 |
# coding=utf-8
# Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan,
# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pvt model configuration"""
from collections import OrderedDict
from collections.abc import Mapping
from typing import Callable
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class PvtConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PvtModel`]. It is used to instantiate an Pvt
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Pvt
[Xrenya/pvt-tiny-224](https://huggingface.co/Xrenya/pvt-tiny-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`int`, *optional*, defaults to 224):
The input image size
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_encoder_blocks (`int`, *optional*, defaults to 4):
The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
depths (`list[int]`, *optional*, defaults to `[2, 2, 2, 2]`):
The number of layers in each encoder block.
sequence_reduction_ratios (`list[int]`, *optional*, defaults to `[8, 4, 2, 1]`):
Sequence reduction ratios in each encoder block.
hidden_sizes (`list[int]`, *optional*, defaults to `[64, 128, 320, 512]`):
Dimension of each of the encoder blocks.
patch_sizes (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
Patch size before each encoder block.
strides (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
Stride before each encoder block.
num_attention_heads (`list[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
Number of attention heads for each attention layer in each block of the Transformer encoder.
mlp_ratios (`list[int]`, *optional*, defaults to `[8, 8, 4, 4]`):
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
num_labels ('int', *optional*, defaults to 1000):
The number of classes.
Example:
```python
>>> from transformers import PvtModel, PvtConfig
>>> # Initializing a PVT Xrenya/pvt-tiny-224 style configuration
>>> configuration = PvtConfig()
>>> # Initializing a model from the Xrenya/pvt-tiny-224 style configuration
>>> model = PvtModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "pvt"
def __init__(
self,
image_size: int = 224,
num_channels: int = 3,
num_encoder_blocks: int = 4,
depths: list[int] = [2, 2, 2, 2],
sequence_reduction_ratios: list[int] = [8, 4, 2, 1],
hidden_sizes: list[int] = [64, 128, 320, 512],
patch_sizes: list[int] = [4, 2, 2, 2],
strides: list[int] = [4, 2, 2, 2],
num_attention_heads: list[int] = [1, 2, 5, 8],
mlp_ratios: list[int] = [8, 8, 4, 4],
hidden_act: Mapping[str, Callable] = "gelu",
hidden_dropout_prob: float = 0.0,
attention_probs_dropout_prob: float = 0.0,
initializer_range: float = 0.02,
drop_path_rate: float = 0.0,
layer_norm_eps: float = 1e-6,
qkv_bias: bool = True,
num_labels: int = 1000,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.num_channels = num_channels
self.num_encoder_blocks = num_encoder_blocks
self.depths = depths
self.sequence_reduction_ratios = sequence_reduction_ratios
self.hidden_sizes = hidden_sizes
self.patch_sizes = patch_sizes
self.strides = strides
self.mlp_ratios = mlp_ratios
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.drop_path_rate = drop_path_rate
self.layer_norm_eps = layer_norm_eps
self.num_labels = num_labels
self.qkv_bias = qkv_bias
class PvtOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4
@property
def default_onnx_opset(self) -> int:
return 12
__all__ = ["PvtConfig", "PvtOnnxConfig"]
| transformers/src/transformers/models/pvt/configuration_pvt.py/0 | {
"file_path": "transformers/src/transformers/models/pvt/configuration_pvt.py",
"repo_id": "transformers",
"token_count": 2751
} | 482 |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_qwen2_5_omni.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ...configuration_utils import PretrainedConfig, layer_type_validation
from ...modeling_rope_utils import rope_config_validation
from ...utils import logging
logger = logging.get_logger(__name__)
class Qwen2_5OmniVisionEncoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2_5OmniThinkerVision`]. It is used to instantiate a
Qwen2.5-VL vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the audio encoder of the Qwen2.5-VL
architecture.
e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
depth (`int`, *optional*, defaults to 32):
Number of layers (depth) in the model.
hidden_size (`int`, *optional*, defaults to 3584):
The size of the hidden layers.
hidden_act (`str`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function used in the model. Supported options include `"quick_gelu"` and others as applicable.
mlp_ratio (`float`, *optional*, defaults to 4):
The ratio used to determine the size of the MLP (Multi-Layer Perceptron) hidden layer.
num_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer.
in_channels (`int`, *optional*, defaults to 3):
Number of input channels.
patch_size (`int`, *optional*, defaults to 14):
The size of the patches extracted from the input.
spatial_merge_size (`int`, *optional*, defaults to 2):
The size used for merging spatial dimensions.
temporal_patch_size (`int`, *optional*, defaults to 2):
The size used for patches along the temporal dimension.
Example:
```python
>>> from transformers import Qwen2_5OmniVisionEncoderConfig, Qwen2_5OmniVisionEncoder
>>> # Initializing a Qwen2_5OmniVisionEncoderConfig
>>> configuration = Qwen2_5OmniVisionEncoderConfig()
>>> # Initializing a Qwen2_5OmniVisionEncoder (with random weights)
>>> model = Qwen2_5OmniVisionEncoder(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2_5_omni_vision_encoder"
base_config_key = "vision_config"
def __init__(
self,
depth=32,
hidden_size=3584,
hidden_act="silu",
intermediate_size=3420,
num_heads=16,
in_channels=3,
patch_size=14,
spatial_merge_size=2,
temporal_patch_size=2,
window_size=112,
out_hidden_size=3584,
fullatt_block_indexes=[7, 15, 23, 31],
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.depth = depth
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.num_heads = num_heads
self.in_channels = in_channels
self.patch_size = patch_size
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
self.window_size = window_size
self.fullatt_block_indexes = fullatt_block_indexes
self.out_hidden_size = out_hidden_size
self.initializer_range = initializer_range
class Qwen2_5OmniAudioEncoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2_5OmniAudioEncoder`]. It is used to instantiate a
Qwen2.5-Omni-Thinker audio encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the audio encoder of the Qwen2-Audio
architecture.
e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_mel_bins (`int`, *optional*, defaults to 128):
Number of mel features used per input features. Should correspond to the value used in the
`Qwen2_5OmniProcessor` class.
encoder_layers (`int`, *optional*, defaults to 32):
Number of encoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 20):
Number of attention heads for each attention layer in the Transformer encoder.
encoder_ffn_dim (`int`, *optional*, defaults to 5120):
Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
d_model (`int`, *optional*, defaults to 1280):
Dimensionality of the layers.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_function (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(d_model).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
max_source_positions (`int`, *optional*, defaults to 1500):
The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
n_window (`int`, *optional*, defaults to 100):
The chunk for conv and flash attn in AudioEncoder.
output_dim (`int`, *optional*, defaults to 3584):
The output dimension of AudioEncoder.
Example:
```python
>>> from transformers import Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniAudioEncoder
>>> # Initializing a Qwen2_5OmniAudioEncoderConfig
>>> configuration = Qwen2_5OmniAudioEncoderConfig()
>>> # Initializing a Qwen2_5OmniAudioEncoder (with random weights)
>>> model = Qwen2_5OmniAudioEncoder(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2_5_omni_audio_encoder"
def __init__(
self,
num_mel_bins=128,
encoder_layers=32,
encoder_attention_heads=20,
encoder_ffn_dim=5120,
d_model=1280,
dropout=0,
attention_dropout=0,
activation_function="gelu",
activation_dropout=0,
scale_embedding=False,
initializer_range=0.02,
max_source_positions=1500,
n_window=100,
output_dim=3584,
**kwargs,
):
super().__init__(**kwargs)
self.num_mel_bins = num_mel_bins
self.d_model = d_model
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.encoder_ffn_dim = encoder_ffn_dim
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_function = activation_function
self.activation_dropout = activation_dropout
self.num_hidden_layers = encoder_layers
self.initializer_range = initializer_range
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.max_source_positions = max_source_positions
self.n_window = n_window
self.output_dim = output_dim
class Qwen2_5OmniTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2_5OmniThinkerForConditionalGeneration`]. It is used to instantiate an
Qwen2.5-Omni-Thinker model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Qwen2.5-Omni-Thinker.
e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 152064):
Vocabulary size of the QwenOmni model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen2VLModel`]
hidden_size (`int`, *optional*, defaults to 3584):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 18944):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 28):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 4):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details, check out [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 32768):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 28):
The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
additional layer afterwards will use SWA (Sliding Window Attention).
layer_types (`list`, *optional*):
Attention pattern for each layer.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig
>>> # Initializing a Qwen2_5OmniAudioEncoder config
>>> audio_config = Qwen2_5OmniAudioEncoderConfig()
>>> # Initializing a Qwen2_5OmniVisionEncoder config
>>> vision_config = Qwen2_5OmniVisionEncoderConfig()
>>> # Initializing a Qwen2.5OmniThinker configuration
>>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config)
>>> # Initializing a model from the Qwen-Omni style configuration
>>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2_5_omni_text"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Qwen25OmniText`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=152064,
hidden_size=3584,
intermediate_size=18944,
num_hidden_layers=28,
num_attention_heads=28,
num_key_value_heads=4,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=1000000.0,
rope_scaling=None,
use_sliding_window=False,
sliding_window=32768,
max_window_layers=28,
layer_types=None,
attention_dropout=0.0,
**kwargs,
):
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window if self.use_sliding_window else None
self.max_window_layers = max_window_layers
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_dropout = attention_dropout
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
if self.rope_scaling is None:
self.rope_scaling = {"mrope_section": [16, 24, 24], "rope_type": "default", "type": "default"}
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
"sliding_attention"
if self.sliding_window is not None and i >= self.max_window_layers
else "full_attention"
for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types)
class Qwen2_5OmniThinkerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2_5OmniThinkerForConditionalGeneration`]. It is used to instantiate an
Qwen2.5-Omni-Thinker model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Qwen2.5-Omni-Thinker.
e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
audio_config (`dict`, *optional*):
The config dictionary of the audio backbone.
vision_config (`dict`, *optional*):
The config dictionary of the vision backbone.
text_config (`dict`, *optional*):
The config dictionary of the text backbone.
audio_token_index (`int`, *optional*, defaults to 151646):
The audio token index to encode the audio prompt.
image_token_index (`int`, *optional*, defaults to 151655):
The image token index to encode the image prompt.
video_token_index (`int`, *optional*, defaults to 151656):
The video token index to encode the video prompt.
position_id_per_seconds (`int`, *optional*, defaults to 25):
The increment of position id per second.
seconds_per_chunk (`int`, *optional*, defaults to 2):
The duration in seconds of the chunk of audio and video data.
audio_start_token_id (`int`, *optional*, defaults to 151647):
The audio start token index to encode the audio prompt.
audio_end_token_id (`int`, *optional*, defaults to 151648):
The audio end token index to encode the audio prompt.
user_token_id (`int, *optional*, defaults to 872):
The user token index to encode the user token.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig
>>> # Initializing a Qwen2_5OmniAudioEncoder config
>>> audio_config = Qwen2_5OmniAudioEncoderConfig()
>>> # Initializing a Qwen2_5OmniVisionEncoder config
>>> vision_config = Qwen2_5OmniVisionEncoderConfig()
>>> # Initializing a Qwen2_5OmniTextConfig config
>>> text_config = Qwen2_5OmniTextConfig()
>>> # Initializing a Qwen2.5OmniThinker configuration
>>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config, text_config)
>>> # Initializing a model from the Qwen-Omni style configuration
>>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2_5_omni_thinker"
attribute_map = {
"image_token_id": "image_token_index",
"video_token_id": "video_token_index",
"audio_token_id": "audio_token_index",
}
sub_configs = {
"audio_config": Qwen2_5OmniAudioEncoderConfig,
"vision_config": Qwen2_5OmniVisionEncoderConfig,
"text_config": Qwen2_5OmniTextConfig,
}
def __init__(
self,
audio_config=None,
vision_config=None,
text_config=None,
audio_token_index=151646,
image_token_index=151655,
video_token_index=151656,
position_id_per_seconds=25,
seconds_per_chunk=2,
audio_start_token_id=151647,
audio_end_token_id=151648,
user_token_id=872,
initializer_range=0.02,
**kwargs,
):
self.audio_token_index = audio_token_index
self.image_token_index = image_token_index
self.video_token_index = video_token_index
self.user_token_id = user_token_id
self.position_id_per_seconds = position_id_per_seconds
self.seconds_per_chunk = seconds_per_chunk
self.audio_start_token_id = audio_start_token_id
self.audio_end_token_id = audio_end_token_id
self.initializer_range = initializer_range
if isinstance(vision_config, dict):
vision_config = Qwen2_5OmniVisionEncoderConfig(**vision_config)
elif vision_config is None:
vision_config = Qwen2_5OmniVisionEncoderConfig()
self.vision_config = vision_config
if isinstance(audio_config, dict):
audio_config = Qwen2_5OmniAudioEncoderConfig(**audio_config)
elif audio_config is None:
audio_config = Qwen2_5OmniAudioEncoderConfig()
self.audio_config = audio_config
if isinstance(text_config, dict):
text_config = Qwen2_5OmniTextConfig(**text_config)
elif text_config is None:
text_config = Qwen2_5OmniTextConfig()
self.text_config = text_config
super().__init__(**kwargs)
class Qwen2_5OmniTalkerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2_5OmniTalkerForConditionalGeneration`]. It is used to instantiate an
Qwen2.5-Omni-Talker model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Qwen2.5-Omni-Thinker.
e.g. [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
audio_token_index (`int`, *optional*, defaults to 151646):
The audio token index to encode the audio prompt.
image_token_index (`int`, *optional*, defaults to 151655):
The image token index to encode the image prompt.
video_token_index (`int`, *optional*, defaults to 151656):
The video token index to encode the video prompt.
vocab_size (`int`, *optional*, defaults to 8448):
Vocabulary size of the QwenOmni model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen2VLModel`]
tts_text_start_token_id (`int`, *optional*, defaults to 151860):
The tts text start token index to encode the start of tts text.
tts_text_end_token_id (`int`, *optional*, defaults to 151861):
The tts text end token index to encode the end of tts text.
tts_text_pad_token_id (`int`, *optional*, defaults to 151859):
The tts text pad token index to encode the pad of tts text.
tts_codec_start_token_id (`int`, *optional*, defaults to 8293):
The tts codec start token index to encode the start of tts codec.
tts_codec_end_token_id (`int`, *optional*, defaults to 8294):
The tts codec end token index to encode the end of tts codec.
tts_codec_pad_token_id (`int`, *optional*, defaults to 8292):
The tts codec pad token index to encode the pad of tts codec.
tts_codec_mask_token_id (`int`, *optional*, defaults to 8296):
The tts codec mask token index to encode the mask of tts codec.
vision_start_token_id (`int`, *optional*, defaults to 151652):
The tts vision start token index to encode the start of vision.
vision_end_token_id (`int`, *optional*, defaults to 151653):
The tts vision end token index to encode the end of vision.
embedding_size (`int`, *optional*, defaults to 3584):
Dimension of the embedding representations.
hidden_size (`int`, *optional*, defaults to 3584):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 18944):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 28):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 4):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details, check out [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
head_dim (`int`, *optional*, defaults to 128):
The dimension of each attention head.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 32768):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 28):
The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
additional layer afterwards will use SWA (Sliding Window Attention).
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
position_id_per_seconds (`int`, *optional*, defaults to 25):
The increment of position id per second.
seconds_per_chunk (`int`, *optional*, defaults to 2):
The duration in seconds of the chunk of audio and video data.
audio_start_token_id (`int`, *optional*, defaults to 151647):
The audio start token index to encode the audio prompt.
audio_end_token_id (`int`, *optional*, defaults to 151648):
The audio end token index to encode the audio prompt.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
spatial_merge_size (`int`, *optional*, defaults to 2):
The size used for merging spatial dimensions.
layer_types (`list`, *optional*):
Attention pattern for each layer.
Example:
```python
>>> from transformers import Qwen2_5OmniTalkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig
>>> # Initializing a Qwen2_5OmniAudioEncoder config
>>> audio_config = Qwen2_5OmniAudioEncoderConfig()
>>> # Initializing a Qwen2 config
>>> text_config = Qwen2Config()
>>> # Initializing a Qwen2_5Omni configuration
>>> configuration = Qwen2_5OmniThinkerConfig(audio_config, text_config)
>>> # Initializing a model from the qwen2-audio style configuration
>>> model = Qwen2_5OmniTalkerForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2_5_omni_talker"
attribute_map = {
"image_token_id": "image_token_index",
"video_token_id": "video_token_index",
"audio_token_id": "audio_token_index",
}
def __init__(
self,
audio_token_index=151646,
image_token_index=151655,
video_token_index=151656,
vocab_size=8448,
tts_text_start_token_id=151860,
tts_text_end_token_id=151861,
tts_text_pad_token_id=151859,
tts_codec_start_token_id=8293,
tts_codec_end_token_id=8294,
tts_codec_pad_token_id=8292,
tts_codec_mask_token_id=8296,
vision_start_token_id=151652,
vision_end_token_id=151653,
embedding_size=3584,
hidden_size=3584,
intermediate_size=18944,
num_hidden_layers=28,
num_attention_heads=28,
num_key_value_heads=4,
hidden_act="silu",
max_position_embeddings=32768,
rms_norm_eps=1e-06,
head_dim=128,
use_cache=True,
tie_word_embeddings=False,
rope_theta=1000000.0,
use_sliding_window=False,
sliding_window=32768,
max_window_layers=28,
attention_dropout=0.0,
rope_scaling=None,
position_id_per_seconds=25,
seconds_per_chunk=2,
audio_start_token_id=151647,
audio_end_token_id=151648,
initializer_range=0.02,
spatial_merge_size=2,
layer_types=None,
**kwargs,
):
self.audio_token_index = audio_token_index
self.image_token_index = image_token_index
self.video_token_index = video_token_index
self.tts_text_start_token_id = tts_text_start_token_id
self.tts_text_end_token_id = tts_text_end_token_id
self.tts_text_pad_token_id = tts_text_pad_token_id
self.tts_codec_start_token_id = tts_codec_start_token_id
self.tts_codec_end_token_id = tts_codec_end_token_id
self.tts_codec_pad_token_id = tts_codec_pad_token_id
self.tts_codec_mask_token_id = tts_codec_mask_token_id
self.vision_start_token_id = vision_start_token_id
self.vision_end_token_id = vision_end_token_id
self.vocab_size = vocab_size
self.head_dim = head_dim
self.embedding_size = embedding_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window if self.use_sliding_window else None
self.max_window_layers = max_window_layers
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.rope_scaling = rope_scaling
self.position_id_per_seconds = position_id_per_seconds # zf
self.seconds_per_chunk = seconds_per_chunk # zf
self.audio_start_token_id = audio_start_token_id # zf
self.audio_end_token_id = audio_end_token_id # zf
self.initializer_range = initializer_range
self.spatial_merge_size = spatial_merge_size
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
"sliding_attention"
if self.sliding_window is not None and i >= self.max_window_layers
else "full_attention"
for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types)
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
class Qwen2_5OmniDiTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of the Qwen2_5OmniToken2WavDiT used in the Qwen2.5-Omni-Token2Wav model.
It defines the architecture of the DiT model, which is used for generating mel-spectrograms from tokens.
Args:
hidden_size (`int`, *optional*, defaults to 1024):
The dimension of the model.
num_hidden_layers (`int`, *optional*, defaults to 22):
The number of transformer blocks in the DiT model.
num_attention_heads (`int`, *optional*, defaults to 16):
The number of attention heads in each transformer block.
ff_mult (`int`, *optional*, defaults to 2):
The multiplier for the feedforward layer in each transformer block.
emb_dim (`int`, *optional*, defaults to 512):
The dimension of the embedding layer.
head_dim (`int`, *optional*, defaults to 64):
The dimension of each attention head.
repeats (`int`, *optional*, defaults to 2):
The number of times the codec embeddings are repeated.
num_embeds (`int`, *optional*, defaults to 8193):
The number of unique embeddings in the codec.
mel_dim (`int`, *optional*, defaults to 80):
The dimension of the mel-spectrogram.
dropout (`float`, *optional*, defaults to 0.1):
The dropout rate for the transformer blocks.
enc_emb_dim (`int`, *optional*, defaults to 192):
The dimension of the pre-trained speaker embedding.
enc_dim (`int`, *optional*, defaults to 128):
The dimension of the encoder output.
enc_channels (`list[int]`, *optional*, defaults to `[256, 256, 256, 256, 768]`):
A list of output channels for each TDNN/SERes2Net layer in the encoder.
enc_kernel_sizes (`list[int]`, *optional*, defaults to `[5, 3, 3, 3, 1]`):
A list of kernel sizes for each layer in the encoder.
enc_dilations (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 1]`):
A list of dilations for each layer in the encoder.
enc_attention_channels (`int`, *optional*, defaults to 64):
The number of attention channels in the SqueezeExcitationBlock.
enc_res2net_scale (`int`, *optional*, defaults to 2):
The scale of the Res2Net block in the encoder.
enc_se_channels (`int`, *optional*, defaults to 64):
The number of output channels after squeeze in the SqueezeExcitationBlock.
"""
model_type = "qwen2_5_omni_dit"
def __init__(
self,
hidden_size=1024,
num_hidden_layers=22,
num_attention_heads=16,
ff_mult=2,
emb_dim=512,
head_dim=64,
rope_theta=10000.0,
max_position_embeddings=32768,
block_size=24,
look_ahead_layers=[10],
look_backward_layers=[0, 20],
repeats=2,
num_embeds=8193,
mel_dim=80,
dropout=0.1,
enc_emb_dim=192,
enc_dim=128,
enc_channels=[256, 256, 256, 256, 768],
enc_kernel_sizes=[5, 3, 3, 3, 1],
enc_dilations=[1, 2, 3, 4, 1],
enc_attention_channels=64,
enc_res2net_scale=2,
enc_se_channels=64,
**kwargs,
):
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.ff_mult = ff_mult
self.emb_dim = emb_dim
self.head_dim = head_dim
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.block_size = block_size
self.look_ahead_layers = look_ahead_layers
self.look_backward_layers = look_backward_layers
self.repeats = repeats
self.num_embeds = num_embeds
self.mel_dim = mel_dim
self.dropout = dropout
self.enc_emb_dim = enc_emb_dim
self.enc_dim = enc_dim
self.enc_channels = enc_channels
self.enc_kernel_sizes = enc_kernel_sizes
self.enc_dilations = enc_dilations
self.enc_attention_channels = enc_attention_channels
self.enc_res2net_scale = enc_res2net_scale
self.enc_se_channels = enc_se_channels
super().__init__(**kwargs)
class Qwen2_5OmniBigVGANConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of the Qwen2_5OmniToken2WavBigVGAN module used in the Qwen2.5-Omni-Token2Wav model.
It defines the architecture of the BigVGAN model, which is used for converting mel-spectrograms to waveforms.
Args:
mel_dim (`int`, *optional*, defaults to 80):
The dimension of the mel-spectrogram.
upsample_initial_channel (`int`, *optional*, defaults to 1536):
The number of channels in the initial upsampling layer.
resblock_kernel_sizes (`list[int]`, *optional*, defaults to `[3, 7, 11]`):
A list of kernel sizes for each residual block.
resblock_dilation_sizes (`list[list[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`):
A list of dilation sizes for each residual block.
upsample_rates (`list[int]`, *optional*, defaults to `[5, 3, 2, 2, 2, 2]`):
A list of upsampling rates for each upsampling layer.
upsample_kernel_sizes (`list[int]`, *optional*, defaults to `[11, 7, 4, 4, 4, 4]`):
A list of kernel sizes for each upsampling layer.
"""
model_type = "qwen2_5_omni_bigvgan"
def __init__(
self,
mel_dim=80,
upsample_initial_channel=1536,
resblock_kernel_sizes=[3, 7, 11],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
upsample_rates=[5, 3, 2, 2, 2, 2],
upsample_kernel_sizes=[11, 7, 4, 4, 4, 4],
**kwargs,
):
self.mel_dim = mel_dim
self.upsample_initial_channel = upsample_initial_channel
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.upsample_rates = upsample_rates
self.upsample_kernel_sizes = upsample_kernel_sizes
super().__init__(**kwargs)
class Qwen2_5OmniToken2WavConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2_5OmniToken2WavModel`].
It is used to instantiate the Qwen2.5-Omni-Token2Wav model which combines a Diffusion Transformer (DiT) for mel-spectrogram generation with a BigVGAN model for waveform synthesis. The configuration contains sub-configurations for both components.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
dit_config ([`DiT_Args`], *optional*):
Configuration class for the Diffusion Transformer (DiT) module responsible for generating mel-spectrograms.
bigvgan_config ([`BigVGAN_Args`], *optional*):
Configuration class for the BigVGAN module responsible for converting mel-spectrograms to waveforms.
Example:
```python
>>> from transformers import Qwen2_5OmniToken2WavModel, DiT_Args, BigVGAN_Args
>>> # Initialize DiT configuration
>>> dit_config = DiT_Args(
... dim=1024,
... depth=22,
... heads=16,
... ff_mult=2
... )
>>> # Initialize BigVGAN configuration
>>> bigvgan_config = BigVGAN_Args(
... mel_dim=80,
... upsample_rates=[5,3,2,2,2,2]
... )
>>> # Initialize main configuration
>>> config = Qwen2_5OmniToken2WavConfig(dit_config, bigvgan_config)
>>> # Initialize model with config
>>> model = Qwen2_5OmniToken2Wav(config)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "qwen2_5_omni_token2wav"
sub_configs = {
"dit_config": Qwen2_5OmniDiTConfig,
"bigvgan_config": Qwen2_5OmniBigVGANConfig,
}
def __init__(self, dit_config=None, bigvgan_config=None, **kwargs):
if dit_config is None:
dit_config = {}
if bigvgan_config is None:
bigvgan_config = {}
self.dit_config = Qwen2_5OmniDiTConfig(**dit_config)
self.bigvgan_config = Qwen2_5OmniBigVGANConfig(**bigvgan_config)
super().__init__(**kwargs)
class Qwen2_5OmniConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`Qwen2_5OmniForConditionalGeneration`]. It is used to instantiate a Qwen2.5Omni
model according to the specified sub-models configurations, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the
[Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
thinker_config (`dict`, *optional*): Configuration of the underlying thinker sub-model.
talker_config (`dict`, *optional*): Configuration of the underlying talker sub-model.
token2wav_config (`dict`, *optional*): Configuration of the underlying codec sub-model.
enable_audio_output (`bool`, *optional*, defaults to `True`): Whether enable audio output and load talker and token2wav module.
Example:
```python
>>> from transformers import (
... Qwen2_5OmniThinkerConfig,
... Qwen2_5OmniTalkerConfig,
... Qwen2_5OmniToken2WavConfig,
... Qwen2_5OmniForConditionalGeneration,
... Qwen2_5OmniConfig,
... )
>>> # Initializing sub-modules configurations.
>>> thinker_config = Qwen2_5OmniThinkerConfig()
>>> talker_config = Qwen2_5OmniTalkerConfig()
>>> token2wav_config = Qwen2_5OmniToken2WavConfig()
>>> # Initializing a module style configuration
>>> configuration = Qwen2_5OmniConfig.from_sub_model_configs(
... thinker_config, talker_config, token2wav_config
... )
>>> # Initializing a model (with random weights)
>>> model = Qwen2_5OmniForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "qwen2_5_omni"
sub_configs = {
"thinker_config": Qwen2_5OmniThinkerConfig,
"talker_config": Qwen2_5OmniTalkerConfig,
"token2wav_config": Qwen2_5OmniToken2WavConfig,
}
def __init__(
self,
thinker_config=None,
talker_config=None,
token2wav_config=None,
enable_audio_output: bool = True,
**kwargs,
):
if thinker_config is None:
thinker_config = {}
logger.info("thinker_config is None. Initializing thinker model with default values")
if talker_config is None:
talker_config = {}
logger.info("talker_config is None. Initializing talker model with default values")
if token2wav_config is None:
token2wav_config = {}
logger.info("token2wav_config is None. Initializing token2wav model with default values")
self.thinker_config = Qwen2_5OmniThinkerConfig(**thinker_config)
self.talker_config = Qwen2_5OmniTalkerConfig(**talker_config)
self.token2wav_config = Qwen2_5OmniToken2WavConfig(**token2wav_config)
self.enable_audio_output = enable_audio_output
super().__init__(**kwargs)
def get_text_config(self, *args, **kwargs):
"""
Returns the config that is meant to be used with text IO. On most models, it is the original config instance
itself. On specific composite models, it is under a set of valid names.
Args:
decoder (`Optional[bool]`, *optional*, defaults to `False`):
If set to `True`, then only search for decoder config names.
"""
# Overridden for deeply nested config like Qwen2-Omni. We don't have any omni model
# except for Qwen yet. This has to be generalized if more deeply nested configs are
# added. NOTE: currently method used only by vLLM
return self.thinker_config.get_text_config(*args, **kwargs)
__all__ = ["Qwen2_5OmniConfig", "Qwen2_5OmniThinkerConfig", "Qwen2_5OmniTalkerConfig", "Qwen2_5OmniToken2WavConfig"]
| transformers/src/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py/0 | {
"file_path": "transformers/src/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py",
"repo_id": "transformers",
"token_count": 21681
} | 483 |
# coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""RAG model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
RAG_CONFIG_DOC = r"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.
Args:
title_sep (`str`, *optional*, defaults to `" / "`):
Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
doc_sep (`str`, *optional*, defaults to `" // "`):
Separator inserted between the text of the retrieved document and the original input when calling
[`RagRetriever`].
n_docs (`int`, *optional*, defaults to 5):
Number of documents to retrieve.
max_combined_length (`int`, *optional*, defaults to 300):
Max length of contextualized input returned by [`~RagRetriever.__call__`].
retrieval_vector_size (`int`, *optional*, defaults to 768):
Dimensionality of the document embeddings indexed by [`RagRetriever`].
retrieval_batch_size (`int`, *optional*, defaults to 8):
Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
[`RagRetriever`].
dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
using `datasets.list_datasets()`).
dataset_split (`str`, *optional*, defaults to `"train"`)
Which split of the `dataset` to load.
index_name (`str`, *optional*, defaults to `"compressed"`)
The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
`"compressed"`.
index_path (`str`, *optional*)
The path to the serialized faiss index on disk.
passages_path (`str`, *optional*):
A path to text passages compatible with the faiss index. Required if using
[`~models.rag.retrieval_rag.LegacyIndex`]
use_dummy_dataset (`bool`, *optional*, defaults to `False`)
Whether to load a "dummy" variant of the dataset specified by `dataset`.
label_smoothing (`float`, *optional*, defaults to 0.0):
Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
in the loss calculation. If set to 0, no label smoothing is performed.
do_marginalize (`bool`, *optional*, defaults to `False`):
If `True`, the logits are marginalized over all documents by making use of
`torch.nn.functional.log_softmax`.
reduce_loss (`bool`, *optional*, defaults to `False`):
Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
do_deduplication (`bool`, *optional*, defaults to `True`):
Whether or not to deduplicate the generations from different context documents for a given input. Has to be
set to `False` if used while training with distributed backend.
exclude_bos_score (`bool`, *optional*, defaults to `False`):
Whether or not to disregard the BOS token when computing the loss.
output_retrieved(`bool`, *optional*, defaults to `False`):
If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
`context_attention_mask` are returned. See returned tensors for more detail.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
"""
@add_start_docstrings(RAG_CONFIG_DOC)
class RagConfig(PretrainedConfig):
model_type = "rag"
has_no_defaults_at_init = True
def __init__(
self,
vocab_size=None,
is_encoder_decoder=True,
prefix=None,
bos_token_id=None,
pad_token_id=None,
eos_token_id=None,
decoder_start_token_id=None,
title_sep=" / ",
doc_sep=" // ",
n_docs=5,
max_combined_length=300,
retrieval_vector_size=768,
retrieval_batch_size=8,
dataset="wiki_dpr",
dataset_split="train",
index_name="compressed",
index_path=None,
passages_path=None,
use_dummy_dataset=False,
reduce_loss=False,
label_smoothing=0.0,
do_deduplication=True,
exclude_bos_score=False,
do_marginalize=False,
output_retrieved=False,
use_cache=True,
forced_eos_token_id=None,
dataset_revision=None,
**kwargs,
):
super().__init__(
bos_token_id=bos_token_id,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
is_encoder_decoder=is_encoder_decoder,
prefix=prefix,
vocab_size=vocab_size,
**kwargs,
)
if "question_encoder" not in kwargs or "generator" not in kwargs:
raise ValueError(
f"A configuration of type {self.model_type} cannot be instantiated because "
f"both `question_encoder` and `generator` sub-configurations were not passed, only {kwargs}"
)
question_encoder_config = kwargs.pop("question_encoder")
question_encoder_model_type = question_encoder_config.pop("model_type")
decoder_config = kwargs.pop("generator")
decoder_model_type = decoder_config.pop("model_type")
from ..auto.configuration_auto import AutoConfig
self.question_encoder = AutoConfig.for_model(question_encoder_model_type, **question_encoder_config)
self.generator = AutoConfig.for_model(decoder_model_type, **decoder_config)
self.reduce_loss = reduce_loss
self.label_smoothing = label_smoothing
self.exclude_bos_score = exclude_bos_score
self.do_marginalize = do_marginalize
self.title_sep = title_sep
self.doc_sep = doc_sep
self.n_docs = n_docs
self.max_combined_length = max_combined_length
self.dataset = dataset
self.dataset_split = dataset_split
self.index_name = index_name
self.retrieval_vector_size = retrieval_vector_size
self.retrieval_batch_size = retrieval_batch_size
self.passages_path = passages_path
self.index_path = index_path
self.use_dummy_dataset = use_dummy_dataset
self.dataset_revision = dataset_revision
self.output_retrieved = output_retrieved
self.do_deduplication = do_deduplication
self.use_cache = use_cache
if self.forced_eos_token_id is None:
self.forced_eos_token_id = getattr(self.generator, "forced_eos_token_id", None)
@classmethod
def from_question_encoder_generator_configs(
cls, question_encoder_config: PretrainedConfig, generator_config: PretrainedConfig, **kwargs
) -> PretrainedConfig:
r"""
Instantiate a [`EncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model configuration and
decoder model configuration.
Returns:
[`EncoderDecoderConfig`]: An instance of a configuration object
"""
return cls(question_encoder=question_encoder_config.to_dict(), generator=generator_config.to_dict(), **kwargs)
__all__ = ["RagConfig"]
| transformers/src/transformers/models/rag/configuration_rag.py/0 | {
"file_path": "transformers/src/transformers/models/rag/configuration_rag.py",
"repo_id": "transformers",
"token_count": 3456
} | 484 |
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""RegNet model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class RegNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RegNetModel`]. It is used to instantiate a RegNet
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the RegNet
[facebook/regnet-y-040](https://huggingface.co/facebook/regnet-y-040) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embedding_size (`int`, *optional*, defaults to 64):
Dimensionality (hidden size) for the embedding layer.
hidden_sizes (`list[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
Dimensionality (hidden size) at each stage.
depths (`list[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
Depth (number of layers) for each stage.
layer_type (`str`, *optional*, defaults to `"y"`):
The layer to use, it can be either `"x" or `"y"`. An `x` layer is a ResNet's BottleNeck layer with
`reduction` fixed to `1`. While a `y` layer is a `x` but with squeeze and excitation. Please refer to the
paper for a detailed explanation of how these layers were constructed.
hidden_act (`str`, *optional*, defaults to `"relu"`):
The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
are supported.
downsample_in_first_stage (`bool`, *optional*, defaults to `False`):
If `True`, the first stage will downsample the inputs using a `stride` of 2.
Example:
```python
>>> from transformers import RegNetConfig, RegNetModel
>>> # Initializing a RegNet regnet-y-40 style configuration
>>> configuration = RegNetConfig()
>>> # Initializing a model from the regnet-y-40 style configuration
>>> model = RegNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "regnet"
layer_types = ["x", "y"]
def __init__(
self,
num_channels=3,
embedding_size=32,
hidden_sizes=[128, 192, 512, 1088],
depths=[2, 6, 12, 2],
groups_width=64,
layer_type="y",
hidden_act="relu",
**kwargs,
):
super().__init__(**kwargs)
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}")
self.num_channels = num_channels
self.embedding_size = embedding_size
self.hidden_sizes = hidden_sizes
self.depths = depths
self.groups_width = groups_width
self.layer_type = layer_type
self.hidden_act = hidden_act
# always downsample in the first stage
self.downsample_in_first_stage = True
__all__ = ["RegNetConfig"]
| transformers/src/transformers/models/regnet/configuration_regnet.py/0 | {
"file_path": "transformers/src/transformers/models/regnet/configuration_regnet.py",
"repo_id": "transformers",
"token_count": 1433
} | 485 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Optional
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.traverse_util import flatten_dict, unflatten_dict
from ...modeling_flax_outputs import (
FlaxBaseModelOutputWithNoAttention,
FlaxBaseModelOutputWithPoolingAndNoAttention,
FlaxImageClassifierOutputWithNoAttention,
)
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward
from .configuration_resnet import ResNetConfig
RESNET_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a
[flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
RESNET_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`jax.numpy.float32` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`AutoImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class Identity(nn.Module):
"""Identity function."""
@nn.compact
def __call__(self, x, **kwargs):
return x
class FlaxResNetConvLayer(nn.Module):
out_channels: int
kernel_size: int = 3
stride: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(self.kernel_size, self.kernel_size),
strides=self.stride,
padding=self.kernel_size // 2,
dtype=self.dtype,
use_bias=False,
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="normal", dtype=self.dtype),
)
self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
self.activation_func = ACT2FN[self.activation] if self.activation is not None else Identity()
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = self.convolution(x)
hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxResNetEmbeddings(nn.Module):
"""
ResNet Embeddings (stem) composed of a single aggressive convolution.
"""
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embedder = FlaxResNetConvLayer(
self.config.embedding_size,
kernel_size=7,
stride=2,
activation=self.config.hidden_act,
dtype=self.dtype,
)
self.max_pool = partial(nn.max_pool, window_shape=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1)))
def __call__(self, pixel_values: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
num_channels = pixel_values.shape[-1]
if num_channels != self.config.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embedding = self.embedder(pixel_values, deterministic=deterministic)
embedding = self.max_pool(embedding)
return embedding
class FlaxResNetShortCut(nn.Module):
"""
ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
out_channels: int
stride: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(1, 1),
strides=self.stride,
use_bias=False,
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
dtype=self.dtype,
)
self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = self.convolution(x)
hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
return hidden_state
class FlaxResNetBasicLayerCollection(nn.Module):
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layer = [
FlaxResNetConvLayer(self.out_channels, stride=self.stride, dtype=self.dtype),
FlaxResNetConvLayer(self.out_channels, activation=None, dtype=self.dtype),
]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
for layer in self.layer:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxResNetBasicLayer(nn.Module):
"""
A classic ResNet's residual layer composed by two `3x3` convolutions.
"""
in_channels: int
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
self.shortcut = (
FlaxResNetShortCut(self.out_channels, stride=self.stride, dtype=self.dtype)
if should_apply_shortcut
else None
)
self.layer = FlaxResNetBasicLayerCollection(
out_channels=self.out_channels,
stride=self.stride,
dtype=self.dtype,
)
self.activation_func = ACT2FN[self.activation]
def __call__(self, hidden_state, deterministic: bool = True):
residual = hidden_state
hidden_state = self.layer(hidden_state, deterministic=deterministic)
if self.shortcut is not None:
residual = self.shortcut(residual, deterministic=deterministic)
hidden_state += residual
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxResNetBottleNeckLayerCollection(nn.Module):
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
reduction: int = 4
dtype: jnp.dtype = jnp.float32
def setup(self):
reduces_channels = self.out_channels // self.reduction
self.layer = [
FlaxResNetConvLayer(reduces_channels, kernel_size=1, dtype=self.dtype, name="0"),
FlaxResNetConvLayer(reduces_channels, stride=self.stride, dtype=self.dtype, name="1"),
FlaxResNetConvLayer(self.out_channels, kernel_size=1, activation=None, dtype=self.dtype, name="2"),
]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
for layer in self.layer:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxResNetBottleNeckLayer(nn.Module):
"""
A classic ResNet's bottleneck layer composed by three `3x3` convolutions. The first `1x1` convolution reduces the
input by a factor of `reduction` in order to make the second `3x3` convolution faster. The last `1x1` convolution
remaps the reduced features to `out_channels`.
"""
in_channels: int
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
reduction: int = 4
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
self.shortcut = (
FlaxResNetShortCut(self.out_channels, stride=self.stride, dtype=self.dtype)
if should_apply_shortcut
else None
)
self.layer = FlaxResNetBottleNeckLayerCollection(
self.out_channels,
stride=self.stride,
activation=self.activation,
reduction=self.reduction,
dtype=self.dtype,
)
self.activation_func = ACT2FN[self.activation]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
residual = hidden_state
if self.shortcut is not None:
residual = self.shortcut(residual, deterministic=deterministic)
hidden_state = self.layer(hidden_state, deterministic)
hidden_state += residual
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxResNetStageLayersCollection(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
config: ResNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
layer = FlaxResNetBottleNeckLayer if self.config.layer_type == "bottleneck" else FlaxResNetBasicLayer
layers = [
# downsampling is done in the first layer with stride of 2
layer(
self.in_channels,
self.out_channels,
stride=self.stride,
activation=self.config.hidden_act,
dtype=self.dtype,
name="0",
),
]
for i in range(self.depth - 1):
layers.append(
layer(
self.out_channels,
self.out_channels,
activation=self.config.hidden_act,
dtype=self.dtype,
name=str(i + 1),
)
)
self.layers = layers
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = x
for layer in self.layers:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxResNetStage(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
config: ResNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = FlaxResNetStageLayersCollection(
self.config,
in_channels=self.in_channels,
out_channels=self.out_channels,
stride=self.stride,
depth=self.depth,
dtype=self.dtype,
)
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
return self.layers(x, deterministic=deterministic)
class FlaxResNetStageCollection(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
in_out_channels = zip(self.config.hidden_sizes, self.config.hidden_sizes[1:])
stages = [
FlaxResNetStage(
self.config,
self.config.embedding_size,
self.config.hidden_sizes[0],
stride=2 if self.config.downsample_in_first_stage else 1,
depth=self.config.depths[0],
dtype=self.dtype,
name="0",
)
]
for i, ((in_channels, out_channels), depth) in enumerate(zip(in_out_channels, self.config.depths[1:])):
stages.append(
FlaxResNetStage(self.config, in_channels, out_channels, depth=depth, dtype=self.dtype, name=str(i + 1))
)
self.stages = stages
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
deterministic: bool = True,
) -> FlaxBaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
hidden_state = stage_module(hidden_state, deterministic=deterministic)
return hidden_state, hidden_states
class FlaxResNetEncoder(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.stages = FlaxResNetStageCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
) -> FlaxBaseModelOutputWithNoAttention:
hidden_state, hidden_states = self.stages(
hidden_state, output_hidden_states=output_hidden_states, deterministic=deterministic
)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return FlaxBaseModelOutputWithNoAttention(
last_hidden_state=hidden_state,
hidden_states=hidden_states,
)
class FlaxResNetPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ResNetConfig
base_model_prefix = "resnet"
main_input_name = "pixel_values"
module_class: nn.Module = None
def __init__(
self,
config: ResNetConfig,
input_shape=(1, 224, 224, 3),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
if input_shape is None:
input_shape = (1, config.image_size, config.image_size, config.num_channels)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
pixel_values = jnp.zeros(input_shape, dtype=self.dtype)
rngs = {"params": rng}
random_params = self.module.init(rngs, pixel_values, return_dict=False)
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
def __call__(
self,
pixel_values,
params: Optional[dict] = None,
train: bool = False,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# Handle any PRNG if needed
rngs = {}
return self.module.apply(
{
"params": params["params"] if params is not None else self.params["params"],
"batch_stats": params["batch_stats"] if params is not None else self.params["batch_stats"],
},
jnp.array(pixel_values, dtype=jnp.float32),
not train,
output_hidden_states,
return_dict,
rngs=rngs,
mutable=["batch_stats"] if train else False, # Returning tuple with batch_stats only when train is True
)
class FlaxResNetModule(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embedder = FlaxResNetEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxResNetEncoder(self.config, dtype=self.dtype)
# Adaptive average pooling used in resnet
self.pooler = partial(
nn.avg_pool,
padding=((0, 0), (0, 0)),
)
def __call__(
self,
pixel_values,
deterministic: bool = True,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> FlaxBaseModelOutputWithPoolingAndNoAttention:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
embedding_output = self.embedder(pixel_values, deterministic=deterministic)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(
last_hidden_state,
window_shape=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
strides=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
).transpose(0, 3, 1, 2)
last_hidden_state = last_hidden_state.transpose(0, 3, 1, 2)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return FlaxBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top.",
RESNET_START_DOCSTRING,
)
class FlaxResNetModel(FlaxResNetPreTrainedModel):
module_class = FlaxResNetModule
FLAX_VISION_MODEL_DOCSTRING = """
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, FlaxResNetModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
>>> model = FlaxResNetModel.from_pretrained("microsoft/resnet-50")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```
"""
overwrite_call_docstring(FlaxResNetModel, FLAX_VISION_MODEL_DOCSTRING)
append_replace_return_docstrings(
FlaxResNetModel, output_type=FlaxBaseModelOutputWithPoolingAndNoAttention, config_class=ResNetConfig
)
class FlaxResNetClassifierCollection(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype, name="1")
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
return self.classifier(x)
class FlaxResNetForImageClassificationModule(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.resnet = FlaxResNetModule(config=self.config, dtype=self.dtype)
if self.config.num_labels > 0:
self.classifier = FlaxResNetClassifierCollection(self.config, dtype=self.dtype)
else:
self.classifier = Identity()
def __call__(
self,
pixel_values=None,
deterministic: bool = True,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.resnet(
pixel_values,
deterministic=deterministic,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output[:, :, 0, 0])
if not return_dict:
output = (logits,) + outputs[2:]
return output
return FlaxImageClassifierOutputWithNoAttention(logits=logits, hidden_states=outputs.hidden_states)
@add_start_docstrings(
"""
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
RESNET_START_DOCSTRING,
)
class FlaxResNetForImageClassification(FlaxResNetPreTrainedModel):
module_class = FlaxResNetForImageClassificationModule
FLAX_VISION_CLASSIF_DOCSTRING = """
Returns:
Example:
```python
>>> from transformers import AutoImageProcessor, FlaxResNetForImageClassification
>>> from PIL import Image
>>> import jax
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
>>> model = FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1)
>>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()])
```
"""
overwrite_call_docstring(FlaxResNetForImageClassification, FLAX_VISION_CLASSIF_DOCSTRING)
append_replace_return_docstrings(
FlaxResNetForImageClassification, output_type=FlaxImageClassifierOutputWithNoAttention, config_class=ResNetConfig
)
__all__ = ["FlaxResNetForImageClassification", "FlaxResNetModel", "FlaxResNetPreTrainedModel"]
| transformers/src/transformers/models/resnet/modeling_flax_resnet.py/0 | {
"file_path": "transformers/src/transformers/models/resnet/modeling_flax_resnet.py",
"repo_id": "transformers",
"token_count": 10404
} | 486 |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/sam2_video/modular_sam2_video.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_sam2_video.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ...configuration_utils import PretrainedConfig
from ..auto import CONFIG_MAPPING, AutoConfig
class Sam2VideoPromptEncoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Sam2VideoPromptEncoder`]. The [`Sam2VideoPromptEncoder`]
module is used to encode the input 2D points and bounding boxes.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the hidden states.
image_size (`int`, *optional*, defaults to 1024):
The expected output resolution of the image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
mask_input_channels (`int`, *optional*, defaults to 16):
The number of channels to be fed to the `MaskDecoder` module.
num_point_embeddings (`int`, *optional*, defaults to 4):
The number of point embeddings to be used.
hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function in the encoder and pooler.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
scale (`float`, *optional*, defaults to 1):
The scale factor for the prompt encoder.
"""
base_config_key = "prompt_encoder_config"
def __init__(
self,
hidden_size=256,
image_size=1024,
patch_size=16,
mask_input_channels=16,
num_point_embeddings=4,
hidden_act="gelu",
layer_norm_eps=1e-6,
scale=1,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.image_size = image_size
self.patch_size = patch_size
self.mask_input_channels = mask_input_channels
self.num_point_embeddings = num_point_embeddings
self.hidden_act = hidden_act
self.layer_norm_eps = layer_norm_eps
self.scale = scale
class Sam2VideoMaskDecoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Sam2VideoMaskDecoder`]. It is used to instantiate a SAM2_VIDEO
memory encoder according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the hidden states.
hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function in the SAM2_VIDEO mask decoder.
mlp_dim (`int`, *optional*, defaults to 2048):
The dimension of the MLP in the two-way transformer.
num_hidden_layers (`int`, *optional*, defaults to 2):
The number of hidden layers in the two-way transformer.
num_attention_heads (`int`, *optional*, defaults to 8):
The number of attention heads in the two-way transformer.
attention_downsample_rate (`int`, *optional*, defaults to 2):
The downsample rate for the attention layers.
num_multimask_outputs (`int`, *optional*, defaults to 3):
The number of multimask outputs.
iou_head_depth (`int`, *optional*, defaults to 3):
The depth of the IoU head.
iou_head_hidden_dim (`int`, *optional*, defaults to 256):
The hidden dimension of the IoU head.
dynamic_multimask_via_stability (`bool`, *optional*, defaults to `True`):
Whether to use dynamic multimask via stability.
dynamic_multimask_stability_delta (`float`, *optional*, defaults to 0.05):
The stability delta for the dynamic multimask.
dynamic_multimask_stability_thresh (`float`, *optional*, defaults to 0.98):
The stability threshold for the dynamic multimask.
"""
base_config_key = "mask_decoder_config"
def __init__(
self,
hidden_size=256,
hidden_act="gelu",
mlp_dim=2048,
num_hidden_layers=2,
num_attention_heads=8,
attention_downsample_rate=2,
num_multimask_outputs=3,
iou_head_depth=3,
iou_head_hidden_dim=256,
dynamic_multimask_via_stability=True,
dynamic_multimask_stability_delta=0.05,
dynamic_multimask_stability_thresh=0.98,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_multimask_outputs = num_multimask_outputs
self.hidden_act = hidden_act
self.iou_head_depth = iou_head_depth
self.iou_head_hidden_dim = iou_head_hidden_dim
self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta
self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh
# TwoWayTransformer configuration
self.num_hidden_layers = num_hidden_layers
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.mlp_dim = mlp_dim
self.attention_downsample_rate = attention_downsample_rate
class Sam2VideoConfig(PretrainedConfig):
r"""
[`Sam2Config`] is the configuration class to store the configuration of a [`Sam2Model`]. It is used to instantiate a
SAM2 model according to the specified arguments, defining the memory attention, memory encoder, and image encoder
configs. Instantiating a configuration defaults will yield a similar configuration to that of the SAM 2.1 Hiera-tiny
[facebook/sam2.1-hiera-tiny](https://huggingface.co/facebook/sam2.1-hiera-tiny) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (Union[`dict`, `Sam2VisionConfig`], *optional*):
Dictionary of configuration options used to initialize [`Sam2VisionConfig`].
prompt_encoder_config (Union[`dict`, `Sam2PromptEncoderConfig`], *optional*):
Dictionary of configuration options used to initialize [`Sam2PromptEncoderConfig`].
mask_decoder_config (Union[`dict`, `Sam2MaskDecoderConfig`], *optional*):
Dictionary of configuration options used to initialize [`Sam2MaskDecoderConfig`].
initializer_range (`float`, *optional*, defaults to 0.02):
Standard deviation for parameter initialization.
num_maskmem (`int`, *optional*, defaults to 7):
The number of memory slots for the mask memory.
image_size (`int`, *optional*, defaults to 1024):
The size of the input images.
sigmoid_scale_for_mem_enc (`float`, *optional*, defaults to 20.0):
Scale factor for the sigmoid function in the memory encoder.
sigmoid_bias_for_mem_enc (`float`, *optional*, defaults to -10.0):
Bias for the sigmoid function in the memory encoder.
enable_occlusion_spatial_embedding (`bool`, *optional*, defaults to `True`):
Whether to enable spatial embedding for occlusions.
multimask_output_in_sam (`bool`, *optional*, defaults to `True`):
Whether to output multiple masks from the SAM head.
multimask_min_pt_num (`int`, *optional*, defaults to 0):
The minimum number of points to trigger multimask output.
multimask_max_pt_num (`int`, *optional*, defaults to 1):
The maximum number of points to trigger multimask output.
multimask_output_for_tracking (`bool`, *optional*, defaults to `True`):
Whether to use multimask output for tracking.
max_object_pointers_in_encoder (`int`, *optional*, defaults to 16):
The maximum number of object pointers in the encoder.
enable_temporal_pos_encoding_for_object_pointers (`bool`, *optional*, defaults to `True`):
Whether to enable temporal positional encoding for object pointers.
memory_attention_hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the memory attention hidden states.
memory_attention_num_layers (`int`, *optional*, defaults to 4):
The number of layers in the memory attention module.
memory_attention_num_attention_heads (`int`, *optional*, defaults to 1):
Number of attention heads for each attention layer in the memory attention.
memory_attention_downsample_rate (`int`, *optional*, defaults to 1):
The downsample rate for the attention layers.
memory_attention_feed_forward_hidden_size (`int`, *optional*, defaults to 2048):
The dimension of the feedforward network in the memory attention module.
memory_attention_feed_forward_hidden_act (`str`, *optional*, defaults to `"relu"`):
The non-linear activation function in the feedforward network in the memory attention module.
memory_attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout rate for the memory attention module.
memory_attention_rope_theta (`float`, *optional*, defaults to 10000):
The Rope theta parameter.
memory_attention_rope_feat_sizes (`list[int]`, *optional*, defaults to `[64, 64]`):
The feature sizes for the Rope positional encoding.
memory_attention_rope_dropout (`float`, *optional*, defaults to 0.1):
The dropout rate for the Rope positional encoding.
memory_encoder_hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the memory encoder hidden states.
memory_encoder_output_channels (`int`, *optional*, defaults to 64):
The number of output channels for the memory encoder.
mask_downsampler_embed_dim (`int`, *optional*, defaults to 256):
The dimension of the mask downsampler embedding.
mask_downsampler_kernel_size (`int`, *optional*, defaults to 3):
The kernel size for the mask downsampler.
mask_downsampler_stride (`int`, *optional*, defaults to 2):
The stride for the mask downsampler.
mask_downsampler_padding (`int`, *optional*, defaults to 1):
The padding for the mask downsampler.
mask_downsampler_total_stride (`int`, *optional*, defaults to 16):
The total stride for the mask downsampler.
mask_downsampler_hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function in the mask downsampler.
memory_fuser_num_layers (`int`, *optional*, defaults to 2):
The number of layers in the memory fuser.
memory_fuser_embed_dim (`int`, *optional*, defaults to 256):
The dimension of the embedding layer in the memory fuser.
memory_fuser_intermediate_dim (`int`, *optional*, defaults to 1024):
The dimension of the intermediate layer in the memory fuser.
memory_fuser_kernel_size (`int`, *optional*, defaults to 7):
The kernel size for the memory fuser.
memory_fuser_padding (`int`, *optional*, defaults to 3):
The padding for the memory fuser.
memory_fuser_layer_scale_init_value (`float`, *optional*, defaults to 1e-06):
The initial value for the layer scale in the memory fuser.
memory_fuser_hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function in the memory fuser.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import (
... Sam2VisionConfig,
... Sam2PromptEncoderConfig,
... Sam2MaskDecoderConfig,
... Sam2Model,
... )
>>> # Initializing a Sam2Config with `"facebook/sam2.1_hiera_tiny"` style configuration
>>> configuration = Sam2config()
>>> # Initializing a Sam2Model (with random weights) from the `"facebook/sam2.1_hiera_tiny"` style configuration
>>> model = Sam2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a Sam2Config from a Sam2VisionConfig, Sam2PromptEncoderConfig, and Sam2MaskDecoderConfig
>>> # Initializing SAM2 vision encoder, memory attention, and memory encoder configurations
>>> vision_config = Sam2VisionConfig()
>>> prompt_encoder_config = Sam2PromptEncoderConfig()
>>> mask_decoder_config = Sam2MaskDecoderConfig()
>>> config = Sam2Config(vision_config, prompt_encoder_config, mask_decoder_config)
```"""
model_type = "sam2_video"
sub_configs = {
"vision_config": AutoConfig,
"prompt_encoder_config": Sam2VideoPromptEncoderConfig,
"mask_decoder_config": Sam2VideoMaskDecoderConfig,
}
def __init__(
self,
vision_config=None,
prompt_encoder_config=None,
mask_decoder_config=None,
initializer_range=0.02,
num_maskmem=7,
image_size=1024,
sigmoid_scale_for_mem_enc=20.0,
sigmoid_bias_for_mem_enc=-10.0,
enable_occlusion_spatial_embedding=True,
multimask_output_in_sam=True,
multimask_min_pt_num=0,
multimask_max_pt_num=1,
multimask_output_for_tracking=True,
max_object_pointers_in_encoder=16,
enable_temporal_pos_encoding_for_object_pointers=True,
# memory attention
memory_attention_hidden_size=256,
memory_attention_num_layers=4,
memory_attention_num_attention_heads=1,
memory_attention_downsample_rate=1,
memory_attention_feed_forward_hidden_size=2048,
memory_attention_feed_forward_hidden_act="relu",
memory_attention_dropout=0.1,
memory_attention_rope_theta=10000,
memory_attention_rope_feat_sizes=None,
memory_attention_rope_dropout=0.1,
# memory encoder
memory_encoder_hidden_size=256,
memory_encoder_output_channels=64,
mask_downsampler_embed_dim=256,
mask_downsampler_kernel_size=3,
mask_downsampler_stride=2,
mask_downsampler_padding=1,
mask_downsampler_total_stride=16,
mask_downsampler_hidden_act="gelu",
memory_fuser_num_layers=2,
memory_fuser_embed_dim=256,
memory_fuser_intermediate_dim=1024,
memory_fuser_kernel_size=7,
memory_fuser_padding=3,
memory_fuser_layer_scale_init_value=1e-6,
memory_fuser_hidden_act="gelu",
**kwargs,
):
super().__init__(**kwargs)
vision_config = vision_config if vision_config is not None else {}
prompt_encoder_config = prompt_encoder_config if prompt_encoder_config is not None else {}
mask_decoder_config = mask_decoder_config if mask_decoder_config is not None else {}
memory_attention_rope_feat_sizes = (
[64, 64] if memory_attention_rope_feat_sizes is None else memory_attention_rope_feat_sizes
)
if isinstance(vision_config, dict):
vision_config["model_type"] = vision_config.get("model_type", "sam2_vision_model")
vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
elif isinstance(vision_config, PretrainedConfig):
vision_config = vision_config
if isinstance(prompt_encoder_config, Sam2VideoPromptEncoderConfig):
prompt_encoder_config = prompt_encoder_config.to_dict()
if isinstance(mask_decoder_config, Sam2VideoMaskDecoderConfig):
mask_decoder_config = mask_decoder_config.to_dict()
self.vision_config = vision_config
self.prompt_encoder_config = Sam2VideoPromptEncoderConfig(**prompt_encoder_config)
self.mask_decoder_config = Sam2VideoMaskDecoderConfig(**mask_decoder_config)
self.initializer_range = initializer_range
self.num_maskmem = num_maskmem # default 1 input frame + 6 previous frames
self.image_size = image_size
self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc
self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc
self.multimask_output_in_sam = multimask_output_in_sam
self.multimask_min_pt_num = multimask_min_pt_num
self.multimask_max_pt_num = multimask_max_pt_num
self.multimask_output_for_tracking = multimask_output_for_tracking
self.max_object_pointers_in_encoder = max_object_pointers_in_encoder
# The next 4 are True for sam2.1 and False for sam2
self.enable_occlusion_spatial_embedding = enable_occlusion_spatial_embedding
self.enable_temporal_pos_encoding_for_object_pointers = enable_temporal_pos_encoding_for_object_pointers
# memory attention
self.memory_attention_hidden_size = memory_attention_hidden_size
self.memory_attention_num_layers = memory_attention_num_layers
self.memory_attention_num_attention_heads = memory_attention_num_attention_heads
self.memory_attention_downsample_rate = memory_attention_downsample_rate
self.memory_attention_feed_forward_hidden_size = memory_attention_feed_forward_hidden_size
self.memory_attention_feed_forward_hidden_act = memory_attention_feed_forward_hidden_act
self.memory_attention_dropout = memory_attention_dropout
self.memory_attention_rope_theta = memory_attention_rope_theta
self.memory_attention_rope_feat_sizes = memory_attention_rope_feat_sizes
self.memory_attention_rope_dropout = memory_attention_rope_dropout
# memory encoder
self.memory_encoder_hidden_size = memory_encoder_hidden_size
self.memory_encoder_output_channels = memory_encoder_output_channels
self.mask_downsampler_embed_dim = mask_downsampler_embed_dim
self.mask_downsampler_kernel_size = mask_downsampler_kernel_size
self.mask_downsampler_stride = mask_downsampler_stride
self.mask_downsampler_padding = mask_downsampler_padding
self.mask_downsampler_total_stride = mask_downsampler_total_stride
self.mask_downsampler_hidden_act = mask_downsampler_hidden_act
self.memory_fuser_num_layers = memory_fuser_num_layers
self.memory_fuser_embed_dim = memory_fuser_embed_dim
self.memory_fuser_intermediate_dim = memory_fuser_intermediate_dim
self.memory_fuser_kernel_size = memory_fuser_kernel_size
self.memory_fuser_padding = memory_fuser_padding
self.memory_fuser_layer_scale_init_value = memory_fuser_layer_scale_init_value
self.memory_fuser_hidden_act = memory_fuser_hidden_act
__all__ = ["Sam2VideoMaskDecoderConfig", "Sam2VideoPromptEncoderConfig", "Sam2VideoConfig"]
| transformers/src/transformers/models/sam2_video/configuration_sam2_video.py/0 | {
"file_path": "transformers/src/transformers/models/sam2_video/configuration_sam2_video.py",
"repo_id": "transformers",
"token_count": 8183
} | 487 |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization class for SigLIP model."""
import os
import re
import string
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Optional
import sentencepiece as spm
from ...convert_slow_tokenizer import import_protobuf
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging, requires_backends
from ...utils.import_utils import requires
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
SPIECE_UNDERLINE = "▁"
@requires(backends=("sentencepiece",))
class SiglipTokenizer(PreTrainedTokenizer):
"""
Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"</s>"`):
The token used for padding, for example when batching sequences of different lengths.
additional_special_tokens (`list[str]`, *optional*):
Additional special tokens used by the tokenizer.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
model_max_length (`int`, *optional*, defaults to 64):
The maximum length (in number of tokens) for model inputs.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
eos_token="</s>",
unk_token="<unk>",
pad_token="</s>",
additional_special_tokens=None,
sp_model_kwargs: Optional[dict[str, Any]] = None,
model_max_length=64,
do_lower_case=True,
**kwargs,
) -> None:
requires_backends(self, "protobuf")
pad_token = (
AddedToken(pad_token, rstrip=True, lstrip=True, normalized=False, special=True)
if isinstance(pad_token, str)
else pad_token
)
unk_token = (
AddedToken(unk_token, rstrip=True, lstrip=True, normalized=False, special=True)
if isinstance(unk_token, str)
else unk_token
)
eos_token = (
AddedToken(eos_token, rstrip=True, lstrip=True, normalized=False, special=True)
if isinstance(eos_token, str)
else eos_token
)
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
self.do_lower_case = do_lower_case
self.vocab_file = vocab_file
self.sp_model = self.get_spm_processor()
self.vocab_file = vocab_file
super().__init__(
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
additional_special_tokens=additional_special_tokens,
sp_model_kwargs=self.sp_model_kwargs,
model_max_length=model_max_length,
do_lower_case=do_lower_case,
**kwargs,
)
def get_spm_processor(self):
tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
with open(self.vocab_file, "rb") as f:
sp_model = f.read()
model_pb2 = import_protobuf()
model = model_pb2.ModelProto.FromString(sp_model)
normalizer_spec = model_pb2.NormalizerSpec()
normalizer_spec.add_dummy_prefix = False
model.normalizer_spec.MergeFrom(normalizer_spec)
sp_model = model.SerializeToString()
tokenizer.LoadFromSerializedProto(sp_model)
return tokenizer
@property
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.vocab_size
def vocab_size(self):
return self.sp_model.get_piece_size()
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_vocab
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_special_tokens_mask
def get_special_tokens_mask(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
) -> list[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
# normal case: some special tokens
if token_ids_1 is None:
return ([0] * len(token_ids_0)) + [1]
return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._add_eos_if_not_present
def _add_eos_if_not_present(self, token_ids: list[int]) -> list[int]:
"""Do not add eos again if user already added it."""
if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added."
)
return token_ids
else:
return token_ids + [self.eos_token_id]
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.create_token_type_ids_from_sequences
def create_token_type_ids_from_sequences(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
) -> list[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of zeros.
"""
eos = [self.eos_token_id]
if token_ids_1 is None:
return len(token_ids_0 + eos) * [0]
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.build_inputs_with_special_tokens
def build_inputs_with_special_tokens(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
) -> list[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A sequence has the following format:
- single sequence: `X </s>`
- pair of sequences: `A </s> B </s>`
Args:
token_ids_0 (`list[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
token_ids_0 = self._add_eos_if_not_present(token_ids_0)
if token_ids_1 is None:
return token_ids_0
else:
token_ids_1 = self._add_eos_if_not_present(token_ids_1)
return token_ids_0 + token_ids_1
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__getstate__
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__setstate__
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def remove_punctuation(self, text: str) -> str:
return text.translate(str.maketrans("", "", string.punctuation))
# source: https://github.com/google-research/big_vision/blob/3b8e5ab6ad4f96e32b32826f9e1b8fd277914f9c/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
def canonicalize_text(self, text, *, keep_punctuation_exact_string=None):
"""Returns canonicalized `text` (puncuation removed).
Args:
text (`str`):
String to be canonicalized.
keep_punctuation_exact_string (`str`, *optional*):
If provided, then this exact string is kept. For example providing '{}' will keep any occurrences of '{}'
(but will still remove '{' and '}' that appear separately).
"""
if keep_punctuation_exact_string:
text = keep_punctuation_exact_string.join(
self.remove_punctuation(part) for part in text.split(keep_punctuation_exact_string)
)
else:
text = self.remove_punctuation(text)
text = re.sub(r"\s+", " ", text)
text = text.strip()
return text
def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> list[str]:
"""
Converts a string to a list of tokens.
"""
tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs)
if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
tokens = tokens[1:]
return tokens
@property
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.unk_token_length
def unk_token_length(self):
return len(self.sp_model.encode(str(self.unk_token)))
def _tokenize(self, text, **kwargs):
"""
Returns a tokenized string.
We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
SPIECE_UNDERLINE.
For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`.
Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
"""
text = self.canonicalize_text(text, keep_punctuation_exact_string=None)
tokens = self.sp_model.encode(text, out_type=str)
# 1. Encode string + prefix ex: "<unk> Hey"
tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_token_to_id
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.piece_to_id(token)
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_id_to_token
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = self.sp_model.IdToPiece(index)
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
return out_string.strip()
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
__all__ = ["SiglipTokenizer"]
| transformers/src/transformers/models/siglip/tokenization_siglip.py/0 | {
"file_path": "transformers/src/transformers/models/siglip/tokenization_siglip.py",
"repo_id": "transformers",
"token_count": 6957
} | 488 |
# coding=utf-8
# Copyright 2021 Tel AViv University, AllenAI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fast Tokenization classes for Splinter."""
import json
from typing import Optional
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
class SplinterTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" Splinter tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
question_token (`str`, *optional*, defaults to `"[QUESTION]"`):
The token used for constructing question representations.
clean_text (`bool`, *optional*, defaults to `True`):
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
The prefix for subwords.
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = SplinterTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
question_token="[QUESTION]",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs,
):
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
additional_special_tokens=(question_token,),
**kwargs,
)
pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
):
pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
pre_tok_state["lowercase"] = do_lower_case
pre_tok_state["strip_accents"] = strip_accents
self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)
self.do_lower_case = do_lower_case
@property
def question_token_id(self):
"""
`Optional[int]`: Id of the question token in the vocabulary, used to condition the answer on a question
representation.
"""
return self.convert_tokens_to_ids(self.question_token)
def build_inputs_with_special_tokens(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
) -> list[int]:
"""
Build model inputs from a pair of sequence for question answering tasks by concatenating and adding special
tokens. A Splinter sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences for question answering: `[CLS] question_tokens [QUESTION] . [SEP] context_tokens [SEP]`
Args:
token_ids_0 (`list[int]`):
The question token IDs if pad_on_right, else context tokens IDs
token_ids_1 (`list[int]`, *optional*):
The context token IDs if pad_on_right, else question token IDs
Returns:
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
question_suffix = [self.question_token_id] + [self.convert_tokens_to_ids(".")]
if self.padding_side == "right":
# Input is question-then-context
return cls + token_ids_0 + question_suffix + sep + token_ids_1 + sep
else:
# Input is context-then-question
return cls + token_ids_0 + sep + token_ids_1 + question_suffix + sep
def create_token_type_ids_from_sequences(
self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
) -> list[int]:
"""
Create the token type IDs corresponding to the sequences passed. [What are token type
IDs?](../glossary#token-type-ids)
Should be overridden in a subclass if the model has a special way of building those.
Args:
token_ids_0 (`list[int]`): The first tokenized sequence.
token_ids_1 (`list[int]`, *optional*): The second tokenized sequence.
Returns:
`list[int]`: The token type ids.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
question_suffix = [self.question_token_id] + [self.convert_tokens_to_ids(".")]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
if self.padding_side == "right":
# Input is question-then-context
return len(cls + token_ids_0 + question_suffix + sep) * [0] + len(token_ids_1 + sep) * [1]
else:
# Input is context-then-question
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + question_suffix + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
__all__ = ["SplinterTokenizerFast"]
| transformers/src/transformers/models/splinter/tokenization_splinter_fast.py/0 | {
"file_path": "transformers/src/transformers/models/splinter/tokenization_splinter_fast.py",
"repo_id": "transformers",
"token_count": 3455
} | 489 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for SuperPoint."""
from typing import TYPE_CHECKING, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
ImageType,
PILImageResampling,
get_image_type,
infer_channel_dimension_format,
is_pil_image,
is_scaled_image,
is_torch_available,
is_valid_image,
is_vision_available,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging, requires_backends
from ...utils.import_utils import requires
if is_torch_available():
import torch
if TYPE_CHECKING:
from .modeling_superglue import KeypointMatchingOutput
if is_vision_available():
import PIL
from PIL import Image, ImageDraw
logger = logging.get_logger(__name__)
# Copied from transformers.models.superpoint.image_processing_superpoint.is_grayscale
def is_grayscale(
image: np.ndarray,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
if input_data_format == ChannelDimension.FIRST:
if image.shape[0] == 1:
return True
return np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...])
elif input_data_format == ChannelDimension.LAST:
if image.shape[-1] == 1:
return True
return np.all(image[..., 0] == image[..., 1]) and np.all(image[..., 1] == image[..., 2])
# Copied from transformers.models.superpoint.image_processing_superpoint.convert_to_grayscale
def convert_to_grayscale(
image: ImageInput,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> ImageInput:
"""
Converts an image to grayscale format using the NTSC formula. Only support numpy and PIL Image. TODO support torch
and tensorflow grayscale conversion
This function is supposed to return a 1-channel image, but it returns a 3-channel image with the same value in each
channel, because of an issue that is discussed in :
https://github.com/huggingface/transformers/pull/25786#issuecomment-1730176446
Args:
image (Image):
The image to convert.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image.
"""
requires_backends(convert_to_grayscale, ["vision"])
if isinstance(image, np.ndarray):
if is_grayscale(image, input_data_format=input_data_format):
return image
if input_data_format == ChannelDimension.FIRST:
gray_image = image[0, ...] * 0.2989 + image[1, ...] * 0.5870 + image[2, ...] * 0.1140
gray_image = np.stack([gray_image] * 3, axis=0)
elif input_data_format == ChannelDimension.LAST:
gray_image = image[..., 0] * 0.2989 + image[..., 1] * 0.5870 + image[..., 2] * 0.1140
gray_image = np.stack([gray_image] * 3, axis=-1)
return gray_image
if not isinstance(image, PIL.Image.Image):
return image
image = image.convert("L")
return image
def validate_and_format_image_pairs(images: ImageInput):
error_message = (
"Input images must be a one of the following :",
" - A pair of PIL images.",
" - A pair of 3D arrays.",
" - A list of pairs of PIL images.",
" - A list of pairs of 3D arrays.",
)
def _is_valid_image(image):
"""images is a PIL Image or a 3D array."""
return is_pil_image(image) or (
is_valid_image(image) and get_image_type(image) != ImageType.PIL and len(image.shape) == 3
)
if isinstance(images, list):
if len(images) == 2 and all((_is_valid_image(image)) for image in images):
return images
if all(
isinstance(image_pair, list)
and len(image_pair) == 2
and all(_is_valid_image(image) for image in image_pair)
for image_pair in images
):
return [image for image_pair in images for image in image_pair]
raise ValueError(error_message)
@requires(backends=("torch",))
class SuperGlueImageProcessor(BaseImageProcessor):
r"""
Constructs a SuperGlue image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden
by `do_resize` in the `preprocess` method.
size (`dict[str, int]` *optional*, defaults to `{"height": 480, "width": 640}`):
Resolution of the output image after `resize` is applied. Only has an effect if `do_resize` is set to
`True`. Can be overridden by `size` in the `preprocess` method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
method.
do_grayscale (`bool`, *optional*, defaults to `True`):
Whether to convert the image to grayscale. Can be overridden by `do_grayscale` in the `preprocess` method.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Optional[dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: float = 1 / 255,
do_grayscale: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"height": 480, "width": 640}
size = get_size_dict(size, default_to_square=False)
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_grayscale = do_grayscale
# Copied from transformers.models.superpoint.image_processing_superpoint.SuperPointImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: dict[str, int],
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
):
"""
Resize an image.
Args:
image (`np.ndarray`):
Image to resize.
size (`dict[str, int]`):
Dictionary of the form `{"height": int, "width": int}`, specifying the size of the output image.
data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the output image. If not provided, it will be inferred from the input
image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
size = get_size_dict(size, default_to_square=False)
return resize(
image,
size=(size["height"], size["width"]),
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def preprocess(
self,
images,
do_resize: Optional[bool] = None,
size: Optional[dict[str, int]] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_grayscale: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: ChannelDimension = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> BatchFeature:
"""
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image pairs to preprocess. Expects either a list of 2 images or a list of list of 2 images list with
pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set
`do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`dict[str, int]`, *optional*, defaults to `self.size`):
Size of the output image after `resize` has been applied. If `size["shortest_edge"]` >= 384, the image
is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the
image will be matched to `int(size["shortest_edge"]/ crop_pct)`, after which the image is cropped to
`(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of `PILImageResampling`, filters. Only
has an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_grayscale (`bool`, *optional*, defaults to `self.do_grayscale`):
Whether to convert the image to grayscale.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_grayscale = do_grayscale if do_grayscale is not None else self.do_grayscale
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
# Validate and convert the input images into a flattened list of images for all subsequent processing steps.
images = validate_and_format_image_pairs(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
do_resize=do_resize,
size=size,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
)
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_rescale:
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
all_images = []
for image in images:
if do_resize:
image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
if do_rescale:
image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
if do_grayscale:
image = convert_to_grayscale(image, input_data_format=input_data_format)
image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
all_images.append(image)
# Convert back the flattened list of images into a list of pairs of images.
image_pairs = [all_images[i : i + 2] for i in range(0, len(all_images), 2)]
data = {"pixel_values": image_pairs}
return BatchFeature(data=data, tensor_type=return_tensors)
def post_process_keypoint_matching(
self,
outputs: "KeypointMatchingOutput",
target_sizes: Union[TensorType, list[tuple]],
threshold: float = 0.0,
) -> list[dict[str, torch.Tensor]]:
"""
Converts the raw output of [`KeypointMatchingOutput`] into lists of keypoints, scores and descriptors
with coordinates absolute to the original image sizes.
Args:
outputs ([`KeypointMatchingOutput`]):
Raw outputs of the model.
target_sizes (`torch.Tensor` or `list[tuple[tuple[int, int]]]`, *optional*):
Tensor of shape `(batch_size, 2, 2)` or list of tuples of tuples (`tuple[int, int]`) containing the
target size `(height, width)` of each image in the batch. This must be the original image size (before
any processing).
threshold (`float`, *optional*, defaults to 0.0):
Threshold to filter out the matches with low scores.
Returns:
`list[Dict]`: A list of dictionaries, each dictionary containing the keypoints in the first and second image
of the pair, the matching scores and the matching indices.
"""
if outputs.mask.shape[0] != len(target_sizes):
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the mask")
if not all(len(target_size) == 2 for target_size in target_sizes):
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
if isinstance(target_sizes, list):
image_pair_sizes = torch.tensor(target_sizes, device=outputs.mask.device)
else:
if target_sizes.shape[1] != 2 or target_sizes.shape[2] != 2:
raise ValueError(
"Each element of target_sizes must contain the size (h, w) of each image of the batch"
)
image_pair_sizes = target_sizes
keypoints = outputs.keypoints.clone()
keypoints = keypoints * image_pair_sizes.flip(-1).reshape(-1, 2, 1, 2)
keypoints = keypoints.to(torch.int32)
results = []
for mask_pair, keypoints_pair, matches, scores in zip(
outputs.mask, keypoints, outputs.matches[:, 0], outputs.matching_scores[:, 0]
):
mask0 = mask_pair[0] > 0
mask1 = mask_pair[1] > 0
keypoints0 = keypoints_pair[0][mask0]
keypoints1 = keypoints_pair[1][mask1]
matches0 = matches[mask0]
scores0 = scores[mask0]
# Filter out matches with low scores
valid_matches = torch.logical_and(scores0 > threshold, matches0 > -1)
matched_keypoints0 = keypoints0[valid_matches]
matched_keypoints1 = keypoints1[matches0[valid_matches]]
matching_scores = scores0[valid_matches]
results.append(
{
"keypoints0": matched_keypoints0,
"keypoints1": matched_keypoints1,
"matching_scores": matching_scores,
}
)
return results
# Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor.visualize_keypoint_matching with EfficientLoFTR->SuperGlue
def visualize_keypoint_matching(
self,
images: ImageInput,
keypoint_matching_output: list[dict[str, torch.Tensor]],
) -> list["Image.Image"]:
"""
Plots the image pairs side by side with the detected keypoints as well as the matching between them.
Args:
images (`ImageInput`):
Image pairs to plot. Same as `SuperGlueImageProcessor.preprocess`. Expects either a list of 2
images or a list of list of 2 images list with pixel values ranging from 0 to 255.
keypoint_matching_output (List[Dict[str, torch.Tensor]]]):
A post processed keypoint matching output
Returns:
`List[PIL.Image.Image]`: A list of PIL images, each containing the image pairs side by side with the detected
keypoints as well as the matching between them.
"""
images = validate_and_format_image_pairs(images)
images = [to_numpy_array(image) for image in images]
image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]
results = []
for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
height0, width0 = image_pair[0].shape[:2]
height1, width1 = image_pair[1].shape[:2]
plot_image = np.zeros((max(height0, height1), width0 + width1, 3), dtype=np.uint8)
plot_image[:height0, :width0] = image_pair[0]
plot_image[:height1, width0:] = image_pair[1]
plot_image_pil = Image.fromarray(plot_image)
draw = ImageDraw.Draw(plot_image_pil)
keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
):
color = self._get_color(matching_score)
draw.line(
(keypoint0_x, keypoint0_y, keypoint1_x + width0, keypoint1_y),
fill=color,
width=3,
)
draw.ellipse((keypoint0_x - 2, keypoint0_y - 2, keypoint0_x + 2, keypoint0_y + 2), fill="black")
draw.ellipse(
(keypoint1_x + width0 - 2, keypoint1_y - 2, keypoint1_x + width0 + 2, keypoint1_y + 2),
fill="black",
)
results.append(plot_image_pil)
return results
# Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor._get_color
def _get_color(self, score):
"""Maps a score to a color."""
r = int(255 * (1 - score))
g = int(255 * score)
b = 0
return (r, g, b)
__all__ = ["SuperGlueImageProcessor"]
| transformers/src/transformers/models/superglue/image_processing_superglue.py/0 | {
"file_path": "transformers/src/transformers/models/superglue/image_processing_superglue.py",
"repo_id": "transformers",
"token_count": 9393
} | 490 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def get_swin_config(swin_name):
config = SwinConfig()
name_split = swin_name.split("_")
model_size = name_split[1]
img_size = int(name_split[4])
window_size = int(name_split[3][-1])
if model_size == "tiny":
embed_dim = 96
depths = (2, 2, 6, 2)
num_heads = (3, 6, 12, 24)
elif model_size == "small":
embed_dim = 96
depths = (2, 2, 18, 2)
num_heads = (3, 6, 12, 24)
elif model_size == "base":
embed_dim = 128
depths = (2, 2, 18, 2)
num_heads = (4, 8, 16, 32)
else:
embed_dim = 192
depths = (2, 2, 18, 2)
num_heads = (6, 12, 24, 48)
if "in22k" in swin_name:
num_classes = 21841
else:
num_classes = 1000
repo_id = "huggingface/label-files"
filename = "imagenet-1k-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
config.image_size = img_size
config.num_labels = num_classes
config.embed_dim = embed_dim
config.depths = depths
config.num_heads = num_heads
config.window_size = window_size
return config
def rename_key(name):
if "patch_embed.proj" in name:
name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection")
if "patch_embed.norm" in name:
name = name.replace("patch_embed.norm", "embeddings.norm")
if "layers" in name:
name = "encoder." + name
if "attn.proj" in name:
name = name.replace("attn.proj", "attention.output.dense")
if "attn" in name:
name = name.replace("attn", "attention.self")
if "norm1" in name:
name = name.replace("norm1", "layernorm_before")
if "norm2" in name:
name = name.replace("norm2", "layernorm_after")
if "mlp.fc1" in name:
name = name.replace("mlp.fc1", "intermediate.dense")
if "mlp.fc2" in name:
name = name.replace("mlp.fc2", "output.dense")
if name == "norm.weight":
name = "layernorm.weight"
if name == "norm.bias":
name = "layernorm.bias"
if "head" in name:
name = name.replace("head", "classifier")
else:
name = "swin." + name
return name
def convert_state_dict(orig_state_dict, model):
for key in orig_state_dict.copy():
val = orig_state_dict.pop(key)
if "mask" in key:
continue
elif "qkv" in key:
key_split = key.split(".")
layer_num = int(key_split[1])
block_num = int(key_split[3])
dim = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight"] = (
val[:dim, :]
)
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = val[
dim : dim * 2, :
]
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight"] = (
val[-dim:, :]
)
else:
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = val[
:dim
]
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[
dim : dim * 2
]
orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = val[
-dim:
]
else:
orig_state_dict[rename_key(key)] = val
return orig_state_dict
def convert_swin_checkpoint(swin_name, pytorch_dump_folder_path):
timm_model = timm.create_model(swin_name, pretrained=True)
timm_model.eval()
config = get_swin_config(swin_name)
model = SwinForImageClassification(config)
model.eval()
new_state_dict = convert_state_dict(timm_model.state_dict(), model)
model.load_state_dict(new_state_dict)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image_processor = AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_", "-")))
image = Image.open(requests.get(url, stream=True).raw)
inputs = image_processor(images=image, return_tensors="pt")
timm_outs = timm_model(inputs["pixel_values"])
hf_outs = model(**inputs).logits
assert torch.allclose(timm_outs, hf_outs, atol=1e-3)
print(f"Saving model {swin_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving image processor to {pytorch_dump_folder_path}")
image_processor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swin_name",
default="swin_tiny_patch4_window7_224",
type=str,
help="Name of the Swin timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
args = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| transformers/src/transformers/models/swin/convert_swin_timm_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/swin/convert_swin_timm_to_pytorch.py",
"repo_id": "transformers",
"token_count": 2718
} | 491 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert SwitchTransformersX checkpoints from the original repository to JAX/FLAX model."""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from t5x import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
MOE_LAYER_NAME_MAPPING = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def rename_keys(s_dict):
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
keys = list(s_dict.keys())
for key in keys:
layer_to_block_of_layer = r".*/layers_(\d+)"
new_key = key
if re.match(layer_to_block_of_layer, key):
new_key = re.sub(r"layers_(\d+)", r"block/\1/layer", new_key)
layer_to_block_of_layer = r"(encoder|decoder)\/"
if re.match(layer_to_block_of_layer, key):
groups = re.match(layer_to_block_of_layer, new_key).groups()
if groups[0] == "encoder":
new_key = re.sub(r"/mlp/", r"/1/mlp/", new_key)
new_key = re.sub(r"/pre_mlp_layer_norm/", r"/1/layer_norm/", new_key)
elif groups[0] == "decoder":
new_key = re.sub(r"/mlp/", r"/2/mlp/", new_key)
new_key = re.sub(r"/pre_mlp_layer_norm/", r"/2/layer_norm/", new_key)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
new_key = new_key.replace(old_key, temp_key)
print(f"{key} -> {new_key}")
s_dict[new_key] = s_dict.pop(key)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
s_dict["encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"] = s_dict[
"encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
s_dict["decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"] = s_dict[
"decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
num_experts = s_dict[key].shape[0]
expert_weihts = s_dict[key]
for idx in range(num_experts):
s_dict[key.replace("expert/", f"experts/expert_{idx}/")] = expert_weihts[idx]
print(f"{key} -> {key.replace('expert/', f'experts/expert_{idx}/')}")
s_dict.pop(key)
return s_dict
GIN_TO_CONFIG_MAPPING = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def convert_gin_to_config(gin_file, num_experts):
# Convert a google style config to the hugging face format
import regex as re
with open(gin_file, "r") as f:
raw_gin = f.read()
regex_match = re.findall(r"(.*) = ([0-9.]*)", raw_gin)
args = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
args[GIN_TO_CONFIG_MAPPING[param]] = float(value) if "." in value else int(value)
activation = re.findall(r"(.*activations) = \(\'(.*)\',\)", raw_gin)[0]
args[GIN_TO_CONFIG_MAPPING[activation[0]]] = str(activation[1])
args["num_experts"] = num_experts
config = SwitchTransformersConfig(**args)
return config
def convert_flax_checkpoint_to_pytorch(
flax_checkpoint_path, config_file, gin_file=None, pytorch_dump_path="./", num_experts=8
):
# Initialise PyTorch model
print(f"Loading flax weights from : {flax_checkpoint_path}")
flax_params = checkpoints.load_t5x_checkpoint(flax_checkpoint_path)
if gin_file is not None:
config = convert_gin_to_config(gin_file, num_experts)
else:
config = SwitchTransformersConfig.from_pretrained(config_file)
pt_model = SwitchTransformersForConditionalGeneration(config)
flax_params = flax_params["target"]
flax_params = flatten_dict(flax_params, sep="/")
flax_params = rename_keys(flax_params)
flax_params = unflatten_dict(flax_params, sep="/")
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(pt_model, flax_params)
print(f"Save PyTorch model to {pytorch_dump_path}")
pt_model.save_pretrained(pytorch_dump_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
args = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_t5x_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| transformers/src/transformers/models/switch_transformers/convert_switch_transformers_original_flax_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/switch_transformers/convert_switch_transformers_original_flax_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 3256
} | 492 |
# coding=utf-8
# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Optional, Union
import torch
import torch.nn as nn
from transformers.utils.generic import OutputRecorder, check_model_inputs
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...configuration_utils import PretrainedConfig
from ...generation import GenerationMixin
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import (
TransformersKwargs,
auto_docstring,
can_return_tuple,
is_torch_flex_attn_available,
is_torchdynamo_compiling,
logging,
)
from ...utils.deprecation import deprecate_kwarg
from ..gemma2.configuration_gemma2 import Gemma2Config
from ..gemma2.modeling_gemma2 import (
Gemma2Attention,
Gemma2MLP,
Gemma2PreTrainedModel,
Gemma2RMSNorm,
Gemma2RotaryEmbedding,
create_causal_mask,
create_sliding_window_causal_mask,
eager_attention_forward,
)
_CHECKPOINT_FOR_DOC = "google/t5gemma-2b-2b-prefixlm-it"
if is_torch_flex_attn_available():
pass
logger = logging.get_logger(__name__)
class T5GemmaModuleConfig(Gemma2Config):
pass
class T5GemmaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`T5GemmaModel`]. It is used to instantiate an T5Gemma
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to a hypothetical balanced Gemma2 encoder-decoder model.
e.g. [google/t5gemma-2b-2b-prefixlm-it](https://huggingface.co/google/t5gemma-2b-2b-prefixlm-it)
```python
>>> from transformers import T5GemmaConfig, T5GemmaModel
>>> t5gemma_config = T5GemmaConfig.from_pretrained("google/t5gemma-2b-2b-prefixlm-it")
>>> model = T5GemmaModel(t5gemma_config)
```
Configuration objects inherit from [PretrainedConfig] and can be used to control the model outputs. Read the
documentation from [PretrainedConfig] for more information.
Args:
encoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*):
Configuration for the encoder.
decoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*):
Configuration for the decoder.
is_encoder_decoder (bool, optional, *optional*, defaults to `True`):
Whether the model is used as an encoder/decoder or not.
dropout_rate (`float`, *optional*, defaults to 0.0):
The ratio for all dropout layers (following T5).
classifier_dropout_rate (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier (following T5).
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for attention.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether tie input and output embeddings.
vocab_size (`int`, *optional*, defaults to 256000):
Vocabulary size of the T5Gemma model (the same as Gemma 2).
kwargs (additional keyword arguments, optional, *optional*):
Will be passed to the PretrainedConfig base class.
"""
model_type = "t5gemma"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
# encoder
"encoder.layers.*.self_attn.q_proj": "colwise",
"encoder.layers.*.self_attn.k_proj": "colwise",
"encoder.layers.*.self_attn.v_proj": "colwise",
"encoder.layers.*.self_attn.o_proj": "rowwise",
"encoder.layers.*.mlp.gate_proj": "colwise",
"encoder.layers.*.mlp.up_proj": "colwise",
"encoder.layers.*.mlp.down_proj": "rowwise",
# decoder
"decoder.layers.*.self_attn.q_proj": "colwise",
"decoder.layers.*.self_attn.k_proj": "colwise",
"decoder.layers.*.self_attn.v_proj": "colwise",
"decoder.layers.*.self_attn.o_proj": "rowwise",
"decoder.layers.*.cross_attn.q_proj": "colwise",
"decoder.layers.*.cross_attn.k_proj": "colwise",
"decoder.layers.*.cross_attn.v_proj": "colwise",
"decoder.layers.*.cross_attn.o_proj": "rowwise",
"decoder.layers.*.mlp.gate_proj": "colwise",
"decoder.layers.*.mlp.up_proj": "colwise",
"decoder.layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
# encoder
"encoder.embed_tokens": (["input_ids"], ["inputs_embeds"]),
"encoder.layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"encoder.norm": (["hidden_states"], ["hidden_states"]),
# decoder
"decoder.embed_tokens": (["input_ids"], ["inputs_embeds"]),
"decoder.layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"decoder.norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
encoder: Optional[Union[T5GemmaModuleConfig, dict[Any, Any]]] = None,
decoder: Optional[Union[T5GemmaModuleConfig, dict[Any, Any]]] = None,
is_encoder_decoder: bool = True,
dropout_rate: float = 0.0,
classifier_dropout_rate: float = 0.0,
attention_dropout: float = 0.0,
tie_word_embeddings: bool = True,
vocab_size: int = 256000,
**kwargs,
):
if isinstance(encoder, dict):
encoder = T5GemmaModuleConfig(**encoder)
elif encoder is None:
encoder = T5GemmaModuleConfig()
else:
assert isinstance(encoder, T5GemmaModuleConfig), f"{type(encoder)} is not supported."
if isinstance(decoder, dict):
decoder = T5GemmaModuleConfig(**decoder)
elif decoder is None:
decoder = encoder
else:
assert isinstance(decoder, T5GemmaModuleConfig), f"{type(decoder)} is not supported."
encoder = T5GemmaModuleConfig(**encoder.to_dict())
decoder = T5GemmaModuleConfig(**decoder.to_dict())
encoder.is_decoder = False
encoder.dropout_rate = dropout_rate
encoder.attention_dropout = attention_dropout
self.encoder = encoder
decoder.is_decoder = True
decoder.use_cache = True
decoder.dropout_rate = dropout_rate
decoder.attention_dropout = attention_dropout
decoder.cross_attention_hidden_size = encoder.hidden_size
self.decoder = decoder
for special_token_key in ["bos_token_id", "pad_token_id", "eos_token_id"]:
if special_token_key not in kwargs:
kwargs[special_token_key] = getattr(decoder, special_token_key)
super().__init__(**kwargs)
self.is_encoder_decoder = is_encoder_decoder
self.use_cache = kwargs.get("use_cache", decoder.use_cache)
self.initializer_range = kwargs.get("initializer_range", decoder.initializer_range)
self.dropout_rate = dropout_rate
self.attention_dropout = attention_dropout
self.classifier_dropout_rate = classifier_dropout_rate
self.tie_word_embeddings = tie_word_embeddings
# Used in pipeline generation.
self.vocab_size = vocab_size
def __setattr__(self, key, value):
shared_attr_with_submodules = [
"output_hidden_states",
"output_attentions",
"_attn_implementation",
"dropout_rate",
"attention_dropout",
"vocab_size",
]
if key in shared_attr_with_submodules:
setattr(self.encoder, key, value)
setattr(self.decoder, key, value)
super().__setattr__(key, value)
def get_text_config(self, *args, **kwargs):
# Always return self, regardless of the decoder option.
return self
class T5GemmaRMSNorm(Gemma2RMSNorm):
pass
class T5GemmaMLP(Gemma2MLP):
def __init__(self, config):
super().__init__(config)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, x):
hidden_states = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
hidden_states = self.dropout(hidden_states)
down_proj = self.down_proj(hidden_states)
return down_proj
class T5GemmaRotaryEmbedding(Gemma2RotaryEmbedding):
def __init__(self, config, device=None):
super().__init__(config, device)
class T5GemmaSelfAttention(Gemma2Attention):
def __init__(self, config: T5GemmaModuleConfig, layer_idx: int):
super().__init__(config, layer_idx)
# Requied by flash attention: encoder selfattention is non-causal
self.is_causal = config.is_decoder
class T5GemmaCrossAttention(Gemma2Attention):
def __init__(self, config: T5GemmaModuleConfig, layer_idx: int):
super().__init__(config, layer_idx)
del self.sliding_window
self.is_causal = False
if config.cross_attention_hidden_size is None:
raise ValueError("Cross-attention needs cross_attention_hidden_size to be specified.")
self.k_proj = nn.Linear(
config.cross_attention_hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
config.cross_attention_hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor],
encoder_hidden_states: Optional[torch.Tensor],
past_key_values: Optional[Cache] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
if encoder_hidden_states is None:
raise ValueError("Encoder hidden state is required for cross attention.")
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
if past_key_values is not None:
is_updated = past_key_values.is_updated.get(self.layer_idx)
curr_past_key_value = past_key_values.cross_attention_cache
if past_key_values is None or not is_updated:
encoder_input_shape = encoder_hidden_states.shape[:-1]
encoder_hidden_shape = (*encoder_input_shape, -1, self.head_dim)
key_states = self.k_proj(encoder_hidden_states).view(encoder_hidden_shape).transpose(1, 2)
value_states = self.v_proj(encoder_hidden_states).view(encoder_hidden_shape).transpose(1, 2)
if past_key_values is not None:
key_states, value_states = curr_past_key_value.update(key_states, value_states, self.layer_idx)
past_key_values.is_updated[self.layer_idx] = True
else:
key_states = curr_past_key_value.layers[self.layer_idx].keys
value_states = curr_past_key_value.layers[self.layer_idx].values
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=self.attention_dropout if self.training else 0.0,
scaling=self.scaling,
sliding_window=None,
softcap=self.attn_logit_softcapping,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
def bidirectional_mask_function(attention_mask: Optional[torch.Tensor]) -> Callable:
"""
This creates bidirectional attention mask.
"""
def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
if attention_mask is None:
return torch.ones((), dtype=torch.bool)
return attention_mask[batch_idx, kv_idx].to(torch.bool)
return inner_mask
def sliding_window_bidirectional_mask_function(sliding_window: int) -> Callable:
"""
This creates bidirectional attention mask with sliding window.
"""
def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
return (q_idx - sliding_window < kv_idx) & (kv_idx < q_idx + sliding_window)
return inner_mask
class T5GemmaEncoderLayer(GradientCheckpointingLayer):
"""Encoder sub-layer."""
def __init__(self, config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.config = config
self.layer_idx = layer_idx
self.attention_type = config.layer_types[layer_idx]
self.self_attn = T5GemmaSelfAttention(
config=config,
layer_idx=layer_idx,
)
self.pre_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.mlp = T5GemmaMLP(config)
self.pre_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
**kwargs,
) -> tuple[torch.FloatTensor,]:
residual = hidden_states
hidden_states = self.pre_self_attn_layernorm(hidden_states)
hidden_states, _ = self.self_attn(
hidden_states=hidden_states,
position_embeddings=position_embeddings,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=None,
**kwargs,
)
hidden_states = self.post_self_attn_layernorm(hidden_states)
hidden_states = residual + self.dropout(hidden_states)
residual = hidden_states
hidden_states = self.pre_feedforward_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = self.post_feedforward_layernorm(hidden_states)
hidden_states = residual + self.dropout(hidden_states)
return hidden_states
class T5GemmaDecoderLayer(T5GemmaEncoderLayer):
"""Decoder sub-layer: an extra cross-attention layer."""
def __init__(self, config, layer_idx: int):
super().__init__(config, layer_idx)
self.cross_attn = T5GemmaCrossAttention(config=config, layer_idx=layer_idx)
self.pre_cross_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_cross_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[EncoderDecoderCache] = None,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
hidden_states = self.pre_self_attn_layernorm(hidden_states)
hidden_states, _ = self.self_attn(
hidden_states=hidden_states,
position_embeddings=position_embeddings,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values.self_attention_cache if past_key_values is not None else None,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = self.post_self_attn_layernorm(hidden_states)
hidden_states = residual + self.dropout(hidden_states)
residual = hidden_states
hidden_states = self.pre_cross_attn_layernorm(hidden_states)
hidden_states, _ = self.cross_attn(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
**kwargs,
)
hidden_states = self.post_cross_attn_layernorm(hidden_states)
hidden_states = residual + self.dropout(hidden_states)
residual = hidden_states
hidden_states = self.pre_feedforward_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = self.post_feedforward_layernorm(hidden_states)
hidden_states = residual + self.dropout(hidden_states)
return hidden_states
class T5GemmaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, hidden_size: int, num_labels: int, classifier_dropout_rate: float = 0.0):
super().__init__()
self.dropout = nn.Dropout(p=classifier_dropout_rate)
self.out_proj = nn.Linear(hidden_size, num_labels)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
class T5GemmaLMHead(nn.Module):
"""Head for language modeling (generation) tasks."""
def __init__(self, hidden_size: int, vocab_size: int, bias: bool = False):
super().__init__()
self.out_proj = nn.Linear(hidden_size, vocab_size, bias=bias)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
logits = self.out_proj(hidden_states)
return logits
@auto_docstring
class T5GemmaPreTrainedModel(Gemma2PreTrainedModel):
config: T5GemmaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["T5GemmaBlock"]
def _init_weights(self, module):
# TODO: support intialization for encoders and decoders separately(?)
Gemma2PreTrainedModel._init_weights(self, module)
std = self.config.initializer_range
if isinstance(module, T5GemmaClassificationHead):
scale = module.out_proj.weight.shape[0] ** -0.5
module.out_proj.weight.data.normal_(mean=0.0, std=std * scale)
if hasattr(module.out_proj, "bias") and module.out_proj.bias is not None:
module.out_proj.bias.data.zero_()
elif isinstance(module, T5GemmaLMHead):
if not self.config.tie_word_embeddings:
scale = module.out_proj.weight.shape[0] ** -0.5
module.out_proj.weight.data.normal_(mean=0.0, std=std * scale)
def _shift_right(self, input_ids):
"""
Shifts input_ids to the right, prepends the decoder_start_token_id, and handles
pad_token_id replacement for labels that were -100.
This is a common preparation step for decoder inputs in sequence-to-sequence models.
"""
decoder_start_token_id = self.config.decoder.bos_token_id
pad_token_id = self.config.decoder.pad_token_id
if decoder_start_token_id is None:
raise ValueError("self.model.config.decoder.bos_token_id has to be defined. ")
# shift inputs to the right
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
shifted_input_ids[..., 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.decoder.pad_token_id has to be defined.")
# Is this T5 specific?
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
def make_default_2d_attention_mask(
token_ids: Optional[torch.LongTensor],
hidden_states: torch.Tensor,
pad_token_id: Optional[int],
) -> torch.Tensor:
"""Construct the default attention mask."""
if token_ids is not None:
if pad_token_id is None:
raise ValueError("`pad_token_id` is required for padding information.")
attention_mask = (token_ids != pad_token_id).to(hidden_states.device, torch.long)
else:
attention_mask = torch.ones(
(hidden_states.shape[0], hidden_states.shape[1]), device=hidden_states.device, dtype=torch.long
)
return attention_mask
class T5GemmaEncoder(T5GemmaPreTrainedModel):
_can_record_outputs = {
"attentions": T5GemmaSelfAttention,
"hidden_states": T5GemmaEncoderLayer,
}
def __init__(self, config):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.norm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = T5GemmaRotaryEmbedding(config=config)
self.gradient_checkpointing = False
self.layers = nn.ModuleList(
[T5GemmaEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.dropout = nn.Dropout(config.dropout_rate)
# Initialize weights and apply final processing
self.post_init()
@check_model_inputs
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutput:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
# As we want to pass `past_key_values=None` explicitly everwhere, we need to pop them from kwargs if present
kwargs.pop("past_key_values", None)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
if attention_mask is None:
attention_mask = make_default_2d_attention_mask(input_ids, inputs_embeds, self.config.pad_token_id)
if not isinstance(self_attn_mask_mapping := attention_mask, dict):
mask_kwargs = {
"config": self.config,
"input_embeds": inputs_embeds,
"attention_mask": attention_mask,
"cache_position": cache_position,
"past_key_values": None,
"position_ids": position_ids,
}
self_attn_mask_mapping = {
"full_attention": create_causal_mask(
**mask_kwargs,
or_mask_function=bidirectional_mask_function(attention_mask),
),
"sliding_attention": create_sliding_window_causal_mask(
**mask_kwargs,
or_mask_function=sliding_window_bidirectional_mask_function(self.config.sliding_window),
and_mask_function=bidirectional_mask_function(attention_mask),
),
}
hidden_states = inputs_embeds
position_embeddings = self.rotary_emb(hidden_states, position_ids)
normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
hidden_states = hidden_states * normalizer
hidden_states = self.dropout(hidden_states)
for layer_module in self.layers[: self.config.num_hidden_layers]:
hidden_states = layer_module(
hidden_states,
position_embeddings,
self_attn_mask_mapping[layer_module.attention_type],
position_ids,
**kwargs,
)
hidden_states = self.norm(hidden_states)
hidden_states = self.dropout(hidden_states)
return BaseModelOutput(
last_hidden_state=hidden_states,
)
class T5GemmaDecoder(T5GemmaEncoder):
_can_record_outputs = {
"attentions": OutputRecorder(T5GemmaSelfAttention, index=1),
"cross_attentions": OutputRecorder(T5GemmaCrossAttention, index=1),
"hidden_states": T5GemmaDecoderLayer,
}
def __init__(self, config):
super().__init__(config)
self.layers = nn.ModuleList(
[T5GemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.post_init()
@check_model_inputs
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[EncoderDecoderCache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutputWithPastAndCrossAttentions:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if encoder_hidden_states is None:
raise ValueError("`encoder_hidden_states` must be given in decoder")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if not self.training and use_cache and past_key_values is None:
past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
if attention_mask is None and past_key_values is None:
attention_mask = make_default_2d_attention_mask(input_ids, inputs_embeds, self.config.pad_token_id)
if not isinstance(self_attn_mask_mapping := attention_mask, dict):
mask_kwargs = {
"config": self.config,
"input_embeds": inputs_embeds,
"attention_mask": attention_mask,
"cache_position": cache_position,
"past_key_values": past_key_values.self_attention_cache if past_key_values is not None else None,
"position_ids": position_ids,
}
self_attn_mask_mapping = {
"full_attention": create_causal_mask(**mask_kwargs),
"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
}
if not isinstance(cross_attn_mask_mapping := encoder_attention_mask, dict):
mask_kwargs = {
"config": self.config,
"input_embeds": encoder_hidden_states,
"attention_mask": encoder_attention_mask,
"cache_position": cache_position,
"past_key_values": None,
"position_ids": None,
}
cross_attn_mask_mapping = {
"full_attention": create_causal_mask(
**mask_kwargs,
or_mask_function=bidirectional_mask_function(encoder_attention_mask),
),
}
hidden_states = inputs_embeds
position_embeddings = self.rotary_emb(hidden_states, position_ids)
normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
hidden_states = hidden_states * normalizer
hidden_states = self.dropout(hidden_states)
for layer_module in self.layers[: self.config.num_hidden_layers]:
hidden_states = layer_module(
hidden_states,
position_embeddings,
self_attn_mask_mapping[layer_module.attention_type],
position_ids,
past_key_values,
use_cache,
cache_position,
encoder_hidden_states,
cross_attn_mask_mapping["full_attention"],
**kwargs,
)
hidden_states = self.norm(hidden_states)
hidden_states = self.dropout(hidden_states)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
)
@auto_docstring
class T5GemmaModel(T5GemmaPreTrainedModel):
def __init__(self, config: T5GemmaConfig):
super().__init__(config)
if not config.is_encoder_decoder:
raise ValueError("T5GemmaModel only support encoder-decoder modeling. Use `T5GemmaEncoderModel` instead.")
self.encoder = T5GemmaEncoder(config.encoder)
self.decoder = T5GemmaDecoder(config.decoder)
self.post_init()
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def get_input_embeddings(self):
return self.encoder.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
return self.encoder.set_input_embeddings(new_embeddings)
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
decoder_position_ids: Optional[torch.LongTensor] = None,
encoder_outputs: Optional[BaseModelOutput] = None,
past_key_values: Optional[EncoderDecoderCache] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> Seq2SeqModelOutput:
r"""
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
"""
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
**kwargs,
)
encoder_hidden_states = encoder_outputs.last_hidden_state
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=attention_mask,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states
if kwargs.get("output_hidden_states", False)
else (decoder_outputs.last_hidden_state,),
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@auto_docstring
class T5GemmaEncoderModel(T5GemmaPreTrainedModel):
def __init__(self, config: T5GemmaConfig):
super().__init__(config)
if config.is_encoder_decoder:
raise ValueError("T5GemmaEncoderModel only supports encoder-only model. Use `T5GemmaModel` instead.")
self.encoder = T5GemmaEncoder(config.encoder)
self.post_init()
def get_input_embeddings(self):
return self.encoder.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
return self.encoder.set_input_embeddings(new_embeddings)
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutput:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
**kwargs,
)
return encoder_outputs
class T5GemmaForConditionalGeneration(T5GemmaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["model.decoder.embed_tokens.weight", "lm_head.out_proj.weight"]
_tp_plan = {"lm_head.out_proj": "colwise_rep"}
_pp_plan = {"lm_head.out_proj": (["hidden_states"], ["logits"])}
def __init__(self, config: T5GemmaConfig):
config.is_encoder_decoder = True
super().__init__(config)
self.model = T5GemmaModel(config)
self.vocab_size = config.decoder.vocab_size
self.lm_head = T5GemmaLMHead(config.decoder.hidden_size, self.vocab_size)
self.loss_type = "ForMaskedLM"
self.post_init()
def set_output_embeddings(self, new_embeddings):
self.lm_head.out_proj = new_embeddings
def get_output_embeddings(self):
return self.lm_head.out_proj
def _tie_weights(self):
# Decoder input and output embeddings are tied.
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.lm_head.out_proj, self.get_decoder().get_input_embeddings())
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
return self.model.decoder
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
decoder_position_ids: Optional[torch.LongTensor] = None,
encoder_outputs: Optional[BaseModelOutput] = None,
past_key_values: Optional[EncoderDecoderCache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[TransformersKwargs],
) -> Union[tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r"""
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
"""
if self.training and self.config._attn_implementation != "eager":
msg = (
"It is strongly recommended to train T5Gemma models with the `eager` attention implementation "
f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
)
if is_torchdynamo_compiling():
raise ValueError(msg)
else:
logger.warning_once(msg)
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
# get decoder inputs from shifting lm labels to the right
decoder_input_ids = self._shift_right(labels)
decoder_outputs: Seq2SeqModelOutput = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
hidden_states = decoder_outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
decoder_config = self.get_decoder().config
if decoder_config.final_logit_softcapping is not None:
logits = logits / decoder_config.final_logit_softcapping
logits = torch.tanh(logits)
logits = logits * decoder_config.final_logit_softcapping
loss = None
if labels is not None:
# Input has right-shifted so we directly perform masked lm loss
loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
return Seq2SeqLMOutput(
loss=loss,
logits=logits,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.decoder_hidden_states,
decoder_attentions=decoder_outputs.decoder_attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=decoder_outputs.encoder_last_hidden_state,
encoder_hidden_states=decoder_outputs.encoder_hidden_states,
encoder_attentions=decoder_outputs.encoder_attentions,
)
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return self._shift_right(labels)
@auto_docstring
class T5GemmaForSequenceClassification(T5GemmaPreTrainedModel):
def __init__(self, config: T5GemmaConfig, is_encoder_decoder: Optional[bool] = None):
r"""
is_encoder_decoder (`Optional`, *optional*):
Whether use encoder_decoder for sequence classification. When set to False, only encoder is used.
"""
if is_encoder_decoder is not None:
config.is_encoder_decoder = is_encoder_decoder
super().__init__(config)
self.num_labels = config.num_labels
if config.is_encoder_decoder:
self.model = T5GemmaModel(config)
else:
self.model = T5GemmaEncoderModel(config)
hidden_size = config.encoder.hidden_size
if config.is_encoder_decoder:
hidden_size = config.decoder.hidden_size
classifier_dropout = getattr(config, "classifier_dropout_rate", 0.1)
self.score = T5GemmaClassificationHead(hidden_size, self.num_labels, classifier_dropout)
self.post_init()
def get_input_embeddings(self):
return self.model.get_input_embeddings()
def set_input_embeddings(self, value):
self.model.set_input_embeddings(value)
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.Tensor] = None,
decoder_position_ids: Optional[torch.LongTensor] = None,
encoder_outputs: Optional[BaseModelOutput] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> SequenceClassifierOutput:
r"""
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
if self.config.is_encoder_decoder and (input_ids is None and inputs_embeds is not None):
raise NotImplementedError(
f"Passing input embeddings is currently not supported for {self.__class__.__name__} in encoder-decoder mode."
)
# Following T5, we automatically creates decoder_input_ids from input_ids if no decoder_input_ids are provided
if self.config.is_encoder_decoder and (decoder_input_ids is None and decoder_inputs_embeds is None):
if input_ids is None:
raise ValueError(
"If no `decoder_input_ids` or `decoder_inputs_embeds` are "
"passed, `input_ids` cannot be `None`. Please pass either "
"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
)
decoder_input_ids = self._shift_right(input_ids)
if self.config.is_encoder_decoder:
outputs: Seq2SeqModelOutput = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=False,
**kwargs,
)
last_hidden_state = outputs.last_hidden_state
hidden_states = outputs.decoder_hidden_states
attentions = outputs.decoder_attentions
else:
outputs: BaseModelOutput = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
**kwargs,
)
last_hidden_state = outputs.last_hidden_state
hidden_states = outputs.hidden_states
attentions = outputs.attentions
logits = self.score(last_hidden_state)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
if self.config.is_encoder_decoder:
last_non_pad_token += 1 # due to the right shift.
last_non_pad_token = torch.clamp(last_non_pad_token, max=decoder_input_ids.shape[-1] - 1)
else:
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
return SequenceClassifierOutput(
loss=loss,
logits=pooled_logits,
hidden_states=hidden_states,
attentions=attentions,
)
@auto_docstring
class T5GemmaForTokenClassification(T5GemmaPreTrainedModel):
def __init__(self, config: T5GemmaConfig, is_encoder_decoder: Optional[bool] = None):
r"""
is_encoder_decoder (`Optional`, *optional*):
Whether use encoder_decoder for token classification. When set to False, only encoder is used.
"""
if is_encoder_decoder is not None:
config.is_encoder_decoder = is_encoder_decoder
super().__init__(config)
self.num_labels = config.num_labels
if config.is_encoder_decoder:
self.model = T5GemmaModel(config)
else:
self.model = T5GemmaEncoderModel(config)
hidden_size = config.encoder.hidden_size
if config.is_encoder_decoder:
hidden_size = config.decoder.hidden_size
classifier_dropout = getattr(config, "classifier_dropout_rate", 0.1)
self.score = T5GemmaClassificationHead(hidden_size, self.num_labels, classifier_dropout)
self.post_init()
def get_input_embeddings(self):
return self.model.get_input_embeddings()
def set_input_embeddings(self, value):
self.model.set_input_embeddings(value)
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.Tensor] = None,
decoder_position_ids: Optional[torch.LongTensor] = None,
encoder_outputs: Optional[BaseModelOutput] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> TokenClassifierOutput:
r"""
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
if self.config.is_encoder_decoder and (input_ids is None and inputs_embeds is not None):
raise NotImplementedError(
f"Passing input embeddings is currently not supported for {self.__class__.__name__} in encoder-decoder mode."
)
if self.config.is_encoder_decoder and (decoder_input_ids is None and decoder_inputs_embeds is None):
if input_ids is None:
raise ValueError(
"If no `decoder_input_ids` or `decoder_inputs_embeds` are "
"passed, `input_ids` cannot be `None`. Please pass either "
"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
)
decoder_input_ids = self._shift_right(input_ids)
if self.config.is_encoder_decoder:
outputs: Seq2SeqModelOutput = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=False,
**kwargs,
)
last_hidden_state = outputs.last_hidden_state
hidden_states = outputs.decoder_hidden_states
attentions = outputs.decoder_attentions
else:
outputs: BaseModelOutput = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
**kwargs,
)
last_hidden_state = outputs.last_hidden_state
hidden_states = outputs.hidden_states
attentions = outputs.attentions
logits = self.score(last_hidden_state)
loss = None
if labels is not None:
loss = self.loss_function(logits, labels, self.config)
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=hidden_states,
attentions=attentions,
)
__all__ = [
"T5GemmaConfig",
"T5GemmaModuleConfig",
"T5GemmaForConditionalGeneration",
"T5GemmaModel",
"T5GemmaEncoderModel",
"T5GemmaPreTrainedModel", # noqa: F822
"T5GemmaForSequenceClassification",
"T5GemmaForTokenClassification",
]
| transformers/src/transformers/models/t5gemma/modular_t5gemma.py/0 | {
"file_path": "transformers/src/transformers/models/t5gemma/modular_t5gemma.py",
"repo_id": "transformers",
"token_count": 23782
} | 493 |
# coding=utf-8
# Copyright 2025 the Fast authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fast Image processor class for TextNet."""
from typing import Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_processing_utils_fast import BaseImageProcessorFast, DefaultFastImageProcessorKwargs
from ...image_transforms import (
get_resize_output_image_size,
group_images_by_shape,
reorder_images,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
SizeDict,
)
from ...processing_utils import Unpack
from ...utils import (
TensorType,
auto_docstring,
is_torch_available,
is_torchvision_available,
is_torchvision_v2_available,
)
if is_torch_available():
import torch
if is_torchvision_available():
if is_torchvision_v2_available():
from torchvision.transforms.v2 import functional as F
else:
from torchvision.transforms import functional as F
class TextNetFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
"""
size_divisor (`int`, *optional*, defaults to 32):
Ensures height and width are rounded to a multiple of this value after resizing.
"""
size_divisor: Optional[int]
@auto_docstring
class TextNetImageProcessorFast(BaseImageProcessorFast):
resample = PILImageResampling.BILINEAR
image_mean = IMAGENET_DEFAULT_MEAN
image_std = IMAGENET_DEFAULT_STD
size = {"shortest_edge": 640}
default_to_square = False
crop_size = {"height": 224, "width": 224}
do_resize = True
do_center_crop = False
do_rescale = True
do_normalize = True
do_convert_rgb = True
size_divisor = 32
valid_kwargs = TextNetFastImageProcessorKwargs
def __init__(self, **kwargs: Unpack[TextNetFastImageProcessorKwargs]) -> None:
super().__init__(**kwargs)
@auto_docstring
def preprocess(self, images: ImageInput, **kwargs: Unpack[TextNetFastImageProcessorKwargs]) -> BatchFeature:
return super().preprocess(images, **kwargs)
def resize(
self,
image: "torch.Tensor",
size: SizeDict,
interpolation: "F.InterpolationMode" = None,
antialias: bool = True,
size_divisor: int = 32,
**kwargs,
) -> "torch.Tensor":
if size.shortest_edge:
new_size = get_resize_output_image_size(
image,
size=size.shortest_edge,
default_to_square=False,
input_data_format=ChannelDimension.FIRST,
)
else:
raise ValueError(f"Size must contain 'shortest_edge' key. Got {size}.")
# ensure height and width are divisible by size_divisor
height, width = new_size
if height % size_divisor != 0:
height += size_divisor - (height % size_divisor)
if width % size_divisor != 0:
width += size_divisor - (width % size_divisor)
return super().resize(
image, SizeDict(height=height, width=width), interpolation=interpolation, antialias=antialias
)
def _preprocess(
self,
images: list["torch.Tensor"],
do_resize: bool,
size: SizeDict,
size_divisor: int,
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: SizeDict,
do_rescale: bool,
rescale_factor: float,
do_normalize: bool,
image_mean: Optional[Union[float, list[float]]],
image_std: Optional[Union[float, list[float]]],
disable_grouping: Optional[bool],
return_tensors: Optional[Union[str, TensorType]],
**kwargs,
) -> BatchFeature:
# Group images by size for batched resizing
grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
resized_images_grouped = {}
for shape, stacked_images in grouped_images.items():
if do_resize:
stacked_images = self.resize(
image=stacked_images, size=size, interpolation=interpolation, size_divisor=size_divisor
)
resized_images_grouped[shape] = stacked_images
resized_images = reorder_images(resized_images_grouped, grouped_images_index)
# Group images by size for further processing
# Needed in case do_resize is False, or resize returns images with different sizes
grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
processed_images_grouped = {}
for shape, stacked_images in grouped_images.items():
if do_center_crop:
stacked_images = self.center_crop(stacked_images, crop_size)
# Fused rescale and normalize
stacked_images = self.rescale_and_normalize(
stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
)
processed_images_grouped[shape] = stacked_images
processed_images = reorder_images(processed_images_grouped, grouped_images_index)
processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
__all__ = ["TextNetImageProcessorFast"]
| transformers/src/transformers/models/textnet/image_processing_textnet_fast.py/0 | {
"file_path": "transformers/src/transformers/models/textnet/image_processing_textnet_fast.py",
"repo_id": "transformers",
"token_count": 2399
} | 494 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Union
import torch
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class TimmBackbone(PreTrainedModel, BackboneMixin):
"""
Wrapper class for timm models to be used as backbones. This enables using the timm models interchangeably with the
other models in the library keeping the same API.
"""
main_input_name = "pixel_values"
supports_gradient_checkpointing = False
config: TimmBackboneConfig
def __init__(self, config, **kwargs):
requires_backends(self, "timm")
super().__init__(config)
self.config = config
if config.backbone is None:
raise ValueError("backbone is not set in the config. Please set it to a timm model name.")
if hasattr(config, "out_features") and config.out_features is not None:
raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead.")
pretrained = getattr(config, "use_pretrained_backbone", None)
if pretrained is None:
raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False.")
# We just take the final layer by default. This matches the default for the transformers models.
out_indices = config.out_indices if getattr(config, "out_indices", None) is not None else (-1,)
in_chans = kwargs.pop("in_chans", config.num_channels)
self._backbone = timm.create_model(
config.backbone,
pretrained=pretrained,
# This is currently not possible for transformer architectures.
features_only=config.features_only,
in_chans=in_chans,
out_indices=out_indices,
**kwargs,
)
# Converts all `BatchNorm2d` and `SyncBatchNorm` or `BatchNormAct2d` and `SyncBatchNormAct2d` layers of provided module into `FrozenBatchNorm2d` or `FrozenBatchNormAct2d` respectively
if getattr(config, "freeze_batch_norm_2d", False):
self.freeze_batch_norm_2d()
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
self._return_layers = {
layer["module"]: str(layer["index"]) for layer in self._backbone.feature_info.get_dicts()
}
self._all_layers = {layer["module"]: str(i) for i, layer in enumerate(self._backbone.feature_info.info)}
super()._init_backbone(config)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
requires_backends(cls, ["vision", "timm"])
from ...models.timm_backbone import TimmBackboneConfig
config = kwargs.pop("config", TimmBackboneConfig())
use_timm = kwargs.pop("use_timm_backbone", True)
if not use_timm:
raise ValueError("use_timm_backbone must be True for timm backbones")
num_channels = kwargs.pop("num_channels", config.num_channels)
features_only = kwargs.pop("features_only", config.features_only)
use_pretrained_backbone = kwargs.pop("use_pretrained_backbone", config.use_pretrained_backbone)
out_indices = kwargs.pop("out_indices", config.out_indices)
config = TimmBackboneConfig(
backbone=pretrained_model_name_or_path,
num_channels=num_channels,
features_only=features_only,
use_pretrained_backbone=use_pretrained_backbone,
out_indices=out_indices,
)
return super()._from_config(config, **kwargs)
def freeze_batch_norm_2d(self):
timm.utils.model.freeze_batch_norm_2d(self._backbone)
def unfreeze_batch_norm_2d(self):
timm.utils.model.unfreeze_batch_norm_2d(self._backbone)
def _init_weights(self, module):
"""
Empty init weights function to ensure compatibility of the class in the library.
"""
pass
def forward(
self,
pixel_values: torch.FloatTensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[BackboneOutput, tuple[Tensor, ...]]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("Cannot output attentions for timm backbones at the moment")
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
self._backbone.return_layers = self._all_layers
hidden_states = self._backbone(pixel_values, **kwargs)
self._backbone.return_layers = self._return_layers
feature_maps = tuple(hidden_states[i] for i in self.out_indices)
else:
feature_maps = self._backbone(pixel_values, **kwargs)
hidden_states = None
feature_maps = tuple(feature_maps)
hidden_states = tuple(hidden_states) if hidden_states is not None else None
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=feature_maps, hidden_states=hidden_states, attentions=None)
__all__ = ["TimmBackbone"]
| transformers/src/transformers/models/timm_backbone/modeling_timm_backbone.py/0 | {
"file_path": "transformers/src/transformers/models/timm_backbone/modeling_timm_backbone.py",
"repo_id": "transformers",
"token_count": 2572
} | 495 |
# coding=utf-8
# Copyright The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""VisionTextDualEncoder model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..chinese_clip.configuration_chinese_clip import ChineseCLIPVisionConfig
from ..clip.configuration_clip import CLIPVisionConfig
from ..siglip.configuration_siglip import SiglipVisionConfig
logger = logging.get_logger(__name__)
VISION_MODEL_CONFIGS = {
"clip_vision_model": CLIPVisionConfig,
"chinese_clip_vision_model": ChineseCLIPVisionConfig,
"siglip_vision_model": SiglipVisionConfig,
}
class VisionTextDualEncoderConfig(PretrainedConfig):
r"""
[`VisionTextDualEncoderConfig`] is the configuration class to store the configuration of a
[`VisionTextDualEncoderModel`]. It is used to instantiate [`VisionTextDualEncoderModel`] model according to the
specified arguments, defining the text model and vision model configs.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and vision projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The initial value of the *logit_scale* parameter. Default is used as per the original CLIP implementation.
kwargs (*optional*):
Dictionary of keyword arguments.
Examples:
```python
>>> from transformers import ViTConfig, BertConfig, VisionTextDualEncoderConfig, VisionTextDualEncoderModel
>>> # Initializing a BERT and ViT configuration
>>> config_vision = ViTConfig()
>>> config_text = BertConfig()
>>> config = VisionTextDualEncoderConfig.from_vision_text_configs(config_vision, config_text, projection_dim=512)
>>> # Initializing a BERT and ViT model (with random weights)
>>> model = VisionTextDualEncoderModel(config=config)
>>> # Accessing the model configuration
>>> config_vision = model.config.vision_config
>>> config_text = model.config.text_config
>>> # Saving the model, including its configuration
>>> model.save_pretrained("vit-bert")
>>> # loading model and config from pretrained folder
>>> vision_text_config = VisionTextDualEncoderConfig.from_pretrained("vit-bert")
>>> model = VisionTextDualEncoderModel.from_pretrained("vit-bert", config=vision_text_config)
```"""
model_type = "vision-text-dual-encoder"
sub_configs = {"vision_config": AutoConfig, "text_config": AutoConfig}
has_no_defaults_at_init = True
def __init__(self, projection_dim=512, logit_scale_init_value=2.6592, **kwargs):
super().__init__(**kwargs)
if "vision_config" not in kwargs:
raise ValueError("`vision_config` can not be `None`.")
if "text_config" not in kwargs:
raise ValueError("`text_config` can not be `None`.")
vision_config = kwargs.pop("vision_config")
text_config = kwargs.pop("text_config")
vision_model_type = vision_config.pop("model_type")
text_model_type = text_config.pop("model_type")
vision_config_class = VISION_MODEL_CONFIGS.get(vision_model_type)
if vision_config_class is not None:
self.vision_config = vision_config_class(**vision_config)
else:
self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config)
if hasattr(self.vision_config, "vision_config"):
self.vision_config = self.vision_config.vision_config
self.text_config = AutoConfig.for_model(text_model_type, **text_config)
self.projection_dim = projection_dim
self.logit_scale_init_value = logit_scale_init_value
@classmethod
def from_vision_text_configs(cls, vision_config: PretrainedConfig, text_config: PretrainedConfig, **kwargs):
r"""
Instantiate a [`VisionTextDualEncoderConfig`] (or a derived class) from text model configuration and vision
model configuration.
Returns:
[`VisionTextDualEncoderConfig`]: An instance of a configuration object
"""
return cls(vision_config=vision_config.to_dict(), text_config=text_config.to_dict(), **kwargs)
__all__ = ["VisionTextDualEncoderConfig"]
| transformers/src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py/0 | {
"file_path": "transformers/src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py",
"repo_id": "transformers",
"token_count": 1704
} | 496 |
# coding=utf-8
# Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxBaseModelOutputWithPooling, FlaxSequenceClassifierOutput
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward
from .configuration_vit import ViTConfig
VIT_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a
[flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`ViTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
VIT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class FlaxViTPatchEmbeddings(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
image_size = self.config.image_size
patch_size = self.config.patch_size
num_patches = (image_size // patch_size) * (image_size // patch_size)
self.num_patches = num_patches
self.num_channels = self.config.num_channels
self.projection = nn.Conv(
self.config.hidden_size,
kernel_size=(patch_size, patch_size),
strides=(patch_size, patch_size),
padding="VALID",
dtype=self.dtype,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, "fan_in", "truncated_normal"
),
)
def __call__(self, pixel_values):
num_channels = pixel_values.shape[-1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embeddings = self.projection(pixel_values)
batch_size, _, _, channels = embeddings.shape
return jnp.reshape(embeddings, (batch_size, -1, channels))
class FlaxViTEmbeddings(nn.Module):
"""Construct the CLS token, position and patch embeddings."""
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.cls_token = self.param(
"cls_token",
jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"),
(1, 1, self.config.hidden_size),
)
self.patch_embeddings = FlaxViTPatchEmbeddings(self.config, dtype=self.dtype)
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = self.param(
"position_embeddings",
jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"),
(1, num_patches + 1, self.config.hidden_size),
)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
def __call__(self, pixel_values, deterministic=True):
batch_size = pixel_values.shape[0]
embeddings = self.patch_embeddings(pixel_values)
cls_tokens = jnp.broadcast_to(self.cls_token, (batch_size, 1, self.config.hidden_size))
embeddings = jnp.concatenate((cls_tokens, embeddings), axis=1)
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings, deterministic=deterministic)
return embeddings
class FlaxViTSelfAttention(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
if self.config.hidden_size % self.config.num_attention_heads != 0:
raise ValueError(
"`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads`:"
" {self.config.num_attention_heads}"
)
self.query = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"
),
use_bias=self.config.qkv_bias,
)
self.key = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"
),
use_bias=self.config.qkv_bias,
)
self.value = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"
),
use_bias=self.config.qkv_bias,
)
def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False):
head_dim = self.config.hidden_size // self.config.num_attention_heads
query_states = self.query(hidden_states).reshape(
hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
)
value_states = self.value(hidden_states).reshape(
hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
)
key_states = self.key(hidden_states).reshape(
hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
)
dropout_rng = None
if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
dropout_rng=dropout_rng,
dropout_rate=self.config.attention_probs_dropout_prob,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
return outputs
class FlaxViTSelfOutput(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, "fan_in", "truncated_normal"
),
dtype=self.dtype,
)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
return hidden_states
class FlaxViTAttention(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.attention = FlaxViTSelfAttention(self.config, dtype=self.dtype)
self.output = FlaxViTSelfOutput(self.config, dtype=self.dtype)
def __call__(self, hidden_states, deterministic=True, output_attentions: bool = False):
attn_outputs = self.attention(hidden_states, deterministic=deterministic, output_attentions=output_attentions)
attn_output = attn_outputs[0]
hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_outputs[1],)
return outputs
class FlaxViTIntermediate(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, "fan_in", "truncated_normal"
),
dtype=self.dtype,
)
self.activation = ACT2FN[self.config.hidden_act]
def __call__(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
class FlaxViTOutput(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, "fan_in", "truncated_normal"
),
dtype=self.dtype,
)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
def __call__(self, hidden_states, attention_output, deterministic: bool = True):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = hidden_states + attention_output
return hidden_states
class FlaxViTLayer(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxViTAttention(self.config, dtype=self.dtype)
self.intermediate = FlaxViTIntermediate(self.config, dtype=self.dtype)
self.output = FlaxViTOutput(self.config, dtype=self.dtype)
self.layernorm_before = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.layernorm_after = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False):
attention_outputs = self.attention(
self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention
deterministic=deterministic,
output_attentions=output_attentions,
)
attention_output = attention_outputs[0]
# first residual connection
attention_output = attention_output + hidden_states
# in ViT, layernorm is also applied after self-attention
layer_output = self.layernorm_after(attention_output)
hidden_states = self.intermediate(layer_output)
hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)
outputs = (hidden_states,)
if output_attentions:
outputs += (attention_outputs[1],)
return outputs
class FlaxViTLayerCollection(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxViTLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_states,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = layer(hidden_states, deterministic=deterministic, output_attentions=output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions += (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states,)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
class FlaxViTEncoder(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layer = FlaxViTLayerCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_states,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
return self.layer(
hidden_states,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
class FlaxViTPooler(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.pooler_output_size,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, "fan_in", "truncated_normal"
),
dtype=self.dtype,
)
self.activation = ACT2FN[self.config.pooler_act]
def __call__(self, hidden_states):
cls_hidden_state = hidden_states[:, 0]
cls_hidden_state = self.dense(cls_hidden_state)
return self.activation(cls_hidden_state)
class FlaxViTPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViTConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
module_class: nn.Module = None
def __init__(
self,
config: ViTConfig,
input_shape=None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
if input_shape is None:
input_shape = (1, config.image_size, config.image_size, config.num_channels)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
pixel_values = jnp.zeros(input_shape, dtype=self.dtype)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(rngs, pixel_values, return_dict=False)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
@add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def __call__(
self,
pixel_values,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
return self.module.apply(
{"params": params or self.params},
jnp.array(pixel_values, dtype=jnp.float32),
not train,
output_attentions,
output_hidden_states,
return_dict,
rngs=rngs,
)
class FlaxViTModule(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
add_pooling_layer: bool = True
def setup(self):
self.embeddings = FlaxViTEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxViTEncoder(self.config, dtype=self.dtype)
self.layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.pooler = FlaxViTPooler(self.config, dtype=self.dtype) if self.add_pooling_layer else None
def __call__(
self,
pixel_values,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
hidden_states = self.embeddings(pixel_values, deterministic=deterministic)
outputs = self.encoder(
hidden_states,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
hidden_states = self.layernorm(hidden_states)
pooled = self.pooler(hidden_states) if self.add_pooling_layer else None
if not return_dict:
# if pooled is None, don't return it
if pooled is None:
return (hidden_states,) + outputs[1:]
return (hidden_states, pooled) + outputs[1:]
return FlaxBaseModelOutputWithPooling(
last_hidden_state=hidden_states,
pooler_output=pooled,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"The bare ViT Model transformer outputting raw hidden-states without any specific head on top.",
VIT_START_DOCSTRING,
)
class FlaxViTModel(FlaxViTPreTrainedModel):
module_class = FlaxViTModule
FLAX_VISION_MODEL_DOCSTRING = """
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, FlaxViTModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> model = FlaxViTModel.from_pretrained("google/vit-base-patch16-224-in21k")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```
"""
overwrite_call_docstring(FlaxViTModel, FLAX_VISION_MODEL_DOCSTRING)
append_replace_return_docstrings(FlaxViTModel, output_type=FlaxBaseModelOutputWithPooling, config_class=ViTConfig)
class FlaxViTForImageClassificationModule(nn.Module):
config: ViTConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.vit = FlaxViTModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
self.classifier = nn.Dense(
self.config.num_labels,
dtype=self.dtype,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializer_range**2, "fan_in", "truncated_normal"
),
)
def __call__(
self,
pixel_values=None,
deterministic: bool = True,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.vit(
pixel_values,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.classifier(hidden_states[:, 0, :])
if not return_dict:
output = (logits,) + outputs[2:]
return output
return FlaxSequenceClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.
""",
VIT_START_DOCSTRING,
)
class FlaxViTForImageClassification(FlaxViTPreTrainedModel):
module_class = FlaxViTForImageClassificationModule
FLAX_VISION_CLASSIF_DOCSTRING = """
Returns:
Example:
```python
>>> from transformers import AutoImageProcessor, FlaxViTForImageClassification
>>> from PIL import Image
>>> import jax
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
>>> model = FlaxViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1)
>>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()])
```
"""
overwrite_call_docstring(FlaxViTForImageClassification, FLAX_VISION_CLASSIF_DOCSTRING)
append_replace_return_docstrings(
FlaxViTForImageClassification, output_type=FlaxSequenceClassifierOutput, config_class=ViTConfig
)
__all__ = ["FlaxViTForImageClassification", "FlaxViTModel", "FlaxViTPreTrainedModel"]
| transformers/src/transformers/models/vit/modeling_flax_vit.py/0 | {
"file_path": "transformers/src/transformers/models/vit/modeling_flax_vit.py",
"repo_id": "transformers",
"token_count": 10905
} | 497 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""VitMatte model configuration"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import verify_backbone_config_arguments
from ..auto.configuration_auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
class VitMatteConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of [`VitMatteForImageMatting`]. It is used to
instantiate a ViTMatte model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the ViTMatte
[hustvl/vitmatte-small-composition-1k](https://huggingface.co/hustvl/vitmatte-small-composition-1k) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `VitDetConfig()`):
The configuration of the backbone model.
backbone (`str`, *optional*):
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
Whether to use pretrained weights for the backbone.
use_timm_backbone (`bool`, *optional*, defaults to `False`):
Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
library.
backbone_kwargs (`dict`, *optional*):
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
hidden_size (`int`, *optional*, defaults to 384):
The number of input channels of the decoder.
batch_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the batch norm layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
convstream_hidden_sizes (`list[int]`, *optional*, defaults to `[48, 96, 192]`):
The output channels of the ConvStream module.
fusion_hidden_sizes (`list[int]`, *optional*, defaults to `[256, 128, 64, 32]`):
The output channels of the Fusion blocks.
Example:
```python
>>> from transformers import VitMatteConfig, VitMatteForImageMatting
>>> # Initializing a ViTMatte hustvl/vitmatte-small-composition-1k style configuration
>>> configuration = VitMatteConfig()
>>> # Initializing a model (with random weights) from the hustvl/vitmatte-small-composition-1k style configuration
>>> model = VitMatteForImageMatting(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "vitmatte"
def __init__(
self,
backbone_config: PretrainedConfig = None,
backbone=None,
use_pretrained_backbone=False,
use_timm_backbone=False,
backbone_kwargs=None,
hidden_size: int = 384,
batch_norm_eps: float = 1e-5,
initializer_range: float = 0.02,
convstream_hidden_sizes: list[int] = [48, 96, 192],
fusion_hidden_sizes: list[int] = [256, 128, 64, 32],
**kwargs,
):
super().__init__(**kwargs)
if backbone_config is None and backbone is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `VitDet` backbone.")
backbone_config = CONFIG_MAPPING["vitdet"](out_features=["stage4"])
elif isinstance(backbone_config, dict):
backbone_model_type = backbone_config.get("model_type")
config_class = CONFIG_MAPPING[backbone_model_type]
backbone_config = config_class.from_dict(backbone_config)
verify_backbone_config_arguments(
use_timm_backbone=use_timm_backbone,
use_pretrained_backbone=use_pretrained_backbone,
backbone=backbone,
backbone_config=backbone_config,
backbone_kwargs=backbone_kwargs,
)
self.backbone_config = backbone_config
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.use_timm_backbone = use_timm_backbone
self.backbone_kwargs = backbone_kwargs
self.batch_norm_eps = batch_norm_eps
self.hidden_size = hidden_size
self.initializer_range = initializer_range
self.convstream_hidden_sizes = convstream_hidden_sizes
self.fusion_hidden_sizes = fusion_hidden_sizes
@property
def sub_configs(self):
return (
{"backbone_config": type(self.backbone_config)}
if getattr(self, "backbone_config", None) is not None
else {}
)
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns:
`dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
output["backbone_config"] = self.backbone_config.to_dict()
output["model_type"] = self.__class__.model_type
return output
__all__ = ["VitMatteConfig"]
| transformers/src/transformers/models/vitmatte/configuration_vitmatte.py/0 | {
"file_path": "transformers/src/transformers/models/vitmatte/configuration_vitmatte.py",
"repo_id": "transformers",
"token_count": 2393
} | 498 |
# coding=utf-8
# Copyright 2023 The Kakao Enterprise Authors and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch VITS model."""
import math
from dataclasses import dataclass
from typing import Any, Optional, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...integrations.fsdp import is_fsdp_managed_module
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, ModelOutput
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, logging
from .configuration_vits import VitsConfig
logger = logging.get_logger(__name__)
@dataclass
@auto_docstring(
custom_intro="""
Describes the outputs for the VITS model, with potential hidden states and attentions.
"""
)
class VitsModelOutput(ModelOutput):
r"""
waveform (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
The final audio waveform predicted by the model.
sequence_lengths (`torch.FloatTensor` of shape `(batch_size,)`):
The length in samples of each element in the `waveform` batch.
spectrogram (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_bins)`):
The log-mel spectrogram predicted at the output of the flow model. This spectrogram is passed to the Hi-Fi
GAN decoder model to obtain the final audio waveform.
"""
waveform: Optional[torch.FloatTensor] = None
sequence_lengths: Optional[torch.FloatTensor] = None
spectrogram: Optional[tuple[torch.FloatTensor]] = None
hidden_states: Optional[tuple[torch.FloatTensor]] = None
attentions: Optional[tuple[torch.FloatTensor]] = None
@dataclass
@auto_docstring(
custom_intro="""
Describes the outputs for the VITS text encoder model, with potential hidden states and attentions.
"""
)
class VitsTextEncoderOutput(ModelOutput):
r"""
prior_means (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
The predicted mean values of the prior distribution for the latent text variables.
prior_log_variances (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
The predicted log-variance values of the prior distribution for the latent text variables.
"""
last_hidden_state: Optional[torch.FloatTensor] = None
prior_means: Optional[torch.FloatTensor] = None
prior_log_variances: Optional[torch.FloatTensor] = None
hidden_states: Optional[tuple[torch.FloatTensor]] = None
attentions: Optional[tuple[torch.FloatTensor]] = None
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, num_channels):
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :num_channels, :])
s_act = torch.sigmoid(in_act[:, num_channels:, :])
acts = t_act * s_act
return acts
def _unconstrained_rational_quadratic_spline(
inputs,
unnormalized_widths,
unnormalized_heights,
unnormalized_derivatives,
reverse=False,
tail_bound=5.0,
min_bin_width=1e-3,
min_bin_height=1e-3,
min_derivative=1e-3,
):
"""
This transformation represents a monotonically increasing piecewise rational quadratic function. Outside of the
`tail_bound`, the transform behaves as an identity function.
Args:
inputs (`torch.FloatTensor` of shape `(batch_size, channels, seq_len)`:
Second half of the hidden-states input to the Vits convolutional flow module.
unnormalized_widths (`torch.FloatTensor` of shape `(batch_size, channels, seq_len, duration_predictor_flow_bins)`):
First `duration_predictor_flow_bins` of the hidden-states from the output of the convolution projection
layer in the convolutional flow module
unnormalized_heights (`torch.FloatTensor` of shape `(batch_size, channels, seq_len, duration_predictor_flow_bins)`):
Second `duration_predictor_flow_bins` of the hidden-states from the output of the convolution projection
layer in the convolutional flow module
unnormalized_derivatives (`torch.FloatTensor` of shape `(batch_size, channels, seq_len, duration_predictor_flow_bins)`):
Third `duration_predictor_flow_bins` of the hidden-states from the output of the convolution projection
layer in the convolutional flow module
reverse (`bool`, *optional*, defaults to `False`):
Whether the model is being run in reverse mode.
tail_bound (`float`, *optional* defaults to 5):
Upper and lower limit bound for the rational quadratic function. Outside of this `tail_bound`, the
transform behaves as an identity function.
min_bin_width (`float`, *optional*, defaults to 1e-3):
Minimum bin value across the width dimension for the piecewise rational quadratic function.
min_bin_height (`float`, *optional*, defaults to 1e-3):
Minimum bin value across the height dimension for the piecewise rational quadratic function.
min_derivative (`float`, *optional*, defaults to 1e-3):
Minimum bin value across the derivatives for the piecewise rational quadratic function.
Returns:
outputs (`torch.FloatTensor` of shape `(batch_size, channels, seq_len)`:
Hidden-states as transformed by the piecewise rational quadratic function with the `tail_bound` limits
applied.
log_abs_det (`torch.FloatTensor` of shape `(batch_size, channels, seq_len)`:
Logarithm of the absolute value of the determinants corresponding to the `outputs` with the `tail_bound`
limits applied.
"""
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
outside_interval_mask = ~inside_interval_mask
outputs = torch.zeros_like(inputs)
log_abs_det = torch.zeros_like(inputs)
constant = np.log(np.exp(1 - min_derivative) - 1)
unnormalized_derivatives = nn.functional.pad(unnormalized_derivatives, pad=(1, 1))
unnormalized_derivatives[..., 0] = constant
unnormalized_derivatives[..., -1] = constant
outputs[outside_interval_mask] = inputs[outside_interval_mask]
log_abs_det[outside_interval_mask] = 0.0
outputs[inside_interval_mask], log_abs_det[inside_interval_mask] = _rational_quadratic_spline(
inputs=inputs[inside_interval_mask],
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
reverse=reverse,
tail_bound=tail_bound,
min_bin_width=min_bin_width,
min_bin_height=min_bin_height,
min_derivative=min_derivative,
)
return outputs, log_abs_det
def _rational_quadratic_spline(
inputs,
unnormalized_widths,
unnormalized_heights,
unnormalized_derivatives,
reverse,
tail_bound,
min_bin_width,
min_bin_height,
min_derivative,
):
"""
This transformation represents a monotonically increasing piecewise rational quadratic function. Unlike the
function `_unconstrained_rational_quadratic_spline`, the function behaves the same across the `tail_bound`.
Args:
inputs (`torch.FloatTensor` of shape `(batch_size, channels, seq_len)`:
Second half of the hidden-states input to the Vits convolutional flow module.
unnormalized_widths (`torch.FloatTensor` of shape `(batch_size, channels, seq_len, duration_predictor_flow_bins)`):
First `duration_predictor_flow_bins` of the hidden-states from the output of the convolution projection
layer in the convolutional flow module
unnormalized_heights (`torch.FloatTensor` of shape `(batch_size, channels, seq_len, duration_predictor_flow_bins)`):
Second `duration_predictor_flow_bins` of the hidden-states from the output of the convolution projection
layer in the convolutional flow module
unnormalized_derivatives (`torch.FloatTensor` of shape `(batch_size, channels, seq_len, duration_predictor_flow_bins)`):
Third `duration_predictor_flow_bins` of the hidden-states from the output of the convolution projection
layer in the convolutional flow module
reverse (`bool`):
Whether the model is being run in reverse mode.
tail_bound (`float`):
Upper and lower limit bound for the rational quadratic function. Outside of this `tail_bound`, the
transform behaves as an identity function.
min_bin_width (`float`):
Minimum bin value across the width dimension for the piecewise rational quadratic function.
min_bin_height (`float`):
Minimum bin value across the height dimension for the piecewise rational quadratic function.
min_derivative (`float`):
Minimum bin value across the derivatives for the piecewise rational quadratic function.
Returns:
outputs (`torch.FloatTensor` of shape `(batch_size, channels, seq_len)`:
Hidden-states as transformed by the piecewise rational quadratic function.
log_abs_det (`torch.FloatTensor` of shape `(batch_size, channels, seq_len)`:
Logarithm of the absolute value of the determinants corresponding to the `outputs`.
"""
upper_bound = tail_bound
lower_bound = -tail_bound
if torch.min(inputs) < lower_bound or torch.max(inputs) > upper_bound:
raise ValueError("Input to a transform is not within its domain")
num_bins = unnormalized_widths.shape[-1]
if min_bin_width * num_bins > 1.0:
raise ValueError(f"Minimal bin width {min_bin_width} too large for the number of bins {num_bins}")
if min_bin_height * num_bins > 1.0:
raise ValueError(f"Minimal bin height {min_bin_height} too large for the number of bins {num_bins}")
widths = nn.functional.softmax(unnormalized_widths, dim=-1)
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
cumwidths = torch.cumsum(widths, dim=-1)
cumwidths = nn.functional.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
cumwidths = (upper_bound - lower_bound) * cumwidths + lower_bound
cumwidths[..., 0] = lower_bound
cumwidths[..., -1] = upper_bound
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
derivatives = min_derivative + nn.functional.softplus(unnormalized_derivatives)
heights = nn.functional.softmax(unnormalized_heights, dim=-1)
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
cumheights = torch.cumsum(heights, dim=-1)
cumheights = nn.functional.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
cumheights = (upper_bound - lower_bound) * cumheights + lower_bound
cumheights[..., 0] = lower_bound
cumheights[..., -1] = upper_bound
heights = cumheights[..., 1:] - cumheights[..., :-1]
bin_locations = cumheights if reverse else cumwidths
bin_locations[..., -1] += 1e-6
bin_idx = torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
bin_idx = bin_idx[..., None]
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
delta = heights / widths
input_delta = delta.gather(-1, bin_idx)[..., 0]
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
input_heights = heights.gather(-1, bin_idx)[..., 0]
intermediate1 = input_derivatives + input_derivatives_plus_one - 2 * input_delta
if not reverse:
theta = (inputs - input_cumwidths) / input_bin_widths
theta_one_minus_theta = theta * (1 - theta)
numerator = input_heights * (input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta)
denominator = input_delta + intermediate1 * theta_one_minus_theta
outputs = input_cumheights + numerator / denominator
derivative_numerator = input_delta.pow(2) * (
input_derivatives_plus_one * theta.pow(2)
+ 2 * input_delta * theta_one_minus_theta
+ input_derivatives * (1 - theta).pow(2)
)
log_abs_det = torch.log(derivative_numerator) - 2 * torch.log(denominator)
return outputs, log_abs_det
else:
# find the roots of a quadratic equation
intermediate2 = inputs - input_cumheights
intermediate3 = intermediate2 * intermediate1
a = input_heights * (input_delta - input_derivatives) + intermediate3
b = input_heights * input_derivatives - intermediate3
c = -input_delta * intermediate2
discriminant = b.pow(2) - 4 * a * c
if not (discriminant >= 0).all():
raise RuntimeError(f"invalid discriminant {discriminant}")
root = (2 * c) / (-b - torch.sqrt(discriminant))
outputs = root * input_bin_widths + input_cumwidths
theta_one_minus_theta = root * (1 - root)
denominator = input_delta + intermediate1 * theta_one_minus_theta
derivative_numerator = input_delta.pow(2) * (
input_derivatives_plus_one * root.pow(2)
+ 2 * input_delta * theta_one_minus_theta
+ input_derivatives * (1 - root).pow(2)
)
log_abs_det = torch.log(derivative_numerator) - 2 * torch.log(denominator)
return outputs, -log_abs_det
class VitsWaveNet(torch.nn.Module):
def __init__(self, config: VitsConfig, num_layers: int):
super().__init__()
self.hidden_size = config.hidden_size
self.num_layers = num_layers
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.dropout = nn.Dropout(config.wavenet_dropout)
if hasattr(nn.utils.parametrizations, "weight_norm"):
weight_norm = nn.utils.parametrizations.weight_norm
else:
weight_norm = nn.utils.weight_norm
if config.speaker_embedding_size != 0:
cond_layer = torch.nn.Conv1d(config.speaker_embedding_size, 2 * config.hidden_size * num_layers, 1)
self.cond_layer = weight_norm(cond_layer, name="weight")
for i in range(num_layers):
dilation = config.wavenet_dilation_rate**i
padding = (config.wavenet_kernel_size * dilation - dilation) // 2
in_layer = torch.nn.Conv1d(
in_channels=config.hidden_size,
out_channels=2 * config.hidden_size,
kernel_size=config.wavenet_kernel_size,
dilation=dilation,
padding=padding,
)
in_layer = weight_norm(in_layer, name="weight")
self.in_layers.append(in_layer)
# last one is not necessary
if i < num_layers - 1:
res_skip_channels = 2 * config.hidden_size
else:
res_skip_channels = config.hidden_size
res_skip_layer = torch.nn.Conv1d(config.hidden_size, res_skip_channels, 1)
res_skip_layer = weight_norm(res_skip_layer, name="weight")
self.res_skip_layers.append(res_skip_layer)
def forward(self, inputs, padding_mask, global_conditioning=None):
outputs = torch.zeros_like(inputs)
num_channels_tensor = torch.IntTensor([self.hidden_size])
if global_conditioning is not None:
global_conditioning = self.cond_layer(global_conditioning)
for i in range(self.num_layers):
hidden_states = self.in_layers[i](inputs)
if global_conditioning is not None:
cond_offset = i * 2 * self.hidden_size
global_states = global_conditioning[:, cond_offset : cond_offset + 2 * self.hidden_size, :]
else:
global_states = torch.zeros_like(hidden_states)
acts = fused_add_tanh_sigmoid_multiply(hidden_states, global_states, num_channels_tensor[0])
acts = self.dropout(acts)
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.num_layers - 1:
res_acts = res_skip_acts[:, : self.hidden_size, :]
inputs = (inputs + res_acts) * padding_mask
outputs = outputs + res_skip_acts[:, self.hidden_size :, :]
else:
outputs = outputs + res_skip_acts
return outputs * padding_mask
def remove_weight_norm(self):
if self.speaker_embedding_size != 0:
torch.nn.utils.remove_weight_norm(self.cond_layer)
for layer in self.in_layers:
torch.nn.utils.remove_weight_norm(layer)
for layer in self.res_skip_layers:
torch.nn.utils.remove_weight_norm(layer)
class VitsPosteriorEncoder(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.out_channels = config.flow_size
self.conv_pre = nn.Conv1d(config.spectrogram_bins, config.hidden_size, 1)
self.wavenet = VitsWaveNet(config, num_layers=config.posterior_encoder_num_wavenet_layers)
self.conv_proj = nn.Conv1d(config.hidden_size, self.out_channels * 2, 1)
def forward(self, inputs, padding_mask, global_conditioning=None):
inputs = self.conv_pre(inputs) * padding_mask
inputs = self.wavenet(inputs, padding_mask, global_conditioning)
stats = self.conv_proj(inputs) * padding_mask
mean, log_stddev = torch.split(stats, self.out_channels, dim=1)
sampled = (mean + torch.randn_like(mean) * torch.exp(log_stddev)) * padding_mask
return sampled, mean, log_stddev
# Copied from transformers.models.speecht5.modeling_speecht5.HifiGanResidualBlock
class HifiGanResidualBlock(nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1):
super().__init__()
self.leaky_relu_slope = leaky_relu_slope
self.convs1 = nn.ModuleList(
[
nn.Conv1d(
channels,
channels,
kernel_size,
stride=1,
dilation=dilation[i],
padding=self.get_padding(kernel_size, dilation[i]),
)
for i in range(len(dilation))
]
)
self.convs2 = nn.ModuleList(
[
nn.Conv1d(
channels,
channels,
kernel_size,
stride=1,
dilation=1,
padding=self.get_padding(kernel_size, 1),
)
for _ in range(len(dilation))
]
)
def get_padding(self, kernel_size, dilation=1):
return (kernel_size * dilation - dilation) // 2
def apply_weight_norm(self):
weight_norm = nn.utils.weight_norm
if hasattr(nn.utils.parametrizations, "weight_norm"):
weight_norm = nn.utils.parametrizations.weight_norm
for layer in self.convs1:
weight_norm(layer)
for layer in self.convs2:
weight_norm(layer)
def remove_weight_norm(self):
for layer in self.convs1:
nn.utils.remove_weight_norm(layer)
for layer in self.convs2:
nn.utils.remove_weight_norm(layer)
def forward(self, hidden_states):
for conv1, conv2 in zip(self.convs1, self.convs2):
residual = hidden_states
hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope)
hidden_states = conv1(hidden_states)
hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope)
hidden_states = conv2(hidden_states)
hidden_states = hidden_states + residual
return hidden_states
class VitsHifiGan(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.config = config
self.num_kernels = len(config.resblock_kernel_sizes)
self.num_upsamples = len(config.upsample_rates)
self.conv_pre = nn.Conv1d(
config.flow_size,
config.upsample_initial_channel,
kernel_size=7,
stride=1,
padding=3,
)
self.upsampler = nn.ModuleList()
for i, (upsample_rate, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes)):
self.upsampler.append(
nn.ConvTranspose1d(
config.upsample_initial_channel // (2**i),
config.upsample_initial_channel // (2 ** (i + 1)),
kernel_size=kernel_size,
stride=upsample_rate,
padding=(kernel_size - upsample_rate) // 2,
)
)
self.resblocks = nn.ModuleList()
for i in range(len(self.upsampler)):
channels = config.upsample_initial_channel // (2 ** (i + 1))
for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes):
self.resblocks.append(HifiGanResidualBlock(channels, kernel_size, dilation, config.leaky_relu_slope))
self.conv_post = nn.Conv1d(channels, 1, kernel_size=7, stride=1, padding=3, bias=False)
if config.speaker_embedding_size != 0:
self.cond = nn.Conv1d(config.speaker_embedding_size, config.upsample_initial_channel, 1)
def apply_weight_norm(self):
weight_norm = nn.utils.weight_norm
if hasattr(nn.utils.parametrizations, "weight_norm"):
weight_norm = nn.utils.parametrizations.weight_norm
for layer in self.upsampler:
weight_norm(layer)
for layer in self.resblocks:
layer.apply_weight_norm()
def remove_weight_norm(self):
for layer in self.upsampler:
nn.utils.remove_weight_norm(layer)
for layer in self.resblocks:
layer.remove_weight_norm()
def forward(
self, spectrogram: torch.FloatTensor, global_conditioning: Optional[torch.FloatTensor] = None
) -> torch.FloatTensor:
r"""
Converts a spectrogram into a speech waveform.
Args:
spectrogram (`torch.FloatTensor` of shape `(batch_size, config.spectrogram_bins, sequence_length)`):
Tensor containing the spectrograms.
global_conditioning (`torch.FloatTensor` of shape `(batch_size, config.speaker_embedding_size, 1)`, *optional*):
Tensor containing speaker embeddings, for multispeaker models.
Returns:
`torch.FloatTensor`: Tensor of shape shape `(batch_size, 1, num_frames)` containing the speech waveform.
"""
hidden_states = self.conv_pre(spectrogram)
if global_conditioning is not None:
hidden_states = hidden_states + self.cond(global_conditioning)
for i in range(self.num_upsamples):
hidden_states = nn.functional.leaky_relu(hidden_states, self.config.leaky_relu_slope)
hidden_states = self.upsampler[i](hidden_states)
res_state = self.resblocks[i * self.num_kernels](hidden_states)
for j in range(1, self.num_kernels):
res_state += self.resblocks[i * self.num_kernels + j](hidden_states)
hidden_states = res_state / self.num_kernels
hidden_states = nn.functional.leaky_relu(hidden_states)
hidden_states = self.conv_post(hidden_states)
waveform = torch.tanh(hidden_states)
return waveform
class VitsResidualCouplingLayer(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.half_channels = config.flow_size // 2
self.conv_pre = nn.Conv1d(self.half_channels, config.hidden_size, 1)
self.wavenet = VitsWaveNet(config, num_layers=config.prior_encoder_num_wavenet_layers)
self.conv_post = nn.Conv1d(config.hidden_size, self.half_channels, 1)
def forward(self, inputs, padding_mask, global_conditioning=None, reverse=False):
first_half, second_half = torch.split(inputs, [self.half_channels] * 2, dim=1)
hidden_states = self.conv_pre(first_half) * padding_mask
hidden_states = self.wavenet(hidden_states, padding_mask, global_conditioning)
mean = self.conv_post(hidden_states) * padding_mask
log_stddev = torch.zeros_like(mean)
if not reverse:
second_half = mean + second_half * torch.exp(log_stddev) * padding_mask
outputs = torch.cat([first_half, second_half], dim=1)
log_determinant = torch.sum(log_stddev, [1, 2])
return outputs, log_determinant
else:
second_half = (second_half - mean) * torch.exp(-log_stddev) * padding_mask
outputs = torch.cat([first_half, second_half], dim=1)
return outputs, None
class VitsResidualCouplingBlock(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.flows = nn.ModuleList()
for _ in range(config.prior_encoder_num_flows):
self.flows.append(VitsResidualCouplingLayer(config))
def forward(self, inputs, padding_mask, global_conditioning=None, reverse=False):
if not reverse:
for flow in self.flows:
inputs, _ = flow(inputs, padding_mask, global_conditioning)
inputs = torch.flip(inputs, [1])
else:
for flow in reversed(self.flows):
inputs = torch.flip(inputs, [1])
inputs, _ = flow(inputs, padding_mask, global_conditioning, reverse=True)
return inputs
class VitsDilatedDepthSeparableConv(nn.Module):
def __init__(self, config: VitsConfig, dropout_rate=0.0):
super().__init__()
kernel_size = config.duration_predictor_kernel_size
channels = config.hidden_size
self.num_layers = config.depth_separable_num_layers
self.dropout = nn.Dropout(dropout_rate)
self.convs_dilated = nn.ModuleList()
self.convs_pointwise = nn.ModuleList()
self.norms_1 = nn.ModuleList()
self.norms_2 = nn.ModuleList()
for i in range(self.num_layers):
dilation = kernel_size**i
padding = (kernel_size * dilation - dilation) // 2
self.convs_dilated.append(
nn.Conv1d(
in_channels=channels,
out_channels=channels,
kernel_size=kernel_size,
groups=channels,
dilation=dilation,
padding=padding,
)
)
self.convs_pointwise.append(nn.Conv1d(channels, channels, 1))
self.norms_1.append(nn.LayerNorm(channels))
self.norms_2.append(nn.LayerNorm(channels))
def forward(self, inputs, padding_mask, global_conditioning=None):
if global_conditioning is not None:
inputs = inputs + global_conditioning
for i in range(self.num_layers):
hidden_states = self.convs_dilated[i](inputs * padding_mask)
hidden_states = self.norms_1[i](hidden_states.transpose(1, -1)).transpose(1, -1)
hidden_states = nn.functional.gelu(hidden_states)
hidden_states = self.convs_pointwise[i](hidden_states)
hidden_states = self.norms_2[i](hidden_states.transpose(1, -1)).transpose(1, -1)
hidden_states = nn.functional.gelu(hidden_states)
hidden_states = self.dropout(hidden_states)
inputs = inputs + hidden_states
return inputs * padding_mask
class VitsConvFlow(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.filter_channels = config.hidden_size
self.half_channels = config.depth_separable_channels // 2
self.num_bins = config.duration_predictor_flow_bins
self.tail_bound = config.duration_predictor_tail_bound
self.conv_pre = nn.Conv1d(self.half_channels, self.filter_channels, 1)
self.conv_dds = VitsDilatedDepthSeparableConv(config)
self.conv_proj = nn.Conv1d(self.filter_channels, self.half_channels * (self.num_bins * 3 - 1), 1)
def forward(self, inputs, padding_mask, global_conditioning=None, reverse=False):
first_half, second_half = torch.split(inputs, [self.half_channels] * 2, dim=1)
hidden_states = self.conv_pre(first_half)
hidden_states = self.conv_dds(hidden_states, padding_mask, global_conditioning)
hidden_states = self.conv_proj(hidden_states) * padding_mask
batch_size, channels, length = first_half.shape
hidden_states = hidden_states.reshape(batch_size, channels, -1, length).permute(0, 1, 3, 2)
unnormalized_widths = hidden_states[..., : self.num_bins] / math.sqrt(self.filter_channels)
unnormalized_heights = hidden_states[..., self.num_bins : 2 * self.num_bins] / math.sqrt(self.filter_channels)
unnormalized_derivatives = hidden_states[..., 2 * self.num_bins :]
second_half, log_abs_det = _unconstrained_rational_quadratic_spline(
second_half,
unnormalized_widths,
unnormalized_heights,
unnormalized_derivatives,
reverse=reverse,
tail_bound=self.tail_bound,
)
outputs = torch.cat([first_half, second_half], dim=1) * padding_mask
if not reverse:
log_determinant = torch.sum(log_abs_det * padding_mask, [1, 2])
return outputs, log_determinant
else:
return outputs, None
class VitsElementwiseAffine(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.channels = config.depth_separable_channels
self.translate = nn.Parameter(torch.zeros(self.channels, 1))
self.log_scale = nn.Parameter(torch.zeros(self.channels, 1))
def forward(self, inputs, padding_mask, global_conditioning=None, reverse=False):
if not reverse:
outputs = self.translate + torch.exp(self.log_scale) * inputs
outputs = outputs * padding_mask
log_determinant = torch.sum(self.log_scale * padding_mask, [1, 2])
return outputs, log_determinant
else:
outputs = (inputs - self.translate) * torch.exp(-self.log_scale) * padding_mask
return outputs, None
class VitsStochasticDurationPredictor(nn.Module):
def __init__(self, config):
super().__init__()
embed_dim = config.speaker_embedding_size
filter_channels = config.hidden_size
self.conv_pre = nn.Conv1d(filter_channels, filter_channels, 1)
self.conv_proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.conv_dds = VitsDilatedDepthSeparableConv(
config,
dropout_rate=config.duration_predictor_dropout,
)
if embed_dim != 0:
self.cond = nn.Conv1d(embed_dim, filter_channels, 1)
self.flows = nn.ModuleList()
self.flows.append(VitsElementwiseAffine(config))
for _ in range(config.duration_predictor_num_flows):
self.flows.append(VitsConvFlow(config))
self.post_conv_pre = nn.Conv1d(1, filter_channels, 1)
self.post_conv_proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.post_conv_dds = VitsDilatedDepthSeparableConv(
config,
dropout_rate=config.duration_predictor_dropout,
)
self.post_flows = nn.ModuleList()
self.post_flows.append(VitsElementwiseAffine(config))
for _ in range(config.duration_predictor_num_flows):
self.post_flows.append(VitsConvFlow(config))
def forward(self, inputs, padding_mask, global_conditioning=None, durations=None, reverse=False, noise_scale=1.0):
inputs = torch.detach(inputs)
inputs = self.conv_pre(inputs)
if global_conditioning is not None:
global_conditioning = torch.detach(global_conditioning)
inputs = inputs + self.cond(global_conditioning)
inputs = self.conv_dds(inputs, padding_mask)
inputs = self.conv_proj(inputs) * padding_mask
if not reverse:
hidden_states = self.post_conv_pre(durations)
hidden_states = self.post_conv_dds(hidden_states, padding_mask)
hidden_states = self.post_conv_proj(hidden_states) * padding_mask
random_posterior = (
torch.randn(durations.size(0), 2, durations.size(2)).to(device=inputs.device, dtype=inputs.dtype)
* padding_mask
)
log_determinant_posterior_sum = 0
latents_posterior = random_posterior
for flow in self.post_flows:
latents_posterior, log_determinant = flow(
latents_posterior, padding_mask, global_conditioning=inputs + hidden_states
)
latents_posterior = torch.flip(latents_posterior, [1])
log_determinant_posterior_sum += log_determinant
first_half, second_half = torch.split(latents_posterior, [1, 1], dim=1)
log_determinant_posterior_sum += torch.sum(
(nn.functional.logsigmoid(first_half) + nn.functional.logsigmoid(-first_half)) * padding_mask, [1, 2]
)
logq = (
torch.sum(-0.5 * (math.log(2 * math.pi) + (random_posterior**2)) * padding_mask, [1, 2])
- log_determinant_posterior_sum
)
first_half = (durations - torch.sigmoid(first_half)) * padding_mask
first_half = torch.log(torch.clamp_min(first_half, 1e-5)) * padding_mask
log_determinant_sum = torch.sum(-first_half, [1, 2])
latents = torch.cat([first_half, second_half], dim=1)
for flow in self.flows:
latents, log_determinant = flow(latents, padding_mask, global_conditioning=inputs)
latents = torch.flip(latents, [1])
log_determinant_sum += log_determinant
nll = torch.sum(0.5 * (math.log(2 * math.pi) + (latents**2)) * padding_mask, [1, 2]) - log_determinant_sum
return nll + logq
else:
flows = list(reversed(self.flows))
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
latents = (
torch.randn(inputs.size(0), 2, inputs.size(2)).to(device=inputs.device, dtype=inputs.dtype)
* noise_scale
)
for flow in flows:
latents = torch.flip(latents, [1])
latents, _ = flow(latents, padding_mask, global_conditioning=inputs, reverse=True)
log_duration, _ = torch.split(latents, [1, 1], dim=1)
return log_duration
class VitsDurationPredictor(nn.Module):
def __init__(self, config):
super().__init__()
kernel_size = config.duration_predictor_kernel_size
filter_channels = config.duration_predictor_filter_channels
self.dropout = nn.Dropout(config.duration_predictor_dropout)
self.conv_1 = nn.Conv1d(config.hidden_size, filter_channels, kernel_size, padding=kernel_size // 2)
self.norm_1 = nn.LayerNorm(filter_channels, eps=config.layer_norm_eps)
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.norm_2 = nn.LayerNorm(filter_channels, eps=config.layer_norm_eps)
self.proj = nn.Conv1d(filter_channels, 1, 1)
if config.speaker_embedding_size != 0:
self.cond = nn.Conv1d(config.speaker_embedding_size, config.hidden_size, 1)
def forward(self, inputs, padding_mask, global_conditioning=None):
inputs = torch.detach(inputs)
if global_conditioning is not None:
global_conditioning = torch.detach(global_conditioning)
inputs = inputs + self.cond(global_conditioning)
inputs = self.conv_1(inputs * padding_mask)
inputs = torch.relu(inputs)
inputs = self.norm_1(inputs.transpose(1, -1)).transpose(1, -1)
inputs = self.dropout(inputs)
inputs = self.conv_2(inputs * padding_mask)
inputs = torch.relu(inputs)
inputs = self.norm_2(inputs.transpose(1, -1)).transpose(1, -1)
inputs = self.dropout(inputs)
inputs = self.proj(inputs * padding_mask)
return inputs * padding_mask
class VitsAttention(nn.Module):
"""Multi-headed attention with relative positional representation."""
def __init__(self, config: VitsConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.dropout = config.attention_dropout
self.window_size = config.window_size
self.head_dim = self.embed_dim // self.num_heads
self.scaling = self.head_dim**-0.5
if (self.head_dim * self.num_heads) != self.embed_dim:
raise ValueError(
f"hidden_size must be divisible by num_attention_heads (got `hidden_size`: {self.embed_dim}"
f" and `num_attention_heads`: {self.num_heads})."
)
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.use_bias)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.use_bias)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.use_bias)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.use_bias)
if self.window_size:
self.emb_rel_k = nn.Parameter(torch.randn(1, self.window_size * 2 + 1, self.head_dim) * self.scaling)
self.emb_rel_v = nn.Parameter(torch.randn(1, self.window_size * 2 + 1, self.head_dim) * self.scaling)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if self.window_size is not None:
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, src_len)
relative_logits = torch.matmul(query_states, key_relative_embeddings.transpose(-2, -1))
rel_pos_bias = self._relative_position_to_absolute_position(relative_logits)
attn_weights += rel_pos_bias
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
if self.window_size is not None:
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, src_len)
relative_weights = self._absolute_position_to_relative_position(attn_probs)
rel_pos_bias = torch.matmul(relative_weights, value_relative_embeddings)
attn_output += rel_pos_bias
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
def _get_relative_embeddings(self, relative_embeddings, length):
pad_length = max(length - (self.window_size + 1), 0)
if pad_length > 0:
relative_embeddings = nn.functional.pad(relative_embeddings, [0, 0, pad_length, pad_length, 0, 0])
slice_start_position = max((self.window_size + 1) - length, 0)
slice_end_position = slice_start_position + 2 * length - 1
return relative_embeddings[:, slice_start_position:slice_end_position]
def _relative_position_to_absolute_position(self, x):
batch_heads, length, _ = x.size()
# Concat columns of pad to shift from relative to absolute indexing.
x = nn.functional.pad(x, [0, 1, 0, 0, 0, 0])
# Concat extra elements so to add up to shape (len+1, 2*len-1).
x_flat = x.view([batch_heads, length * 2 * length])
x_flat = nn.functional.pad(x_flat, [0, length - 1, 0, 0])
# Reshape and slice out the padded elements.
x_final = x_flat.view([batch_heads, length + 1, 2 * length - 1])
x_final = x_final[:, :length, length - 1 :]
return x_final
def _absolute_position_to_relative_position(self, x):
batch_heads, length, _ = x.size()
# Pad along column
x = nn.functional.pad(x, [0, length - 1, 0, 0, 0, 0])
x_flat = x.view([batch_heads, length * (2 * length - 1)])
# Add 0's in the beginning that will skew the elements after reshape
x_flat = nn.functional.pad(x_flat, [length, 0, 0, 0])
x_final = x_flat.view([batch_heads, length, 2 * length])[:, :, 1:]
return x_final
class VitsFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.conv_1 = nn.Conv1d(config.hidden_size, config.ffn_dim, config.ffn_kernel_size)
self.conv_2 = nn.Conv1d(config.ffn_dim, config.hidden_size, config.ffn_kernel_size)
self.dropout = nn.Dropout(config.activation_dropout)
if isinstance(config.hidden_act, str):
self.act_fn = ACT2FN[config.hidden_act]
else:
self.act_fn = config.hidden_act
if config.ffn_kernel_size > 1:
pad_left = (config.ffn_kernel_size - 1) // 2
pad_right = config.ffn_kernel_size // 2
self.padding = [pad_left, pad_right, 0, 0, 0, 0]
else:
self.padding = None
def forward(self, hidden_states, padding_mask):
hidden_states = hidden_states.permute(0, 2, 1)
padding_mask = padding_mask.permute(0, 2, 1)
hidden_states = hidden_states * padding_mask
if self.padding is not None:
hidden_states = nn.functional.pad(hidden_states, self.padding)
hidden_states = self.conv_1(hidden_states)
hidden_states = self.act_fn(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states * padding_mask
if self.padding is not None:
hidden_states = nn.functional.pad(hidden_states, self.padding)
hidden_states = self.conv_2(hidden_states)
hidden_states = hidden_states * padding_mask
hidden_states = hidden_states.permute(0, 2, 1)
return hidden_states
class VitsEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: VitsConfig):
super().__init__()
self.attention = VitsAttention(config)
self.dropout = nn.Dropout(config.hidden_dropout)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.feed_forward = VitsFeedForward(config)
self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
padding_mask: torch.FloatTensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
):
residual = hidden_states
hidden_states, attn_weights = self.attention(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = self.dropout(hidden_states)
hidden_states = self.layer_norm(residual + hidden_states)
residual = hidden_states
hidden_states = self.feed_forward(hidden_states, padding_mask)
hidden_states = self.dropout(hidden_states)
hidden_states = self.final_layer_norm(residual + hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class VitsEncoder(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([VitsEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
self.layerdrop = config.layerdrop
def forward(
self,
hidden_states: torch.FloatTensor,
padding_mask: torch.FloatTensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
hidden_states = hidden_states * padding_mask
synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
for encoder_layer in self.layers:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
dropout_probability = np.random.uniform(0, 1)
skip_the_layer = self.training and (dropout_probability < self.layerdrop)
if not skip_the_layer or synced_gpus:
# under fsdp or deepspeed zero3 all gpus must run in sync
layer_outputs = encoder_layer(
hidden_states,
attention_mask=attention_mask,
padding_mask=padding_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
hidden_states = hidden_states * padding_mask
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class VitsTextEncoder(nn.Module):
"""
Transformer encoder that uses relative positional representation instead of absolute positional encoding.
"""
def __init__(self, config: VitsConfig):
super().__init__()
self.config = config
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
self.encoder = VitsEncoder(config)
self.project = nn.Conv1d(config.hidden_size, config.flow_size * 2, kernel_size=1)
def forward(
self,
input_ids: torch.Tensor,
padding_mask: torch.FloatTensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = True,
) -> Union[tuple[torch.Tensor], VitsTextEncoderOutput]:
hidden_states = self.embed_tokens(input_ids) * math.sqrt(self.config.hidden_size)
encoder_outputs = self.encoder(
hidden_states=hidden_states,
padding_mask=padding_mask,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0] if not return_dict else encoder_outputs.last_hidden_state
stats = self.project(last_hidden_state.transpose(1, 2)).transpose(1, 2) * padding_mask
prior_means, prior_log_variances = torch.split(stats, self.config.flow_size, dim=2)
if not return_dict:
outputs = (last_hidden_state, prior_means, prior_log_variances) + encoder_outputs[1:]
return outputs
return VitsTextEncoderOutput(
last_hidden_state=last_hidden_state,
prior_means=prior_means,
prior_log_variances=prior_log_variances,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@auto_docstring
class VitsPreTrainedModel(PreTrainedModel):
config: VitsConfig
base_model_prefix = "vits"
main_input_name = "input_ids"
supports_gradient_checkpointing = True
def _init_weights(self, module: nn.Module):
"""Initialize the weights"""
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, (nn.Conv1d, nn.ConvTranspose1d)):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
nn.init.uniform_(module.bias, a=-k, b=k)
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, VitsAttention):
if self.config.window_size:
head_dim = self.config.hidden_size // self.config.num_attention_heads
nn.init.normal_(module.emb_rel_k, std=head_dim**-0.5)
nn.init.normal_(module.emb_rel_v, std=head_dim**-0.5)
elif isinstance(module, VitsElementwiseAffine):
module.translate.data.zero_()
module.log_scale.data.zero_()
@auto_docstring(
custom_intro="""
The complete VITS model, for text-to-speech synthesis.
"""
)
class VitsModel(VitsPreTrainedModel):
def __init__(self, config: VitsConfig):
super().__init__(config)
self.config = config
self.text_encoder = VitsTextEncoder(config)
self.flow = VitsResidualCouplingBlock(config)
self.decoder = VitsHifiGan(config)
if config.use_stochastic_duration_prediction:
self.duration_predictor = VitsStochasticDurationPredictor(config)
else:
self.duration_predictor = VitsDurationPredictor(config)
if config.num_speakers > 1:
self.embed_speaker = nn.Embedding(config.num_speakers, config.speaker_embedding_size)
# This is used only for training.
self.posterior_encoder = VitsPosteriorEncoder(config)
# These parameters control the synthesised speech properties
self.speaking_rate = config.speaking_rate
self.noise_scale = config.noise_scale
self.noise_scale_duration = config.noise_scale_duration
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.text_encoder
@auto_docstring
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
speaker_id: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.FloatTensor] = None,
) -> Union[tuple[Any], VitsModelOutput]:
r"""
speaker_id (`int`, *optional*):
Which speaker embedding to use. Only used for multispeaker models.
labels (`torch.FloatTensor` of shape `(batch_size, config.spectrogram_bins, sequence_length)`, *optional*):
Float values of target spectrogram. Timesteps set to `-100.0` are ignored (masked) for the loss
computation.
Example:
```python
>>> from transformers import VitsTokenizer, VitsModel, set_seed
>>> import torch
>>> tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng")
>>> model = VitsModel.from_pretrained("facebook/mms-tts-eng")
>>> inputs = tokenizer(text="Hello - my dog is cute", return_tensors="pt")
>>> set_seed(555) # make deterministic
>>> with torch.no_grad():
... outputs = model(inputs["input_ids"])
>>> outputs.waveform.shape
torch.Size([1, 45824])
```
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
raise NotImplementedError("Training of VITS is not supported yet.")
mask_dtype = self.text_encoder.embed_tokens.weight.dtype
if attention_mask is not None:
input_padding_mask = attention_mask.unsqueeze(-1).to(mask_dtype)
else:
input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).to(mask_dtype)
if self.config.num_speakers > 1 and speaker_id is not None:
if not 0 <= speaker_id < self.config.num_speakers:
raise ValueError(f"Set `speaker_id` in the range 0-{self.config.num_speakers - 1}.")
if isinstance(speaker_id, int):
speaker_id = torch.full(size=(1,), fill_value=speaker_id, device=self.device)
speaker_embeddings = self.embed_speaker(speaker_id).unsqueeze(-1)
else:
speaker_embeddings = None
text_encoder_output = self.text_encoder(
input_ids=input_ids,
padding_mask=input_padding_mask,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state
hidden_states = hidden_states.transpose(1, 2)
input_padding_mask = input_padding_mask.transpose(1, 2)
prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means
prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances
if self.config.use_stochastic_duration_prediction:
log_duration = self.duration_predictor(
hidden_states,
input_padding_mask,
speaker_embeddings,
reverse=True,
noise_scale=self.noise_scale_duration,
)
else:
log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
length_scale = 1.0 / self.speaking_rate
duration = torch.ceil(torch.exp(log_duration) * input_padding_mask * length_scale)
predicted_lengths = torch.clamp_min(torch.sum(duration, [1, 2]), 1).long()
# Create a padding mask for the output lengths of shape (batch, 1, max_output_length)
indices = torch.arange(predicted_lengths.max(), dtype=predicted_lengths.dtype, device=predicted_lengths.device)
output_padding_mask = indices.unsqueeze(0) < predicted_lengths.unsqueeze(1)
output_padding_mask = output_padding_mask.unsqueeze(1).to(input_padding_mask.dtype)
# Reconstruct an attention tensor of shape (batch, 1, out_length, in_length)
attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(output_padding_mask, -1)
batch_size, _, output_length, input_length = attn_mask.shape
cum_duration = torch.cumsum(duration, -1).view(batch_size * input_length, 1)
indices = torch.arange(output_length, dtype=duration.dtype, device=duration.device)
valid_indices = indices.unsqueeze(0) < cum_duration
valid_indices = valid_indices.to(attn_mask.dtype).view(batch_size, input_length, output_length)
padded_indices = valid_indices - nn.functional.pad(valid_indices, [0, 0, 1, 0, 0, 0])[:, :-1]
attn = padded_indices.unsqueeze(1).transpose(2, 3) * attn_mask
# Expand prior distribution
prior_means = torch.matmul(attn.squeeze(1), prior_means).transpose(1, 2)
prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances).transpose(1, 2)
prior_latents = prior_means + torch.randn_like(prior_means) * torch.exp(prior_log_variances) * self.noise_scale
latents = self.flow(prior_latents, output_padding_mask, speaker_embeddings, reverse=True)
spectrogram = latents * output_padding_mask
waveform = self.decoder(spectrogram, speaker_embeddings)
waveform = waveform.squeeze(1)
sequence_lengths = predicted_lengths * np.prod(self.config.upsample_rates)
if not return_dict:
outputs = (waveform, sequence_lengths, spectrogram) + text_encoder_output[3:]
return outputs
return VitsModelOutput(
waveform=waveform,
sequence_lengths=sequence_lengths,
spectrogram=spectrogram,
hidden_states=text_encoder_output.hidden_states,
attentions=text_encoder_output.attentions,
)
__all__ = ["VitsModel", "VitsPreTrainedModel"]
| transformers/src/transformers/models/vits/modeling_vits.py/0 | {
"file_path": "transformers/src/transformers/models/vits/modeling_vits.py",
"repo_id": "transformers",
"token_count": 27287
} | 499 |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/voxtral/modular_voxtral.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_voxtral.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Callable, Optional, Union
import torch
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...generation import GenerationMixin
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
from ...utils.generic import check_model_inputs
from ..auto import AutoModel, AutoModelForCausalLM
from .configuration_voxtral import VoxtralConfig, VoxtralEncoderConfig
logger = logging.get_logger(__name__)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: Optional[float] = None,
dropout: float = 0.0,
head_mask: Optional[torch.Tensor] = None,
**kwargs,
):
if scaling is None:
scaling = query.size(-1) ** -0.5
attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
if attention_mask is not None and attention_mask.ndim == 4:
attn_weights = attn_weights + attention_mask[:, :, :, : key.shape[-2]]
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if head_mask is not None:
attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class VoxtralAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
layer_idx: Optional[int] = None,
config: Optional[VoxtralConfig] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
if layer_idx is None and is_decoder:
logger.warning_once(
f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
"will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.layer_idx = layer_idx
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
**kwargs,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, _ = hidden_states.size()
# Scaling is susceptible to floating point arithmetics' inprecisions
# which can lead to different results (this is dependent from model
# to model, e.g. whisper is one such case). We therefore keep the
# original order of scaling to follow the original implementation
# and enforce no scaling (1.0) in the attention call below.
query_states = self._shape(self.q_proj(hidden_states) * self.scaling, tgt_len, bsz)
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.dropout,
scaling=1.0,
output_attentions=output_attentions,
head_mask=layer_head_mask,
**kwargs,
)
attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
class VoxtralEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: VoxtralConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = VoxtralAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
config=config,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16:
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
return hidden_states, attn_weights
@auto_docstring
class VoxtralPreTrainedModel(PreTrainedModel):
config: VoxtralConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = None
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn = True
_supports_sdpa = True
_supports_flex_attn = True
_supports_cache_class = True
_supports_attention_backend = True
_can_compile_fullgraph = True
def _init_weights(self, module):
# important: this ported version of Voxtral isn't meant for training from scratch - only
# inference and fine-tuning - so the proper init weights code has been removed
std = (
self.config.initializer_range
if hasattr(self.config, "initializer_range")
else self.config.audio_config.initializer_range
)
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.weight.data.fill_(1.0)
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
@auto_docstring(
custom_intro="""
The Voxtral encoder, which is a Whisper encoder.
"""
)
class VoxtralEncoder(VoxtralPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`VoxtralEncoderLayer`].
Args:
config: VoxtralEncoderConfig
"""
# Ignore copy
config: VoxtralEncoderConfig
main_input_name = "input_features"
_no_split_modules = ["VoxtralEncoderLayer"]
_can_record_outputs = {
"attentions": VoxtralAttention,
"hidden_states": VoxtralEncoderLayer,
}
def __init__(self, config: VoxtralEncoderConfig):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.num_mel_bins = config.num_mel_bins
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_source_positions
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)
self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim)
self.embed_positions.requires_grad_(False)
self.layers = nn.ModuleList([VoxtralEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
# Ignore copy
self.avg_pooler = nn.AvgPool1d(2, stride=2)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def _freeze_parameters(self):
for param in self.parameters():
param.requires_grad = False
self._requires_grad = False
def get_input_embeddings(self) -> nn.Module:
return self.conv1
def set_input_embeddings(self, value: nn.Module):
self.conv1 = value
@check_model_inputs
def forward(
self,
input_features,
attention_mask=None,
**kwargs: Unpack[TransformersKwargs],
):
r"""
Args:
input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
`numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
`input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
attention_mask (`torch.Tensor`)`, *optional*):
Voxtral does not support masking of the `input_features`, this argument is preserved for compatibility,
but it is not used. By default the silence in the input log mel spectrogram are ignored.
"""
expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0]
if input_features.shape[-1] != expected_seq_length:
raise ValueError(
f"Qwen2Audio expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
)
input_features = input_features.to(dtype=self.conv1.weight.dtype, device=self.conv1.weight.device)
inputs_embeds = nn.functional.gelu(self.conv1(input_features))
inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
inputs_embeds = inputs_embeds.permute(0, 2, 1)
embed_pos = self.embed_positions.weight
hidden_states = (inputs_embeds + embed_pos).to(inputs_embeds.dtype)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
for idx, encoder_layer in enumerate(self.layers):
layer_outputs = encoder_layer(
hidden_states,
attention_mask=attention_mask,
layer_head_mask=None,
)
hidden_states = layer_outputs[0]
hidden_states = self.layer_norm(hidden_states)
return BaseModelOutput(
last_hidden_state=hidden_states,
)
# Ignore copy
def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
"""
Computes the output length of the convolutional layers and the output length of the audio encoder
"""
input_lengths = (input_lengths - 1) // 2 + 1
output_lengths = (input_lengths - 2) // 2 + 1
return input_lengths, output_lengths
class VoxtralMultiModalProjector(nn.Module):
def __init__(self, config: VoxtralConfig):
super().__init__()
self.linear_1 = nn.Linear(config.audio_config.intermediate_size, config.text_config.hidden_size, bias=False)
self.act = ACT2FN[config.projector_hidden_act]
self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=False)
def forward(self, audio_features):
hidden_states = self.linear_1(audio_features)
hidden_states = self.act(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states
@auto_docstring(
custom_intro="""
The Voxtral model, which consists of Whisper encoder, a multi-modal projector and a LLama language model.
"""
)
class VoxtralForConditionalGeneration(VoxtralPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
_keep_in_fp32_modules_strict = ["embed_positions"]
def __init__(self, config):
super().__init__(config)
self.vocab_size = config.text_config.vocab_size
self.audio_tower = AutoModel.from_config(config.audio_config)
self.language_model = AutoModelForCausalLM.from_config(config.text_config)
self.multi_modal_projector = VoxtralMultiModalProjector(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.set_input_embeddings(value)
def get_output_embeddings(self):
return self.language_model.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
self.language_model.set_output_embeddings(new_embeddings)
def set_decoder(self, decoder):
self.language_model.set_decoder(decoder)
def get_decoder(self):
return self.language_model.get_decoder()
def get_audio_embeds(self, input_features: torch.FloatTensor):
"""
This method is used to get the audio embeddings from input features (a log mel spectrogram), meaning inferring the audio encoder and the multi-modal projector.
Args:
input_features (`torch.FloatTensor`):
Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
`numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
`input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
Returns:
`torch.FloatTensor`:
The audio embeddings.
"""
audio_outputs = self.audio_tower(input_features)
audio_hidden_states = audio_outputs.last_hidden_state
audio_hidden_states = audio_hidden_states.reshape(-1, self.config.audio_config.intermediate_size)
audio_embeds = self.multi_modal_projector(audio_hidden_states)
return audio_embeds
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
input_features: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[TransformersKwargs],
) -> CausalLMOutputWithPast:
r"""
Example:
```python
>>> from transformers import VoxtralForConditionalGeneration, AutoProcessor
>>> import torch
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> repo_id = "mistralai/Voxtral-Mini-3B-2507"
>>> processor = AutoProcessor.from_pretrained(repo_id)
>>> model = VoxtralForConditionalGeneration.from_pretrained(repo_id, dtype=torch.bfloat16, device_map=device)
>>> conversation = [
{
"role": "user",
"content": [
{
"type": "audio",
"url": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/dude_where_is_my_car.wav",
},
{"type": "text", "text": "What can you tell me about this audio?"},
],
}
]
>>> inputs = processor.apply_chat_template(conversation)
>>> inputs = inputs.to(device, dtype=torch.bfloat16)
>>> outputs = model.generate(**inputs, max_new_tokens=30)
>>> processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
["This audio is a humorous conversation between two friends, likely in English, where one of them is trying to figure out what the other's tattoo says."]
```"""
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
if input_features is not None:
audio_embeds = self.get_audio_embeds(input_features)
# replace text-audio token placeholders with audio embeddings
audio_token_mask = input_ids == self.config.audio_token_id
inputs_embeds[audio_token_mask] = audio_embeds
outputs: BaseModelOutputWithPast = self.language_model(
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
cache_position=cache_position,
logits_to_keep=logits_to_keep,
**kwargs,
)
return outputs
def prepare_inputs_for_generation(self, *args, **kwargs):
# Overwritten -- we should not pass input_features when we are in cached decoding stage
input_features = kwargs.pop("input_features", None)
cache_position = kwargs.get("cache_position")
model_inputs = super().prepare_inputs_for_generation(*args, **kwargs)
if cache_position is not None and cache_position[0] == 0:
# input_features should only be passed when we are not in cached decoding stage
model_inputs["input_features"] = input_features
return model_inputs
__all__ = ["VoxtralPreTrainedModel", "VoxtralEncoder", "VoxtralForConditionalGeneration"]
| transformers/src/transformers/models/voxtral/modeling_voxtral.py/0 | {
"file_path": "transformers/src/transformers/models/voxtral/modeling_voxtral.py",
"repo_id": "transformers",
"token_count": 9853
} | 500 |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/wav2vec2_bert/modular_wav2vec2_bert.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_wav2vec2_bert.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
import math
import warnings
from typing import Optional, Union
import numpy as np
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...integrations.fsdp import is_fsdp_managed_module
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
BaseModelOutput,
CausalLMOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
Wav2Vec2BaseModelOutput,
XVectorOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, is_peft_available
from .configuration_wav2vec2_bert import Wav2Vec2BertConfig
class Wav2Vec2BertRotaryPositionalEmbedding(nn.Module):
"""Rotary positional embedding
Reference : https://blog.eleuther.ai/rotary-embeddings/ Paper: https://huggingface.co/papers/2104.09864
"""
def __init__(self, config):
super().__init__()
dim = config.hidden_size // config.num_attention_heads
base = config.rotary_embedding_base
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim))
# Ignore copy
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.cached_sequence_length = None
self.cached_rotary_positional_embedding = None
def forward(self, hidden_states):
sequence_length = hidden_states.shape[1]
if sequence_length == self.cached_sequence_length and self.cached_rotary_positional_embedding is not None:
return self.cached_rotary_positional_embedding
self.cached_sequence_length = sequence_length
# Embeddings are computed in the dtype of the inv_freq constant
time_stamps = torch.arange(sequence_length).type_as(self.inv_freq)
freqs = torch.einsum("i,j->ij", time_stamps, self.inv_freq)
embeddings = torch.cat((freqs, freqs), dim=-1)
cos_embeddings = embeddings.cos()[:, None, None, :]
sin_embeddings = embeddings.sin()[:, None, None, :]
# Computed embeddings are cast to the dtype of the hidden state inputs
self.cached_rotary_positional_embedding = torch.stack([cos_embeddings, sin_embeddings]).type_as(hidden_states)
return self.cached_rotary_positional_embedding
class Wav2Vec2BertRelPositionalEmbedding(nn.Module):
"""Relative positional encoding module."""
def __init__(self, config):
super().__init__()
self.max_len = config.max_source_positions
self.d_model = config.hidden_size
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, self.max_len))
def extend_pe(self, x):
# Reset the positional encodings
if self.pe is not None:
# self.pe contains both positive and negative parts
# the length of self.pe is 2 * input_len - 1
if self.pe.size(1) >= x.size(1) * 2 - 1:
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
# Suppose `i` is the position of query vector and `j` is the
# position of key vector. We use positive relative positions when keys
# are to the left (i>j) and negative relative positions otherwise (i<j).
pe_positive = torch.zeros(x.size(1), self.d_model)
pe_negative = torch.zeros(x.size(1), self.d_model)
position = torch.arange(0, x.size(1), dtype=torch.int64).float().unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.d_model, 2, dtype=torch.int64).float() * -(math.log(10000.0) / self.d_model)
)
pe_positive[:, 0::2] = torch.sin(position * div_term)
pe_positive[:, 1::2] = torch.cos(position * div_term)
pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
# Reverse the order of positive indices and concat both positive and
# negative indices. This is used to support the shifting trick
# as in https://huggingface.co/papers/1901.02860
pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
pe_negative = pe_negative[1:].unsqueeze(0)
pe = torch.cat([pe_positive, pe_negative], dim=1)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, hidden_states: torch.Tensor):
self.extend_pe(hidden_states)
start_idx = self.pe.size(1) // 2 - hidden_states.size(1) + 1
end_idx = self.pe.size(1) // 2 + hidden_states.size(1)
relative_position_embeddings = self.pe[:, start_idx:end_idx]
return relative_position_embeddings
class Wav2Vec2BertFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.feature_projection_input_dim, eps=config.layer_norm_eps)
self.projection = nn.Linear(config.feature_projection_input_dim, config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_dropout)
def forward(self, hidden_states):
# non-projected hidden states are needed for quantization
norm_hidden_states = self.layer_norm(hidden_states)
hidden_states = self.projection(norm_hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states, norm_hidden_states
class Wav2Vec2BertFeedForward(nn.Module):
def __init__(self, config, act_fn=None, hidden_size=None):
super().__init__()
act_fn = act_fn if act_fn is not None else config.hidden_act
hidden_size = hidden_size if hidden_size is not None else config.hidden_size
self.intermediate_dropout = nn.Dropout(config.activation_dropout)
self.intermediate_dense = nn.Linear(hidden_size, config.intermediate_size)
self.intermediate_act_fn = ACT2FN[act_fn] if isinstance(act_fn, str) else act_fn
self.output_dense = nn.Linear(config.intermediate_size, hidden_size)
self.output_dropout = nn.Dropout(config.hidden_dropout)
def forward(self, hidden_states):
hidden_states = self.intermediate_dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
hidden_states = self.intermediate_dropout(hidden_states)
hidden_states = self.output_dense(hidden_states)
hidden_states = self.output_dropout(hidden_states)
return hidden_states
class Wav2Vec2BertConvolutionModule(nn.Module):
"""Convolution block used in the conformer block"""
def __init__(self, config):
super().__init__()
if (config.conv_depthwise_kernel_size - 1) % 2 == 1:
raise ValueError("`config.conv_depthwise_kernel_size` should be a odd number for 'SAME' padding")
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pointwise_conv1 = nn.Conv1d(
config.hidden_size,
2 * config.hidden_size,
kernel_size=1,
stride=1,
padding=0,
bias=False,
)
self.glu = nn.GLU(dim=1)
self.depthwise_conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
config.conv_depthwise_kernel_size,
stride=1,
padding=0,
groups=config.hidden_size,
bias=False,
)
self.depthwise_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.activation = ACT2FN[config.hidden_act]
self.pointwise_conv2 = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=1,
stride=1,
padding=0,
bias=False,
)
self.dropout = nn.Dropout(config.conformer_conv_dropout)
def forward(self, hidden_states, attention_mask=None):
hidden_states = self.layer_norm(hidden_states)
# Ensure that we do not leak padded positions in depthwise convolution if attention mask is passed.
# Put 0 where necessary
if attention_mask is not None:
hidden_states = hidden_states.masked_fill(~attention_mask.bool().unsqueeze(-1), 0.0)
# exchange the temporal dimension and the feature dimension
hidden_states = hidden_states.transpose(1, 2)
# GLU mechanism
# => (batch, 2*channel, dim)
hidden_states = self.pointwise_conv1(hidden_states)
# => (batch, channel, dim)
hidden_states = self.glu(hidden_states)
# Pad the sequence entirely on the left because of causal convolution.
hidden_states = torch.nn.functional.pad(hidden_states, (self.depthwise_conv.kernel_size[0] - 1, 0))
# 1D Depthwise Conv
hidden_states = self.depthwise_conv(hidden_states)
hidden_states = self.depthwise_layer_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
hidden_states = self.activation(hidden_states)
hidden_states = self.pointwise_conv2(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
return hidden_states
class Wav2Vec2BertSelfAttention(nn.Module):
"""Construct an Wav2Vec2BertSelfAttention object.
Can be enhanced with rotary or relative position embeddings.
"""
def __init__(self, config, is_adapter_attention=False):
super().__init__()
hidden_size = config.hidden_size if not is_adapter_attention else config.output_hidden_size
self.head_size = hidden_size // config.num_attention_heads
self.num_heads = config.num_attention_heads
self.position_embeddings_type = config.position_embeddings_type if not is_adapter_attention else None
self.linear_q = nn.Linear(hidden_size, hidden_size)
self.linear_k = nn.Linear(hidden_size, hidden_size)
self.linear_v = nn.Linear(hidden_size, hidden_size)
self.linear_out = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(p=config.attention_dropout)
if self.position_embeddings_type == "relative":
# linear transformation for positional encoding
self.linear_pos = nn.Linear(hidden_size, hidden_size, bias=False)
# these two learnable bias are used in matrix c and matrix d
# as described in https://huggingface.co/papers/1901.02860 Section 3.3
self.pos_bias_u = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
self.pos_bias_v = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
if self.position_embeddings_type == "relative_key":
self.left_max_position_embeddings = config.left_max_position_embeddings
self.right_max_position_embeddings = config.right_max_position_embeddings
num_positions = self.left_max_position_embeddings + self.right_max_position_embeddings + 1
self.distance_embedding = nn.Embedding(num_positions, self.head_size)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
relative_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
# self-attention mechanism
batch_size, sequence_length, hidden_size = hidden_states.size()
# make sure query/key states can be != value states
query_key_states = hidden_states
value_states = hidden_states
if self.position_embeddings_type == "rotary":
if relative_position_embeddings is None:
raise ValueError(
"`relative_position_embeddings` has to be defined when `self.position_embeddings_type == 'rotary'"
)
query_key_states = self._apply_rotary_embedding(query_key_states, relative_position_embeddings)
# project query_key_states and value_states
query = self.linear_q(query_key_states).view(batch_size, -1, self.num_heads, self.head_size)
key = self.linear_k(query_key_states).view(batch_size, -1, self.num_heads, self.head_size)
value = self.linear_v(value_states).view(batch_size, -1, self.num_heads, self.head_size)
# => (batch, head, time1, d_k)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
if self.position_embeddings_type == "relative":
if relative_position_embeddings is None:
raise ValueError(
"`relative_position_embeddings` has to be defined when `self.position_embeddings_type =="
" 'relative'"
)
# apply relative_position_embeddings to qk scores
# as proposed in Transformer_XL: https://huggingface.co/papers/1901.02860
scores = self._apply_relative_embeddings(
query=query, key=key, relative_position_embeddings=relative_position_embeddings
)
else:
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_size)
if self.position_embeddings_type == "relative_key":
query_length, key_length = query.shape[2], key.shape[2]
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_r - position_ids_l
distance = torch.clamp(distance, -self.left_max_position_embeddings, self.right_max_position_embeddings)
positional_embedding = self.distance_embedding(distance + self.left_max_position_embeddings)
positional_embedding = positional_embedding.to(dtype=query.dtype) # fp16 compatibility
relative_position_attn_weights = torch.einsum("bhld,lrd->bhlr", query, positional_embedding)
scores = scores + (relative_position_attn_weights / math.sqrt(self.head_size))
# apply attention_mask if necessary
if attention_mask is not None:
scores = scores + attention_mask
# => (batch, head, time1, time2)
probs = torch.softmax(scores, dim=-1)
probs = self.dropout(probs)
# => (batch, head, time1, d_k)
hidden_states = torch.matmul(probs, value)
# => (batch, time1, hidden_size)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_size)
hidden_states = self.linear_out(hidden_states)
return hidden_states, probs
def _apply_rotary_embedding(self, hidden_states, relative_position_embeddings):
batch_size, sequence_length, hidden_size = hidden_states.size()
hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads, self.head_size)
cos = relative_position_embeddings[0, :sequence_length, ...]
sin = relative_position_embeddings[1, :sequence_length, ...]
# rotate hidden_states with rotary embeddings
hidden_states = hidden_states.transpose(0, 1)
rotated_states_begin = hidden_states[..., : self.head_size // 2]
rotated_states_end = hidden_states[..., self.head_size // 2 :]
rotated_states = torch.cat((-rotated_states_end, rotated_states_begin), dim=rotated_states_begin.ndim - 1)
hidden_states = (hidden_states * cos) + (rotated_states * sin)
hidden_states = hidden_states.transpose(0, 1)
hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads * self.head_size)
return hidden_states
def _apply_relative_embeddings(self, query, key, relative_position_embeddings):
# 1. project positional embeddings
# => (batch, head, 2*time1-1, d_k)
proj_relative_position_embeddings = self.linear_pos(relative_position_embeddings)
proj_relative_position_embeddings = proj_relative_position_embeddings.view(
relative_position_embeddings.size(0), -1, self.num_heads, self.head_size
)
proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(1, 2)
proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(2, 3)
# 2. Add bias to query
# => (batch, head, time1, d_k)
query = query.transpose(1, 2)
q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2)
q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2)
# 3. attention score: first compute matrix a and matrix c
# as described in https://huggingface.co/papers/1901.02860 Section 3.3
# => (batch, head, time1, time2)
scores_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1))
# 4. then compute matrix b and matrix d
# => (batch, head, time1, 2*time1-1)
scores_bd = torch.matmul(q_with_bias_v, proj_relative_position_embeddings)
# 5. shift matrix b and matrix d
zero_pad = torch.zeros((*scores_bd.size()[:3], 1), device=scores_bd.device, dtype=scores_bd.dtype)
scores_bd_padded = torch.cat([zero_pad, scores_bd], dim=-1)
scores_bd_padded_shape = scores_bd.size()[:2] + (scores_bd.shape[3] + 1, scores_bd.shape[2])
scores_bd_padded = scores_bd_padded.view(*scores_bd_padded_shape)
scores_bd = scores_bd_padded[:, :, 1:].view_as(scores_bd)
scores_bd = scores_bd[:, :, :, : scores_bd.size(-1) // 2 + 1]
# 6. sum matrices
# => (batch, head, time1, time2)
scores = (scores_ac + scores_bd) / math.sqrt(self.head_size)
return scores
class Wav2Vec2BertEncoderLayer(GradientCheckpointingLayer):
"""Conformer block based on https://huggingface.co/papers/2005.08100."""
def __init__(self, config):
super().__init__()
embed_dim = config.hidden_size
dropout = config.attention_dropout
# Feed-forward 1
self.ffn1_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.ffn1 = Wav2Vec2BertFeedForward(config)
# Self-Attention
self.self_attn_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.self_attn_dropout = nn.Dropout(dropout)
self.self_attn = Wav2Vec2BertSelfAttention(config)
# Conformer Convolution
self.conv_module = Wav2Vec2BertConvolutionModule(config)
# Feed-forward 2
self.ffn2_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.ffn2 = Wav2Vec2BertFeedForward(config)
self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states,
attention_mask: Optional[torch.Tensor] = None,
relative_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
conv_attention_mask: Optional[torch.Tensor] = None,
):
hidden_states = hidden_states
# 1. Feed-Forward 1 layer
residual = hidden_states
hidden_states = self.ffn1_layer_norm(hidden_states)
hidden_states = self.ffn1(hidden_states)
hidden_states = hidden_states * 0.5 + residual
residual = hidden_states
# 2. Self-Attention layer
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weigts = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
relative_position_embeddings=relative_position_embeddings,
output_attentions=output_attentions,
)
hidden_states = self.self_attn_dropout(hidden_states)
hidden_states = hidden_states + residual
# 3. Convolutional Layer
residual = hidden_states
hidden_states = self.conv_module(hidden_states, attention_mask=conv_attention_mask)
hidden_states = residual + hidden_states
# 4. Feed-Forward 2 Layer
residual = hidden_states
hidden_states = self.ffn2_layer_norm(hidden_states)
hidden_states = self.ffn2(hidden_states)
hidden_states = hidden_states * 0.5 + residual
hidden_states = self.final_layer_norm(hidden_states)
return hidden_states, attn_weigts
class Wav2Vec2BertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
if config.position_embeddings_type == "relative":
self.embed_positions = Wav2Vec2BertRelPositionalEmbedding(config)
elif config.position_embeddings_type == "rotary":
self.embed_positions = Wav2Vec2BertRotaryPositionalEmbedding(config)
else:
self.embed_positions = None
self.dropout = nn.Dropout(config.hidden_dropout)
self.layers = nn.ModuleList([Wav2Vec2BertEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
conv_attention_mask = attention_mask
if attention_mask is not None:
# make sure padded tokens output 0
hidden_states = hidden_states.masked_fill(~attention_mask.bool().unsqueeze(-1), 0.0)
# extend attention_mask
attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)
attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min
attention_mask = attention_mask.expand(
attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
)
hidden_states = self.dropout(hidden_states)
if self.embed_positions is not None:
relative_position_embeddings = self.embed_positions(hidden_states)
else:
relative_position_embeddings = None
synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
for i, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
dropout_probability = torch.rand([])
skip_the_layer = self.training and dropout_probability < self.config.layerdrop
if not skip_the_layer or synced_gpus:
# under fsdp or deepspeed zero3 all gpus must run in sync
layer_outputs = layer(
hidden_states,
attention_mask=attention_mask,
relative_position_embeddings=relative_position_embeddings,
output_attentions=output_attentions,
conv_attention_mask=conv_attention_mask,
)
hidden_states = layer_outputs[0]
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class Wav2Vec2BertAdapter(nn.Module):
def __init__(self, config):
super().__init__()
# feature dim might need to be down-projected
if config.output_hidden_size != config.hidden_size:
self.proj = nn.Linear(config.hidden_size, config.output_hidden_size)
self.proj_layer_norm = nn.LayerNorm(config.output_hidden_size, eps=config.layer_norm_eps)
else:
self.proj = self.proj_layer_norm = None
self.layers = nn.ModuleList(Wav2Vec2BertAdapterLayer(config) for _ in range(config.num_adapter_layers))
self.layerdrop = config.layerdrop
self.kernel_size = config.adapter_kernel_size
self.stride = config.adapter_stride
def _compute_sub_sample_lengths_from_attention_mask(self, seq_lens):
if seq_lens is None:
return seq_lens
pad = self.kernel_size // 2
seq_lens = ((seq_lens + 2 * pad - self.kernel_size) / self.stride) + 1
return seq_lens.floor()
def forward(self, hidden_states, attention_mask=None):
# down project hidden_states if necessary
if self.proj is not None and self.proj_layer_norm is not None:
hidden_states = self.proj(hidden_states)
hidden_states = self.proj_layer_norm(hidden_states)
sub_sampled_lengths = None
if attention_mask is not None:
sub_sampled_lengths = (attention_mask.size(1) - (1 - attention_mask.int()).sum(1)).to(hidden_states.device)
for layer in self.layers:
layerdrop_prob = torch.rand([])
sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(sub_sampled_lengths)
if not self.training or (layerdrop_prob > self.layerdrop):
hidden_states = layer(
hidden_states, attention_mask=attention_mask, sub_sampled_lengths=sub_sampled_lengths
)
return hidden_states
# Copied from transformers.models.seamless_m4t_v2.modeling_seamless_m4t_v2._compute_new_attention_mask
def _compute_new_attention_mask(hidden_states: torch.Tensor, seq_lens: torch.Tensor):
"""
Computes an attention mask of the form `(batch, seq_len)` with an attention for each element in the batch that
stops at the corresponding element in `seq_lens`.
Args:
hidden_states (`torch.FloatTensor` of shape `(batch, seq_len, *)`):
The sequences to mask, where `*` is any number of sequence-specific dimensions including none.
seq_lens (`torch.Tensor` of shape `(batch)`:
Each element represents the length of the sequence at the same index in `hidden_states`
Returns:
`torch.FloatTensor`: The float attention mask of shape `(batch, seq_len)`
"""
batch_size, mask_seq_len = hidden_states.shape[:2]
indices = torch.arange(mask_seq_len, device=seq_lens.device).expand(batch_size, -1)
bool_mask = indices >= seq_lens.unsqueeze(1).expand(-1, mask_seq_len)
mask = hidden_states.new_ones((batch_size, mask_seq_len))
mask = mask.masked_fill(bool_mask, 0)
return mask
class Wav2Vec2BertAdapterLayer(nn.Module):
def __init__(self, config):
super().__init__()
embed_dim = config.output_hidden_size
dropout = config.conformer_conv_dropout
self.kernel_size = config.adapter_kernel_size
self.stride = config.adapter_stride
# 1. residual convolution
self.residual_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.residual_conv = nn.Conv1d(
embed_dim,
2 * embed_dim,
self.kernel_size,
stride=self.stride,
padding=self.stride // 2,
)
self.activation = nn.GLU(dim=1)
# Self-Attention
self.self_attn_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.self_attn_conv = nn.Conv1d(
embed_dim,
2 * embed_dim,
self.kernel_size,
stride=self.stride,
padding=self.stride // 2,
)
self.self_attn = Wav2Vec2BertSelfAttention(config, is_adapter_attention=True)
self.self_attn_dropout = nn.Dropout(dropout)
# Feed-forward
self.ffn_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.ffn = Wav2Vec2BertFeedForward(config, act_fn=config.adapter_act, hidden_size=embed_dim)
def forward(
self,
hidden_states,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
sub_sampled_lengths: Optional[torch.Tensor] = None,
):
residual = self.residual_layer_norm(hidden_states)
# Apply pooling to the residual to match the sequence length of the
# multi-head attention output.
# (batch, seq_len, feature_dim) -> (batch, feature_dim, seq_len)
residual = residual.transpose(1, 2)
residual = self.residual_conv(residual)
residual = self.activation(residual)
# (batch, feature_dim, seq_len) -> (batch, seq_len, feature_dim)
residual = residual.transpose(1, 2)
hidden_states = self.self_attn_layer_norm(hidden_states)
# Apply pooling before feeding to the multihead-attention layer.
# (batch, seq_len, feature_dim) -> (batch, feature_dim, seq_len)
hidden_states = hidden_states.transpose(1, 2)
hidden_states = self.self_attn_conv(hidden_states)
hidden_states = self.activation(hidden_states)
# (batch, feature_dim, seq_len) -> (batch, seq_len, feature_dim)
hidden_states = hidden_states.transpose(1, 2)
if attention_mask is not None:
attention_mask = _compute_new_attention_mask(hidden_states=hidden_states, seq_lens=sub_sampled_lengths)
attention_mask = _prepare_4d_attention_mask(
attention_mask,
hidden_states.dtype,
)
# The rest of the computation is identical to a vanilla Transformer
# encoder layer.
hidden_states, attn_weights = self.self_attn(
hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = self.self_attn_dropout(hidden_states)
hidden_states = hidden_states + residual
residual = hidden_states
hidden_states = self.ffn_layer_norm(hidden_states)
hidden_states = self.ffn(hidden_states) + residual
return hidden_states
@auto_docstring
class Wav2Vec2BertPreTrainedModel(PreTrainedModel):
config: Wav2Vec2BertConfig
base_model_prefix = "wav2vec2_bert"
main_input_name = "input_features"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, Wav2Vec2BertSelfAttention):
if hasattr(module, "pos_bias_u"):
nn.init.xavier_uniform_(module.pos_bias_u)
if hasattr(module, "pos_bias_v"):
nn.init.xavier_uniform_(module.pos_bias_v)
elif isinstance(module, Wav2Vec2BertFeatureProjection):
k = math.sqrt(1 / module.projection.in_features)
nn.init.uniform_(module.projection.weight, a=-k, b=k)
nn.init.uniform_(module.projection.bias, a=-k, b=k)
elif isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Conv1d):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
nn.init.uniform_(module.bias, a=-k, b=k)
elif isinstance(module, Wav2Vec2BertModel):
if hasattr(module, "masked_spec_embed"):
module.masked_spec_embed.data.uniform_()
elif isinstance(
module,
(Wav2Vec2BertForSequenceClassification, Wav2Vec2BertForAudioFrameClassification, Wav2Vec2BertForXVector),
):
if hasattr(module, "layer_weights"):
module.layer_weights.data.fill_(1.0 / (self.config.num_hidden_layers + 1))
elif isinstance(module, AMSoftmaxLoss): # noqa: F821
module.weight.data.normal_()
# Ignore copy
def _get_feat_extract_output_lengths(
self, input_lengths: Union[torch.LongTensor, int], add_adapter: Optional[bool] = None
):
"""
Computes the output length of the convolutional layers
"""
add_adapter = self.config.add_adapter if add_adapter is None else add_adapter
def _conv_out_length(input_length, kernel_size, stride, padding):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return torch.div(input_length + 2 * padding - kernel_size, stride, rounding_mode="floor") + 1
if add_adapter:
padding = self.config.adapter_kernel_size // 2
for _ in range(self.config.num_adapter_layers):
input_lengths = _conv_out_length(
input_lengths, self.config.adapter_kernel_size, self.config.adapter_stride, padding
)
return input_lengths
def _get_feature_vector_attention_mask(
self, feature_vector_length: int, attention_mask: torch.LongTensor, add_adapter=None
):
# Effectively attention_mask.sum(-1), but not inplace to be able to run
# on inference mode.
non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1]
output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter)
output_lengths = output_lengths.to(torch.long)
batch_size = attention_mask.shape[0]
attention_mask = torch.zeros(
(batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
)
# these two operations makes sure that all values before the output lengths idxs are attended to
attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1
attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
return attention_mask
def _compute_mask_indices(
shape: tuple[int, int],
mask_prob: float,
mask_length: int,
attention_mask: Optional[torch.LongTensor] = None,
min_masks: int = 0,
) -> np.ndarray:
"""
Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
ASR](https://huggingface.co/papers/1904.08779). Note that this method is not optimized to run on TPU and should be run on
CPU as part of the preprocessing during training.
Args:
shape: The shape for which to compute masks. This should be of a tuple of size 2 where
the first element is the batch size and the second element is the length of the axis to span.
mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of
independently generated mask spans of length `mask_length` is computed by
`mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
actual percentage will be smaller.
mask_length: size of the mask
min_masks: minimum number of masked spans
attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
each batch dimension.
"""
batch_size, sequence_length = shape
if mask_length < 1:
raise ValueError("`mask_length` has to be bigger than 0.")
if mask_length > sequence_length:
raise ValueError(
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
f" and `sequence_length`: {sequence_length}`"
)
# epsilon is used for probabilistic rounding
epsilon = np.random.rand(1).item()
def compute_num_masked_span(input_length):
"""Given input length, compute how many spans should be masked"""
num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
num_masked_span = max(num_masked_span, min_masks)
# make sure num masked span <= sequence_length
if num_masked_span * mask_length > sequence_length:
num_masked_span = sequence_length // mask_length
# make sure num_masked span is also <= input_length - (mask_length - 1)
if input_length - (mask_length - 1) < num_masked_span:
num_masked_span = max(input_length - (mask_length - 1), 0)
return num_masked_span
# compute number of masked spans in batch
input_lengths = (
attention_mask.detach().sum(-1).tolist()
if attention_mask is not None
else [sequence_length for _ in range(batch_size)]
)
# SpecAugment mask to fill
spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
spec_aug_mask_idxs = []
max_num_masked_span = compute_num_masked_span(sequence_length)
if max_num_masked_span == 0:
return spec_aug_mask
for input_length in input_lengths:
# compute num of masked spans for this input
num_masked_span = compute_num_masked_span(input_length)
# get random indices to mask
spec_aug_mask_idx = np.random.choice(
np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
)
# pick first sampled index that will serve as a dummy index to pad vector
# to ensure same dimension for all batches due to probabilistic rounding
# Picking first sample just pads those vectors twice.
if len(spec_aug_mask_idx) == 0:
# this case can only happen if `input_length` is strictly smaller then
# `sequence_length` in which case the last token has to be a padding
# token which we can use as a dummy mask id
dummy_mask_idx = sequence_length - 1
else:
dummy_mask_idx = spec_aug_mask_idx[0]
spec_aug_mask_idx = np.concatenate(
[spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
)
spec_aug_mask_idxs.append(spec_aug_mask_idx)
spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)
# expand masked indices to masked spans
spec_aug_mask_idxs = np.broadcast_to(
spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
)
spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)
# add offset to the starting indexes so that indexes now create a span
offsets = np.arange(mask_length)[None, None, :]
offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
batch_size, max_num_masked_span * mask_length
)
spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
# ensure that we cannot have indices larger than sequence_length
if spec_aug_mask_idxs.max() > sequence_length - 1:
spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
# scatter indices to mask
np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
return spec_aug_mask
Wav2Vec2BertBaseModelOutput = Wav2Vec2BaseModelOutput
@auto_docstring
class Wav2Vec2BertModel(Wav2Vec2BertPreTrainedModel):
def __init__(self, config: Wav2Vec2BertConfig):
super().__init__(config)
self.config = config
self.feature_projection = Wav2Vec2BertFeatureProjection(config)
# model only needs masking vector if mask prob is > 0.0
if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
self.masked_spec_embed = nn.Parameter(torch.Tensor(config.hidden_size).uniform_())
self.encoder = Wav2Vec2BertEncoder(config)
self.adapter = Wav2Vec2BertAdapter(config) if config.add_adapter else None
self.intermediate_ffn = None
if config.use_intermediate_ffn_before_adapter:
self.intermediate_ffn = Wav2Vec2BertFeedForward(config, act_fn="relu")
# Initialize weights and apply final processing
self.post_init()
def _mask_hidden_states(
self,
hidden_states: torch.FloatTensor,
mask_time_indices: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
):
"""
Masks extracted features along time axis and/or along feature axis according to
[SpecAugment](https://huggingface.co/papers/1904.08779).
"""
# `config.apply_spec_augment` can set masking to False
if not getattr(self.config, "apply_spec_augment", True):
return hidden_states
# generate indices & apply SpecAugment along time axis
batch_size, sequence_length, hidden_size = hidden_states.size()
if mask_time_indices is not None:
# apply SpecAugment along time axis with given mask_time_indices
hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
elif self.config.mask_time_prob > 0 and self.training:
mask_time_indices = _compute_mask_indices(
(batch_size, sequence_length),
mask_prob=self.config.mask_time_prob,
mask_length=self.config.mask_time_length,
attention_mask=attention_mask,
min_masks=self.config.mask_time_min_masks,
)
mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool)
hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
if self.config.mask_feature_prob > 0 and self.training:
# generate indices & apply SpecAugment along feature axis
mask_feature_indices = _compute_mask_indices(
(batch_size, hidden_size),
mask_prob=self.config.mask_feature_prob,
mask_length=self.config.mask_feature_length,
min_masks=self.config.mask_feature_min_masks,
)
mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool)
mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1)
hidden_states[mask_feature_indices] = 0
return hidden_states
@auto_docstring
def forward(
self,
input_features: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, Wav2Vec2BertBaseModelOutput]:
r"""
mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
masked extracted features in *config.proj_codevector_dim* space.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
hidden_states, extract_features = self.feature_projection(input_features)
hidden_states = self._mask_hidden_states(
hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
)
encoder_outputs = self.encoder(
hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = encoder_outputs[0]
if self.intermediate_ffn:
expanded_hidden_states = self.intermediate_ffn(hidden_states)
hidden_states = hidden_states + 0.5 * expanded_hidden_states
if self.adapter is not None:
hidden_states = self.adapter(hidden_states, attention_mask=attention_mask)
if not return_dict:
return (hidden_states, extract_features) + encoder_outputs[1:]
return Wav2Vec2BertBaseModelOutput(
last_hidden_state=hidden_states,
extract_features=extract_features,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
_HIDDEN_STATES_START_POSITION = 2
@auto_docstring(
custom_intro="""
Wav2Vec2Bert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
"""
)
class Wav2Vec2BertForCTC(Wav2Vec2BertPreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
r"""
target_lang (`str`, *optional*):
Language id of adapter weights. Adapter weights are stored in the format adapter.<lang>.safetensors or
adapter.<lang>.bin. Only relevant when using an instance of [`UniSpeechSatForCTC`] with adapters. Uses 'eng' by
default.
"""
super().__init__(config)
self.wav2vec2_bert = Wav2Vec2BertModel(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate {self.__class__} with a configuration that "
"does not define the vocabulary size of the language model head. Please "
"instantiate the model as follows: `Wav2Vec2BertForCTC.from_pretrained(..., vocab_size=vocab_size)`. "
"or define `vocab_size` of your model's configuration."
)
output_hidden_size = (
config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size
)
self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
# Initialize weights and apply final processing
self.post_init()
@auto_docstring
def forward(
self,
input_features: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size - 1]`.
"""
if labels is not None and labels.max() >= self.config.vocab_size:
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.wav2vec2_bert(
input_features,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
hidden_states = self.dropout(hidden_states)
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# retrieve loss input_lengths from attention_mask
attention_mask = (
attention_mask
if attention_mask is not None
else torch.ones(input_features.shape[:2], device=input_features.device, dtype=torch.long)
)
input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum([-1])).to(torch.long)
# assuming that padded tokens are filled with -100
# when not being attended to
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
# ctc_loss doesn't support fp16
log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
with torch.backends.cudnn.flags(enabled=False):
loss = nn.functional.ctc_loss(
log_probs,
flattened_targets,
input_lengths,
target_lengths,
blank=self.config.pad_token_id,
reduction=self.config.ctc_loss_reduction,
zero_infinity=self.config.ctc_zero_infinity,
)
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)
@auto_docstring(
custom_intro="""
Wav2Vec2Bert Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting.
"""
)
class Wav2Vec2BertForSequenceClassification(Wav2Vec2BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Sequence classification does not support the use of Wav2Vec2Bert adapters (config.add_adapter=True)"
)
self.wav2vec2_bert = Wav2Vec2BertModel(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
be updated during training. Only the classification head will be updated.
"""
for param in self.wav2vec2_bert.parameters():
param.requires_grad = False
@auto_docstring
def forward(
self,
input_features: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
outputs = self.wav2vec2_bert(
input_features,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if self.config.use_weighted_layer_sum:
hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
hidden_states = torch.stack(hidden_states, dim=1)
norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
else:
hidden_states = outputs[0]
hidden_states = self.projector(hidden_states)
if attention_mask is None:
pooled_output = hidden_states.mean(dim=1)
else:
padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
hidden_states[~expand_padding_mask] = 0.0
pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@auto_docstring
class Wav2Vec2BertForAudioFrameClassification(Wav2Vec2BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Audio frame classification does not support the use of Wav2Vec2Bert adapters (config.add_adapter=True)"
)
self.wav2vec2_bert = Wav2Vec2BertModel(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.num_labels = config.num_labels
self.init_weights()
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
be updated during training. Only the classification head will be updated.
"""
for param in self.wav2vec2_bert.parameters():
param.requires_grad = False
@auto_docstring
def forward(
self,
input_features: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
outputs = self.wav2vec2_bert(
input_features,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if self.config.use_weighted_layer_sum:
hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
hidden_states = torch.stack(hidden_states, dim=1)
norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
else:
hidden_states = outputs[0]
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), torch.argmax(labels.view(-1, self.num_labels), axis=1))
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class AMSoftmaxLoss(nn.Module):
def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
super().__init__()
self.scale = scale
self.margin = margin
self.num_labels = num_labels
self.weight = nn.Parameter(torch.randn(input_dim, num_labels), requires_grad=True)
self.loss = nn.CrossEntropyLoss()
def forward(self, hidden_states, labels):
labels = labels.flatten()
weight = nn.functional.normalize(self.weight, dim=0)
hidden_states = nn.functional.normalize(hidden_states, dim=1)
cos_theta = torch.mm(hidden_states, weight)
psi = cos_theta - self.margin
onehot = nn.functional.one_hot(labels, self.num_labels)
logits = self.scale * torch.where(onehot.bool(), psi, cos_theta)
loss = self.loss(logits, labels)
return loss
class TDNNLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
self.out_conv_dim = config.tdnn_dim[layer_id]
self.kernel_size = config.tdnn_kernel[layer_id]
self.dilation = config.tdnn_dilation[layer_id]
self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
self.activation = nn.ReLU()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if is_peft_available():
from peft.tuners.lora import LoraLayer
if is_peft_available():
if isinstance(self.kernel, LoraLayer):
warnings.warn(
"Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. "
"You should exclude TDNNLayer from LoRA's target modules.",
)
# for backward compatibility, we keep nn.Linear but call F.conv1d for speed up
hidden_states = hidden_states.transpose(1, 2)
weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2)
hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation)
hidden_states = hidden_states.transpose(1, 2)
hidden_states = self.activation(hidden_states)
return hidden_states
@auto_docstring(
custom_intro="""
Wav2Vec2Bert Model with an XVector feature extraction head on top for tasks like Speaker Verification.
"""
)
class Wav2Vec2BertForXVector(Wav2Vec2BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wav2vec2_bert = Wav2Vec2BertModel(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
self.projector = nn.Linear(config.hidden_size, config.tdnn_dim[0])
tdnn_layers = [TDNNLayer(config, i) for i in range(len(config.tdnn_dim))]
self.tdnn = nn.ModuleList(tdnn_layers)
self.feature_extractor = nn.Linear(config.tdnn_dim[-1] * 2, config.xvector_output_dim)
self.classifier = nn.Linear(config.xvector_output_dim, config.xvector_output_dim)
self.objective = AMSoftmaxLoss(config.xvector_output_dim, config.num_labels)
self.init_weights()
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
be updated during training. Only the classification head will be updated.
"""
for param in self.wav2vec2_bert.parameters():
param.requires_grad = False
def _get_tdnn_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
"""
Computes the output length of the TDNN layers
"""
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return (input_length - kernel_size) // stride + 1
for kernel_size in self.config.tdnn_kernel:
input_lengths = _conv_out_length(input_lengths, kernel_size, 1)
return input_lengths
@auto_docstring
def forward(
self,
input_features: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[tuple, XVectorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
outputs = self.wav2vec2_bert(
input_features,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if self.config.use_weighted_layer_sum:
hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
hidden_states = torch.stack(hidden_states, dim=1)
norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
else:
hidden_states = outputs[0]
hidden_states = self.projector(hidden_states)
for tdnn_layer in self.tdnn:
hidden_states = tdnn_layer(hidden_states)
# Statistic Pooling
if attention_mask is None:
mean_features = hidden_states.mean(dim=1)
std_features = hidden_states.std(dim=1)
else:
feat_extract_output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(dim=1))
tdnn_output_lengths = self._get_tdnn_output_lengths(feat_extract_output_lengths)
mean_features = []
std_features = []
for i, length in enumerate(tdnn_output_lengths):
mean_features.append(hidden_states[i, :length].mean(dim=0))
std_features.append(hidden_states[i, :length].std(dim=0))
mean_features = torch.stack(mean_features)
std_features = torch.stack(std_features)
statistic_pooling = torch.cat([mean_features, std_features], dim=-1)
output_embeddings = self.feature_extractor(statistic_pooling)
logits = self.classifier(output_embeddings)
loss = None
if labels is not None:
loss = self.objective(logits, labels)
if not return_dict:
output = (logits, output_embeddings) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return XVectorOutput(
loss=loss,
logits=logits,
embeddings=output_embeddings,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = [
"Wav2Vec2BertForAudioFrameClassification",
"Wav2Vec2BertForCTC",
"Wav2Vec2BertForSequenceClassification",
"Wav2Vec2BertForXVector",
"Wav2Vec2BertModel",
"Wav2Vec2BertPreTrainedModel",
]
| transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py/0 | {
"file_path": "transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py",
"repo_id": "transformers",
"token_count": 28811
} | 501 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def get_xclip_config(model_name, num_frames):
text_config = XCLIPTextConfig()
# derive patch size from model name
start_idx = model_name.find("patch")
patch_size = int(model_name[start_idx + len("patch") : start_idx + len("patch") + 2])
vision_config = XCLIPVisionConfig(patch_size=patch_size, num_frames=num_frames)
if "large" in model_name:
text_config.hidden_size = 768
text_config.intermediate_size = 3072
text_config.num_attention_heads = 12
vision_config.hidden_size = 1024
vision_config.intermediate_size = 4096
vision_config.num_attention_heads = 16
vision_config.num_hidden_layers = 24
vision_config.mit_hidden_size = 768
vision_config.mit_intermediate_size = 3072
if model_name == "xclip-large-patch14-16-frames":
vision_config.image_size = 336
config = XCLIPConfig.from_text_vision_configs(text_config, vision_config)
if "large" in model_name:
config.projection_dim = 768
return config
def rename_key(name):
# text encoder
if name == "token_embedding.weight":
name = name.replace("token_embedding.weight", "text_model.embeddings.token_embedding.weight")
if name == "positional_embedding":
name = name.replace("positional_embedding", "text_model.embeddings.position_embedding.weight")
if "ln_1" in name:
name = name.replace("ln_1", "layer_norm1")
if "ln_2" in name:
name = name.replace("ln_2", "layer_norm2")
if "c_fc" in name:
name = name.replace("c_fc", "fc1")
if "c_proj" in name:
name = name.replace("c_proj", "fc2")
if name.startswith("transformer.resblocks"):
name = name.replace("transformer.resblocks", "text_model.encoder.layers")
if "attn.out_proj" in name and "message" not in name:
name = name.replace("attn.out_proj", "self_attn.out_proj")
if "ln_final" in name:
name = name.replace("ln_final", "text_model.final_layer_norm")
# visual encoder
if name == "visual.class_embedding":
name = name.replace("visual.class_embedding", "vision_model.embeddings.class_embedding")
if name == "visual.positional_embedding":
name = name.replace("visual.positional_embedding", "vision_model.embeddings.position_embedding.weight")
if name.startswith("visual.transformer.resblocks"):
name = name.replace("visual.transformer.resblocks", "vision_model.encoder.layers")
if "visual.conv1" in name:
name = name.replace("visual.conv1", "vision_model.embeddings.patch_embedding")
if "visual.ln_pre" in name:
name = name.replace("visual.ln_pre", "vision_model.pre_layernorm")
if "visual.ln_post" in name:
name = name.replace("visual.ln_post", "vision_model.post_layernorm")
if "visual.proj" in name:
name = name.replace("visual.proj", "visual_projection.weight")
if "text_projection" in name:
name = name.replace("text_projection", "text_projection.weight")
# things on top
if "prompts_visual_proj" in name:
name = name.replace("prompts_visual_proj", "prompts_visual_projection")
if "prompts_visual_ln" in name:
name = name.replace("prompts_visual_ln", "prompts_visual_layernorm")
# mit
if name == "mit.positional_embedding":
name = name.replace("positional", "position")
if name.startswith("mit.resblocks"):
name = name.replace("mit.resblocks", "mit.encoder.layers")
# prompts generator
if name.startswith("prompts_generator.norm"):
name = name.replace("prompts_generator.norm", "prompts_generator.layernorm")
return name
def convert_state_dict(orig_state_dict, config):
for key in orig_state_dict.copy():
val = orig_state_dict.pop(key)
if "attn.in_proj" in key:
key_split = key.split(".")
if key.startswith("visual"):
layer_num = key_split[3]
dim = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.q_proj.weight"] = val[
:dim, :
]
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.k_proj.weight"] = val[
dim : dim * 2, :
]
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.v_proj.weight"] = val[
-dim:, :
]
else:
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.q_proj.bias"] = val[
:dim
]
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.k_proj.bias"] = val[
dim : dim * 2
]
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.v_proj.bias"] = val[
-dim:
]
else:
if "weight" in key:
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[
:dim, :
]
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[
dim : dim * 2, :
]
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[
-dim:, :
]
else:
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[
dim : dim * 2
]
orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
elif key.startswith("mit"):
layer_num = key_split[2]
dim = config.vision_config.mit_hidden_size
if "weight" in key:
orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[dim : dim * 2, :]
orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
else:
orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
else:
layer_num = key_split[2]
dim = config.text_config.hidden_size
if "weight" in key:
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[
dim : dim * 2, :
]
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
else:
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[
dim : dim * 2
]
orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
else:
new_key_name = rename_key(key)
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
val = val.T
orig_state_dict[new_key_name] = val
return orig_state_dict
def prepare_video(num_frames):
if num_frames == 8:
filename = "eating_spaghetti_8_frames.npy"
elif num_frames == 16:
filename = "eating_spaghetti.npy"
elif num_frames == 32:
filename = "eating_spaghetti_32_frames.npy"
file = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video",
filename=filename,
repo_type="dataset",
)
video = np.load(file)
return list(video)
def convert_xclip_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False):
model_to_url = {
# fully supervised kinetics-400 checkpoints
"xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
"xclip-base-patch32-16-frames": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
),
"xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
"xclip-base-patch16-16-frames": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
),
"xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
"xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f",
# fully supervised kinetics-600 checkpoints
"xclip-base-patch16-kinetics-600": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
),
"xclip-base-patch16-kinetics-600-16-frames": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
),
"xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
# few shot
"xclip-base-patch16-hmdb-2-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
),
"xclip-base-patch16-hmdb-4-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
),
"xclip-base-patch16-hmdb-8-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
),
"xclip-base-patch16-hmdb-16-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
),
"xclip-base-patch16-ucf-2-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
),
"xclip-base-patch16-ucf-4-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
),
"xclip-base-patch16-ucf-8-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
),
"xclip-base-patch16-ucf-16-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
),
# zero shot
"xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
}
checkpoint_url = model_to_url[model_name]
num_frames = 8
if "16-frames" in model_name:
num_frames = 16
elif "shot" in model_name:
num_frames = 32
config = get_xclip_config(model_name, num_frames)
model = XCLIPModel(config)
model.eval()
if "drive" in checkpoint_url:
output = "pytorch_model.bin"
gdown.cached_download(checkpoint_url, output, quiet=False)
state_dict = torch.load(output, map_location="cpu", weights_only=True)["model"]
else:
state_dict = torch.hub.load_state_dict_from_url(checkpoint_url)["model"]
state_dict = convert_state_dict(state_dict, config)
model = XCLIPModel(config)
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
size = 336 if model_name == "xclip-large-patch14-16-frames" else 224
image_processor = VideoMAEImageProcessor(size=size)
slow_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
fast_tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32")
processor = XCLIPProcessor(image_processor=image_processor, tokenizer=fast_tokenizer)
video = prepare_video(num_frames)
inputs = processor(
text=["playing sports", "eating spaghetti", "go shopping"], videos=video, return_tensors="pt", padding=True
)
print("Shape of pixel values:", inputs.pixel_values.shape)
with torch.no_grad():
outputs = model(**inputs)
# Verify outputs
logits_per_video = outputs.logits_per_video
probs = logits_per_video.softmax(dim=1)
print("Probs:", probs)
# kinetics-400
if model_name == "xclip-base-patch32":
expected_probs = torch.tensor([[0.0019, 0.9951, 0.0030]])
elif model_name == "xclip-base-patch32-16-frames":
expected_probs = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]])
elif model_name == "xclip-base-patch16":
expected_probs = torch.tensor([[0.0083, 0.9681, 0.0236]])
elif model_name == "xclip-base-patch16-16-frames":
expected_probs = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]])
elif model_name == "xclip-large-patch14":
expected_probs = torch.tensor([[0.0062, 0.9864, 0.0075]])
elif model_name == "xclip-large-patch14-16-frames":
expected_probs = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]])
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
expected_probs = torch.tensor([[0.0555, 0.8914, 0.0531]])
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
expected_probs = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]])
elif model_name == "xclip-large-patch14-kinetics-600":
expected_probs = torch.tensor([[0.0036, 0.9920, 0.0045]])
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
expected_probs = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]])
elif model_name == "xclip-base-patch16-hmdb-4-shot":
expected_probs = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]])
elif model_name == "xclip-base-patch16-hmdb-8-shot":
expected_probs = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]])
elif model_name == "xclip-base-patch16-hmdb-16-shot":
expected_probs = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]])
elif model_name == "xclip-base-patch16-ucf-2-shot":
expected_probs = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]])
elif model_name == "xclip-base-patch16-ucf-4-shot":
expected_probs = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]])
elif model_name == "xclip-base-patch16-ucf-8-shot":
expected_probs = torch.tensor([[0.0027, 0.9904, 0.0070]])
elif model_name == "xclip-base-patch16-ucf-16-shot":
expected_probs = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]])
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
expected_probs = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]])
else:
raise ValueError(f"Model name {model_name} not supported")
assert torch.allclose(probs, expected_probs, atol=1e-3)
print("Looks ok!")
if pytorch_dump_folder_path is not None:
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
print("Pushing model, processor and slow tokenizer files to the hub...")
model.push_to_hub(model_name, organization="nielsr")
processor.push_to_hub(model_name, organization="nielsr")
slow_tokenizer.push_to_hub(model_name, organization="nielsr")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="xclip-base-patch32",
type=str,
help="Name of the model.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
args = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| transformers/src/transformers/models/x_clip/convert_x_clip_original_pytorch_to_hf.py/0 | {
"file_path": "transformers/src/transformers/models/x_clip/convert_x_clip_original_pytorch_to_hf.py",
"repo_id": "transformers",
"token_count": 8833
} | 502 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert YOLOS checkpoints from the original repository. URL: https://github.com/hustvl/YOLOS"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_yolos_config(yolos_name: str) -> YolosConfig:
config = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
config.hidden_size = 192
config.intermediate_size = 768
config.num_hidden_layers = 12
config.num_attention_heads = 3
config.image_size = [800, 1333]
config.use_mid_position_embeddings = False
elif yolos_name == "yolos_s_dWr":
config.hidden_size = 330
config.num_hidden_layers = 14
config.num_attention_heads = 6
config.intermediate_size = 1320
elif "yolos_s" in yolos_name:
config.hidden_size = 384
config.intermediate_size = 1536
config.num_hidden_layers = 12
config.num_attention_heads = 6
elif "yolos_b" in yolos_name:
config.image_size = [800, 1344]
config.num_labels = 91
repo_id = "huggingface/label-files"
filename = "coco-detection-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
return config
# we split up the matrix of each encoder layer into queries, keys and values
def read_in_q_k_v(state_dict: dict, config: YolosConfig, base_model: bool = False):
for i in range(config.num_hidden_layers):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight")
in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[: config.hidden_size, :]
state_dict[f"encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size]
state_dict[f"encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
state_dict[f"encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
state_dict[f"encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[-config.hidden_size :, :]
state_dict[f"encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :]
def rename_key(name: str) -> str:
if "backbone" in name:
name = name.replace("backbone", "vit")
if "cls_token" in name:
name = name.replace("cls_token", "embeddings.cls_token")
if "det_token" in name:
name = name.replace("det_token", "embeddings.detection_tokens")
if "mid_pos_embed" in name:
name = name.replace("mid_pos_embed", "encoder.mid_position_embeddings")
if "pos_embed" in name:
name = name.replace("pos_embed", "embeddings.position_embeddings")
if "patch_embed.proj" in name:
name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection")
if "blocks" in name:
name = name.replace("blocks", "encoder.layer")
if "attn.proj" in name:
name = name.replace("attn.proj", "attention.output.dense")
if "attn" in name:
name = name.replace("attn", "attention.self")
if "norm1" in name:
name = name.replace("norm1", "layernorm_before")
if "norm2" in name:
name = name.replace("norm2", "layernorm_after")
if "mlp.fc1" in name:
name = name.replace("mlp.fc1", "intermediate.dense")
if "mlp.fc2" in name:
name = name.replace("mlp.fc2", "output.dense")
if "class_embed" in name:
name = name.replace("class_embed", "class_labels_classifier")
if "bbox_embed" in name:
name = name.replace("bbox_embed", "bbox_predictor")
if "vit.norm" in name:
name = name.replace("vit.norm", "vit.layernorm")
return name
def convert_state_dict(orig_state_dict: dict, model: YolosForObjectDetection) -> dict:
for key in orig_state_dict.copy():
val = orig_state_dict.pop(key)
if "qkv" in key:
key_split = key.split(".")
layer_num = int(key_split[2])
dim = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.query.weight"] = val[:dim, :]
orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.key.weight"] = val[
dim : dim * 2, :
]
orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.value.weight"] = val[-dim:, :]
else:
orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.query.bias"] = val[:dim]
orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2]
orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.value.bias"] = val[-dim:]
else:
orig_state_dict[rename_key(key)] = val
return orig_state_dict
# We will verify our results on an image of cute cats
def prepare_img() -> torch.Tensor:
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_yolos_checkpoint(
yolos_name: str, checkpoint_path: str, pytorch_dump_folder_path: str, push_to_hub: bool = False
):
"""
Copy/paste/tweak model's weights to our YOLOS structure.
"""
config = get_yolos_config(yolos_name)
# load original state_dict
state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=True)["model"]
# load 🤗 model
model = YolosForObjectDetection(config)
model.eval()
new_state_dict = convert_state_dict(state_dict, model)
model.load_state_dict(new_state_dict)
# Check outputs on an image, prepared by YolosImageProcessor
size = 800 if yolos_name != "yolos_ti" else 512
image_processor = YolosImageProcessor(format="coco_detection", size=size)
encoding = image_processor(images=prepare_img(), return_tensors="pt")
outputs = model(**encoding)
logits, pred_boxes = outputs.logits, outputs.pred_boxes
expected_slice_logits, expected_slice_boxes = None, None
if yolos_name == "yolos_ti":
expected_slice_logits = torch.tensor(
[[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]
)
expected_slice_boxes = torch.tensor(
[[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]
)
elif yolos_name == "yolos_s_200_pre":
expected_slice_logits = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]
)
expected_slice_boxes = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]
)
elif yolos_name == "yolos_s_300_pre":
expected_slice_logits = torch.tensor(
[[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]
)
expected_slice_boxes = torch.tensor(
[[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]
)
elif yolos_name == "yolos_s_dWr":
expected_slice_logits = torch.tensor(
[[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]
)
expected_slice_boxes = torch.tensor(
[[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]
)
elif yolos_name == "yolos_base":
expected_slice_logits = torch.tensor(
[[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]
)
expected_slice_boxes = torch.tensor(
[[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]
)
else:
raise ValueError(f"Unknown yolos_name: {yolos_name}")
assert torch.allclose(logits[0, :3, :3], expected_slice_logits, atol=1e-4)
assert torch.allclose(pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4)
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model {yolos_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving image processor to {pytorch_dump_folder_path}")
image_processor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
model_mapping = {
"yolos_ti": "yolos-tiny",
"yolos_s_200_pre": "yolos-small",
"yolos_s_300_pre": "yolos-small-300",
"yolos_s_dWr": "yolos-small-dwr",
"yolos_base": "yolos-base",
}
print("Pushing to the hub...")
model_name = model_mapping[yolos_name]
image_processor.push_to_hub(model_name, organization="hustvl")
model.push_to_hub(model_name, organization="hustvl")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--yolos_name",
default="yolos_s_200_pre",
type=str,
help=(
"Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
" 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
),
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
args = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| transformers/src/transformers/models/yolos/convert_yolos_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/yolos/convert_yolos_to_pytorch.py",
"repo_id": "transformers",
"token_count": 5085
} | 503 |
# coding=utf-8
# Copyright 2024 Zyphra Technologies and the HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import re
from itertools import cycle
from typing import Callable, Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import BaseModelOutputWithPast
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import (
logging,
)
from ...utils.deprecation import deprecate_kwarg
from ...utils.import_utils import (
is_causal_conv1d_available,
is_mamba_ssm_available,
)
from ..llama.modeling_llama import LlamaRotaryEmbedding, apply_rotary_pos_emb
from ..mamba2.modeling_mamba2 import pad_tensor_by_size, reshape_into_chunks, segment_sum
from ..zamba.modeling_zamba import (
ZambaAttention,
ZambaAttentionDecoderLayer,
ZambaForCausalLM,
ZambaForSequenceClassification,
ZambaHybridDynamicCache,
ZambaHybridLayer,
ZambaMambaDecoderLayer,
ZambaModel,
ZambaRMSNorm,
eager_attention_forward,
)
from .configuration_zamba2 import Zamba2Config
if is_mamba_ssm_available():
from mamba_ssm.ops.triton.selective_state_update import selective_state_update
from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined
else:
selective_state_update, mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined = None, None, None
if is_causal_conv1d_available():
from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
else:
causal_conv1d_update, causal_conv1d_fn = None, None
is_fast_path_available = all((selective_state_update, causal_conv1d_fn, causal_conv1d_update))
_CONFIG_FOR_DOC = "Zyphra/Zamba2-2.7B"
logger = logging.get_logger(__name__)
class Zamba2RMSNormGated(torch.nn.Module):
def __init__(self, hidden_size, group_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
self.group_size = group_size
def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
*prefix_dims, last_dim = hidden_states.shape
group_count = last_dim // self.group_size
hidden_states_group = hidden_states.view(*prefix_dims, group_count, self.group_size)
variance = hidden_states_group.pow(2).mean(-1, keepdim=True)
hidden_states_group = hidden_states_group * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = hidden_states_group.view(*prefix_dims, group_count * self.group_size)
return self.weight * hidden_states.to(input_dtype)
class Zamba2RMSNorm(ZambaRMSNorm):
pass
class Zamba2HybridDynamicCache(ZambaHybridDynamicCache):
"""
A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
(which has a constant shape regardless of seq_len).
This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
"""
def __init__(
self, config: Zamba2Config, batch_size: int, dtype: torch.dtype = torch.float16, device: Optional[str] = None
):
self.dtype = dtype
self.layers_block_type = config.layers_block_type
self.has_previous_state = False
self.intermediate_size = int(config.mamba_expand * config.hidden_size)
self.ssm_state_size = config.mamba_d_state
self.conv_kernel_size = config.mamba_d_conv
self.n_mamba_heads = config.n_mamba_heads
self.transformer_layers = []
self._modules = {}
self._parameters = {}
self._buffers = {}
self.conv_states = {}
self.ssm_states = {}
for i in range(config.num_hidden_layers):
self.conv_states[i] = torch.zeros(
batch_size,
self.intermediate_size + 2 * config.mamba_ngroups * config.mamba_d_state,
self.conv_kernel_size,
device=device,
dtype=dtype,
)
self.ssm_states[i] = torch.zeros(
batch_size, self.n_mamba_heads, config.mamba_headdim, self.ssm_state_size, device=device, dtype=dtype
)
if self.layers_block_type[i] == "hybrid":
self.transformer_layers.append(i)
self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
def update_conv_state(
self, layer_idx: int, new_conv_state: torch.Tensor, cache_position: torch.LongTensor
) -> torch.Tensor:
conv_state = self.conv_states[layer_idx]
cache_position = cache_position.clamp(0, self.conv_kernel_size - 1)
conv_state = conv_state.roll(shifts=-1, dims=-1)
conv_state[:, :, cache_position] = new_conv_state.to(conv_state.device)
self.conv_states[layer_idx].zero_()
self.conv_states[layer_idx] += conv_state
return self.conv_states[layer_idx]
def reset(self):
self.conv_states.zero_()
self.ssm_states.zero_()
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
"""Returns the sequence length of the cached states. A layer index can be optionally passed."""
# take any layer that contains cache and not empty tensor
layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
return 0
return self.key_cache[layer_idx].shape[-2]
class Zamba2RotaryEmbedding(LlamaRotaryEmbedding):
pass
class Zamba2Attention(ZambaAttention):
"""
Multi-headed attention from 'Attention Is All You Need' paper.
Adapted from transformers.models.mistral.modeling_mistral.MistralAttention:
The input dimension here is attention_hidden_size = 2 * hidden_size, and head_dim = attention_hidden_size // num_heads.
The extra factor of 2 comes from the input being the concatenation of original_hidden_states with the output of the previous (mamba) layer
(see fig. 2 in https://huggingface.co/papers/2405.16712).
Additionally, replaced
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) with
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim/2)
Finally, this attention layer contributes to tied transformer blocks aimed to increasing compute without increasing model size. Because this
layer is tied, un-tied adapters (formally the same as LoRA but used in the base model) modules are added to the q, k, v projectors to increase
expressivity with a small memory overhead (see Fig. 2 of https://huggingface.co/papers/2411.15242).
"""
def __init__(
self,
config: Zamba2Config,
layer_idx: Optional[int] = None,
num_fwd_mem_blocks: Optional[int] = None,
block_id: Optional[int] = None,
):
super().__init__(config, layer_idx)
self.num_fwd_mem_blocks = num_fwd_mem_blocks
self.layer_block_map = config.hybrid_layer_ids
self.block_id = block_id
if config.use_shared_attention_adapter:
self.linear_q_adapter_list = nn.ModuleList([])
self.linear_k_adapter_list = nn.ModuleList([])
self.linear_v_adapter_list = nn.ModuleList([])
for i in range(self.num_fwd_mem_blocks):
if i % config.num_mem_blocks == block_id:
linear_q_adapter = nn.Sequential(
nn.Linear(self.attention_hidden_size, self.config.adapter_rank, bias=False),
nn.Linear(self.config.adapter_rank, self.attention_hidden_size, bias=False),
)
linear_k_adapter = nn.Sequential(
nn.Linear(self.attention_hidden_size, self.config.adapter_rank, bias=False),
nn.Linear(self.config.adapter_rank, self.attention_hidden_size, bias=False),
)
linear_v_adapter = nn.Sequential(
nn.Linear(self.attention_hidden_size, self.config.adapter_rank, bias=False),
nn.Linear(self.config.adapter_rank, self.attention_hidden_size, bias=False),
)
else:
linear_q_adapter = nn.Identity()
linear_k_adapter = nn.Identity()
linear_v_adapter = nn.Identity()
self.linear_q_adapter_list.append(linear_q_adapter)
self.linear_k_adapter_list.append(linear_k_adapter)
self.linear_v_adapter_list.append(linear_v_adapter)
self.layer_dic = {value: index for index, value in enumerate(self.layer_block_map)}
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
layer_idx: int,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Zamba2HybridDynamicCache] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
if self.config.use_shared_attention_adapter:
adapter_layer_idx = self.layer_dic[layer_idx]
query_states = query_states + self.linear_q_adapter_list[adapter_layer_idx](hidden_states)
key_states = key_states + self.linear_k_adapter_list[adapter_layer_idx](hidden_states)
value_states = value_states + self.linear_v_adapter_list[adapter_layer_idx](hidden_states)
query_states = query_states.view(hidden_shape).transpose(1, 2)
key_states = key_states.view(hidden_shape).transpose(1, 2)
value_states = value_states.view(hidden_shape).transpose(1, 2)
if self.config.use_mem_rope:
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_values is not None:
key_states, value_states = past_key_values.update(key_states, value_states, layer_idx)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
class Zamba2MambaMixer(nn.Module):
"""
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
and is why Mamba is called **selective** state spaces)
"""
def __init__(self, config: Zamba2Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.ssm_state_size = config.mamba_d_state
self.conv_kernel_size = config.mamba_d_conv
self.intermediate_size = int(config.mamba_expand * self.hidden_size)
self.layer_idx = layer_idx
self.use_conv_bias = config.use_conv_bias
self.activation = "silu"
self.act = nn.SiLU()
self.use_mem_eff_path = config.use_mem_eff_path
self.n_groups = config.mamba_ngroups
self.head_dim = config.mamba_headdim
self.num_heads = self.config.n_mamba_heads
self.chunk_size = config.chunk_size
self.time_step_limit = config.time_step_limit
self.time_step_min = config.time_step_min
self.time_step_max = config.time_step_max
self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
self.conv1d = nn.Conv1d(
in_channels=self.conv_dim,
out_channels=self.conv_dim,
bias=True,
kernel_size=config.mamba_d_conv,
groups=self.conv_dim,
padding=config.mamba_d_conv - 1,
)
# projection of the input hidden states
projection_size = self.intermediate_size + self.conv_dim + self.num_heads
self.in_proj = nn.Linear(
self.hidden_size,
projection_size,
bias=config.add_bias_linear,
)
# selective projection used to make dt, B and C input dependent
# time step projection (discretization)
# instantiate once and copy inv_dt in init_weights of PretrainedModel
self.dt_bias = nn.Parameter(torch.ones(self.num_heads))
# S4D real initialization. These are not discretized!
# The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
A = torch.arange(1, self.num_heads + 1)
self.A_log = nn.Parameter(torch.log(A))
self.A_log._no_weight_decay = True
self.norm = Zamba2RMSNormGated(
self.intermediate_size, group_size=self.intermediate_size // self.n_groups, eps=1e-5
)
self.D = nn.Parameter(torch.ones(self.num_heads))
self.D._no_weight_decay = True
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.add_bias_linear)
if not is_fast_path_available:
logger.warning_once(
"The fast path is not available because on of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)`"
" is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and"
" https://github.com/Dao-AILab/causal-conv1d"
)
def cuda_kernels_forward(
self,
hidden_states: torch.Tensor,
cache_params: Optional[Zamba2HybridDynamicCache] = None,
attention_mask: Optional[torch.Tensor] = None,
):
# set up dimensions for reshapes later
batch_size, seq_len, _ = hidden_states.shape
groups_time_state_size = self.n_groups * self.ssm_state_size
d_to_remove = 2 * self.intermediate_size + 2 * self.n_groups * self.ssm_state_size + self.num_heads
# getting projected states from cache if it exists
if cache_params is not None and cache_params.has_previous_state:
in_projected_states = self.in_proj(hidden_states.squeeze(1)) # (B 2D)
d_mlp = (in_projected_states.shape[-1] - d_to_remove) // 2
split_projection_dim = [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads]
_, _, gate, hidden_states_B_C, dt = torch.split(in_projected_states, split_projection_dim, dim=-1)
hidden_states_B_C = causal_conv1d_update(
hidden_states_B_C,
cache_params.conv_states[self.layer_idx],
self.conv1d.weight.squeeze(1),
self.conv1d.bias,
self.activation,
)
hidden_states, B, C = torch.split(
hidden_states_B_C,
[self.intermediate_size, groups_time_state_size, groups_time_state_size],
dim=-1,
)
A = -torch.exp(self.A_log.float()) # (nheads,)
A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
dt = dt[:, :, None].expand(-1, -1, self.head_dim)
dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
D = self.D[:, None, ...].expand(-1, self.head_dim)
B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups)
C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
hidden_states = selective_state_update(
cache_params.ssm_states[self.layer_idx],
hidden_states_reshaped,
dt,
A,
B,
C,
D,
z=None,
dt_bias=dt_bias,
dt_softplus=True,
)
hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim)
hidden_states = self.norm(hidden_states, gate)
out = self.out_proj(hidden_states)[:, None, ...]
# if no cache is found, calling the kernel
else:
if attention_mask is not None and not torch.all(attention_mask == 1):
# tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
dtype = hidden_states.dtype
hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
# 1. Gated MLP's linear projection
projected_states = self.in_proj(hidden_states)
A = -torch.exp(self.A_log.float()) # (num_heads) or (intermediate_size, state_size)
dt_limit_kwargs = {} if self.time_step_limit is None else {"dt_limit": self.time_step_limit}
if attention_mask is not None:
input_not_masked = torch.all(attention_mask == 1)
else:
input_not_masked = True
if self.use_mem_eff_path and self.training and cache_params is None and input_not_masked:
out, ssm_state = mamba_split_conv1d_scan_combined(
projected_states,
self.conv1d.weight.squeeze(1),
self.conv1d.bias,
self.dt_bias,
A,
D=self.D,
chunk_size=self.chunk_size,
seq_idx=None,
activation=self.activation,
rmsnorm_weight=self.norm.weight,
rmsnorm_eps=self.norm.variance_epsilon,
outproj_weight=self.out_proj.weight,
outproj_bias=self.out_proj.bias,
headdim=self.head_dim,
ngroups=self.n_groups,
norm_before_gate=False,
return_final_states=True,
**dt_limit_kwargs,
)
else:
gate, hidden_states_B_C, time_step = torch.split(
projected_states,
[self.intermediate_size, self.conv_dim, self.num_heads],
dim=-1,
)
# 1D Convolution
if cache_params is not None:
hidden_states_B_C_t = hidden_states_B_C.transpose(1, 2)
conv_state = nn.functional.pad(
hidden_states_B_C_t, (self.conv_kernel_size - hidden_states_B_C_t.shape[-1], 0)
)
cache_params.conv_states[self.layer_idx].copy_(conv_state)
if causal_conv1d_fn is None or self.activation not in ["silu", "swish"]:
hidden_states_B_C = self.act(
self.conv1d(hidden_states_B_C.transpose(1, 2)).transpose(1, 2)[:, :seq_len]
) # (B, L, self.d_inner + 2 * ngroups * d_state)
else:
hidden_states_B_C = causal_conv1d_fn(
x=hidden_states_B_C.transpose(1, 2),
weight=self.conv1d.weight.squeeze(1),
bias=self.conv1d.bias,
activation=self.activation,
).transpose(1, 2)[:, :seq_len]
hidden_states, B, C = torch.split(
hidden_states_B_C,
[self.intermediate_size, groups_time_state_size, groups_time_state_size],
dim=-1,
)
if attention_mask is not None and not torch.all(attention_mask == 1):
# tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
dtype = hidden_states.dtype
hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
scan_output, ssm_state = mamba_chunk_scan_combined(
hidden_states.view(batch_size, seq_len, -1, self.head_dim),
time_step,
A,
B.view(batch_size, seq_len, self.n_groups, -1),
C.view(batch_size, seq_len, self.n_groups, -1),
chunk_size=self.chunk_size,
D=self.D,
z=None,
seq_idx=None,
return_final_states=True,
dt_bias=self.dt_bias,
dt_softplus=True,
**dt_limit_kwargs,
)
if ssm_state is not None and cache_params is not None:
cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
scan_output = scan_output.view(batch_size, seq_len, -1)
# Multiply "gate" branch and apply extra normalization layer
scan_output = self.norm(scan_output, gate)
out = self.out_proj(scan_output)
return out
# fmt: off
def torch_forward(self, input_states, cache_params: Optional[Zamba2HybridDynamicCache]=None, attention_mask: Optional[torch.Tensor]=None):
batch_size, seq_len, _ = input_states.shape
dtype = input_states.dtype
# Gated MLP's linear projection
if cache_params is not None and cache_params.has_previous_state:
projected_states = self.in_proj(input_states.squeeze(1))
else:
if attention_mask is not None and not torch.all(attention_mask==1):
# tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
input_states = (input_states * attention_mask[:, :, None]).to(dtype)
projected_states = self.in_proj(input_states)
d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.ssm_state_size- self.num_heads) // 2
_, _, gate, hidden_states, dt = projected_states.split(
[d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
)
# Convolution sequence transformation
if cache_params is not None:
ssm_state = cache_params.ssm_states[self.layer_idx].clone()
ssm_state = ssm_state.to(hidden_states.device)
if cache_params.has_previous_state:
gate = gate.unsqueeze(1)
conv_state = cache_params.conv_states[self.layer_idx] # [batch, intermediate_size, conv_kernel_size]
conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
# handle batched generation - states are copied through
conv_state[:, :, -1] = hidden_states[:, 0, :] if hidden_states.ndim == 3 else hidden_states
cache_params.conv_states[self.layer_idx].copy_(conv_state)
hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
if self.use_conv_bias:
hidden_states += self.conv1d.bias
hidden_states = self.act(hidden_states).to(dtype)[:, None, ...] # [batch, 1, intermediate_size] : decoding
else:
hidden_states = hidden_states.transpose(1,2)
conv_state = nn.functional.pad(
hidden_states,
(self.conv_kernel_size - hidden_states.shape[-1], 0)
)
cache_params.conv_states[self.layer_idx].copy_(conv_state)
hidden_states = self.act(self.conv1d(hidden_states).transpose(1,2))[:, :seq_len, :] # [batch, intermediate_size, seq_len]
if attention_mask is not None and not torch.all(attention_mask==1):
dtype = hidden_states.dtype
# tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
else:
ssm_state = torch.zeros(
(batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
device=hidden_states.device, dtype=dtype
)
hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
A = -torch.exp(self.A_log.float()) # [num_heads]
if cache_params is not None and cache_params.has_previous_state:
# Note: there is no need to pad parameter matrices here, as there is just one new token
# for batched generation
dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...]
dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim)
# [num_heads] -> [num_heads, head_dim]
dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim)
dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype))
dt = torch.clamp(dt, self.time_step_min) #, self.time_step_max)
A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
# [bsz, num_heads, head_dim, state_size]
dA = torch.exp(dt[..., None] * A)
# Discretize B
# [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] ->
# -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size]
B = B.reshape(batch_size, self.n_groups, -1)[..., None, :]
B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous()
B = B.reshape(batch_size, -1, B.shape[-1])
# [bsz, num_heads, head_dim, state_size]
dB = dt[..., None] * B[..., None, :]
# Discretize x into dB
# [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim)
dBx = dB * hidden_states[..., None]
# State calculation
cache_params.ssm_states[self.layer_idx].copy_(
cache_params.ssm_states[self.layer_idx] * dA + dBx
)
# Subsequent output
# [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
C = C.reshape(batch_size, self.n_groups, -1)[..., None, :]
C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous()
C = C.reshape(batch_size, -1, C.shape[-1])
# [bsz, num_heads, head_dim]
ssm_states = cache_params.ssm_states[self.layer_idx].to(C.dtype) # Shape: [b, h, d, n]
# Reshape ssm_states to merge the first two dimensions
ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size) # Shape: [b*h, d, n]
C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1) # Shape: [b*h, n, 1]
y = torch.bmm(ssm_states_reshaped, C_reshaped)
y = y.view(batch_size, self.num_heads, self.head_dim)
# D skip connection
# [num_heads] -> [num_heads, head_dim]
D = self.D[..., None].expand(self.D.shape[0], self.head_dim)
y = (y + hidden_states * D).to(y.dtype)
# [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size]
y = y.reshape(batch_size, -1)[:, None, ...]
else:
# begin ssd naive implementation without einsums
dt = nn.functional.softplus(dt + self.dt_bias)
dt = torch.clamp(dt, self.time_step_min)
hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float()
B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
B = B.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
C = C.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size
D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size)
# Discretize x and A
hidden_states = hidden_states * dt[..., None]
A = A.to(hidden_states.dtype) * dt
# Rearrange into blocks/chunks
hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)]
# [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size]
A = A.permute(0, 3, 1, 2)
A_cumsum = torch.cumsum(A, dim=-1)
# 1. Compute the output for each intra-chunk (diagonal blocks)
# This is the analog of a causal mask
L = torch.exp(segment_sum(A))
# First, contraction of C and B to get G (attention-weights like)
G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, : ,:] # shape: (b, c, l, s, h, n)
G = G_intermediate.sum(dim=-1) # shape: (b, c, l, s, h)
# Step 2: Compute M, equivalent to applying attention mask to weights
M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None]
M = M_intermediate.sum(dim=-1)
# Step 3: Compute Y_diag (apply to values)
Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(3)
# (right term of low-rank factorization of off-diagonal blocks; B terms)
decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum)
B_decay_contraction = B * decay_states.permute(0, 2, 3, 1)[..., None]
# permute back B * decay states
states = (B_decay_contraction.permute(0, 1, 3, 2, 4)[..., None] * hidden_states.permute(0, 1, 3, 2, 4)[..., None, :]).sum(dim=3).permute(0, 1, 2, 4, 3)
if cache_params is not None and cache_params.has_previous_state:
previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...]
else:
previous_states = torch.zeros_like(states[:, :1])
states = torch.cat([previous_states, states], dim=1)
decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
states_permuted = states.permute(0, 2, 1, 3, 4)
result = (decay_chunk[..., None, None] * states_permuted[:, :, None, ...]).sum(dim=2)
new_states = result.permute(0, 2, 1, 3, 4)
states, ssm_state = new_states[:, :-1], new_states[:, -1]
# Compute state -> output conversion per chunk
# (left term of low-rank factorization of off-diagonal blocks; C terms)
state_decay_out = torch.exp(A_cumsum)
# compute Yoff
C_times_states = (C[..., None, :] * states[:, :, None, ...])
state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1)
Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None])
# Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks)
y = Y_diag + Y_off
# [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim]
y = y.reshape(batch_size, -1, self.num_heads, self.head_dim)
y = y + D_residual
# Cutting off padded chunks
if pad_size > 0:
y = y[:, :seq_len, :, :]
y = y.reshape(batch_size, seq_len, -1)
if ssm_state is not None and cache_params is not None:
cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
scan_output = self.norm(y, gate)
# end ssd naive
# 4. Final linear projection
contextualized_states = self.out_proj(scan_output.to(dtype)) # [batch, seq_len, hidden_size]
return contextualized_states
# fmt: on
def forward(
self,
hidden_states,
cache_params: Optional[Zamba2HybridDynamicCache] = None,
attention_mask: Optional[torch.Tensor] = None,
):
if is_fast_path_available and "cuda" in self.in_proj.weight.device.type:
return self.cuda_kernels_forward(hidden_states, cache_params, attention_mask)
return self.torch_forward(hidden_states, cache_params, attention_mask)
class Zamba2MLP(nn.Module):
def __init__(self, config: Zamba2Config, num_fwd_mem_blocks=None, block_id: Optional[int] = None):
"""
This MLP layer contributes to tied transformer blocks aimed to increasing compute without increasing model size. Because this layer
is tied, un-tied adapter modules (formally same as LoRA, but used in the base model) are added to the up and gate projectors to increase expressivity with a small memory overhead.
"""
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.num_fwd_mem_blocks = num_fwd_mem_blocks
self.block_id = block_id
self.gate_up_proj = nn.Linear(self.hidden_size, 2 * self.intermediate_size, bias=config.add_bias_linear)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.add_bias_linear)
self.act_fn = ACT2FN[config.hidden_act]
self.gate_up_proj_adapter_list = nn.ModuleList([])
for i in range(self.num_fwd_mem_blocks):
if i % config.num_mem_blocks == block_id:
gate_up_proj_adapter = nn.Sequential(
nn.Linear(self.config.hidden_size, self.config.adapter_rank, bias=False),
nn.Linear(self.config.adapter_rank, 2 * self.intermediate_size, bias=False),
)
else:
gate_up_proj_adapter = nn.Identity()
self.gate_up_proj_adapter_list.append(gate_up_proj_adapter)
layer_block_map = config.hybrid_layer_ids
self.layer_dic = {value: index for index, value in enumerate(layer_block_map)}
def forward(self, hidden_state, layer_idx=None):
gate_up_state = self.gate_up_proj(hidden_state)
layer_idx = self.layer_dic[layer_idx]
gate_up_state = gate_up_state + self.gate_up_proj_adapter_list[layer_idx](hidden_state)
gate_up_state = torch.chunk(gate_up_state, 2, dim=-1)
hidden_state = self.act_fn(gate_up_state[0]) * gate_up_state[1]
output = self.down_proj(hidden_state)
return output
class Zamba2AttentionDecoderLayer(ZambaAttentionDecoderLayer):
def __init__(self, config: Zamba2Config, block_id: Optional[int] = None, layer_idx: Optional[int] = None):
self.block_id = block_id
num_gs = len(config.hybrid_layer_ids)
super().__init__(config, layer_idx)
self.self_attn = Zamba2Attention(config, layer_idx=-1, num_fwd_mem_blocks=num_gs, block_id=block_id)
self.feed_forward = Zamba2MLP(config, num_fwd_mem_blocks=num_gs, block_id=block_id)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
original_hidden_states: torch.Tensor,
layer_idx: int,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Zamba2HybridDynamicCache] = None,
output_attentions: Optional[bool] = False,
position_embeddings: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): output of previous Mamba layer of shape `(batch, seq_len, embed_dim)`
original_hidden_states (`torch.FloatTensor`): word embedding output of shape `(batch, seq_len, embed_dim)`.
This is concatenated with `hidden_states` (which is the output of the previous (mamba) layer). The
concatenated tensor is then used as input of the pre-attention RMSNorm
(see fig. 2 in https://huggingface.co/papers/2405.16712).
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, sequence_length)` where padding elements are indicated by 0.
past_key_values (`Zamba2HybridDynamicCache`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
"""
hidden_states = torch.concatenate([hidden_states, original_hidden_states], dim=-1)
hidden_states = self.input_layernorm(hidden_states)
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
layer_idx=layer_idx,
attention_mask=attention_mask,
past_key_values=past_key_values,
output_attentions=output_attentions,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = self.pre_ff_layernorm(hidden_states)
hidden_states = self.feed_forward(hidden_states, layer_idx)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class Zamba2MambaDecoderLayer(ZambaMambaDecoderLayer):
def __init__(self, config: Zamba2Config, layer_idx: int):
super().__init__(config, layer_idx)
self.mamba = Zamba2MambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = Zamba2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
class Zamba2HybridLayer(ZambaHybridLayer):
def __init__(
self, shared_transformer: Zamba2AttentionDecoderLayer, linear: nn.Linear, mamba: Zamba2MambaDecoderLayer
):
super().__init__(shared_transformer, linear, mamba)
del self.shared_transf
self.shared_transformer = shared_transformer
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
original_hidden_states: Optional[torch.Tensor] = None,
layer_idx: Optional[int] = None,
attention_mask: Optional[torch.Tensor] = None,
causal_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Zamba2HybridDynamicCache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
position_embeddings: Optional[torch.LongTensor] = None,
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
original_hidden_states (`torch.FloatTensor`): word embedding output that will be concatenated with
hidden activations to form the input of the shared transformer layer.
layer_idx (`int`): layer number.
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, sequence_length)` where padding elements are indicated by 0.
past_key_values (`Zamba2HybridDynamicCache`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
"""
layer_outputs = self.shared_transformer(
hidden_states,
original_hidden_states=original_hidden_states,
layer_idx=layer_idx,
attention_mask=causal_mask,
past_key_values=past_key_values,
output_attentions=output_attentions,
position_embeddings=position_embeddings,
)
transformer_hidden_states = layer_outputs[0]
if output_attentions:
self_attn_weights = layer_outputs[1]
transformer_hidden_states = self.linear(transformer_hidden_states)
layer_outputs = self.mamba_decoder(
hidden_states,
transformer_hidden_states=transformer_hidden_states,
attention_mask=attention_mask,
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
position_embeddings=position_embeddings,
)
if output_attentions:
layer_outputs = (layer_outputs[0], self_attn_weights) + layer_outputs[2:]
return layer_outputs
class Zamba2PreTrainedModel(PreTrainedModel):
config: Zamba2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Zamba2AttentionDecoderLayer", "Zamba2MambaDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn = True
_supports_flex_attn = True
_supports_sdpa = True
# Note: only supports Zamba2HybridDynamicCache
_is_stateful = True
def _init_weights(self, module):
super()._init_weights(module)
if isinstance(module, Zamba2MambaMixer):
dt = torch.exp(
torch.rand(self.config.n_mamba_heads)
* (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
+ math.log(self.config.time_step_min)
).clamp(min=self.config.time_step_floor)
# # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
inv_dt = dt + torch.log(-torch.expm1(-dt))
module.dt_bias.data.copy_(inv_dt)
A = torch.arange(1, module.num_heads + 1)
module.A_log.data.copy_(torch.log(A))
module.D.data.fill_(1.0)
class Zamba2Model(ZambaModel, Zamba2PreTrainedModel):
"""
Model consisting of *config.num_hidden_layers* layers.
Args:
config: Zamba2Config
"""
def __init__(self, config: Zamba2Config):
Zamba2PreTrainedModel.__init__(self, config)
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
blocks = [Zamba2AttentionDecoderLayer(config, block_id=k) for k in range(config.num_mem_blocks)]
mamba_layers = []
linear_layers = []
self.layers_block_type = config.layers_block_type
for i in range(config.num_hidden_layers):
if config.layers_block_type[i] == "mamba":
mamba_layers.append(Zamba2MambaDecoderLayer(config, layer_idx=i))
elif config.layers_block_type[i] == "hybrid":
linear_layers.append(nn.Linear(self.config.hidden_size, self.config.hidden_size, bias=False))
mamba_layers.append(Zamba2MambaDecoderLayer(config, layer_idx=i))
mamba_layers = iter(mamba_layers)
linear_layers = iter(linear_layers)
blocks = cycle(blocks)
layers = self.get_layers(blocks, linear_layers, mamba_layers)
self.layers = nn.ModuleList(layers)
self._attn_implementation = config._attn_implementation
self.final_layernorm = Zamba2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
if config.use_mem_rope:
if config.use_long_context:
logger.warning_once(
"`use_long_context` set to `True`: using rescaled `rope_theta` and extended `max_position_embeddings`."
)
self.rotary_emb = Zamba2RotaryEmbedding(config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_layers(self, blocks, linear_layers, mamba_layers):
layers = []
self._tied_weights_keys = []
self.first_transformer_layer_id = 0
for layer_id, layer_type in enumerate(self.layers_block_type):
if layer_type == "hybrid":
if self.first_transformer_layer_id == 0:
self.first_transformer_layer_id = layer_id
block = next(blocks)
if self.config.num_mem_blocks * len(self.config.hybrid_layer_ids) > 1:
prefix_pattern = rf"^layers\.{layer_id}\.shared_transformer\."
main_keys_pattern = re.compile(
prefix_pattern
+ r"(?:"
+ r"self_attn\.(?:q_proj|k_proj|v_proj|o_proj)\.weight|"
+ r"feed_forward\.(?:gate_up_proj|down_proj)\.weight|"
+ r"(?:input_layernorm|pre_ff_layernorm)\.weight"
+ r")$"
)
self._tied_weights_keys.append(main_keys_pattern)
adapter_id = 0
for _layer_type in self.layers_block_type:
if _layer_type == "hybrid" and adapter_id % self.config.num_mem_blocks == block.block_id:
adapter_pattern = re.compile(
r"^shared_transformer\.feed_forward\.gate_up_proj_adapter_list\."
+ str(adapter_id)
+ r"\.(?:0|1)\.weight$"
)
self._tied_weights_keys.append(adapter_pattern)
adapter_id += 1
if self.config.use_shared_attention_adapter:
adapter_id = 0
for _layer_type in self.layers_block_type:
if _layer_type == "hybrid" and adapter_id % self.config.num_mem_blocks == block.block_id:
attn_adapter_pattern = re.compile(
r"^shared_transformer\.self_attn\."
+ r"(?:linear_q_adapter_list|linear_k_adapter_list|linear_v_adapter_list)\."
+ str(adapter_id)
+ r"\.(?:0|1)\.weight$"
)
self._tied_weights_keys.append(attn_adapter_pattern)
adapter_id += 1
layers.append(Zamba2HybridLayer(block, next(linear_layers), next(mamba_layers)))
else:
layers.append(next(mamba_layers))
return layers
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Zamba2HybridDynamicCache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
)
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
hidden_states = inputs_embeds
original_hidden_states = torch.clone(inputs_embeds)
# original_hidden_states: word embedding output that will be concatenated with hidden activations to form the input of the shared transformer layer
if use_cache and past_key_values is None:
batch_size = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0]
past_key_values = Zamba2HybridDynamicCache(self.config, batch_size, dtype=self.dtype, device=self.device)
if cache_position is None:
past_seen_tokens = (
past_key_values.get_seq_length(layer_idx=self.first_transformer_layer_id)
if past_key_values is not None
else 0
)
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position)
# create position embeddings to be shared across the decoder layers
if self.config.use_mem_rope:
position_embeddings = self.rotary_emb(hidden_states, position_ids)
else:
position_embeddings = None
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for layer_idx, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer.__call__,
hidden_states,
original_hidden_states,
layer_idx,
attention_mask,
causal_mask,
past_key_values,
output_attentions,
use_cache,
position_embeddings,
)
else:
layer_outputs = layer(
hidden_states,
original_hidden_states=original_hidden_states,
layer_idx=layer_idx,
attention_mask=attention_mask,
causal_mask=causal_mask,
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
position_embeddings=position_embeddings,
)
hidden_states = layer_outputs[0]
if output_attentions:
if layer_outputs[1] is not None:
# append attentions only of attention layers. Mamba layers return `None` as the attention weights
all_self_attns += (layer_outputs[1],)
hidden_states = self.final_layernorm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
if past_key_values is not None and not past_key_values.has_previous_state:
past_key_values.has_previous_state = True
output = BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
return output if return_dict else output.to_tuple()
class Zamba2ForCausalLM(ZambaForCausalLM):
pass
class Zamba2ForSequenceClassification(ZambaForSequenceClassification):
pass
__all__ = [
"Zamba2ForCausalLM",
"Zamba2ForSequenceClassification",
"Zamba2Model",
"Zamba2PreTrainedModel",
]
| transformers/src/transformers/models/zamba2/modular_zamba2.py/0 | {
"file_path": "transformers/src/transformers/models/zamba2/modular_zamba2.py",
"repo_id": "transformers",
"token_count": 26483
} | 504 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import subprocess
from typing import Any, Union
import numpy as np
import requests
from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, is_torchcodec_available, logging
from .base import Pipeline, build_pipeline_init_args
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
logger = logging.get_logger(__name__)
def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.ndarray:
"""
Helper function to read an audio file through ffmpeg.
"""
ar = f"{sampling_rate}"
ac = "1"
format_for_conversion = "f32le"
ffmpeg_command = [
"ffmpeg",
"-i",
"pipe:0",
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
try:
ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
except FileNotFoundError:
raise ValueError("ffmpeg was not found but is required to load audio files from filename")
output_stream = ffmpeg_process.communicate(bpayload)
out_bytes = output_stream[0]
audio = np.frombuffer(out_bytes, np.float32)
if audio.shape[0] == 0:
raise ValueError("Malformed soundfile")
return audio
@add_end_docstrings(build_pipeline_init_args(has_feature_extractor=True))
class AudioClassificationPipeline(Pipeline):
"""
Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a
raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio
formats.
Example:
```python
>>> from transformers import pipeline
>>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks")
>>> classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
[{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"audio-classification"`.
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=audio-classification).
"""
_load_processor = False
_load_image_processor = False
_load_feature_extractor = True
_load_tokenizer = False
def __init__(self, *args, **kwargs):
# Only set default top_k if explicitly provided
if "top_k" in kwargs and kwargs["top_k"] is None:
kwargs["top_k"] = None
elif "top_k" not in kwargs:
kwargs["top_k"] = 5
super().__init__(*args, **kwargs)
if self.framework != "pt":
raise ValueError(f"The {self.__class__} is only available in PyTorch.")
self.check_model_type(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES)
def __call__(self, inputs: Union[np.ndarray, bytes, str, dict], **kwargs: Any) -> list[dict[str, Any]]:
"""
Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more
information.
Args:
inputs (`np.ndarray` or `bytes` or `str` or `dict`):
The inputs is either :
- `str` that is the filename of the audio file, the file will be read at the correct sampling rate
to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system.
- `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the
same way.
- (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
Raw audio at the correct sampling rate (no further check will be done)
- `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this
pipeline do the resampling. The dict must be either be in the format `{"sampling_rate": int,
"raw": np.array}`, or `{"sampling_rate": int, "array": np.array}`, where the key `"raw"` or
`"array"` is used to denote the raw audio waveform.
top_k (`int`, *optional*, defaults to None):
The number of top labels that will be returned by the pipeline. If the provided number is `None` or
higher than the number of labels available in the model configuration, it will default to the number of
labels.
function_to_apply(`str`, *optional*, defaults to "softmax"):
The function to apply to the model output. By default, the pipeline will apply the softmax function to
the output of the model. Valid options: ["softmax", "sigmoid", "none"]. Note that passing Python's
built-in `None` will default to "softmax", so you need to pass the string "none" to disable any
post-processing.
Return:
A list of `dict` with the following keys:
- **label** (`str`) -- The label predicted.
- **score** (`float`) -- The corresponding probability.
"""
return super().__call__(inputs, **kwargs)
def _sanitize_parameters(self, top_k=None, function_to_apply=None, **kwargs):
postprocess_params = {}
# If top_k is None, use all labels
if top_k is None:
postprocess_params["top_k"] = self.model.config.num_labels
else:
if top_k > self.model.config.num_labels:
top_k = self.model.config.num_labels
postprocess_params["top_k"] = top_k
if function_to_apply is not None:
if function_to_apply not in ["softmax", "sigmoid", "none"]:
raise ValueError(
f"Invalid value for `function_to_apply`: {function_to_apply}. "
"Valid options are ['softmax', 'sigmoid', 'none']"
)
postprocess_params["function_to_apply"] = function_to_apply
else:
postprocess_params["function_to_apply"] = "softmax"
return {}, {}, postprocess_params
def preprocess(self, inputs):
if isinstance(inputs, str):
if inputs.startswith("http://") or inputs.startswith("https://"):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
inputs = requests.get(inputs).content
else:
with open(inputs, "rb") as f:
inputs = f.read()
if isinstance(inputs, bytes):
inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
if is_torch_available():
import torch
if isinstance(inputs, torch.Tensor):
inputs = inputs.cpu().numpy()
if is_torchcodec_available():
import torch
import torchcodec
if isinstance(inputs, torchcodec.decoders.AudioDecoder):
_audio_samples = inputs.get_all_samples()
_array = _audio_samples.data
inputs = {"array": _array, "sampling_rate": _audio_samples.sample_rate}
if isinstance(inputs, dict):
inputs = inputs.copy() # So we don't mutate the original dictionary outside the pipeline
# Accepting `"array"` which is the key defined in `datasets` for
# better integration
if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
raise ValueError(
"When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
'"raw" key containing the numpy array or torch tensor representing the audio and a "sampling_rate" key, '
"containing the sampling_rate associated with that array"
)
_inputs = inputs.pop("raw", None)
if _inputs is None:
# Remove path which will not be used from `datasets`.
inputs.pop("path", None)
_inputs = inputs.pop("array", None)
in_sampling_rate = inputs.pop("sampling_rate")
inputs = _inputs
if in_sampling_rate != self.feature_extractor.sampling_rate:
import torch
if is_torchaudio_available():
from torchaudio import functional as F
else:
raise ImportError(
"torchaudio is required to resample audio samples in AudioClassificationPipeline. "
"The torchaudio package can be installed through: `pip install torchaudio`."
)
inputs = F.resample(
torch.from_numpy(inputs) if isinstance(inputs, np.ndarray) else inputs,
in_sampling_rate,
self.feature_extractor.sampling_rate,
).numpy()
if not isinstance(inputs, np.ndarray):
raise TypeError("We expect a numpy ndarray or torch tensor as input")
if len(inputs.shape) != 1:
raise ValueError("We expect a single channel audio input for AudioClassificationPipeline")
processed = self.feature_extractor(
inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
)
if self.dtype is not None:
processed = processed.to(dtype=self.dtype)
return processed
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs, top_k=5, function_to_apply="softmax"):
if function_to_apply == "softmax":
probs = model_outputs.logits[0].softmax(-1)
elif function_to_apply == "sigmoid":
probs = model_outputs.logits[0].sigmoid()
else:
probs = model_outputs.logits[0]
scores, ids = probs.topk(top_k)
scores = scores.tolist()
ids = ids.tolist()
labels = [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
return labels
| transformers/src/transformers/pipelines/audio_classification.py/0 | {
"file_path": "transformers/src/transformers/pipelines/audio_classification.py",
"repo_id": "transformers",
"token_count": 4797
} | 505 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class PipelineDataset(Dataset):
def __init__(self, dataset, process, params):
self.dataset = dataset
self.process = process
self.params = params
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
item = self.dataset[i]
processed = self.process(item, **self.params)
return processed
class PipelineIterator(IterableDataset):
def __init__(self, loader, infer, params, loader_batch_size=None):
"""
Roughly equivalent to
```
for item in loader:
yield infer(item, **params)
```
Arguments:
loader (`torch.utils.data.DataLoader` or `Iterable`):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `infer` along with every item
loader_batch_size (`int`, *optional*):
If specified, the items of `loader` are supposed to come as batch, and are loader_batched here
making it roughly behave as
```
for items in loader:
for i in loader_batch_size:
item = items[i]
yield infer(item, **params)
```"""
self.loader = loader
self.infer = infer
self.params = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
loader_batch_size = None
self.loader_batch_size = loader_batch_size
# Internal bookkeeping
self._loader_batch_index = None
self._loader_batch_data = None
def __len__(self):
return len(self.loader)
def __iter__(self):
self.iterator = iter(self.loader)
return self
def loader_batch_item(self):
"""
Return item located at `loader_batch_index` within the current `loader_batch_data`.
"""
if isinstance(self._loader_batch_data, torch.Tensor):
# Batch data is simple tensor, just fetch the slice
result = self._loader_batch_data[self._loader_batch_index].unsqueeze(0)
else:
# Batch data is assumed to be BaseModelOutput (or dict)
loader_batched = {}
for k, element in self._loader_batch_data.items():
if isinstance(element, ModelOutput):
# Convert ModelOutput to tuple first
element = element.to_tuple()
if isinstance(element[0], torch.Tensor):
loader_batched[k] = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0], np.ndarray):
loader_batched[k] = tuple(np.expand_dims(el[self._loader_batch_index], 0) for el in element)
continue
if k in {"hidden_states", "attentions"} and isinstance(element, tuple):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0], torch.Tensor):
loader_batched[k] = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0], np.ndarray):
loader_batched[k] = tuple(np.expand_dims(el[self._loader_batch_index], 0) for el in element)
continue
if k == "past_key_values":
continue
if element is None:
# This can happen for optional data that get passed around
loader_batched[k] = None
elif isinstance(element[self._loader_batch_index], torch.Tensor):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
loader_batched[k] = element[self._loader_batch_index].unsqueeze(0)
elif isinstance(element[self._loader_batch_index], np.ndarray):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
loader_batched[k] = np.expand_dims(element[self._loader_batch_index], 0)
else:
# This is typically a list, so no need to `unsqueeze`.
loader_batched[k] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
result = self._loader_batch_data.__class__(loader_batched)
self._loader_batch_index += 1
return result
def __next__(self):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
item = next(self.iterator)
processed = self.infer(item, **self.params)
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(processed, torch.Tensor):
first_tensor = processed
elif isinstance(processed, tuple):
first_tensor = processed[0]
else:
key = list(processed.keys())[0]
first_tensor = processed[key]
if isinstance(first_tensor, list):
observed_batch_size = len(first_tensor)
else:
observed_batch_size = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
self.loader_batch_size = observed_batch_size
# Setting internal index to unwrap the batch
self._loader_batch_data = processed[0] if isinstance(processed, tuple) else processed
self._loader_batch_index = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class PipelineChunkIterator(PipelineIterator):
def __init__(self, loader, infer, params, loader_batch_size=None):
"""
Roughly equivalent to
```
for iterator in loader:
for item in iterator:
yield infer(item, **params)
```
Arguments:
loader (`torch.utils.data.DataLoader` or `Iterable`):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `infer` along with every item
"""
super().__init__(loader, infer, params)
def __iter__(self):
self.iterator = iter(self.loader)
self.subiterator = None
return self
def __next__(self):
if self.subiterator is None:
"Subiterator None means we haven't started a `preprocess` iterator. so start it"
self.subiterator = self.infer(next(self.iterator), **self.params)
try:
# Try to return next item
processed = next(self.subiterator)
except StopIteration:
# When a preprocess iterator ends, we can start looking at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
self.subiterator = self.infer(next(self.iterator), **self.params)
processed = next(self.subiterator)
return processed
class PipelinePackIterator(PipelineIterator):
"""
Roughly equivalent to
```
packed = []
for item in loader:
packed.append(item)
if item["is_last"]:
yield packed
packed = []
```
but it also handles cases where `item` are batched (meaning it's a dict of Tensor with first dimension > 1. In
that case it does
```
packed = []
for batch in loader:
# item is batched
for item in batch:
packed.append(item)
if item["is_last"]:
yield packed
packed = []
```
Arguments:
loader (`torch.utils.data.DataLoader` or `Iterable`):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `infer` along with every item
loader_batch_size (`int`, *optional*):
If specified, the items of `loader` are supposed to come as batch, and are loader_batched here making
it roughly behave as
```
for items in loader:
for i in loader_batch_size:
item = items[i]
yield infer(item, **params)
```"""
def __iter__(self):
self.iterator = iter(self.loader)
return self
def __next__(self):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
is_last = False
accumulator = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
item = self.loader_batch_item()
is_last = item.pop("is_last")
accumulator.append(item)
if is_last:
return accumulator
while not is_last:
processed = self.infer(next(self.iterator), **self.params)
if self.loader_batch_size is not None:
if isinstance(processed, torch.Tensor):
first_tensor = processed
else:
key = list(processed.keys())[0]
first_tensor = processed[key]
if isinstance(first_tensor, list):
observed_batch_size = len(first_tensor)
else:
observed_batch_size = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
self.loader_batch_size = observed_batch_size
self._loader_batch_data = processed
self._loader_batch_index = 0
while self._loader_batch_index < self.loader_batch_size:
item = self.loader_batch_item()
is_last = item.pop("is_last")
accumulator.append(item)
if is_last:
return accumulator
else:
item = processed
is_last = item.pop("is_last")
accumulator.append(item)
return accumulator
class KeyDataset(Dataset):
def __init__(self, dataset: Dataset, key: str):
self.dataset = dataset
self.key = key
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
return self.dataset[i][self.key]
class KeyPairDataset(Dataset):
def __init__(self, dataset: Dataset, key1: str, key2: str):
self.dataset = dataset
self.key1 = key1
self.key2 = key2
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
return {"text": self.dataset[i][self.key1], "text_pair": self.dataset[i][self.key2]}
| transformers/src/transformers/pipelines/pt_utils.py/0 | {
"file_path": "transformers/src/transformers/pipelines/pt_utils.py",
"repo_id": "transformers",
"token_count": 5997
} | 506 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import inspect
from functools import lru_cache, wraps
from typing import Callable
import torch
from safetensors.torch import storage_ptr, storage_size
from torch import nn
from .utils import (
is_torch_greater_or_equal,
is_torch_xla_available,
is_torch_xpu_available,
is_torchdynamo_compiling,
logging,
)
ALL_LAYERNORM_LAYERS = [nn.LayerNorm]
logger = logging.get_logger(__name__)
is_torch_greater_or_equal_than_2_8 = is_torch_greater_or_equal("2.8", accept_dev=True)
is_torch_greater_or_equal_than_2_6 = is_torch_greater_or_equal("2.6", accept_dev=True)
is_torch_greater_or_equal_than_2_4 = is_torch_greater_or_equal("2.4", accept_dev=True)
is_torch_greater_or_equal_than_2_3 = is_torch_greater_or_equal("2.3", accept_dev=True)
# For backwards compatibility (e.g. some remote codes on Hub using those variables).
is_torch_greater_or_equal_than_2_2 = is_torch_greater_or_equal("2.2", accept_dev=True)
is_torch_greater_or_equal_than_2_1 = is_torch_greater_or_equal("2.1", accept_dev=True)
is_torch_greater_or_equal_than_2_0 = is_torch_greater_or_equal("2.0", accept_dev=True)
is_torch_greater_or_equal_than_1_13 = is_torch_greater_or_equal("1.13", accept_dev=True)
is_torch_greater_or_equal_than_1_12 = is_torch_greater_or_equal("1.12", accept_dev=True)
# Cache this result has it's a C FFI call which can be pretty time-consuming
_torch_distributed_available = torch.distributed.is_available()
def softmax_backward_data(parent, grad_output, output, dim, self):
"""
A function that calls the internal `_softmax_backward_data` PyTorch method and that adjusts the arguments according
to the torch version detected.
"""
from torch import _softmax_backward_data
return _softmax_backward_data(grad_output, output, parent.dim, self.dtype)
def prune_linear_layer(layer: nn.Linear, index: torch.LongTensor, dim: int = 0) -> nn.Linear:
"""
Prune a linear layer to keep only entries in index.
Used to remove heads.
Args:
layer (`torch.nn.Linear`): The layer to prune.
index (`torch.LongTensor`): The indices to keep in the layer.
dim (`int`, *optional*, defaults to 0): The dimension on which to keep the indices.
Returns:
`torch.nn.Linear`: The pruned layer as a new layer with `requires_grad=True`.
"""
index = index.to(layer.weight.device)
W = layer.weight.index_select(dim, index).detach().clone()
if layer.bias is not None:
if dim == 1:
b = layer.bias.detach().clone()
else:
b = layer.bias[index].detach().clone()
new_size = list(layer.weight.size())
new_size[dim] = len(index)
new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
new_layer.weight.requires_grad = False
new_layer.weight.copy_(W.contiguous())
new_layer.weight.requires_grad = True
if layer.bias is not None:
new_layer.bias.requires_grad = False
new_layer.bias.copy_(b.contiguous())
new_layer.bias.requires_grad = True
return new_layer
class Conv1D(nn.Module):
"""
1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).
Basically works like a linear layer but the weights are transposed.
Args:
nf (`int`): The number of output features.
nx (`int`): The number of input features.
"""
def __init__(self, nf, nx):
super().__init__()
self.nf = nf
self.nx = nx
self.weight = nn.Parameter(torch.empty(nx, nf))
self.bias = nn.Parameter(torch.zeros(nf))
nn.init.normal_(self.weight, std=0.02)
def __repr__(self) -> str:
return "Conv1D(nf={nf}, nx={nx})".format(**self.__dict__)
def forward(self, x):
size_out = x.size()[:-1] + (self.nf,)
x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
x = x.view(size_out)
return x
def prune_conv1d_layer(layer: Conv1D, index: torch.LongTensor, dim: int = 1) -> Conv1D:
"""
Prune a Conv1D layer to keep only entries in index. A Conv1D work as a Linear layer (see e.g. BERT) but the weights
are transposed.
Used to remove heads.
Args:
layer ([`~pytorch_utils.Conv1D`]): The layer to prune.
index (`torch.LongTensor`): The indices to keep in the layer.
dim (`int`, *optional*, defaults to 1): The dimension on which to keep the indices.
Returns:
[`~pytorch_utils.Conv1D`]: The pruned layer as a new layer with `requires_grad=True`.
"""
index = index.to(layer.weight.device)
W = layer.weight.index_select(dim, index).detach().clone()
if dim == 0:
b = layer.bias.detach().clone()
else:
b = layer.bias[index].detach().clone()
new_size = list(layer.weight.size())
new_size[dim] = len(index)
new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)
new_layer.weight.requires_grad = False
new_layer.weight.copy_(W.contiguous())
new_layer.weight.requires_grad = True
new_layer.bias.requires_grad = False
new_layer.bias.copy_(b.contiguous())
new_layer.bias.requires_grad = True
return new_layer
def prune_layer(layer: nn.Linear | Conv1D, index: torch.LongTensor, dim: int | None = None) -> nn.Linear | Conv1D:
"""
Prune a Conv1D or linear layer to keep only entries in index.
Used to remove heads.
Args:
layer (`Union[torch.nn.Linear, Conv1D]`): The layer to prune.
index (`torch.LongTensor`): The indices to keep in the layer.
dim (`int`, *optional*): The dimension on which to keep the indices.
Returns:
`torch.nn.Linear` or [`~pytorch_utils.Conv1D`]: The pruned layer as a new layer with `requires_grad=True`.
"""
if isinstance(layer, nn.Linear):
return prune_linear_layer(layer, index, dim=0 if dim is None else dim)
elif isinstance(layer, Conv1D):
return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)
else:
raise ValueError(f"Can't prune layer of class {layer.__class__}")
def apply_chunking_to_forward(
forward_fn: Callable[..., torch.Tensor],
chunk_size: int,
chunk_dim: int,
*input_tensors,
) -> torch.Tensor:
"""
This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension
`chunk_dim`. It then applies a layer `forward_fn` to each chunk independently to save memory.
If the `forward_fn` is independent across the `chunk_dim` this function will yield the same result as directly
applying `forward_fn` to `input_tensors`.
Args:
forward_fn (`Callable[..., torch.Tensor]`):
The forward function of the model.
chunk_size (`int`):
The chunk size of a chunked tensor: `num_chunks = len(input_tensors[0]) / chunk_size`.
chunk_dim (`int`):
The dimension over which the `input_tensors` should be chunked.
input_tensors (`tuple[torch.Tensor]`):
The input tensors of `forward_fn` which will be chunked
Returns:
`torch.Tensor`: A tensor with the same shape as the `forward_fn` would have given if applied`.
Examples:
```python
# rename the usual forward() fn to forward_chunk()
def forward_chunk(self, hidden_states):
hidden_states = self.decoder(hidden_states)
return hidden_states
# implement a chunked forward function
def forward(self, hidden_states):
return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)
```"""
assert len(input_tensors) > 0, f"{input_tensors} has to be a tuple/list of tensors"
# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
if num_args_in_forward_chunk_fn != len(input_tensors):
raise ValueError(
f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(input_tensors)} input "
"tensors are given"
)
if chunk_size > 0:
tensor_shape = input_tensors[0].shape[chunk_dim]
for input_tensor in input_tensors:
if input_tensor.shape[chunk_dim] != tensor_shape:
raise ValueError(
f"All input tenors have to be of the same shape: {tensor_shape}, "
f"found shape {input_tensor.shape[chunk_dim]}"
)
if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
raise ValueError(
f"The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk "
f"size {chunk_size}"
)
num_chunks = input_tensors[0].shape[chunk_dim] // chunk_size
# chunk input tensor into tuples
input_tensors_chunks = tuple(input_tensor.chunk(num_chunks, dim=chunk_dim) for input_tensor in input_tensors)
# apply forward fn to every tuple
output_chunks = tuple(forward_fn(*input_tensors_chunk) for input_tensors_chunk in zip(*input_tensors_chunks))
# concatenate output at same dimension
return torch.cat(output_chunks, dim=chunk_dim)
return forward_fn(*input_tensors)
def find_pruneable_heads_and_indices(
heads: list[int], n_heads: int, head_size: int, already_pruned_heads: set[int]
) -> tuple[set[int], torch.LongTensor]:
"""
Finds the heads and their indices taking `already_pruned_heads` into account.
Args:
heads (`list[int]`): List of the indices of heads to prune.
n_heads (`int`): The number of heads in the model.
head_size (`int`): The size of each head.
already_pruned_heads (`Set[int]`): A set of already pruned heads.
Returns:
`tuple[Set[int], torch.LongTensor]`: A tuple with the indices of heads to prune taking `already_pruned_heads`
into account and the indices of rows/columns to keep in the layer weight.
"""
mask = torch.ones(n_heads, head_size)
heads = set(heads) - already_pruned_heads # Convert to set and remove already pruned heads
for head in heads:
# Compute how many pruned heads are before the head and move the index accordingly
head = head - sum(1 if h < head else 0 for h in already_pruned_heads)
mask[head] = 0
mask = mask.view(-1).contiguous().eq(1)
index: torch.LongTensor = torch.arange(len(mask))[mask].long()
return heads, index
def meshgrid(*tensors: torch.Tensor | list[torch.Tensor], indexing: str | None = None) -> tuple[torch.Tensor, ...]:
"""
Wrapper around torch.meshgrid to avoid warning messages about the introduced `indexing` argument.
Reference: https://pytorch.org/docs/1.13/generated/torch.meshgrid.html
"""
return torch.meshgrid(*tensors, indexing=indexing)
def id_tensor_storage(tensor: torch.Tensor) -> tuple[torch.device, int, int]:
"""
Unique identifier to a tensor storage. Multiple different tensors can share the same underlying storage. For
example, "meta" tensors all share the same storage, and thus their identifier will all be equal. This identifier is
guaranteed to be unique and constant for this tensor's storage during its lifetime. Two tensor storages with
non-overlapping lifetimes may have the same id.
"""
if _torch_distributed_available and is_torch_greater_or_equal("2.5"):
from torch.distributed.tensor import DTensor
if isinstance(tensor, DTensor):
local_tensor = tensor.to_local()
return tensor.device, local_tensor.storage().data_ptr(), tensor.nbytes
if tensor.device.type == "xla" and is_torch_xla_available():
# NOTE: xla tensors dont have storage
# use some other unique id to distinguish.
# this is a XLA tensor, it must be created using torch_xla's
# device. So the following import is safe:
import torch_xla
unique_id = torch_xla._XLAC._xla_get_tensor_id(tensor)
else:
unique_id = storage_ptr(tensor)
return tensor.device, unique_id, storage_size(tensor)
def isin_mps_friendly(elements: torch.Tensor, test_elements: torch.Tensor | int) -> torch.Tensor:
"""
Same as `torch.isin` without flags, but MPS-friendly. We can remove this function when we stop supporting
torch <= 2.3. See https://github.com/pytorch/pytorch/issues/77764#issuecomment-2067838075
Args:
elements (`torch.Tensor`): Input elements
test_elements (`torch.Tensor` or `int`): The elements to check against.
Returns:
`torch.Tensor`: A boolean tensor of the same shape as `elements` that is True for `elements` in `test_elements`
and False otherwise
"""
if elements.device.type == "mps" and not is_torch_greater_or_equal_than_2_4:
test_elements = torch.tensor(test_elements)
if test_elements.ndim == 0:
test_elements = test_elements.unsqueeze(0)
return elements.tile(test_elements.shape[0], 1).eq(test_elements.unsqueeze(1)).sum(dim=0).bool().squeeze()
else:
# Note: don't use named arguments in `torch.isin`, see https://github.com/pytorch/pytorch/issues/126045
return torch.isin(elements, test_elements)
@wraps(lru_cache)
def compile_compatible_method_lru_cache(*lru_args, **lru_kwargs):
"""
LRU cache decorator from standard functools library, but with a workaround to disable
caching when torchdynamo is compiling. Expected to work with class methods.
"""
def decorator(func):
func_with_cache = lru_cache(*lru_args, **lru_kwargs)(func)
@wraps(func)
def wrapper(*args, **kwargs):
if is_torchdynamo_compiling():
return func(*args, **kwargs)
else:
return func_with_cache(*args, **kwargs)
return wrapper
return decorator
def infer_device():
"""
Infers available device.
"""
torch_device = "cpu"
if torch.cuda.is_available():
torch_device = "cuda"
elif is_torch_xpu_available():
torch_device = "xpu"
return torch_device
| transformers/src/transformers/pytorch_utils.py/0 | {
"file_path": "transformers/src/transformers/pytorch_utils.py",
"repo_id": "transformers",
"token_count": 5941
} | 507 |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Optional
from ..utils.logging import tqdm
from .base import HfQuantizer
from .quantizers_utils import get_module_from_name
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..utils import is_accelerate_available, is_flute_available, is_hadamard_available, is_torch_available, logging
from ..utils.quantization_config import QuantizationConfigMixin
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class HiggsHfQuantizer(HfQuantizer):
"""
Quantizer of the HIGGS method. Enables the loading of prequantized models and in-flight quantization of full-precision models.
"""
requires_calibration = False
requires_parameters_quantization = True
required_packages = ["flute-kernel", "fast_hadamard_transform"]
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
super().__init__(quantization_config, **kwargs)
self.quantization_config = quantization_config
def validate_environment(self, device_map, **kwargs):
if not torch.cuda.is_available():
raise NotImplementedError("HIGGS quantization is only supported on GPU. Please use a different quantizer.")
if not is_accelerate_available():
raise ImportError("Using `higgs` quantization requires Accelerate: `pip install accelerate`")
if not is_flute_available():
raise ImportError("Using `higgs` quantization requires FLUTE: `pip install flute-kernel>=0.3.0`")
if not is_hadamard_available():
raise ImportError(
"Using `higgs` quantization requires fast_hadamard_transform: `pip install fast_hadamard_transform`"
)
if device_map is None:
raise ValueError(
"You are attempting to load a HIGGS model without setting device_map."
" Please set device_map comprised of 'cuda' devices."
)
elif isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()):
raise ValueError(
"You are attempting to load a HIGGS model with a device_map that contains a CPU or disk device."
" This is not supported. Please remove the CPU or disk device from the device_map."
)
def update_dtype(self, dtype: "torch.dtype") -> "torch.dtype":
if dtype is None:
logger.info("`dtype` is None. Setting `dtype=torch.float16` for FLUTE compatibility.")
dtype = torch.float16
elif dtype != torch.float16 and dtype != torch.bfloat16:
raise ValueError(
f"Invalid `dtype` {dtype}. HIGGS quantization only supports `dtype=torch.float16` or `dtype=torch.bfloat16`."
)
return dtype
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
state_dict: dict[str, Any],
unexpected_keys: Optional[list[str]] = None,
):
from ..integrations import quantize_with_higgs
"""
Quantizes weights into weight and weight_scale
"""
flute_dict = quantize_with_higgs(
param_value.to(target_device),
self.quantization_config.bits,
self.quantization_config.p,
self.quantization_config.group_size,
self.quantization_config.hadamard_size,
)
del param_value
module, _ = get_module_from_name(model, param_name)
module_name = ".".join(param_name.split(".")[:-1])
for key, value in flute_dict.items():
if key in module._parameters:
module._parameters[key] = torch.nn.Parameter(value, requires_grad=False)
elif key in module._buffers:
module._buffers[key] = torch.nn.Buffer(value)
elif key == "tune_metadata":
module.tune_metadata = value
self.quantization_config.tune_metadata[module_name] = value.to_dict()
else:
raise ValueError(f"Unexpected key {key} in module {module}")
if unexpected_keys is not None and param_name in unexpected_keys:
unexpected_keys.remove(param_name)
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
keep_in_fp32_modules: Optional[list[str]] = None,
**kwargs,
):
from ..integrations import replace_with_higgs_linear
self.modules_to_not_convert = self.get_modules_to_not_convert(
model, self.quantization_config.modules_to_not_convert, keep_in_fp32_modules
)
replace_with_higgs_linear(
model,
quantization_config=self.quantization_config,
modules_to_not_convert=self.modules_to_not_convert,
)
model.config.quantization_config = self.quantization_config
def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
from flute.tune import TuneMetaData, maybe_tune_and_repack
from flute.utils import make_workspace_streamk
from ..integrations import HiggsLinear
flute_workspaces = {}
flute_modules = {name: module for name, module in model.named_modules() if isinstance(module, HiggsLinear)}
for name, module in tqdm(flute_modules.items(), desc="Repacking HIGGS modules", leave=False):
# Every HiggsLinear needs a "workspace": a buffer for the unpacking operation.
# This buffer needs to be on the same device as the weights, but can be reused across modules otherwise.
if module.weight.device not in flute_workspaces:
flute_workspaces[module.weight.device] = make_workspace_streamk(device=module.weight.device)
module.workspace = flute_workspaces[module.weight.device]
# FLUTE weights are packed in a way that is optimized for a specific number of SMs (GPU streaming multiprocessors).
# If the model is loaded on a different device than the one it was saved on, we need to repack the weights.
module.tune_metadata = TuneMetaData.from_dict(self.quantization_config.tune_metadata[name])
module.weight.data, module.tune_metadata = maybe_tune_and_repack(
weight=module.weight.data,
scales=module.scales.data,
metadata=module.tune_metadata,
)
self.quantization_config.tune_metadata[name] = module.tune_metadata.to_dict()
def update_missing_keys(self, model, missing_keys: list[str], prefix: str) -> list[str]:
from ..integrations import HiggsLinear
higgs_names = {name for name, module in model.named_modules() if isinstance(module, HiggsLinear)}
def should_update(key: str) -> bool:
if key.endswith(".weight") or key.endswith(".bias"):
return False
full_key = f"{prefix}.{key}"
return any(name in key or name in full_key for name in higgs_names)
return [key for key in missing_keys if not should_update(key)]
@property
def is_trainable(self) -> bool:
return False
def is_serializable(self, safe_serialization=None):
return True
def check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: dict[str, Any],
**kwargs,
) -> bool:
from ..integrations import HiggsLinear
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module, HiggsLinear) and tensor_name == "weight" and param_value.dtype != torch.int16:
# Only quantize weights of HiggsLinear modules that are not already quantized
return True
else:
return False
def _dequantize(self, model):
from ..integrations import dequantize_higgs
model = dequantize_higgs(model)
return model
| transformers/src/transformers/quantizers/quantizer_higgs.py/0 | {
"file_path": "transformers/src/transformers/quantizers/quantizer_higgs.py",
"repo_id": "transformers",
"token_count": 3513
} | 508 |
# Copyright 2025 Mistral AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import warnings
from collections.abc import Mapping, Sized
from enum import Enum
from pathlib import Path
from typing import Any, Callable, Optional, Union, overload
import numpy as np
from transformers.audio_utils import load_audio_as
from transformers.tokenization_utils_base import (
LARGE_INTEGER,
VERY_LARGE_INTEGER,
BatchEncoding,
EncodedInput,
PreTokenizedInput,
PreTrainedTokenizerBase,
TextInput,
TruncationStrategy,
)
from transformers.utils import PaddingStrategy, TensorType, add_end_docstrings, logging, to_py_obj
from transformers.utils.generic import is_torch_tensor
from transformers.utils.hub import PushToHubMixin
from transformers.utils.import_utils import is_mistral_common_available, is_torch_available, requires
if is_mistral_common_available():
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.protocol.instruct.validator import ValidationMode
from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, TokenizerVersion
from mistral_common.tokens.tokenizers.image import MultiModalVersion
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.tokenizers.tekken import Tekkenizer
from mistral_common.tokens.tokenizers.utils import download_tokenizer_from_hf_hub
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
ENCODE_KWARGS_DOCSTRING = r"""
add_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to add special tokens when encoding the sequences. This will use the underlying
`PretrainedTokenizerBase.build_inputs_with_special_tokens` function, which defines which tokens are
automatically added to the input ids. This is useful if you want to add `bos` or `eos` tokens
automatically.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence is provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
to the maximum acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (`int`, *optional*, defaults to 0):
If set to a number along with `max_length`, the overflowing tokens returned when
`return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value. Requires `padding` to be activated.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
padding_side (`str`, *optional*):
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'pt'`: Return PyTorch `torch.Tensor` objects.
"""
ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
return_attention_mask (`bool`, *optional*):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
of returning overflowing tokens.
return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
Whether or not to return special tokens mask information.
return_offsets_mapping (`bool`, *optional*, defaults to `False`):
Whether or not to return `(char_start, char_end)` for each token.
This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
Python's tokenizer, this method will raise `NotImplementedError`.
return_length (`bool`, *optional*, defaults to `False`):
Whether or not to return the lengths of the encoded inputs.
verbose (`bool`, *optional*, defaults to `True`):
Whether or not to print more information and warnings.
**kwargs: passed to the `self.tokenize()` method
Return:
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model.
[What are input IDs?](../glossary#input-ids)
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
[What are attention masks?](../glossary#attention-mask)
- **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
`return_overflowing_tokens=True`).
- **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
`return_overflowing_tokens=True`).
- **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
- **length** -- The length of the inputs (when `return_length=True`)
"""
class MistralTokenizerType(str, Enum):
"""Enum for the different type of tokenizer."""
spm = "spm"
tekken = "tekken"
@requires(backends=("mistral-common",))
class MistralCommonTokenizer(PushToHubMixin):
"""
Class to wrap `mistral-common` tokenizers.
`mistral-common` is the official tokenizer library for Mistral AI models. To use it, you need to install it with:
```bash
pip install transformers[mistral-common]
```
Otherwise the tokenizer falls back to the Transformers implementation of the tokenizer.
For more info on `mistral-common`, see [mistral-common](https://github.com/mistralai/mistral-common).
This class is a wrapper around a `mistral_common.tokens.tokenizers.mistral.MistralTokenizer`.
It provides a Hugging Face compatible interface to tokenize using the official mistral-common tokenizer.
Supports the following methods from the `PreTrainedTokenizerBase` class:
- [`~MistralCommonTokenizer.get_vocab`]: Returns the vocabulary as a dictionary of token to index.
- [`~MistralCommonTokenizer.encode`]: Encode a string to a list of integers.
- [`~MistralCommonTokenizer.decode`]: Decode a list of integers to a string.
- [`~MistralCommonTokenizer.batch_decode`]: Decode a batch of list of integers to a list of strings.
- [`~MistralCommonTokenizer.convert_tokens_to_ids`]: Convert a list of tokens to a list of integers.
- [`~MistralCommonTokenizer.convert_ids_to_tokens`]: Convert a list of integers to a list of tokens.
- [`~MistralCommonTokenizer.tokenize`]: Tokenize a string.
- [`~MistralCommonTokenizer.get_special_tokens_mask`]: Get the special tokens mask for a list of tokens.
- [`~MistralCommonTokenizer.prepare_for_model`]: Prepare a list of inputs for the model.
- [`~MistralCommonTokenizer.pad`]: Pad a list of inputs to the same length.
- [`~MistralCommonTokenizer.truncate_sequences`]: Truncate a list of sequences to the same length.
- [`~MistralCommonTokenizer.apply_chat_template`]: Apply a chat template to a list of messages.
- [`~MistralCommonTokenizer.__call__`]: Tokenize a string or a list of strings.
- [`~MistralCommonTokenizer.from_pretrained`]: Download and cache a pretrained tokenizer from the Hugging Face model hub or local directory.
- [`~MistralCommonTokenizer.save_pretrained`]: Save a tokenizer to a directory, so it can be reloaded using the `from_pretrained` class method.
- [`~MistralCommonTokenizer.push_to_hub`]: Upload tokenizer to the Hugging Face model hub.
Here are the key differences with the `PreTrainedTokenizerBase` class:
- Pair of sequences are not supported. The signature have been kept for compatibility but all arguments related to pair of sequences are ignored. The return values of pairs are returned as `None`.
- The `is_split_into_words` argument is not supported.
- The `return_token_type_ids` argument is not supported.
- It is not possible to add new tokens to the tokenizer. Also the special tokens are handled differently from Transformers. In `mistral-common`, special tokens are never encoded directly. This means that: `tokenizer.encode("<s>")` will not return the ID of the `<s>` token. Instead, it will return a list of IDs corresponding to the tokenization of the string `"<s>"`. For more information, see the [mistral-common documentation](https://mistralai.github.io/mistral-common/usage/tokenizers/#special-tokens).
If you have suggestions to improve this class, please open an issue on the [mistral-common GitHub repository](https://github.com/mistralai/mistral-common/issues) if it is related to the tokenizer or on the [Transformers GitHub repository](https://github.com/huggingface/transformers/issues) if it is related to the Hugging Face interface.
"""
model_input_names: list[str] = ["input_ids", "attention_mask"]
padding_side: str = "left"
truncation_side: str = "right"
def __init__(
self,
tokenizer_path: Union[str, os.PathLike, Path],
mode: ValidationMode = ValidationMode.test,
model_max_length: int = VERY_LARGE_INTEGER,
padding_side: str = "left",
truncation_side: str = "right",
model_input_names: Optional[list[str]] = None,
clean_up_tokenization_spaces: bool = False,
**kwargs,
):
"""
Constructs a `MistralCommonTokenizer`.
- **model_input_names** (`List[str]`) -- A list of inputs expected in the forward pass of the model.
- **padding_side** (`str`) -- The default value for the side on which the model should have padding applied.
Should be `'right'` or `'left'`.
- **truncation_side** (`str`) -- The default value for the side on which the model should have truncation
applied. Should be `'right'` or `'left'`.
Args:
tokenizer_path (`str` or `os.PathLike` or `Path`):
Path to the tokenizer file to load the `MistralTokenizer`.
mode (`ValidationMode`, *optional*, defaults to `ValidationMode.test`):
The mode to use for the tokenizer. This will be passed to the `MistralTokenizer` constructor.
model_max_length (`int`, *optional*):
The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is
loaded with [`~tokenization_utils_base.PreTrainedTokenizerBase.from_pretrained`], this will be set to the
value stored for the associated model in `max_model_input_sizes` (see above). If no value is provided, will
default to VERY_LARGE_INTEGER (`int(1e30)`).
padding_side (`str`, *optional*):
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
truncation_side (`str`, *optional*):
The side on which the model should have truncation applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
model_input_names (`List[string]`, *optional*):
The list of inputs accepted by the forward pass of the model (like `"token_type_ids"` or
`"attention_mask"`). Default value is picked from the class attribute of the same name.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not the model should cleanup the spaces that were added when splitting the input text during the
tokenization process.
"""
if kwargs:
raise ValueError(f"Kwargs {list(kwargs.keys())} are not supported to init `MistralCommonTokenizer`.")
self._tokenizer_path = Path(tokenizer_path)
self.tokenizer: MistralTokenizer = MistralTokenizer.from_file(str(self._tokenizer_path), mode=mode)
self._tokenizer_type = (
MistralTokenizerType.tekken
if isinstance(self.tokenizer.instruct_tokenizer.tokenizer, Tekkenizer)
else MistralTokenizerType.spm
)
self.truncation_side = truncation_side
self.padding_side = padding_side
self.model_max_length = model_max_length
self.cleanup_tokenization_spaces = clean_up_tokenization_spaces
self.deprecation_warnings = {} # Use to store when we have already noticed a deprecation warning (avoid overlogging).
if model_input_names is not None:
if (
not isinstance(model_input_names, (list, tuple))
and len(model_input_names) == 0
and not all(isinstance(i, str) for i in model_input_names)
):
raise ValueError(
"`model_input_names` should be a non-empty list or tuple of str but got an empty value."
)
self.model_input_names = model_input_names
self._cache_get_vocab: Optional[dict[str, int]] = None
@property
def bos_token_id(self) -> int:
"""
Id of the beginning of sentence token in the vocabulary.
"""
return self.tokenizer.instruct_tokenizer.tokenizer.bos_id
@property
def eos_token_id(self) -> int:
"""
Id of the end of sentence token in the vocabulary.
"""
return self.tokenizer.instruct_tokenizer.tokenizer.eos_id
@property
def unk_token_id(self) -> int:
"""
Id of the unknown token in the vocabulary.
"""
return self.tokenizer.instruct_tokenizer.tokenizer.unk_id
@property
def pad_token_id(self) -> int:
"""
Id of the padding token in the vocabulary.
"""
return self.tokenizer.instruct_tokenizer.tokenizer.pad_id
@property
def bos_token(self) -> str:
"""
String associated to the beginning of sentence token in the vocabulary.
"""
return self.convert_ids_to_tokens(self.bos_token_id)
@property
def eos_token(self) -> str:
"""
String associated to the end of sentence token in the vocabulary.
"""
return self.convert_ids_to_tokens(self.eos_token_id)
@property
def unk_token(self) -> str:
"""
String associated to the unknown token in the vocabulary.
"""
return self.convert_ids_to_tokens(self.unk_token_id)
@property
def pad_token(self) -> str:
"""
String associated to the padding token in the vocabulary.
"""
return self.convert_ids_to_tokens(self.pad_token_id)
@property
def vocab_size(self) -> int:
"""
Returns the size of the vocabulary.
`int`: Size of the vocabulary.
"""
return self.tokenizer.instruct_tokenizer.tokenizer.n_words
def get_vocab(self) -> dict[str, int]:
"""
Returns the vocabulary as a dictionary of token to index.
This is a lossy conversion. There may be multiple token ids that decode to the same
string due to partial UTF-8 byte sequences being converted to �.
Returns:
`Dict[str, int]`: The vocabulary.
"""
if self._cache_get_vocab is None:
self._cache_get_vocab = {
token: idx for idx, token in enumerate(self.tokenizer.instruct_tokenizer.tokenizer.vocab())
}
return self._cache_get_vocab
def __len__(self):
"""
Size of the full vocabulary with the added tokens.
"""
return self.vocab_size
@add_end_docstrings(
ENCODE_KWARGS_DOCSTRING,
"""
**kwargs: Not supported by `MistralCommonTokenizer.encode`.
Will raise an error if used.
""",
"""
Returns:
`List[int]`, `torch.Tensor`: The tokenized ids of the text.
""",
)
def encode(
self,
text: Union[TextInput, EncodedInput],
text_pair: None = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy, None] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
verbose: bool = True,
**kwargs,
) -> list[int]:
"""
Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary.
Args:
text (`str` or `List[int]`):
The first sequence to be encoded. This can be a string or a list of integers (tokenized string ids).
text_pair (`None`, *optional*):
Not supported by `MistralCommonTokenizer.encode`. Kept to match `PreTrainedTokenizerBase.encode` signature.
"""
if kwargs:
raise ValueError(f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.encode`.")
if text_pair:
raise ValueError("`MistralCommonTokenizer.encode` does not support `text_pair`.")
padding_strategy, truncation_strategy, max_length, _ = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
)
encoded_inputs = self._encode_plus(
text,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_attention_mask=False,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_length=False,
verbose=verbose,
)
return encoded_inputs["input_ids"]
def decode(
self,
token_ids: Union[int, list[int], "np.ndarray", "torch.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: Optional[bool] = None,
**kwargs,
) -> str:
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces. If `None`, will default to
`self.clean_up_tokenization_spaces`.
kwargs (additional keyword arguments, *optional*):
Not supported by `MistralCommonTokenizer.decode`.
Will raise an error if used.
Returns:
`str`: The decoded sentence.
"""
if kwargs:
raise ValueError(f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.decode`.")
clean_up_tokenization_spaces = clean_up_tokenization_spaces or self.cleanup_tokenization_spaces
# Convert inputs to python lists
token_ids = to_py_obj(token_ids)
special_token_policy = SpecialTokenPolicy.IGNORE if skip_special_tokens else SpecialTokenPolicy.KEEP
decoded_string = self.tokenizer.decode(token_ids, special_token_policy=special_token_policy)
if clean_up_tokenization_spaces:
decoded_string = PreTrainedTokenizerBase.clean_up_tokenization(decoded_string)
return decoded_string
def batch_decode(
self,
sequences: Union[list[int], list[list[int]], "np.ndarray", "torch.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: Optional[bool] = None,
**kwargs,
) -> list[str]:
"""
Convert a list of lists of token ids into a list of strings by calling decode.
Args:
sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (`bool`, *optional*):
Whether or not to clean up the tokenization spaces. If `None`, will default to
`self.clean_up_tokenization_spaces`.
kwargs (additional keyword arguments, *optional*):
Not supported by `MistralCommonTokenizer.batch_decode`.
Will raise an error if used.
Returns:
`List[str]`: The list of decoded sentences.
"""
return [
self.decode(
seq,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
for seq in sequences
]
def _is_control_token(self, token_id: int) -> bool:
if self._tokenizer_type == MistralTokenizerType.spm:
return token_id in self.tokenizer.instruct_tokenizer.tokenizer._control_tokens()
elif self._tokenizer_type == MistralTokenizerType.tekken:
return token_id < self.tokenizer.instruct_tokenizer.tokenizer.num_special_tokens
else:
raise ValueError(f"Unknown tokenizer type: {self._tokenizer_type}")
@overload
def convert_ids_to_tokens(self, ids: int, skip_special_tokens: bool = False) -> str: ...
@overload
def convert_ids_to_tokens(self, ids: list[int], skip_special_tokens: bool = False) -> list[str]: ...
def convert_ids_to_tokens(
self, ids: Union[int, list[int]], skip_special_tokens: bool = False
) -> Union[str, list[str]]:
"""
Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
added tokens.
Args:
ids (`int` or `List[int]`):
The token id (or token ids) to convert to tokens.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
Returns:
`str` or `List[str]`: The decoded token(s).
"""
if isinstance(ids, int):
one_token = True
ids = [ids]
else:
one_token = False
tokens: list[str] = []
for token_id in ids:
if self._is_control_token(token_id) and skip_special_tokens:
continue
tokens.append(self.tokenizer.instruct_tokenizer.tokenizer.id_to_piece(token_id))
if one_token:
if tokens == []:
raise ValueError(f"Invalid token id {ids}.")
return tokens[0]
return tokens
def _piece_to_id(self, piece: str) -> int:
if self._tokenizer_type == MistralTokenizerType.spm:
return self.tokenizer.instruct_tokenizer.tokenizer._model.piece_to_id(piece)
elif self._tokenizer_type == MistralTokenizerType.tekken:
pieces = self.tokenizer.instruct_tokenizer.tokenizer._model.encode(
piece, allowed_special="all", disallowed_special=set()
)
assert len(pieces) == 1, f"Expected to decode 1 token, got {len(pieces)}"
return pieces[0]
else:
raise ValueError(f"Unknown tokenizer type: {self._tokenizer_type}")
def convert_tokens_to_ids(self, tokens: Union[str, list[str]]) -> Union[int, list[int]]:
"""
Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
vocabulary.
Args:
tokens (`str` or `List[str]`): One or several token(s) to convert to token id(s).
Returns:
`int` or `List[int]`: The token id or list of token ids.
"""
if isinstance(tokens, str):
one_token = True
tokens = [tokens]
else:
one_token = False
ids: list[int] = []
for token in tokens:
ids.append(self._piece_to_id(token))
if one_token:
return ids[0]
return ids
def _text_to_ids(self, text: TextInput, add_special_tokens: bool) -> list[int]:
"""
Converts a string into a sequence of tokens ids, using the tokenizer.
"""
tokens_ids = self.tokenizer.instruct_tokenizer.tokenizer.encode(
text, bos=add_special_tokens, eos=add_special_tokens
)
return tokens_ids
def tokenize(self, text: TextInput, **kwargs) -> list[str]:
"""
Converts a string into a sequence of tokens, using the tokenizer.
Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies.
Args:
text (`str`):
The sequence to be encoded.
**kwargs (additional keyword arguments):
Not supported by `MistralCommonTokenizer.tokenize`.
Will raise an error if used.
Returns:
`List[str]`: The list of tokens.
"""
if kwargs:
raise ValueError(f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.tokenize`.")
return self.convert_ids_to_tokens(self._text_to_ids(text, add_special_tokens=False), skip_special_tokens=False)
def _encode_plus(
self,
text: Union[TextInput, EncodedInput],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
if kwargs:
raise ValueError(
f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer._encode_plus`."
)
def get_input_ids(text):
if isinstance(text, str):
return self._text_to_ids(text, add_special_tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
raise ValueError(f"Input {text} is not valid. Should be a string, or a list/tuple of integers.")
ids = get_input_ids(text)
return self.prepare_for_model(
ids,
add_special_tokens=add_special_tokens,
padding=padding_strategy.value,
truncation=truncation_strategy.value,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
)
def _batch_encode_plus(
self,
batch_text: Union[
list[TextInput],
list[EncodedInput],
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
def get_input_ids(text):
if isinstance(text, str):
return self._text_to_ids(text, add_special_tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
raise ValueError("Input is not valid. Should be a string or a list/tuple of integers.")
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
input_ids = []
for ids in batch_text:
input_ids.append(get_input_ids(ids))
batch_outputs = self._batch_prepare_for_model(
input_ids,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=return_tensors,
verbose=verbose,
)
return BatchEncoding(batch_outputs)
def _all_special_ids(self) -> set[int]:
if self._tokenizer_type == MistralTokenizerType.tekken:
return {t["rank"] for t in self.tokenizer.instruct_tokenizer.tokenizer._all_special_tokens}
elif self._tokenizer_type == MistralTokenizerType.spm:
return self.tokenizer.instruct_tokenizer.tokenizer._control_tokens()
else:
raise ValueError(f"Unknown tokenizer type: {self._tokenizer_type}")
def get_special_tokens_mask(
self, token_ids_0: list, token_ids_1: None = None, already_has_special_tokens: bool = False
) -> list[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
Args:
token_ids_0 (`List[int]`):
List of ids of the sequence.
token_ids_1 (`List[int]`, *optional*):
Not supported by `MistralCommonTokenizer`. Kept to match the interface of `PreTrainedTokenizerBase`.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if token_ids_1 is not None:
raise ValueError(
"`token_ids_1` is not supported by `MistralCommonTokenizer` and should be `None`, kept for compatibility."
)
if already_has_special_tokens:
raise ValueError(
"`already_has_special_tokens` is not supported by `MistralCommonTokenizer` and should be `False`."
)
all_special_ids = self._all_special_ids() # cache the ids
special_tokens_mask = [1 if token in all_special_ids else 0 for token in token_ids_0]
return special_tokens_mask
def _batch_prepare_for_model(
self,
batch_ids: list[Union[PreTokenizedInput, list[int]]],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_tensors: Optional[str] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_length: bool = False,
verbose: bool = True,
) -> BatchEncoding:
"""
Prepares a sequence of input id so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens.
Args:
batch_ids: list of tokenized input ids
"""
batch_outputs = {}
for ids in batch_ids:
outputs = self.prepare_for_model(
ids,
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
truncation=truncation_strategy.value,
max_length=max_length,
stride=stride,
pad_to_multiple_of=None, # we pad in batch afterward
padding_side=None, # we pad in batch afterward
return_attention_mask=False, # we pad in batch afterward
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=None, # We convert the whole batch to tensors at the end
prepend_batch_axis=False,
verbose=verbose,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding_strategy.value,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_attention_mask=return_attention_mask,
)
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
return batch_outputs
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def prepare_for_model(
self,
ids: list[int],
pair_ids: None = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy, None] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_length: bool = False,
verbose: bool = True,
prepend_batch_axis: bool = False,
**kwargs,
) -> BatchEncoding:
"""
Prepares a sequence of input id so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens.
Args:
ids (`List[int]`):
Tokenized input ids of the first sequence.
pair_ids (`None`, *optional*):
Not supported by `MistralCommonTokenizer`. Kept to match the interface of `PreTrainedTokenizerBase`.
"""
if pair_ids is not None:
raise ValueError(
"`pair_ids` is not supported by `MistralCommonTokenizer` and should be `None`, kept for compatibility."
)
if kwargs:
raise ValueError(
f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.prepare_for_model`."
)
padding_strategy, truncation_strategy, max_length, _ = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
)
len_ids = len(ids)
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
encoded_inputs = {}
# Truncation: Handle max sequence length
overflowing_tokens = []
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and len_ids > max_length:
ids, _, overflowing_tokens = self.truncate_sequences(
ids,
num_tokens_to_remove=len_ids - max_length,
truncation_strategy=truncation_strategy,
stride=stride,
)
if return_overflowing_tokens:
encoded_inputs["overflowing_tokens"] = overflowing_tokens
encoded_inputs["num_truncated_tokens"] = len_ids - max_length
# Build output dictionary
encoded_inputs[self.model_input_names[0]] = ids
if return_special_tokens_mask:
if add_special_tokens:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, None)
else:
encoded_inputs["special_tokens_mask"] = [0] * len(ids)
# Padding
if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
padding=padding_strategy.value,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_attention_mask=return_attention_mask,
)
if return_length:
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
batch_outputs = BatchEncoding(
encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
)
return batch_outputs
def _get_padding_truncation_strategies(
self,
padding: Union[str, PaddingStrategy, bool] = False,
truncation: Optional[Union[str, TruncationStrategy, bool]] = None,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
verbose: bool = True,
**kwargs,
):
"""
Find the correct padding/truncation strategy.
"""
# Backward compatibility for previous behavior, maybe we should deprecate it:
# If you only set max_length, it activates truncation for max_length
if max_length is not None and padding is False and truncation is None:
if verbose:
if not self.deprecation_warnings.get("Truncation-not-explicitly-activated", False):
logger.warning(
"Truncation was not explicitly activated but `max_length` is provided a specific value, please"
" use `truncation=True` to explicitly truncate examples to max length. Defaulting to"
" 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the"
" tokenizer you can select this strategy more precisely by providing a specific strategy to"
" `truncation`."
)
self.deprecation_warnings["Truncation-not-explicitly-activated"] = True
truncation = "longest_first"
# Get padding strategy
if padding is not False:
if padding is True:
if verbose:
if max_length is not None and (
truncation is None or truncation is False or truncation == "do_not_truncate"
):
warnings.warn(
"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. "
"To pad to max length, use `padding='max_length'`."
)
padding_strategy = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(padding, PaddingStrategy):
padding_strategy = PaddingStrategy(padding)
elif isinstance(padding, PaddingStrategy):
padding_strategy = padding
else:
padding_strategy = PaddingStrategy.DO_NOT_PAD
# Get truncation strategy
if truncation is not False and truncation is not None:
if truncation is True:
truncation_strategy = (
TruncationStrategy.LONGEST_FIRST
) # Default to truncate the longest sequences in pairs of inputs
elif not isinstance(truncation, TruncationStrategy):
truncation_strategy = TruncationStrategy(truncation)
elif isinstance(truncation, TruncationStrategy):
truncation_strategy = truncation
if truncation in [TruncationStrategy.ONLY_FIRST, TruncationStrategy.ONLY_SECOND]:
raise ValueError(
"Truncation strategy `only_first` and `only_second` are not supported by `MistralCommonTokenizer`."
)
else:
truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
if self.model_max_length > LARGE_INTEGER:
if verbose:
if not self.deprecation_warnings.get("Asking-to-pad-to-max_length", False):
logger.warning(
"Asking to pad to max_length but no maximum length is provided and the model has no"
" predefined maximum length. Default to no padding."
)
self.deprecation_warnings["Asking-to-pad-to-max_length"] = True
padding_strategy = PaddingStrategy.DO_NOT_PAD
else:
max_length = self.model_max_length
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
if self.model_max_length > LARGE_INTEGER:
if verbose:
if not self.deprecation_warnings.get("Asking-to-truncate-to-max_length", False):
logger.warning(
"Asking to truncate to max_length but no maximum length is provided and the model has"
" no predefined maximum length. Default to no truncation."
)
self.deprecation_warnings["Asking-to-truncate-to-max_length"] = True
truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
else:
max_length = self.model_max_length
# Test if we have a padding token
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.pad_token is None or self.pad_token_id < 0):
raise ValueError(
"Asking to pad but the tokenizer does not have a padding token. "
"Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` "
"or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`."
)
# Check that we will truncate to a multiple of pad_to_multiple_of if both are provided
if (
truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE
and padding_strategy != PaddingStrategy.DO_NOT_PAD
and pad_to_multiple_of is not None
and max_length is not None
and (max_length % pad_to_multiple_of != 0)
):
raise ValueError(
"Truncation and padding are both activated but "
f"truncation length ({max_length}) is not a multiple of pad_to_multiple_of ({pad_to_multiple_of})."
)
return padding_strategy, truncation_strategy, max_length, kwargs
def _pad(
self,
encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in `padding_side` argument:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
padding_side:
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(required_input)
if needs_to_be_padded:
difference = max_length - len(required_input)
padding_side = padding_side if padding_side is not None else self.padding_side
if padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
elif padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
else:
raise ValueError(f"Invalid padding strategy:{padding_side}")
return encoded_inputs
def pad(
self,
encoded_inputs: Union[
BatchEncoding,
list[BatchEncoding],
dict[str, EncodedInput],
dict[str, list[EncodedInput]],
list[dict[str, EncodedInput]],
],
padding: Union[bool, str, PaddingStrategy] = True,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_attention_mask: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
verbose: bool = True,
) -> BatchEncoding:
"""
Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
in the batch.
Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`,
`self.pad_token_id`).
<Tip>
If the `encoded_inputs` passed are dictionary of numpy arrays, PyTorch tensors, the
result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of
PyTorch tensors, you will lose the specific device of your tensors however.
</Tip>
Args:
encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `Dict[str, List[int]]`, `Dict[str, List[List[int]]` or `List[Dict[str, List[int]]]`):
Tokenized inputs. Can represent one input ([`BatchEncoding`] or `Dict[str, List[int]]`) or a batch of
tokenized inputs (list of [`BatchEncoding`], *Dict[str, List[List[int]]]* or *List[Dict[str,
List[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
collate function.
Instead of `List[int]` you can have tensors (numpy arrays, PyTorch tensors), see
the note above for the return type.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
padding_side (`str`, *optional*):
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
return_attention_mask (`bool`, *optional*):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
verbose (`bool`, *optional*, defaults to `True`):
Whether or not to print more information and warnings.
"""
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping):
encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0]}
# The model's main input name, usually `input_ids`, has been passed for padding
if self.model_input_names[0] not in encoded_inputs:
raise ValueError(
"You should supply an encoding or a list of encodings to this method "
f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
)
required_input = encoded_inputs[self.model_input_names[0]]
if required_input is None or (isinstance(required_input, Sized) and len(required_input) == 0):
if return_attention_mask:
encoded_inputs["attention_mask"] = []
return encoded_inputs
# If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
first_element = required_input[0]
if isinstance(first_element, (list, tuple)):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
for item in required_input:
if len(item) != 0:
first_element = item[0]
break
# At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
if not isinstance(first_element, (int, list, tuple)):
if is_torch_tensor(first_element):
return_tensors = "pt" if return_tensors is None else return_tensors
elif isinstance(first_element, np.ndarray):
return_tensors = "np" if return_tensors is None else return_tensors
else:
raise ValueError(
f"type of {first_element} unknown: {type(first_element)}. "
"Should be one of a python, numpy, pytorch or tensorflow object."
)
for key, value in encoded_inputs.items():
encoded_inputs[key] = to_py_obj(value)
# Convert padding_strategy in PaddingStrategy
padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
padding=padding, max_length=max_length, verbose=verbose
)
required_input = encoded_inputs[self.model_input_names[0]]
if required_input and not isinstance(required_input[0], (list, tuple)):
encoded_inputs = self._pad(
encoded_inputs,
max_length=max_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_attention_mask=return_attention_mask,
)
return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
batch_size = len(required_input)
assert all(len(v) == batch_size for v in encoded_inputs.values()), (
"Some items in the output dictionary have a different batch size than others."
)
if padding_strategy == PaddingStrategy.LONGEST:
max_length = max(len(inputs) for inputs in required_input)
padding_strategy = PaddingStrategy.MAX_LENGTH
batch_outputs = {}
for i in range(batch_size):
inputs = {k: v[i] for k, v in encoded_inputs.items()}
outputs = self._pad(
inputs,
max_length=max_length,
padding_strategy=padding_strategy,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_attention_mask=return_attention_mask,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
def truncate_sequences(
self,
ids: list[int],
pair_ids: None = None,
num_tokens_to_remove: int = 0,
truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
stride: int = 0,
**kwargs,
) -> tuple[list[int], None, list[int]]:
"""
Truncates a sequence pair in-place following the strategy.
Args:
ids (`List[int]`):
Tokenized input ids. Can be obtained from a string by chaining the `tokenize` and
`convert_tokens_to_ids` methods.
pair_ids (`None`, *optional*):
Not supported by `MistralCommonTokenizer`. Kept to match the signature of `PreTrainedTokenizerBase.truncate_sequences`.
num_tokens_to_remove (`int`, *optional*, defaults to 0):
Number of tokens to remove using the truncation strategy.
truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `'longest_first'`):
The strategy to follow for truncation. Can be:
- `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
- `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
than the model maximum admissible input size).
stride (`int`, *optional*, defaults to 0):
If set to a positive number, the overflowing tokens returned will contain some tokens from the main
sequence returned. The value of this argument defines the number of additional tokens.
Returns:
`Tuple[List[int], None, List[int]]`: The truncated `ids` and the list of
overflowing tokens. `None` is returned to match Transformers signature.
"""
if kwargs:
raise ValueError(
f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.truncate_sequences`."
)
if pair_ids:
raise ValueError("`pair_ids` is not supported by `MistralCommonTokenizer.truncate_sequences`.")
if num_tokens_to_remove <= 0:
return (ids, None, [])
if not isinstance(truncation_strategy, TruncationStrategy):
truncation_strategy = TruncationStrategy(truncation_strategy)
if truncation_strategy in [TruncationStrategy.ONLY_FIRST, TruncationStrategy.ONLY_SECOND]:
raise ValueError(
f"Only {TruncationStrategy.LONGEST_FIRST} and {TruncationStrategy.DO_NOT_TRUNCATE} are supported."
)
overflowing_tokens = []
if truncation_strategy == TruncationStrategy.LONGEST_FIRST:
if len(ids) > num_tokens_to_remove:
window_len = min(len(ids), stride + num_tokens_to_remove)
if self.truncation_side == "left":
overflowing_tokens = ids[:window_len]
ids = ids[num_tokens_to_remove:]
elif self.truncation_side == "right":
overflowing_tokens = ids[-window_len:]
ids = ids[:-num_tokens_to_remove]
else:
raise ValueError(f"invalid truncation strategy: {self.truncation_side}, use 'left' or 'right'.")
else:
error_msg = (
f"We need to remove {num_tokens_to_remove} to truncate the input "
f"but the first sequence has a length {len(ids)}. "
)
logger.error(error_msg)
return (ids, None, overflowing_tokens)
def apply_chat_template(
self,
conversation: Union[list[dict[str, str]], list[list[dict[str, str]]]],
tools: Optional[list[Union[dict, Callable]]] = None,
continue_final_message: bool = False,
tokenize: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: bool = False,
max_length: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_dict: bool = False,
**kwargs,
) -> Union[str, list[int], list[str], list[list[int]], BatchEncoding]:
"""
Converts a list of dictionaries with `"role"` and `"content"` keys to a list of token
ids.
Args:
conversation (Union[List[Dict[str, str]], List[List[Dict[str, str]]]]): A list of dicts
with "role" and "content" keys, representing the chat history so far.
tools (`List[Union[Dict, Callable]]`, *optional*):
A list of tools (callable functions) that will be accessible to the model. If the template does not
support function calling, this argument will have no effect. Each tool should be passed as a JSON Schema,
giving the name, description and argument types for the tool. See our
[chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#automated-function-conversion-for-tool-use)
for more information.
continue_final_message (bool, *optional*):
If this is set, the chat will be formatted so that the final
message in the chat is open-ended, without any EOS tokens. The model will continue this message
rather than starting a new one. This allows you to "prefill" part of
the model's response for it. Cannot be used at the same time as `add_generation_prompt`.
tokenize (`bool`, defaults to `True`):
Whether to tokenize the output. If `False`, the output will be a string.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, defaults to `False`):
Whether to truncate sequences at the maximum length. Has no effect if tokenize is `False`.
max_length (`int`, *optional*):
Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is `False`. If
not specified, the tokenizer's `max_length` attribute will be used as a default.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Has no effect if tokenize is `False`. Acceptable
values are:
- `'pt'`: Return PyTorch `torch.Tensor` objects.
return_dict (`bool`, defaults to `False`):
Whether to return a dictionary with named outputs. Has no effect if tokenize is `False`.
If at least one conversation contains an image, its pixel values will be returned in the `pixel_values` key.
kwargs (additional keyword arguments, *optional*):
Not supported by `MistralCommonTokenizer.apply_chat_template`.
Will raise an error if used.
Returns:
`Union[str, List[int], List[str], List[List[int]], BatchEncoding]`: A list of token ids representing the tokenized chat so far, including control
tokens. This output is ready to pass to the model, either directly or via methods like `generate()`.
"""
if kwargs:
raise ValueError(
f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.apply_chat_template`."
)
if not isinstance(truncation, bool):
raise ValueError("`truncation` must be a boolean for `apply_chat_template` method.")
if isinstance(conversation, (list, tuple)) and (
isinstance(conversation[0], (list, tuple)) or hasattr(conversation[0], "messages")
):
conversations = conversation
is_batched = True
else:
conversations = [conversation]
is_batched = False
def _maybe_adapt_message(message: dict[str, Any]) -> None:
"""Adapt message to `mistral-common` format and leave validation to `mistral-common`."""
if not isinstance(message, dict):
return
maybe_list_content: Optional[Union[str, list[dict[str, Union[str, dict[str, Any]]]]]] = message.get(
"content"
)
if not maybe_list_content or isinstance(maybe_list_content, str):
return
normalized_content: list[dict[str, Union[str, dict[str, Any]]]] = []
for content in maybe_list_content:
content_type = content.get("type", None)
if not content_type:
continue
elif content_type == "image":
maybe_url: Optional[str] = content.get("url")
maybe_path: Optional[str] = content.get("path")
maybe_base64: Optional[str] = content.get("base64")
if maybe_url:
image_content = maybe_url
elif maybe_path:
if not maybe_path.startswith("file://"):
maybe_path = Path(maybe_path).resolve().as_uri()
image_content = maybe_path
elif maybe_base64:
if not maybe_base64.startswith("data:image"):
maybe_base64 = "data:image/unk;base64," + maybe_base64
image_content = maybe_base64
else:
raise ValueError("Image content must be specified.")
normalized_content.append({"type": "image_url", "image_url": {"url": image_content}})
elif content_type == "audio":
maybe_url: Optional[str] = content.get("url")
maybe_path: Optional[str] = content.get("path")
maybe_base64: Optional[str] = content.get("base64")
if maybe_url or maybe_path:
audio_data = load_audio_as(maybe_url or maybe_path, return_format="dict", force_mono=True)
normalized_content.append({"type": "input_audio", "input_audio": audio_data})
continue
if not maybe_base64:
raise ValueError("Audio content must be specified.")
normalized_content.append({"type": "audio_url", "audio_url": {"url": maybe_base64}})
else:
normalized_content.append(content)
message["content"] = normalized_content
outputs = []
images: list[np.ndarray] = []
audios: list[np.ndarray] = []
for conversation in conversations:
messages: list[dict[str, Union[str, list[dict[str, Union[str, dict[str, Any]]]]]]] = []
for message in conversation:
_maybe_adapt_message(message)
messages.append(message)
chat_request = ChatCompletionRequest.from_openai(
messages=messages,
tools=tools,
continue_final_message=continue_final_message,
)
tokenized_request = self.tokenizer.encode_chat_completion(chat_request)
if tokenize:
outputs.append(tokenized_request.tokens)
else:
outputs.append(tokenized_request.text)
images.extend(tokenized_request.images)
audios.extend([el.audio_array for el in tokenized_request.audios])
if not is_batched:
outputs = outputs[0]
if tokenize:
out = self(
outputs,
padding=padding,
truncation=truncation,
max_length=max_length,
add_special_tokens=False,
return_tensors=return_tensors,
)
if return_dict:
if images:
pixel_values: Union[list[np.ndarray], np.ndarray, torch.Tensor]
if return_tensors == "pt":
if not is_torch_available():
raise ImportError(
"Unable to convert output to PyTorch tensors format, PyTorch is not installed."
)
pixel_values = torch.tensor(images)
elif return_tensors == "np":
pixel_values = np.array(images)
elif return_tensors is None:
pixel_values = images
else:
raise ValueError(f"Unsupported return_tensors type: {return_tensors}")
out.data["pixel_values"] = pixel_values
if audios:
if return_tensors is not None:
raise NotImplementedError(
"When passing audio content in apply_chat_template, `return_tensors` must be None since we cannot batch the audio inputs. The returned audio will be a list of numpy arrays."
)
# Transformers convention is audio for plural audio (audio does not take a "s")
out.data["audio"] = audios
return out
else:
return out["input_ids"]
else:
logger.warning(
"`MistralCommonTokenizer.apply_chat_template(..., tokenize=False)` is unsafe and may lead to unexpected behavior."
" Please consider using `tokenize=True` instead and don't encode the output manually."
)
return outputs
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, EncodedInput, list[TextInput], list[EncodedInput], None] = None,
text_pair: None = None,
text_target: None = None,
text_pair_target: None = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy, None] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[str] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.
Args:
text (`str`, `List[str]`, `List[List[str]]`, *optional*):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of int
(encoded strings).
text_pair (`None`, *optional*):
Not supported by `MistralCommonTokenizer`. Kept to match the signature of `PreTrainedTokenizerBase.__call__`.
text_target (`None`, *optional*):
Not supported by `MistralCommonTokenizer`. Kept to match the signature of `PreTrainedTokenizerBase.__call__`.
text_pair_target (`None`, *optional*):
Not supported by `MistralCommonTokenizer`. Kept to match the signature of `PreTrainedTokenizerBase.__call__`.
"""
if kwargs:
raise ValueError(f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.__call__`.")
if text_pair or text_target or text_pair_target:
raise ValueError(
"`text_pair`, `text_target` and `text_pair_target` are not supported by `MistralCommonTokenizer`."
)
if return_tensors in ("tf", "jax"):
raise ValueError(
"`MistralCommonTokenizer` does not support `return_tensors='tf'` or `return_tensors='jax'`."
)
def _is_valid_text_input(t):
if isinstance(t, str):
# Strings are fine
return True
elif isinstance(t, (list, tuple)):
# List are fine as long as they are...
if len(t) == 0:
# ... empty
return True
elif isinstance(t[0], (str, int)):
# ... list of strings or int
return True
elif isinstance(t[0], (list, tuple)):
# ... list with an empty list or with a list of strings or with a list of ints
return len(t[0]) == 0 or isinstance(t[0][0], (str, int))
else:
return False
else:
return False
if not _is_valid_text_input(text):
raise ValueError(
"text input must be of type `str` (single example), `List[str]` (batch or single encoded example) "
"or `List[List[int]]` (batch of encoded examples)."
)
is_batched = isinstance(text, (list, tuple)) and isinstance(text[0], (str, list, tuple))
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
if is_batched:
return self._batch_encode_plus(
batch_text=text,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self._encode_plus(
text=text,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
**kwargs,
)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
*init_inputs,
mode: ValidationMode = ValidationMode.test,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = None,
revision: str = "main",
model_max_length: int = VERY_LARGE_INTEGER,
padding_side: str = "left",
truncation_side: str = "right",
model_input_names: Optional[list[str]] = None,
clean_up_tokenization_spaces: bool = False,
**kwargs,
):
r"""
Instantiate a `MistralCommonTokenizer` from a predefined
tokenizer.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
Can be either:
- A string, the *model id* of a predefined tokenizer hosted inside a model repo on huggingface.co.
- A path to a *directory* containing the tokenizer config, for instance saved
using the [`MistralCommonTokenizer.tokenization_mistral_common.save_pretrained`] method, e.g.,
`./my_model_directory/`.
mode (`ValidationMode`, *optional*, defaults to `ValidationMode.test`):
Validation mode for the `MistralTokenizer` tokenizer.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download the vocabulary files and override the cached versions if they
exist.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `hf auth login` (stored in `~/.huggingface`).
local_files_only (`bool`, *optional*, defaults to `False`):
Whether or not to only rely on local files and not to attempt to download any files.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
padding_side (`str`, *optional*, defaults to `"left"`):
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
truncation_side (`str`, *optional*, defaults to `"right"`):
The side on which the model should have truncation applied. Should be selected between ['right', 'left'].
model_input_names (`List[string]`, *optional*):
The list of inputs accepted by the forward pass of the model (like `"token_type_ids"` or
`"attention_mask"`). Default value is picked from the class attribute of the same name.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not the model should cleanup the spaces that were added when splitting the input text during the
tokenization process.
kwargs (additional keyword arguments, *optional*):
Not supported by `MistralCommonTokenizer.from_pretrained`.
Will raise an error if used.
"""
if init_inputs:
raise ValueError("`init_inputs` are not supported by `MistralCommonTokenizer.from_pretrained`.")
# Handle kwargs and AutoTokenizer case
if kwargs and not set(kwargs.keys()).issubset({"_from_auto", "trust_remote_code"}):
raise ValueError(
f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.from_pretrained`."
)
if not os.path.isdir(pretrained_model_name_or_path):
tokenizer_path = download_tokenizer_from_hf_hub(
repo_id=pretrained_model_name_or_path,
cache_dir=cache_dir,
token=token,
revision=revision,
force_download=force_download,
local_files_only=local_files_only,
)
else:
valid_tokenizer_files = []
tokenizer_file: str
instruct_versions = list(TokenizerVersion.__members__)
mm_versions = list(MultiModalVersion.__members__) + [""] # allow no mm version
sentencepiece_suffixes = [f".model.{v}{m}" for v in instruct_versions for m in mm_versions] + [".model"]
for path in os.listdir(pretrained_model_name_or_path):
pathlib_repo_file = Path(path)
file_name = pathlib_repo_file.name
suffix = "".join(pathlib_repo_file.suffixes)
if file_name == "tekken.json" or suffix in sentencepiece_suffixes:
valid_tokenizer_files.append(file_name)
if len(valid_tokenizer_files) == 0:
raise ValueError(f"No tokenizer file found in directory: {pretrained_model_name_or_path}")
# If there are multiple tokenizer files, we use tekken.json if it exists, otherwise the versioned one.
if len(valid_tokenizer_files) > 1:
if "tekken.json" in valid_tokenizer_files:
tokenizer_file = "tekken.json"
else:
tokenizer_file = sorted(valid_tokenizer_files)[-1]
logger.warning(
f"Multiple tokenizer files found in directory: {pretrained_model_name_or_path}. Using {tokenizer_file}."
)
else:
tokenizer_file = valid_tokenizer_files[0]
tokenizer_path = os.path.join(pretrained_model_name_or_path, tokenizer_file)
return cls(
tokenizer_path=tokenizer_path,
mode=mode,
model_max_length=model_max_length,
padding_side=padding_side,
truncation_side=truncation_side,
model_input_names=model_input_names,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
)
def save_pretrained(
self,
save_directory: Union[str, os.PathLike, Path],
push_to_hub: bool = False,
token: Optional[Union[str, bool]] = None,
commit_message: Optional[str] = None,
repo_id: Optional[str] = None,
private: Optional[bool] = None,
repo_url: Optional[str] = None,
organization: Optional[str] = None,
**kwargs,
) -> tuple[str]:
"""
Save the full tokenizer state.
This method make sure the full tokenizer can then be re-loaded using the
[`~MistralCommonTokenizer.tokenization_mistral_common.from_pretrained`] class method.
Args:
save_directory (`str` or `os.PathLike`): The path to a directory where the tokenizer will be saved.
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
token (`str` or *bool*, *optional*, defaults to `None`):
The token to use to push to the model hub. If `True`, will use the token in the `HF_TOKEN` environment
variable.
commit_message (`str`, *optional*): The commit message to use when pushing to the hub.
repo_id (`str`, *optional*): The name of the repository to which push to the Hub.
private (`bool`, *optional*): Whether the model repository is private or not.
repo_url (`str`, *optional*): The URL to the Git repository to which push to the Hub.
organization (`str`, *optional*): The name of the organization in which you would like to push your model.
kwargs (`Dict[str, Any]`, *optional*):
Not supported by `MistralCommonTokenizer.save_pretrained`.
Will raise an error if used.
Returns:
A tuple of `str`: The files saved.
"""
# `save_jinja_files`` must be skipped to be able to save from a processor
kwargs.pop("save_jinja_files", None)
if kwargs:
raise ValueError(
f"Kwargs {list(kwargs.keys())} are not supported by `MistralCommonTokenizer.save_pretrained`."
)
save_directory = Path(save_directory)
save_directory.mkdir(parents=True, exist_ok=True)
shutil.copy(self._tokenizer_path, save_directory)
if push_to_hub:
repo_id = repo_id or str(save_directory).split(os.path.sep)[-1]
repo_id = self._create_repo(
repo_id, token=token, private=private, repo_url=repo_url, organization=organization
)
files_timestamps = self._get_files_timestamps(save_directory)
self._upload_modified_files(
save_directory,
repo_id,
files_timestamps,
commit_message=commit_message,
token=token,
)
return (str(save_directory / self._tokenizer_path.name),)
| transformers/src/transformers/tokenization_mistral_common.py/0 | {
"file_path": "transformers/src/transformers/tokenization_mistral_common.py",
"repo_id": "transformers",
"token_count": 40413
} | 509 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
warnings.warn(
"transformers.utils.bitsandbytes module is deprecated and will be removed in a future version. Please import bitsandbytes modules directly from transformers.integrations",
FutureWarning,
)
from ..integrations import ( # noqa
get_keys_to_not_convert,
replace_8bit_linear,
replace_with_bnb_linear,
set_module_8bit_tensor_to_device,
set_module_quantized_tensor_to_device,
)
| transformers/src/transformers/utils/bitsandbytes.py/0 | {
"file_path": "transformers/src/transformers/utils/bitsandbytes.py",
"repo_id": "transformers",
"token_count": 303
} | 510 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class TimmWrapperImageProcessor(metaclass=DummyObject):
_backends = ["timm", "torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["timm", "torchvision"])
| transformers/src/transformers/utils/dummy_timm_and_torchvision_objects.py/0 | {
"file_path": "transformers/src/transformers/utils/dummy_timm_and_torchvision_objects.py",
"repo_id": "transformers",
"token_count": 115
} | 511 |
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: sentencepiece_model.proto
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name="sentencepiece_model.proto",
package="sentencepiece",
syntax="proto2",
serialized_options=b"H\003",
create_key=_descriptor._internal_create_key,
serialized_pb=(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\xa1\n\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01'
b" \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02"
b" \x01(\t\x12\x41\n\nmodel_type\x18\x03"
b" \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04"
b" \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12"
b' \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n'
b" \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b"
b" \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12"
b' \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r'
b" \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e"
b" \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f"
b" \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12"
b" \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10"
b" \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11"
b" \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14"
b" \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15"
b" \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17"
b" \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16"
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b" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18)"
b" \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+"
b" \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18."
b" \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30"
b" \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87"
b" \x12+\n\x1ctrain_extremely_large_corpus\x18\x31"
b' \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01'
b" \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03"
b" \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12"
b" \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06"
b' \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01'
b' \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01'
b" \x01(\t\x12\x10\n\x08\x65xpected\x18\x02"
b' \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01'
b" \x03(\x0b\x32'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02"
b" \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03"
b" \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04"
b" \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05"
b" \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01"
b" \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03"
b' \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
),
)
_TRAINERSPEC_MODELTYPE = _descriptor.EnumDescriptor(
name="ModelType",
full_name="sentencepiece.TrainerSpec.ModelType",
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name="UNIGRAM",
index=0,
number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.EnumValueDescriptor(
name="BPE",
index=1,
number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.EnumValueDescriptor(
name="WORD",
index=2,
number=3,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.EnumValueDescriptor(
name="CHAR",
index=3,
number=4,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
],
containing_type=None,
serialized_options=None,
serialized_start=1294,
serialized_end=1347,
)
_sym_db.RegisterEnumDescriptor(_TRAINERSPEC_MODELTYPE)
_MODELPROTO_SENTENCEPIECE_TYPE = _descriptor.EnumDescriptor(
name="Type",
full_name="sentencepiece.ModelProto.SentencePiece.Type",
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name="NORMAL",
index=0,
number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.EnumValueDescriptor(
name="UNKNOWN",
index=1,
number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.EnumValueDescriptor(
name="CONTROL",
index=2,
number=3,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.EnumValueDescriptor(
name="USER_DEFINED",
index=3,
number=4,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.EnumValueDescriptor(
name="BYTE",
index=4,
number=6,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.EnumValueDescriptor(
name="UNUSED",
index=5,
number=5,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key,
),
],
containing_type=None,
serialized_options=None,
serialized_start=2100,
serialized_end=2184,
)
_sym_db.RegisterEnumDescriptor(_MODELPROTO_SENTENCEPIECE_TYPE)
_TRAINERSPEC = _descriptor.Descriptor(
name="TrainerSpec",
full_name="sentencepiece.TrainerSpec",
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name="input",
full_name="sentencepiece.TrainerSpec.input",
index=0,
number=1,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="input_format",
full_name="sentencepiece.TrainerSpec.input_format",
index=1,
number=7,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="model_prefix",
full_name="sentencepiece.TrainerSpec.model_prefix",
index=2,
number=2,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="model_type",
full_name="sentencepiece.TrainerSpec.model_type",
index=3,
number=3,
type=14,
cpp_type=8,
label=1,
has_default_value=True,
default_value=1,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="vocab_size",
full_name="sentencepiece.TrainerSpec.vocab_size",
index=4,
number=4,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=8000,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="accept_language",
full_name="sentencepiece.TrainerSpec.accept_language",
index=5,
number=5,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="self_test_sample_size",
full_name="sentencepiece.TrainerSpec.self_test_sample_size",
index=6,
number=6,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="character_coverage",
full_name="sentencepiece.TrainerSpec.character_coverage",
index=7,
number=10,
type=2,
cpp_type=6,
label=1,
has_default_value=True,
default_value=0.9995,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="input_sentence_size",
full_name="sentencepiece.TrainerSpec.input_sentence_size",
index=8,
number=11,
type=4,
cpp_type=4,
label=1,
has_default_value=True,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="shuffle_input_sentence",
full_name="sentencepiece.TrainerSpec.shuffle_input_sentence",
index=9,
number=19,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=True,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="mining_sentence_size",
full_name="sentencepiece.TrainerSpec.mining_sentence_size",
index=10,
number=12,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=b"\030\001",
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="training_sentence_size",
full_name="sentencepiece.TrainerSpec.training_sentence_size",
index=11,
number=13,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=b"\030\001",
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="seed_sentencepiece_size",
full_name="sentencepiece.TrainerSpec.seed_sentencepiece_size",
index=12,
number=14,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=1000000,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="shrinking_factor",
full_name="sentencepiece.TrainerSpec.shrinking_factor",
index=13,
number=15,
type=2,
cpp_type=6,
label=1,
has_default_value=True,
default_value=0.75,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="max_sentence_length",
full_name="sentencepiece.TrainerSpec.max_sentence_length",
index=14,
number=18,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=4192,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="num_threads",
full_name="sentencepiece.TrainerSpec.num_threads",
index=15,
number=16,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=16,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="num_sub_iterations",
full_name="sentencepiece.TrainerSpec.num_sub_iterations",
index=16,
number=17,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=2,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="max_sentencepiece_length",
full_name="sentencepiece.TrainerSpec.max_sentencepiece_length",
index=17,
number=20,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=16,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="split_by_unicode_script",
full_name="sentencepiece.TrainerSpec.split_by_unicode_script",
index=18,
number=21,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=True,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="split_by_number",
full_name="sentencepiece.TrainerSpec.split_by_number",
index=19,
number=23,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=True,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="split_by_whitespace",
full_name="sentencepiece.TrainerSpec.split_by_whitespace",
index=20,
number=22,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=True,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="treat_whitespace_as_suffix",
full_name="sentencepiece.TrainerSpec.treat_whitespace_as_suffix",
index=21,
number=24,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="split_digits",
full_name="sentencepiece.TrainerSpec.split_digits",
index=22,
number=25,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="control_symbols",
full_name="sentencepiece.TrainerSpec.control_symbols",
index=23,
number=30,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="user_defined_symbols",
full_name="sentencepiece.TrainerSpec.user_defined_symbols",
index=24,
number=31,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="required_chars",
full_name="sentencepiece.TrainerSpec.required_chars",
index=25,
number=36,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="byte_fallback",
full_name="sentencepiece.TrainerSpec.byte_fallback",
index=26,
number=35,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="vocabulary_output_piece_score",
full_name="sentencepiece.TrainerSpec.vocabulary_output_piece_score",
index=27,
number=32,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=True,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="hard_vocab_limit",
full_name="sentencepiece.TrainerSpec.hard_vocab_limit",
index=28,
number=33,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=True,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="use_all_vocab",
full_name="sentencepiece.TrainerSpec.use_all_vocab",
index=29,
number=34,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="unk_id",
full_name="sentencepiece.TrainerSpec.unk_id",
index=30,
number=40,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="bos_id",
full_name="sentencepiece.TrainerSpec.bos_id",
index=31,
number=41,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=1,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="eos_id",
full_name="sentencepiece.TrainerSpec.eos_id",
index=32,
number=42,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=2,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="pad_id",
full_name="sentencepiece.TrainerSpec.pad_id",
index=33,
number=43,
type=5,
cpp_type=1,
label=1,
has_default_value=True,
default_value=-1,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="unk_piece",
full_name="sentencepiece.TrainerSpec.unk_piece",
index=34,
number=45,
type=9,
cpp_type=9,
label=1,
has_default_value=True,
default_value=b"<unk>".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="bos_piece",
full_name="sentencepiece.TrainerSpec.bos_piece",
index=35,
number=46,
type=9,
cpp_type=9,
label=1,
has_default_value=True,
default_value=b"<s>".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="eos_piece",
full_name="sentencepiece.TrainerSpec.eos_piece",
index=36,
number=47,
type=9,
cpp_type=9,
label=1,
has_default_value=True,
default_value=b"</s>".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="pad_piece",
full_name="sentencepiece.TrainerSpec.pad_piece",
index=37,
number=48,
type=9,
cpp_type=9,
label=1,
has_default_value=True,
default_value=b"<pad>".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="unk_surface",
full_name="sentencepiece.TrainerSpec.unk_surface",
index=38,
number=44,
type=9,
cpp_type=9,
label=1,
has_default_value=True,
default_value=b" \342\201\207 ".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="train_extremely_large_corpus",
full_name="sentencepiece.TrainerSpec.train_extremely_large_corpus",
index=39,
number=49,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
],
extensions=[],
nested_types=[],
enum_types=[
_TRAINERSPEC_MODELTYPE,
],
serialized_options=None,
is_extendable=True,
syntax="proto2",
extension_ranges=[
(200, 536870912),
],
oneofs=[],
serialized_start=45,
serialized_end=1358,
)
_NORMALIZERSPEC = _descriptor.Descriptor(
name="NormalizerSpec",
full_name="sentencepiece.NormalizerSpec",
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name="name",
full_name="sentencepiece.NormalizerSpec.name",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="precompiled_charsmap",
full_name="sentencepiece.NormalizerSpec.precompiled_charsmap",
index=1,
number=2,
type=12,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"",
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="add_dummy_prefix",
full_name="sentencepiece.NormalizerSpec.add_dummy_prefix",
index=2,
number=3,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=True,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="remove_extra_whitespaces",
full_name="sentencepiece.NormalizerSpec.remove_extra_whitespaces",
index=3,
number=4,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=True,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="escape_whitespaces",
full_name="sentencepiece.NormalizerSpec.escape_whitespaces",
index=4,
number=5,
type=8,
cpp_type=7,
label=1,
has_default_value=True,
default_value=True,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="normalization_rule_tsv",
full_name="sentencepiece.NormalizerSpec.normalization_rule_tsv",
index=5,
number=6,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=True,
syntax="proto2",
extension_ranges=[
(200, 536870912),
],
oneofs=[],
serialized_start=1361,
serialized_end=1570,
)
_SELFTESTDATA_SAMPLE = _descriptor.Descriptor(
name="Sample",
full_name="sentencepiece.SelfTestData.Sample",
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name="input",
full_name="sentencepiece.SelfTestData.Sample.input",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="expected",
full_name="sentencepiece.SelfTestData.Sample.expected",
index=1,
number=2,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto2",
extension_ranges=[],
oneofs=[],
serialized_start=1641,
serialized_end=1682,
)
_SELFTESTDATA = _descriptor.Descriptor(
name="SelfTestData",
full_name="sentencepiece.SelfTestData",
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name="samples",
full_name="sentencepiece.SelfTestData.samples",
index=0,
number=1,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
],
extensions=[],
nested_types=[
_SELFTESTDATA_SAMPLE,
],
enum_types=[],
serialized_options=None,
is_extendable=True,
syntax="proto2",
extension_ranges=[
(200, 536870912),
],
oneofs=[],
serialized_start=1572,
serialized_end=1693,
)
_MODELPROTO_SENTENCEPIECE = _descriptor.Descriptor(
name="SentencePiece",
full_name="sentencepiece.ModelProto.SentencePiece",
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name="piece",
full_name="sentencepiece.ModelProto.SentencePiece.piece",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="score",
full_name="sentencepiece.ModelProto.SentencePiece.score",
index=1,
number=2,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="type",
full_name="sentencepiece.ModelProto.SentencePiece.type",
index=2,
number=3,
type=14,
cpp_type=8,
label=1,
has_default_value=True,
default_value=1,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
],
extensions=[],
nested_types=[],
enum_types=[
_MODELPROTO_SENTENCEPIECE_TYPE,
],
serialized_options=None,
is_extendable=True,
syntax="proto2",
extension_ranges=[
(200, 536870912),
],
oneofs=[],
serialized_start=1985,
serialized_end=2195,
)
_MODELPROTO = _descriptor.Descriptor(
name="ModelProto",
full_name="sentencepiece.ModelProto",
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name="pieces",
full_name="sentencepiece.ModelProto.pieces",
index=0,
number=1,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="trainer_spec",
full_name="sentencepiece.ModelProto.trainer_spec",
index=1,
number=2,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="normalizer_spec",
full_name="sentencepiece.ModelProto.normalizer_spec",
index=2,
number=3,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="self_test_data",
full_name="sentencepiece.ModelProto.self_test_data",
index=3,
number=4,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
_descriptor.FieldDescriptor(
name="denormalizer_spec",
full_name="sentencepiece.ModelProto.denormalizer_spec",
index=4,
number=5,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
),
],
extensions=[],
nested_types=[
_MODELPROTO_SENTENCEPIECE,
],
enum_types=[],
serialized_options=None,
is_extendable=True,
syntax="proto2",
extension_ranges=[
(200, 536870912),
],
oneofs=[],
serialized_start=1696,
serialized_end=2206,
)
_TRAINERSPEC.fields_by_name["model_type"].enum_type = _TRAINERSPEC_MODELTYPE
_TRAINERSPEC_MODELTYPE.containing_type = _TRAINERSPEC
_SELFTESTDATA_SAMPLE.containing_type = _SELFTESTDATA
_SELFTESTDATA.fields_by_name["samples"].message_type = _SELFTESTDATA_SAMPLE
_MODELPROTO_SENTENCEPIECE.fields_by_name["type"].enum_type = _MODELPROTO_SENTENCEPIECE_TYPE
_MODELPROTO_SENTENCEPIECE.containing_type = _MODELPROTO
_MODELPROTO_SENTENCEPIECE_TYPE.containing_type = _MODELPROTO_SENTENCEPIECE
_MODELPROTO.fields_by_name["pieces"].message_type = _MODELPROTO_SENTENCEPIECE
_MODELPROTO.fields_by_name["trainer_spec"].message_type = _TRAINERSPEC
_MODELPROTO.fields_by_name["normalizer_spec"].message_type = _NORMALIZERSPEC
_MODELPROTO.fields_by_name["self_test_data"].message_type = _SELFTESTDATA
_MODELPROTO.fields_by_name["denormalizer_spec"].message_type = _NORMALIZERSPEC
DESCRIPTOR.message_types_by_name["TrainerSpec"] = _TRAINERSPEC
DESCRIPTOR.message_types_by_name["NormalizerSpec"] = _NORMALIZERSPEC
DESCRIPTOR.message_types_by_name["SelfTestData"] = _SELFTESTDATA
DESCRIPTOR.message_types_by_name["ModelProto"] = _MODELPROTO
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
TrainerSpec = _reflection.GeneratedProtocolMessageType(
"TrainerSpec",
(_message.Message,),
{
"DESCRIPTOR": _TRAINERSPEC,
"__module__": "sentencepiece_model_pb2",
# @@protoc_insertion_point(class_scope:sentencepiece.TrainerSpec)
},
)
_sym_db.RegisterMessage(TrainerSpec)
NormalizerSpec = _reflection.GeneratedProtocolMessageType(
"NormalizerSpec",
(_message.Message,),
{
"DESCRIPTOR": _NORMALIZERSPEC,
"__module__": "sentencepiece_model_pb2",
# @@protoc_insertion_point(class_scope:sentencepiece.NormalizerSpec)
},
)
_sym_db.RegisterMessage(NormalizerSpec)
SelfTestData = _reflection.GeneratedProtocolMessageType(
"SelfTestData",
(_message.Message,),
{
"Sample": _reflection.GeneratedProtocolMessageType(
"Sample",
(_message.Message,),
{
"DESCRIPTOR": _SELFTESTDATA_SAMPLE,
"__module__": "sentencepiece_model_pb2",
# @@protoc_insertion_point(class_scope:sentencepiece.SelfTestData.Sample)
},
),
"DESCRIPTOR": _SELFTESTDATA,
"__module__": "sentencepiece_model_pb2",
# @@protoc_insertion_point(class_scope:sentencepiece.SelfTestData)
},
)
_sym_db.RegisterMessage(SelfTestData)
_sym_db.RegisterMessage(SelfTestData.Sample)
ModelProto = _reflection.GeneratedProtocolMessageType(
"ModelProto",
(_message.Message,),
{
"SentencePiece": _reflection.GeneratedProtocolMessageType(
"SentencePiece",
(_message.Message,),
{
"DESCRIPTOR": _MODELPROTO_SENTENCEPIECE,
"__module__": "sentencepiece_model_pb2",
# @@protoc_insertion_point(class_scope:sentencepiece.ModelProto.SentencePiece)
},
),
"DESCRIPTOR": _MODELPROTO,
"__module__": "sentencepiece_model_pb2",
# @@protoc_insertion_point(class_scope:sentencepiece.ModelProto)
},
)
_sym_db.RegisterMessage(ModelProto)
_sym_db.RegisterMessage(ModelProto.SentencePiece)
DESCRIPTOR._options = None
_TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
_TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# @@protoc_insertion_point(module_scope)
| transformers/src/transformers/utils/sentencepiece_model_pb2.py/0 | {
"file_path": "transformers/src/transformers/utils/sentencepiece_model_pb2.py",
"repo_id": "transformers",
"token_count": 28257
} | 512 |
import time
import unittest
from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from transformers.testing_utils import require_flash_attn, require_torch_gpu, slow
_TEST_PROMPTS = [
"A man is a walking his dog down the street, and a the turn he sees",
"Describe a fruit that is of orange color and round. It is a sweet fruit and a great source of Vitamine C. The fruit I'm thinking of is an",
"A plane is flying high in the sky, out of the window are clouds and mountains. Where could the plane be located?",
"Please fill in the form to",
"For safety reasons, the train is stopped in the middle of the",
]
_EXPECTED_OUTPUTS = [
"a woman standing on the sidewalk, looking at him. He is immediately drawn to her and feels a strong attraction. He walks up to her and strikes up a conversation, and they quickly discover that they have a lot in common. They exchange numbers and",
"orange.\n\n## Step 1: Identify the key characteristics of the fruit\nThe fruit is described as being orange in color and round in shape.\n\n## Step 2: Determine the taste and nutritional value of the fruit\nThe fruit is described as sweet",
"This riddle is a classic example of a lateral thinking puzzle, which requires the test-taker to think creatively and consider multiple possibilities. The answer is not a straightforward one, and it requires some lateral thinking to arrive at the correct solution.",
"get in touch with us. We will respond to your message as soon as possible.\n\n[Your Name]\n[Your Email]\n[Your Phone Number]\n[Your Message]\n\nWe are looking forward to hearing from you!\n\n[Insert Contact Information]\n\nNote:",
"track. The train is stopped for 30 minutes. The train is moving at a speed of 60 km/h. How many kilometers does the train travel in 30 minutes?\n## Step 1: Convert the speed from km/h to km/min",
]
@slow
@require_flash_attn
@require_torch_gpu
class TestBatchGeneration(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.2-3b-Instruct", dtype="bfloat16", device_map="auto"
).eval()
cls.tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3b-Instruct", padding_side="left")
if cls.tokenizer.pad_token is None:
cls.tokenizer.pad_token = cls.tokenizer.eos_token
cls.model.config.pad_token_id = cls.model.config.eos_token_id
cls.model.use_cache = False
@parameterized.expand(
[
("eager_paged", 64, 128, 64),
("sdpa_paged", 32, 256, 128),
("paged_attention", 16, 512, 256),
("flex_paged", 64, 128, 64),
]
)
def test_generate_batch_consistency(self, attn_impl, num_blocks, block_size, max_batch_tokens):
self.model.config.attn_implementation = attn_impl
generation_config = GenerationConfig(
max_new_tokens=50,
top_k=0,
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=self.tokenizer.pad_token_id,
use_cache=False,
num_blocks=num_blocks,
block_size=block_size,
max_batch_tokens=max_batch_tokens,
)
tokenized = self.tokenizer(_TEST_PROMPTS, truncation=True, max_length=512)
batch_inputs = list(tokenized["input_ids"])
start = time.time()
batch_outputs = self.model.generate_batch(
inputs=batch_inputs,
generation_config=generation_config,
)
end = time.time()
print(
f"\n[{attn_impl}] Batch took {end - start:.2f}s with config: blocks={num_blocks}, block_size={block_size}, max_batch_tokens={max_batch_tokens}"
)
for i, req_id in enumerate(batch_outputs):
generated = self.tokenizer.decode(batch_outputs[req_id].static_outputs, skip_special_tokens=False).strip()
expected = _EXPECTED_OUTPUTS[i].strip()
self.assertTrue(
generated.startswith(expected),
msg=f"[{attn_impl}] Mismatch in request {i}:\nExpected start: {expected}\nGot: {generated}",
)
| transformers/tests/generation/test_paged_attention.py/0 | {
"file_path": "transformers/tests/generation/test_paged_attention.py",
"repo_id": "transformers",
"token_count": 1668
} | 513 |
# coding=utf-8
# Copyright 2025 the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
VIDEO_PROCESSOR_MAPPING,
AutoConfig,
AutoVideoProcessor,
LlavaOnevisionConfig,
LlavaOnevisionVideoProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_torch
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_video_processing import CustomVideoProcessor # noqa E402
@require_torch
class AutoVideoProcessorTest(unittest.TestCase):
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
def test_video_processor_from_model_shortcut(self):
config = AutoVideoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf")
self.assertIsInstance(config, LlavaOnevisionVideoProcessor)
def test_video_processor_from_local_directory_from_key(self):
with tempfile.TemporaryDirectory() as tmpdirname:
processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json"
config_tmpfile = Path(tmpdirname) / "config.json"
json.dump(
{
"video_processor_type": "LlavaOnevisionVideoProcessor",
"processor_class": "LlavaOnevisionProcessor",
},
open(processor_tmpfile, "w"),
)
json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w"))
config = AutoVideoProcessor.from_pretrained(tmpdirname)
self.assertIsInstance(config, LlavaOnevisionVideoProcessor)
def test_video_processor_from_local_directory_from_preprocessor_key(self):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
config_tmpfile = Path(tmpdirname) / "config.json"
json.dump(
{
"video_processor_type": "LlavaOnevisionVideoProcessor",
"processor_class": "LlavaOnevisionProcessor",
},
open(processor_tmpfile, "w"),
)
json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w"))
config = AutoVideoProcessor.from_pretrained(tmpdirname)
self.assertIsInstance(config, LlavaOnevisionVideoProcessor)
def test_video_processor_from_local_directory_from_config(self):
with tempfile.TemporaryDirectory() as tmpdirname:
model_config = LlavaOnevisionConfig()
# Create a dummy config file with image_proceesor_type
processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json"
config_tmpfile = Path(tmpdirname) / "config.json"
json.dump(
{
"video_processor_type": "LlavaOnevisionVideoProcessor",
"processor_class": "LlavaOnevisionProcessor",
},
open(processor_tmpfile, "w"),
)
json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w"))
# remove video_processor_type to make sure config.json alone is enough to load image processor locally
config_dict = AutoVideoProcessor.from_pretrained(tmpdirname).to_dict()
config_dict.pop("video_processor_type")
config = LlavaOnevisionVideoProcessor(**config_dict)
# save in new folder
model_config.save_pretrained(tmpdirname)
config.save_pretrained(tmpdirname)
config = AutoVideoProcessor.from_pretrained(tmpdirname)
# make sure private variable is not incorrectly saved
dict_as_saved = json.loads(config.to_json_string())
self.assertTrue("_processor_class" not in dict_as_saved)
self.assertIsInstance(config, LlavaOnevisionVideoProcessor)
def test_video_processor_from_local_file(self):
with tempfile.TemporaryDirectory() as tmpdirname:
processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json"
json.dump(
{
"video_processor_type": "LlavaOnevisionVideoProcessor",
"processor_class": "LlavaOnevisionProcessor",
},
open(processor_tmpfile, "w"),
)
config = AutoVideoProcessor.from_pretrained(processor_tmpfile)
self.assertIsInstance(config, LlavaOnevisionVideoProcessor)
def test_repo_not_found(self):
with self.assertRaisesRegex(
EnvironmentError,
"llava-hf/llava-doesnt-exist is not a local folder and is not a valid model identifier",
):
_ = AutoVideoProcessor.from_pretrained("llava-hf/llava-doesnt-exist")
def test_revision_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
):
_ = AutoVideoProcessor.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
def test_video_processor_not_found(self):
with self.assertRaisesRegex(
EnvironmentError,
"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.",
):
_ = AutoVideoProcessor.from_pretrained("hf-internal-testing/config-no-model")
def test_from_pretrained_dynamic_video_processor(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
video_processor = AutoVideoProcessor.from_pretrained("hf-internal-testing/test_dynamic_video_processor")
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
video_processor = AutoVideoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_video_processor", trust_remote_code=False
)
video_processor = AutoVideoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True
)
self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor")
# Test the dynamic module is loaded only once.
reloaded_video_processor = AutoVideoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True
)
self.assertIs(video_processor.__class__, reloaded_video_processor.__class__)
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
video_processor.save_pretrained(tmp_dir)
reloaded_video_processor = AutoVideoProcessor.from_pretrained(tmp_dir, trust_remote_code=True)
self.assertEqual(reloaded_video_processor.__class__.__name__, "NewVideoProcessor")
def test_new_video_processor_registration(self):
try:
AutoConfig.register("custom", CustomConfig)
AutoVideoProcessor.register(CustomConfig, CustomVideoProcessor)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(ValueError):
AutoVideoProcessor.register(LlavaOnevisionConfig, LlavaOnevisionVideoProcessor)
with tempfile.TemporaryDirectory() as tmpdirname:
processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json"
config_tmpfile = Path(tmpdirname) / "config.json"
json.dump(
{
"video_processor_type": "LlavaOnevisionVideoProcessor",
"processor_class": "LlavaOnevisionProcessor",
},
open(processor_tmpfile, "w"),
)
json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w"))
video_processor = CustomVideoProcessor.from_pretrained(tmpdirname)
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
video_processor.save_pretrained(tmp_dir)
new_video_processor = AutoVideoProcessor.from_pretrained(tmp_dir)
self.assertIsInstance(new_video_processor, CustomVideoProcessor)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in VIDEO_PROCESSOR_MAPPING._extra_content:
del VIDEO_PROCESSOR_MAPPING._extra_content[CustomConfig]
def test_from_pretrained_dynamic_video_processor_conflict(self):
class NewVideoProcessor(LlavaOnevisionVideoProcessor):
is_local = True
try:
AutoConfig.register("custom", CustomConfig)
AutoVideoProcessor.register(CustomConfig, NewVideoProcessor)
# If remote code is not set, the default is to use local
video_processor = AutoVideoProcessor.from_pretrained("hf-internal-testing/test_dynamic_video_processor")
self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor")
self.assertTrue(video_processor.is_local)
# If remote code is disabled, we load the local one.
video_processor = AutoVideoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_video_processor", trust_remote_code=False
)
self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor")
self.assertTrue(video_processor.is_local)
# If remote is enabled, we load from the Hub
video_processor = AutoVideoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True
)
self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor")
self.assertTrue(not hasattr(video_processor, "is_local"))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in VIDEO_PROCESSOR_MAPPING._extra_content:
del VIDEO_PROCESSOR_MAPPING._extra_content[CustomConfig]
| transformers/tests/models/auto/test_video_processing_auto.py/0 | {
"file_path": "transformers/tests/models/auto/test_video_processing_auto.py",
"repo_id": "transformers",
"token_count": 4713
} | 514 |
# Copyright 2021, The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch BlenderbotSmall model."""
import tempfile
import unittest
from transformers import BlenderbotSmallConfig, is_torch_available
from transformers.testing_utils import (
require_torch,
require_torch_fp16,
slow,
torch_device,
)
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel, BlenderbotSmallTokenizer
from transformers.models.blenderbot_small.modeling_blenderbot_small import (
BlenderbotSmallDecoder,
BlenderbotSmallEncoder,
BlenderbotSmallForCausalLM,
)
def prepare_blenderbot_small_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class BlenderbotSmallModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=50,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
3,
)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.get_config()
inputs_dict = prepare_blenderbot_small_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def get_config(self):
return BlenderbotSmallConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = BlenderbotSmallModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
head_mask = inputs_dict["head_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = BlenderbotSmallModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = BlenderbotSmallEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = BlenderbotSmallDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class BlenderbotSmallModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (BlenderbotSmallModel, BlenderbotSmallForConditionalGeneration) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": BlenderbotSmallModel,
"summarization": BlenderbotSmallForConditionalGeneration,
"text-generation": BlenderbotSmallForCausalLM,
"text2text-generation": BlenderbotSmallForConditionalGeneration,
"translation": BlenderbotSmallForConditionalGeneration,
}
if is_torch_available()
else {}
)
is_encoder_decoder = True
fx_compatible = True
test_pruning = False
test_missing_keys = False
# TODO: Fix the failed tests when this model gets more usage
def is_pipeline_test_to_skip(
self,
pipeline_test_case_name,
config_class,
model_architecture,
tokenizer_name,
image_processor_name,
feature_extractor_name,
processor_name,
):
return pipeline_test_case_name == "TextGenerationPipelineTests"
def setUp(self):
self.model_tester = BlenderbotSmallModelTester(self)
self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
@require_torch_fp16
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = BlenderbotSmallForConditionalGeneration(config).eval().to(torch_device)
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def assert_tensors_close(a, b, atol=1e-12, prefix=""):
"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
if a.numel() > 100:
msg = f"tensor values are {pct_different:.1%} percent different."
else:
msg = f"{a} != {b}"
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
@require_torch
class Blenderbot90MIntegrationTests(unittest.TestCase):
ckpt = "facebook/blenderbot-90M"
@cached_property
def model(self):
model = BlenderbotSmallForConditionalGeneration.from_pretrained(self.ckpt).to(torch_device)
if torch_device == "cuda":
model = model.half()
return model
@cached_property
def tokenizer(self):
return BlenderbotSmallTokenizer.from_pretrained(self.ckpt)
@slow
def test_90_generation_from_long_input(self):
src_text = [
"Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel"
" like i'm going to throw up.\nand why is that?"
]
model_inputs = self.tokenizer(src_text, return_tensors="pt").to(torch_device)
assert isinstance(self.tokenizer, BlenderbotSmallTokenizer)
generated_ids = self.model.generate(**model_inputs)[0]
reply = self.tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
assert reply in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
)
@slow
def test_90_generation_from_short_input(self):
model_inputs = self.tokenizer(["sam"], return_tensors="pt").to(torch_device)
generated_utterances = self.model.generate(**model_inputs)
clean_txt = self.tokenizer.decode(
generated_utterances[0], skip_special_tokens=True, clean_up_tokenization_spaces=True
)
assert clean_txt in (
"have you ever been to a sam club? it's a great club in the south.",
"have you ever heard of sam harris? he's an american singer, songwriter, and actor.",
)
class BlenderbotSmallStandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=2,
encoder_attention_heads=4,
decoder_attention_heads=4,
max_position_embeddings=50,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = BlenderbotSmallConfig(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
num_hidden_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = BlenderbotSmallDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = BlenderbotSmallDecoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(
next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, use_cache=True
)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class BlenderbotSmallStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (BlenderbotSmallDecoder, BlenderbotSmallForCausalLM) if is_torch_available() else ()
test_pruning = False
is_encoder_decoder = False
def setUp(
self,
):
self.model_tester = BlenderbotSmallStandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
@unittest.skip(reason="decoder cannot keep gradients")
def test_retain_grad_hidden_states_attentions(self):
return
@unittest.skip(reason="Decoder cannot keep gradients")
def test_flex_attention_with_grads():
return
| transformers/tests/models/blenderbot_small/test_modeling_blenderbot_small.py/0 | {
"file_path": "transformers/tests/models/blenderbot_small/test_modeling_blenderbot_small.py",
"repo_id": "transformers",
"token_count": 9876
} | 515 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available
from ...test_processing_common import ProcessorTesterMixin
if is_vision_available():
from transformers import (
AutoProcessor,
BridgeTowerImageProcessor,
BridgeTowerProcessor,
RobertaTokenizerFast,
)
@require_vision
class BridgeTowerProcessorTest(ProcessorTesterMixin, unittest.TestCase):
processor_class = BridgeTowerProcessor
@classmethod
def setUpClass(cls):
cls.tmpdirname = tempfile.mkdtemp()
image_processor = BridgeTowerImageProcessor()
tokenizer = RobertaTokenizerFast.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")
processor = BridgeTowerProcessor(image_processor, tokenizer)
processor.save_pretrained(cls.tmpdirname)
def get_tokenizer(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
def get_image_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
@classmethod
def tearDownClass(cls):
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
# Some kwargs tests are overridden from common tests to handle shortest_edge
# and size_divisor behaviour
@require_torch
@require_vision
def test_image_processor_defaults_preserved_by_image_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component(
"image_processor",
crop_size={"shortest_edge": 234, "longest_edge": 234},
)
tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertEqual(len(inputs["pixel_values"][0][0]), 234)
@require_torch
@require_vision
def test_structured_kwargs_nested_from_dict(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {
"crop_size": {"shortest_edge": 214},
},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}
inputs = processor(text=input_str, images=image_input, **all_kwargs)
self.assertEqual(inputs["pixel_values"].shape[2], 214)
self.assertEqual(len(inputs["input_ids"][0]), 76)
@require_torch
@require_vision
def test_kwargs_overrides_default_image_processor_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor", crop_size={"shortest_edge": 234})
tokenizer = self.get_component("tokenizer", max_length=117)
if not tokenizer.pad_token:
tokenizer.pad_token = "[TEST_PAD]"
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input, crop_size={"shortest_edge": 224})
self.assertEqual(len(inputs["pixel_values"][0][0]), 224)
@require_torch
@require_vision
def test_unstructured_kwargs_batched(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
if not tokenizer.pad_token:
tokenizer.pad_token = "[TEST_PAD]"
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = ["lower newer", "upper older longer string"]
image_input = self.prepare_image_inputs(batch_size=2)
inputs = processor(
text=input_str,
images=image_input,
return_tensors="pt",
crop_size={"shortest_edge": 214},
padding="longest",
max_length=76,
)
self.assertEqual(inputs["pixel_values"].shape[2], 214)
self.assertEqual(len(inputs["input_ids"][0]), 6)
@require_torch
@require_vision
def test_unstructured_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
if not tokenizer.pad_token:
tokenizer.pad_token = "[TEST_PAD]"
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(
text=input_str,
images=image_input,
return_tensors="pt",
crop_size={"shortest_edge": 214},
padding="max_length",
max_length=76,
)
self.assertEqual(inputs["pixel_values"].shape[2], 214)
self.assertEqual(len(inputs["input_ids"][0]), 76)
@require_torch
@require_vision
def test_structured_kwargs_nested(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
if not tokenizer.pad_token:
tokenizer.pad_token = "[TEST_PAD]"
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {"crop_size": {"shortest_edge": 214}},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}
inputs = processor(text=input_str, images=image_input, **all_kwargs)
self.skip_processor_without_typed_kwargs(processor)
self.assertEqual(inputs["pixel_values"].shape[2], 214)
self.assertEqual(len(inputs["input_ids"][0]), 76)
| transformers/tests/models/bridgetower/test_processing_bridgetower.py/0 | {
"file_path": "transformers/tests/models/bridgetower/test_processing_bridgetower.py",
"repo_id": "transformers",
"token_count": 3309
} | 516 |
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch GotOcr2 model."""
import unittest
from transformers import (
AutoProcessor,
Cohere2VisionConfig,
is_torch_available,
)
from transformers.testing_utils import (
Expectations,
cleanup,
get_device_properties,
require_deterministic_for_xpu,
require_read_token,
require_torch,
require_torch_accelerator,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
Cohere2VisionForConditionalGeneration,
Cohere2VisionModel,
)
class Cohere2VisionText2TextModelTester:
def __init__(
self,
parent,
batch_size=3,
seq_length=7,
downsample_factor=2,
alignment_intermediate_size=32,
ignore_index=-100,
image_token_id=2,
num_channels=3,
image_size=64,
is_training=True,
text_config={
"model_type": "cohere2",
"vocab_size": 99,
"hidden_size": 128,
"intermediate_size": 37,
"num_hidden_layers": 4,
"num_attention_heads": 4,
"output_channels": 64,
"hidden_act": "silu",
"max_position_embeddings": 512,
"tie_word_embeddings": True,
"bos_token_id": 0,
"eos_token_id": 0,
"pad_token_id": 0,
},
vision_config={
"model_type": "siglip_vision_model",
"hidden_size": 32,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"intermediate_size": 128,
"image_size": 64,
"patch_size": 8,
"vision_use_head": False,
},
):
self.parent = parent
self.ignore_index = ignore_index
self.bos_token_id = text_config["bos_token_id"]
self.eos_token_id = text_config["eos_token_id"]
self.pad_token_id = text_config["pad_token_id"]
self.image_token_id = image_token_id
self.text_config = text_config
self.vision_config = vision_config
self.batch_size = batch_size
self.downsample_factor = downsample_factor
self.alignment_intermediate_size = alignment_intermediate_size
self.is_training = is_training
self.num_channels = num_channels
self.image_size = image_size
self.image_seq_length = 16
self.seq_length = seq_length + self.image_seq_length
self.num_hidden_layers = text_config["num_hidden_layers"]
self.vocab_size = text_config["vocab_size"]
self.hidden_size = text_config["hidden_size"]
self.num_attention_heads = text_config["num_attention_heads"]
def get_config(self):
return Cohere2VisionConfig(
text_config=self.text_config,
vision_config=self.vision_config,
image_token_id=self.image_token_id,
downsample_factor=self.downsample_factor,
alignment_intermediate_size=self.alignment_intermediate_size,
)
def prepare_config_and_inputs(self):
config = self.get_config()
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
return config, pixel_values
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
input_ids[input_ids == self.image_token_id] = self.pad_token_id
input_ids[:, : self.image_seq_length] = self.image_token_id
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class Cohere2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
Cohere2VisionModel,
Cohere2VisionForConditionalGeneration,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (Cohere2VisionForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"image-text-to-text": Cohere2VisionForConditionalGeneration,
}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_torchscript = False
test_head_masking = False
_is_composite = True
def setUp(self):
self.model_tester = Cohere2VisionText2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=Cohere2VisionConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="Siglip backbone uses the same initialization scheme as the Flax original implementation")
def test_initialization(self):
pass
@require_read_token
@require_torch
class Cohere2IntegrationTest(unittest.TestCase):
def setUp(self):
self.model_checkpoint = "CohereLabs/command-a-vision-07-2025"
def tearDown(self):
cleanup(torch_device, gc_collect=True)
def get_model(self, dummy=True):
device_type, major, _ = get_device_properties()
dtype = torch.float16
# too large to fit into A10
config = Cohere2VisionConfig.from_pretrained(self.model_checkpoint)
if dummy:
config.text_config.num_hidden_layers = 4
config.text_config.layer_types = config.text_config.layer_types[:4]
model = Cohere2VisionForConditionalGeneration.from_pretrained(
self.model_checkpoint,
config=config,
dtype=dtype,
device_map="auto",
)
return model
@slow
@require_torch_accelerator
def test_model_integration_forward(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model(dummy=False)
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
{"type": "text", "text": "Please describe the image explicitly."},
],
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.float16)
# Forward
with torch.inference_mode():
output = model(**inputs)
actual_logits = output.logits[0, -1, :5].cpu()
EXPECTED_LOGITS = Expectations(
{
("xpu", 3): [0.4109, 0.1532, 0.8018, 2.1328, 0.5483],
# 4-bit
("cuda", 7): [0.1097, 0.3481, 3.8340, 9.7969, 2.0488],
("cuda", 8): [2.4277, 1.6875, 1.8789, 2.1875, 1.9375],
}
) # fmt: skip
expected_logits = torch.tensor(EXPECTED_LOGITS.get_expectation(), dtype=torch.float16)
self.assertTrue(
torch.allclose(actual_logits, expected_logits, atol=0.1),
f"Actual logits: {actual_logits}"
f"\nExpected logits: {expected_logits}"
f"\nDifference: {torch.abs(actual_logits - expected_logits)}",
)
@slow
@require_torch_accelerator
@require_deterministic_for_xpu
def test_model_integration_generate_text_only(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model()
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "Write a haiku"},
],
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.float16)
with torch.no_grad():
generate_ids = model.generate(**inputs, max_new_tokens=10, do_sample=False)
decoded_output = processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_outputs = Expectations(
{
("cuda", 8): "<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>",
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(decoded_output, expected_output)
@slow
@require_torch_accelerator
@require_deterministic_for_xpu
def test_model_integration_generate_chat_template(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model()
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
{"type": "text", "text": "Please describe the image explicitly."},
],
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.float16)
with torch.no_grad():
generate_ids = model.generate(**inputs, max_new_tokens=10, do_sample=False)
decoded_output = processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_outputs = Expectations(
{
("cuda", 8): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(decoded_output, expected_output)
@slow
@require_torch_accelerator
def test_model_integration_batched_generate(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model(dummy=False)
# Prepare inputs
messages = [
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
{"type": "text", "text": "Write a haiku for this image"},
],
},
],
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
{"type": "text", "text": "Describe this image"},
],
},
],
]
inputs = processor.apply_chat_template(
messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.float16)
output = model.generate(**inputs, do_sample=False, max_new_tokens=5)
# Check first output
decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
expected_outputs = Expectations(
{
("cuda", 8): 'Dock stretches to calm',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
# Check second output
decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
expected_outputs = Expectations(
{
("cuda", 8): 'The image depicts a',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
@slow
@require_torch_accelerator
@require_deterministic_for_xpu
def test_model_integration_batched_generate_multi_image(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model()
# Prepare inputs
messages = [
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
{"type": "text", "text": "Write a haiku for this image"},
],
},
],
[
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
},
{
"type": "image",
"url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
},
{
"type": "text",
"text": "These images depict two different landmarks. Can you identify them?",
},
],
},
],
]
inputs = processor.apply_chat_template(
messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.float16)
output = model.generate(**inputs, do_sample=False, max_new_tokens=10)
# Check first output
decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
# Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
expected_outputs = Expectations(
{
("cuda", 8): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
# Check second output
decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
expected_outputs = Expectations(
{
("cuda", 8): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
| transformers/tests/models/cohere2_vision/test_modeling_cohere2_vision.py/0 | {
"file_path": "transformers/tests/models/cohere2_vision/test_modeling_cohere2_vision.py",
"repo_id": "transformers",
"token_count": 8205
} | 517 |
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Dia model."""
import copy
import pathlib
import tempfile
import unittest
import pytest
from transformers.models.dia import DiaConfig, DiaDecoderConfig, DiaEncoderConfig
from transformers.testing_utils import (
cleanup,
is_flaky,
require_torch,
require_torch_accelerator,
slow,
torch_device,
)
from transformers.utils import is_soundfile_available, is_torch_available, is_torchaudio_available
from transformers.utils.import_utils import is_datasets_available
from ...generation.test_utils import GenerationTesterMixin, has_similar_generate_outputs
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_datasets_available():
from datasets import Audio, load_dataset
if is_torch_available():
import torch
from transformers import (
DiaForConditionalGeneration,
DiaModel,
DiaProcessor,
PretrainedConfig,
PreTrainedModel,
)
from transformers.cache_utils import (
Cache,
StaticCache,
)
from transformers.models.dia.modeling_dia import DiaDecoder, DiaEncoder
if is_torchaudio_available():
import torchaudio
if is_soundfile_available():
import soundfile as sf
@require_torch
class DiaModelTester:
def __init__(
self,
parent,
batch_size=3, # need batch_size != num_hidden_layers
seq_length=7,
max_length=50,
is_training=True,
vocab_size=100,
hidden_size=16,
intermediate_size=37,
num_hidden_layers=2,
num_attention_heads=2,
head_dim=8,
decoder_hidden_size=32, # typically larger than encoder
hidden_act="silu",
eos_token_id=97, # special tokens all occur after eos
pad_token_id=98,
bos_token_id=99,
delay_pattern=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.max_length = max_length
self.is_training = is_training
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim = head_dim
self.decoder_hidden_size = decoder_hidden_size
self.hidden_act = hidden_act
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
# Set default delay pattern if not provided
self.delay_pattern = delay_pattern if delay_pattern is not None else [0, 1, 2]
self.num_channels = len(self.delay_pattern)
def get_config(self):
encoder_config = DiaEncoderConfig(
max_position_embeddings=self.max_length,
num_hidden_layers=self.num_hidden_layers,
hidden_size=self.hidden_size,
num_attention_heads=self.num_attention_heads,
num_key_value_heads=self.num_attention_heads, # same as num_attention_heads for testing
head_dim=self.head_dim,
intermediate_size=self.intermediate_size,
vocab_size=self.vocab_size,
hidden_act=self.hidden_act,
)
decoder_config = DiaDecoderConfig(
max_position_embeddings=self.max_length,
num_hidden_layers=self.num_hidden_layers,
hidden_size=self.decoder_hidden_size,
intermediate_size=self.intermediate_size,
num_attention_heads=self.num_attention_heads,
num_key_value_heads=1, # GQA
head_dim=self.head_dim,
cross_num_attention_heads=self.num_attention_heads,
cross_head_dim=self.head_dim,
cross_num_key_value_heads=1, # GQA
cross_hidden_size=self.hidden_size, # match encoder hidden size
vocab_size=self.vocab_size,
hidden_act=self.hidden_act,
num_channels=self.num_channels,
)
config = DiaConfig(
encoder_config=encoder_config,
decoder_config=decoder_config,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
bos_token_id=self.bos_token_id,
delay_pattern=self.delay_pattern,
)
return config
def prepare_config_and_inputs(self) -> tuple[DiaConfig, dict]:
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = input_ids.ne(self.pad_token_id)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length, self.num_channels], self.vocab_size)
decoder_attention_mask = decoder_input_ids[..., 0].ne(self.pad_token_id)
config = self.get_config()
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return config, inputs_dict
def prepare_config_and_inputs_for_common(self) -> tuple[DiaConfig, dict]:
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_model_forward(self, config, inputs_dict):
model = DiaModel(config=config).to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
# first forward pass
last_hidden_state = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids).last_hidden_state
self.parent.assertTrue(
last_hidden_state.shape, (self.batch_size, self.seq_length, config.decoder_config.hidden_size)
)
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = DiaModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = DiaEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(
input_ids=inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"]
)[0]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 3e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = DiaDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 3e-3)
@require_torch
class DiaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (DiaModel, DiaForConditionalGeneration) if is_torch_available() else ()
# We only allow greedy search / sampling with one sequence; see `skip_non_greedy_generate`
all_generative_model_classes = (DiaForConditionalGeneration,)
# TODO: support new pipeline behavior in tests
pipeline_model_mapping = {}
# pipeline_model_mapping = {"text-to-audio": DiaForConditionalGeneration} if is_torch_available() else {}
test_pruning = False
test_head_masking = False
test_resize_embeddings = False
is_encoder_decoder = True
# Indicates VLMs usually but there are many audio models which are also composite
_is_composite = True
def setUp(self):
self.model_tester = DiaModelTester(self)
# Skipping `has_text_modality` but manually testing down below
self.config_tester = ConfigTester(self, has_text_modality=False, config_class=DiaConfig)
self.skip_non_greedy_generate()
def skip_non_greedy_generate(self):
skippable_tests = [
"test_sample_generate_dict_output", # return sequences > 1
"test_beam",
"test_group_beam",
"test_constrained_beam",
"test_contrastive",
"test_assisted",
"test_dola",
"test_prompt_lookup",
"test_model_parallel_beam_search",
"test_generate_without_input_ids",
"test_generate_with_head_masking",
]
for test in skippable_tests:
if self._testMethodName.startswith(test):
self.skipTest(reason="Dia only supports greedy search / sampling with one sequence.")
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
"""Overriden to account for the 2D flattened structure"""
inputs_dict = copy.deepcopy(inputs_dict)
if return_labels:
inputs_dict["labels"] = torch.ones(
(
self.model_tester.batch_size * self.model_tester.num_channels,
self.model_tester.seq_length,
),
dtype=torch.long,
device=torch_device,
)
return inputs_dict
def test_config(self):
self.config_tester.run_common_tests()
# Manual testing because of composite configs
config = self.model_tester.prepare_config_and_inputs()[0]
self.assertTrue(hasattr(config.encoder_config, "vocab_size"), msg="Encoder `vocab_size` does not exist")
self.assertTrue(hasattr(config.decoder_config, "vocab_size"), msg="Decoder `vocab_size` does not exist")
def test_model_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_forward(*config_and_inputs)
@is_flaky
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
# Overriding shape checks as Dia has different shapes on encoder/decoder using a composite config
# + additional special cases where 3D x 2D meshes confuse the expected shape
def _check_logits(self, batch_size, logits, config):
batch_size *= len(config.delay_pattern) # Account for flattening
vocab_size = config.decoder_config.vocab_size
self.assertIsInstance(logits, tuple)
self.assertListEqual([iter_logits.shape[0] for iter_logits in logits], [batch_size] * len(logits))
# vocabulary difference equal to one (imagegptmodel?) or zero (all other models)
vocab_diff = vocab_size - logits[0].shape[-1]
self.assertTrue(vocab_diff in [0, 1])
self.assertListEqual([vocab_size - score.shape[-1] for score in logits], [vocab_diff] * len(logits))
def _check_attentions_for_generate(
self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (output_length - prompt_length))
use_cache = decoder_past_key_values is not None
has_static_cache = isinstance(decoder_past_key_values, StaticCache)
# When `output_attentions=True`, each iteration of generate appends the attentions corresponding to the new
# token(s)
for generated_length, iter_attentions in enumerate(attentions):
# regardless of using cache, the first forward pass will have the full prompt as input
if use_cache and generated_length > 0:
model_input_length = 1
else:
model_input_length = prompt_length + generated_length
query_length = (
prompt_length + generated_length
if not has_static_cache
else decoder_past_key_values.get_max_cache_shape()
)
expected_shape = (
batch_size,
config.decoder_config.num_attention_heads, # Decoder config
model_input_length,
query_length,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_encoder_attention_for_generate(self, attentions, batch_size, config, prompt_length):
# Encoder config
encoder_expected_shape = (batch_size, config.encoder_config.num_attention_heads, prompt_length, prompt_length)
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[layer_attentions.shape for layer_attentions in attentions],
[encoder_expected_shape] * len(attentions),
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, prompt_length, output_length, config, use_cache=False
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (output_length - prompt_length))
# When `output_hidden_states=True`, each iteration of generate appends the hidden states corresponding to the
# new token(s)
for generated_length, iter_hidden_states in enumerate(hidden_states):
# regardless of using cache, the first forward pass will have the full prompt as input
if use_cache and generated_length > 0:
model_input_length = 1
else:
model_input_length = prompt_length + generated_length
# check hidden size
# we can have different hidden sizes between encoder and decoder --> check both
expected_shape_encoder = (batch_size, model_input_length, config.encoder_config.hidden_size)
expected_shape_decoder = (batch_size, model_input_length, config.decoder_config.hidden_size)
self.assertTrue(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]
== [expected_shape_encoder] * len(iter_hidden_states)
or [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]
== [expected_shape_decoder] * len(iter_hidden_states)
)
def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, prompt_length):
# Encoder config
encoder_expected_shape = (batch_size, prompt_length, config.encoder_config.hidden_size)
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in hidden_states],
[encoder_expected_shape] * len(hidden_states),
)
def _check_past_key_values_for_generate(self, batch_size, decoder_past_key_values, cache_length, config):
self.assertIsInstance(decoder_past_key_values, (tuple, Cache))
# we need the decoder config here
config = config.decoder_config
# (batch, head, seq_length, head_features)
expected_shape = (
batch_size,
config.num_key_value_heads if hasattr(config, "num_key_value_heads") else config.num_attention_heads,
cache_length,
config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads,
)
if isinstance(decoder_past_key_values, Cache):
self.assertListEqual(
[layer.keys.shape for layer in decoder_past_key_values.layers],
[expected_shape] * len(decoder_past_key_values.layers),
)
self.assertListEqual(
[layer.values.shape for layer in decoder_past_key_values.layers],
[expected_shape] * len(decoder_past_key_values.layers),
)
def _check_scores(self, batch_size, scores, generated_length, config):
# Special case where Dia keeps score in a 2D mesh of (bsz * channels, vocab)
vocab_size = config.decoder_config.vocab_size
expected_shape = (batch_size * len(config.delay_pattern), vocab_size)
self.assertIsInstance(scores, tuple)
self.assertEqual(len(scores), generated_length)
self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))
def test_sdpa_can_dispatch_composite_models(self):
"""
Overwritten as it relies on hardcoded namings atm - checking for our case here specifically
"""
for model_class in self.all_model_classes:
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
sub_models_supporting_sdpa = [
(module._supports_sdpa or module._supports_attention_backend)
for name, module in model.named_modules()
if isinstance(module, PreTrainedModel) and name != ""
]
supports_sdpa_all_modules = (
all(sub_models_supporting_sdpa)
if len(sub_models_supporting_sdpa) > 0
else (model._supports_sdpa or model._supports_attention_backend)
)
if not supports_sdpa_all_modules:
with self.assertRaises(ValueError):
model_sdpa = model_class.from_pretrained(tmpdirname, attn_implementation="sdpa")
else:
model_sdpa = model_class.from_pretrained(tmpdirname, attn_implementation="sdpa")
for key in model_sdpa.config:
if isinstance(getattr(model_sdpa.config, key), PretrainedConfig):
sub_config = getattr(model_sdpa.config, key)
self.assertTrue(sub_config._attn_implementation == "sdpa")
@pytest.mark.generate
@unittest.skip(reason="Custom processor `DiaEOSDelayPatternLogitsProcessor` forces eos token.")
def test_generate_continue_from_past_key_values(self):
"""Only a small change due to the expected shapes"""
# Tests that we can continue generating from past key values, returned from a previous `generate` call
for model_class in self.all_generative_model_classes:
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
# Let's make it always:
# 1. use cache (for obvious reasons)
# 2. generate to max length (which can be achieved by setting the eos token to an invalid value), which
# would make the test flaky (e.g. EOS is generated on iteration 1 on both generations, but the
# continuation would force it to generate beyond an EOS token)
# 3. ignore `token_type_ids` for simplicity
# 4. ignore `forced_eos_token_id`, which requires further manipulation of the continuation inputs and is
# active by default on some models
# 5. ignore `encoder_no_repeat_ngram_size`, which is set by default in some encoder-decoder models. When
# we use their decoder as a stand-alone model, `encoder_no_repeat_ngram_size` actually prevents
# repetition exclusively from the prompt. This test relies on comparing one call vs 2 calls
# with cache, what is considered a prompt is different in the two cases.
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
model = model_class(config).to(torch_device)
model.eval()
generate_kwargs = {
"pad_token_id": -1,
"eos_token_id": -1,
"forced_eos_token_id": None,
"encoder_no_repeat_ngram_size": 0,
"use_cache": True,
"do_sample": False,
"return_dict_in_generate": True,
"output_scores": True,
}
# Traditional way of generating text, with `return_dict_in_generate` to return the past key values
outputs = model.generate(**inputs, **generate_kwargs, max_new_tokens=4)
# Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
# inputs may need to be tweaked across `generate` calls (like the attention mask).
outputs_cached = model.generate(**inputs, **generate_kwargs, max_new_tokens=3)
# Continue from the tokens generated above, preparing the inputs accordingly
inputs["past_key_values"] = outputs_cached.past_key_values
new_attention_len = outputs_cached.sequences.shape[1] # the only real modification in this test
inputs["decoder_input_ids"] = outputs_cached.sequences
if "decoder_attention_mask" in inputs:
inputs["decoder_attention_mask"] = torch.nn.functional.pad(
inputs["decoder_attention_mask"],
(0, new_attention_len - inputs["decoder_attention_mask"].shape[1]),
mode="constant",
value=1,
)
first_caches_scores = outputs_cached.scores
outputs_cached = model.generate(**inputs, **generate_kwargs, max_new_tokens=1)
full_cached_scores = first_caches_scores + outputs_cached.scores
outputs_cached.scores = full_cached_scores
# The two sets of generated text and past kv should be equal to each other
self.assertTrue(has_similar_generate_outputs(outputs, outputs_cached))
for layer_idx in range(len(outputs_cached.past_key_values)):
for kv_idx in range(len(outputs_cached.past_key_values[layer_idx])):
self.assertTrue(
torch.allclose(
outputs.past_key_values[layer_idx][kv_idx],
outputs_cached.past_key_values[layer_idx][kv_idx],
)
)
@unittest.skip(reason="Indirectly checked in Dia through the generate methods.")
def test_past_key_values_format(self, custom_all_cache_shapes=None):
pass
@unittest.skip(reason="Indirectly checked in Dia through the generate methods.")
def test_hidden_states_output(self):
pass
@unittest.skip(
reason="Dia has too many mixed embedding types which would cause unintentional side effects, e.g. attempts at tying embeddings"
)
def test_model_get_set_embeddings(self):
pass
@unittest.skip(reason="Theoretically works but kernel library causes issues.")
def test_torchscript_output_hidden_state(self):
pass
@unittest.skip(reason="Theoretically works but kernel library causes issues.")
def test_torchscript_simple(self):
pass
@unittest.skip(reason="Encoder-Decoder cache can not be initialized.")
def test_multi_gpu_data_parallel_forward(self):
pass
class DiaForConditionalGenerationIntegrationTest(unittest.TestCase):
"""
See https://gist.github.com/vasqu/0e3b06360373a4e612aa3b9a7c09185e for generating the integration tests
NOTE: We add a single `eos` line for the last channel which is skipped in the original Dia
(It doesn't change the behaviour as we cut by the eos token position)
"""
def setUp(self):
# it's a dummy ckpt but should suffice for testing purposes
self.model_checkpoint = "AntonV/Dia-1.6B"
self.sampling_rate = 44100
# prepare audio
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=self.sampling_rate))
audio_sample_1 = librispeech_dummy[-1]["audio"]["array"]
audio_sample_2 = librispeech_dummy[-2]["audio"]["array"]
# 10 and 5 codebooks as prefix - saved as files as we need wav files for the original Dia
dac_chunk_len = 512
self.audio_prompt_1_path = "/tmp/dia_test_sample_1.mp3"
self.audio_prompt_2_path = "/tmp/dia_test_sample_2.mp3"
sf.write(self.audio_prompt_1_path, audio_sample_1[: (dac_chunk_len * 10)], self.sampling_rate)
sf.write(self.audio_prompt_2_path, audio_sample_2[: (dac_chunk_len * 5)], self.sampling_rate)
def tearDown(self):
pathlib.Path(self.audio_prompt_1_path).unlink()
pathlib.Path(self.audio_prompt_2_path).unlink()
cleanup(torch_device, gc_collect=True)
@slow
@require_torch_accelerator
def test_dia_model_integration_generate_tts(self):
text = ["[S1] Dia is an open weights text to dialogue model.", "This is a test"]
processor = DiaProcessor.from_pretrained(self.model_checkpoint)
inputs = processor(text=text, padding=True, return_tensors="pt").to(torch_device)
model = DiaForConditionalGeneration.from_pretrained(self.model_checkpoint).to(torch_device)
outputs = model.generate(**inputs, max_new_tokens=32, do_sample=False)
# fmt: off
EXPECTED_OUTPUT_TOKENS = torch.tensor([[[1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 778, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 778, 338, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 804, 10, 524, 1026, 1026, 1026, 1026, 1026],
[ 568, 804, 10, 674, 967, 1026, 1026, 1026, 1026],
[ 568, 804, 10, 674, 364, 360, 1026, 1026, 1026],
[ 568, 804, 10, 674, 364, 981, 728, 1026, 1026],
[ 568, 804, 10, 674, 364, 981, 741, 550, 1026],
[ 568, 804, 10, 674, 364, 981, 568, 378, 90],
[1024, 804, 10, 674, 364, 981, 568, 378, 731],
[1025, 804, 10, 674, 364, 981, 568, 378, 731],
[1025, 804, 10, 674, 364, 981, 568, 378, 731],
[1025, 804, 10, 674, 364, 981, 568, 378, 731],
[1025, 804, 10, 674, 364, 981, 568, 378, 731],
[1025, 804, 10, 674, 364, 981, 568, 378, 731],
[1025, 804, 10, 674, 364, 981, 568, 378, 731],
[1025, 804, 10, 674, 364, 981, 568, 378, 731],
[1025, 1024, 10, 674, 364, 981, 568, 378, 731],
[1025, 1025, 1024, 674, 364, 981, 568, 378, 731],
[1025, 1025, 1025, 1024, 364, 981, 568, 378, 731],
[1025, 1025, 1025, 1025, 1024, 981, 568, 378, 731],
[1025, 1025, 1025, 1025, 1025, 1024, 568, 378, 731],
[1025, 1025, 1025, 1025, 1025, 1025, 1024, 378, 731],
[1025, 1025, 1025, 1025, 1025, 1025, 1025, 1024, 731],
[1025, 1025, 1025, 1025, 1025, 1025, 1025, 1025, 1024]],
[[1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 568, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 698, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 592, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 592, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 592, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 592, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 592, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 592, 778, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 592, 778, 338, 1026, 1026, 1026, 1026, 1026, 1026],
[ 592, 697, 10, 524, 1026, 1026, 1026, 1026, 1026],
[ 592, 288, 476, 649, 967, 1026, 1026, 1026, 1026],
[ 592, 740, 386, 674, 364, 360, 1026, 1026, 1026],
[ 592, 402, 386, 347, 362, 981, 728, 1026, 1026],
[ 592, 402, 721, 728, 327, 981, 741, 550, 1026],
[ 592, 402, 721, 728, 460, 62, 676, 378, 90],
[1024, 402, 721, 728, 837, 595, 195, 982, 784],
[1025, 402, 721, 677, 497, 102, 692, 24, 330],
[1025, 402, 721, 677, 511, 102, 503, 871, 609],
[1025, 402, 721, 677, 511, 96, 801, 871, 894],
[1025, 402, 721, 677, 511, 745, 314, 498, 775],
[1025, 402, 721, 677, 511, 745, 314, 498, 105],
[1025, 402, 721, 677, 511, 745, 314, 861, 889],
[1025, 893, 721, 677, 511, 744, 314, 871, 353],
[1025, 1024, 888, 677, 511, 744, 314, 871, 332],
[1025, 1025, 1024, 518, 511, 744, 314, 871, 366],
[1025, 1025, 1025, 1024, 611, 744, 314, 871, 366],
[1025, 1025, 1025, 1025, 1024, 980, 314, 871, 366],
[1025, 1025, 1025, 1025, 1025, 1024, 45, 124, 366],
[1025, 1025, 1025, 1025, 1025, 1025, 1024, 871, 366],
[1025, 1025, 1025, 1025, 1025, 1025, 1025, 1024, 719],
[1025, 1025, 1025, 1025, 1025, 1025, 1025, 1025, 1024]]])
# fmt: on
torch.testing.assert_close(outputs.cpu(), EXPECTED_OUTPUT_TOKENS)
@slow
@require_torch_accelerator
def test_dia_model_integration_generate_audio_context(self):
text = ["[S1] Dia is an open weights text to dialogue model.", "This is a test"]
audio_sample_1 = (
torchaudio.load(self.audio_prompt_1_path, channels_first=True, backend="soundfile")[0].squeeze().numpy()
)
audio_sample_2 = (
torchaudio.load(self.audio_prompt_2_path, channels_first=True, backend="soundfile")[0].squeeze().numpy()
)
audio = [audio_sample_1, audio_sample_2]
processor = DiaProcessor.from_pretrained(self.model_checkpoint)
inputs = processor(text=text, audio=audio, padding=True, return_tensors="pt").to(torch_device)
model = DiaForConditionalGeneration.from_pretrained(self.model_checkpoint).to(torch_device)
# dia has right padding while we have left padding (for faster prefill)
# additionally we have new tokens vs dia's max tokens (hence we compare each in the respective settings)
outputs_1 = model.generate(**inputs, max_new_tokens=22, do_sample=False)
outputs_2 = model.generate(**inputs, max_new_tokens=27, do_sample=False)
# fmt: off
EXPECTED_OUTPUT_TOKENS_1 = torch.tensor([[1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 578, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 592, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 494, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 330, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 330, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 330, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 330, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 330, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 330, 501, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 330, 204, 34, 1026, 1026, 1026, 1026, 1026, 1026],
[ 330, 254, 915, 863, 1026, 1026, 1026, 1026, 1026],
[ 330, 215, 458, 313, 50, 1026, 1026, 1026, 1026],
[ 330, 615, 529, 216, 801, 237, 1026, 1026, 1026],
[ 330, 580, 563, 233, 337, 37, 1018, 1026, 1026],
[ 330, 567, 530, 753, 607, 179, 954, 242, 1026],
[ 330, 627, 6, 1010, 500, 189, 598, 858, 247],
[1024, 432, 480, 530, 122, 3, 788, 149, 814],
[1025, 875, 826, 458, 98, 540, 181, 122, 608],
[1025, 495, 840, 413, 337, 784, 591, 150, 1017],
[1025, 808, 189, 137, 445, 0, 227, 658, 345],
[1025, 397, 89, 753, 1016, 173, 984, 0, 910],
[1025, 875, 460, 934, 50, 335, 670, 818, 722],
[1025, 875, 460, 762, 119, 372, 503, 858, 584],
[1025, 348, 555, 475, 469, 458, 963, 41, 664],
[1025, 1024, 852, 683, 761, 193, 595, 895, 885],
[1025, 1025, 1024, 135, 761, 902, 163, 623, 385],
[1025, 1025, 1025, 1024, 852, 282, 581, 623, 70],
[1025, 1025, 1025, 1025, 1024, 41, 661, 790, 977],
[1025, 1025, 1025, 1025, 1025, 1024, 580, 401, 464],
[1025, 1025, 1025, 1025, 1025, 1025, 1024, 756, 61],
[1025, 1025, 1025, 1025, 1025, 1025, 1025, 1024, 752],
[1025, 1025, 1025, 1025, 1025, 1025, 1025, 1025, 1024]])
EXPECTED_OUTPUT_TOKENS_2 = torch.tensor([[1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 619, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 968, 1026, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 1007, 458, 1026, 1026, 1026, 1026, 1026, 1026],
[ 315, 35, 266, 68, 1026, 1026, 1026, 1026, 1026],
[ 315, 359, 285, 811, 154, 1026, 1026, 1026, 1026],
[ 315, 906, 407, 297, 785, 649, 1026, 1026, 1026],
[ 315, 249, 678, 868, 899, 257, 950, 1026, 1026],
[ 315, 249, 217, 471, 292, 908, 196, 469, 1026],
[ 315, 249, 825, 771, 839, 802, 633, 590, 531],
[1024, 249, 150, 53, 126, 76, 794, 626, 442],
[1025, 249, 825, 218, 359, 864, 526, 626, 770],
[1025, 249, 150, 137, 530, 845, 877, 600, 111],
[1025, 249, 150, 287, 730, 991, 135, 259, 39],
[1025, 249, 825, 104, 198, 1020, 719, 625, 208],
[1025, 249, 825, 997, 602, 256, 859, 322, 518],
[1025, 668, 825, 979, 584, 256, 98, 665, 589],
[1025, 954, 458, 54, 206, 52, 244, 822, 599],
[1025, 1024, 104, 914, 435, 579, 860, 92, 661],
[1025, 1025, 1024, 848, 126, 74, 304, 92, 753],
[1025, 1025, 1025, 1024, 362, 376, 304, 586, 753],
[1025, 1025, 1025, 1025, 1024, 633, 996, 586, 83],
[1025, 1025, 1025, 1025, 1025, 1024, 179, 898, 928],
[1025, 1025, 1025, 1025, 1025, 1025, 1024, 506, 102],
[1025, 1025, 1025, 1025, 1025, 1025, 1025, 1024, 79],
[1025, 1025, 1025, 1025, 1025, 1025, 1025, 1025, 1024]])
# fmt: on
torch.testing.assert_close(outputs_1[0].cpu(), EXPECTED_OUTPUT_TOKENS_1)
torch.testing.assert_close(outputs_2[1, 5:].cpu(), EXPECTED_OUTPUT_TOKENS_2) # left padding
| transformers/tests/models/dia/test_modeling_dia.py/0 | {
"file_path": "transformers/tests/models/dia/test_modeling_dia.py",
"repo_id": "transformers",
"token_count": 17307
} | 518 |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch emu3 model."""
import tempfile
import unittest
import numpy as np
from transformers import Emu3Processor, GPT2TokenizerFast
from transformers.utils import is_vision_available
from ...test_processing_common import ProcessorTesterMixin
if is_vision_available():
from transformers import Emu3ImageProcessor
class Emu3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
processor_class = Emu3Processor
@classmethod
def setUpClass(cls):
cls.tmpdirname = tempfile.mkdtemp()
image_processor = Emu3ImageProcessor(min_pixels=28 * 28, max_pixels=56 * 56)
extra_special_tokens = {
"image_token": "<image>",
"boi_token": "<|image start|>",
"eoi_token": "<|image end|>",
"image_wrapper_token": "<|image token|>",
"eof_token": "<|extra_201|>",
}
tokenizer = GPT2TokenizerFast.from_pretrained(
"openai-community/gpt2", extra_special_tokens=extra_special_tokens
)
tokenizer.pad_token_id = 0
tokenizer.sep_token_id = 1
processor = cls.processor_class(
image_processor=image_processor, tokenizer=tokenizer, chat_template="dummy_template"
)
processor.save_pretrained(cls.tmpdirname)
cls.image_token = processor.image_token
@staticmethod
def prepare_processor_dict():
return {
"chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}",
} # fmt: skip
def test_processor_for_generation(self):
processor_components = self.prepare_components()
processor = self.processor_class(**processor_components)
# we don't need an image as input because the model will generate one
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, return_for_image_generation=True, return_tensors="pt")
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "image_sizes"])
self.assertEqual(inputs[self.text_input_name].shape[-1], 8)
# when `return_for_image_generation` is set, we raise an error that image should not be provided
with self.assertRaises(ValueError):
inputs = processor(
text=input_str, images=image_input, return_for_image_generation=True, return_tensors="pt"
)
def test_processor_postprocess(self):
processor_components = self.prepare_components()
processor = self.processor_class(**processor_components)
input_str = "lower newer"
orig_image_input = self.prepare_image_inputs()
orig_image = np.array(orig_image_input).transpose(2, 0, 1)
inputs = processor(text=input_str, images=orig_image, do_resize=False, return_tensors="np")
normalized_image_input = inputs.pixel_values
unnormalized_images = processor.postprocess(normalized_image_input, return_tensors="np")["pixel_values"]
# For an image where pixels go from 0 to 255 the diff can be 1 due to some numerical precision errors when scaling and unscaling
self.assertTrue(np.abs(orig_image - unnormalized_images).max() >= 1)
# Copied from tests.models.llava.test_processing_llava.LlavaProcessorTest.test_get_num_vision_tokens
def test_get_num_vision_tokens(self):
"Tests general functionality of the helper used internally in vLLM"
processor = self.get_processor()
output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
self.assertTrue("num_image_tokens" in output)
self.assertEqual(len(output["num_image_tokens"]), 3)
self.assertTrue("num_image_patches" in output)
self.assertEqual(len(output["num_image_patches"]), 3)
| transformers/tests/models/emu3/test_processing_emu3.py/0 | {
"file_path": "transformers/tests/models/emu3/test_processing_emu3.py",
"repo_id": "transformers",
"token_count": 1905
} | 519 |
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch ESM model."""
import tempfile
import unittest
import pytest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
is_flaky,
require_bitsandbytes,
require_flash_attn,
require_torch,
require_torch_gpu,
slow,
torch_device,
)
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
EsmEmbeddings,
create_position_ids_from_input_ids,
)
# copied from tests.test_modeling_roberta
class EsmModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=False,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=33,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
position_embedding_type="rotary",
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.position_embedding_type = position_embedding_type
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return EsmConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
pad_token_id=1,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
position_embedding_type=self.position_embedding_type,
)
def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = EsmModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = EsmForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_token_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = EsmForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_forward_and_backwards(
self,
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
gradient_checkpointing=False,
):
model = EsmForMaskedLM(config)
if gradient_checkpointing:
model.gradient_checkpointing_enable()
model.to(torch_device)
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
result.loss.backward()
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class EsmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_mismatched_shapes = False
all_model_classes = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": EsmModel,
"fill-mask": EsmForMaskedLM,
"text-classification": EsmForSequenceClassification,
"token-classification": EsmForTokenClassification,
"zero-shot": EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
test_sequence_classification_problem_types = True
model_split_percents = [0.5, 0.8, 0.9]
def setUp(self):
self.model_tester = EsmModelTester(self)
self.config_tester = ConfigTester(self, config_class=EsmConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_esm_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
@slow
def test_model_from_pretrained(self):
model_name = "facebook/esm2_t6_8M_UR50D"
model = EsmModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_create_position_ids_respects_padding_index(self):
"""This is a regression test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is EsmEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
model = EsmEmbeddings(config=config)
input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
expected_positions = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
]
)
position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
def test_create_position_ids_from_inputs_embeds(self):
"""This is a regression test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is EsmEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
embeddings = EsmEmbeddings(config=config)
inputs_embeds = torch.empty(2, 4, 30)
expected_single_positions = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
@unittest.skip(reason="Esm does not support embedding resizing")
def test_resize_embeddings_untied(self):
pass
@unittest.skip(reason="Esm does not support embedding resizing")
def test_resize_tokens_embeddings(self):
pass
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test
@is_flaky()
@slow
def test_flash_attn_2_equivalence(self):
for model_class in self.all_model_classes:
if not model_class._supports_flash_attn:
self.skipTest(reason="Model does not support Flash Attention 2")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model_fa = model_class.from_pretrained(
tmpdirname, dtype=torch.float16, attn_implementation="flash_attention_2"
)
model_fa.to(torch_device)
model = model_class.from_pretrained(tmpdirname, dtype=torch.float16, attn_implementation="eager")
model.to(torch_device)
dummy_input = inputs_dict[model_class.main_input_name]
dummy_input = dummy_input.to(torch_device)
outputs = model(dummy_input, output_hidden_states=True)
outputs_fa = model_fa(dummy_input, output_hidden_states=True)
logits = outputs.hidden_states[-1]
logits_fa = outputs_fa.hidden_states[-1]
torch.testing.assert_close(logits_fa, logits, atol=1e-2, rtol=1e-3)
@slow
@require_torch
class EsmModelIntegrationTest(TestCasePlus):
def test_inference_masked_lm(self):
with torch.no_grad():
model = EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
model.eval()
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
vocab_size = 33
expected_shape = torch.Size((1, 6, vocab_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]
)
torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
def test_inference_no_head(self):
with torch.no_grad():
model = EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
model.eval()
input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]
)
torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
@require_bitsandbytes
def test_inference_bitsandbytes(self):
model = EsmForMaskedLM.from_pretrained("facebook/esm2_t36_3B_UR50D", load_in_8bit=True)
input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]).to(model.device)
# Just test if inference works
with torch.no_grad():
_ = model(input_ids)[0]
model = EsmForMaskedLM.from_pretrained("facebook/esm2_t36_3B_UR50D", load_in_4bit=True)
input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]).to(model.device)
# Just test if inference works
_ = model(input_ids)[0]
| transformers/tests/models/esm/test_modeling_esm.py/0 | {
"file_path": "transformers/tests/models/esm/test_modeling_esm.py",
"repo_id": "transformers",
"token_count": 7005
} | 520 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import unittest
from transformers import ImageGPTConfig
from transformers.testing_utils import require_torch, require_vision, run_test_using_subprocess, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
ImageGPTForCausalImageModeling,
ImageGPTForImageClassification,
ImageGPTModel,
)
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class ImageGPTModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = None
def get_large_model_config(self):
return ImageGPTConfig.from_pretrained("imagegpt")
def prepare_config_and_inputs(
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config(
gradient_checkpointing=gradient_checkpointing,
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
reorder_and_upcast_attn=reorder_and_upcast_attn,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def get_config(
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
):
return ImageGPTConfig(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
n_inner=self.intermediate_size,
activation_function=self.hidden_act,
resid_pdrop=self.hidden_dropout_prob,
attn_pdrop=self.attention_probs_dropout_prob,
n_positions=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
use_cache=True,
gradient_checkpointing=gradient_checkpointing,
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
reorder_and_upcast_attn=reorder_and_upcast_attn,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 513
config.max_position_embeddings = 1024
return config
def create_and_check_imagegpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = ImageGPTModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(len(result.past_key_values), config.n_layer)
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = ImageGPTForCausalImageModeling(config)
model.to(torch_device)
model.eval()
labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
result = model(input_ids, token_type_ids=token_type_ids, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
# ImageGPTForCausalImageModeling doesn't have tied input- and output embeddings
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size - 1))
def create_and_check_imagegpt_for_image_classification(
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
):
config.num_labels = self.num_labels
model = ImageGPTForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel) if is_torch_available() else ()
)
pipeline_model_mapping = (
{"image-feature-extraction": ImageGPTModel, "image-classification": ImageGPTForImageClassification}
if is_torch_available()
else {}
)
test_missing_keys = False
test_torch_exportable = True
# as ImageGPTForImageClassification isn't included in any auto mapping, we add labels here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "ImageGPTForImageClassification":
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
# we overwrite the _check_scores method of GenerationTesterMixin, as ImageGPTForCausalImageModeling doesn't have tied input- and output embeddings
def _check_scores(self, batch_size, scores, generated_length, config):
expected_shape = (batch_size, config.vocab_size - 1)
self.assertIsInstance(scores, tuple)
self.assertEqual(len(scores), generated_length)
self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))
@run_test_using_subprocess
def test_beam_search_generate_dict_outputs_use_cache(self):
super().test_beam_search_generate_dict_outputs_use_cache()
def setUp(self):
self.model_tester = ImageGPTModelTester(self)
self.config_tester = ConfigTester(self, config_class=ImageGPTConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_imagegpt_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_imagegpt_model(*config_and_inputs)
def test_imagegpt_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_imagegpt_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_imagegpt_for_image_classification(*config_and_inputs)
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@slow
def test_model_from_pretrained(self):
model_name = "openai/imagegpt-small"
model = ImageGPTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_ids"]
self.assertListEqual(arg_names[:1], expected_arg_names)
@unittest.skip(reason="The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
@unittest.skip(reason="Model inputs don't fit test pattern") # and it's not used enough to be worth fixing :)
def test_past_key_values_format(self):
pass
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class ImageGPTModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small") if is_vision_available() else None
@slow
def test_inference_causal_lm_head(self):
model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1024, 512))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor(
[[2.3445, 2.6889, 2.7313], [1.0530, 1.2416, 0.5699], [0.2205, 0.7749, 0.3953]]
).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
| transformers/tests/models/imagegpt/test_modeling_imagegpt.py/0 | {
"file_path": "transformers/tests/models/imagegpt/test_modeling_imagegpt.py",
"repo_id": "transformers",
"token_count": 6039
} | 521 |
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LayoutLMForMaskedLM,
LayoutLMForQuestionAnswering,
LayoutLMForSequenceClassification,
LayoutLMForTokenClassification,
LayoutLMModel,
)
class LayoutLMModelTester:
"""You can also import this e.g from .test_modeling_layoutlm import LayoutLMModelTester"""
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
range_bbox=1000,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.range_bbox = range_bbox
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
t = bbox[i, j, 3]
bbox[i, j, 3] = bbox[i, j, 1]
bbox[i, j, 1] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
t = bbox[i, j, 2]
bbox[i, j, 2] = bbox[i, j, 0]
bbox[i, j, 0] = t
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return LayoutLMConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
def create_and_check_model(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LayoutLMModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, bbox, token_type_ids=token_type_ids)
result = model(input_ids, bbox)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LayoutLMForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = LayoutLMForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(
input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = LayoutLMForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LayoutLMForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
bbox=bbox,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
bbox,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class LayoutLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
LayoutLMModel,
LayoutLMForMaskedLM,
LayoutLMForSequenceClassification,
LayoutLMForTokenClassification,
LayoutLMForQuestionAnswering,
)
if is_torch_available()
else None
)
pipeline_model_mapping = (
{
"document-question-answering": LayoutLMForQuestionAnswering,
"feature-extraction": LayoutLMModel,
"fill-mask": LayoutLMForMaskedLM,
"text-classification": LayoutLMForSequenceClassification,
"token-classification": LayoutLMForTokenClassification,
"zero-shot": LayoutLMForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = False # Cannot support if `can_return_tuple`
def setUp(self):
self.model_tester = LayoutLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
def prepare_layoutlm_batch_inputs():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]],device=torch_device) # noqa: E231
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],device=torch_device) # noqa: E231
bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]],device=torch_device) # noqa: E231
token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],device=torch_device) # noqa: E231
# these are sequence labels (i.e. at the token level)
labels = torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],device=torch_device) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_torch
class LayoutLMModelIntegrationTest(unittest.TestCase):
@slow
def test_forward_pass_no_head(self):
model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device)
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
# test the sequence output on [0, :3, :3]
expected_slice = torch.tensor(
[[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],
device=torch_device,
)
torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-3, atol=1e-3)
# test the pooled output on [1, :3]
expected_slice = torch.tensor([-0.6580, -0.0214, 0.8552], device=torch_device)
torch.testing.assert_close(outputs.pooler_output[1, :3], expected_slice, rtol=1e-3, atol=1e-3)
@slow
def test_forward_pass_sequence_classification(self):
# initialize model with randomly initialized sequence classification head
model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2).to(
torch_device
)
input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
labels=torch.tensor([1, 1], device=torch_device),
)
# test whether we get a loss as a scalar
loss = outputs.loss
expected_shape = torch.Size([])
self.assertEqual(loss.shape, expected_shape)
# test the shape of the logits
logits = outputs.logits
expected_shape = torch.Size((2, 2))
self.assertEqual(logits.shape, expected_shape)
@slow
def test_forward_pass_token_classification(self):
# initialize model with randomly initialized token classification head
model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13).to(
torch_device
)
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
)
# test the loss calculation to be around 2.65
# expected_loss = torch.tensor(2.65, device=torch_device)
# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))
# test the shape of the logits
logits = outputs.logits
expected_shape = torch.Size((2, 25, 13))
self.assertEqual(logits.shape, expected_shape)
@slow
def test_forward_pass_question_answering(self):
# initialize model with randomly initialized token classification head
model = LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device)
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
# test the shape of the logits
expected_shape = torch.Size((2, 25))
self.assertEqual(outputs.start_logits.shape, expected_shape)
self.assertEqual(outputs.end_logits.shape, expected_shape)
| transformers/tests/models/layoutlm/test_modeling_layoutlm.py/0 | {
"file_path": "transformers/tests/models/layoutlm/test_modeling_layoutlm.py",
"repo_id": "transformers",
"token_count": 8043
} | 522 |
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torchvision_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_vision_available() and is_torchvision_available():
from transformers import Llama4ImageProcessorFast
class Llama4ImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
max_patches=1,
do_resize=True,
size=None,
do_normalize=True,
do_pad=False,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
do_convert_rgb=True,
):
super().__init__()
size = size if size is not None else {"height": 20, "width": 20}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.max_patches = max_patches
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_pad = do_pad
self.do_convert_rgb = do_convert_rgb
def prepare_image_processor_dict(self):
return {
"max_patches": self.max_patches,
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
"do_pad": self.do_pad,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class Llama4ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
test_slow_image_processor = False
fast_image_processing_class = Llama4ImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()
self.image_processor_tester = Llama4ImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
def test_split_tiles(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class(**self.image_processor_dict)
image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)[0]
processed_images = image_processor(
image,
max_patches=16,
)
self.assertEqual(len(processed_images.pixel_values), 1)
self.assertEqual(processed_images.pixel_values[0].shape[0], 17)
self.assertEqual(processed_images.pixel_values[0].shape[-2:], (20, 20))
@unittest.skip("Broken on main right now. Should be fixable!")
def test_image_processor_save_load_with_autoimageprocessor(self):
pass
| transformers/tests/models/llama4/test_image_processing_llama4.py/0 | {
"file_path": "transformers/tests/models/llama4/test_image_processing_llama4.py",
"repo_id": "transformers",
"token_count": 2045
} | 523 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import pickle
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
AutoTokenizer,
PreTrainedTokenizerFast,
SpecialTokensMixin,
)
from transformers.convert_slow_tokenizer import MoshiConverter
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import SMALL_TRAINING_CORPUS, TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class MoshiTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = ["kmhf/hf-moshiko"]
rust_tokenizer_class = PreTrainedTokenizerFast
test_slow_tokenizer = False
test_rust_tokenizer = True
from_pretrained_kwargs = {}
@classmethod
def setUpClass(cls):
super().setUpClass()
# We have a SentencePiece fixture for testing
tokenizer = PreTrainedTokenizerFast(
tokenizer_object=MoshiConverter(vocab_file=SAMPLE_VOCAB).converted(),
bos_token="<s>",
unk_token="<unk>",
eos_token="</s>",
)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.save_pretrained(cls.tmpdirname)
def get_rust_tokenizer(cls, pretrained_name=None, **kwargs) -> PreTrainedTokenizerFast:
pretrained_name = pretrained_name or cls.tmpdirname
return cls.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
@unittest.skip(reason="No slow tokenizer")
def test_added_tokens_serialization(self):
pass
@unittest.skip(reason="PreTrainedTokenizerFast doesn't have tokenizer_file in its signature")
def test_rust_tokenizer_signature(self):
pass
@unittest.skip(reason="No slow tokenizer")
def test_encode_decode_with_spaces(self):
pass
def test_full_tokenizer(self):
tokenizer = PreTrainedTokenizerFast(
tokenizer_object=MoshiConverter(vocab_file=SAMPLE_VOCAB).converted(),
bos_token="<s>",
unk_token="<unk>",
eos_token="</s>",
)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
def test_special_tokens_initialization(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
added_tokens = [AddedToken("<special>", lstrip=True)]
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
r_output = tokenizer_r.encode("Hey this is a <special> token")
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
self.assertTrue(special_token_id in r_output)
def test_picklable(self):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(SAMPLE_VOCAB, f.name)
tokenizer = PreTrainedTokenizerFast(
tokenizer_object=MoshiConverter(vocab_file=f.name).converted(),
bos_token="<s>",
unk_token="<unk>",
eos_token="</s>",
)
pickled_tokenizer = pickle.dumps(tokenizer)
pickle.loads(pickled_tokenizer)
def test_training_new_tokenizer(self):
# This feature only exists for fast tokenizers
if not self.test_rust_tokenizer:
self.skipTest(reason="test_rust_tokenizer is set to False")
tokenizer = self.get_rust_tokenizer()
new_tokenizer = tokenizer.train_new_from_iterator(SMALL_TRAINING_CORPUS, 100)
# Test we can use the new tokenizer with something not seen during training
inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."])
self.assertEqual(len(inputs["input_ids"]), 2)
decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
expected_result = "This is the first sentence"
self.assertEqual(expected_result, decoded_input)
# We check that the parameters of the tokenizer remained the same
# Check we have the same number of added_tokens for both pair and non-pair inputs.
self.assertEqual(tokenizer.num_special_tokens_to_add(False), new_tokenizer.num_special_tokens_to_add(False))
self.assertEqual(tokenizer.num_special_tokens_to_add(True), new_tokenizer.num_special_tokens_to_add(True))
# Check we have the correct max_length for both pair and non-pair inputs.
self.assertEqual(tokenizer.max_len_single_sentence, new_tokenizer.max_len_single_sentence)
self.assertEqual(tokenizer.max_len_sentences_pair, new_tokenizer.max_len_sentences_pair)
# Assert the set of special tokens match as we didn't ask to change them
self.assertSequenceEqual(
tokenizer.all_special_tokens_extended,
new_tokenizer.all_special_tokens_extended,
)
self.assertDictEqual(tokenizer.special_tokens_map, new_tokenizer.special_tokens_map)
def test_training_new_tokenizer_with_special_tokens_change(self):
# This feature only exists for fast tokenizers
if not self.test_rust_tokenizer:
self.skipTest(reason="test_rust_tokenizer is set to False")
tokenizer = self.get_rust_tokenizer()
# Test with a special tokens map
class_signature = inspect.signature(tokenizer.__class__)
if "cls_token" in class_signature.parameters:
new_tokenizer = tokenizer.train_new_from_iterator(
SMALL_TRAINING_CORPUS, 100, special_tokens_map={tokenizer.cls_token: "<cls>"}
)
cls_id = new_tokenizer.get_vocab()["<cls>"]
self.assertEqual(new_tokenizer.cls_token, "<cls>")
self.assertEqual(new_tokenizer.cls_token_id, cls_id)
# Create a new mapping from the special tokens defined in the original tokenizer
special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
special_tokens_list.remove("additional_special_tokens")
special_tokens_map = {}
for token in special_tokens_list:
# Get the private one to avoid unnecessary warnings.
if getattr(tokenizer, token) is not None:
special_token = getattr(tokenizer, token)
special_tokens_map[special_token] = f"{special_token}a"
# Train new tokenizer
new_tokenizer = tokenizer.train_new_from_iterator(
SMALL_TRAINING_CORPUS, 100, special_tokens_map=special_tokens_map
)
# Check the changes
for token in special_tokens_list:
# Get the private one to avoid unnecessary warnings.
if getattr(tokenizer, token) is None:
continue
special_token = getattr(tokenizer, token)
if special_token in special_tokens_map:
new_special_token = getattr(new_tokenizer, token)
self.assertEqual(special_tokens_map[special_token], new_special_token)
new_id = new_tokenizer.get_vocab()[new_special_token]
self.assertEqual(getattr(new_tokenizer, f"{token}_id"), new_id)
# Check if the AddedToken / string format has been kept
for special_token in tokenizer.all_special_tokens_extended:
if isinstance(special_token, AddedToken) and special_token.content not in special_tokens_map:
# The special token must appear identically in the list of the new tokenizer.
self.assertTrue(
special_token in new_tokenizer.all_special_tokens_extended,
f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
)
elif isinstance(special_token, AddedToken):
# The special token must appear in the list of the new tokenizer as an object of type AddedToken with
# the same parameters as the old AddedToken except the content that the user has requested to change.
special_token_str = special_token.content
new_special_token_str = special_tokens_map[special_token_str]
find = False
for candidate in new_tokenizer.all_special_tokens_extended:
if (
isinstance(candidate, AddedToken)
and candidate.content == new_special_token_str
and candidate.lstrip == special_token.lstrip
and candidate.rstrip == special_token.rstrip
and candidate.normalized == special_token.normalized
and candidate.single_word == special_token.single_word
):
find = True
break
special_token.content = new_special_token_str
self.assertTrue(
find,
f"'{special_token.__repr__()}' should appear as an `AddedToken` in the all_special_tokens_extended = "
f"{[k for k in new_tokenizer.all_special_tokens_extended if str(k) == new_special_token_str]} but it is missing"
", this means that the new tokenizers did not keep the `rstrip`, `lstrip`, `normalized` etc attributes.",
)
elif special_token not in special_tokens_map:
# The special token must appear identically in the list of the new tokenizer.
self.assertTrue(
special_token in new_tokenizer.all_special_tokens_extended,
f"'{special_token.__repr__()}' should be in {new_tokenizer.all_special_tokens_extended}",
)
else:
# The special token must appear in the list of the new tokenizer as an object of type string.
self.assertTrue(special_tokens_map[special_token] in new_tokenizer.all_special_tokens_extended)
# Test we can use the new tokenizer with something not seen during training
inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."])
self.assertEqual(len(inputs["input_ids"]), 2)
decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
expected_result = "This is the first sentence"
self.assertEqual(expected_result, decoded_input)
def test_alignement_methods(self):
# TODO: @ArthurZucker - alignment is broken
pass
def test_added_tokens_do_lower_case(self):
# TODO: @ArthurZucker
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class MoshiIntegrationTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
checkpoint_name = "kmhf/hf-moshiko"
cls.rust_tokenizer = AutoTokenizer.from_pretrained(checkpoint_name)
return cls
@require_torch
def integration_tests(self):
inputs = self.tokenizer(
["The following string should be properly encoded: Hello.", "But ird and ปี ird ด"],
return_tensors="pt",
)
long_attention_mask = [1] * 21
# fmt: off
self.assertEqual(
nested_simplify(inputs),
{
"input_ids": [
[287, 547, 2359, 457, 297, 3708, 11488, 279, 11725, 263],
[588, 478, 1442, 267, 260, 228, 188, 159, 228, 188, 185, 260, 260, 478, 1442, 260, 260, 260, 228, 188, 152],
],
"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], long_attention_mask],
},
)
# fmt: on
def test_fast_special_tokens(self):
fast_tokenizer = self.rust_tokenizer
fast_tokenizer.add_eos_token = False
fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
assert fast == [318, 1145, 694]
fast_tokenizer.add_eos_token = True
fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
assert fast == [318, 1145, 694]
self.rust_tokenizer.add_eos_token = False
def test_simple_encode_decode(self):
rust_tokenizer = self.rust_tokenizer
self.assertEqual(rust_tokenizer.encode("This is a test"), [353, 275, 272, 694])
self.assertEqual(rust_tokenizer.decode([353, 275, 272, 694], skip_special_tokens=True), "This is a test")
# bytefallback showcase
bytefallback_tokens = [260, 235, 152, 163, 234, 184, 191, 13340, 235, 160, 163, 236, 180, 159, 234, 156, 179] # fmt: skip
self.assertEqual(rust_tokenizer.encode("生活的真谛是"), bytefallback_tokens)
self.assertEqual(
rust_tokenizer.decode(bytefallback_tokens, skip_special_tokens=True),
"生活的真谛是",
)
# Inner spaces showcase
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [2769, 260, 11725])
self.assertEqual(rust_tokenizer.decode([2769, 260, 11725], skip_special_tokens=True), "Hi Hello")
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [2769, 260, 260, 11725])
self.assertEqual(rust_tokenizer.decode([2769, 260, 260, 11725], skip_special_tokens=True), "Hi Hello")
# TODO: @ArthurZucker
# self.assertEqual(rust_tokenizer.encode(""), [])
# self.assertEqual(rust_tokenizer.encode(" "), [260, 260])
# self.assertEqual(rust_tokenizer.encode(" "), [260, 260, 260])
# self.assertEqual(rust_tokenizer.encode(" Hello"), [260, 11725])
# self.assertEqual(rust_tokenizer.encode("<s>"), [607, 266, 578])
def test_no_differences_decode(self):
rust_tokenizer = self.rust_tokenizer
self.assertEqual(rust_tokenizer.decode([869]), "levels")
self.assertEqual(rust_tokenizer.decode([30112, 869]), "unanswered levels")
@require_sentencepiece
@require_tokenizers
class CommonSpmIntegrationTests(unittest.TestCase):
"""
A class that regroups important test to make sure that we properly handle the special tokens.
"""
def test_edge_case_tabulation(self):
fast_tokenizer = AutoTokenizer.from_pretrained("kmhf/hf-moshiko")
input_text = "Hey<eos>. \t\t \n\nyou é @#😈 🤗! , 1234 15 5,61"
EXPECTED_IDS = [11510, 934, 4451, 266, 578, 263, 260, 13, 13, 260, 14, 14, 5209, 260, 260, 1202, 260, 527, 1322, 244, 163, 156, 140, 260, 260, 244, 163, 168, 155, 430, 1047, 261, 260, 265, 270, 278, 281, 260, 265, 280, 260, 280, 261, 285, 265] # fmt: skip
EXPECTED_TOKENS = ['▁Hey', '<', 'eo', 's', '>', '.', '▁', '<0x09>', '<0x09>', '▁', '<0x0A>', '<0x0A>', 'you', '▁', '▁', 'é', '▁', '▁@', '#', '<0xF0>', '<0x9F>', '<0x98>', '<0x88>', '▁', '▁', '<0xF0>', '<0x9F>', '<0xA4>', '<0x97>', '!', '▁▁▁▁▁▁▁', ',', '▁', '1', '2', '3', '4', '▁', '1', '5', '▁', '5', ',', '6', '1'] # fmt: skip
tokens = fast_tokenizer.tokenize(input_text)
with self.subTest("test fast edge case fast"):
self.assertEqual(tokens, EXPECTED_TOKENS)
input_ids = fast_tokenizer.encode(input_text)
with self.subTest("test fast edge case fast"):
self.assertEqual(input_ids, EXPECTED_IDS)
text = fast_tokenizer.decode(EXPECTED_IDS)
with self.subTest("test fast edge case fast"):
self.assertEqual(text, "Hey<eos>. \t\t \n\nyou é @#😈 🤗! , 1234 15 5,61")
input_text = "\t\t\t\t \n\n61"
EXPECTED_IDS = [260, 13, 13, 13, 13, 260, 14, 14, 285, 265]
EXPECTED_TOKENS = ["▁", "<0x09>", "<0x09>", "<0x09>", "<0x09>", "▁", "<0x0A>", "<0x0A>", "6", "1"]
tokens = fast_tokenizer.tokenize(input_text)
with self.subTest("test fast edge case fast"):
self.assertEqual(tokens, EXPECTED_TOKENS)
input_ids = fast_tokenizer.encode(input_text)
with self.subTest("test fast edge case fast"):
self.assertEqual(input_ids, EXPECTED_IDS)
text = fast_tokenizer.decode(EXPECTED_IDS)
with self.subTest("test fast edge case fast"):
self.assertEqual(text, "\t\t\t\t \n\n61")
| transformers/tests/models/moshi/test_tokenization_moshi.py/0 | {
"file_path": "transformers/tests/models/moshi/test_tokenization_moshi.py",
"repo_id": "transformers",
"token_count": 8969
} | 524 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for the MusicGen processor."""
import random
import shutil
import tempfile
import unittest
import numpy as np
from transformers import T5Tokenizer, T5TokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torchaudio_available
if is_torchaudio_available():
from transformers import MusicgenMelodyFeatureExtractor, MusicgenMelodyProcessor
global_rng = random.Random()
# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
@require_torch
@require_sentencepiece
@require_torchaudio
# Copied from tests.models.musicgen.test_processing_musicgen.MusicgenProcessorTest with Musicgen->MusicgenMelody, Encodec->MusicgenMelody, padding_mask->attention_mask, input_values->input_features
class MusicgenMelodyProcessorTest(unittest.TestCase):
def setUp(self):
# Ignore copy
self.checkpoint = "facebook/musicgen-melody"
self.tmpdirname = tempfile.mkdtemp()
def get_tokenizer(self, **kwargs):
return T5Tokenizer.from_pretrained(self.checkpoint, **kwargs)
def get_feature_extractor(self, **kwargs):
return MusicgenMelodyFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = MusicgenMelodyProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, T5TokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, MusicgenMelodyFeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = MusicgenMelodyProcessor(
tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
)
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
processor = MusicgenMelodyProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, T5TokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, MusicgenMelodyFeatureExtractor)
def test_feature_extractor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
raw_speech = floats_list((3, 1000))
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
input_processor = processor(raw_speech, return_tensors="np")
for key in input_feat_extract:
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
input_str = "This is a test string"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok:
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_tokenizer_decode(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(sequences=predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
# Ignore copy
def test_decode_audio(self):
feature_extractor = self.get_feature_extractor(padding_side="left")
tokenizer = self.get_tokenizer()
processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
attention_mask = np.zeros((3, 20))
attention_mask[0, -5:] = 1
attention_mask[1, -20:] = 1
attention_mask[2, -10:] = 1
generated_speech = np.asarray(floats_list((3, 20)))[:, None, :]
decoded_audios = processor.batch_decode(generated_speech, attention_mask=attention_mask)
self.assertIsInstance(decoded_audios, list)
for audio in decoded_audios:
self.assertIsInstance(audio, np.ndarray)
self.assertTrue(decoded_audios[0].shape == (1, 5))
self.assertTrue(decoded_audios[1].shape == (1, 20))
self.assertTrue(decoded_audios[2].shape == (1, 10))
| transformers/tests/models/musicgen_melody/test_processing_musicgen_melody.py/0 | {
"file_path": "transformers/tests/models/musicgen_melody/test_processing_musicgen_melody.py",
"repo_id": "transformers",
"token_count": 2446
} | 525 |
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "openai-community/openai-gpt"
"""Tests OpenAIGPTTokenizer that uses BERT BasicTokenizer."""
tokenizer_class = OpenAIGPTTokenizer
rust_tokenizer_class = OpenAIGPTTokenizerFast
test_rust_tokenizer = True
test_seq2seq = False
@classmethod
def setUpClass(cls):
super().setUpClass()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
cls.merges_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(cls.vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(cls.merges_file, "w") as fp:
fp.write("\n".join(merges))
def get_input_output_texts(self, tokenizer):
return "lower newer", "lower newer"
def test_full_tokenizer(self):
tokenizer = OpenAIGPTTokenizer(self.vocab_file, self.merges_file)
text = "lower"
bpe_tokens = ["low", "er</w>"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + ["<unk>"]
input_bpe_tokens = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def test_padding(self, max_length=15):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs)
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input 1", "This is a simple input 2"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
s2,
max_length=max_length,
padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
p2,
max_length=max_length,
padding="max_length",
)
@unittest.skip(reason="tokenizer has no padding token")
def test_padding_different_model_input_name(self):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class OpenAIGPTTokenizationTestWithSpacy(OpenAIGPTTokenizationTest):
"""Tests OpenAIGPTTokenizer that uses SpaCy and ftfy."""
pass
| transformers/tests/models/openai/test_tokenization_openai.py/0 | {
"file_path": "transformers/tests/models/openai/test_tokenization_openai.py",
"repo_id": "transformers",
"token_count": 2442
} | 526 |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch PaliGemma model."""
import copy
import unittest
import requests
from transformers import (
PaliGemmaConfig,
PaliGemmaForConditionalGeneration,
PaliGemmaModel,
PaliGemmaProcessor,
is_torch_available,
is_vision_available,
)
from transformers.testing_utils import (
Expectations,
cleanup,
require_read_token,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
class PaliGemmaVisionText2TextModelTester:
def __init__(
self,
parent,
ignore_index=-100,
image_token_index=0,
projector_hidden_act="gelu",
seq_length=25,
vision_feature_select_strategy="default",
vision_feature_layer=-1,
projection_dim=32,
text_config={
"model_type": "gemma",
"seq_length": 128,
"is_training": True,
# "use_input_mask": True,
"use_token_type_ids": False,
"use_labels": True,
"vocab_size": 99,
"hidden_size": 32,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"num_key_value_heads": 1,
"head_dim": 8,
"intermediate_size": 37,
"hidden_activation": "gelu_pytorch_tanh",
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 512,
"type_vocab_size": 16,
"type_sequence_label_size": 2,
"initializer_range": 0.02,
"num_labels": 3,
"num_choices": 4,
"pad_token_id": 1,
},
is_training=True,
vision_config={
"use_labels": True,
"image_size": 20,
"patch_size": 5,
"num_image_tokens": 4,
"num_channels": 3,
"is_training": True,
"hidden_size": 32,
"projection_dim": 32,
"num_key_value_heads": 1,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"intermediate_size": 37,
"dropout": 0.1,
"attention_dropout": 0.1,
"initializer_range": 0.02,
},
use_cache=False,
):
self.parent = parent
self.ignore_index = ignore_index
# `image_token_index` is set to 0 to pass "resize_embeddings" test, do not modify
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
self.vision_feature_select_strategy = vision_feature_select_strategy
self.vision_feature_layer = vision_feature_layer
self.text_config = text_config
self.vision_config = vision_config
self.seq_length = seq_length
self.projection_dim = projection_dim
self.pad_token_id = text_config["pad_token_id"]
self.num_hidden_layers = text_config["num_hidden_layers"]
self.vocab_size = text_config["vocab_size"]
self.hidden_size = text_config["hidden_size"]
self.num_attention_heads = text_config["num_attention_heads"]
self.is_training = is_training
self.batch_size = 3
self.num_channels = vision_config["num_channels"]
self.image_size = vision_config["image_size"]
self.encoder_seq_length = seq_length
self.use_cache = use_cache
def get_config(self):
return PaliGemmaConfig(
text_config=self.text_config,
vision_config=self.vision_config,
ignore_index=self.ignore_index,
image_token_index=self.image_token_index,
projector_hidden_act=self.projector_hidden_act,
projection_dim=self.projection_dim,
vision_feature_select_strategy=self.vision_feature_select_strategy,
vision_feature_layer=self.vision_feature_layer,
)
def prepare_config_and_inputs(self):
pixel_values = floats_tensor(
[
self.batch_size,
self.vision_config["num_channels"],
self.vision_config["image_size"],
self.vision_config["image_size"],
]
)
config = self.get_config()
return config, pixel_values
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
attention_mask = input_ids.ne(self.pad_token_id).to(torch_device)
# set the 16 first tokens to be image, and ensure that no other tokens are image tokens
# do not change this unless you modified image size or patch size
input_ids[input_ids == config.image_token_index] = self.pad_token_id
input_ids[:, :16] = config.image_token_index
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": input_ids,
"token_type_ids": torch.zeros_like(input_ids),
}
return config, inputs_dict
@require_torch
class PaliGemmaForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
"""
Model tester for `PaliGemmaForConditionalGeneration`.
"""
all_model_classes = (
(
PaliGemmaModel,
PaliGemmaForConditionalGeneration,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = {"image-text-to-text": PaliGemmaForConditionalGeneration}
fx_compatible = False
test_pruning = False
test_torchscript = False
test_head_masking = False
_is_composite = True
def setUp(self):
self.model_tester = PaliGemmaVisionText2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=PaliGemmaConfig, has_text_modality=False)
# Copied from tests.models.llava.test_modeling_llava.LlavaForConditionalGenerationModelTest.test_mismatching_num_image_tokens
def test_mismatching_num_image_tokens(self):
"""
Tests that VLMs through an error with explicit message saying what is wrong
when number of images doesn't match number of image tokens in the text.
Also we need to test multi-image cases when one prompr has multiple image tokens.
"""
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config).to(torch_device)
curr_input_dict = copy.deepcopy(input_dict) # in=place modifications further
_ = model(**curr_input_dict) # successful forward with no modifications
# remove one image but leave the image token in text
curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-1:, ...]
with self.assertRaises(ValueError):
_ = model(**curr_input_dict)
# simulate multi-image case by concatenating inputs where each has exactly one image/image-token
input_ids = curr_input_dict["input_ids"][:1]
pixel_values = curr_input_dict["pixel_values"][:1]
input_ids = torch.cat([input_ids, input_ids], dim=0)
# one image and two image tokens raise an error
with self.assertRaises(ValueError):
_ = model(input_ids=input_ids, pixel_values=pixel_values)
# two images and two image tokens don't raise an error
pixel_values = torch.cat([pixel_values, pixel_values], dim=0)
_ = model(input_ids=input_ids, pixel_values=pixel_values)
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
def test_cpu_offload(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
def test_disk_offload_bin(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
def test_disk_offload_safetensors(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
def test_model_parallelism(self):
pass
@unittest.skip(
reason="PaliGemma's SigLip encoder uses the same initialization scheme as the Flax original implementation"
)
def test_initialization(self):
pass
# TODO extend valid outputs to include this test @Molbap
@unittest.skip(reason="PaliGemma has currently one output format.")
def test_model_outputs_equivalence(self):
pass
# TODO fix the loss = nan in the testing configuration chosen @Molbap
@unittest.skip(reason="Edge case giving loss nan values in testing configuration.")
def test_determinism(self):
pass
@unittest.skip(reason="PaliGemma does not use feedforward chunking.")
def test_feed_forward_chunking(self):
pass
@unittest.skip(
"VLMs need lots of steps to prepare images/mask correctly to get pad-free inputs. Can be tested as part of LLM test"
)
def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
pass
@unittest.skip("Paligemma position ids are 1 indexed")
def test_eager_padding_matches_padding_free_with_position_ids(self):
pass
@unittest.skip("Paloigemma position ids are 1 indexed")
def test_sdpa_padding_matches_padding_free_with_position_ids(self):
pass
def test_attention_mask_with_token_types(self):
"""Test that attention masking works correctly both with and without token type IDs."""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class._from_config(config, attn_implementation="eager")
config = model.config
model.to(torch_device)
model.eval()
# Case 1: With token_type_ids
outputs_with_types = model(
**inputs_dict,
output_attentions=True,
)
# Case 2: Without token_type_ids
inputs_no_types = {k: v for k, v in inputs_dict.items() if k != "token_type_ids"}
outputs_no_types = model(
**inputs_no_types,
output_attentions=True,
)
attention_outputs_with_types = outputs_with_types.attentions
attention_outputs_no_types = outputs_no_types.attentions
# Verify pad tokens remain masked in both cases
attention_mask = inputs_dict["attention_mask"]
pad_positions = attention_mask == 0
for layer_attentions in [attention_outputs_with_types, attention_outputs_no_types]:
for layer_attn in layer_attentions:
# Check if pad tokens are properly masked
for batch_idx in range(layer_attn.shape[0]):
for seq_idx in range(layer_attn.shape[-1]):
if pad_positions[batch_idx, seq_idx]:
# Verify attention weights for pad tokens are zero
self.assertTrue(
torch.all(layer_attn[batch_idx, :, :, seq_idx] == 0),
f"Found non-zero attention weights for padding token at batch {batch_idx}, sequence position {seq_idx}",
)
@slow
@require_torch
@require_read_token
class PaliGemmaForConditionalGenerationIntegrationTest(unittest.TestCase):
def setUp(self):
self.processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-pt-224")
def tearDown(self):
cleanup(torch_device, gc_collect=True)
def test_small_model_integration_test(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
prompt = ""
image_file = (
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt")
EXPECTED_INPUT_IDS = torch.tensor([[257152] * 256 + [2, 108]])
self.assertTrue(torch.equal(inputs["input_ids"], EXPECTED_INPUT_IDS))
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = "\ncow on the beach" # fmt: skip
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
def test_small_model_integration_test_multiimage(self):
model_id = "google/paligemma-3b-ft-nlvr2-448" # checkpoint tuned for multiple images
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
processor = PaliGemmaProcessor.from_pretrained(model_id)
prompt = "answer en There is no snowman in any of the images. Is this true or false?"
stop_sign_image = Image.open(
requests.get("https://www.ilankelman.org/stopsigns/australia.jpg", stream=True).raw
)
snow_image = Image.open(
requests.get(
"https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg", stream=True
).raw
)
inputs = processor(text=prompt, images=[[snow_image, snow_image]], return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = "answer en There is no snowman in any of the images. Is this true or false?\nFalse"
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
# try another prompt with two different image this time
prompt = "answer en There is exactly one snowman. Is this true or false?"
inputs = processor(text=prompt, images=[[snow_image, stop_sign_image]], return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = "answer en There is exactly one snowman. Is this true or false?\nTrue"
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
def test_small_model_integration_test_paligemma_VQA(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
prompt = "answer en Where is the cow standing?"
image_file = (
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt").to(torch.float16)
output = model.generate(**inputs, max_new_tokens=900, do_sample=False)
EXPECTED_DECODED_TEXT = "answer en Where is the cow standing?\nbeach" # fmt: skip
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
def test_small_model_integration_test_paligemma_empty_prompt(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
prompt = ""
image_file = (
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt").to(torch.float16)
output = model.generate(**inputs, max_new_tokens=900, do_sample=False)
EXPECTED_DECODED_TEXT = "\ncow on the beach" # fmt: skip
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
def test_small_model_integration_test_paligemma_batched(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
prompts = [
"answer en Where is the cow standing?",
"",
]
image1 = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
stream=True,
).raw
)
image2 = image1
inputs = self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = ["answer en Where is the cow standing?\nbeach", "\ncow on the beach"] # fmt: skip
self.assertEqual(self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT)
def test_small_model_integration_test_paligemma_batched_bf16(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(
model_id, revision="bfloat16", dtype=torch.bfloat16
).to(torch_device)
# The first batch is longer in terms of text, the second will be padded.
prompts = [
"answer en Where is the cow standing?",
"",
]
image1 = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
stream=True,
).raw
)
image2 = image1
inputs = (
self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)
.to(torch.bfloat16)
.to(torch_device)
)
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = ["answer en Where is the cow standing?\nbeach", "\ncow on the beach"] # fmt: skip
self.assertEqual(self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT)
def test_small_model_integration_test_paligemma_batched_f16(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(
model_id, revision="float16", dtype=torch.float16
).to(torch_device)
# The first batch is longer in terms of text, the second will be padded.
prompts = [
"answer en Where is the cow standing?",
"",
]
image1 = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
stream=True,
).raw
)
image2 = image1
inputs = (
self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)
.to(torch.float16)
.to(torch_device)
)
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = ["answer en Where is the cow standing?\nbeach", "\ncow on the beach"] # fmt: skip
self.assertEqual(self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT)
def test_integration_detection_bug(self):
# this is a reproducer of https://github.com/huggingface/transformers/issues/31425 where not enough context
# impacted negatively segmentation generations.
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(
model_id, revision="bfloat16", dtype=torch.bfloat16
).to(torch_device)
prompt = ("detect shoe",)
image = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/shoe.png",
stream=True,
).raw
)
inputs = self.processor(images=image, text=prompt, return_tensors="pt").to(torch.bfloat16).to(torch_device)
output = model.generate(**inputs, max_new_tokens=20)
expected_decoded_texts = Expectations(
{
("rocm", (9, 5)): "detect shoe\n<loc0051><loc0309><loc0708><loc0644> shoe",
(None, None): "detect shoe\n<loc0051><loc0309><loc0708><loc0646> shoe",
("cuda", 8): "detect shoe\n<loc0045><loc0309><loc0708><loc0646> shoe",
}
) # fmt: skip
EXPECTED_DECODED_TEXT = expected_decoded_texts.get_expectation()
self.assertEqual(self.processor.decode(output[0], skip_special_tokens=True), EXPECTED_DECODED_TEXT)
def test_paligemma_index_error_bug(self):
# This is a reproducer of https://github.com/huggingface/transformers/pull/28032 and makes sure it does not happen anymore
# Please refer to that PR, or specifically https://github.com/huggingface/transformers/pull/28032#issuecomment-1860650043 for
# more details
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
# Simulate a super long prompt
prompt = "\n" * 200
image_file = (
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = self.processor(
images=raw_image,
text=prompt,
return_tensors="pt",
).to(torch.float16)
# Make sure that `generate` works
_ = model.generate(**inputs, max_new_tokens=20)
def test_paligemma_finetuning_with_suffixes_bf16(self):
# this is a supplementary test to ensure paligemma fine-tuning that relies on token_type_ids is robust to future changes
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(
model_id, revision="bfloat16", dtype=torch.bfloat16
).to(torch_device)
# The first batch is longer in terms of text, the second will be padded.
prompts = [
"answer en Where is the cow standing?",
"",
]
suffixes = ["beach", "cow standing on the beach"]
image1 = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
stream=True,
).raw
)
image2 = image1
inputs = (
self.processor(images=[image1, image2], text=prompts, suffix=suffixes, return_tensors="pt", padding=True)
.to(torch.bfloat16)
.to(torch_device)
)
expected_labels = torch.tensor(
[266 * [-100] + [54901, 1], 262 * [-100] + [14706, 9980, 611, 573, 8318, 1]]
).to(torch_device)
assert torch.equal(inputs["labels"], expected_labels)
expected_token_type_ids = torch.tensor([266 * [0] + 2 * [1], 262 * [0] + 6 * [1]]).to(torch_device)
assert torch.equal(inputs["token_type_ids"], expected_token_type_ids)
output = model(**inputs)
# check that loss does not error out
_ = output.loss
| transformers/tests/models/paligemma/test_modeling_paligemma.py/0 | {
"file_path": "transformers/tests/models/paligemma/test_modeling_paligemma.py",
"repo_id": "transformers",
"token_count": 11468
} | 527 |
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import re
import shutil
import tempfile
import unittest
from functools import lru_cache
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible
if is_torch_available():
FRAMEWORK = "pt"
elif is_tf_available():
FRAMEWORK = "tf"
else:
FRAMEWORK = "jax"
class PerceiverTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "deepmind/language-perceiver"
tokenizer_class = PerceiverTokenizer
test_rust_tokenizer = False
@classmethod
def setUpClass(cls):
super().setUpClass()
tokenizer = PerceiverTokenizer()
tokenizer.save_pretrained(cls.tmpdirname)
@cached_property
def perceiver_tokenizer(self):
return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver")
@classmethod
@use_cache_if_possible
@lru_cache(maxsize=64)
def get_tokenizer(cls, pretrained_name=None, **kwargs) -> PerceiverTokenizer:
pretrained_name = pretrained_name or cls.tmpdirname
return cls.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5) -> tuple[str, list]:
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for Perceiver because single bytes might not be
# valid utf-8 (byte 128 for instance).
# Here we're overriding the smallest possible method to provide
# a clean sequence without making the same assumption.
toks = []
for i in range(len(tokenizer)):
try:
tok = tokenizer.decode([i], clean_up_tokenization_spaces=False)
except UnicodeDecodeError:
pass
toks.append((i, tok))
toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks))
toks = list(filter(lambda t: [t[0]] == tokenizer.encode(t[1], add_special_tokens=False), toks))
if max_length is not None and len(toks) > max_length:
toks = toks[:max_length]
if min_length is not None and len(toks) < min_length and len(toks) > 0:
while len(toks) < min_length:
toks = toks + toks
# toks_str = [t[1] for t in toks]
toks_ids = [t[0] for t in toks]
# Ensure consistency
output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False)
if " " not in output_txt and len(toks_ids) > 1:
output_txt = (
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
+ " "
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
)
if with_prefix_space:
output_txt = " " + output_txt
output_ids = tokenizer.encode(output_txt, add_special_tokens=False)
return output_txt, output_ids
def test_multibytes_char(self):
tokenizer = self.perceiver_tokenizer
src_text = "Unicode €."
encoded = tokenizer(src_text)
encoded_ids = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded["input_ids"], encoded_ids)
# decoding
decoded = tokenizer.decode(encoded_ids)
self.assertEqual(decoded, "[CLS]Unicode €.[SEP]")
encoded = tokenizer("e è é ê ë")
encoded_ids = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded["input_ids"], encoded_ids)
# decoding
decoded = tokenizer.decode(encoded_ids)
self.assertEqual(decoded, "[CLS]e è é ê ë[SEP]")
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë")), "[CLS]e è é ê ë[SEP]")
def test_prepare_batch_integration(self):
tokenizer = self.perceiver_tokenizer
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
expected_src_tokens = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0] # fmt: skip
batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
self.assertIsInstance(batch, BatchEncoding)
if FRAMEWORK != "jax":
result = list(batch.input_ids.numpy()[0])
else:
result = list(batch.input_ids.tolist()[0])
self.assertListEqual(expected_src_tokens, result)
self.assertEqual((2, 38), batch.input_ids.shape)
self.assertEqual((2, 38), batch.attention_mask.shape)
def test_empty_target_text(self):
tokenizer = self.perceiver_tokenizer
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
# check if input_ids are returned and no decoder_input_ids
self.assertIn("input_ids", batch)
self.assertIn("attention_mask", batch)
self.assertNotIn("decoder_input_ids", batch)
self.assertNotIn("decoder_attention_mask", batch)
def test_max_length_integration(self):
tokenizer = self.perceiver_tokenizer
tgt_text = [
"Summary of the text.",
"Another summary.",
]
targets = tokenizer(
text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
)
self.assertEqual(32, targets["input_ids"].shape[1])
# cannot use default save_and_load_tokenizer test method because tokenizer has no vocab
def test_save_and_load_tokenizer(self):
# safety check on max_len default value so we are sure the test works
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.model_max_length, 42)
# Now let's start the test
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00e9d,running"
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
shutil.rmtree(tmpdirname)
tokenizers = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00e9d,running"
tokenizer.add_tokens(["bim", "bambam"])
additional_special_tokens = tokenizer.additional_special_tokens
additional_special_tokens.append("new_additional_special_token")
tokenizer.add_special_tokens(
{"additional_special_tokens": additional_special_tokens}, replace_additional_special_tokens=False
)
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
self.assertIn("new_additional_special_token", after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length, 42)
tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
self.assertEqual(tokenizer.model_max_length, 43)
shutil.rmtree(tmpdirname)
# There is a conflict between the default value of extra_ids and adding a new special token through additional_special_tokens
# We need to add the extra_ids in the list of the arg additional_special_tokens
def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
tokenizer_list = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(tmp_dir)
with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
special_tokens_map = json.load(json_file)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
tokenizer_config = json.load(json_file)
added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(125)]
special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [
"an_additional_special_token"
]
tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [
"an_additional_special_token"
]
with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
json.dump(special_tokens_map, outfile)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
json.dump(tokenizer_config, outfile)
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
tmp_dir,
)
self.assertIn(
"an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
)
self.assertEqual(
["an_additional_special_token"],
tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
),
)
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)]
tokenizer = tokenizer_class.from_pretrained(
tmp_dir,
additional_special_tokens=new_added_tokens,
)
self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
self.assertEqual(
["a_new_additional_special_token"],
tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
),
)
def test_decode_invalid_byte_id(self):
tokenizer = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178]), "�")
@unittest.skip(reason="tokenizer does not have vocabulary")
def test_get_vocab(self):
pass
@unittest.skip(reason="inputs cannot be pretokenized")
def test_pretokenized_inputs(self):
# inputs cannot be pretokenized since ids depend on whole input string and not just on single characters
pass
@unittest.skip(reason="vocab does not exist")
def test_conversion_reversible(self):
pass
def test_convert_tokens_to_string_format(self):
# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
# strings and special added tokens as tokens
tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
tokens = ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
string = tokenizer.convert_tokens_to_string(tokens)
self.assertIsInstance(string, str)
| transformers/tests/models/perceiver/test_tokenization_perceiver.py/0 | {
"file_path": "transformers/tests/models/perceiver/test_tokenization_perceiver.py",
"repo_id": "transformers",
"token_count": 6203
} | 528 |
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torchvision_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_vision_available():
from transformers import PoolFormerImageProcessor
if is_torchvision_available():
from transformers import PoolFormerImageProcessorFast
class PoolFormerImageProcessingTester:
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
min_resolution=30,
max_resolution=400,
do_resize_and_center_crop=True,
size=None,
crop_pct=0.9,
crop_size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
):
size = size if size is not None else {"shortest_edge": 30}
crop_size = crop_size if crop_size is not None else {"height": 30, "width": 30}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize_and_center_crop = do_resize_and_center_crop
self.size = size
self.crop_pct = crop_pct
self.crop_size = crop_size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
def prepare_image_processor_dict(self):
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.crop_size["height"], self.crop_size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class PoolFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = PoolFormerImageProcessor if is_vision_available() else None
fast_image_processing_class = PoolFormerImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()
self.image_processor_tester = PoolFormerImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize_and_center_crop"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "crop_pct"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
def test_image_processor_from_dict_with_kwargs(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"shortest_edge": 30})
self.assertEqual(image_processor.crop_size, {"height": 30, "width": 30})
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
self.assertEqual(image_processor.size, {"shortest_edge": 42})
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
@require_torch
@require_vision
class PoolFormerImageProcessingNoCropPctTest(PoolFormerImageProcessingTest):
def setUp(self):
super().setUp()
self.image_processor_tester = PoolFormerImageProcessingTester(self, crop_pct=None)
| transformers/tests/models/poolformer/test_image_processing_poolformer.py/0 | {
"file_path": "transformers/tests/models/poolformer/test_image_processing_poolformer.py",
"repo_id": "transformers",
"token_count": 2024
} | 529 |
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import ReformerConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
require_torch_fp16,
require_torch_multi_gpu,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerModel,
ReformerModelWithLMHead,
ReformerTokenizer,
)
from transformers.models.reformer.modeling_reformer import ReformerLayer
class ReformerModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=32,
text_seq_length=None,
is_training=True,
is_decoder=True,
use_input_mask=True,
use_labels=True,
vocab_size=32,
attention_head_size=16,
hidden_size=32,
num_attention_heads=2,
local_attn_chunk_length=4,
local_num_chunks_before=1,
local_num_chunks_after=0,
num_buckets=None,
num_hashes=1,
lsh_attn_chunk_length=None,
lsh_num_chunks_before=None,
lsh_num_chunks_after=None,
chunk_size_lm_head=0,
chunk_size_feed_forward=0,
feed_forward_size=32,
hidden_act="gelu",
hidden_dropout_prob=0.1,
local_attention_probs_dropout_prob=0.1,
lsh_attention_probs_dropout_prob=None,
max_position_embeddings=512,
initializer_range=0.02,
axial_norm_std=1.0,
layer_norm_eps=1e-12,
axial_pos_embds=True,
axial_pos_shape=[4, 8],
axial_pos_embds_dim=[16, 16],
attn_layers=["local", "local", "local", "local"],
pad_token_id=0,
eos_token_id=2,
scope=None,
hash_seed=0,
num_labels=2,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.is_decoder = is_decoder
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.attention_head_size = attention_head_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = len(attn_layers) if attn_layers is not None else 0
self.local_attn_chunk_length = local_attn_chunk_length
self.local_num_chunks_after = local_num_chunks_after
self.local_num_chunks_before = local_num_chunks_before
self.num_hashes = num_hashes
self.num_buckets = tuple(num_buckets) if isinstance(num_buckets, list) else num_buckets
self.lsh_attn_chunk_length = lsh_attn_chunk_length
self.lsh_num_chunks_after = lsh_num_chunks_after
self.lsh_num_chunks_before = lsh_num_chunks_before
self.hidden_act = hidden_act
self.feed_forward_size = feed_forward_size
self.hidden_dropout_prob = hidden_dropout_prob
self.local_attention_probs_dropout_prob = local_attention_probs_dropout_prob
self.lsh_attention_probs_dropout_prob = lsh_attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.axial_pos_embds = axial_pos_embds
self.axial_pos_shape = tuple(axial_pos_shape)
self.axial_pos_embds_dim = tuple(axial_pos_embds_dim)
self.axial_norm_std = axial_norm_std
self.chunk_size_lm_head = chunk_size_lm_head
self.chunk_size_feed_forward = chunk_size_feed_forward
self.scope = scope
self.attn_layers = attn_layers
self.pad_token_id = pad_token_id
self.hash_seed = hash_seed
self.text_seq_length = text_seq_length or seq_length
attn_chunk_length = local_attn_chunk_length if local_attn_chunk_length is not None else lsh_attn_chunk_length
num_chunks_after = local_num_chunks_after if local_num_chunks_after is not None else lsh_num_chunks_after
num_chunks_before = local_num_chunks_before if local_num_chunks_before is not None else lsh_num_chunks_before
self.encoder_seq_length = seq_length // attn_chunk_length + (self.seq_length % attn_chunk_length != 0)
self.key_length = (num_chunks_before + num_chunks_after + 1) * attn_chunk_length
self.chunk_length = attn_chunk_length
self.num_labels = num_labels
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
choice_labels = None
if self.use_labels:
choice_labels = ids_tensor([self.batch_size], 2)
config = self.get_config()
return (
config,
input_ids,
input_mask,
choice_labels,
)
def get_config(self):
return ReformerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
feed_forward_size=self.feed_forward_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
local_attention_probs_dropout_prob=self.local_attention_probs_dropout_prob,
lsh_attention_probs_dropout_prob=self.lsh_attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
is_decoder=self.is_decoder,
axial_pos_embds=self.axial_pos_embds,
axial_pos_shape=self.axial_pos_shape,
axial_pos_embds_dim=self.axial_pos_embds_dim,
local_attn_chunk_length=self.local_attn_chunk_length,
local_num_chunks_after=self.local_num_chunks_after,
local_num_chunks_before=self.local_num_chunks_before,
num_hashes=self.num_hashes,
num_buckets=self.num_buckets,
lsh_attn_chunk_length=self.lsh_attn_chunk_length,
lsh_num_chunks_after=self.lsh_num_chunks_after,
lsh_num_chunks_before=self.lsh_num_chunks_before,
attn_layers=self.attn_layers,
pad_token_id=self.pad_token_id,
hash_seed=self.hash_seed,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 100
config.max_position_embeddings = 100
config.axial_pos_shape = (4, 25)
config.is_decoder = False
return config
def create_and_check_reformer_model(self, config, input_ids, input_mask, choice_labels):
model = ReformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
# 2 * hidden_size because we use reversible resnet layers
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.seq_length, 2 * self.hidden_size)
)
def create_and_check_reformer_model_with_lm_backward(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = False
config.lsh_num_chunks_after = 1
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.train()
loss = model(input_ids, attention_mask=input_mask, labels=input_ids)["loss"]
loss.backward()
def create_and_check_reformer_with_lm(self, config, input_ids, input_mask, choice_labels):
config.lsh_num_chunks_after = 0
config.is_decoder = True
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_reformer_with_mlm(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = False
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_reformer_model_with_attn_mask(
self, config, input_ids, input_mask, choice_labels, is_decoder=False
):
# no special position embeddings
config.axial_pos_embds = False
config.is_decoder = is_decoder
if self.lsh_attn_chunk_length is not None:
# need to set chunk length equal sequence length to be certain that chunking works
config.lsh_attn_chunk_length = self.seq_length
model = ReformerModel(config=config)
model.to(torch_device)
model.eval()
# set all position encodings to zero so that positions don't matter
with torch.no_grad():
embedding = model.embeddings.position_embeddings.embedding
embedding.weight = nn.Parameter(torch.zeros(embedding.weight.shape).to(torch_device))
embedding.weight.requires_grad = False
half_seq_len = self.seq_length // 2
roll = self.chunk_length
half_input_ids = input_ids[:, :half_seq_len]
# normal padded
attn_mask = torch.cat(
[torch.ones_like(half_input_ids), torch.zeros_like(half_input_ids)],
dim=-1,
)
input_ids_padded = torch.cat(
[half_input_ids, ids_tensor((self.batch_size, half_seq_len), self.vocab_size)],
dim=-1,
)
# shifted padded
input_ids_roll = torch.cat(
[half_input_ids, ids_tensor((self.batch_size, half_seq_len), self.vocab_size)],
dim=-1,
)
input_ids_roll = torch.roll(input_ids_roll, roll, dims=-1)
attn_mask_roll = torch.roll(attn_mask, roll, dims=-1)
output_padded = model(input_ids_padded, attention_mask=attn_mask)[0][:, :half_seq_len]
output_padded_rolled = model(input_ids_roll, attention_mask=attn_mask_roll)[0][:, roll : half_seq_len + roll]
self.parent.assertTrue(torch.allclose(output_padded, output_padded_rolled, atol=1e-3))
def create_and_check_reformer_layer_dropout_seed(
self, config, input_ids, input_mask, choice_labels, is_decoder=False
):
config.is_decoder = is_decoder
layer = ReformerLayer(config).to(torch_device)
layer.train()
shape = (
self.batch_size,
self.seq_length,
config.hidden_size,
) # Batch x SeqLen x hiddenSize
# get random tensors
hidden_states = floats_tensor(shape)
prev_attn_output = floats_tensor(shape)
# now the random seeds for attention and feed forward is initialized
# forward tensors with dropout
layer_outputs = layer(prev_attn_output, hidden_states, attention_mask=input_mask)
next_attn_output = layer_outputs.attn_output
next_hidden_states = layer_outputs.hidden_states
torch.manual_seed(layer.attention_seed)
attn_outputs = layer.attention(hidden_states, attention_mask=input_mask)
self.parent.assertTrue(
torch.allclose(
prev_attn_output + attn_outputs.hidden_states,
next_attn_output,
atol=1e-3,
)
)
torch.manual_seed(layer.feed_forward_seed)
feed_forward_hidden_states = layer.feed_forward(next_attn_output)
self.parent.assertTrue(
torch.allclose(
next_hidden_states,
hidden_states + feed_forward_hidden_states,
atol=1e-3,
)
)
def create_and_check_reformer_feed_backward_chunking(self, config, input_ids, input_mask, choice_labels):
# disable dropout
config.hidden_dropout_prob = 0
config.local_attention_probs_dropout_prob = 0
config.lsh_attention_probs_dropout_prob = 0
config.lsh_num_chunks_after = 1
config.is_decoder = False
torch.manual_seed(0)
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.train()
model.zero_grad()
loss_no_chunk, output_no_chunk = model(input_ids, labels=input_ids, attention_mask=input_mask)[:2]
loss_no_chunk.backward()
grad_slice_word_no_chunk = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
grad_slice_position_factor_1_no_chunk = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
grad_slice_position_factor_2_no_chunk = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
config.chunk_size_lm_head = 1
config.chunk_size_feed_forward = 1
torch.manual_seed(0)
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.train()
model.zero_grad()
loss_chunk, output_chunk = model(input_ids, labels=input_ids, attention_mask=input_mask)[:2]
loss_chunk.backward()
grad_slice_word_chunk = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
grad_slice_position_factor_1_chunk = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
grad_slice_position_factor_2_chunk = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
self.parent.assertTrue(torch.allclose(loss_chunk, loss_no_chunk, atol=1e-3))
self.parent.assertTrue(torch.allclose(grad_slice_word_no_chunk, grad_slice_word_chunk, atol=1e-3))
self.parent.assertTrue(
torch.allclose(grad_slice_position_factor_1_chunk, grad_slice_position_factor_1_no_chunk, atol=1e-3)
)
self.parent.assertTrue(
torch.allclose(grad_slice_position_factor_2_chunk, grad_slice_position_factor_2_no_chunk, atol=1e-3)
)
def create_and_check_reformer_random_seed(self, config, input_ids, input_mask, choice_labels):
layer = ReformerLayer(config).to(torch_device)
layer.train()
shape = (
self.batch_size,
self.seq_length,
config.hidden_size,
) # Batch x SeqLen x hiddenSize
hidden_states = floats_tensor(shape)
attn_output = floats_tensor(shape)
seeds = []
for _ in range(100):
layer_outputs = layer(attn_output, hidden_states, attention_mask=input_mask)
attn_output = layer_outputs.attn_output
hidden_states = layer_outputs.hidden_states
torch.manual_seed(layer.attention_seed)
seeds.append(layer.attention_seed)
self.parent.assertGreater(len(set(seeds)), 70)
seeds = []
for _ in range(100):
layer_outputs = layer(attn_output, hidden_states, attention_mask=input_mask)
attn_output = layer_outputs.attn_output
hidden_states = layer_outputs.hidden_states
torch.manual_seed(layer.feed_forward_seed)
seeds.append(layer.feed_forward_seed)
self.parent.assertGreater(len(set(seeds)), 70)
def create_and_check_reformer_model_fp16_forward(self, config, input_ids, input_mask, choice_labels):
model = ReformerModel(config=config)
model.to(torch_device)
model.half()
model.eval()
output = model(input_ids, attention_mask=input_mask)["last_hidden_state"]
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_reformer_model_generate(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = True
config.lsh_num_chunks_after = 0
config.bos_token_id = 0
config.eos_token_id = None
config.max_length = 20
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
output = model.generate()
self.parent.assertIsNotNone(output)
def create_and_check_reformer_model_fp16_generate(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = True
config.lsh_num_chunks_after = 0
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.half()
model.eval()
# only use last 10 inputs for generation
output = model.generate(input_ids[:, -10:], attention_mask=input_mask, do_sample=False)
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_reformer_no_chunking(self, config, input_ids, input_mask, choice_labels):
# force chunk length to be bigger than input_ids
config.lsh_attn_chunk_length = 2 * input_ids.shape[-1]
config.local_attn_chunk_length = 2 * input_ids.shape[-1]
config.lsh_num_chunks_after = 1
config.is_decoder = False
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
output_logits = model(input_ids, attention_mask=input_mask)["logits"]
self.parent.assertTrue(output_logits.shape[1] == input_ids.shape[-1])
def create_and_check_reformer_for_question_answering(self, config, input_ids, input_mask, choice_labels):
model = ReformerForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
start_positions=choice_labels,
end_positions=choice_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_past_buckets_states(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = True
config.lsh_num_chunks_before = 1
config.lsh_num_chunks_after = 0
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
input_ids_first = input_ids[:, :-1]
input_ids_second = input_ids[:, -1:]
# return saved cache
past_buckets_states = model(input_ids_first, use_cache=True)["past_buckets_states"]
# calculate last output with and without cache
outputs_with_cache = model(input_ids_second, past_buckets_states=past_buckets_states, use_cache=True)["logits"]
outputs_without_cache = model(input_ids)["logits"][:, -1]
# select random slice idx
random_slice_idx = torch.randint(outputs_without_cache.shape[-1], (1, 1), device=torch_device).item()
# outputs should be similar within range
self.parent.assertTrue(
torch.allclose(
outputs_with_cache[:, 0, random_slice_idx], outputs_without_cache[:, random_slice_idx], atol=1e-2
)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, choice_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
def create_and_check_reformer_for_sequence_classification(
self, config, input_ids, input_mask, choice_labels, is_decoder
):
config.is_decoder = is_decoder
sequence_labels = ids_tensor([self.batch_size], config.num_labels)
model = ReformerForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
class ReformerTesterMixin:
"""
Reformer Local and Reformer LSH run essentially the same tests
"""
def test_config(self):
self.config_tester.run_common_tests()
def test_reformer_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model(*config_and_inputs)
def test_reformer_lm_model_backward(self):
if not self.model_tester.is_training:
self.skipTest(reason="model_tester.is_training is set to False")
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_with_lm_backward(*config_and_inputs)
def test_reformer_model_attn_masking(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, is_decoder=True)
self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, is_decoder=False)
def test_reformer_with_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_with_lm(*config_and_inputs)
def test_reformer_with_mlm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_with_mlm(*config_and_inputs)
def test_reformer_layer_training_dropout(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=True)
self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=False)
def test_reformer_chunking_backward_equality(self):
if not self.model_tester.is_training:
self.skipTest(reason="model_tester.is_training is set to False")
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_feed_backward_chunking(*config_and_inputs)
def test_reformer_no_chunking(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_no_chunking(*config_and_inputs)
def test_reformer_qa_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_for_question_answering(*config_and_inputs)
def test_reformer_cached_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_past_buckets_states(*config_and_inputs)
def test_reformer_cached_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_generate(*config_and_inputs)
@slow
def test_dropout_random_seed_is_changing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_random_seed(*config_and_inputs)
@require_torch_fp16
def test_reformer_model_fp16_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_fp16_forward(*config_and_inputs)
@require_torch_fp16
def test_reformer_model_fp16_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_fp16_generate(*config_and_inputs)
@require_torch_multi_gpu
@unittest.skip(
reason=(
"Reformer does not work with data parallel (DP) because of a bug in PyTorch:"
" https://github.com/pytorch/pytorch/issues/36035"
)
)
def test_multi_gpu_data_parallel_forward(self):
pass
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_for_sequence_classification(*config_and_inputs, is_decoder=False)
@unittest.skip(reason="Reformer cannot keep gradients in attentions or hidden states")
def test_retain_grad_hidden_states_attentions(self):
return
@unittest.skip(reason="Reformer cannot resize embeddings that easily")
def test_resize_embeddings_untied(self):
return
@require_torch
class ReformerLocalAttnModelTest(ReformerTesterMixin, GenerationTesterMixin, ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
if is_torch_available()
else ()
)
test_pruning = False
test_headmasking = False
test_torchscript = False
test_sequence_classification_problem_types = True
def setUp(self):
self.model_tester = ReformerModelTester(self, text_seq_length=16)
self.config_tester = ConfigTester(self, config_class=ReformerConfig, hidden_size=37)
@slow
def test_model_from_pretrained(self):
model_name = "google/reformer-crime-and-punishment"
model = ReformerModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)
def _check_attentions_for_generate(
self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values
):
# NOTE (joao): this function is substancially different from the original, the attention has different
# *number* of shapes in certain conditions
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, list) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (output_length - prompt_length))
for generated_length, iter_attentions in enumerate(attentions):
use_cache = decoder_past_key_values is not None and generated_length > 0
model_input_length = prompt_length + generated_length if not use_cache else 1
num_chunks = model_input_length // config.local_attn_chunk_length + (
model_input_length % config.local_attn_chunk_length != 0
)
model_input_chunk_len = config.local_attn_chunk_length
query_chunk_len = config.local_attn_chunk_length * (
1 + config.local_num_chunks_after + config.local_num_chunks_before
)
if use_cache:
expected_shape = (
batch_size,
config.num_attention_heads,
model_input_length,
prompt_length // config.local_attn_chunk_length + generated_length,
)
else:
expected_shape = (
batch_size,
config.num_attention_heads,
num_chunks,
model_input_chunk_len,
query_chunk_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, prompt_length, output_length, config, use_cache=False
):
# NOTE (joao): this function is substancially different from the original, the hidden states have different
# length in certain conditions
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, list) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (output_length - prompt_length))
for generation_length, iter_hidden_states in enumerate(hidden_states):
use_cache_this_iter = use_cache and generation_length > 0
model_input_length = prompt_length + generation_length
model_output_length = config.local_attn_chunk_length * (
model_input_length // config.local_attn_chunk_length
+ (model_input_length % config.local_attn_chunk_length != 0)
)
if use_cache_this_iter:
model_output_length = 1
expected_shape = (batch_size, model_output_length, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
@unittest.skip(reason="The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
def prepare_config_and_inputs_for_generate(self, *args, **kwargs):
# override because otherwise we hit max possible seq length for model (4*8=32)
# decreasing the seq_length in tester causes errors for "training_tests", those need exactly max seq length
# NOTE: seq_length has to be multiple of 4, otherwise it fails for other tests
original_sequence_length = self.model_tester.seq_length
self.model_tester.seq_length = self.model_tester.text_seq_length
test_inputs = super().prepare_config_and_inputs_for_generate(*args, **kwargs)
self.model_tester.seq_length = original_sequence_length
return test_inputs
@require_torch
class ReformerLSHAttnModelTest(
ReformerTesterMixin, ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
):
all_model_classes = (
(ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": ReformerModel,
"fill-mask": ReformerForMaskedLM,
"question-answering": ReformerForQuestionAnswering,
"text-classification": ReformerForSequenceClassification,
"text-generation": ReformerModelWithLMHead,
"zero-shot": ReformerForSequenceClassification,
}
if is_torch_available()
else {}
)
test_pruning = False
test_headmasking = False
test_torchscript = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self,
pipeline_test_case_name,
config_class,
model_architecture,
tokenizer_name,
image_processor_name,
feature_extractor_name,
processor_name,
):
if (
pipeline_test_case_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = ReformerModelTester(
self,
batch_size=13,
seq_length=13,
use_input_mask=True,
use_labels=True,
is_training=False,
is_decoder=True,
vocab_size=32,
attention_head_size=16,
hidden_size=64,
num_attention_heads=2,
num_buckets=2,
num_hashes=4,
lsh_attn_chunk_length=4,
lsh_num_chunks_before=1,
lsh_num_chunks_after=0,
chunk_size_lm_head=5,
chunk_size_feed_forward=6,
feed_forward_size=32,
hidden_act="relu",
hidden_dropout_prob=0.1,
lsh_attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
initializer_range=0.02,
axial_norm_std=1.0,
layer_norm_eps=1e-12,
axial_pos_embds=True,
axial_pos_shape=[4, 8],
axial_pos_embds_dim=[16, 48],
# sanotheu
# attn_layers=[lsh,lsh,lsh,lsh],
attn_layers=["lsh"],
pad_token_id=0,
eos_token_id=2,
scope=None,
hash_seed=0,
num_labels=2,
)
self.config_tester = ConfigTester(self, config_class=ReformerConfig, hidden_size=37)
def _check_attentions_for_generate(
self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values
):
# NOTE (joao): this function is substancially different from the original, the attention has different
# *number* of shapes in certain conditions
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, list) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (output_length - prompt_length))
for generated_length, iter_attentions in enumerate(attentions):
use_cache = decoder_past_key_values is not None and generated_length > 0
model_input_len = prompt_length + generated_length if not use_cache else 1
num_chunks = model_input_len // config.lsh_attn_chunk_length + (
model_input_len % config.lsh_attn_chunk_length != 0
)
model_input_chunk_len = config.lsh_attn_chunk_length
query_chunk_len = config.lsh_attn_chunk_length * (
1 + config.lsh_num_chunks_after + config.lsh_num_chunks_before
)
if use_cache:
expected_shape = (
batch_size,
config.num_attention_heads,
config.num_hashes,
model_input_len,
config.num_hashes * (1 + config.lsh_num_chunks_after + config.lsh_num_chunks_before),
)
else:
expected_shape = (
batch_size,
config.num_attention_heads,
num_chunks * config.num_hashes,
model_input_chunk_len,
query_chunk_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, prompt_length, output_length, config, use_cache=False
):
# NOTE (joao): this function is substancially different from the original, the hidden states have different
# length in certain conditions
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, list) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (output_length - prompt_length))
for generation_length, iter_hidden_states in enumerate(hidden_states):
use_cache_this_iter = use_cache and generation_length > 0
model_input_length = prompt_length + generation_length
model_output_length = config.local_attn_chunk_length * (
model_input_length // config.local_attn_chunk_length
+ (model_input_length % config.local_attn_chunk_length != 0)
)
if use_cache_this_iter:
model_output_length = 1
expected_shape = (batch_size, model_output_length, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
@unittest.skip(reason="Fails because the sequence length is not a multiple of 4")
def test_problem_types(self):
pass
@unittest.skip(reason="Fails because the sequence length is not a multiple of 4")
def test_past_key_values_format(self):
pass
@unittest.skip(reason="The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class ReformerIntegrationTests(unittest.TestCase):
"""
These integration tests test the current layer activations and gradients against the output of the Hugging Face Reformer model at time of integration: 29/06/2020. During integration, the model was tested against the output of the official Trax ReformerLM model for various cases ("lsh" only, "lsh" only, masked / non-masked, different chunk length, ....). In order to recover the original trax integration tests, one should use patrickvonplaten's fork of trax and the code that lives on the branch `reformer_trax_tests`.
"""
def _get_basic_config_and_input(self):
config = {
"vocab_size": 320,
"attention_head_size": 8,
"hidden_size": 16,
"num_attention_heads": 2,
"num_buckets": 2,
"num_hashes": 4,
"lsh_attn_chunk_length": 4,
"local_attn_chunk_length": 4,
"lsh_num_chunks_before": 1,
"lsh_num_chunks_after": 0,
"local_num_chunks_before": 1,
"local_num_chunks_after": 0,
"chunk_size_lm_head": 0,
"chunk_size_feed_forward": 0,
"feed_forward_size": 32,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"lsh_attention_probs_dropout_prob": 0.0,
"local_attention_probs_dropout_prob": 0.0,
"max_position_embeddings": 32,
"initializer_range": 0.02,
"axial_norm_std": 1.0,
"layer_norm_eps": 1e-12,
"sinusoidal_pos_embds": False,
"axial_pos_embds": True,
"axial_pos_shape": [4, 8],
"axial_pos_embds_dim": [8, 8],
"hash_seed": 0,
"is_decoder": True,
}
return config
def _get_hidden_states(self):
return torch.tensor(
[
[
[
1.90826353e00,
-1.45999730e00,
-6.20405462e-01,
1.52503433e00,
-3.64464232e-01,
-8.27359235e-01,
8.39670803e-01,
2.44492178e-01,
4.98332758e-01,
2.69175139e00,
-7.08081422e-03,
1.04915401e00,
-1.83476661e00,
7.67220476e-01,
2.98580543e-01,
2.84803992e-02,
],
[
-2.66374286e-02,
4.33497576e-01,
3.10386309e-01,
5.46039944e-01,
-2.47292666e-04,
-7.52305019e-01,
2.39162103e-01,
7.25216186e-01,
-7.58357372e-01,
4.20635998e-01,
-4.04739919e-02,
1.59924145e-01,
2.05135748e00,
-1.15997978e00,
5.37166397e-01,
2.62873606e-01,
],
[
1.85247482e-01,
7.07046037e-01,
-6.77089715e-01,
-2.24209655e00,
-3.75307980e-02,
-8.59380874e-01,
-2.81027884e00,
1.01276376e00,
-1.69438001e00,
4.17574660e-01,
-1.49196962e00,
-1.76483717e00,
-1.94566312e-01,
-1.71183858e00,
7.72903565e-01,
-1.11557056e00,
],
[
9.46069193e-01,
1.53417623e-01,
-9.58686996e-01,
1.18126669e-01,
1.75967724e00,
1.62194590e00,
-5.74108159e-01,
6.79920443e-01,
5.44028163e-01,
2.05466114e-01,
-3.63045868e-01,
2.41865062e-01,
3.20348382e-01,
-9.05611176e-01,
-1.92690727e-01,
-1.19917547e00,
],
]
],
dtype=torch.float32,
device=torch_device,
)
def _get_attn_mask(self):
return torch.tensor([[0, 1, 0, 0]], dtype=torch.long, device=torch_device)
def _get_input_ids_and_mask(self):
mask = torch.tensor(
[
[1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0],
],
dtype=torch.long,
device=torch_device,
)
input_ids = torch.tensor(
[
[
89,
279,
286,
84,
194,
316,
182,
28,
283,
37,
169,
7,
253,
267,
107,
250,
44,
7,
102,
62,
3,
243,
171,
265,
302,
48,
164,
264,
148,
229,
280,
150,
],
[
9,
192,
66,
112,
163,
83,
135,
70,
224,
96,
31,
80,
196,
80,
63,
22,
85,
100,
47,
283,
0,
163,
126,
143,
195,
82,
53,
82,
18,
27,
182,
52,
],
],
dtype=torch.long,
device=torch_device,
)
return input_ids, mask
def test_lsh_layer_forward(self):
config = self._get_basic_config_and_input()
config["lsh_num_chunks_before"] = 0
config["attn_layers"] = ["lsh"]
config["is_decoder"] = False
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(prev_attn_output=hidden_states.clone(), hidden_states=hidden_states)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.6879, -1.3083, -0.4708, 1.3555, -0.6292],
dtype=torch.float,
device=torch_device,
)
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_lsh_layer_forward_complex(self):
config = self._get_basic_config_and_input()
config["lsh_num_chunks_before"] = 0
config["attn_layers"] = ["lsh"]
config["num_buckets"] = [2, 4]
attn_mask = self._get_attn_mask()
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(
prev_attn_output=hidden_states.clone(),
hidden_states=hidden_states,
attention_mask=attn_mask,
)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.6439, -1.2306, -0.5108, 1.3006, -0.6537],
dtype=torch.float,
device=torch_device,
)
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_local_layer_forward(self):
config = self._get_basic_config_and_input()
config["local_num_chunks_before"] = 0
config["attn_layers"] = ["local"]
config["is_decoder"] = False
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(prev_attn_output=hidden_states, hidden_states=hidden_states)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.4212, -2.0576, -0.9688, 1.4599, -0.1344],
dtype=torch.float,
device=torch_device,
)
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_local_layer_forward_complex(self):
config = self._get_basic_config_and_input()
config["local_num_chunks_before"] = 0
config["attn_layers"] = ["local"]
attn_mask = self._get_attn_mask()
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(
prev_attn_output=hidden_states,
hidden_states=hidden_states,
attention_mask=attn_mask,
)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.4750, -2.0235, -0.9743, 1.4463, -0.1269],
dtype=torch.float,
device=torch_device,
)
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_lsh_model_forward(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["lsh", "lsh", "lsh", "lsh"]
config["num_buckets"] = [2, 4]
torch.manual_seed(0)
model = ReformerModel(ReformerConfig(**config)).to(torch_device)
model.eval()
input_ids, attn_mask = self._get_input_ids_and_mask()
hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]
output_slice = hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[-0.9896, -0.9396, -1.0831, -0.0597, 0.2456],
dtype=torch.float,
device=torch_device,
)
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_local_model_forward(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["local", "local", "local", "local"]
torch.manual_seed(0)
model = ReformerModel(ReformerConfig(**config)).to(torch_device)
model.eval()
input_ids, attn_mask = self._get_input_ids_and_mask()
hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]
output_slice = hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[-1.6791, 0.7171, 0.1594, 0.4063, 1.2584],
dtype=torch.float,
device=torch_device,
)
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_lm_model_forward(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["local", "lsh", "local", "lsh", "local", "lsh"]
config["num_buckets"] = [2, 4]
config["is_decoder"] = False
torch.manual_seed(0)
model = ReformerForMaskedLM(ReformerConfig(**config)).to(torch_device)
model.eval()
input_ids, attn_mask = self._get_input_ids_and_mask()
hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]
output_slice = hidden_states[1, -1, :5]
expected_output_slice = torch.tensor(
[0.1018, -0.2026, 0.2116, 0.0270, -0.1233],
dtype=torch.float,
device=torch_device,
)
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_local_lm_model_grad(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["local", "local", "local", "local"]
config["hidden_dropout_prob"] = 0.0
config["local_attention_probs_dropout_prob"] = 0.0
torch.manual_seed(0)
model = ReformerModelWithLMHead(ReformerConfig(**config)).to(torch_device)
model.train()
model.zero_grad()
input_ids, _ = self._get_input_ids_and_mask()
loss = model(input_ids=input_ids, labels=input_ids)[0]
torch.testing.assert_close(
loss, torch.tensor(5.8019, dtype=torch.float, device=torch_device), rtol=1e-3, atol=1e-3
)
loss.backward()
# check last grads to cover all probable errors
grad_slice_word = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
expected_grad_slice_word = torch.tensor(
[-0.0005, -0.0001, -0.0002, -0.0006, -0.0006],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_1 = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
expected_grad_slice_pos_fac_1 = torch.tensor(
[-0.5235, 0.5704, 0.0922, -0.3140, 0.9928],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_2 = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
expected_grad_slice_pos_fac_2 = torch.tensor(
[1.7960, 1.7668, 0.5593, 0.0907, 1.8342],
dtype=torch.float,
device=torch_device,
)
torch.testing.assert_close(grad_slice_word, expected_grad_slice_word, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, rtol=1e-3, atol=1e-3)
def test_lsh_lm_model_grad(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["lsh", "lsh", "lsh", "lsh"]
config["hidden_dropout_prob"] = 0.0
config["lsh_attention_probs_dropout_prob"] = 0.0
config["num_buckets"] = [2, 4]
config["num_hashes"] = 6
torch.manual_seed(0)
model = ReformerModelWithLMHead(ReformerConfig(**config)).to(torch_device)
model.train()
model.zero_grad()
input_ids, _ = self._get_input_ids_and_mask()
loss = model(input_ids=input_ids, labels=input_ids)[0]
torch.testing.assert_close(
loss, torch.tensor(5.7854, dtype=torch.float, device=torch_device), rtol=1e-3, atol=1e-3
)
loss.backward()
# check last grads to cover all probable errors
grad_slice_word = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
expected_grad_slice_word = torch.tensor(
[0.0004, 0.0003, 0.0006, -0.0004, 0.0002],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_1 = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
expected_grad_slice_pos_fac_1 = torch.tensor(
[-0.3792, 0.5593, -1.6993, 0.2033, 0.4131],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_2 = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
expected_grad_slice_pos_fac_2 = torch.tensor(
[-1.4212, -0.3201, -1.1944, 0.1258, 0.2856],
dtype=torch.float,
device=torch_device,
)
torch.testing.assert_close(grad_slice_word, expected_grad_slice_word, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, rtol=1e-3, atol=1e-3)
@slow
def test_pretrained_generate_crime_and_punish(self):
model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment").to(torch_device)
tokenizer = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
model.eval()
input_ids = tokenizer.encode("A few months later", return_tensors="pt").to(torch_device)
output_ids = model.generate(
input_ids, max_length=50, num_beams=4, early_stopping=True, do_sample=False, num_hashes=8
)
output = tokenizer.decode(output_ids[0])
self.assertEqual(
output,
"A few months later state expression in his ideas, at the first entrance. He was positively for an inst",
)
@slow
def test_pretrained_generate_use_cache_equality(self):
model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment").to(torch_device)
tokenizer = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
model.eval()
input_ids = tokenizer.encode("A few months later", return_tensors="pt").to(torch_device)
output_ids_with_cache = model.generate(input_ids, max_length=130, num_hashes=8, use_cache=False)
output_ids_without_cache = model.generate(input_ids, max_length=130, num_hashes=8, use_cache=True)
output_with_cache = tokenizer.decode(output_ids_with_cache[0])
output_without_cache = tokenizer.decode(output_ids_without_cache[0])
self.assertEqual(output_with_cache, output_without_cache)
| transformers/tests/models/reformer/test_modeling_reformer.py/0 | {
"file_path": "transformers/tests/models/reformer/test_modeling_reformer.py",
"repo_id": "transformers",
"token_count": 27714
} | 530 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "weiweishi/roc-bert-base-zh"
tokenizer_class = RoCBertTokenizer
rust_tokenizer_class = None
test_rust_tokenizer = False
space_between_special_tokens = True
from_pretrained_filter = filter_non_english
@classmethod
def setUpClass(cls):
super().setUpClass()
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
word_shape = {}
word_pronunciation = {}
for i, value in enumerate(vocab_tokens):
word_shape[value] = i
word_pronunciation[value] = i
cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
cls.word_shape_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["word_shape_file"])
cls.word_pronunciation_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["word_pronunciation_file"])
with open(cls.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
with open(cls.word_shape_file, "w", encoding="utf-8") as word_shape_writer:
json.dump(word_shape, word_shape_writer, ensure_ascii=False)
with open(cls.word_pronunciation_file, "w", encoding="utf-8") as word_pronunciation_writer:
json.dump(word_pronunciation, word_pronunciation_writer, ensure_ascii=False)
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file, self.word_shape_file, self.word_pronunciation_file)
tokens = tokenizer.tokenize("你好[SEP]你是谁")
self.assertListEqual(tokens, ["你", "好", "[SEP]", "你", "是", "谁"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [5, 6, 2, 5, 7, 8])
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(tokens), [5, 6, 2, 5, 7, 8])
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(tokens), [5, 6, 2, 5, 7, 8])
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_chinese with BasicTokenizer->RoCBertBasicTokenizer
def test_chinese(self):
tokenizer = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535a\u63a8zz"), ["ah", "\u535a", "\u63a8", "zz"])
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_basic_tokenizer_lower with BasicTokenizer->RoCBertBasicTokenizer
def test_basic_tokenizer_lower(self):
tokenizer = RoCBertBasicTokenizer(do_lower_case=True)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["hello", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00e9llo"), ["hello"])
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_basic_tokenizer_lower_strip_accents_false with BasicTokenizer->RoCBertBasicTokenizer
def test_basic_tokenizer_lower_strip_accents_false(self):
tokenizer = RoCBertBasicTokenizer(do_lower_case=True, strip_accents=False)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hällo", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00e9llo"), ["h\u00e9llo"])
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_basic_tokenizer_lower_strip_accents_true with BasicTokenizer->RoCBertBasicTokenizer
def test_basic_tokenizer_lower_strip_accents_true(self):
tokenizer = RoCBertBasicTokenizer(do_lower_case=True, strip_accents=True)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00e9llo"), ["hello"])
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_basic_tokenizer_lower_strip_accents_default with BasicTokenizer->RoCBertBasicTokenizer
def test_basic_tokenizer_lower_strip_accents_default(self):
tokenizer = RoCBertBasicTokenizer(do_lower_case=True)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00e9llo"), ["hello"])
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_basic_tokenizer_no_lower with BasicTokenizer->RoCBertBasicTokenizer
def test_basic_tokenizer_no_lower(self):
tokenizer = RoCBertBasicTokenizer(do_lower_case=False)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["HeLLo", "!", "how", "Are", "yoU", "?"]
)
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_basic_tokenizer_no_lower_strip_accents_false with BasicTokenizer->RoCBertBasicTokenizer
def test_basic_tokenizer_no_lower_strip_accents_false(self):
tokenizer = RoCBertBasicTokenizer(do_lower_case=False, strip_accents=False)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HäLLo", "!", "how", "Are", "yoU", "?"]
)
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_basic_tokenizer_no_lower_strip_accents_true with BasicTokenizer->RoCBertBasicTokenizer
def test_basic_tokenizer_no_lower_strip_accents_true(self):
tokenizer = RoCBertBasicTokenizer(do_lower_case=False, strip_accents=True)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HaLLo", "!", "how", "Are", "yoU", "?"]
)
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_basic_tokenizer_respects_never_split_tokens with BasicTokenizer->RoCBertBasicTokenizer
def test_basic_tokenizer_respects_never_split_tokens(self):
tokenizer = RoCBertBasicTokenizer(do_lower_case=False, never_split=["[UNK]"])
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]"), ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]
)
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_wordpiece_tokenizer with WordpieceTokenizer->RoCBertWordpieceTokenizer
def test_wordpiece_tokenizer(self):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
vocab = {}
for i, token in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = RoCBertWordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize(""), [])
self.assertListEqual(tokenizer.tokenize("unwanted running"), ["un", "##want", "##ed", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_is_whitespace
def test_is_whitespace(self):
self.assertTrue(_is_whitespace(" "))
self.assertTrue(_is_whitespace("\t"))
self.assertTrue(_is_whitespace("\r"))
self.assertTrue(_is_whitespace("\n"))
self.assertTrue(_is_whitespace("\u00a0"))
self.assertFalse(_is_whitespace("A"))
self.assertFalse(_is_whitespace("-"))
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_is_control
def test_is_control(self):
self.assertTrue(_is_control("\u0005"))
self.assertFalse(_is_control("A"))
self.assertFalse(_is_control(" "))
self.assertFalse(_is_control("\t"))
self.assertFalse(_is_control("\r"))
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_is_punctuation
def test_is_punctuation(self):
self.assertTrue(_is_punctuation("-"))
self.assertTrue(_is_punctuation("$"))
self.assertTrue(_is_punctuation("`"))
self.assertTrue(_is_punctuation("."))
self.assertFalse(_is_punctuation("A"))
self.assertFalse(_is_punctuation(" "))
def test_clean_text(self):
tokenizer = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(t) for t in ["Test", "\xad", "test"]], [["[UNK]"], [], ["[UNK]"]])
if self.test_rust_tokenizer:
rust_tokenizer = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(t) for t in ["Test", "\xad", "test"]], [["[UNK]"], [], ["[UNK]"]]
)
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_offsets_with_special_characters
def test_offsets_with_special_characters(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs)
sentence = f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
tokens = tokenizer_r.encode_plus(
sentence,
return_attention_mask=False,
return_token_type_ids=False,
return_offsets_mapping=True,
add_special_tokens=True,
)
do_lower_case = tokenizer_r.do_lower_case if hasattr(tokenizer_r, "do_lower_case") else False
expected_results = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results], tokenizer_r.convert_ids_to_tokens(tokens["input_ids"])
)
self.assertEqual([e[0] for e in expected_results], tokens["offset_mapping"])
# Copied from tests.models.bert.test_tokenization_bert.BertTokenizationTest.test_change_tokenize_chinese_chars
def test_change_tokenize_chinese_chars(self):
list_of_commun_chinese_char = ["的", "人", "有"]
text_with_chinese_char = "".join(list_of_commun_chinese_char)
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
kwargs["tokenize_chinese_chars"] = True
tokenizer_p = self.get_tokenizer(pretrained_name, **kwargs)
tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs)
ids_without_spe_char_p = tokenizer_p.encode(text_with_chinese_char, add_special_tokens=False)
ids_without_spe_char_r = tokenizer_r.encode(text_with_chinese_char, add_special_tokens=False)
tokens_without_spe_char_r = tokenizer_r.convert_ids_to_tokens(ids_without_spe_char_r)
tokens_without_spe_char_p = tokenizer_p.convert_ids_to_tokens(ids_without_spe_char_p)
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(tokens_without_spe_char_p, list_of_commun_chinese_char)
self.assertListEqual(tokens_without_spe_char_r, list_of_commun_chinese_char)
kwargs["tokenize_chinese_chars"] = False
tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs)
tokenizer_p = self.get_tokenizer(pretrained_name, **kwargs)
ids_without_spe_char_r = tokenizer_r.encode(text_with_chinese_char, add_special_tokens=False)
ids_without_spe_char_p = tokenizer_p.encode(text_with_chinese_char, add_special_tokens=False)
tokens_without_spe_char_r = tokenizer_r.convert_ids_to_tokens(ids_without_spe_char_r)
tokens_without_spe_char_p = tokenizer_p.convert_ids_to_tokens(ids_without_spe_char_p)
# it is expected that only the first Chinese character is not preceded by "##".
expected_tokens = [
f"##{token}" if idx != 0 else token for idx, token in enumerate(list_of_commun_chinese_char)
]
self.assertListEqual(tokens_without_spe_char_p, expected_tokens)
self.assertListEqual(tokens_without_spe_char_r, expected_tokens)
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_class(self.vocab_file, self.word_shape_file, self.word_pronunciation_file)
text = tokenizer.encode("你好", add_special_tokens=False)
text_2 = tokenizer.encode("你是谁", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_2 + [2]
def test_prepare_for_model(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
string_sequence = "你好，你是谁"
tokens = tokenizer.tokenize(string_sequence)
tokens_ids = tokenizer.convert_tokens_to_ids(tokens)
tokens_shape_ids = tokenizer.convert_tokens_to_shape_ids(tokens)
tokens_proun_ids = tokenizer.convert_tokens_to_pronunciation_ids(tokens)
prepared_input_dict = tokenizer.prepare_for_model(
tokens_ids, tokens_shape_ids, tokens_proun_ids, add_special_tokens=True
)
input_dict = tokenizer.encode_plus(string_sequence, add_special_tokens=True)
self.assertEqual(input_dict, prepared_input_dict)
| transformers/tests/models/roc_bert/test_tokenization_roc_bert.py/0 | {
"file_path": "transformers/tests/models/roc_bert/test_tokenization_roc_bert.py",
"repo_id": "transformers",
"token_count": 7475
} | 531 |
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Swin2SR model."""
import unittest
from transformers import Swin2SRConfig
from transformers.testing_utils import Expectations, require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import Swin2SRForImageSuperResolution, Swin2SRModel
if is_vision_available():
from PIL import Image
from transformers import Swin2SRImageProcessor
class Swin2SRModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=1,
num_channels=3,
num_channels_out=1,
embed_dim=16,
depths=[1, 2, 1],
num_heads=[2, 2, 4],
window_size=2,
mlp_ratio=2.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
is_training=True,
scope=None,
use_labels=False,
upscale=2,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.num_channels_out = num_channels_out
self.embed_dim = embed_dim
self.depths = depths
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.patch_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.is_training = is_training
self.scope = scope
self.use_labels = use_labels
self.upscale = upscale
# here we set some attributes to make tests pass
self.num_hidden_layers = len(depths)
self.hidden_size = embed_dim
self.seq_length = (image_size // patch_size) ** 2
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return Swin2SRConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
num_channels_out=self.num_channels_out,
embed_dim=self.embed_dim,
depths=self.depths,
num_heads=self.num_heads,
window_size=self.window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
drop_path_rate=self.drop_path_rate,
hidden_act=self.hidden_act,
use_absolute_embeddings=self.use_absolute_embeddings,
path_norm=self.patch_norm,
layer_norm_eps=self.layer_norm_eps,
initializer_range=self.initializer_range,
upscale=self.upscale,
)
def create_and_check_model(self, config, pixel_values, labels):
model = Swin2SRModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.embed_dim, self.image_size, self.image_size)
)
def create_and_check_for_image_super_resolution(self, config, pixel_values, labels):
model = Swin2SRForImageSuperResolution(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_image_size = self.image_size * self.upscale
self.parent.assertEqual(
result.reconstruction.shape,
(self.batch_size, self.num_channels_out, expected_image_size, expected_image_size),
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Swin2SRModel, Swin2SRForImageSuperResolution) if is_torch_available() else ()
pipeline_model_mapping = (
{"image-feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torchscript = False
test_torch_exportable = True
def setUp(self):
self.model_tester = Swin2SRModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=Swin2SRConfig,
embed_dim=37,
has_text_modality=False,
common_properties=["image_size", "patch_size", "num_channels"],
)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_for_image_super_resolution(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_super_resolution(*config_and_inputs)
# TODO: check if this works again for PyTorch 2.x.y
@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip(reason="Swin2SR does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Swin2SR does not support training yet")
def test_training(self):
pass
@unittest.skip(reason="Swin2SR does not support training yet")
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
def test_model_get_set_embeddings(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
@slow
def test_model_from_pretrained(self):
model_name = "caidas/swin2SR-classical-sr-x2-64"
model = Swin2SRModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# overwriting because of `logit_scale` parameter
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if "logit_scale" in name:
continue
if param.requires_grad:
# See PR #38607 (to avoid flakiness)
data = torch.flatten(param.data)
n_elements = torch.numel(data)
# skip 2.5% of elements on each side to avoid issues caused by `nn.init.trunc_normal_` described in
# https://github.com/huggingface/transformers/pull/27906#issuecomment-1846951332
n_elements_to_skip_on_each_side = int(n_elements * 0.025)
data_to_check = torch.sort(data).values
if n_elements_to_skip_on_each_side > 0:
data_to_check = data_to_check[n_elements_to_skip_on_each_side:-n_elements_to_skip_on_each_side]
self.assertIn(
((data_to_check.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class._from_config(config, attn_implementation="eager")
config = model.config
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
expected_num_attentions = len(self.model_tester.depths)
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
window_size_squared = config.window_size**2
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 1, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
)
@require_vision
@require_torch
@slow
class Swin2SRModelIntegrationTest(unittest.TestCase):
def test_inference_image_super_resolution_head(self):
processor = Swin2SRImageProcessor()
model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64").to(torch_device)
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size([1, 3, 976, 1296])
self.assertEqual(outputs.reconstruction.shape, expected_shape)
expected_slice = torch.tensor(
[[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]
).to(torch_device)
torch.testing.assert_close(outputs.reconstruction[0, 0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
def test_inference_fp16(self):
processor = Swin2SRImageProcessor()
model = Swin2SRForImageSuperResolution.from_pretrained(
"caidas/swin2SR-classical-sr-x2-64", dtype=torch.float16
).to(torch_device)
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=image, return_tensors="pt").to(model.dtype).to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size([1, 3, 976, 1296])
self.assertEqual(outputs.reconstruction.shape, expected_shape)
expectations = Expectations(
{
(None, None): [[0.5454, 0.5542, 0.5640], [0.5518, 0.5562, 0.5649], [0.5391, 0.5425, 0.5620]],
("cuda", 8): [[0.5454, 0.5547, 0.5640], [0.5522, 0.5562, 0.5649], [0.5391, 0.5425, 0.5620]],
}
)
expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device, dtype=model.dtype)
torch.testing.assert_close(outputs.reconstruction[0, 0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)
| transformers/tests/models/swin2sr/test_modeling_swin2sr.py/0 | {
"file_path": "transformers/tests/models/swin2sr/test_modeling_swin2sr.py",
"repo_id": "transformers",
"token_count": 6692
} | 532 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch VisionTextDualEncoder model."""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_bert import BertModelTester
from ..clip.test_modeling_clip import CLIPVisionModelTester
from ..deit.test_modeling_deit import DeiTModelTester
from ..roberta.test_modeling_roberta import RobertaModelTester
from ..vit.test_modeling_vit import ViTModelTester
if is_torch_available():
import torch
from transformers import (
BertModel,
CLIPVisionModel,
DeiTModel,
RobertaModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderModel,
ViTModel,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
# Inspired by
# https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py
# From PyTorch internals
def to_2tuple(x):
if isinstance(x, collections.abc.Iterable):
return x
return (x, x)
@require_torch
class VisionTextDualEncoderMixin:
def get_vision_text_model(self, config, text_config):
pass
def prepare_config_and_inputs(self):
pass
def get_pretrained_model_and_inputs(self):
pass
def check_model_from_pretrained_configs(
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
):
config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
model = VisionTextDualEncoderModel(config)
model.to(torch_device)
model.eval()
output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], config.projection_dim))
self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], config.projection_dim))
def check_vision_text_dual_encoder_model(
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
):
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
model.to(torch_device)
model.eval()
output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim))
self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim))
def check_vision_text_dual_encoder_from_pretrained(
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
):
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
kwargs = {"vision_model": vision_model, "text_model": text_model}
model = VisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
model.to(torch_device)
model.eval()
output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim))
self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim))
def check_save_load(self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs):
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
out_1 = output[0].cpu().numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = VisionTextDualEncoderModel.from_pretrained(tmpdirname).eval()
model.to(torch_device)
after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
out_2 = after_output[0].cpu().numpy()
max_diff = np.amax(np.abs(out_2 - out_1))
self.assertLessEqual(max_diff, 1e-5)
def check_vision_text_output_attention(
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
):
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
model.to(torch_device)
model.eval()
output = model(
input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True
)
vision_attentions = output.vision_model_output.attentions
self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers)
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
image_size = to_2tuple(vision_model.config.image_size)
patch_size = to_2tuple(vision_model.config.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
seq_len = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len))
text_attentions = output.text_model_output.attentions
self.assertEqual(len(text_attentions), text_config.num_hidden_layers)
self.assertEqual(
text_attentions[0].shape[-3:],
(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
)
def test_vision_text_dual_encoder_model(self):
inputs_dict = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**inputs_dict)
def test_model_from_pretrained_configs(self):
inputs_dict = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**inputs_dict)
def test_vision_text_dual_encoder_from_pretrained(self):
inputs_dict = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**inputs_dict)
def test_save_load(self):
inputs_dict = self.prepare_config_and_inputs()
self.check_save_load(**inputs_dict)
def test_vision_text_output_attention(self):
inputs_dict = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**inputs_dict)
@slow
def test_real_model_save_load_from_pretrained(self):
model_2, inputs = self.get_pretrained_model_and_inputs()
model_2.to(torch_device)
with torch.no_grad():
outputs = model_2(**inputs)
out_2 = outputs[0].cpu().numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_2.save_pretrained(tmp_dirname)
model_1 = VisionTextDualEncoderModel.from_pretrained(tmp_dirname)
model_1.to(torch_device)
after_outputs = model_1(**inputs)
out_1 = after_outputs[0].cpu().numpy()
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
@require_torch
class ViTBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = VisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit", "hf-internal-testing/tiny-bert"
)
batch_size = 13
pixel_values = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
]
)
input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
attention_mask = random_attention_mask([batch_size, 4])
inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def get_vision_text_model(self, vision_config, text_config):
vision_model = ViTModel(vision_config).eval()
text_model = BertModel(text_config).eval()
return vision_model, text_model
def prepare_config_and_inputs(self):
vit_model_tester = ViTModelTester(self)
bert_model_tester = BertModelTester(self)
vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
vision_config, pixel_values, _ = vision_config_and_inputs
(
text_config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_torch
class DeiTRobertaModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = VisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-deit", "hf-internal-testing/tiny-random-roberta"
)
batch_size = 13
pixel_values = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
]
)
input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
attention_mask = random_attention_mask([batch_size, 4])
inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def check_vision_text_output_attention(
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
):
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
model.to(torch_device)
model.eval()
output = model(
input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True
)
vision_attentions = output.vision_model_output.attentions
self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers)
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
image_size = to_2tuple(vision_model.config.image_size)
patch_size = to_2tuple(vision_model.config.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
seq_len = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len))
text_attentions = output.text_model_output.attentions
self.assertEqual(len(text_attentions), text_config.num_hidden_layers)
self.assertEqual(
text_attentions[0].shape[-3:],
(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
)
def get_vision_text_model(self, vision_config, text_config):
vision_model = DeiTModel(vision_config).eval()
text_model = RobertaModel(text_config).eval()
return vision_model, text_model
def prepare_config_and_inputs(self):
vit_model_tester = DeiTModelTester(self)
bert_model_tester = RobertaModelTester(self)
vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
vision_config, pixel_values, _ = vision_config_and_inputs
(
text_config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_torch
class CLIPVisionBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = VisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-clip", "hf-internal-testing/tiny-bert"
)
batch_size = 13
pixel_values = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
]
)
input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
attention_mask = random_attention_mask([batch_size, 4])
inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def get_vision_text_model(self, vision_config, text_config):
vision_model = CLIPVisionModel(vision_config).eval()
text_model = BertModel(text_config).eval()
return vision_model, text_model
def prepare_config_and_inputs(self):
clip_model_tester = CLIPVisionModelTester(self)
bert_model_tester = BertModelTester(self)
vision_config_and_inputs = clip_model_tester.prepare_config_and_inputs()
text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
vision_config, pixel_values = vision_config_and_inputs
(
text_config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_torch
class VisionTextDualEncoderIntegrationTest(unittest.TestCase):
@slow
def test_inference(self):
model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", logit_scale_init_value=1.0)
processor = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(
text=["una foto di un gatto", "una foto di un cane"], images=image, padding=True, return_tensors="pt"
)
outputs = model(**inputs)
# verify the logits
self.assertEqual(outputs.logits_per_image.shape, (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
self.assertEqual(
outputs.logits_per_text.shape,
(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),
)
expected_logits = torch.tensor([[1.2284727, 0.3104122]])
torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3)
| transformers/tests/models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py/0 | {
"file_path": "transformers/tests/models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py",
"repo_id": "transformers",
"token_count": 7564
} | 533 |
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import random
import unittest
import numpy as np
from transformers import Wav2Vec2Config, Wav2Vec2FeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
global_rng = random.Random()
# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
class Wav2Vec2FeatureExtractionTester:
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=1,
padding_value=0.0,
sampling_rate=16000,
return_attention_mask=True,
do_normalize=True,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.feature_size = feature_size
self.padding_value = padding_value
self.sampling_rate = sampling_rate
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
def _flatten(list_of_lists):
return list(itertools.chain(*list_of_lists))
if equal_length:
speech_inputs = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
speech_inputs = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
speech_inputs = [np.asarray(x) for x in speech_inputs]
return speech_inputs
class Wav2Vec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = Wav2Vec2FeatureExtractor
def setUp(self):
self.feat_extract_tester = Wav2Vec2FeatureExtractionTester(self)
def _check_zero_mean_unit_variance(self, input_vector):
self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test not batched input
encoded_sequences_1 = feat_extract(speech_inputs[0], return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs[0], return_tensors="np").input_values
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_zero_mean_unit_variance_normalization_np(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 1600, None]
for max_length, padding in zip(max_lengths, paddings):
processed = feat_extract(speech_inputs, padding=padding, max_length=max_length, return_tensors="np")
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800])
self.assertTrue(input_values[0][800:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[1][:1000])
self.assertTrue(input_values[0][1000:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[2][:1200])
def test_zero_mean_unit_variance_normalization(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
lengths = range(800, 1400, 200)
speech_inputs = [floats_list((1, x))[0] for x in lengths]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 1600, None]
for max_length, padding in zip(max_lengths, paddings):
processed = feat_extract(speech_inputs, max_length=max_length, padding=padding)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800])
self._check_zero_mean_unit_variance(input_values[1][:1000])
self._check_zero_mean_unit_variance(input_values[2][:1200])
def test_zero_mean_unit_variance_normalization_trunc_np_max_length(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = feat_extract(
speech_inputs, truncation=True, max_length=1000, padding="max_length", return_tensors="np"
)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800])
self._check_zero_mean_unit_variance(input_values[1])
self._check_zero_mean_unit_variance(input_values[2])
def test_zero_mean_unit_variance_normalization_trunc_np_longest(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = feat_extract(
speech_inputs, truncation=True, max_length=1000, padding="longest", return_tensors="np"
)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800])
self._check_zero_mean_unit_variance(input_values[1, :1000])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000))
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = feat_extract(
speech_inputs, truncation=True, max_length=2000, padding="longest", return_tensors="np"
)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800])
self._check_zero_mean_unit_variance(input_values[1, :1000])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200))
@require_torch
def test_double_precision_pad(self):
import torch
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_speech_inputs = np.random.rand(100).astype(np.float64)
py_speech_inputs = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
np_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_values.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_values.dtype == torch.float32)
@slow
@require_torch
def test_pretrained_checkpoints_are_set_correctly(self):
# this test makes sure that models that are using
# group norm don't have their feature extractor return the
# attention_mask
model_id = "facebook/wav2vec2-base-960h"
config = Wav2Vec2Config.from_pretrained(model_id)
feat_extract = Wav2Vec2FeatureExtractor.from_pretrained(model_id)
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask, config.feat_extract_norm == "layer")
| transformers/tests/models/wav2vec2/test_feature_extraction_wav2vec2.py/0 | {
"file_path": "transformers/tests/models/wav2vec2/test_feature_extraction_wav2vec2.py",
"repo_id": "transformers",
"token_count": 4341
} | 534 |
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import WhisperFeatureExtractor
from transformers.testing_utils import (
check_json_file_has_correct_format,
require_torch,
require_torch_accelerator,
)
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
global_rng = random.Random()
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
class WhisperFeatureExtractionTester:
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=10,
hop_length=160,
chunk_length=8,
padding_value=0.0,
sampling_rate=4_000,
return_attention_mask=False,
do_normalize=True,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.padding_value = padding_value
self.sampling_rate = sampling_rate
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
self.feature_size = feature_size
self.chunk_length = chunk_length
self.hop_length = hop_length
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
def _flatten(list_of_lists):
return list(itertools.chain(*list_of_lists))
if equal_length:
speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
else:
# make sure that inputs increase in size
speech_inputs = [
floats_list((x, self.feature_size))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
speech_inputs = [np.asarray(x) for x in speech_inputs]
return speech_inputs
class WhisperFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = WhisperFeatureExtractor
def setUp(self):
self.feat_extract_tester = WhisperFeatureExtractionTester(self)
def test_feat_extract_from_and_save_pretrained(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
mel_1 = feat_extract_first.mel_filters
mel_2 = feat_extract_second.mel_filters
self.assertTrue(np.allclose(mel_1, mel_2))
self.assertEqual(dict_first, dict_second)
def test_feat_extract_to_json_file(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "feat_extract.json")
feat_extract_first.to_json_file(json_file_path)
feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
mel_1 = feat_extract_first.mel_filters
mel_2 = feat_extract_second.mel_filters
self.assertTrue(np.allclose(mel_1, mel_2))
self.assertEqual(dict_first, dict_second)
def test_feat_extract_from_pretrained_kwargs(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
feat_extract_second = self.feature_extraction_class.from_pretrained(
tmpdirname, feature_size=2 * self.feat_extract_dict["feature_size"]
)
mel_1 = feat_extract_first.mel_filters
mel_2 = feat_extract_second.mel_filters
self.assertTrue(2 * mel_1.shape[1] == mel_2.shape[1])
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test feature size
input_features = feature_extractor(np_speech_inputs, padding="max_length", return_tensors="np").input_features
self.assertTrue(input_features.ndim == 3)
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames)
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size)
# Test not batched input
encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test truncation required
speech_inputs = [floats_list((1, x))[0] for x in range(200, (feature_extractor.n_samples + 500), 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
speech_inputs_truncated = [x[: feature_extractor.n_samples] for x in speech_inputs]
np_speech_inputs_truncated = [np.asarray(speech_input) for speech_input in speech_inputs_truncated]
encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs_truncated, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_dither(self):
np.random.seed(42) # seed the dithering randn()
# Tests that features with and without little dithering are similar, but not the same
dict_no_dither = self.feat_extract_tester.prepare_feat_extract_dict()
dict_no_dither["dither"] = 0.0
dict_dither = self.feat_extract_tester.prepare_feat_extract_dict()
dict_dither["dither"] = 0.00003 # approx. 1/32k
feature_extractor_no_dither = self.feature_extraction_class(**dict_no_dither)
feature_extractor_dither = self.feature_extraction_class(**dict_dither)
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# compute features
input_features_no_dither = feature_extractor_no_dither(
np_speech_inputs, padding=True, return_tensors="np", sampling_rate=dict_no_dither["sampling_rate"]
).input_features
input_features_dither = feature_extractor_dither(
np_speech_inputs, padding=True, return_tensors="np", sampling_rate=dict_dither["sampling_rate"]
).input_features
# test there is a difference between features (there's added noise to input signal)
diff = input_features_dither - input_features_no_dither
# features are not identical
self.assertTrue(np.abs(diff).mean() > 1e-6)
# features are not too different
self.assertTrue(np.abs(diff).mean() <= 1e-4)
self.assertTrue(np.abs(diff).max() <= 5e-3)
@require_torch
def test_double_precision_pad(self):
import torch
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
py_speech_inputs = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_features.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_features.dtype == torch.float32)
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id")[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
@require_torch_accelerator
@require_torch
def test_torch_integration(self):
# fmt: off
EXPECTED_INPUT_FEATURES = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
]
)
# fmt: on
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
self.assertEqual(input_features.shape, (1, 80, 3000))
torch.testing.assert_close(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)
@unittest.mock.patch("transformers.models.whisper.feature_extraction_whisper.is_torch_available", lambda: False)
def test_numpy_integration(self):
# fmt: off
EXPECTED_INPUT_FEATURES = np.array(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
]
)
# fmt: on
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="np").input_features
self.assertEqual(input_features.shape, (1, 80, 3000))
self.assertTrue(np.allclose(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, atol=1e-4))
def test_zero_mean_unit_variance_normalization_trunc_np_longest(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
audio = self._load_datasamples(1)[0]
audio = ((audio - audio.min()) / (audio.max() - audio.min())) * 65535 # Rescale to [0, 65535] to show issue
audio = feat_extract.zero_mean_unit_var_norm([audio], attention_mask=None)[0]
self.assertTrue(np.all(np.mean(audio) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(audio) - 1) < 1e-3))
@require_torch_accelerator
@require_torch
def test_torch_integration_batch(self):
# fmt: off
EXPECTED_INPUT_FEATURES = torch.tensor(
[
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
],
[
-0.4696, -0.0751, 0.0276, -0.0312, -0.0540, -0.0383, 0.1295, 0.0568,
-0.2071, -0.0548, 0.0389, -0.0316, -0.2346, -0.1068, -0.0322, 0.0475,
-0.1709, -0.0041, 0.0872, 0.0537, 0.0075, -0.0392, 0.0371, 0.0189,
-0.1522, -0.0270, 0.0744, 0.0738, -0.0245, -0.0667
],
[
-0.2337, -0.0060, -0.0063, -0.2353, -0.0431, 0.1102, -0.1492, -0.0292,
0.0787, -0.0608, 0.0143, 0.0582, 0.0072, 0.0101, -0.0444, -0.1701,
-0.0064, -0.0027, -0.0826, -0.0730, -0.0099, -0.0762, -0.0170, 0.0446,
-0.1153, 0.0960, -0.0361, 0.0652, 0.1207, 0.0277
]
]
)
# fmt: on
with torch.device("cuda"):
input_speech = self._load_datasamples(3)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
self.assertEqual(input_features.shape, (3, 80, 3000))
torch.testing.assert_close(input_features[:, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)
| transformers/tests/models/whisper/test_feature_extraction_whisper.py/0 | {
"file_path": "transformers/tests/models/whisper/test_feature_extraction_whisper.py",
"repo_id": "transformers",
"token_count": 7182
} | 535 |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Zamba model."""
import math
import tempfile
import unittest
import pytest
from transformers import AutoTokenizer, ZambaConfig, is_torch_available
from transformers.testing_utils import (
is_flaky,
require_bitsandbytes,
require_flash_attn,
require_torch,
require_torch_gpu,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
ZambaForCausalLM,
ZambaForSequenceClassification,
ZambaModel,
)
from transformers.models.zamba.modeling_zamba import (
ZambaHybridDynamicCache,
)
class ZambaModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=64,
mamba_dt_rank=32,
num_hidden_layers=5,
attn_layer_offset=1,
attn_layer_period=8,
num_attention_heads=4,
num_key_value_heads=4,
n_mamba_heads=2,
intermediate_size=37,
hidden_act="gelu",
hidden_mamba_act="silu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.mamba_dt_rank = mamba_dt_rank
self.num_hidden_layers = num_hidden_layers
self.attn_layer_offset = attn_layer_offset
self.attn_layer_period = attn_layer_period
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.n_mamba_heads = n_mamba_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_mamba_act = hidden_mamba_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return ZambaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
mamba_dt_rank=self.mamba_dt_rank,
num_hidden_layers=self.num_hidden_layers,
attn_layer_offset=self.attn_layer_offset,
attn_layer_period=self.attn_layer_period,
num_attention_heads=self.num_attention_heads,
num_key_value_heads=self.num_key_value_heads,
n_mamba_heads=self.n_mamba_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_mamba_act=self.hidden_mamba_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=True,
initializer_range=self.initializer_range,
use_mamba_kernels=False,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = ZambaModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = ZambaForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids, labels=token_labels)
result = model(input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
config.add_cross_attention = True
model = ZambaForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
# Attention: Zamba needs the cache to be initialized to return a cache!
past_key_values = ZambaHybridDynamicCache(config, input_ids.shape[0], model.dtype, device=model.device)
outputs = model(
input_ids,
attention_mask=input_mask,
past_key_values=past_key_values,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
cache_position=torch.arange(
input_ids.shape[1], input_ids.shape[1] + next_tokens.shape[1], device=model.device
),
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_sequence_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = ZambaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class ZambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
ZambaModel,
ZambaForCausalLM,
ZambaForSequenceClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": ZambaModel,
"text-classification": ZambaForSequenceClassification,
"text-generation": ZambaForCausalLM,
"zero-shot": ZambaForSequenceClassification,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
def _check_past_key_values_for_generate(self, batch_size, decoder_past_key_values, cache_length, config):
self.assertIsInstance(decoder_past_key_values, ZambaHybridDynamicCache)
# (batch, head, seq_length, head_features)
expected_shape = (
batch_size,
config.num_key_value_heads if hasattr(config, "num_key_value_heads") else config.num_attention_heads,
cache_length,
config.hidden_size // config.num_attention_heads,
)
self.assertListEqual(
[key_tensor.shape for key_tensor in decoder_past_key_values.key_cache],
[expected_shape] * len(decoder_past_key_values.key_cache),
)
self.assertListEqual(
[value_cache.shape for value_cache in decoder_past_key_values.value_cache],
[expected_shape] * len(decoder_past_key_values.value_cache),
)
def setUp(self):
self.model_tester = ZambaModelTester(self)
self.config_tester = ConfigTester(self, config_class=ZambaConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_casual_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
@is_flaky(description="TODO: ydshieh")
def test_initialization(self):
r"""
Overriding the test_initialization test as the A_log and D params of the Mamba block are initialized differently
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
if "A_log" in name:
A = torch.arange(1, config.mamba_d_state + 1, dtype=torch.float32)[None, :]
intermediate_dim = config.mamba_expand * config.hidden_size
A = A.expand(intermediate_dim, -1).reshape(
config.n_mamba_heads, intermediate_dim // config.n_mamba_heads, -1
)
torch.testing.assert_close(param.data, torch.log(A), rtol=1e-5, atol=1e-5)
elif "D" in name:
# check if it's a ones like
torch.testing.assert_close(param.data, torch.ones_like(param.data), rtol=1e-5, atol=1e-5)
elif "x_proj" in name or "dt_proj_weight" in name:
self.assertIn(
((param.data.mean() * 1e2).round() / 1e2).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized (raw value {param.data.mean()})",
)
elif "dt_proj_bias" in name:
dt = torch.exp(
torch.tensor([0, 1]) * (math.log(config.time_step_max) - math.log(config.time_step_min))
+ math.log(config.time_step_min)
).clamp(min=config.time_step_floor)
inv_dt = dt + torch.log(-torch.expm1(-dt))
if param.requires_grad:
self.assertTrue(param.data.max().item() <= inv_dt[1])
self.assertTrue(param.data.min().item() >= inv_dt[0])
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_mismatched_shapes_have_properly_initialized_weights(self):
r"""
Overriding the test_mismatched_shapes_have_properly_initialized_weights test because A_log and D params of the
Mamba block are initialized differently and we tested that in test_initialization
"""
self.skipTest("Cumbersome and redundant for Zamba")
def test_attention_outputs(self):
r"""
Overriding the test_attention_outputs test as the Zamba model outputs attention only for its attention layers
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
expected_num_attentions = (
math.ceil(
(self.model_tester.num_hidden_layers - self.model_tester.attn_layer_offset)
/ self.model_tester.attn_layer_period
)
+ 1
)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class._from_config(config, attn_implementation="eager")
config = model.config
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def _get_input_ids_and_config(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
return config, input_ids, input_mask
def test_left_padding_compatibility(self):
r"""
Overriding the test_left_padding_compatibility test as the mamba layers accentuate the numerical differences
effect of the left padding discussed in the issue in the note. Using a more permissive tolerance value.
"""
import inspect
# NOTE: left-padding results in small numerical differences. This is expected.
# See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535
# First, filter out models that don't support left padding - generative and decoder-only.
# Zamba is a decoder-only architecture
decoder_only_classes = self.all_generative_model_classes
# Then, test left-padding
def _prepare_model_kwargs(input_ids, attention_mask, signature):
model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask}
if "position_ids" in signature:
position_ids = torch.cumsum(attention_mask, dim=-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
model_kwargs["position_ids"] = position_ids
if "cache_position" in signature:
cache_position = torch.arange(input_ids.shape[-1], device=torch_device)
model_kwargs["cache_position"] = cache_position
return model_kwargs
for model_class in decoder_only_classes:
config, input_ids, attention_mask = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
signature = inspect.signature(model.forward).parameters.keys()
# Without padding
model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, signature)
next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :]
# With left-padding (length 32)
pad_size = (input_ids.shape[0], 32)
padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * config.pad_token_id
padded_input_ids = torch.cat((padding, input_ids), dim=1)
padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
model_kwargs = _prepare_model_kwargs(padded_input_ids, padded_attention_mask, signature)
next_logits_with_padding = model(**model_kwargs).logits[:, -1, :]
# They should result in very similar logits
torch.testing.assert_close(next_logits_wo_padding, next_logits_with_padding, rtol=3e-3, atol=3e-3)
@require_flash_attn
@require_torch_gpu
@require_bitsandbytes
@pytest.mark.flash_attn_test
@slow
def test_flash_attn_2_fp32_ln(self):
r"""
Overriding the test_flash_attn_2_fp32_ln test as the Zamba model, like Mixtral, doesn't support
right padding + use cache with FA2
"""
for model_class in self.all_generative_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
dummy_input = inputs_dict[model.main_input_name]
dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input))
# NOTE: Zamba does not support right padding + use_cache with FA2.
dummy_attention_mask[:, -1] = 1
model = model_class.from_pretrained(
tmpdirname,
dtype=torch.float16,
attn_implementation="flash_attention_2",
load_in_4bit=True,
)
for _, param in model.named_parameters():
# upcast only layer norms
if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
param.data = param.data.to(torch.float32)
_ = model(dummy_input)
# with attention mask
_ = model(dummy_input, attention_mask=dummy_attention_mask)
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test
@slow
def test_flash_attn_2_inference_equivalence_right_padding(self):
r"""
Overriding the test_flash_attn_2_inference_padding_right test as the Zamba model, like Mixtral, doesn't support
right padding + use cache with FA2
"""
self.skipTest(reason="Zamba flash attention does not support right padding")
@require_torch
class ZambaModelIntegrationTest(unittest.TestCase):
model = None
tokenizer = None
@classmethod
@slow
def setUpClass(cls):
model_id = "Zyphra/Zamba-7B-v1"
cls.model = ZambaForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, use_mamba_kernels=False)
cls.tokenizer = AutoTokenizer.from_pretrained(model_id)
@slow
def test_simple_generate(self):
self.model.to(torch_device)
input_ids = self.tokenizer("Hey how are you doing on this lovely evening?", return_tensors="pt")[
"input_ids"
].to(torch_device)
out = self.model.generate(input_ids, do_sample=False, max_new_tokens=10)
output_sentence = self.tokenizer.decode(out[0, :])
self.assertEqual(
output_sentence,
"<s> Hey how are you doing on this lovely evening? I hope you are all doing well. I am",
)
with torch.no_grad():
logits = self.model(input_ids=input_ids).logits
EXPECTED_LOGITS_NO_GRAD = torch.tensor(
[
-7.9375, 8.1875, 1.3984, -6.0000, -7.9375, -7.9375, -7.9375, -7.9375,
-7.9375, -7.9375, -7.9375, -7.9375, 2.7500, 13.0625, -7.9375, -7.9375,
-7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375,
-7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375,
-7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375
]
, dtype=torch.float32) # fmt: skip
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
@slow
def test_simple_batched_generate_with_padding(self):
self.model.to(torch_device)
self.tokenizer.add_special_tokens({"pad_token": "[PAD]"})
self.model.resize_token_embeddings(len(self.tokenizer))
inputs = self.tokenizer(
["Hey how are you doing on this lovely evening?", "Tell me a story"], padding=True, return_tensors="pt"
).to(torch_device)
out = self.model.generate(**inputs, do_sample=False, max_new_tokens=10)
output_sentences = self.tokenizer.batch_decode(out)
self.assertEqual(
output_sentences[0],
"<s> Hey how are you doing on this lovely evening? I hope you are all doing well. I am",
)
self.assertEqual(
output_sentences[1],
"[PAD][PAD][PAD][PAD][PAD][PAD]<s> Tell me a story about a time when you were in a difficult situation",
)
with torch.no_grad():
logits = self.model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]).logits
EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
[
-7.9375, 8.1250, 1.3594, -6.0000, -7.9375, -7.9375, -7.9375, -7.9375,
-7.9375, -7.9375, -7.9375, -7.9375, 2.7344, 13.0625, -7.9375, -7.9375,
-7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375,
-7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375,
-7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375
]
, dtype=torch.float32) # fmt: skip
EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
[
-6.3750, 3.4219, 0.6719, -5.0312, -8.5000, -8.5000, -8.5000, -8.5000,
-8.5000, -8.5000, -8.5000, -8.5000, 2.0625, 10.3750, -8.5000, -8.5000,
-8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000,
-8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000,
-8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000
]
, dtype=torch.float32) # fmt: skip
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1e-3)
| transformers/tests/models/zamba/test_modeling_zamba.py/0 | {
"file_path": "transformers/tests/models/zamba/test_modeling_zamba.py",
"repo_id": "transformers",
"token_count": 13346
} | 536 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
backend_empty_cache,
is_pipeline_test,
is_torch_available,
nested_simplify,
require_torch,
require_torch_accelerator,
slow,
torch_device,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class FillMaskPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_MASKED_LM_MAPPING
tf_model_mapping = TF_MODEL_FOR_MASKED_LM_MAPPING
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
backend_empty_cache(torch_device)
@require_torch
def test_small_model_pt(self):
unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", top_k=2, framework="pt")
outputs = unmasker("My name is <mask>")
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
{"sequence": "My name is Maul", "score": 2.2e-05, "token": 35676, "token_str": " Maul"},
{"sequence": "My name isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
],
)
outputs = unmasker("The largest city in France is <mask>")
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
{
"sequence": "The largest city in France is Maul",
"score": 2.2e-05,
"token": 35676,
"token_str": " Maul",
},
{"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
],
)
outputs = unmasker("My name is <mask>", targets=[" Patrick", " Clara", " Teven"], top_k=3)
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
{"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3499, "token_str": " Patrick"},
{"sequence": "My name is Te", "score": 2e-05, "token": 2941, "token_str": " Te"},
{"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
],
)
outputs = unmasker("My name is <mask> <mask>", top_k=2)
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
[
{
"score": 2.2e-05,
"token": 35676,
"token_str": " Maul",
"sequence": "<s>My name is Maul<mask></s>",
},
{"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
],
[
{
"score": 2.2e-05,
"token": 35676,
"token_str": " Maul",
"sequence": "<s>My name is<mask> Maul</s>",
},
{"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
],
],
)
@require_torch_accelerator
def test_fp16_casting(self):
pipe = pipeline(
"fill-mask",
model="hf-internal-testing/tiny-random-distilbert",
device=torch_device,
framework="pt",
)
# convert model to fp16
pipe.model.half()
response = pipe("Paris is the [MASK] of France.")
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(response, list)
@slow
@require_torch
def test_large_model_pt(self):
unmasker = pipeline(task="fill-mask", model="distilbert/distilroberta-base", top_k=2, framework="pt")
self.run_large_test(unmasker)
def run_large_test(self, unmasker):
outputs = unmasker("My name is <mask>")
self.assertEqual(
nested_simplify(outputs),
[
{"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
{"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
],
)
outputs = unmasker("The largest city in France is <mask>")
self.assertEqual(
nested_simplify(outputs),
[
{
"sequence": "The largest city in France is Paris",
"score": 0.251,
"token": 2201,
"token_str": " Paris",
},
{
"sequence": "The largest city in France is Lyon",
"score": 0.214,
"token": 12790,
"token_str": " Lyon",
},
],
)
outputs = unmasker("My name is <mask>", targets=[" Patrick", " Clara", " Teven"], top_k=3)
self.assertEqual(
nested_simplify(outputs),
[
{"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
{"sequence": "My name is Clara", "score": 0.000, "token": 13606, "token_str": " Clara"},
{"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
],
)
dummy_str = "Lorem ipsum dolor sit amet, consectetur adipiscing elit," * 100
outputs = unmasker(
"My name is <mask>" + dummy_str,
tokenizer_kwargs={"truncation": True},
)
simplified = nested_simplify(outputs, decimals=4)
self.assertEqual(
[{"sequence": x["sequence"][:100]} for x in simplified],
[
{"sequence": f"My name is,{dummy_str}"[:100]},
{"sequence": f"My name is:,{dummy_str}"[:100]},
],
)
self.assertEqual(
[{k: x[k] for k in x if k != "sequence"} for x in simplified],
[
{"score": 0.2819, "token": 6, "token_str": ","},
{"score": 0.0954, "token": 46686, "token_str": ":,"},
],
)
@require_torch
def test_model_no_pad_pt(self):
unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", framework="pt")
unmasker.tokenizer.pad_token_id = None
unmasker.tokenizer.pad_token = None
self.run_pipeline_test(unmasker, [])
def get_test_pipeline(
self,
model,
tokenizer=None,
image_processor=None,
feature_extractor=None,
processor=None,
dtype="float32",
):
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest(reason="The provided tokenizer has no mask token, (probably reformer or wav2vec2)")
fill_masker = FillMaskPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
dtype=dtype,
)
examples = [
f"This is another {tokenizer.mask_token} test",
]
return fill_masker, examples
def run_pipeline_test(self, fill_masker, examples):
tokenizer = fill_masker.tokenizer
model = fill_masker.model
outputs = fill_masker(
f"This is a {tokenizer.mask_token}",
)
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
outputs = fill_masker([f"This is a {tokenizer.mask_token}"])
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
outputs = fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token} great test."])
self.assertEqual(
outputs,
[
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
],
)
with self.assertRaises(ValueError):
fill_masker([None])
# No mask_token is not supported
with self.assertRaises(PipelineException):
fill_masker("This is")
self.run_test_top_k(model, tokenizer)
self.run_test_targets(model, tokenizer)
self.run_test_top_k_targets(model, tokenizer)
self.fill_mask_with_duplicate_targets_and_top_k(model, tokenizer)
self.fill_mask_with_multiple_masks(model, tokenizer)
def run_test_targets(self, model, tokenizer):
vocab = tokenizer.get_vocab()
targets = sorted(vocab.keys())[:2]
# Pipeline argument
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer, targets=targets)
outputs = fill_masker(f"This is a {tokenizer.mask_token}")
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
target_ids = {vocab[el] for el in targets}
self.assertEqual({el["token"] for el in outputs}, target_ids)
processed_targets = [tokenizer.decode([x]) for x in target_ids]
self.assertEqual({el["token_str"] for el in outputs}, set(processed_targets))
# Call argument
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=targets)
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
target_ids = {vocab[el] for el in targets}
self.assertEqual({el["token"] for el in outputs}, target_ids)
processed_targets = [tokenizer.decode([x]) for x in target_ids]
self.assertEqual({el["token_str"] for el in outputs}, set(processed_targets))
# Score equivalence
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=targets)
tokens = [top_mask["token_str"] for top_mask in outputs]
scores = [top_mask["score"] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(tokens) == set(targets):
unmasked_targets = fill_masker(f"This is a {tokenizer.mask_token}", targets=tokens)
target_scores = [top_mask["score"] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(scores), nested_simplify(target_scores))
# Raises with invalid
with self.assertRaises(ValueError):
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=[])
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(ValueError):
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=[""])
with self.assertRaises(ValueError):
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets="")
def run_test_top_k(self, model, tokenizer):
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer, top_k=2)
outputs = fill_masker(f"This is a {tokenizer.mask_token}")
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
outputs2 = fill_masker(f"This is a {tokenizer.mask_token}", top_k=2)
self.assertEqual(
outputs2,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
self.assertEqual(nested_simplify(outputs), nested_simplify(outputs2))
def run_test_top_k_targets(self, model, tokenizer):
vocab = tokenizer.get_vocab()
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
# top_k=2, ntargets=3
targets = sorted(vocab.keys())[:3]
outputs = fill_masker(f"This is a {tokenizer.mask_token}", top_k=2, targets=targets)
# If we use the most probably targets, and filter differently, we should still
# have the same results
targets2 = [el["token_str"] for el in sorted(outputs, key=lambda x: x["score"], reverse=True)]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(targets2).issubset(targets):
outputs2 = fill_masker(f"This is a {tokenizer.mask_token}", top_k=3, targets=targets2)
# They should yield exactly the same result
self.assertEqual(nested_simplify(outputs), nested_simplify(outputs2))
def fill_mask_with_duplicate_targets_and_top_k(self, model, tokenizer):
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
vocab = tokenizer.get_vocab()
# String duplicates + id duplicates
targets = sorted(vocab.keys())[:3]
targets = [targets[0], targets[1], targets[0], targets[2], targets[1]]
outputs = fill_masker(f"My name is {tokenizer.mask_token}", targets=targets, top_k=10)
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(outputs), 3)
def fill_mask_with_multiple_masks(self, model, tokenizer):
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
outputs = fill_masker(
f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}", top_k=2
)
self.assertEqual(
outputs,
[
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
],
)
| transformers/tests/pipelines/test_pipelines_fill_mask.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_fill_mask.py",
"repo_id": "transformers",
"token_count": 8777
} | 537 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers import (
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
AutoModelForTokenClassification,
AutoTokenizer,
TokenClassificationPipeline,
pipeline,
)
from transformers.pipelines import AggregationStrategy, TokenClassificationArgumentHandler
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_torch,
require_torch_accelerator,
slow,
torch_device,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]]
# These 2 model types require different inputs than those of the usual text models.
_TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"}
@is_pipeline_test
class TokenClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
if not hasattr(model_mapping, "is_dummy"):
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if not hasattr(tf_model_mapping, "is_dummy"):
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def get_test_pipeline(
self,
model,
tokenizer=None,
image_processor=None,
feature_extractor=None,
processor=None,
dtype="float32",
):
token_classifier = TokenClassificationPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
dtype=dtype,
)
return token_classifier, ["A simple string", "A simple string that is quite a bit longer"]
def run_pipeline_test(self, token_classifier, _):
model = token_classifier.model
tokenizer = token_classifier.tokenizer
if not tokenizer.is_fast:
return # Slow tokenizers do not return offsets mappings, so this test will fail
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"index": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
outputs = token_classifier(["list of strings", "A simple string that is quite a bit longer"])
self.assertIsInstance(outputs, list)
self.assertEqual(len(outputs), 2)
n = len(outputs[0])
m = len(outputs[1])
self.assertEqual(
nested_simplify(outputs),
[
[
{
"entity": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"index": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
[
{
"entity": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"index": ANY(int),
"word": ANY(str),
}
for i in range(m)
],
],
)
self.run_aggregation_strategy(model, tokenizer)
def run_aggregation_strategy(self, model, tokenizer):
token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="simple")
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE)
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity_group": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="first")
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST)
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity_group": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="max")
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.MAX)
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity_group": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="average"
)
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.AVERAGE)
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity_group": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
with self.assertWarns(UserWarning):
token_classifier = pipeline(task="ner", model=model, tokenizer=tokenizer, grouped_entities=True)
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE)
with self.assertWarns(UserWarning):
token_classifier = pipeline(
task="ner", model=model, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True
)
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST)
@slow
@require_torch
def test_chunking(self):
NER_MODEL = "elastic/distilbert-base-uncased-finetuned-conll03-english"
model = AutoModelForTokenClassification.from_pretrained(NER_MODEL)
tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True)
tokenizer.model_max_length = 10
stride = 5
sentence = (
"Hugging Face, Inc. is a French company that develops tools for building applications using machine learning. "
"The company, based in New York City was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf."
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="simple", stride=stride
)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30},
{"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174},
{"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205},
{"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222},
{"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239},
],
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="first", stride=stride
)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30},
{"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174},
{"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205},
{"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222},
{"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239},
],
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="max", stride=stride
)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30},
{"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174},
{"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205},
{"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222},
{"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239},
],
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="average", stride=stride
)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30},
{"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174},
{"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205},
{"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222},
{"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239},
],
)
@require_torch
@slow
def test_is_split_into_words(self):
"""
Tests the pipeline with pre-tokenized inputs (is_split_into_words=True)
and validates that the character offsets are correct.
"""
token_classifier = pipeline(task="ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
# Input is a list of words
words = ["Hello", "Sarah", "lives", "in", "New", "York"]
# The reconstructed sentence will be "Hello Sarah lives in New York"
# - "Sarah": starts at index 6, ends at 11
# - "New York": starts at index 21, ends at 29
output = token_classifier(words, is_split_into_words=True)
self.assertEqual(
nested_simplify(output),
[
[
{"entity_group": "PER", "score": ANY(float), "word": "Sarah", "start": 6, "end": 11},
{"entity_group": "LOC", "score": ANY(float), "word": "New York", "start": 21, "end": 29},
]
],
)
# Also test batching with pre-tokenized inputs
words2 = ["My", "name", "is", "Wolfgang", "and", "I", "live", "in", "Berlin"]
batch_output = token_classifier([words, words2], is_split_into_words=True)
# Expected for second sentence ("My name is Wolfgang and I live in Berlin")
# - "Wolfgang": starts at 12, ends at 20
# - "Berlin": starts at 36, ends at 42
self.assertEqual(
nested_simplify(batch_output),
[
[
{"entity_group": "PER", "score": ANY(float), "word": "Sarah", "start": 6, "end": 11},
{"entity_group": "LOC", "score": ANY(float), "word": "New York", "start": 21, "end": 29},
],
[
{"entity_group": "PER", "score": ANY(float), "word": "Wolfgang", "start": 11, "end": 19},
{"entity_group": "LOC", "score": ANY(float), "word": "Berlin", "start": 34, "end": 40},
],
],
)
@require_torch
def test_chunking_fast(self):
# Note: We cannot run the test on "conflicts" on the chunking.
# The problem is that the model is random, and thus the results do heavily
# depend on the chunking, so we cannot expect "abcd" and "bcd" to find
# the same entities. We defer to slow tests for this.
pipe = pipeline(model="hf-internal-testing/tiny-bert-for-token-classification")
sentence = "The company, based in New York City was founded in 2016 by French entrepreneurs"
results = pipe(sentence, aggregation_strategy="first")
# This is what this random model gives on the full sentence
self.assertEqual(
nested_simplify(results),
[
# This is 2 actual tokens
{"end": 39, "entity_group": "MISC", "score": 0.115, "start": 31, "word": "city was"},
{"end": 79, "entity_group": "MISC", "score": 0.115, "start": 66, "word": "entrepreneurs"},
],
)
# This will force the tokenizer to split after "city was".
pipe.tokenizer.model_max_length = 12
self.assertEqual(
pipe.tokenizer.decode(pipe.tokenizer.encode(sentence, truncation=True)),
"[CLS] the company, based in new york city was [SEP]",
)
stride = 4
results = pipe(sentence, aggregation_strategy="first", stride=stride)
self.assertEqual(
nested_simplify(results),
[
{"end": 39, "entity_group": "MISC", "score": 0.115, "start": 31, "word": "city was"},
# This is an extra entity found by this random model, but at least both original
# entities are there
{"end": 58, "entity_group": "MISC", "score": 0.115, "start": 56, "word": "by"},
{"end": 79, "entity_group": "MISC", "score": 0.115, "start": 66, "word": "entrepreneurs"},
],
)
@require_torch
@slow
def test_spanish_bert(self):
# https://github.com/huggingface/transformers/pull/4987
NER_MODEL = "mrm8488/bert-spanish-cased-finetuned-ner"
model = AutoModelForTokenClassification.from_pretrained(NER_MODEL)
tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True)
sentence = """Consuelo Araújo Noguera, ministra de cultura del presidente Andrés Pastrana (1998.2002) fue asesinada por las Farc luego de haber permanecido secuestrada por algunos meses."""
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity": "B-PER", "score": 0.999, "word": "Cons", "start": 0, "end": 4, "index": 1},
{"entity": "B-PER", "score": 0.803, "word": "##uelo", "start": 4, "end": 8, "index": 2},
{"entity": "I-PER", "score": 0.999, "word": "Ara", "start": 9, "end": 12, "index": 3},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.999, "word": "Cons", "start": 0, "end": 4},
{"entity_group": "PER", "score": 0.966, "word": "##uelo Araújo Noguera", "start": 4, "end": 23},
{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.999, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23},
{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
{"entity_group": "ORG", "score": 0.999, "word": "Farc", "start": 110, "end": 114},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.999, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23},
{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
{"entity_group": "ORG", "score": 0.999, "word": "Farc", "start": 110, "end": 114},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="average")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.966, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23},
{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
{"entity_group": "ORG", "score": 0.542, "word": "Farc", "start": 110, "end": 114},
],
)
@require_torch_accelerator
@slow
def test_accelerator(self):
sentence = "This is dummy sentence"
ner = pipeline(
"token-classification",
device=torch_device,
aggregation_strategy=AggregationStrategy.SIMPLE,
)
output = ner(sentence)
self.assertEqual(nested_simplify(output), [])
@require_torch
@slow
def test_dbmdz_english(self):
# Other sentence
NER_MODEL = "dbmdz/bert-large-cased-finetuned-conll03-english"
model = AutoModelForTokenClassification.from_pretrained(NER_MODEL)
tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True)
sentence = """Enzo works at the UN"""
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity": "I-PER", "score": 0.998, "word": "En", "start": 0, "end": 2, "index": 1},
{"entity": "I-PER", "score": 0.997, "word": "##zo", "start": 2, "end": 4, "index": 2},
{"entity": "I-ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20, "index": 6},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.998, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.998, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="average")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20},
],
)
@require_torch
@slow
def test_aggregation_strategy_byte_level_tokenizer(self):
sentence = "Groenlinks praat over Schiphol."
ner = pipeline("ner", model="FacebookAI/xlm-roberta-large-finetuned-conll02-dutch", aggregation_strategy="max")
self.assertEqual(
nested_simplify(ner(sentence)),
[
{"end": 10, "entity_group": "ORG", "score": 0.994, "start": 0, "word": "Groenlinks"},
{"entity_group": "LOC", "score": 1.0, "word": "Schiphol.", "start": 22, "end": 31},
],
)
@require_torch
def test_aggregation_strategy_no_b_i_prefix(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
# Just to understand scores indexes in this test
token_classifier.model.config.id2label = {0: "O", 1: "MISC", 2: "PER", 3: "ORG", 4: "LOC"}
example = [
{
"scores": np.array([0, 0, 0, 0, 0.9968166351318359]), # fmt : skip
"index": 1,
"is_subword": False,
"word": "En",
"start": 0,
"end": 2,
},
{
"scores": np.array([0, 0, 0, 0, 0.9957635998725891]), # fmt : skip
"index": 2,
"is_subword": True,
"word": "##zo",
"start": 2,
"end": 4,
},
{
"scores": np.array([0, 0, 0, 0.9986497163772583, 0]), # fmt : skip
"index": 7,
"word": "UN",
"is_subword": False,
"start": 11,
"end": 13,
},
]
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.NONE)),
[
{"end": 2, "entity": "LOC", "score": 0.997, "start": 0, "word": "En", "index": 1},
{"end": 4, "entity": "LOC", "score": 0.996, "start": 2, "word": "##zo", "index": 2},
{"end": 13, "entity": "ORG", "score": 0.999, "start": 11, "word": "UN", "index": 7},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.SIMPLE)),
[
{"entity_group": "LOC", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
@require_torch
def test_aggregation_strategy(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
# Just to understand scores indexes in this test
self.assertEqual(
token_classifier.model.config.id2label,
{0: "O", 1: "B-MISC", 2: "I-MISC", 3: "B-PER", 4: "I-PER", 5: "B-ORG", 6: "I-ORG", 7: "B-LOC", 8: "I-LOC"},
)
example = [
{
"scores": np.array([0, 0, 0, 0, 0.9968166351318359, 0, 0, 0]), # fmt : skip
"index": 1,
"is_subword": False,
"word": "En",
"start": 0,
"end": 2,
},
{
"scores": np.array([0, 0, 0, 0, 0.9957635998725891, 0, 0, 0]), # fmt : skip
"index": 2,
"is_subword": True,
"word": "##zo",
"start": 2,
"end": 4,
},
{
"scores": np.array([0, 0, 0, 0, 0, 0.9986497163772583, 0, 0]), # fmt : skip
"index": 7,
"word": "UN",
"is_subword": False,
"start": 11,
"end": 13,
},
]
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.NONE)),
[
{"end": 2, "entity": "I-PER", "score": 0.997, "start": 0, "word": "En", "index": 1},
{"end": 4, "entity": "I-PER", "score": 0.996, "start": 2, "word": "##zo", "index": 2},
{"end": 13, "entity": "B-ORG", "score": 0.999, "start": 11, "word": "UN", "index": 7},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.SIMPLE)),
[
{"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.FIRST)),
[
{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.MAX)),
[
{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.AVERAGE)),
[
{"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
@require_torch
def test_aggregation_strategy_example2(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
# Just to understand scores indexes in this test
self.assertEqual(
token_classifier.model.config.id2label,
{0: "O", 1: "B-MISC", 2: "I-MISC", 3: "B-PER", 4: "I-PER", 5: "B-ORG", 6: "I-ORG", 7: "B-LOC", 8: "I-LOC"},
)
example = [
{
# Necessary for AVERAGE
"scores": np.array([0, 0.55, 0, 0.45, 0, 0, 0, 0, 0, 0]),
"is_subword": False,
"index": 1,
"word": "Ra",
"start": 0,
"end": 2,
},
{
"scores": np.array([0, 0, 0, 0.2, 0, 0, 0, 0.8, 0, 0]),
"is_subword": True,
"word": "##ma",
"start": 2,
"end": 4,
"index": 2,
},
{
# 4th score will have the higher average
# 4th score is B-PER for this model
# It's does not correspond to any of the subtokens.
"scores": np.array([0, 0, 0, 0.4, 0, 0, 0.6, 0, 0, 0]),
"is_subword": True,
"word": "##zotti",
"start": 11,
"end": 13,
"index": 3,
},
]
self.assertEqual(
token_classifier.aggregate(example, AggregationStrategy.NONE),
[
{"end": 2, "entity": "B-MISC", "score": 0.55, "start": 0, "word": "Ra", "index": 1},
{"end": 4, "entity": "B-LOC", "score": 0.8, "start": 2, "word": "##ma", "index": 2},
{"end": 13, "entity": "I-ORG", "score": 0.6, "start": 11, "word": "##zotti", "index": 3},
],
)
self.assertEqual(
token_classifier.aggregate(example, AggregationStrategy.FIRST),
[{"entity_group": "MISC", "score": 0.55, "word": "Ramazotti", "start": 0, "end": 13}],
)
self.assertEqual(
token_classifier.aggregate(example, AggregationStrategy.MAX),
[{"entity_group": "LOC", "score": 0.8, "word": "Ramazotti", "start": 0, "end": 13}],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.AVERAGE)),
[{"entity_group": "PER", "score": 0.35, "word": "Ramazotti", "start": 0, "end": 13}],
)
@require_torch
@slow
def test_aggregation_strategy_offsets_with_leading_space(self):
sentence = "We're from New York"
model_name = "brandon25/deberta-base-finetuned-ner"
ner = pipeline("ner", model=model_name, ignore_labels=[], aggregation_strategy="max")
self.assertEqual(
nested_simplify(ner(sentence)),
[
{"entity_group": "O", "score": 1.0, "word": " We're from", "start": 0, "end": 10},
{"entity_group": "LOC", "score": 1.0, "word": " New York", "start": 10, "end": 19},
],
)
@require_torch
def test_gather_pre_entities(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
sentence = "Hello there"
tokens = tokenizer(
sentence,
return_attention_mask=False,
return_tensors="pt",
truncation=True,
return_special_tokens_mask=True,
return_offsets_mapping=True,
)
offset_mapping = tokens.pop("offset_mapping").cpu().numpy()[0]
special_tokens_mask = tokens.pop("special_tokens_mask").cpu().numpy()[0]
input_ids = tokens["input_ids"].numpy()[0]
# First element in [CLS]
scores = np.array([[1, 0, 0], [0.1, 0.3, 0.6], [0.8, 0.1, 0.1]])
pre_entities = token_classifier.gather_pre_entities(
sentence,
input_ids,
scores,
offset_mapping,
special_tokens_mask,
aggregation_strategy=AggregationStrategy.NONE,
)
self.assertEqual(
nested_simplify(pre_entities),
[
{"word": "Hello", "scores": [0.1, 0.3, 0.6], "start": 0, "end": 5, "is_subword": False, "index": 1},
{
"word": "there",
"scores": [0.8, 0.1, 0.1],
"index": 2,
"start": 6,
"end": 11,
"is_subword": False,
},
],
)
@require_torch
def test_word_heuristic_leading_space(self):
model_name = "hf-internal-testing/tiny-random-deberta-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
sentence = "I play the theremin"
tokens = tokenizer(
sentence,
return_attention_mask=False,
return_tensors="pt",
return_special_tokens_mask=True,
return_offsets_mapping=True,
)
offset_mapping = tokens.pop("offset_mapping").cpu().numpy()[0]
special_tokens_mask = tokens.pop("special_tokens_mask").cpu().numpy()[0]
input_ids = tokens["input_ids"].numpy()[0]
scores = np.array([[1, 0] for _ in input_ids]) # values irrelevant for heuristic
pre_entities = token_classifier.gather_pre_entities(
sentence,
input_ids,
scores,
offset_mapping,
special_tokens_mask,
aggregation_strategy=AggregationStrategy.FIRST,
)
# ensure expected tokenization and correct is_subword values
self.assertEqual(
[(entity["word"], entity["is_subword"]) for entity in pre_entities],
[("▁I", False), ("▁play", False), ("▁the", False), ("▁there", False), ("min", True)],
)
@require_torch
def test_no_offset_tokenizer(self):
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
token_classifier = pipeline(task="token-classification", model=model_name, tokenizer=tokenizer, framework="pt")
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": None, "end": None},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": None, "end": None},
],
)
@require_torch
def test_small_model_pt(self):
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
token_classifier = pipeline(task="token-classification", model=model_name, framework="pt")
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
],
)
token_classifier = pipeline(
task="token-classification", model=model_name, framework="pt", ignore_labels=["O", "I-MISC"]
)
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[],
)
token_classifier = pipeline(task="token-classification", model=model_name, framework="pt")
# Overload offset_mapping
outputs = token_classifier(
"This is a test !", offset_mapping=[(0, 0), (0, 1), (0, 2), (0, 0), (0, 0), (0, 0), (0, 0)]
)
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 1},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 0, "end": 2},
],
)
# Batch size does not affect outputs (attention_mask are required)
sentences = ["This is a test !", "Another test this is with longer sentence"]
outputs = token_classifier(sentences)
outputs_batched = token_classifier(sentences, batch_size=2)
# Batching does not make a difference in predictions
self.assertEqual(nested_simplify(outputs_batched), nested_simplify(outputs))
self.assertEqual(
nested_simplify(outputs_batched),
[
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
],
[],
],
)
@require_torch
def test_small_model_pt_fp16(self):
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
token_classifier = pipeline(task="token-classification", model=model_name, framework="pt", dtype=torch.float16)
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
],
)
@require_torch
def test_small_model_pt_bf16(self):
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
token_classifier = pipeline(
task="token-classification", model=model_name, framework="pt", dtype=torch.bfloat16
)
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
],
)
@require_torch
def test_pt_ignore_subwords_slow_tokenizer_raises(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
with self.assertRaises(ValueError):
pipeline(task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.FIRST)
with self.assertRaises(ValueError):
pipeline(
task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.AVERAGE
)
with self.assertRaises(ValueError):
pipeline(task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.MAX)
@slow
@require_torch
def test_simple(self):
token_classifier = pipeline(task="ner", model="dslim/bert-base-NER", grouped_entities=True)
sentence = "Hello Sarah Jessica Parker who Jessica lives in New York"
sentence2 = "This is a simple test"
output = token_classifier(sentence)
output_ = nested_simplify(output)
self.assertEqual(
output_,
[
{
"entity_group": "PER",
"score": 0.996,
"word": "Sarah Jessica Parker",
"start": 6,
"end": 26,
},
{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
],
)
output = token_classifier([sentence, sentence2])
output_ = nested_simplify(output)
self.assertEqual(
output_,
[
[
{"entity_group": "PER", "score": 0.996, "word": "Sarah Jessica Parker", "start": 6, "end": 26},
{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
],
[],
],
)
class TokenClassificationArgumentHandlerTestCase(unittest.TestCase):
def setUp(self):
self.args_parser = TokenClassificationArgumentHandler()
def test_simple(self):
string = "This is a simple input"
inputs, is_split_into_words, offset_mapping, delimiter = self.args_parser(string)
self.assertEqual(inputs, [string])
self.assertFalse(is_split_into_words)
self.assertEqual(offset_mapping, None)
inputs, is_split_into_words, offset_mapping, delimiter = self.args_parser([string, string])
self.assertEqual(inputs, [string, string])
self.assertFalse(is_split_into_words)
self.assertEqual(offset_mapping, None)
inputs, is_split_into_words, offset_mapping, delimiter = self.args_parser(
string, offset_mapping=[(0, 1), (1, 2)]
)
self.assertEqual(inputs, [string])
self.assertFalse(is_split_into_words)
self.assertEqual(offset_mapping, [[(0, 1), (1, 2)]])
inputs, is_split_into_words, offset_mapping, delimiter = self.args_parser(
[string, string], offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]]
)
self.assertEqual(inputs, [string, string])
self.assertEqual(offset_mapping, [[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
def test_errors(self):
string = "This is a simple input"
# 2 sentences, 1 offset_mapping, args
with self.assertRaises(TypeError):
self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)]])
# 2 sentences, 1 offset_mapping, args
with self.assertRaises(TypeError):
self.args_parser(string, string, offset_mapping=[(0, 1), (1, 2)])
# 2 sentences, 1 offset_mapping, input_list
with self.assertRaises(ValueError):
self.args_parser([string, string], offset_mapping=[[(0, 1), (1, 2)]])
# 2 sentences, 1 offset_mapping, input_list
with self.assertRaises(ValueError):
self.args_parser([string, string], offset_mapping=[(0, 1), (1, 2)])
# 1 sentences, 2 offset_mapping
with self.assertRaises(ValueError):
self.args_parser(string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
# 0 sentences, 1 offset_mapping
with self.assertRaises(TypeError):
self.args_parser(offset_mapping=[[(0, 1), (1, 2)]])
| transformers/tests/pipelines/test_pipelines_token_classification.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_token_classification.py",
"repo_id": "transformers",
"token_count": 22069
} | 538 |
# Testing mixed int8 quantization
The following is the recipe on how to effectively debug `bitsandbytes` integration on Hugging Face `transformers`.
## Library requirements
+ `transformers>=4.22.0`
+ `accelerate>=0.12.0`
+ `bitsandbytes>=0.31.5`.
## Hardware requirements
The following instructions are tested with 2 NVIDIA-Tesla T4 GPUs. To run successfully `bitsandbytes` you would need a 8-bit core tensor supported GPU. Note that Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100, A6000 should be supported.
## Virtual envs
```bash
conda create --name int8-testing python==3.8
pip install bitsandbytes>=0.31.5
pip install accelerate>=0.12.0
pip install transformers>=4.23.0
```
if `transformers>=4.23.0` is not released yet, then use:
```bash
pip install git+https://github.com/huggingface/transformers.git
```
## Troubleshooting
A list of common errors:
### Torch does not correctly do the operations on GPU
First check that:
```py
import torch
vec = torch.randn(1, 2, 3).to(0)
```
Works without any error. If not, install torch using `conda` like:
```bash
conda create --name int8-testing python==3.8
conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
pip install bitsandbytes>=0.31.5
pip install accelerate>=0.12.0
pip install transformers>=4.23.0
```
For the latest pytorch instructions please see [this](https://pytorch.org/get-started/locally/)
and the snippet above should work.
### ` bitsandbytes operations are not supported under CPU!`
This happens when some Linear weights are set to the CPU when using `accelerate`. Please check carefully `model.hf_device_map` and make sure that there is no `Linear` module that is assigned to CPU. It is fine to have the last module (usually the Lm_head) set on CPU.
### `To use the type as a Parameter, please correct the detach() semantics defined by __torch_dispatch__() implementation.`
Use the latest version of `accelerate` with a command such as: `pip install -U accelerate` and the problem should be solved.
### `Parameter has no attribute .CB`
Same solution as above.
### `RuntimeError: CUDA error: an illegal memory access was encountered ... consider passing CUDA_LAUNCH_BLOCKING=1`
Run your script by prepending `CUDA_LAUNCH_BLOCKING=1` and you should observe an error as described in the next section.
### `CUDA illegal memory error: an illegal memory access at line...`:
Check the CUDA versions with:
```bash
nvcc --version
```
and confirm it is the same version as the one detected by `bitsandbytes`. If not, run:
```bash
ls -l $CONDA_PREFIX/lib/libcudart.so
```
or
```bash
ls -l $LD_LIBRARY_PATH
```
Check if `libcudart.so` has a correct symlink that is set. Sometimes `nvcc` detects the correct CUDA version but `bitsandbytes` doesn't. You have to make sure that the symlink that is set for the file `libcudart.so` is redirected to the correct CUDA file.
Here is an example of a badly configured CUDA installation:
`nvcc --version` gives:
which means that the detected CUDA version is 11.3 but `bitsandbytes` outputs:
First check:
```bash
echo $LD_LIBRARY_PATH
```
If this contains multiple paths separated by `:`. Then you have to make sure that the correct CUDA version is set. By doing:
```bash
ls -l $path/libcudart.so
```
On each path (`$path`) separated by `:`.
If not, simply run
```bash
ls -l $LD_LIBRARY_PATH/libcudart.so
```
and you can see
If you see that the file is linked to the wrong CUDA version (here 10.2), find the correct location for `libcudart.so` (`find --name libcudart.so`) and replace the environment variable `LD_LIBRARY_PATH` with the one containing the correct `libcudart.so` file. | transformers/tests/quantization/bnb/README.md/0 | {
"file_path": "transformers/tests/quantization/bnb/README.md",
"repo_id": "transformers",
"token_count": 1398
} | 539 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from parameterized import parameterized
from transformers import AddedToken, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.testing_utils import (
require_gguf,
require_read_token,
require_torch_accelerator,
slow,
torch_device,
)
from transformers.utils import is_gguf_available, is_torch_available
if is_torch_available():
import torch
if is_gguf_available():
from gguf import GGMLQuantizationType as QuantType
@require_gguf
@require_torch_accelerator
@slow
class GgufQuantizationTests(unittest.TestCase):
"""
Test cases for weights dequantization with GGUF models.
Note: The quantization names should keep aligned with `GGMLQuantizationType` in gguf-py:
https://github.com/ggerganov/llama.cpp/blob/4b0c638b9a68f577cb2066b638c9f622d91ee661/gguf-py/gguf/constants.py#L1545-L1576
So quantization like Q4_K_M or Q4_K_S shouldn't be added to this tests.
"""
example_text = "Hello"
def run_gguf_model(self, gguf_model_id: str, gguf_filename: str, expected_text: str):
tokenizer = AutoTokenizer.from_pretrained(gguf_model_id, gguf_file=gguf_filename)
model = AutoModelForCausalLM.from_pretrained(gguf_model_id, gguf_file=gguf_filename).to(torch_device)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), expected_text)
@parameterized.expand(
[
# standard quants
("Q4_0", "Hello, World!\n\nStep 3: Add"),
("Q5_0", "Hello, World!\n\n5. Use a library"),
("Q8_0", "Hello, World!\n\n5. Use a library"),
],
)
def test_standard_quants(self, quant_type: str, expected_text: str):
gguf_model_id = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
filename_format = "tinyllama-1.1b-chat-v1.0.{quant_type}.gguf"
gguf_filename = filename_format.format(quant_type=quant_type)
self.run_gguf_model(gguf_model_id, gguf_filename, expected_text)
# k-quants
@parameterized.expand(
[
("Q2_K", "Hello, I'm a 22 year old female"),
("Q3_K", "Hello\n\nI am trying to create a simple program that"),
("Q4_K", "Hello\n\nI am trying to create a simple program that"),
("Q5_K", "Helloveda is a 1999 Indian"),
("Q6_K", "Hello\n\nI am trying to create a simple program that"),
],
)
def test_k_quants(self, quant_type: str, expected_text: str):
gguf_model_id = "legraphista/Qwen2.5-0.5B-Instruct-IMat-GGUF"
filename_format = "Qwen2.5-0.5B-Instruct.{quant_type}.gguf"
gguf_filename = filename_format.format(quant_type=quant_type)
self.run_gguf_model(gguf_model_id, gguf_filename, expected_text)
@parameterized.expand(
[
# i-matrix
("IQ1_S", "Hello, I'm a friend of mine, I"),
("IQ1_M", "Hello, I am interested in purching a copy of"),
("IQ2_XXS", "Hello, I'm a software engineer. I'"),
("IQ2_XS", "Hello World!\n\n```\n<|user|"),
("IQ2_S", "Hello World!\n\n```\n<|user|"),
("IQ3_XXS", "Hello, I am interested in your product. Can you"),
("IQ4_XS", "Hello, world!\n\n5. Using a loop"),
("IQ3_S", "Hello, World!\n\n5. Python:\n"),
("IQ4_NL", "Hello, world!\n\n5. Using a loop"),
],
)
def test_imatrix_quants(self, quant_type: str, expected_text: str):
gguf_model_id = "duyntnet/TinyLlama-1.1B-Chat-v1.0-imatrix-GGUF"
filename_format = "TinyLlama-1.1B-Chat-v1.0-{quant_type}.gguf"
gguf_filename = filename_format.format(quant_type=quant_type)
self.run_gguf_model(gguf_model_id, gguf_filename, expected_text)
@require_gguf
@require_torch_accelerator
@slow
class GgufIntegrationTests(unittest.TestCase):
"""
Test cases for basic interoperability with GGUF models:
- Tokenization
- Model dtype casting and serialization
"""
example_text = "Hello"
original_model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
gguf_model_id = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
gguf_filename = "tinyllama-1.1b-chat-v1.0.{quant_type}.gguf"
def test_tokenization_xnli(self):
import tqdm
from datasets import load_dataset
q8_0_gguf_model_id = self.gguf_filename.format(quant_type=QuantType.Q8_0.name)
gguf_tokenizer = AutoTokenizer.from_pretrained(self.gguf_model_id, gguf_file=q8_0_gguf_model_id)
original_tokenizer = AutoTokenizer.from_pretrained(self.original_model_id)
dataset = load_dataset("google/code_x_glue_ct_code_to_text", "go")
for item in tqdm.tqdm(dataset["validation"]):
string = item["code"]
encoded1 = gguf_tokenizer.encode(string)
encoded2 = original_tokenizer.encode(string)
self.assertEqual(encoded1, encoded2)
decoded1 = gguf_tokenizer.decode(encoded1, skip_special_tokens=True)
decoded2 = original_tokenizer.decode(encoded2, skip_special_tokens=True)
self.assertEqual(decoded1, decoded2)
dataset = load_dataset("facebook/xnli", "all_languages")
for i, item in enumerate(tqdm.tqdm(dataset["train"].select(range(100)))):
for string in item["premise"].values():
encoded1 = gguf_tokenizer.encode(string)
encoded2 = original_tokenizer.encode(string)
self.assertEqual(encoded1, encoded2)
decoded1 = gguf_tokenizer.decode(encoded1, skip_special_tokens=True)
decoded2 = original_tokenizer.decode(encoded2, skip_special_tokens=True)
self.assertEqual(decoded1, decoded2)
# With special tokens
gguf_tokenizer = AutoTokenizer.from_pretrained(self.gguf_model_id, gguf_file=q8_0_gguf_model_id)
original_tokenizer = AutoTokenizer.from_pretrained(self.original_model_id)
gguf_tokenizer.add_special_tokens(
{"additional_special_tokens": [AddedToken("<token>", rstrip=False, lstrip=False)]}
)
original_tokenizer.add_special_tokens(
{"additional_special_tokens": [AddedToken("<token>", rstrip=False, lstrip=False)]}
)
text = "Hello <token>. <token> Hello"
encoded1 = gguf_tokenizer.encode(text)
encoded2 = original_tokenizer.encode(text)
self.assertEqual(encoded1, encoded2)
decoded1 = gguf_tokenizer.decode(encoded1, skip_special_tokens=True)
decoded2 = original_tokenizer.decode(encoded2, skip_special_tokens=True)
self.assertEqual(decoded1, decoded2)
def test_q2_k_serialization(self):
q2_k_gguf_model_id = self.gguf_filename.format(quant_type=QuantType.Q2_K.name)
EXPECTED_TEXT = "Hello, World!\n\n[10:0"
tokenizer = AutoTokenizer.from_pretrained(self.gguf_model_id, gguf_file=q2_k_gguf_model_id)
model = AutoModelForCausalLM.from_pretrained(self.gguf_model_id, gguf_file=q2_k_gguf_model_id).to(torch_device)
orig_text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
orig_out = model.generate(**orig_text, max_new_tokens=10)
self.assertEqual(tokenizer.decode(orig_out[0], skip_special_tokens=True), EXPECTED_TEXT)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
tokenizer.save_pretrained(tmpdirname)
model = AutoModelForCausalLM.from_pretrained(tmpdirname).to(torch_device)
tokenizer = AutoTokenizer.from_pretrained(tmpdirname)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_q6_k_fp16(self):
q6_k_gguf_model_id = self.gguf_filename.format(quant_type=QuantType.Q6_K.name)
tokenizer = AutoTokenizer.from_pretrained(self.gguf_model_id, gguf_file=q6_k_gguf_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.gguf_model_id, gguf_file=q6_k_gguf_model_id, dtype=torch.float16
).to(torch_device)
self.assertTrue(model.lm_head.weight.dtype == torch.float16)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, World!\n\nStep 3: Add"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_gguf_errors_disk_offload(self):
from collections import OrderedDict
q2_k_gguf_model_id = self.gguf_filename.format(quant_type=QuantType.Q2_K.name)
with self.assertRaises(RuntimeError):
AutoModelForCausalLM.from_pretrained(
self.gguf_model_id,
device_map=OrderedDict(
[
("model.embed_tokens", "cpu"),
("lm_head", "cpu"),
("model.layers.0", "cpu"),
("model.layers.1", "cpu"),
("model.layers.2", "cpu"),
("model.layers.3", "cpu"),
("model.layers.4", "cpu"),
("model.layers.5", "cpu"),
("model.layers.6", "cpu"),
("model.layers.7", "cpu"),
("model.layers.8", "cpu"),
("model.layers.9", "cpu"),
("model.layers.10", "disk"),
("model.layers.11", "disk"),
("model.layers.12", "disk"),
("model.layers.13", "disk"),
("model.layers.14", "disk"),
("model.layers.15", "disk"),
("model.layers.16", "disk"),
("model.layers.17", "disk"),
("model.layers.18", "disk"),
("model.layers.19", "disk"),
("model.layers.20", "disk"),
("model.layers.21", "disk"),
("model.layers.22", "disk"),
("model.norm", "disk"),
("model.rotary_emb", "disk"),
]
),
gguf_file=q2_k_gguf_model_id,
offload_folder="offload",
offload_state_dict=True,
)
@require_gguf
@require_torch_accelerator
@slow
class GgufModelTests(unittest.TestCase):
mistral_model_id = "TheBloke/Mistral-7B-Instruct-v0.2-GGUF"
qwen2_model_id = "Qwen/Qwen1.5-0.5B-Chat-GGUF"
qwen2moe_model_id = "gdax/Qwen1.5-MoE-A2.7B_gguf"
qwen2moe_original_model_id = "Qwen/Qwen1.5-MoE-A2.7B"
llama3_model_id = "NousResearch/Meta-Llama-3-8B-GGUF"
tinyllama_model_id = "PenutChen/TinyLlama-1.1B-Chat-v1.0-GGUF"
phi3_model_id = "microsoft/Phi-3-mini-4k-instruct-gguf"
bloom_model_id = "afrideva/bloom-560m-GGUF"
original_bloom_model_id = "bigscience/bloom-560m"
falcon7b_model_id_q2 = "xaviviro/falcon-7b-quantized-gguf"
falcon7b_model_id_fp16 = "medmekk/falcon-7b-gguf"
falcon40b_model_id = "maddes8cht/tiiuae-falcon-40b-gguf"
original_flacon7b_model_id = "tiiuae/falcon-7b"
t5_model_id = "repetitio/flan-t5-small"
original_t5_model_id = "google/flan-t5-small"
stablelm_model_id = "afrideva/stablelm-3b-4e1t-GGUF"
stablelm2_model_id = "afrideva/stablelm-2-1_6b-GGUF"
original_stablelm2_model_id = "stabilityai/stablelm-2-1_6b"
gpt2_model_id = "mradermacher/gpt2-GGUF"
gpt2_original_model_id = "openai-community/gpt2"
gpt2_xl_model_id = "RichardErkhov/openai-community_-_gpt2-xl-gguf"
starcoder2_model_id = "QuantFactory/starcoder2-3b-GGUF"
starcoder2_fp16_model_id = "brittlewis12/starcoder2-3b-GGUF"
starcoder2_original_model_id = "bigcode/starcoder2-3b"
mamba_original_model_id = "state-spaces/mamba-2.8b-hf"
mamba_model_id = "jpodivin/mamba-2.8b-hf-GGUF"
nemotron_original_model_id = "nvidia/Nemotron-Mini-4B-Instruct"
nemotron_model_id = "bartowski/Nemotron-Mini-4B-Instruct-GGUF"
original_gemma2_model_id = "google/gemma-2-2b-it"
gemma2_model_id = "bartowski/gemma-2-2b-it-GGUF"
original_gemma3_text_model_id = "google/gemma-3-1b-it"
original_gemma3_vision_model_id = "google/gemma-3-4b-it"
gemma3_qat_model_id = "google/gemma-3-1b-it-qat-q4_0-gguf"
gemma3_text_model_id = "unsloth/gemma-3-1b-it-GGUF"
gemma3_vision_model_id = "unsloth/gemma-3-4b-it-GGUF"
qwen3_model_id = "Qwen/Qwen3-0.6B-GGUF"
qwen3moe_model_id = "Qwen/Qwen3-30B-A3B-GGUF"
q4_0_phi3_model_id = "Phi-3-mini-4k-instruct-q4.gguf"
q4_0_mistral_model_id = "mistral-7b-instruct-v0.2.Q4_0.gguf"
q4_0_qwen2_model_id = "qwen1_5-0_5b-chat-q4_0.gguf"
q8_qwen2moe_model_id = "Qwen1.5-MoE-A2.7B_Q8_0.gguf"
q4_llama3_model_id = "Meta-Llama-3-8B-Q4_K_M.gguf"
fp16_bloom_model_id = "bloom-560m.fp16.gguf"
q4_k_m_stablelm_model_id = "stablelm-3b-4e1t.q4_k_m.gguf"
fp16_stablelm2_model_id = "stablelm-2-1_6b.fp16.gguf"
q8_bloom_model_id = "bloom-560m.q8_0.gguf"
f16_tinyllama_model_id = "TinyLlama-1.1B-Chat-v1.0.FP16.gguf"
q2_k_falcon7b_model_id = "falcon-7b-q2_k.gguf"
fp16_falcon7b_model_id = "falcon-7b-fp16.gguf"
q2_k_falcon40b_model_id = "tiiuae-falcon-40b-Q2_K.gguf"
fp16_t5_model_id = "flan-t5-small-f16.gguf"
q8_0_t5_model_id = "flan-t5-small-q8_0.gguf"
fp16_qwen2moe_model_id = "Qwen1.5-MoE-A2.7B.gguf"
fp16_gpt2_model_id = "gpt2.f16.gguf"
q8_gpt2_model_id = "gpt2.Q8_0.gguf"
q6_k_gpt2_xl_model_id = "gpt2-xl.Q6_K.gguf"
q6_k_starcoder2_model_id = "starcoder2-3b.Q6_K.gguf"
fp16_starcoder2_gguf_model_id = "starcoder2-3b.fp16.gguf"
q6_k_mamba_model_id = "ggml-model-Q6_K.gguf"
fp16_mamba_model_id = "ggml-model-f16.gguf"
q6_k_nemotron_model_id = "Nemotron-Mini-4B-Instruct-Q6_K.gguf"
fp16_nemotron_model_id = "Nemotron-Mini-4B-Instruct-f16.gguf"
q3_k_gemma2_model_id = "gemma-2-2b-it-Q3_K_L.gguf"
q8_0_gemma2_model_id = "gemma-2-2b-it-Q8_0.gguf"
fp32_gemma2_model_id = "gemma-2-2b-it-f32.gguf"
q4_0_gemma3_qat_model_id = "gemma-3-1b-it-q4_0.gguf"
bf16_gemma3_text_model_id = "gemma-3-1b-it-BF16.gguf"
bf16_gemma3_vision_model_id = "gemma-3-4b-it-BF16.gguf"
q8_0_qwen3_model_id = "Qwen3-0.6B-Q8_0.gguf"
q4_k_m_qwen3moe_model_id = "Qwen3-30B-A3B-Q4_K_M.gguf"
example_text = "Hello"
def test_mistral_q4_0(self):
tokenizer = AutoTokenizer.from_pretrained(self.mistral_model_id, gguf_file=self.q4_0_mistral_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.mistral_model_id,
gguf_file=self.q4_0_mistral_model_id,
device_map="auto",
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello,\n\nI'm trying to create a"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_qwen2_q4_0(self):
tokenizer = AutoTokenizer.from_pretrained(self.qwen2_model_id, gguf_file=self.q4_0_qwen2_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.qwen2_model_id,
gguf_file=self.q4_0_qwen2_model_id,
device_map="auto",
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello.jsoup\n\nI am a beginner"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_qwen2moe_q8(self):
tokenizer = AutoTokenizer.from_pretrained(self.qwen2moe_model_id, gguf_file=self.q8_qwen2moe_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.qwen2moe_model_id,
gguf_file=self.q8_qwen2moe_model_id,
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt")
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, I am a 20 year old male"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_qwen2moe_weights_conversion_fp16(self):
quantized_model = AutoModelForCausalLM.from_pretrained(
self.qwen2moe_model_id,
gguf_file=self.fp16_qwen2moe_model_id,
dtype=torch.float16,
)
original_model = AutoModelForCausalLM.from_pretrained(
self.qwen2moe_original_model_id,
dtype=torch.float16,
)
quantized_state_dict = quantized_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in quantized_state_dict:
self.assertTrue(original_params.shape == quantized_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, quantized_state_dict[layer_name])
def test_phi3_q4_0(self):
tokenizer = AutoTokenizer.from_pretrained(self.phi3_model_id, gguf_file=self.q4_0_phi3_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.phi3_model_id, gguf_file=self.q4_0_phi3_model_id, device_map="auto", dtype=torch.float16
)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, I've been reading about the impact of"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_llama3_q4_0_tokenizer(self):
tokenizer = AutoTokenizer.from_pretrained(self.llama3_model_id, gguf_file=self.q4_llama3_model_id)
with tempfile.TemporaryDirectory() as tmpdirname:
tokenizer.save_pretrained(tmpdirname)
tokenizer = AutoTokenizer.from_pretrained(tmpdirname)
special_sentence = "สวัสดี"
predicted_text = tokenizer.decode(tokenizer.encode(special_sentence, return_tensors="pt")[0])
self.assertEqual(predicted_text, "<|begin_of_text|>" + special_sentence)
def test_llama3_q4_0(self):
tokenizer = AutoTokenizer.from_pretrained(self.llama3_model_id, gguf_file=self.q4_llama3_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.llama3_model_id,
gguf_file=self.q4_llama3_model_id,
device_map="auto",
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, I am interested in [The Park]\nThe"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_bloom_fp16(self):
tokenizer = AutoTokenizer.from_pretrained(self.bloom_model_id, gguf_file=self.fp16_bloom_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.bloom_model_id,
gguf_file=self.fp16_bloom_model_id,
device_map="auto",
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, I just want to say that I am very"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_bloom_q8_0(self):
tokenizer = AutoTokenizer.from_pretrained(self.bloom_model_id, gguf_file=self.q8_bloom_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.bloom_model_id,
gguf_file=self.q8_bloom_model_id,
device_map="auto",
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, I just want to say that I am just"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_bloom_weights_conversion_fp16(self):
quantized_model = AutoModelForCausalLM.from_pretrained(
self.bloom_model_id,
gguf_file=self.fp16_bloom_model_id,
device_map="auto",
dtype=torch.float16,
)
original_model = AutoModelForCausalLM.from_pretrained(
self.original_bloom_model_id,
device_map="auto",
dtype=torch.float16,
)
quantized_state_dict = quantized_model.state_dict()
original_state_dict = original_model.state_dict()
for (quantized_name, quantized_param), (original_name, original_param) in zip(
quantized_state_dict.items(), original_state_dict.items()
):
if (
"self_attention.query_key_value" in quantized_name
and "self_attention.query_key_value" in original_name
):
self.assertTrue(quantized_param.shape == original_param.shape)
torch.testing.assert_close(quantized_param, original_param)
def test_t5_f16(self):
tokenizer = AutoTokenizer.from_pretrained(self.t5_model_id, gguf_file=self.fp16_t5_model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(
self.t5_model_id, gguf_file=self.fp16_t5_model_id, device_map="auto", dtype=torch.float16
)
T5_EXAMPLE_TEXT = "translate English to German: How old are you?"
text = tokenizer(T5_EXAMPLE_TEXT, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Wie ich er?"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_t5_q8_0(self):
tokenizer = AutoTokenizer.from_pretrained(self.t5_model_id, gguf_file=self.q8_0_t5_model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(
self.t5_model_id, gguf_file=self.q8_0_t5_model_id, device_map="auto", dtype=torch.float16
)
T5_EXAMPLE_TEXT = "translate English to German: How old are you?"
text = tokenizer(T5_EXAMPLE_TEXT, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Wie ich er?"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_t5_weights_conversion_fp16(self):
quantized_model = AutoModelForSeq2SeqLM.from_pretrained(
self.t5_model_id,
gguf_file=self.fp16_t5_model_id,
device_map="auto",
dtype=torch.float16,
)
original_model = AutoModelForSeq2SeqLM.from_pretrained(
self.original_t5_model_id,
device_map="auto",
dtype=torch.float16,
)
quantized_state_dict = quantized_model.state_dict()
original_state_dict = original_model.state_dict()
for (quantized_name, quantized_param), (original_name, original_param) in zip(
quantized_state_dict.items(), original_state_dict.items()
):
self.assertTrue(quantized_param.shape == original_param.shape)
torch.testing.assert_close(quantized_param, original_param, rtol=5e-04, atol=5e-04)
def test_gpt2_q8(self):
tokenizer = AutoTokenizer.from_pretrained(self.gpt2_model_id, gguf_file=self.q8_gpt2_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.gpt2_model_id,
gguf_file=self.q8_gpt2_model_id,
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt")
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, I'm sorry. I'm sorry. I"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_gpt2_weights_conversion_fp16(self):
quantized_model = AutoModelForCausalLM.from_pretrained(
self.gpt2_model_id,
gguf_file=self.fp16_gpt2_model_id,
dtype=torch.float16,
)
original_model = AutoModelForCausalLM.from_pretrained(
self.gpt2_original_model_id,
dtype=torch.float16,
)
quantized_state_dict = quantized_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in quantized_state_dict:
self.assertTrue(original_params.shape == quantized_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, quantized_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
def test_gpt2_xl_Q6_K(self):
tokenizer = AutoTokenizer.from_pretrained(self.gpt2_xl_model_id, gguf_file=self.q6_k_gpt2_xl_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.gpt2_xl_model_id,
gguf_file=self.q6_k_gpt2_xl_model_id,
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt")
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, I'm a newbie to the world of"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
@unittest.skip(reason="Heavy memory")
def test_falcon40b_q2_k(self):
tokenizer = AutoTokenizer.from_pretrained(self.falcon40b_model_id, gguf_file=self.q2_k_falcon40b_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.falcon40b_model_id,
gguf_file=self.q2_k_falcon40b_model_id,
device_map="auto",
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello All,\nI am new to this forum."
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_falcon7b_q2_k(self):
tokenizer = AutoTokenizer.from_pretrained(self.falcon7b_model_id_q2, gguf_file=self.q2_k_falcon7b_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.falcon7b_model_id_q2,
gguf_file=self.q2_k_falcon7b_model_id,
device_map="auto",
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt")["input_ids"].to(torch_device)
out = model.generate(text, max_new_tokens=16)
EXPECTED_TEXT = "Hello All,\nI am new to this forum.\nI am using the "
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
@unittest.skip("The test causes a torch.OutOfMemoryError on the CI but it passes with enough memory")
def test_falcon7b_weights_conversion_fp16(self):
quantized_model = AutoModelForCausalLM.from_pretrained(
self.falcon7b_model_id_fp16,
gguf_file=self.fp16_falcon7b_model_id,
device_map="auto",
dtype=torch.float16,
)
original_model = AutoModelForCausalLM.from_pretrained(
self.original_flacon7b_model_id,
device_map="auto",
dtype=torch.float16,
)
quantized_state_dict = quantized_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in quantized_state_dict:
self.assertTrue(original_params.shape == quantized_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, quantized_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
def test_stablelm_q4_k_m(self):
model = AutoModelForCausalLM.from_pretrained(
self.stablelm_model_id,
gguf_file=self.q4_k_m_stablelm_model_id,
device_map="auto",
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.stablelm_model_id, gguf_file=self.q4_k_m_stablelm_model_id)
text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello-\nI am trying to create a new user"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_stablelm_fp16(self):
original_model = AutoModelForCausalLM.from_pretrained(
self.original_stablelm2_model_id,
dtype=torch.float16,
)
converted_model = AutoModelForCausalLM.from_pretrained(
self.stablelm2_model_id,
gguf_file=self.fp16_stablelm2_model_id,
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.stablelm2_model_id, gguf_file=self.fp16_stablelm2_model_id)
text = tokenizer(self.example_text, return_tensors="pt")
original_out = original_model.generate(**text, max_new_tokens=10)
converted_out = converted_model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, I am a 20 year old male"
self.assertEqual(tokenizer.decode(converted_out[0], skip_special_tokens=True), EXPECTED_TEXT)
self.assertEqual(
tokenizer.decode(converted_out[0], skip_special_tokens=True),
tokenizer.decode(original_out[0], skip_special_tokens=True),
)
def test_stablelm_weights_conversion_fp16(self):
original_model = AutoModelForCausalLM.from_pretrained(
self.original_stablelm2_model_id,
device_map="auto",
dtype=torch.float16,
)
converted_model = AutoModelForCausalLM.from_pretrained(
self.stablelm2_model_id,
gguf_file=self.fp16_stablelm2_model_id,
device_map="auto",
dtype=torch.float16,
)
converted_state_dict = converted_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in converted_state_dict:
self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, converted_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
def test_starcoder2_weights_conversion_fp16(self):
original_model = AutoModelForCausalLM.from_pretrained(
self.starcoder2_original_model_id,
device_map="auto",
dtype=torch.float16,
)
converted_model = AutoModelForCausalLM.from_pretrained(
self.starcoder2_fp16_model_id,
gguf_file=self.fp16_starcoder2_gguf_model_id,
device_map="auto",
dtype=torch.float16,
)
converted_state_dict = converted_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in converted_state_dict:
self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, converted_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
def test_starcoder2_q6_k(self):
example_function_text = "def print_hello_world():"
model = AutoModelForCausalLM.from_pretrained(
self.starcoder2_model_id,
gguf_file=self.q6_k_starcoder2_model_id,
device_map="auto",
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.starcoder2_model_id, gguf_file=self.q6_k_starcoder2_model_id)
text = tokenizer(example_function_text, return_tensors="pt").to(torch_device)
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = 'def print_hello_world():\n print("Hello World")\n\ndef print'
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_mamba_weights_conversion_fp16(self):
original_model = AutoModelForCausalLM.from_pretrained(
self.mamba_original_model_id,
dtype=torch.float16,
)
converted_model = AutoModelForCausalLM.from_pretrained(
self.mamba_model_id,
gguf_file=self.fp16_mamba_model_id,
dtype=torch.float16,
)
converted_state_dict = converted_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in converted_state_dict:
self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
if "mixer.A_log" in layer_name:
# we should increase tolerance after exponential reversing
# and performing np.log(-weights) operation as numbers are slightly different
torch.testing.assert_close(original_params, converted_state_dict[layer_name], rtol=1e-3, atol=1e-3)
else:
torch.testing.assert_close(original_params, converted_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
def test_mamba_q6_k(self):
model = AutoModelForCausalLM.from_pretrained(
self.mamba_model_id,
gguf_file=self.q6_k_mamba_model_id,
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.mamba_model_id, gguf_file=self.q6_k_mamba_model_id)
text = tokenizer(self.example_text, return_tensors="pt")["input_ids"]
out = model.generate(text, max_new_tokens=10)
EXPECTED_TEXT = "Hello,I answerthe question.\n\nA"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_nemotron_weights_conversion_fp16(self):
original_model = AutoModelForCausalLM.from_pretrained(
self.nemotron_original_model_id,
dtype=torch.float16,
)
converted_model = AutoModelForCausalLM.from_pretrained(
self.nemotron_model_id,
gguf_file=self.fp16_nemotron_model_id,
dtype=torch.float16,
)
converted_state_dict = converted_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in converted_state_dict:
self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, converted_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
def test_nemotron_q6_k(self):
model = AutoModelForCausalLM.from_pretrained(
self.nemotron_model_id,
gguf_file=self.q6_k_nemotron_model_id,
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.nemotron_model_id, gguf_file=self.q6_k_nemotron_model_id)
text = tokenizer(self.example_text, return_tensors="pt")["input_ids"]
out = model.generate(text, max_new_tokens=16)
EXPECTED_TEXT = "Hello.▁hotmail.com</s>"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_gemma2_q3_k(self):
model = AutoModelForCausalLM.from_pretrained(
self.gemma2_model_id,
gguf_file=self.q3_k_gemma2_model_id,
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.gemma2_model_id, gguf_file=self.q3_k_gemma2_model_id)
text = tokenizer(self.example_text, return_tensors="pt")["input_ids"]
out = model.generate(text, max_new_tokens=10)
EXPECTED_TEXT = "Hello! 👋\n\nI'm trying to create a"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_gemma2_q8_0(self):
model = AutoModelForCausalLM.from_pretrained(
self.gemma2_model_id,
gguf_file=self.q8_0_gemma2_model_id,
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.gemma2_model_id, gguf_file=self.q8_0_gemma2_model_id)
text = tokenizer(self.example_text, return_tensors="pt")["input_ids"]
out = model.generate(text, max_new_tokens=10)
EXPECTED_TEXT = "Hello! 👋\n\nI'm a large language model"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_gemma2_fp32(self):
model = AutoModelForCausalLM.from_pretrained(
self.gemma2_model_id,
gguf_file=self.fp32_gemma2_model_id,
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.gemma2_model_id, gguf_file=self.fp32_gemma2_model_id)
text = tokenizer(self.example_text, return_tensors="pt")["input_ids"]
out = model.generate(text, max_new_tokens=10)
EXPECTED_TEXT = "Hello! 👋\n\nI'm a large language model"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
@require_read_token
def test_gemma2_weights_conversion_fp32(self):
original_model = AutoModelForCausalLM.from_pretrained(
self.original_gemma2_model_id,
dtype=torch.float16,
)
converted_model = AutoModelForCausalLM.from_pretrained(
self.gemma2_model_id,
gguf_file=self.fp32_gemma2_model_id,
dtype=torch.float16,
)
converted_state_dict = converted_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in converted_state_dict:
self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, converted_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
@require_read_token
@unittest.skipUnless(is_gguf_available("0.16.0"), "test requires gguf version >= 0.16.0")
def test_gemma3_qat_q4_0(self):
model = AutoModelForCausalLM.from_pretrained(
self.gemma3_qat_model_id,
gguf_file=self.q4_0_gemma3_qat_model_id,
dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(self.gemma3_qat_model_id, gguf_file=self.q4_0_gemma3_qat_model_id)
text = tokenizer(self.example_text, return_tensors="pt")["input_ids"]
out = model.generate(text, max_new_tokens=10)
EXPECTED_TEXT = 'Hello with the prompt, "What is the best way'
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
@require_read_token
@unittest.skipUnless(is_gguf_available("0.16.0"), "test requires gguf version >= 0.16.0")
def test_gemma3_text_weights_conversion_bf16(self):
original_model = AutoModelForCausalLM.from_pretrained(
self.original_gemma3_text_model_id,
dtype=torch.float16,
)
converted_model = AutoModelForCausalLM.from_pretrained(
self.gemma3_text_model_id,
gguf_file=self.bf16_gemma3_text_model_id,
dtype=torch.float16,
)
converted_state_dict = converted_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in converted_state_dict:
self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, converted_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
# Test text backbone conversion for gemma3 vision models
@require_read_token
@unittest.skipUnless(is_gguf_available("0.16.0"), "test requires gguf version >= 0.16.0")
def test_gemma3_vision_weights_conversion_bf16(self):
original_model = AutoModelForCausalLM.from_pretrained(
self.original_gemma3_vision_model_id,
dtype=torch.float16,
).language_model
converted_model = AutoModelForCausalLM.from_pretrained(
self.gemma3_vision_model_id,
gguf_file=self.bf16_gemma3_vision_model_id,
dtype=torch.float16,
)
converted_state_dict = converted_model.state_dict()
original_state_dict = original_model.state_dict()
for layer_name, original_params in original_state_dict.items():
if layer_name in converted_state_dict:
self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
torch.testing.assert_close(original_params, converted_state_dict[layer_name])
else:
raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
@require_read_token
@unittest.skipUnless(is_gguf_available("0.16.0"), "test requires gguf version >= 0.16.0")
def test_qwen3_q8_0(self):
tokenizer = AutoTokenizer.from_pretrained(self.qwen3_model_id, gguf_file=self.q8_0_qwen3_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.qwen3_model_id,
gguf_file=self.q8_0_qwen3_model_id,
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt")["input_ids"]
out = model.generate(text, max_new_tokens=10)
EXPECTED_TEXT = "HelloED\nI need to find the value of the"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
def test_qwen3moe_q4_k_m(self):
tokenizer = AutoTokenizer.from_pretrained(self.qwen3moe_model_id, gguf_file=self.q4_k_m_qwen3moe_model_id)
model = AutoModelForCausalLM.from_pretrained(
self.qwen3moe_model_id,
gguf_file=self.q4_k_m_qwen3moe_model_id,
dtype=torch.float16,
)
text = tokenizer(self.example_text, return_tensors="pt")
out = model.generate(**text, max_new_tokens=10)
EXPECTED_TEXT = "Hello, I am a 20 year old male"
self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
| transformers/tests/quantization/ggml/test_ggml.py/0 | {
"file_path": "transformers/tests/quantization/ggml/test_ggml.py",
"repo_id": "transformers",
"token_count": 21506
} | 540 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True,
reason="Skipping test because should only be run when releasing minor transformers version",
)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "FacebookAI/roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "FacebookAI/roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
]
)
class MultiNodeTest(unittest.TestCase):
def setUp(self):
if self.framework == "pytorch":
subprocess.run(
f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(),
encoding="utf-8",
check=True,
)
assert hasattr(self, "env")
def create_estimator(self, instance_count):
# configuration for running training on smdistributed Model Parallel
mpi_options = {
"enabled": True,
"processes_per_host": 8,
}
smp_options = {
"enabled": True,
"parameters": {
"microbatches": 4,
"placement_strategy": "spread",
"pipeline": "interleaved",
"optimize": "speed",
"partitions": 4,
"ddp": True,
},
}
distribution = {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}
name_extension = "trainer" if self.script == "run_glue.py" else "smtrainer"
# creates estimator
return HuggingFace(
entry_point=self.script,
source_dir=self.env.test_path,
role=self.env.role,
image_uri=self.env.image_uri,
base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}",
instance_count=instance_count,
instance_type=self.instance_type,
debugger_hook_config=False,
hyperparameters={
**self.env.hyperparameters,
"model_name_or_path": self.model_name_or_path,
"max_steps": 500,
},
metric_definitions=self.env.metric_definitions,
distribution=distribution,
py_version="py36",
)
def save_results_as_csv(self, job_name):
TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")
# @parameterized.expand([(2,), (4,),])
@parameterized.expand([(1,)])
def test_scripz(self, instance_count):
# create estimator
estimator = self.create_estimator(instance_count)
# run training
estimator.fit()
# result dataframe
result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()
# extract kpis
eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
# get train time from SageMaker job, this includes starting, preprocessing, stopping
train_runtime = (
Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999)
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
assert all(t <= self.results["eval_loss"] for t in eval_loss)
# dump tests result into json file to share in PR
with open(f"{estimator.latest_training_job.name}.json", "w") as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)
| transformers/tests/sagemaker/test_multi_node_model_parallel.py/0 | {
"file_path": "transformers/tests/sagemaker/test_multi_node_model_parallel.py",
"repo_id": "transformers",
"token_count": 2103
} | 541 |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import json
import os
import tempfile
import warnings
from copy import deepcopy
import numpy as np
import pytest
from packaging import version
from transformers import AutoVideoProcessor
from transformers.testing_utils import (
check_json_file_has_correct_format,
require_torch,
require_torch_accelerator,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
def prepare_video(num_frames, num_channels, width=10, height=10, return_tensors="pil"):
"""This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
video = []
for i in range(num_frames):
video.append(np.random.randint(255, size=(width, height, num_channels), dtype=np.uint8))
if return_tensors == "pil":
# PIL expects the channel dimension as last dimension
video = [Image.fromarray(frame) for frame in video]
elif return_tensors == "torch":
# Torch images are typically in channels first format
video = torch.tensor(video).permute(0, 3, 1, 2)
elif return_tensors == "np":
# Numpy images are typically in channels last format
video = np.array(video)
return video
def prepare_video_inputs(
batch_size,
num_frames,
num_channels,
min_resolution,
max_resolution,
equal_resolution=False,
return_tensors="pil",
):
"""This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
one specifies return_tensors="np", or a list of list of PyTorch tensors if one specifies return_tensors="torch".
One can specify whether the videos are of the same resolution or not.
"""
video_inputs = []
for i in range(batch_size):
if equal_resolution:
width = height = max_resolution
else:
width, height = np.random.choice(np.arange(min_resolution, max_resolution), 2)
video = prepare_video(
num_frames=num_frames,
num_channels=num_channels,
width=width,
height=height,
return_tensors=return_tensors,
)
video_inputs.append(video)
return video_inputs
class VideoProcessingTestMixin:
test_cast_dtype = None
fast_video_processing_class = None
video_processor_list = None
input_name = "pixel_values_videos"
def setUp(self):
video_processor_list = []
if self.fast_video_processing_class:
video_processor_list.append(self.fast_video_processing_class)
self.video_processor_list = video_processor_list
def test_video_processor_to_json_string(self):
for video_processing_class in self.video_processor_list:
video_processor = video_processing_class(**self.video_processor_dict)
obj = json.loads(video_processor.to_json_string())
for key, value in self.video_processor_dict.items():
self.assertEqual(obj[key], value)
def test_video_processor_to_json_file(self):
for video_processing_class in self.video_processor_list:
video_processor_first = video_processing_class(**self.video_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "video_processor.json")
video_processor_first.to_json_file(json_file_path)
video_processor_second = video_processing_class.from_json_file(json_file_path)
self.assertEqual(video_processor_second.to_dict(), video_processor_first.to_dict())
def test_video_processor_from_dict_with_kwargs(self):
video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict)
self.assertEqual(video_processor.size, {"shortest_edge": 20})
self.assertEqual(video_processor.crop_size, {"height": 18, "width": 18})
video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict, size=42, crop_size=84)
self.assertEqual(video_processor.size, {"shortest_edge": 42})
self.assertEqual(video_processor.crop_size, {"height": 84, "width": 84})
def test_video_processor_from_and_save_pretrained(self):
for video_processing_class in self.video_processor_list:
video_processor_first = video_processing_class(**self.video_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = video_processor_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
video_processor_second = video_processing_class.from_pretrained(tmpdirname)
self.assertEqual(video_processor_second.to_dict(), video_processor_first.to_dict())
def test_video_processor_save_load_with_autovideoprocessor(self):
for video_processing_class in self.video_processor_list:
video_processor_first = video_processing_class(**self.video_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = video_processor_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
use_fast = video_processing_class.__name__.endswith("Fast")
video_processor_second = AutoVideoProcessor.from_pretrained(tmpdirname, use_fast=use_fast)
self.assertEqual(video_processor_second.to_dict(), video_processor_first.to_dict())
def test_init_without_params(self):
for video_processing_class in self.video_processor_list:
video_processor = video_processing_class()
self.assertIsNotNone(video_processor)
@slow
@require_torch_accelerator
@require_vision
@pytest.mark.torch_compile_test
def test_can_compile_fast_video_processor(self):
if self.fast_video_processing_class is None:
self.skipTest("Skipping compilation test as fast video processor is not defined")
if version.parse(torch.__version__) < version.parse("2.3"):
self.skipTest(reason="This test requires torch >= 2.3 to run.")
torch.compiler.reset()
video_inputs = self.video_processor_tester.prepare_video_inputs(equal_resolution=False, return_tensors="torch")
video_processor = self.fast_video_processing_class(**self.video_processor_dict)
output_eager = video_processor(video_inputs, device=torch_device, do_sample_frames=False, return_tensors="pt")
video_processor = torch.compile(video_processor, mode="reduce-overhead")
output_compiled = video_processor(
video_inputs, device=torch_device, do_sample_frames=False, return_tensors="pt"
)
torch.testing.assert_close(
output_eager[self.input_name], output_compiled[self.input_name], rtol=1e-4, atol=1e-4
)
@require_torch
@require_vision
def test_cast_dtype_device(self):
for video_processing_class in self.video_processor_list:
if self.test_cast_dtype is not None:
# Initialize video_processor
video_processor = video_processing_class(**self.video_processor_dict)
# create random PyTorch tensors
video_inputs = self.video_processor_tester.prepare_video_inputs(
equal_resolution=False, return_tensors="torch"
)
encoding = video_processor(video_inputs, return_tensors="pt")
self.assertEqual(encoding[self.input_name].device, torch.device("cpu"))
self.assertEqual(encoding[self.input_name].dtype, torch.float32)
encoding = video_processor(video_inputs, return_tensors="pt").to(torch.float16)
self.assertEqual(encoding[self.input_name].device, torch.device("cpu"))
self.assertEqual(encoding[self.input_name].dtype, torch.float16)
encoding = video_processor(video_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
self.assertEqual(encoding[self.input_name].device, torch.device("cpu"))
self.assertEqual(encoding[self.input_name].dtype, torch.bfloat16)
with self.assertRaises(TypeError):
_ = video_processor(video_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
# Try with text + video feature
encoding = video_processor(video_inputs, return_tensors="pt")
encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
encoding = encoding.to(torch.float16)
self.assertEqual(encoding[self.input_name].device, torch.device("cpu"))
self.assertEqual(encoding[self.input_name].dtype, torch.float16)
self.assertEqual(encoding.input_ids.dtype, torch.long)
def test_call_pil(self):
for video_processing_class in self.video_processor_list:
# Initialize video_processing
video_processing = video_processing_class(**self.video_processor_dict)
video_inputs = self.video_processor_tester.prepare_video_inputs(equal_resolution=False)
# Each video is a list of PIL Images
for video in video_inputs:
self.assertIsInstance(video[0], Image.Image)
# Test not batched input
encoded_videos = video_processing(video_inputs[0], return_tensors="pt")[self.input_name]
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
# Test batched
encoded_videos = video_processing(video_inputs, return_tensors="pt")[self.input_name]
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
self.assertEqual(
tuple(encoded_videos.shape), (self.video_processor_tester.batch_size, *expected_output_video_shape)
)
def test_call_numpy(self):
for video_processing_class in self.video_processor_list:
# Initialize video_processing
video_processing = video_processing_class(**self.video_processor_dict)
# create random numpy tensors
video_inputs = self.video_processor_tester.prepare_video_inputs(
equal_resolution=False, return_tensors="np"
)
for video in video_inputs:
self.assertIsInstance(video, np.ndarray)
# Test not batched input
encoded_videos = video_processing(video_inputs[0], return_tensors="pt")[self.input_name]
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
# Test batched
encoded_videos = video_processing(video_inputs, return_tensors="pt")[self.input_name]
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
self.assertEqual(
tuple(encoded_videos.shape), (self.video_processor_tester.batch_size, *expected_output_video_shape)
)
def test_call_pytorch(self):
for video_processing_class in self.video_processor_list:
# Initialize video_processing
video_processing = video_processing_class(**self.video_processor_dict)
# create random PyTorch tensors
video_inputs = self.video_processor_tester.prepare_video_inputs(
equal_resolution=False, return_tensors="torch"
)
for video in video_inputs:
self.assertIsInstance(video, torch.Tensor)
# Test not batched input
encoded_videos = video_processing(video_inputs[0], return_tensors="pt")[self.input_name]
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
# Test batched
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
encoded_videos = video_processing(video_inputs, return_tensors="pt")[self.input_name]
self.assertEqual(
tuple(encoded_videos.shape),
(self.video_processor_tester.batch_size, *expected_output_video_shape),
)
def test_call_sample_frames(self):
for video_processing_class in self.video_processor_list:
video_processing = video_processing_class(**self.video_processor_dict)
prev_num_frames = self.video_processor_tester.num_frames
self.video_processor_tester.num_frames = 8
video_inputs = self.video_processor_tester.prepare_video_inputs(
equal_resolution=False,
return_tensors="torch",
)
# Force set sampling to False. No sampling is expected even when `num_frames` exists
video_processing.do_sample_frames = False
encoded_videos = video_processing(video_inputs[0], return_tensors="pt", num_frames=3)[self.input_name]
encoded_videos_batched = video_processing(video_inputs, return_tensors="pt", num_frames=3)[self.input_name]
self.assertEqual(encoded_videos.shape[1], 8)
self.assertEqual(encoded_videos_batched.shape[1], 8)
# Set sampling to True. Video frames should be sampled with `num_frames` in the output
video_processing.do_sample_frames = True
encoded_videos = video_processing(video_inputs[0], return_tensors="pt", num_frames=3)[self.input_name]
encoded_videos_batched = video_processing(video_inputs, return_tensors="pt", num_frames=3)[self.input_name]
self.assertEqual(encoded_videos.shape[1], 3)
self.assertEqual(encoded_videos_batched.shape[1], 3)
# Sample with `fps` requires metadata to infer number of frames from total duration
with self.assertRaises(ValueError):
encoded_videos = video_processing(video_inputs[0], return_tensors="pt", fps=3)[self.input_name]
encoded_videos_batched = video_processing(video_inputs, return_tensors="pt", fps=3)[self.input_name]
metadata = [[{"duration": 2.0, "total_num_frames": 8, "fps": 4}]]
batched_metadata = metadata * len(video_inputs)
encoded_videos = video_processing(video_inputs[0], return_tensors="pt", fps=3, video_metadata=metadata)[
self.input_name
]
encoded_videos_batched = video_processing(
video_inputs, return_tensors="pt", fps=3, video_metadata=batched_metadata
)[self.input_name]
self.assertEqual(encoded_videos.shape[1], 6)
self.assertEqual(encoded_videos_batched.shape[1], 6)
# We should raise error when asked to sample more frames than there are in input video
with self.assertRaises(ValueError):
encoded_videos = video_processing(video_inputs[0], return_tensors="pt", num_frames=10)[self.input_name]
encoded_videos_batched = video_processing(video_inputs, return_tensors="pt", num_frames=10)[
self.input_name
]
# Assign back the actual num frames in tester
self.video_processor_tester.num_frames = prev_num_frames
def test_nested_input(self):
"""Tests that the processor can work with nested list where each video is a list of arrays"""
for video_processing_class in self.video_processor_list:
video_processing = video_processing_class(**self.video_processor_dict)
video_inputs = self.video_processor_tester.prepare_video_inputs(
equal_resolution=False, return_tensors="np"
)
# Test not batched input
video_inputs = [list(video) for video in video_inputs]
encoded_videos = video_processing(video_inputs[0], return_tensors="pt")[self.input_name]
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
# Test batched
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
encoded_videos = video_processing(video_inputs, return_tensors="pt")[self.input_name]
self.assertEqual(
tuple(encoded_videos.shape),
(self.video_processor_tester.batch_size, *expected_output_video_shape),
)
def test_call_numpy_4_channels(self):
for video_processing_class in self.video_processor_list:
# Test that can process videos which have an arbitrary number of channels
# Initialize video_processing
video_processor = video_processing_class(**self.video_processor_dict)
# create random numpy tensors
self.video_processor_tester.num_channels = 4
video_inputs = self.video_processor_tester.prepare_video_inputs(
equal_resolution=False, return_tensors="pil"
)
# Test not batched input
encoded_videos = video_processor(
video_inputs[0],
return_tensors="pt",
input_data_format="channels_last",
image_mean=0,
image_std=1,
)[self.input_name]
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape([video_inputs[0]])
if video_processor.do_convert_rgb:
expected_output_video_shape = list(expected_output_video_shape)
expected_output_video_shape[1] = 3
self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
# Test batched
encoded_videos = video_processor(
video_inputs,
return_tensors="pt",
input_data_format="channels_last",
image_mean=0,
image_std=1,
)[self.input_name]
expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
if video_processor.do_convert_rgb:
expected_output_video_shape = list(expected_output_video_shape)
expected_output_video_shape[1] = 3
self.assertEqual(
tuple(encoded_videos.shape), (self.video_processor_tester.batch_size, *expected_output_video_shape)
)
def test_video_processor_preprocess_arguments(self):
is_tested = False
for video_processing_class in self.video_processor_list:
video_processor = video_processing_class(**self.video_processor_dict)
# validation done by _valid_processor_keys attribute
if hasattr(video_processor, "_valid_processor_keys") and hasattr(video_processor, "preprocess"):
preprocess_parameter_names = inspect.getfullargspec(video_processor.preprocess).args
preprocess_parameter_names.remove("self")
preprocess_parameter_names.sort()
valid_processor_keys = video_processor._valid_processor_keys
valid_processor_keys.sort()
self.assertEqual(preprocess_parameter_names, valid_processor_keys)
is_tested = True
# validation done by @filter_out_non_signature_kwargs decorator
if hasattr(video_processor.preprocess, "_filter_out_non_signature_kwargs"):
if hasattr(self.video_processor_tester, "prepare_video_inputs"):
inputs = self.video_processor_tester.prepare_video_inputs()
elif hasattr(self.video_processor_tester, "prepare_video_inputs"):
inputs = self.video_processor_tester.prepare_video_inputs()
else:
self.skipTest(reason="No valid input preparation method found")
with warnings.catch_warnings(record=True) as raised_warnings:
warnings.simplefilter("always")
video_processor(inputs, extra_argument=True)
messages = " ".join([str(w.message) for w in raised_warnings])
self.assertGreaterEqual(len(raised_warnings), 1)
self.assertIn("extra_argument", messages)
is_tested = True
if not is_tested:
self.skipTest(reason="No validation found for `preprocess` method")
def test_override_instance_attributes_does_not_affect_other_instances(self):
if self.fast_video_processing_class is None:
self.skipTest(
"Only testing fast video processor, as most slow processors break this test and are to be deprecated"
)
video_processing_class = self.fast_video_processing_class
video_processor_1 = video_processing_class()
video_processor_2 = video_processing_class()
if not (hasattr(video_processor_1, "size") and isinstance(video_processor_1.size, dict)) or not (
hasattr(video_processor_1, "image_mean") and isinstance(video_processor_1.image_mean, list)
):
self.skipTest(
reason="Skipping test as the image processor does not have dict size or list image_mean attributes"
)
original_size_2 = deepcopy(video_processor_2.size)
for key in video_processor_1.size:
video_processor_1.size[key] = -1
modified_copied_size_1 = deepcopy(video_processor_1.size)
original_image_mean_2 = deepcopy(video_processor_2.image_mean)
video_processor_1.image_mean[0] = -1
modified_copied_image_mean_1 = deepcopy(video_processor_1.image_mean)
# check that the original attributes of the second instance are not affected
self.assertEqual(video_processor_2.size, original_size_2)
self.assertEqual(video_processor_2.image_mean, original_image_mean_2)
for key in video_processor_2.size:
video_processor_2.size[key] = -2
video_processor_2.image_mean[0] = -2
# check that the modified attributes of the first instance are not affected by the second instance
self.assertEqual(video_processor_1.size, modified_copied_size_1)
self.assertEqual(video_processor_1.image_mean, modified_copied_image_mean_1)
| transformers/tests/test_video_processing_common.py/0 | {
"file_path": "transformers/tests/test_video_processing_common.py",
"repo_id": "transformers",
"token_count": 9984
} | 542 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import warnings
import pytest
from parameterized import parameterized
from transformers import __version__, is_torch_available
from transformers.testing_utils import require_torch_accelerator, torch_device
from transformers.utils.deprecation import deprecate_kwarg
if is_torch_available():
import torch
INFINITE_VERSION = "9999.0.0"
class DeprecationDecoratorTester(unittest.TestCase):
def test_rename_kwarg(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
@deprecate_kwarg("deprecated_name", new_name="new_name", version=INFINITE_VERSION)
def dummy_function(new_name=None, other_name=None):
return new_name, other_name
# Test keyword argument is renamed
value, other_value = dummy_function(deprecated_name="old_value")
self.assertEqual(value, "old_value")
self.assertIsNone(other_value)
# Test deprecated keyword argument not passed
value, other_value = dummy_function(new_name="new_value")
self.assertEqual(value, "new_value")
self.assertIsNone(other_value)
# Test other keyword argument
value, other_value = dummy_function(other_name="other_value")
self.assertIsNone(value)
self.assertEqual(other_value, "other_value")
# Test deprecated and new args are passed, the new one should be returned
value, other_value = dummy_function(deprecated_name="old_value", new_name="new_value")
self.assertEqual(value, "new_value")
self.assertIsNone(other_value)
def test_rename_multiple_kwargs(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
@deprecate_kwarg("deprecated_name1", new_name="new_name1", version=INFINITE_VERSION)
@deprecate_kwarg("deprecated_name2", new_name="new_name2", version=INFINITE_VERSION)
def dummy_function(new_name1=None, new_name2=None, other_name=None):
return new_name1, new_name2, other_name
# Test keyword argument is renamed
value1, value2, other_value = dummy_function(deprecated_name1="old_value1", deprecated_name2="old_value2")
self.assertEqual(value1, "old_value1")
self.assertEqual(value2, "old_value2")
self.assertIsNone(other_value)
# Test deprecated keyword argument is not passed
value1, value2, other_value = dummy_function(new_name1="new_value1", new_name2="new_value2")
self.assertEqual(value1, "new_value1")
self.assertEqual(value2, "new_value2")
self.assertIsNone(other_value)
# Test other keyword argument is passed and correctly returned
value1, value2, other_value = dummy_function(other_name="other_value")
self.assertIsNone(value1)
self.assertIsNone(value2)
self.assertEqual(other_value, "other_value")
def test_warnings(self):
# Test warning is raised for future version
@deprecate_kwarg("deprecated_name", new_name="new_name", version=INFINITE_VERSION)
def dummy_function(new_name=None, other_name=None):
return new_name, other_name
with self.assertWarns(FutureWarning):
dummy_function(deprecated_name="old_value")
# Test warning is not raised for past version, but arg is still renamed
@deprecate_kwarg("deprecated_name", new_name="new_name", version="0.0.0")
def dummy_function(new_name=None, other_name=None):
return new_name, other_name
with warnings.catch_warnings(record=True) as raised_warnings:
warnings.simplefilter("always")
value, other_value = dummy_function(deprecated_name="old_value")
self.assertEqual(value, "old_value")
self.assertIsNone(other_value)
self.assertEqual(len(raised_warnings), 0, f"Warning raised: {[w.message for w in raised_warnings]}")
# Test warning is raised for future version if warn_if_greater_or_equal_version is set
@deprecate_kwarg("deprecated_name", version="0.0.0", warn_if_greater_or_equal_version=True)
def dummy_function(deprecated_name=None):
return deprecated_name
with self.assertWarns(FutureWarning):
value = dummy_function(deprecated_name="deprecated_value")
self.assertEqual(value, "deprecated_value")
# Test arg is not renamed if new_name is not specified, but warning is raised
@deprecate_kwarg("deprecated_name", version=INFINITE_VERSION)
def dummy_function(deprecated_name=None):
return deprecated_name
with self.assertWarns(FutureWarning):
value = dummy_function(deprecated_name="deprecated_value")
self.assertEqual(value, "deprecated_value")
def test_raises(self):
# Test if deprecated name and new name are both passed and raise_if_both_names is set -> raise error
@deprecate_kwarg("deprecated_name", new_name="new_name", version=INFINITE_VERSION, raise_if_both_names=True)
def dummy_function(new_name=None, other_name=None):
return new_name, other_name
with self.assertRaises(ValueError):
dummy_function(deprecated_name="old_value", new_name="new_value")
# Test for current version == deprecation version
@deprecate_kwarg("deprecated_name", version=__version__, raise_if_greater_or_equal_version=True)
def dummy_function(deprecated_name=None):
return deprecated_name
with self.assertRaises(ValueError):
dummy_function(deprecated_name="old_value")
# Test for current version > deprecation version
@deprecate_kwarg("deprecated_name", version="0.0.0", raise_if_greater_or_equal_version=True)
def dummy_function(deprecated_name=None):
return deprecated_name
with self.assertRaises(ValueError):
dummy_function(deprecated_name="old_value")
def test_additional_message(self):
# Test additional message is added to the warning
@deprecate_kwarg("deprecated_name", version=INFINITE_VERSION, additional_message="Additional message")
def dummy_function(deprecated_name=None):
return deprecated_name
with warnings.catch_warnings(record=True) as raised_warnings:
warnings.simplefilter("always")
dummy_function(deprecated_name="old_value")
self.assertTrue("Additional message" in str(raised_warnings[0].message))
@parameterized.expand(["0.0.0", __version__, INFINITE_VERSION])
def test_warning_for_both_names(self, version):
# We should raise warning if both names are passed for any specified version
@deprecate_kwarg("deprecated_name", new_name="new_name", version=version)
def dummy_function(new_name=None, **kwargs):
return new_name
with self.assertWarns(FutureWarning):
result = dummy_function(deprecated_name="old_value", new_name="new_value")
self.assertEqual(result, "new_value")
@pytest.mark.torch_compile_test
@require_torch_accelerator
def test_compile_safe(self):
@deprecate_kwarg("deprecated_factor", new_name="new_factor", version=INFINITE_VERSION)
def dummy_function(new_factor=None, **kwargs):
return new_factor * torch.ones(1, device=torch_device)
compiled_function = torch.compile(dummy_function, fullgraph=True)
# Check that we can correctly call the compiled function with the old name, without raising errors
out = compiled_function(deprecated_factor=2)
self.assertEqual(out.item(), 2)
# Check that we can correctly call the compiled function with the new name, without raising errors
out = compiled_function(new_factor=2)
self.assertEqual(out.item(), 2)
# Check that we can correctly call the compiled function with both names, without raising errors
out = compiled_function(new_factor=2, deprecated_factor=10)
self.assertEqual(out.item(), 2)
| transformers/tests/utils/test_deprecation.py/0 | {
"file_path": "transformers/tests/utils/test_deprecation.py",
"repo_id": "transformers",
"token_count": 3459
} | 543 |
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import json
import os
import tempfile
import unittest
from pathlib import Path
from transformers import is_torch_available
from transformers.model_debugging_utils import model_addition_debugger_context
if is_torch_available():
import torch
from torch import nn
class ToyModel(nn.Module):
def __init__(self):
super().__init__()
self.embed = nn.Embedding(10, 4)
self.linear_1 = nn.Linear(4, 8)
self.linear_2 = nn.Linear(8, 2)
self.act = nn.ReLU()
def forward(self, input_ids: str):
hidden_states = self.embed(input_ids).mean(dim=1)
hidden_states = self.act(self.linear_1(hidden_states))
return self.linear_2(hidden_states)
class TestModelAdditionDebugger(unittest.TestCase):
def setUp(self):
self.model = ToyModel()
self.inputs = {"input_ids": torch.randint(0, 10, (1, 3))}
def tearDown(self):
gc.collect()
def test_debugger_outputs(self):
with tempfile.TemporaryDirectory() as tmpdir:
with model_addition_debugger_context(self.model, debug_path=str(tmpdir)):
_ = self.model.forward(**self.inputs)
base = f"{self.model.__class__.__name__}_debug_tree"
summary = Path(os.path.join(tmpdir, f"{base}_SUMMARY.json"))
full = Path(os.path.join(tmpdir, f"{base}_FULL_TENSORS.json"))
self.assertTrue(os.path.isfile(summary) and os.path.isfile(full))
data = json.loads(summary.read_text())
self.assertTrue({"module_path", "inputs", "children"} <= data.keys())
self.assertTrue(data["children"])
class ToyLayer(nn.Module):
def __init__(self, layer_index):
super().__init__()
self.layer_index = layer_index
self.layer_operation = nn.Linear(4, 4)
def forward(self, hidden_states):
return self.layer_operation(hidden_states)
class ToyModelWithLayers(nn.Module):
def __init__(self):
super().__init__()
self.input_proj = nn.Linear(4, 4)
self.layers = nn.ModuleList([ToyLayer(layer_index) for layer_index in range(6)])
self.output_proj = nn.Linear(4, 2)
def forward(self, x):
x = self.input_proj(x)
for layer in self.layers:
x = layer(x)
return self.output_proj(x)
class TestModelWithLayers(unittest.TestCase):
def setUp(self):
self.inputs = {"input_ids": torch.randint(0, 10, (1, 3))}
self.model_with_layers = ToyModelWithLayers()
self.dense_input = {"x": torch.randn(1, 4)}
def tearDown(self):
gc.collect()
def test_layer_pruning_behavior(self):
# No pruning: expect all 6 layers
with tempfile.TemporaryDirectory() as tmpdir:
with model_addition_debugger_context(self.model_with_layers, debug_path=tmpdir, do_prune_layers=False):
_ = self.model_with_layers(**self.dense_input)
summary_path = os.path.join(tmpdir, "ToyModelWithLayers_debug_tree_SUMMARY.json")
with open(summary_path) as f:
data = json.load(f)
self.assertEqual(set(data.keys()), {"module_path", "inputs", "children"})
for layer_index in range(6):
self.assertEqual(
data["children"][layer_index + 1]["module_path"],
f"ToyModelWithLayers.layers.{int(layer_index)}",
)
# Pruning: expect only 2 layers (0 and 5)
with tempfile.TemporaryDirectory() as tmpdir:
with model_addition_debugger_context(self.model_with_layers, debug_path=tmpdir, do_prune_layers=True):
_ = self.model_with_layers(**self.dense_input)
summary_path = os.path.join(tmpdir, "ToyModelWithLayers_debug_tree_SUMMARY.json")
with open(summary_path) as f:
data = json.load(f)
self.assertEqual(set(data.keys()), {"module_path", "inputs", "children"})
self.assertEqual(data["children"][1]["module_path"], "ToyModelWithLayers.layers.0")
self.assertEqual(data["children"][2]["module_path"], "ToyModelWithLayers.layers.5")
| transformers/tests/utils/test_model_debugging_utils.py/0 | {
"file_path": "transformers/tests/utils/test_model_debugging_utils.py",
"repo_id": "transformers",
"token_count": 2366
} | 544 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utility that checks whether the copies defined in the library match the original or not. This includes:
- All code commented with `# Copied from` comments,
- The list of models in the main README.md matches the ones in the localized READMEs,
- Files that are registered as full copies of one another in the `FULL_COPIES` constant of this script.
This also checks the list of models in the README is complete (has all models) and adds a line to complete if there is
a model missing.
Use from the root of the repo with:
```bash
python utils/check_copies.py
```
for a check that will error in case of inconsistencies (used by `make repo-consistency`) or
```bash
python utils/check_copies.py --fix_and_overwrite
```
for a check that will fix all inconsistencies automatically (used by `make fix-copies`).
"""
import argparse
import glob
import os
import re
import subprocess
from collections import OrderedDict
from typing import Optional, Union
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
TRANSFORMERS_PATH = "src/transformers"
MODEL_TEST_PATH = "tests/models"
PATH_TO_DOCS = "docs/source/en"
REPO_PATH = "."
# Mapping for files that are full copies of others (keys are copies, values the file to keep them up to data with)
FULL_COPIES = {
"examples/tensorflow/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py",
"examples/flax/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py",
}
LOCALIZED_READMES = {
# If the introduction or the conclusion of the list change, the prompts may need to be updated.
"README.md": {
"start_prompt": "🤗 Transformers currently provides the following architectures",
"end_prompt": "1. Want to contribute a new model?",
"format_model_list": (
"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
" {paper_authors}.{supplements}"
),
},
"README_zh-hans.md": {
"start_prompt": "🤗 Transformers 目前支持如下的架构",
"end_prompt": "1. 想要贡献新的模型？",
"format_model_list": (
"**[{title}]({model_link})** (来自 {paper_affiliations}) 伴随论文 {paper_title_link} 由 {paper_authors}"
" 发布。{supplements}"
),
},
"README_zh-hant.md": {
"start_prompt": "🤗 Transformers 目前支援以下的架構",
"end_prompt": "1. 想要貢獻新的模型？",
"format_model_list": (
"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
" {paper_authors}.{supplements}"
),
},
"README_ko.md": {
"start_prompt": "🤗 Transformers는 다음 모델들을 제공합니다",
"end_prompt": "1. 새로운 모델을 올리고 싶나요?",
"format_model_list": (
"**[{title}]({model_link})** ({paper_affiliations} 에서 제공)은 {paper_authors}.{supplements}의"
" {paper_title_link}논문과 함께 발표했습니다."
),
},
"README_es.md": {
"start_prompt": "🤗 Transformers actualmente proporciona las siguientes arquitecturas",
"end_prompt": "1. ¿Quieres aportar un nuevo modelo?",
"format_model_list": (
"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
" {paper_authors}.{supplements}"
),
},
"README_ja.md": {
"start_prompt": "🤗Transformersは現在、以下のアーキテクチャを提供しています",
"end_prompt": "1. 新しいモデルを投稿したいですか？",
"format_model_list": (
"**[{title}]({model_link})** ({paper_affiliations} から) {paper_authors}.{supplements} から公開された研究論文"
" {paper_title_link}"
),
},
"README_hd.md": {
"start_prompt": "🤗 ट्रांसफॉर्मर वर्तमान में निम्नलिखित आर्किटेक्चर का समर्थन करते हैं",
"end_prompt": "1. एक नए मॉडल में योगदान देना चाहते हैं?",
"format_model_list": (
"**[{title}]({model_link})** ({paper_affiliations} से) {paper_authors}.{supplements} द्वारा"
"अनुसंधान पत्र {paper_title_link} के साथ जारी किया गया"
),
},
"README_ru.md": {
"start_prompt": "🤗 В настоящее время Transformers предоставляет следующие архитектуры",
"end_prompt": "1. Хотите внести новую модель?",
"format_model_list": (
"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
" {paper_authors}.{supplements}"
),
},
"README_pt-br.md": {
"start_prompt": "🤗 Transformers atualmente fornece as seguintes arquiteturas",
"end_prompt": "1. Quer contribuir com um novo modelo?",
"format_model_list": (
"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
" {paper_authors}.{supplements}"
),
},
"README_te.md": {
"start_prompt": "🤗 ట్రాన్స్ఫార్మర్లు ప్రస్తుతం కింది ఆర్కిటెక్చర్లను అందజేస్తున్నాయి",
"end_prompt": "1. కొత్త మోడల్ను అందించాలనుకుంటున్నారా?",
"format_model_list": (
"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
" {paper_authors}.{supplements}"
),
},
"README_fr.md": {
"start_prompt": "🤗 Transformers fournit actuellement les architectures suivantes",
"end_prompt": "1. Vous souhaitez contribuer avec un nouveau modèle ?",
"format_model_list": (
"**[{title}]({model_link})** (de {paper_affiliations}) publié dans l'article {paper_title_link} par"
"{paper_authors}.{supplements}"
),
},
"README_de.md": {
"start_prompt": "🤗 Transformers bietet derzeit die folgenden Architekturen an",
"end_prompt": "1. Möchten Sie ein neues Modell beitragen?",
"format_model_list": (
"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
" {paper_authors}.{supplements}"
),
},
"README_vi.md": {
"start_prompt": "🤗 Transformers hiện đang cung cấp các kiến trúc sau đây",
"end_prompt": "1. Muốn đóng góp một mô hình mới?",
"format_model_list": (
"**[{title}]({model_link})** (từ {paper_affiliations}) được phát hành với bài báo {paper_title_link} by"
" {paper_authors}.{supplements}"
),
},
}
# This is to make sure the transformers module imported is the one in the repo.
transformers_module = direct_transformers_import(TRANSFORMERS_PATH)
def _is_definition_header_ending_line(line: str) -> bool:
# Helper function. Returns `True` if `line` is the end parenthesis of a class/function definition
return re.search(r"^\s*\)(\s*->.*:|:)\s*$", line) is not None
def _should_continue(line: str, indent: str) -> bool:
# Helper function. Returns `True` if `line` is empty, starts with the `indent` or is the end parenthesis of a
# class/function definition
return line.startswith(indent) or len(line.strip()) == 0 or _is_definition_header_ending_line(line)
def _sanity_check_splits(splits_1, splits_2, is_class, filename):
"""Check the two (inner) block structures of the corresponding code block given by `split_code_into_blocks` match.
For the case of `class`, they must be of one of the following 3 cases:
- a single block without name:
class foo:
a = 1
- a consecutive sequence of (1 or more) blocks with name
class foo:
def f(x):
return x
- a block without name, followed by a consecutive sequence of (1 or more) blocks with name
class foo:
a = 1
def f(x):
return x
def g(x):
return None
The 2 code snippets that give `splits_1` and `splits_2` have to be in the same case to pass this check, but the
number of blocks with name in the consecutive sequence is not taken into account.
For the case of `function or method`, we don't require it to be in one of the above 3 cases. However, the structure
of`splits_1` and `splits_2` have to match exactly. In particular, the number of blocks with name in a consecutive
sequence is taken into account.
"""
block_names_1 = []
block_names_2 = []
for block in splits_1[1:]:
if block[0].startswith("_block_without_name_"):
block_names_1.append("block_without_name")
elif not block[0].startswith("_empty_block_") and (
not is_class or len(block_names_1) == 0 or block_names_1[-1].startswith("block_without_name")
):
block_names_1.append("block_with_name")
for block in splits_2[1:]:
if block[0].startswith("_block_without_name_"):
block_names_2.append("block_without_name")
elif not block[0].startswith("_empty_block_") and (
not is_class or len(block_names_2) == 0 or block_names_2[-1].startswith("block_without_name")
):
block_names_2.append("block_with_name")
if is_class:
if block_names_1 not in [
["block_without_name"],
["block_with_name"],
["block_without_name", "block_with_name"],
]:
raise ValueError(
f"""Class defined in {filename} doesn't have the expected structure.
See the docstring of `_sanity_check_splits` in the file `utils/check_copies.py`""",
)
if block_names_1 != block_names_2:
raise ValueError(f"In {filename}, two code blocks expected to be copies have different structures.")
def find_block_end(lines: list[str], start_index: int, indent: int) -> int:
"""
Find the end of the class/func block starting at `start_index` in a source code (defined by `lines`).
Args:
lines (`List[str]`):
The source code, represented by a list of lines.
start_index (`int`):
The starting index of the target class/func block.
indent (`int`):
The indent of the class/func body.
Returns:
`int`: The index of the block's ending line plus by 1 (i.e. exclusive).
"""
indent = " " * indent
# enter the block body
line_index = start_index + 1
while line_index < len(lines) and _should_continue(lines[line_index], indent):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
return line_index
def split_code_into_blocks(
lines: list[str], start_index: int, end_index: int, indent: int, backtrace: bool = False
) -> list[tuple[str, int, int]]:
"""
Split the class/func block starting at `start_index` in a source code (defined by `lines`) into *inner blocks*.
The block's header is included as the first element. The contiguous regions (without empty lines) that are not
inside any inner block are included as blocks. The contiguous regions of empty lines that are not inside any inner
block are also included as (dummy) blocks.
Args:
lines (`List[str]`):
The source code, represented by a list of lines.
start_index (`int`):
The starting index of the target class/func block.
end_index (`int`):
The ending index of the target class/func block.
indent (`int`):
The indent of the class/func body.
backtrace (`bool`, *optional*, defaults to `False`):
Whether or not to include the lines before the inner class/func block's header (e.g. comments, decorators,
etc.) until an empty line is encountered.
Returns:
`List[Tuple[str, int, int]]`: A list of elements with the form `(block_name, start_index, end_index)`.
"""
splits = []
# `indent - 4` is the indent level of the target class/func header
try:
target_block_name = re.search(
rf"^{' ' * (indent - 4)}((class|def)\s+\S+)(\(|\:)", lines[start_index]
).groups()[0]
except Exception:
start_context = min(start_index - 10, 0)
end_context = min(end_index + 10, len(lines))
raise ValueError(
f"Tried to split a class or function. It did not work. Error comes from line {start_index}: \n```\n"
+ "".join(lines[start_context:end_context])
+ "```\n"
)
# from now on, the `block` means inner blocks unless explicitly specified
indent_str = " " * indent
block_without_name_idx = 0
empty_block_idx = 0
# Find the lines for the definition header
index = start_index
if "(" in lines[start_index] and "):" not in lines[start_index] in lines[start_index]:
while index < end_index:
if _is_definition_header_ending_line(lines[index]):
break
index += 1
# the first line outside the definition header
index += 1
splits.append((target_block_name, start_index, index))
block_start_index, prev_block_end_index = index, index
while index < end_index:
# if found, it will be an inner block
block_found = re.search(rf"^{indent_str}((class|def)\s+\S+)(\(|\:)", lines[index])
if block_found:
name = block_found.groups()[0]
block_end_index = find_block_end(lines, index, indent + 4)
# backtrace to include the lines before the found block's definition header (e.g. comments, decorators,
# etc.) until an empty line is encountered.
block_start_index = index
if index > prev_block_end_index and backtrace:
idx = index - 1
for idx in range(index - 1, prev_block_end_index - 2, -1):
if not (len(lines[idx].strip()) > 0 and lines[idx].startswith(indent_str)):
break
idx += 1
if idx < index:
block_start_index = idx
# between the current found block and the previous found block
if block_start_index > prev_block_end_index:
# give it a dummy name
if len("".join(lines[prev_block_end_index:block_start_index]).strip()) == 0:
prev_block_name = f"_empty_block_{empty_block_idx}"
empty_block_idx += 1
else:
prev_block_name = f"_block_without_name_{block_without_name_idx}"
block_without_name_idx += 1
# Add it as a block
splits.append((prev_block_name, prev_block_end_index, block_start_index))
# Add the current found block
splits.append((name, block_start_index, block_end_index))
prev_block_end_index = block_end_index
index = block_end_index - 1
index += 1
if index > prev_block_end_index:
if len("".join(lines[prev_block_end_index:index]).strip()) == 0:
prev_block_name = f"_empty_block_{empty_block_idx}"
else:
prev_block_name = f"_block_without_name_{block_without_name_idx}"
splits.append((prev_block_name, prev_block_end_index, index))
return splits
def find_code_in_transformers(
object_name: str, base_path: Optional[str] = None, return_indices: bool = False
) -> Union[str, tuple[list[str], int, int]]:
"""
Find and return the source code of an object.
Args:
object_name (`str`):
The name of the object we want the source code of.
base_path (`str`, *optional*):
The path to the base folder where files are checked. If not set, it will be set to `TRANSFORMERS_PATH`.
return_indices(`bool`, *optional*, defaults to `False`):
If `False`, will only return the code (as a string), otherwise it will also return the whole lines of the
file where the object specified by `object_name` is defined, together the start/end indices of the block in
the file that defines the object.
Returns:
`Union[str, Tuple[List[str], int, int]]`: If `return_indices=False`, only the source code of the object will be
returned. Otherwise, it also returns the whole lines of the file where the object specified by `object_name` is
defined, together the start/end indices of the block in the file that defines the object.
"""
parts = object_name.split(".")
i = 0
# We can't set this as the default value in the argument, otherwise `CopyCheckTester` will fail, as it uses a
# patched temp directory.
if base_path is None:
base_path = TRANSFORMERS_PATH
# Detail: the `Copied from` statement is originally designed to work with the last part of `TRANSFORMERS_PATH`,
# (which is `transformers`). The same should be applied for `MODEL_TEST_PATH`. However, its last part is `models`
# (to only check and search in it) which is a bit confusing. So we keep the copied statement starting with
# `tests.models.` and change it to `tests` here.
if base_path == MODEL_TEST_PATH:
base_path = "tests"
# First let's find the module where our object lives.
module = parts[i]
while i < len(parts) and not os.path.isfile(os.path.join(base_path, f"{module}.py")):
i += 1
if i < len(parts):
module = os.path.join(module, parts[i])
if i >= len(parts):
raise ValueError(
f"`object_name` should begin with the name of a module of transformers but got {object_name}."
)
with open(os.path.join(base_path, f"{module}.py"), "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
# Now let's find the class / func in the code!
indent = ""
line_index = 0
for name in parts[i + 1 :]:
while (
line_index < len(lines) and re.search(rf"^{indent}(class|def)\s+{name}(\(|\:)", lines[line_index]) is None
):
line_index += 1
# find the target specified in the current level in `parts` -> increase `indent` so we can search the next
indent += " "
# the index of the first line in the (currently found) block *body*
line_index += 1
if line_index >= len(lines):
raise ValueError(f" {object_name} does not match any function or class in {module}.")
# `indent` is already one level deeper than the (found) class/func block's definition header
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
# `start_index` is the index of the class/func block's definition header
start_index = line_index - 1
end_index = find_block_end(lines, start_index, len(indent))
code = "".join(lines[start_index:end_index])
return (code, (lines, start_index, end_index)) if return_indices else code
def replace_code(code: str, replace_pattern: str) -> str:
"""Replace `code` by a pattern of the form `with X1->X2,Y1->Y2,Z1->Z2`.
Args:
code (`str`): The code to be modified.
replace_pattern (`str`): The pattern used to modify `code`.
Returns:
`str`: The modified code.
"""
if len(replace_pattern) > 0:
patterns = replace_pattern.replace("with", "").split(",")
patterns = [_re_replace_pattern.search(p) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
obj1, obj2, option = pattern.groups()
code = re.sub(obj1, obj2, code)
if option.strip() == "all-casing":
code = re.sub(obj1.lower(), obj2.lower(), code)
code = re.sub(obj1.upper(), obj2.upper(), code)
return code
def find_code_and_splits(object_name: str, base_path: str, buffer: Optional[dict] = None):
"""Find the code of an object (specified by `object_name`) and split it into blocks.
Args:
object_name (`str`):
The name of the object, e.g. `transformers.models.bert.modeling_bert.BertAttention` or
`tests.models.llama.test_modeling_llama.LlamaModelTest.test_config`.
base_path (`str`):
The path to the base directory within which the search will be performed. It could be either
`TRANSFORMERS_PATH` or `MODEL_TEST_PATH`.
buffer (`dict`, *optional*):
The buffer used to store the previous results in order to speed up the process.
Returns:
lines (`List[str]`):
The lines of the whole file where the object is defined.
code (`str`):
The object's code.
code_splits (`List[Tuple[str, int, int]]`):
`code` splitted into blocks. See `split_code_into_blocks`.
"""
if buffer is None:
buffer = {}
if (object_name, base_path) in buffer:
lines, code, code_splits = buffer[(object_name, base_path)]
else:
code, (lines, target_start_index, target_end_index) = find_code_in_transformers(
object_name, base_path=base_path, return_indices=True
)
indent = get_indent(code)
# Split the code into blocks
# `indent` is the indent of the class/func definition header, but `code_splits` expects the indent level of the
# block body.
code_splits = split_code_into_blocks(
lines, target_start_index, target_end_index, len(indent) + 4, backtrace=True
)
buffer[(object_name, base_path)] = lines, code, code_splits
return lines, code, code_splits
_re_copy_warning = re.compile(r"^(\s*)#\s*Copied from\s+transformers\.(\S+\.\S+)\s*($|\S.*$)")
_re_copy_warning_for_test_file = re.compile(r"^(\s*)#\s*Copied from\s+tests\.(\S+\.\S+)\s*($|\S.*$)")
_re_replace_pattern = re.compile(r"^\s*(\S+)->(\S+)(\s+.*|$)")
_re_fill_pattern = re.compile(r"<FILL\s+[^>]*>")
def get_indent(code: str) -> str:
"""
Find the indent in the first non empty line in a code sample.
Args:
code (`str`): The code to inspect.
Returns:
`str`: The indent looked at (as string).
"""
lines = code.split("\n")
idx = 0
while idx < len(lines) and len(lines[idx]) == 0:
idx += 1
if idx < len(lines):
return re.search(r"^(\s*)\S", lines[idx]).groups()[0]
return ""
def run_ruff(code, check=False):
if check:
command = ["ruff", "check", "-", "--fix", "--exit-zero"]
else:
command = ["ruff", "format", "-", "--config", "pyproject.toml", "--silent"]
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE)
stdout, _ = process.communicate(input=code.encode())
return stdout.decode()
def stylify(code: str) -> str:
"""
Applies the ruff part of our `make style` command to some code. This formats the code using `ruff format`.
As `ruff` does not provide a python api this cannot be done on the fly.
Args:
code (`str`): The code to format.
Returns:
`str`: The formatted code.
"""
has_indent = len(get_indent(code)) > 0
if has_indent:
code = f"class Bla:\n{code}"
formatted_code = run_ruff(code)
return formatted_code[len("class Bla:\n") :] if has_indent else formatted_code
def check_codes_match(observed_code: str, theoretical_code: str) -> Optional[int]:
"""
Checks if two version of a code match with the exception of the class/function name.
Args:
observed_code (`str`): The code found.
theoretical_code (`str`): The code to match.
Returns:
`Optional[int]`: The index of the first line where there is a difference (if any) and `None` if the codes
match.
"""
observed_code_header = observed_code.split("\n")[0]
theoretical_code_header = theoretical_code.split("\n")[0]
# Catch the function/class name: it is expected that those do not match.
_re_class_match = re.compile(r"class\s+([^\(:]+)(?:\(|:)")
_re_func_match = re.compile(r"def\s+([^\(]+)\(")
for re_pattern in [_re_class_match, _re_func_match]:
if re_pattern.match(observed_code_header) is not None:
try:
observed_obj_name = re_pattern.search(observed_code_header).groups()[0]
except Exception:
raise ValueError(
"Tried to split a class or function. It did not work. Error comes from: \n```\n"
+ observed_code_header
+ "\n```\n"
)
try:
theoretical_name = re_pattern.search(theoretical_code_header).groups()[0]
except Exception:
raise ValueError(
"Tried to split a class or function. It did not work. Error comes from: \n```\n"
+ theoretical_code_header
+ "\n```\n"
)
theoretical_code_header = theoretical_code_header.replace(theoretical_name, observed_obj_name)
# Find the first diff. Line 0 is special since we need to compare with the function/class names ignored.
diff_index = 0
if theoretical_code_header != observed_code_header:
return 0
diff_index = 1
for observed_line, theoretical_line in zip(observed_code.split("\n")[1:], theoretical_code.split("\n")[1:]):
if observed_line != theoretical_line:
return diff_index
diff_index += 1
def is_copy_consistent(
filename: str, overwrite: bool = False, buffer: Optional[dict] = None
) -> Optional[list[tuple[str, int]]]:
"""
Check if the code commented as a copy in a file matches the original.
Args:
filename (`str`):
The name of the file to check.
overwrite (`bool`, *optional*, defaults to `False`):
Whether or not to overwrite the copies when they don't match.
buffer (`dict`, *optional*):
The buffer used to store the previous results in order to speed up the process.
Returns:
`Optional[List[Tuple[str, int]]]`: If `overwrite=False`, returns the list of differences as tuples `(str, int)`
with the name of the object having a diff and the line number where there is the first diff.
"""
base_path = TRANSFORMERS_PATH if not filename.startswith("tests") else MODEL_TEST_PATH
with open(filename, "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
diffs = []
line_index = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
search_re = _re_copy_warning_for_test_file if filename.startswith("tests") else _re_copy_warning
while line_index < len(lines):
search = search_re.search(lines[line_index])
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
indent, object_name, replace_pattern = search.groups()
# Find the file lines, the object's code, and its blocks
try:
target_lines, theoretical_code, theoretical_code_splits = find_code_and_splits(
object_name, base_path, buffer=buffer
)
except Exception as exc:
exc.args = (f"Error while trying to find source code for {filename}.\n\n" + str(exc),)
raise
# code replaced by the patterns
theoretical_code_blocks = OrderedDict()
for name, start, end in theoretical_code_splits:
name = replace_code(name, replace_pattern)
code = "".join(target_lines[start:end])
code = replace_code(code, replace_pattern)
theoretical_code_blocks[name] = code
theoretical_indent = get_indent(theoretical_code)
# `start_index` is the index of the first line (the definition header) after `# Copied from`.
# (`indent != theoretical_indent` doesn't seem to occur so far, not sure what this case is for.)
start_index = line_index + 1 if indent == theoretical_indent else line_index
# enter the block body
line_index = start_index + 1
subcode = "\n".join(theoretical_code.split("\n")[1:])
indent = get_indent(subcode)
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
# We can't call `find_block_end` directly as there is sth. special `# End copy"` here.
should_continue = True
while line_index < len(lines) and should_continue:
line_index += 1
if line_index >= len(lines):
break
line = lines[line_index]
# There is a special pattern `# End copy` to stop early. It's not documented cause it shouldn't really be
# used.
should_continue = _should_continue(line, indent) and re.search(f"^{indent}# End copy", line) is None
# `line_index` is outside the block
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
# Split the observed code into blocks
observed_code_splits = split_code_into_blocks(lines, start_index, line_index, len(indent), backtrace=True)
is_class = lines[start_index].startswith(f"{' ' * (len(indent) - 4)}class ")
# sanity check
_sanity_check_splits(theoretical_code_splits, observed_code_splits, is_class=is_class, filename=filename)
# observed code in a structured way (a dict mapping block names to blocks' code)
observed_code_blocks = OrderedDict()
for name, start, end in observed_code_splits:
code = "".join(lines[start:end])
observed_code_blocks[name] = code
# Below, we change some names in `theoretical_code_blocks` and `observed_code_blocks`. These mappings map the
# original names to the modified names: this is used to restore the original order of the code blocks.
name_mappings_1 = {k: k for k in theoretical_code_blocks}
name_mappings_2 = {k: k for k in observed_code_blocks}
# Update code blocks' name and content:
# If `"# Ignore copy"` is found in a block of the observed code:
# 1. if it's a block only in the observed code --> add it to the theoretical code.
# 2. if it's also in the theoretical code () --> put its content (body) to the corresponding block under the
# same name in the theoretical code.
# In both cases, we change the name to have a prefix `_ignored_` so we know if we can discard them during the
# comparison.
ignored_existing_block_index = 0
ignored_new_block_index = 0
for name in list(observed_code_blocks.keys()):
code = observed_code_blocks[name]
if "# Ignore copy" in code:
if name in theoretical_code_blocks:
# in the target --> just copy the content
del theoretical_code_blocks[name]
theoretical_code_blocks[f"_ignored_existing_block_{ignored_existing_block_index}"] = code
name_mappings_1[name] = f"_ignored_existing_block_{ignored_existing_block_index}"
del observed_code_blocks[name]
observed_code_blocks[f"_ignored_existing_block_{ignored_existing_block_index}"] = code
name_mappings_2[name] = f"_ignored_existing_block_{ignored_existing_block_index}"
ignored_existing_block_index += 1
else:
# not in the target --> add it
theoretical_code_blocks[f"_ignored_new_block_{ignored_new_block_index}"] = code
name_mappings_1[f"_ignored_new_block_{ignored_new_block_index}"] = (
f"_ignored_new_block_{ignored_new_block_index}"
)
del observed_code_blocks[name]
observed_code_blocks[f"_ignored_new_block_{ignored_new_block_index}"] = code
name_mappings_2[name] = f"_ignored_new_block_{ignored_new_block_index}"
ignored_new_block_index += 1
# Respect the original block order:
# 1. in `theoretical_code_blocks`: the new blocks will follow the existing ones
# 2. in `observed_code_blocks`: the original order are kept with names modified potentially. This is necessary
# to compute the correct `diff_index` if `overwrite=True` and there is a diff.
theoretical_code_blocks = {
name_mappings_1[orig_name]: theoretical_code_blocks[name_mappings_1[orig_name]]
for orig_name in name_mappings_1
}
observed_code_blocks = {
name_mappings_2[orig_name]: observed_code_blocks[name_mappings_2[orig_name]]
for orig_name in name_mappings_2
}
# Ignore the blocks specified to be ignored. This is the version used to check if there is a mismatch
theoretical_code_blocks_clean = {
k: v
for k, v in theoretical_code_blocks.items()
if not (k.startswith(("_ignored_existing_block_", "_ignored_new_block_")))
}
theoretical_code = "".join(list(theoretical_code_blocks_clean.values()))
# stylify `theoretical_code` before compare (this is needed only when `replace_pattern` is not empty)
if replace_pattern:
theoretical_code = stylify(theoretical_code)
# Remove `\n\n` in `theoretical_code` before compare (so no empty line)
while "\n\n" in theoretical_code:
theoretical_code = theoretical_code.replace("\n\n", "\n")
# Compute `observed_code` where we don't include any empty line + keep track the line index between the
# original/processed `observed_code` so we can have the correct `diff_index`.
idx_to_orig_idx_mapping_for_observed_code_lines = {}
idx = -1
orig_idx = -1
observed_code = ""
for name, code in observed_code_blocks.items():
if code.endswith("\n"):
code = code[:-1]
for code_line in code.split("\n"):
orig_idx += 1
if code_line.strip() and not name.startswith(("_ignored_existing_block_", "_ignored_new_block_")):
idx += 1
observed_code += code_line + "\n"
idx_to_orig_idx_mapping_for_observed_code_lines[idx] = orig_idx
# Test for a diff and act accordingly.
diff_index = check_codes_match(observed_code, theoretical_code)
if diff_index is not None:
# switch to the index in the original `observed_code` (i.e. before removing empty lines)
diff_index = idx_to_orig_idx_mapping_for_observed_code_lines[diff_index]
diffs.append([object_name, diff_index + start_index + 1])
if overwrite:
# `theoretical_code_to_write` is a single string but may have several lines.
theoretical_code_to_write = stylify("".join(list(theoretical_code_blocks.values())))
lines = lines[:start_index] + [theoretical_code_to_write] + lines[line_index:]
# Here we treat it as a single entry in `lines`.
line_index = start_index + 1
if overwrite and len(diffs) > 0:
# Warn the user a file has been modified.
print(f"Detected changes, rewriting {filename}.")
with open(filename, "w", encoding="utf-8", newline="\n") as f:
f.writelines(lines)
return diffs
def check_copies(overwrite: bool = False, file: Optional[str] = None):
"""
Check every file is copy-consistent with the original. Also check the model list in the main README and other
READMEs are consistent.
Args:
overwrite (`bool`, *optional*, defaults to `False`):
Whether or not to overwrite the copies when they don't match.
file (`bool`, *optional*):
The path to a specific file to check and/or fix.
"""
buffer = {}
if file is None:
all_files = glob.glob(os.path.join(TRANSFORMERS_PATH, "**/*.py"), recursive=True)
all_test_files = glob.glob(os.path.join(MODEL_TEST_PATH, "**/*.py"), recursive=True)
all_files = list(all_files) + list(all_test_files)
else:
all_files = [file]
diffs = []
for filename in all_files:
new_diffs = is_copy_consistent(filename, overwrite, buffer)
diffs += [f"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs]
if not overwrite and len(diffs) > 0:
diff = "\n".join(diffs)
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."
)
def check_full_copies(overwrite: bool = False):
"""
Check the files that are full copies of others (as indicated in `FULL_COPIES`) are copy-consistent.
Args:
overwrite (`bool`, *optional*, defaults to `False`):
Whether or not to overwrite the copies when they don't match.
"""
diffs = []
for target, source in FULL_COPIES.items():
with open(source, "r", encoding="utf-8") as f:
source_code = f.read()
with open(target, "r", encoding="utf-8") as f:
target_code = f.read()
if source_code != target_code:
if overwrite:
with open(target, "w", encoding="utf-8") as f:
print(f"Replacing the content of {target} by the one of {source}.")
f.write(source_code)
else:
diffs.append(f"- {target}: copy does not match {source}.")
if not overwrite and len(diffs) > 0:
diff = "\n".join(diffs)
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."
)
def get_model_list(filename: str, start_prompt: str, end_prompt: str) -> str:
"""
Extracts the model list from a README.
Args:
filename (`str`): The name of the README file to check.
start_prompt (`str`): The string to look for that introduces the model list.
end_prompt (`str`): The string to look for that ends the model list.
Returns:
`str`: The model list.
"""
with open(os.path.join(REPO_PATH, filename), "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
# Find the start of the list.
start_index = 0
while not lines[start_index].startswith(start_prompt):
start_index += 1
start_index += 1
result = []
current_line = ""
end_index = start_index
# Keep going until the end of the list.
while not lines[end_index].startswith(end_prompt):
if lines[end_index].startswith("1."):
if len(current_line) > 1:
result.append(current_line)
current_line = lines[end_index]
elif len(lines[end_index]) > 1:
current_line = f"{current_line[:-1]} {lines[end_index].lstrip()}"
end_index += 1
if len(current_line) > 1:
result.append(current_line)
return "".join(result)
def convert_to_localized_md(model_list: str, localized_model_list: str, format_str: str) -> tuple[bool, str]:
"""
Compare the model list from the main README to the one in a localized README.
Args:
model_list (`str`): The model list in the main README.
localized_model_list (`str`): The model list in one of the localized README.
format_str (`str`):
The template for a model entry in the localized README (look at the `format_model_list` in the entries of
`LOCALIZED_READMES` for examples).
Returns:
`Tuple[bool, str]`: A tuple where the first value indicates if the READMEs match or not, and the second value
is the correct localized README.
"""
def _rep(match):
title, model_link, paper_affiliations, paper_title_link, paper_authors, supplements = match.groups()
return format_str.format(
title=title,
model_link=model_link,
paper_affiliations=paper_affiliations,
paper_title_link=paper_title_link,
paper_authors=paper_authors,
supplements=" " + supplements.strip() if len(supplements) != 0 else "",
)
# This regex captures metadata from an English model description, including model title, model link,
# affiliations of the paper, title of the paper, authors of the paper, and supplemental data (see DistilBERT for
# example).
_re_capture_meta = re.compile(
r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\* \(from ([^)]*)\)[^\[]*([^\)]*\)).*?by (.*?[A-Za-z\*]{2,}?)\. (.*)$"
)
# This regex is used to synchronize title link.
_re_capture_title_link = re.compile(r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\*")
# This regex is used to synchronize paper title and link.
_re_capture_paper_link = re.compile(r" \[([^\]]*)\]\(([^\)]*)\)")
if len(localized_model_list) == 0:
localized_model_index = {}
else:
try:
localized_model_index = {
re.search(r"\*\*\[([^\]]*)", line).groups()[0]: line
for line in localized_model_list.strip().split("\n")
}
except AttributeError:
raise AttributeError("A model name in localized READMEs cannot be recognized.")
model_keys = [re.search(r"\*\*\[([^\]]*)", line).groups()[0] for line in model_list.strip().split("\n")]
# We exclude keys in localized README not in the main one.
readmes_match = not any(k not in model_keys for k in localized_model_index)
localized_model_index = {k: v for k, v in localized_model_index.items() if k in model_keys}
for model in model_list.strip().split("\n"):
title, model_link = _re_capture_title_link.search(model).groups()
if title not in localized_model_index:
readmes_match = False
# Add an anchor white space behind a model description string for regex.
# If metadata cannot be captured, the English version will be directly copied.
localized_model_index[title] = _re_capture_meta.sub(_rep, model + " ")
elif _re_fill_pattern.search(localized_model_index[title]) is not None:
update = _re_capture_meta.sub(_rep, model + " ")
if update != localized_model_index[title]:
readmes_match = False
localized_model_index[title] = update
else:
# Synchronize title link
converted_model = _re_capture_title_link.sub(
f"**[{title}]({model_link})**", localized_model_index[title], count=1
)
# Synchronize paper title and its link (if found)
paper_title_link = _re_capture_paper_link.search(model)
if paper_title_link is not None:
paper_title, paper_link = paper_title_link.groups()
converted_model = _re_capture_paper_link.sub(
f" [{paper_title}]({paper_link})", converted_model, count=1
)
if converted_model != localized_model_index[title]:
readmes_match = False
localized_model_index[title] = converted_model
sorted_index = sorted(localized_model_index.items(), key=lambda x: x[0].lower())
return readmes_match, "\n".join(x[1] for x in sorted_index) + "\n"
# Map a model name with the name it has in the README for the check_readme check
SPECIAL_MODEL_NAMES = {
"Bert Generation": "BERT For Sequence Generation",
"BigBird": "BigBird-RoBERTa",
"Data2VecAudio": "Data2Vec",
"Data2VecText": "Data2Vec",
"Data2VecVision": "Data2Vec",
"DonutSwin": "Swin Transformer",
"Marian": "MarianMT",
"MaskFormerSwin": "Swin Transformer",
"OpenAI GPT-2": "GPT-2",
"OpenAI GPT": "GPT",
"Perceiver": "Perceiver IO",
"SAM": "Segment Anything",
"SAM_HQ": "Segment Anything High Quality",
"ViT": "Vision Transformer (ViT)",
}
# Update this list with the models that shouldn't be in the README. This only concerns modular models or those who do
# not have an associated paper.
MODELS_NOT_IN_README = [
"BertJapanese",
"Encoder decoder",
"FairSeq Machine-Translation",
"HerBERT",
"RetriBERT",
"Speech Encoder decoder",
"Speech2Text",
"Speech2Text2",
"TimmBackbone",
"Vision Encoder decoder",
"VisionTextDualEncoder",
"CLIPVisionModel",
"SiglipVisionModel",
"ChineseCLIPVisionModel",
"VitPoseBackbone",
]
# Template for new entries to add in the main README when we have missing models.
README_TEMPLATE = (
"1. **[{model_name}](https://huggingface.co/docs/main/transformers/model_doc/{model_type})** (from "
"<FILL INSTITUTION>) released with the paper [<FILL PAPER TITLE>](<FILL ARKIV LINK>) by <FILL AUTHORS>."
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--file", type=str, default=None, help="A specific file to check and/or fix")
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
args = parser.parse_args()
check_copies(args.fix_and_overwrite, args.file)
check_full_copies(args.fix_and_overwrite)
| transformers/utils/check_copies.py/0 | {
"file_path": "transformers/utils/check_copies.py",
"repo_id": "transformers",
"token_count": 20157
} | 545 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This script is used to get the list of folders under `tests/models` and split the list into `NUM_SLICES` splits.
The main use case is a GitHub Actions workflow file calling this script to get the (nested) list of folders allowing it
to split the list of jobs to run into multiple slices each containing a smaller number of jobs. This way, we can bypass
the maximum of 256 jobs in a matrix.
See the `setup` and `run_models_gpu` jobs defined in the workflow file `.github/workflows/self-scheduled.yml` for more
details.
Usage:
This script is required to be run under `tests` folder of `transformers` root directory.
Assume we are under `transformers` root directory:
```bash
cd tests
python ../utils/split_model_tests.py --num_splits 64
```
"""
import argparse
import os
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--num_splits",
type=int,
default=1,
help="the number of splits into which the (flat) list of folders will be split.",
)
args = parser.parse_args()
tests = os.getcwd()
model_tests = os.listdir(os.path.join(tests, "models"))
d1 = sorted(filter(os.path.isdir, os.listdir(tests)))
d2 = sorted(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))
d1.remove("models")
d = d2 + d1
num_jobs = len(d)
num_jobs_per_splits = num_jobs // args.num_splits
model_splits = []
end = 0
for idx in range(args.num_splits):
start = end
end = start + num_jobs_per_splits + (1 if idx < num_jobs % args.num_splits else 0)
model_splits.append(d[start:end])
print(model_splits)
| transformers/utils/split_model_tests.py/0 | {
"file_path": "transformers/utils/split_model_tests.py",
"repo_id": "transformers",
"token_count": 759
} | 546 |
# How to contribute to TRL?
Everyone is welcome to contribute, and we value everybody's contribution. Code
contributions are not the only way to help the community. Answering questions, helping
others, and improving the documentation are also immensely valuable.
It also helps us if you spread the word! Reference the library in blog posts
about the awesome projects it made possible, shout out on Twitter every time it has
helped you, or simply ⭐️ the repository to say thank you.
However you choose to contribute, please be mindful and respect our
[code of conduct](https://github.com/huggingface/trl/blob/main/CODE_OF_CONDUCT.md).
**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).**
## Ways to contribute
There are several ways you can contribute to TRL:
* Fix outstanding issues with the existing code.
* Submit issues related to bugs or desired new features.
* Implement trainers for new post-training algorithms.
* Contribute to the examples or the documentation.
If you don't know where to start, there is a special [Good First
Issue](https://github.com/huggingface/trl/labels/%F0%9F%91%B6%20good%20first%20issue) listing. It will give you a list of
open issues that are beginner-friendly and help you start contributing to open-source. The best way to do that is to open a Pull Request and link it to the issue that you'd like to work on. We try to give priority to opened PRs as we can easily track the progress of the fix, and if the contributor does not have time anymore, someone else can take the PR over.
For something slightly more challenging, you can also take a look at the [Good Second Issue](https://github.com/huggingface/trl/labels/Good%20Second%20Issue) list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀
> All contributions are equally valuable to the community. 🥰
Before you start contributing make sure you have installed all the dev tools:
```bash
pip install -e .[dev]
```
## Fixing outstanding issues
If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](#submitting-a-pull-request-pr) and open a Pull Request!
## Submitting a bug-related issue or feature request
Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback.
### Did you find a bug?
The TRL library is robust and reliable thanks to users who report the problems they encounter.
Before you report an issue, we would really appreciate it if you could **make sure the bug was not
already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code.
Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it:
* Your **OS type and version**, **Python**, **PyTorch**, **TRL** and **Transformers** versions.
* A short, self-contained, code snippet that allows us to reproduce the bug in
less than 30s.
* The *full* traceback if an exception is raised.
* Attach any other additional information, like screenshots, you think may help.
To get the OS and software versions automatically, run the following command:
```bash
trl env
```
### Do you want a new feature?
If there is a new feature you'd like to see in TRL, please open an issue and describe:
1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community?
Whatever it is, we'd love to hear about it!
2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you.
3. Provide a *code snippet* that demonstrates the feature's usage.
4. If the feature is related to a paper, please include a link.
If your issue is well written we're already 80% of the way there by the time you create it.
## Do you want to implement a new trainer?
New post-training methods are published frequently and those that satisfy the following criteria are good candidates to be integrated into TRL:
* **Simplicity:** Does the new method achieve similar performance as prior methods, but with less complexity? A good example is Direct Preference Optimization (DPO) [[Rafailov et al, 2023]](https://huggingface.co/papers/2305.18290), which provided a simpler and compelling alternative to RLHF methods.
* **Efficiency:** Does the new method provide a significant improvement in training efficiency? A good example is Odds Ratio Preference Optimization (ORPO) [[Hong et al, 2023]](https://huggingface.co/papers/2403.07691), which utilizes a similar objective as DPO but requires half the GPU VRAM.
Methods that only provide incremental improvements at the expense of added complexity or compute costs are unlikely to be included in TRL.
If you want to implement a trainer for a new post-training method, first open an issue and provide the following information:
* A short description of the method and a link to the paper.
* Link to the implementation if it is open-sourced.
* Link to model weights trained with the method if they are available.
Based on the community and maintainer feedback, the next step will be to implement the trainer and config classes. See the following examples for inspiration:
* Paired preference optimisation: [`dpo_trainer.py`](./trl/trainer/dpo_trainer.py) and [`dpo_config.py`](./trl/trainer/dpo_config.py)
* RL-based optimisation: [`rloo_trainer.py](./trl/trainer/rloo_trainer.py) and [`rloo_config.py](./trl/trainer/rloo_config.py)
* Online optimisation: [`online_dpo_trainer.py`](./trl/trainer/online_dpo_trainer.py) and [`online_dpo_config.py`](./trl/trainer/online_dpo_config.py)
## Do you want to add documentation?
We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved, such as typos, dead links, and any missing, unclear, or inaccurate content... We'll be happy to make the changes or help you contribute if you're interested!
## Submitting a pull request (PR)
Before writing code, we strongly advise you to search through the existing PRs or
issues to make sure that nobody is already working on the same thing. If you are
unsure, it is always a good idea to open an issue to get some feedback.
You will need basic `git` proficiency to be able to contribute to
TRL. `git` is not the easiest tool to use but it has the greatest
manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
Git](https://git-scm.com/book/en/v2) is a very good reference.
Follow these steps to start contributing:
1. Fork the [repository](https://github.com/huggingface/trl) by
clicking on the 'Fork' button on the repository's page. This creates a copy of the code
under your GitHub user account.
2. Clone your fork to your local disk, and add the base repository as a remote. The following command
assumes you have your public SSH key uploaded to GitHub. See the following guide for more
[information](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository).
```bash
$ git clone git@github.com:<your Github handle>/trl.git
$ cd trl
$ git remote add upstream https://github.com/huggingface/trl.git
```
3. Create a new branch to hold your development changes, and do this for every new PR you work on.
Start by synchronizing your `main` branch with the `upstream/main` branch (more details in the [GitHub Docs](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork)):
```bash
$ git checkout main
$ git fetch upstream
$ git merge upstream/main
```
Once your `main` branch is synchronized, create a new branch from it:
```bash
$ git checkout -b a-descriptive-name-for-my-changes
```
**Do not** work on the `main` branch.
4. Set up a development environment by running the following command in a conda or a virtual environment you've created for working on this library:
```bash
$ pip install -e .[dev]
```
(If TRL was already installed in the virtual environment, remove
it with `pip uninstall trl` before reinstalling it.)
Alternatively, if you are using [Visual Studio Code](https://code.visualstudio.com/Download), the fastest way to get set up is by using
the provided Dev Container. Documentation on how to get started with dev containers is available [here](https://code.visualstudio.com/docs/remote/containers).
5. Develop the features on your branch.
As you work on the features, you should make sure that the test suite
passes. You should run the tests impacted by your changes like this (see
below an explanation regarding the environment variable):
```bash
$ pytest tests/<TEST_TO_RUN>.py
```
> For the following commands leveraging the `make` utility.
You can also run the full suite with the following command.
```bash
$ make test
```
TRL relies on `ruff` for maintaining consistent code formatting across its source files. Before submitting any PR, you should apply automatic style corrections and run code verification checks.
We provide a `precommit` target in the `Makefile` that simplifies this process by running all required checks and optimizations on only the files modified by your PR.
To apply these checks and corrections in one step, use:
```bash
$ make precommit
```
This command runs the following:
- Executes `pre-commit` hooks to automatically fix style issues with `ruff` and other tools.
- Runs additional scripts such as adding copyright information.
If you prefer to apply the style corrections separately or review them individually, the `pre-commit` hook will handle the formatting for the files in question.
Once you're happy with your changes, add changed files using `git add` and
make a commit with `git commit` to record your changes locally:
```bash
$ git add modified_file.py
$ git commit
```
Please write [good commit messages](https://chris.beams.io/posts/git-commit/).
It is a good idea to sync your copy of the code with the original
repository regularly. This way you can quickly account for changes:
```bash
$ git fetch upstream
$ git rebase upstream/main
```
Push the changes to your account using:
```bash
$ git push -u origin a-descriptive-name-for-my-changes
```
6. Once you are satisfied (**and the checklist below is happy too**), go to the
webpage of your fork on GitHub. Click on 'Pull request' to send your changes
to the project maintainers for review.
7. It's ok if maintainers ask you for changes. It happens to core contributors too! To ensure everyone can review your changes in the pull request, work on your local branch and push the updates to your fork. They will automatically appear in the pull request.
### Checklist
1. The title of your pull request should be a summary of its contribution;
2. If your pull request addresses an issue, please mention the issue number in
the pull request description to make sure they are linked (and people
consulting the issue know you are working on it);
3. To indicate a work in progress please prefix the title with `[WIP]`, or mark
the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
it from PRs ready to be merged;
4. Make sure existing tests pass;
5. Add high-coverage tests. No quality testing = no merge.
### Tests
An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
the [tests folder](https://github.com/huggingface/trl/tree/main/tests).
We use `pytest` to run the tests. From the root of the
repository here's how to run tests with `pytest` for the library:
```bash
$ python -m pytest -sv ./tests
```
That's how `make test` is implemented (without the `pip install` line)!
You can specify a smaller set of tests to test only the feature
you're working on.
### Default values guidelines
1. **Use defaults when appropriate**:
Provide default values unless the parameter's value varies significantly by use case. For example, datasets or models should not have defaults, but parameters like `learning_rate` should.
2. **Prioritize proven defaults**:
Default values should align with those recommended in the original paper or method. Alternatives require strong evidence of superior performance in most cases.
3. **Ensure safety and predictability**:
Defaults must be safe, expected and reliable. Avoid settings that could lead to surprising outcomes, such as excessive memory usage or poor performance in edge cases.
4. **Balance consistency and flexibility**:
Aim for consistent defaults across similar functions or methods. However, consistency should not be preferred to point 2 or 3.
5. **Opt-in for new features**:
Do not enable new features or improvements (e.g., novel loss functions) by default. Users should explicitly opt-in to use these.
### Writing documentation
High-quality documentation is crucial for maintaining a project that is easy to use, understand, and extend. When adding new features, ensure they are thoroughly documented to maintain consistency and clarity throughout the project.
To illustrate what good documentation looks like, here’s an example of a well-documented function:
````python
def replicate_str(string: str, n: int, sep: str = " ") -> str:
r"""
Replicate a string `n` times with a separator.
Args:
string (`str`):
String to replicate.
n (`int`):
Number of times to replicate the string.
sep (`str`, *optional*, defaults to `" "`):
Separator to use between each replication.
Returns:
`str`: The replicated string.
Examples:
```python
>>> replicate_str("hello", 3)
"hello hello hello"
>>> replicate_str("hello", 3, sep=", ")
"hello, hello, hello"
```
"""
return sep.join([string] * n)
````
* **Line Wrapping:** Applied a consistent line wrap at column 120 to improve readability.
* **Definite Articles:** Removed definite articles where possible to streamline language. (Eg: Changed "The string to replicate" to "String to replicate")
* **Type Annotations:**
* Always include type definitions, indicating if a parameter is optional and specifying the default value.
* Note that `Optional` means that the value can be `None`, and `*optional*` means that it is not required for the user to pass a value.
E.g., for arguments that can't be `None` and aren't required:
```python
foo (`int`, *optional*, defaults to `4`):
```
For arguments that can be `None` and are required:
```python
foo (`Optional[int]`):
```
for arguments that can be `None` and aren't required:
```python
foo (`Optional[int]`, *optional*, defaults to `None`):
```
* **String Defaults:**
* Ensured that default string values are wrapped in double quotes:
```python
defaults to `"foo"`
```
* **Dictionary Typing:**
* Replaced generic `dict` type hints with more explicit `dict[str, Any]` to clarify expected key-value pairs.
* **Default Value Formatting:**
* Consistently surrounded default values with backticks for improved formatting:
```python
defaults to `4`
```
* **Sub-sectioning:** When the number of arguments is large, consider breaking them into sub-sections for better readability.
```python
def calculate_statistics(data: list[float], precision: int = 2, include_variance: bool = False) -> dict[str, float]:
r"""
Calculates basic statistics for a given dataset.
Args:
> Data inputs
data (`list[float]`):
A list of numerical values to analyze.
> Configuration parameters
precision (`int`, *optional*, defaults to `2`):
Number of decimal places to round the results.
include_variance (`bool`, *optional*, defaults to `False`):
Whether to include the variance of the dataset in the results.
Returns:
`dict[str, float]`:
A dictionary containing calculated statistics such as mean, median, and optionally variance.
"""
...
```
### Deprecation and backward compatibility
Our approach to deprecation and backward compatibility is flexible and based on the feature’s usage and impact. Each deprecation is carefully evaluated, aiming to balance innovation with user needs.
When a feature or component is marked for deprecation, its use will emit a warning message. This warning will include:
- **Transition Guidance**: Instructions on how to migrate to the alternative solution or replacement.
- **Removal Version**: The target version when the feature will be removed, providing users with a clear timeframe to transition.
Example:
```python
warnings.warn(
"The `Trainer.foo` method is deprecated and will be removed in version 0.14.0. "
"Please use the `Trainer.bar` class instead.",
FutureWarning,
)
```
The deprecation and removal schedule is based on each feature's usage and impact, with examples at two extremes:
- **Experimental or Low-Use Features**: For a feature that is experimental or has limited usage, backward compatibility may not be maintained between releases. Users should therefore anticipate potential breaking changes from one version to the next.
- **Widely-Used Components**: For a feature with high usage, we aim for a more gradual transition period of approximately **5 months**, generally scheduling deprecation around **5 minor releases** after the initial warning.
These examples represent the two ends of a continuum. The specific timeline for each feature will be determined individually, balancing innovation with user stability needs.
### Working with warnings
Warnings play a critical role in guiding users toward resolving potential issues, but they should be used thoughtfully to avoid unnecessary noise. Unlike logging, which provides informational context or operational details, warnings signal conditions that require attention and action. Overusing warnings can dilute their importance, leading users to ignore them entirely.
#### Definitions
- **Correct**: An operation is correct if it is valid, follows the intended approach, and aligns with the current best practices or guidelines within the codebase. This is the recommended or intended way to perform the operation.
- **Supported**: An operation is supported if it is technically valid and works within the current codebase, but it may not be the most efficient, optimal, or recommended way to perform the task. This includes deprecated features or legacy approaches that still work but may be phased out in the future.
#### Choosing the right message
- **Correct → No warning**:
If the operation is fully valid and expected, no message should be issued. The system is working as intended, so no warning is necessary.
- **Correct but deserves attention → No warning, possibly a log message**:
When an operation is correct but uncommon or requires special attention, providing an informational message can be helpful. This keeps users informed without implying any issue. If available, use the logger to output this message. Example:
```python
logger.info("This is an informational message about a rare but correct operation.")
```
- **Correct but very likely a mistake → Warning with option to disable**:
In rare cases, you may want to issue a warning for a correct operation that’s very likely a mistake. In such cases, you must provide an option to suppress the warning. This can be done with a flag in the function. Example:
```python
def my_function(foo, bar, _warn=True):
if foo == bar:
if _warn:
logger.warning("foo and bar are the same, this is likely a mistake. Ignore this warning by setting `_warn=False`.")
# Do something
```
- **Supported but not correct → Warning**:
If the operation is technically supported but is deprecated, suboptimal, or could cause future issues (e.g., conflicting arguments), a warning should be raised. This message should be actionable, meaning it must explain how to resolve the issue. Example:
```python
def my_function(foo, bar):
if foo and bar:
logger.warning("Both `foo` and `bar` were provided, but only one is allowed. Ignoring `foo`. Please pass only one of these arguments.")
# Do something
```
- **Not supported → Exception**:
If the operation is invalid or unsupported, raise an exception. This indicates that the operation cannot be performed and requires immediate attention. Example:
```python
def my_function(foo, bar):
if foo and bar:
raise ValueError("Both `foo` and `bar` were provided, but only one is allowed. Please pass only one of these arguments.")
```
By following this classification, you ensure that warnings, information, and exceptions are used appropriately, providing clear guidance to the user without cluttering the system with unnecessary messages.
## Making a release
> [!NOTE]
> VERSION needs to be formatted following the `v{major}.{minor}.{patch}` convention. We need to follow this convention to be able to retrieve versioned scripts.
#### 0. Prerequisites
- Dependencies:
- twine: `pip install build twine`
- Create an account in (and join the `trl` project):
- PyPI: https://pypi.org/
- Test PyPI: https://test.pypi.org/
### Major/Minor Release
#### 1. Ensure your local repository is up to date with the upstream repository
```bash
git checkout main
git pull origin main
```
> [!WARNING]
> Do not merge other pull requests into `main` until the release is done. This is to ensure that the release is stable and does not include any untested changes. Announce internally (#trl-internal) to other maintainers that you are doing a release and that they must not merge PRs until the release is done.
#### 2. Create a release branch from main
```bash
git checkout -b release-v{major}.{minor}
```
#### 3. Change the version in the following files
- `.github/workflows/tests_latest.yml`:
```diff
- with: { ref: v{major}.{minor-1}-release }
+ with: { ref: v{major}.{minor}-release }
```
- `CITATION.cff`
```diff
- version: "{major}.{minor-1}"
+ version: "{major}.{minor}"
```
- `trl/__init__.py`
```diff
- __version__ = "{major}.{minor}.0.dev0"
+ __version__ = "{major}.{minor}.0"
```
- `setup.cfg`
```diff
- version = {major}.{minor}.0.dev0
+ version = {major}.{minor}.0
```
#### 4. Commit and push these changes
```shell
git add .github/workflows/tests_latest.yml CITATION.cff trl/__init__.py setup.cfg
git commit -m 'Release: {major}.{minor}'
git push origin release-v{major}.{minor}
```
#### 5. Create a pull request
from `release-v{major}.{minor}` to `main`, named `Release: v{major}.{minor}`, wait for tests to pass, and request a review.
#### 6. Once the pull request is approved, merge it into `main`
#### 7. Add a tag in git to mark the release
```shell
git checkout main
git pull origin main
git tag -a v{major}.{minor}.0 -m 'Adds tag v{major}.{minor}.0 for PyPI'
git push origin v{major}.{minor}.0
```
#### 8. Create a branch `v{major}.{minor}-release` for future patch releases.
```shell
git checkout -b v{major}.{minor}-release
git push origin v{major}.{minor}-release
```
This ensures that future patch releases (`v{major}.{minor}.1`, `v{major}.{minor}.2`, etc.) can be made separately from `main`.
#### 9. Create the wheels for your release
These are the artifacts that will be uploaded to PyPI and installed by users via `pip install trl`.
Clean previous builds:
```shell
rm -rf build dist
```
At the root of your repo, run
```bash
python -m build .
```
This will create a folders named `dist` with the new versions of your package.
#### 10. Upload the package to PyPI Test
> [!IMPORTANT]
> Do not skip this step. It is important to test the package before uploading it to the main PyPI server.
```shell
twine upload dist/* -r testpypi
```
Then in a fresh environment containing all dependencies you need, try to install your new package from the PyPI test server.
```bash
pip install -i https://test.pypi.org/simple/ trl
```
You might get errors for missing dependencies since the PyPI test server does not contain all packages like PyPI does. To make sure you have everything you can do:
```bash
pip install trl
pip uninstall trl
```
(the second line will remove trl but keep all its dependencies).
Also make sure you can actually use the package! Run the following line:
```bash
python -c "from trl import *"
```
along with anything that tests:
- the core feature of your package
- the new features you’re adding in the release
#### 11. Publish on PyPI
> [!WARNING]
> This can't be reverted. Make sure you have tested everything before doing this step.
```shell
twine upload dist/*
```
#### 12. Create a GitHub Release
1. Go to the repo’s [releases section](https://github.com/huggingface/trl/releases) on GitHub.
2. Click **Draft a new release**.
3. Select the `v{major}.{minor}.0` tag you just created in step 7.
4. Add a title (`v{major}.{minor}.0`) and a short description of what’s new.
5. Click **Publish Release**.
#### 13. Bump to dev version
1. Create a branch `bump-dev-version-{major}.{minor+1}` from `main` and checkout to it.
```shell
git checkout -b bump-dev-version-{major}.{minor+1}
```
2. Change the version in the following files:
1. `trl/__init__.py`
```diff
- __version__ = "{major}.{minor}.0"
+ __version__ = "{major}.{minor+1}.0.dev0"
```
2. `setup.cfg`
```diff
- version = {major}.{minor}.0
+ version = {major}.{minor+1}.0.dev0
```
3. Commit and push these changes
```shell
git add trl/__init__.py setup.cfg
git commit -m '⬆️ Bump dev version'
git push origin bump-dev-version-{major}.{minor+1}
```
4. Create a pull request from `bump-dev-version-{major}.{minor+1}` to `main`, named `⬆️ Bump dev version`, and request urgent review.
5. Once the pull request is approved, merge it into `main`.
6. The codebase is now ready for the next development cycle, inform the team in the #trl-internal channel.
## Making a patch release
#### 1. Ensure your local repository is up to date with the upstream repository
```bash
git checkout v{major}.{minor}-release
git pull origin main
```
#### 2. Cherry-pick the changes you want to include in the patch release
```bash
git cherry-pick <commit-hash-0>
git cherry-pick <commit-hash-1>
...
```
#### 3. Change the version in the following files
- `trl/__init__.py`
```diff
- __version__ = "{major}.{minor}.{patch-1}"
+ __version__ = "{major}.{minor}.{patch}"
```
- `setup.cfg`
```diff
- version = {major}.{minor}.{patch-1}
+ version = {major}.{minor}.{patch}
```
#### 4. Commit and push these changes
```shell
git add trl/__init__.py setup.cfg
git commit -m 'Release: {major}.{minor}.{patch}'
git push origin v{major}.{minor}-release
```
#### 5. Wait for the CI to pass
#### 6. Add a tag in git to mark the release
```shell
git tag -a v{major}.{minor}.{patch} -m 'Adds tag v{major}.{minor}.{patch} for PyPI'
git push origin v{major}.{minor}.{patch}
```
#### 7. Create the wheels for your release
These are the artifacts that will be uploaded to PyPI and installed by users via `pip install trl`.
Clean previous builds:
```shell
rm -rf build dist
```
At the root of your repo, run
```bash
python -m build .
```
This will create a folders named `dist` with the new versions of your package.
#### 8. Upload the package to PyPI Test
> [!IMPORTANT]
> Do not skip this step. It is important to test the package before uploading it to the main PyPI server.
```shell
twine upload dist/* -r testpypi
```
Then in a fresh environment containing all dependencies you need, try to install your new package from the PyPI test server.
```bash
pip install -i https://test.pypi.org/simple/ trl
```
You might get errors for missing dependencies since the PyPI test server does not contain all packages like PyPI does. To make sure you have everything you can do:
```bash
pip install trl
pip uninstall trl
```
(the second line will remove trl but keep all its dependencies).
Also make sure you can actually use the package! Run the following line:
```bash
python -c "from trl import *"
```
along with anything that tests:
- the core feature of your package
- the new features you’re adding in the release
#### 9. Publish on PyPI
> [!WARNING]
> This can't be reverted. Make sure you have tested everything before doing this step.
```shell
twine upload dist/*
```
#### 10. Create a GitHub Release
1. Go to the repo’s [releases section](https://github.com/huggingface/trl/releases) on GitHub.
2. Click **Draft a new release**.
3. Select the `v{major}.{minor}.{patch}` tag you just created in step 7.
4. Add a title (`v{major}.{minor}.{patch}`) and a short description of what’s new.
5. Click **Publish Release**.
| trl/CONTRIBUTING.md/0 | {
"file_path": "trl/CONTRIBUTING.md",
"repo_id": "trl",
"token_count": 8575
} | 547 |
# Judges
<Tip warning={true}>
TRL Judges is an experimental API which is subject to change at any time.
</Tip>
TRL provides judges to easily compare two completions.
Make sure to have installed the required dependencies by running:
```bash
pip install trl[judges]
```
## Using the provided judges
TRL provides several judges out of the box. For example, you can use the `HfPairwiseJudge` to compare two completions using a pre-trained model from the Hugging Face model hub:
```python
from trl import HfPairwiseJudge
judge = HfPairwiseJudge()
judge.judge(
prompts=["What is the capital of France?", "What is the biggest planet in the solar system?"],
completions=[["Paris", "Lyon"], ["Saturn", "Jupiter"]],
) # Outputs: [0, 1]
```
## Define your own judge
To define your own judge, we provide several base classes that you can subclass. For rank-based judges, you need to subclass [`BaseRankJudge`] and implement the [`BaseRankJudge.judge`] method. For pairwise judges, you need to subclass [`BasePairJudge`] and implement the [`BasePairJudge.judge`] method. If you want to define a judge that doesn't fit into these categories, you need to subclass [`BaseJudge`] and implement the [`BaseJudge.judge`] method.
As an example, let's define a pairwise judge that prefers shorter completions:
```python
from trl import BasePairwiseJudge
class PrefersShorterJudge(BasePairwiseJudge):
def judge(self, prompts, completions, shuffle_order=False):
return [0 if len(completion[0]) > len(completion[1]) else 1 for completion in completions]
```
You can then use this judge as follows:
```python
judge = PrefersShorterJudge()
judge.judge(
prompts=["What is the capital of France?", "What is the biggest planet in the solar system?"],
completions=[["Paris", "The capital of France is Paris."], ["Jupiter is the biggest planet in the solar system.", "Jupiter"]],
) # Outputs: [0, 1]
```
## Provided judges
### PairRMJudge
[[autodoc]] PairRMJudge
### HfPairwiseJudge
[[autodoc]] HfPairwiseJudge
### OpenAIPairwiseJudge
[[autodoc]] OpenAIPairwiseJudge
### AllTrueJudge
[[autodoc]] AllTrueJudge
## Base classes
### BaseJudge
[[autodoc]] BaseJudge
### BaseBinaryJudge
[[autodoc]] BaseBinaryJudge
### BaseRankJudge
[[autodoc]] BaseRankJudge
### BasePairwiseJudge
[[autodoc]] BasePairwiseJudge
| trl/docs/source/judges.md/0 | {
"file_path": "trl/docs/source/judges.md",
"repo_id": "trl",
"token_count": 732
} | 548 |
# Reducing Memory Usage
<Tip warning={true}>
Section under construction. Feel free to contribute!
</Tip>
## Truncation
Sequence lengths in the dataset can vary widely. When data is batched, sequences are padded to match the longest one in the batch, which can cause high memory usage, even if most sequences are relatively short.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/why_you_should_truncate.png" alt="Truncation prompt-completion" width="600"/>
</div>
To reduce memory usage, it's important to truncate sequences to a reasonable length. While TRL trainers truncate sequences by default, you may want to adjust the default truncation length to better align with your specific use case.
<hfoptions id="truncation">
<hfoption id="DPO">
DPO truncation is applied first to the prompt and to the completion via the `max_prompt_length` and `max_completion_length` parameters. The `max_length` parameter is then used to truncate the resulting sequence.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/truncation_prompt_completion.png" alt="Truncation prompt-completion" width="600"/>
</div>
To set the truncation parameters, use the following code snippet:
```python
from trl import DPOConfig
training_args = DPOConfig(..., max_prompt_length=..., max_length=...)
```
You can also use the `max_completion_length` parameter to truncate the completion, though this is less common since the goal is typically to preserve the completion's full length whenever possible.
```python
from trl import DPOConfig
training_args = DPOConfig(..., max_completion_length=...)
```
</hfoption>
<hfoption id="SFT">
SFT truncation is applied to the input sequence via the `max_length` parameter.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/truncation_input_ids.png" alt="Truncation input ids" width="600"/>
</div>
To set the truncation parameter, use the following code snippet:
```python
from trl import SFTConfig
training_args = SFTConfig(..., max_length=...)
```
</hfoption>
</hfoptions>
### How to choose the `max_length` value?
If `max_length` is too small, a significant portion of your tokens will be discarded and won't contribute to training. If it's too large, memory usage can spike, potentially leading to OOM (Out-Of-Memory) errors. Without packing or padding-free, a large `max_length` may also result in inefficient training, as many tokens will be padding.
To help you choose an appropriate value, we provide a utility to visualize the sequence length distribution in your dataset.
<iframe src="https://trl-lib-dataset-length-profiler.hf.space" frameborder="0" width="100%" height="1000"></iframe>
## Packing
<Tip>
This technique applies only to SFT.
</Tip>
[Truncation](#truncation) has several drawbacks:
1. **Loss of information**: Key data at the end of a sequence may be discarded.
2. **Choosing truncation length**: Too short loses data; too long undermines efficiency.
Packing, introduced in [Raffel et al., 2020](https://huggingface.co/papers/1910.10683), addresses these issues by grouping sequences instead of truncating. It concatenates and splits dataset sequences into the desired lengths.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/packing_2.png" alt="Packing" width="600"/>
</div>
Packing reduces padding by merging several sequences in one row when possible. We use an advanced method to be near-optimal in the way we pack the dataset. To enable packing, use `packing=True` and in the [`SFTConfig`].
<Tip>
In TRL 0.18 and earlier, packing used a more aggressive method that reduced padding to almost nothing, but had the downside of breaking sequence continuity for a large fraction of the dataset. To revert to this strategy, use `packing_strategy="wrapped"` in `SFTConfig`.
</Tip>
```python
from trl import SFTConfig
training_args = SFTConfig(..., packing=True, max_length=512)
```
<Tip warning={true}>
Packing may cause batch contamination, where adjacent sequences influence one another. This can be problematic for some applications. For more details, see [#1230](https://github.com/huggingface/trl/issues/1230).
</Tip>
## Liger for reducing peak memory usage
> [Liger Kernel](https://github.com/linkedin/Liger-Kernel) is a collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%.
For more information, see [Liger Kernel Integration](liger_kernel_integration)
<hfoptions id="liger">
<hfoption id="DPO">
To use Liger for reducing peak memory usage, use the following code snippet:
```python
from trl import DPOConfig
training_args = DPOConfig(..., use_liger_loss=True)
```
</hfoption>
<hfoption id="GRPO">
To use Liger for reducing peak memory usage, use the following code snippet:
```python
from trl import GRPOConfig
training_args = GRPOConfig(..., use_liger_loss=True)
```
</hfoption>
<hfoption id="KTO">
To use Liger for reducing peak memory usage, use the following code snippet:
```python
from trl import KTOConfig
training_args = KTOConfig(..., use_liger_loss=True)
```
</hfoption>
</hfoptions>
## Padding-free
Padding-free batching is an alternative approach for reducing memory usage. In this method, a batch is first sampled and then flattened into a single sequence, avoiding padding. Unlike packing, which can result in incomplete sequences by combining parts of different samples, padding-free batching ensures that all sequences remain complete and intact.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/padding-free.png" alt="Padding-free batching" width="600"/>
</div>
<Tip warning={true}>
It's highly recommended to use padding-free batching with **FlashAttention 2** or **FlashAttention 3**. Otherwise, you may encounter batch contamination issues.
</Tip>
<hfoptions id="padding-free">
<hfoption id="DPO">
```python
from trl import DPOConfig
training_args = DPOConfig(..., padding_free=True, model_init_kwargs={"attn_implementation": "flash_attention_2"})
```
</hfoption>
<hfoption id="SFT">
```python
from trl import SFTConfig
training_args = SFTConfig(..., padding_free=True, model_init_kwargs={"attn_implementation": "flash_attention_2"})
```
</hfoption>
</hfoptions>
## Activation offloading
Activation offloading is a memory efficiency technique that reduces GPU VRAM usage by temporarily moving activation tensors to CPU RAM during the forward pass and bringing them back only when needed for the backward pass. This significantly reduces peak memory usage at the cost of slightly increased training time.
To enable activation offloading in your SFT training configuration:
<hfoptions>
<hfoption id="SFT">
```python
from trl import SFTConfig
training_args = SFTConfig(..., activation_offloading=True)
```
</hfoption>
</hfoptions>
<Tip warning={true}>
When using activation offloading with models that use Liger kernels, you must disable Liger cross entropy due to compatibility issues. The issue occurs specifically with `use_liger_kernel=True` because Liger cross entropy performs in-place operations which conflict with activation offloading. The default setting (`use_liger_kernel=False`) works:
```python
# When using activation offloading with a model that uses Liger kernels:
from trl import SFTConfig
training_args = SFTConfig(
activation_offloading=True,
use_liger_kernel=False, # Disable Liger cross entropy
# Other parameters...
)
```
</Tip>
Under the hood, activation offloading implements PyTorch's [`saved_tensors_hooks`](https://pytorch.org/tutorials/intermediate/autograd_saved_tensors_hooks_tutorial.html#hooks-for-autograd-saved-tensors) to intercept activations during the forward pass. It intelligently manages which tensors to offload based on size and context, avoiding offloading output tensors which would be inefficient. For performance optimization, it can optionally use CUDA streams to overlap computation with CPU-GPU transfers.
## Disabling model gathering for generation in online methods
When using DeepSpeed ZeRO-3, model weights are sharded across multiple GPUs. Online methods involve generating completions from the model as part of the training process. During this step, the model weights are temporarily gathered on a single GPU for generation. For very large models, this gathering can lead to out-of-memory (OOM) errors, as described in this issue: [#2250](https://github.com/huggingface/trl/issues/2250#issue-2598304204).
If you encounter this issue, you can disable the gathering of model weights for generation by setting the following parameter:
<hfoptions id="ds3_gather_for_generation">
<hfoption id="GRPO">
```python
from trl import GRPOConfig
training_args = GRPOConfig(..., ds3_gather_for_generation=False)
```
</hfoption>
<hfoption id="Online DPO">
```python
from trl import OnlineDPOConfig
training_args = OnlineDPOConfig(..., ds3_gather_for_generation=False)
```
</hfoption>
<hfoption id="PPO">
```python
from trl import PPOConfig
training_args = PPOConfig(..., ds3_gather_for_generation=False)
```
</hfoption>
<hfoption id="RLOO">
```python
from trl import RLOOConfig
training_args = RLOOConfig(..., ds3_gather_for_generation=False)
```
</hfoption>
</hfoptions>
This adjustment prevents model weights from being gathered, avoiding OOM errors, but it may result in slower generation speeds.
| trl/docs/source/reducing_memory_usage.md/0 | {
"file_path": "trl/docs/source/reducing_memory_usage.md",
"repo_id": "trl",
"token_count": 2828
} | 549 |
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional
from datasets import load_dataset
from huggingface_hub import ModelCard
from transformers import HfArgumentParser
@dataclass
class ScriptArguments:
r"""
Arguments for the script.
Args:
model_name (`str`, *optional*, defaults to `"gpt-3.5-turbo"`):
Language model to target. Possible values are:
aspect (`str`, *optional*, defaults to `"helpfulness"`):
Aspect to target.
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether to push the dataset to the Hugging Face Hub.
repo_id (`str`, *optional*, defaults to `"trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness"`):
Hugging Face repository ID to push the dataset to.
dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
Number of workers to use for dataset processing.
"""
model_name: str = field(
default="gpt-3.5-turbo",
metadata={
"help": "Language model to target.",
"choices": [
"alpaca-7b",
"bard",
"falcon-40b-instruct",
"gpt-3.5-turbo",
"gpt-4",
"llama-2-13b-chat",
"llama-2-70b-chat",
"llama-2-7b-chat",
"mpt-30b-chat",
"pythia-12b",
"starchat",
"ultralm-13b",
"ultralm-65b",
"vicuna-33b",
"wizardlm-13b",
"wizardlm-70b",
"wizardlm-7b",
],
},
)
aspect: str = field(
default="helpfulness",
metadata={
"help": "Aspect to target. Possible values are: 'helpfulness' (default), 'honesty', "
"'instruction-following', 'truthfulness'.",
"choices": ["helpfulness", "honesty", "instruction-following", "truthfulness"],
},
)
push_to_hub: bool = field(
default=False,
metadata={"help": "Whether to push the dataset to the Hugging Face Hub."},
)
repo_id: str = field(
default="trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness",
metadata={"help": "Hugging Face repository ID to push the dataset to."},
)
dataset_num_proc: Optional[int] = field(
default=None,
metadata={"help": "Number of workers to use for dataset processing."},
)
def to_unpaired_preference(example, model_name, aspect):
prompt = [{"role": "user", "content": example["instruction"]}]
model_index = example["models"].index(model_name)
response_content = example["completions"][model_index]["response"]
completion = [{"role": "assistant", "content": response_content}]
score = int(example["completions"][model_index]["annotations"][aspect]["Rating"])
label = score >= 5
return {"prompt": prompt, "completion": completion, "label": label}
model_card = ModelCard("""
---
tags: [trl]
---
# UltraFeedback GPT-3.5-Turbo Helpfulness Dataset
## Summary
The UltraFeedback GPT-3.5-Turbo Helpfulness dataset contains processed user-assistant interactions filtered for helpfulness, derived from the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset. It is designed for fine-tuning and evaluating models in alignment tasks.
## Data Structure
- **Format**: [Conversational](https://huggingface.co/docs/trl/main/dataset_formats#conversational)
- **Type**: [Unpaired preference](https://huggingface.co/docs/trl/main/dataset_formats#unpaired-preference)
Column:
- `"prompt"`: The input question or instruction provided to the model.
- `"completion"`: The model's response to the prompt.
- `"label"`: A binary value indicating whether the response is sufficiently helpful.
## Generation script
The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/ultrafeedback.py).
""")
if __name__ == "__main__":
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
dataset = load_dataset("openbmb/UltraFeedback", split="train")
dataset = dataset.filter(
lambda example: script_args.model_name in example["models"],
batched=False,
num_proc=script_args.dataset_num_proc,
)
dataset = dataset.map(
to_unpaired_preference,
remove_columns=["source", "instruction", "models", "completions", "correct_answers", "incorrect_answers"],
fn_kwargs={"model_name": script_args.model_name, "aspect": script_args.aspect},
num_proc=script_args.dataset_num_proc,
)
dataset = dataset.train_test_split(test_size=0.05, seed=42)
if script_args.push_to_hub:
dataset.push_to_hub(script_args.repo_id)
model_card.push_to_hub(script_args.repo_id, repo_type="dataset")
| trl/examples/datasets/ultrafeedback.py/0 | {
"file_path": "trl/examples/datasets/ultrafeedback.py",
"repo_id": "trl",
"token_count": 2245
} | 550 |
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