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# Copyright 2024 Stability AI, The HuggingFace Team and The InstantX 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, Dict, List, Optional, Union
import torch
import torch.nn as nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
from ...models.attention import JointTransformerBlock
from ...models.attention_processor import Attention, AttentionProcessor, FusedJointAttnProcessor2_0
from ...models.modeling_utils import ModelMixin
from ...models.normalization import AdaLayerNormContinuous
from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
from ..embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
from ..modeling_outputs import Transformer2DModelOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class SD3Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
"""
The Transformer model introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings.
patch_size (`int`): Patch size to turn the input data into small patches.
in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
num_layers (`int`, *optional*, defaults to 18): The number of layers of Transformer blocks to use.
attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
caption_projection_dim (`int`): Number of dimensions to use when projecting the `encoder_hidden_states`.
pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
out_channels (`int`, defaults to 16): Number of output channels.
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: int = 128,
patch_size: int = 2,
in_channels: int = 16,
num_layers: int = 18,
attention_head_dim: int = 64,
num_attention_heads: int = 18,
joint_attention_dim: int = 4096,
caption_projection_dim: int = 1152,
pooled_projection_dim: int = 2048,
out_channels: int = 16,
pos_embed_max_size: int = 96,
):
super().__init__()
default_out_channels = in_channels
self.out_channels = out_channels if out_channels is not None else default_out_channels
self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
self.pos_embed = PatchEmbed(
height=self.config.sample_size,
width=self.config.sample_size,
patch_size=self.config.patch_size,
in_channels=self.config.in_channels,
embed_dim=self.inner_dim,
pos_embed_max_size=pos_embed_max_size, # hard-code for now.
)
self.time_text_embed = CombinedTimestepTextProjEmbeddings(
embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
)
self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.config.caption_projection_dim)
# `attention_head_dim` is doubled to account for the mixing.
# It needs to crafted when we get the actual checkpoints.
self.transformer_blocks = nn.ModuleList(
[
JointTransformerBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
attention_head_dim=self.config.attention_head_dim,
context_pre_only=i == num_layers - 1,
)
for i in range(self.config.num_layers)
]
)
self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
self.gradient_checkpointing = False
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
"""
Sets the attention processor to use [feed forward
chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
Parameters:
chunk_size (`int`, *optional*):
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=`dim`.
dim (`int`, *optional*, defaults to `0`):
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).
"""
if dim not in [0, 1]:
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
# By default chunk size is 1
chunk_size = chunk_size or 1
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, chunk_size, dim)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
def disable_forward_chunking(self):
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, None, 0)
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor()
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedJointAttnProcessor2_0
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.
<Tip warning={true}>
This API is 🧪 experimental.
</Tip>
"""
self.original_attn_processors = None
for _, attn_processor in self.attn_processors.items():
if "Added" in str(attn_processor.__class__.__name__):
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
self.original_attn_processors = self.attn_processors
for module in self.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
self.set_attn_processor(FusedJointAttnProcessor2_0())
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
def unfuse_qkv_projections(self):
"""Disables the fused QKV projection if enabled.
<Tip warning={true}>
This API is 🧪 experimental.
</Tip>
"""
if self.original_attn_processors is not None:
self.set_attn_processor(self.original_attn_processors)
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
pooled_projections: torch.FloatTensor = None,
timestep: torch.LongTensor = None,
block_controlnet_hidden_states: List = None,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
"""
The [`SD3Transformer2DModel`] forward method.
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input `hidden_states`.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
from the embeddings of input conditions.
timestep ( `torch.LongTensor`):
Used to indicate denoising step.
block_controlnet_hidden_states: (`list` of `torch.Tensor`):
A list of tensors that if specified are added to the residuals of transformer blocks.
joint_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
tuple.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
if joint_attention_kwargs is not None:
joint_attention_kwargs = joint_attention_kwargs.copy()
lora_scale = joint_attention_kwargs.pop("scale", 1.0)
else:
lora_scale = 1.0
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
else:
if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
logger.warning(
"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
)
height, width = hidden_states.shape[-2:]
hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.
temb = self.time_text_embed(timestep, pooled_projections)
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
for index_block, block in enumerate(self.transformer_blocks):
if self.training and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
temb,
**ckpt_kwargs,
)
else:
encoder_hidden_states, hidden_states = block(
hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb
)
# controlnet residual
if block_controlnet_hidden_states is not None and block.context_pre_only is False:
interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
hidden_states = hidden_states + block_controlnet_hidden_states[index_block // interval_control]
hidden_states = self.norm_out(hidden_states, temb)
hidden_states = self.proj_out(hidden_states)
# unpatchify
patch_size = self.config.patch_size
height = height // patch_size
width = width // patch_size
hidden_states = hidden_states.reshape(
shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
)
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
|
diffusers/src/diffusers/models/transformers/transformer_sd3.py/0
|
{
"file_path": "diffusers/src/diffusers/models/transformers/transformer_sd3.py",
"repo_id": "diffusers",
"token_count": 7033
}
| 137
|
# 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 math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import FromOriginalModelMixin
from ...utils import BaseOutput
from ..attention_processor import Attention
from ..modeling_utils import ModelMixin
# Copied from diffusers.pipelines.wuerstchen.modeling_wuerstchen_common.WuerstchenLayerNorm with WuerstchenLayerNorm -> SDCascadeLayerNorm
class SDCascadeLayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, x):
x = x.permute(0, 2, 3, 1)
x = super().forward(x)
return x.permute(0, 3, 1, 2)
class SDCascadeTimestepBlock(nn.Module):
def __init__(self, c, c_timestep, conds=[]):
super().__init__()
self.mapper = nn.Linear(c_timestep, c * 2)
self.conds = conds
for cname in conds:
setattr(self, f"mapper_{cname}", nn.Linear(c_timestep, c * 2))
def forward(self, x, t):
t = t.chunk(len(self.conds) + 1, dim=1)
a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1)
for i, c in enumerate(self.conds):
ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1)
a, b = a + ac, b + bc
return x * (1 + a) + b
class SDCascadeResBlock(nn.Module):
def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0):
super().__init__()
self.depthwise = nn.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c)
self.norm = SDCascadeLayerNorm(c, elementwise_affine=False, eps=1e-6)
self.channelwise = nn.Sequential(
nn.Linear(c + c_skip, c * 4),
nn.GELU(),
GlobalResponseNorm(c * 4),
nn.Dropout(dropout),
nn.Linear(c * 4, c),
)
def forward(self, x, x_skip=None):
x_res = x
x = self.norm(self.depthwise(x))
if x_skip is not None:
x = torch.cat([x, x_skip], dim=1)
x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
return x + x_res
# from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105
class GlobalResponseNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))
def forward(self, x):
agg_norm = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
stand_div_norm = agg_norm / (agg_norm.mean(dim=-1, keepdim=True) + 1e-6)
return self.gamma * (x * stand_div_norm) + self.beta + x
class SDCascadeAttnBlock(nn.Module):
def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0):
super().__init__()
self.self_attn = self_attn
self.norm = SDCascadeLayerNorm(c, elementwise_affine=False, eps=1e-6)
self.attention = Attention(query_dim=c, heads=nhead, dim_head=c // nhead, dropout=dropout, bias=True)
self.kv_mapper = nn.Sequential(nn.SiLU(), nn.Linear(c_cond, c))
def forward(self, x, kv):
kv = self.kv_mapper(kv)
norm_x = self.norm(x)
if self.self_attn:
batch_size, channel, _, _ = x.shape
kv = torch.cat([norm_x.view(batch_size, channel, -1).transpose(1, 2), kv], dim=1)
x = x + self.attention(norm_x, encoder_hidden_states=kv)
return x
class UpDownBlock2d(nn.Module):
def __init__(self, in_channels, out_channels, mode, enabled=True):
super().__init__()
if mode not in ["up", "down"]:
raise ValueError(f"{mode} not supported")
interpolation = (
nn.Upsample(scale_factor=2 if mode == "up" else 0.5, mode="bilinear", align_corners=True)
if enabled
else nn.Identity()
)
mapping = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.blocks = nn.ModuleList([interpolation, mapping] if mode == "up" else [mapping, interpolation])
def forward(self, x):
for block in self.blocks:
x = block(x)
return x
@dataclass
class StableCascadeUNetOutput(BaseOutput):
sample: torch.Tensor = None
class StableCascadeUNet(ModelMixin, ConfigMixin, FromOriginalModelMixin):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
in_channels: int = 16,
out_channels: int = 16,
timestep_ratio_embedding_dim: int = 64,
patch_size: int = 1,
conditioning_dim: int = 2048,
block_out_channels: Tuple[int] = (2048, 2048),
num_attention_heads: Tuple[int] = (32, 32),
down_num_layers_per_block: Tuple[int] = (8, 24),
up_num_layers_per_block: Tuple[int] = (24, 8),
down_blocks_repeat_mappers: Optional[Tuple[int]] = (
1,
1,
),
up_blocks_repeat_mappers: Optional[Tuple[int]] = (1, 1),
block_types_per_layer: Tuple[Tuple[str]] = (
("SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"),
("SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"),
),
clip_text_in_channels: Optional[int] = None,
clip_text_pooled_in_channels=1280,
clip_image_in_channels: Optional[int] = None,
clip_seq=4,
effnet_in_channels: Optional[int] = None,
pixel_mapper_in_channels: Optional[int] = None,
kernel_size=3,
dropout: Union[float, Tuple[float]] = (0.1, 0.1),
self_attn: Union[bool, Tuple[bool]] = True,
timestep_conditioning_type: Tuple[str] = ("sca", "crp"),
switch_level: Optional[Tuple[bool]] = None,
):
"""
Parameters:
in_channels (`int`, defaults to 16):
Number of channels in the input sample.
out_channels (`int`, defaults to 16):
Number of channels in the output sample.
timestep_ratio_embedding_dim (`int`, defaults to 64):
Dimension of the projected time embedding.
patch_size (`int`, defaults to 1):
Patch size to use for pixel unshuffling layer
conditioning_dim (`int`, defaults to 2048):
Dimension of the image and text conditional embedding.
block_out_channels (Tuple[int], defaults to (2048, 2048)):
Tuple of output channels for each block.
num_attention_heads (Tuple[int], defaults to (32, 32)):
Number of attention heads in each attention block. Set to -1 to if block types in a layer do not have
attention.
down_num_layers_per_block (Tuple[int], defaults to [8, 24]):
Number of layers in each down block.
up_num_layers_per_block (Tuple[int], defaults to [24, 8]):
Number of layers in each up block.
down_blocks_repeat_mappers (Tuple[int], optional, defaults to [1, 1]):
Number of 1x1 Convolutional layers to repeat in each down block.
up_blocks_repeat_mappers (Tuple[int], optional, defaults to [1, 1]):
Number of 1x1 Convolutional layers to repeat in each up block.
block_types_per_layer (Tuple[Tuple[str]], optional,
defaults to (
("SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"), ("SDCascadeResBlock",
"SDCascadeTimestepBlock", "SDCascadeAttnBlock")
): Block types used in each layer of the up/down blocks.
clip_text_in_channels (`int`, *optional*, defaults to `None`):
Number of input channels for CLIP based text conditioning.
clip_text_pooled_in_channels (`int`, *optional*, defaults to 1280):
Number of input channels for pooled CLIP text embeddings.
clip_image_in_channels (`int`, *optional*):
Number of input channels for CLIP based image conditioning.
clip_seq (`int`, *optional*, defaults to 4):
effnet_in_channels (`int`, *optional*, defaults to `None`):
Number of input channels for effnet conditioning.
pixel_mapper_in_channels (`int`, defaults to `None`):
Number of input channels for pixel mapper conditioning.
kernel_size (`int`, *optional*, defaults to 3):
Kernel size to use in the block convolutional layers.
dropout (Tuple[float], *optional*, defaults to (0.1, 0.1)):
Dropout to use per block.
self_attn (Union[bool, Tuple[bool]]):
Tuple of booleans that determine whether to use self attention in a block or not.
timestep_conditioning_type (Tuple[str], defaults to ("sca", "crp")):
Timestep conditioning type.
switch_level (Optional[Tuple[bool]], *optional*, defaults to `None`):
Tuple that indicates whether upsampling or downsampling should be applied in a block
"""
super().__init__()
if len(block_out_channels) != len(down_num_layers_per_block):
raise ValueError(
f"Number of elements in `down_num_layers_per_block` must match the length of `block_out_channels`: {len(block_out_channels)}"
)
elif len(block_out_channels) != len(up_num_layers_per_block):
raise ValueError(
f"Number of elements in `up_num_layers_per_block` must match the length of `block_out_channels`: {len(block_out_channels)}"
)
elif len(block_out_channels) != len(down_blocks_repeat_mappers):
raise ValueError(
f"Number of elements in `down_blocks_repeat_mappers` must match the length of `block_out_channels`: {len(block_out_channels)}"
)
elif len(block_out_channels) != len(up_blocks_repeat_mappers):
raise ValueError(
f"Number of elements in `up_blocks_repeat_mappers` must match the length of `block_out_channels`: {len(block_out_channels)}"
)
elif len(block_out_channels) != len(block_types_per_layer):
raise ValueError(
f"Number of elements in `block_types_per_layer` must match the length of `block_out_channels`: {len(block_out_channels)}"
)
if isinstance(dropout, float):
dropout = (dropout,) * len(block_out_channels)
if isinstance(self_attn, bool):
self_attn = (self_attn,) * len(block_out_channels)
# CONDITIONING
if effnet_in_channels is not None:
self.effnet_mapper = nn.Sequential(
nn.Conv2d(effnet_in_channels, block_out_channels[0] * 4, kernel_size=1),
nn.GELU(),
nn.Conv2d(block_out_channels[0] * 4, block_out_channels[0], kernel_size=1),
SDCascadeLayerNorm(block_out_channels[0], elementwise_affine=False, eps=1e-6),
)
if pixel_mapper_in_channels is not None:
self.pixels_mapper = nn.Sequential(
nn.Conv2d(pixel_mapper_in_channels, block_out_channels[0] * 4, kernel_size=1),
nn.GELU(),
nn.Conv2d(block_out_channels[0] * 4, block_out_channels[0], kernel_size=1),
SDCascadeLayerNorm(block_out_channels[0], elementwise_affine=False, eps=1e-6),
)
self.clip_txt_pooled_mapper = nn.Linear(clip_text_pooled_in_channels, conditioning_dim * clip_seq)
if clip_text_in_channels is not None:
self.clip_txt_mapper = nn.Linear(clip_text_in_channels, conditioning_dim)
if clip_image_in_channels is not None:
self.clip_img_mapper = nn.Linear(clip_image_in_channels, conditioning_dim * clip_seq)
self.clip_norm = nn.LayerNorm(conditioning_dim, elementwise_affine=False, eps=1e-6)
self.embedding = nn.Sequential(
nn.PixelUnshuffle(patch_size),
nn.Conv2d(in_channels * (patch_size**2), block_out_channels[0], kernel_size=1),
SDCascadeLayerNorm(block_out_channels[0], elementwise_affine=False, eps=1e-6),
)
def get_block(block_type, in_channels, nhead, c_skip=0, dropout=0, self_attn=True):
if block_type == "SDCascadeResBlock":
return SDCascadeResBlock(in_channels, c_skip, kernel_size=kernel_size, dropout=dropout)
elif block_type == "SDCascadeAttnBlock":
return SDCascadeAttnBlock(in_channels, conditioning_dim, nhead, self_attn=self_attn, dropout=dropout)
elif block_type == "SDCascadeTimestepBlock":
return SDCascadeTimestepBlock(
in_channels, timestep_ratio_embedding_dim, conds=timestep_conditioning_type
)
else:
raise ValueError(f"Block type {block_type} not supported")
# BLOCKS
# -- down blocks
self.down_blocks = nn.ModuleList()
self.down_downscalers = nn.ModuleList()
self.down_repeat_mappers = nn.ModuleList()
for i in range(len(block_out_channels)):
if i > 0:
self.down_downscalers.append(
nn.Sequential(
SDCascadeLayerNorm(block_out_channels[i - 1], elementwise_affine=False, eps=1e-6),
UpDownBlock2d(
block_out_channels[i - 1], block_out_channels[i], mode="down", enabled=switch_level[i - 1]
)
if switch_level is not None
else nn.Conv2d(block_out_channels[i - 1], block_out_channels[i], kernel_size=2, stride=2),
)
)
else:
self.down_downscalers.append(nn.Identity())
down_block = nn.ModuleList()
for _ in range(down_num_layers_per_block[i]):
for block_type in block_types_per_layer[i]:
block = get_block(
block_type,
block_out_channels[i],
num_attention_heads[i],
dropout=dropout[i],
self_attn=self_attn[i],
)
down_block.append(block)
self.down_blocks.append(down_block)
if down_blocks_repeat_mappers is not None:
block_repeat_mappers = nn.ModuleList()
for _ in range(down_blocks_repeat_mappers[i] - 1):
block_repeat_mappers.append(nn.Conv2d(block_out_channels[i], block_out_channels[i], kernel_size=1))
self.down_repeat_mappers.append(block_repeat_mappers)
# -- up blocks
self.up_blocks = nn.ModuleList()
self.up_upscalers = nn.ModuleList()
self.up_repeat_mappers = nn.ModuleList()
for i in reversed(range(len(block_out_channels))):
if i > 0:
self.up_upscalers.append(
nn.Sequential(
SDCascadeLayerNorm(block_out_channels[i], elementwise_affine=False, eps=1e-6),
UpDownBlock2d(
block_out_channels[i], block_out_channels[i - 1], mode="up", enabled=switch_level[i - 1]
)
if switch_level is not None
else nn.ConvTranspose2d(
block_out_channels[i], block_out_channels[i - 1], kernel_size=2, stride=2
),
)
)
else:
self.up_upscalers.append(nn.Identity())
up_block = nn.ModuleList()
for j in range(up_num_layers_per_block[::-1][i]):
for k, block_type in enumerate(block_types_per_layer[i]):
c_skip = block_out_channels[i] if i < len(block_out_channels) - 1 and j == k == 0 else 0
block = get_block(
block_type,
block_out_channels[i],
num_attention_heads[i],
c_skip=c_skip,
dropout=dropout[i],
self_attn=self_attn[i],
)
up_block.append(block)
self.up_blocks.append(up_block)
if up_blocks_repeat_mappers is not None:
block_repeat_mappers = nn.ModuleList()
for _ in range(up_blocks_repeat_mappers[::-1][i] - 1):
block_repeat_mappers.append(nn.Conv2d(block_out_channels[i], block_out_channels[i], kernel_size=1))
self.up_repeat_mappers.append(block_repeat_mappers)
# OUTPUT
self.clf = nn.Sequential(
SDCascadeLayerNorm(block_out_channels[0], elementwise_affine=False, eps=1e-6),
nn.Conv2d(block_out_channels[0], out_channels * (patch_size**2), kernel_size=1),
nn.PixelShuffle(patch_size),
)
self.gradient_checkpointing = False
def _set_gradient_checkpointing(self, value=False):
self.gradient_checkpointing = value
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
torch.nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02)
nn.init.normal_(self.clip_txt_mapper.weight, std=0.02) if hasattr(self, "clip_txt_mapper") else None
nn.init.normal_(self.clip_img_mapper.weight, std=0.02) if hasattr(self, "clip_img_mapper") else None
if hasattr(self, "effnet_mapper"):
nn.init.normal_(self.effnet_mapper[0].weight, std=0.02) # conditionings
nn.init.normal_(self.effnet_mapper[2].weight, std=0.02) # conditionings
if hasattr(self, "pixels_mapper"):
nn.init.normal_(self.pixels_mapper[0].weight, std=0.02) # conditionings
nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings
torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs
nn.init.constant_(self.clf[1].weight, 0) # outputs
# blocks
for level_block in self.down_blocks + self.up_blocks:
for block in level_block:
if isinstance(block, SDCascadeResBlock):
block.channelwise[-1].weight.data *= np.sqrt(1 / sum(self.config.blocks[0]))
elif isinstance(block, SDCascadeTimestepBlock):
nn.init.constant_(block.mapper.weight, 0)
def get_timestep_ratio_embedding(self, timestep_ratio, max_positions=10000):
r = timestep_ratio * max_positions
half_dim = self.config.timestep_ratio_embedding_dim // 2
emb = math.log(max_positions) / (half_dim - 1)
emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
emb = r[:, None] * emb[None, :]
emb = torch.cat([emb.sin(), emb.cos()], dim=1)
if self.config.timestep_ratio_embedding_dim % 2 == 1: # zero pad
emb = nn.functional.pad(emb, (0, 1), mode="constant")
return emb.to(dtype=r.dtype)
def get_clip_embeddings(self, clip_txt_pooled, clip_txt=None, clip_img=None):
if len(clip_txt_pooled.shape) == 2:
clip_txt_pool = clip_txt_pooled.unsqueeze(1)
clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(
clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.config.clip_seq, -1
)
if clip_txt is not None and clip_img is not None:
clip_txt = self.clip_txt_mapper(clip_txt)
if len(clip_img.shape) == 2:
clip_img = clip_img.unsqueeze(1)
clip_img = self.clip_img_mapper(clip_img).view(
clip_img.size(0), clip_img.size(1) * self.config.clip_seq, -1
)
clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1)
else:
clip = clip_txt_pool
return self.clip_norm(clip)
def _down_encode(self, x, r_embed, clip):
level_outputs = []
block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
if self.training and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
for down_block, downscaler, repmap in block_group:
x = downscaler(x)
for i in range(len(repmap) + 1):
for block in down_block:
if isinstance(block, SDCascadeResBlock):
x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, use_reentrant=False)
elif isinstance(block, SDCascadeAttnBlock):
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block), x, clip, use_reentrant=False
)
elif isinstance(block, SDCascadeTimestepBlock):
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block), x, r_embed, use_reentrant=False
)
else:
x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), use_reentrant=False)
if i < len(repmap):
x = repmap[i](x)
level_outputs.insert(0, x)
else:
for down_block, downscaler, repmap in block_group:
x = downscaler(x)
for i in range(len(repmap) + 1):
for block in down_block:
if isinstance(block, SDCascadeResBlock):
x = block(x)
elif isinstance(block, SDCascadeAttnBlock):
x = block(x, clip)
elif isinstance(block, SDCascadeTimestepBlock):
x = block(x, r_embed)
else:
x = block(x)
if i < len(repmap):
x = repmap[i](x)
level_outputs.insert(0, x)
return level_outputs
def _up_decode(self, level_outputs, r_embed, clip):
x = level_outputs[0]
block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
if self.training and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
for i, (up_block, upscaler, repmap) in enumerate(block_group):
for j in range(len(repmap) + 1):
for k, block in enumerate(up_block):
if isinstance(block, SDCascadeResBlock):
skip = level_outputs[i] if k == 0 and i > 0 else None
if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
orig_type = x.dtype
x = torch.nn.functional.interpolate(
x.float(), skip.shape[-2:], mode="bilinear", align_corners=True
)
x = x.to(orig_type)
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block), x, skip, use_reentrant=False
)
elif isinstance(block, SDCascadeAttnBlock):
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block), x, clip, use_reentrant=False
)
elif isinstance(block, SDCascadeTimestepBlock):
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block), x, r_embed, use_reentrant=False
)
else:
x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, use_reentrant=False)
if j < len(repmap):
x = repmap[j](x)
x = upscaler(x)
else:
for i, (up_block, upscaler, repmap) in enumerate(block_group):
for j in range(len(repmap) + 1):
for k, block in enumerate(up_block):
if isinstance(block, SDCascadeResBlock):
skip = level_outputs[i] if k == 0 and i > 0 else None
if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
orig_type = x.dtype
x = torch.nn.functional.interpolate(
x.float(), skip.shape[-2:], mode="bilinear", align_corners=True
)
x = x.to(orig_type)
x = block(x, skip)
elif isinstance(block, SDCascadeAttnBlock):
x = block(x, clip)
elif isinstance(block, SDCascadeTimestepBlock):
x = block(x, r_embed)
else:
x = block(x)
if j < len(repmap):
x = repmap[j](x)
x = upscaler(x)
return x
def forward(
self,
sample,
timestep_ratio,
clip_text_pooled,
clip_text=None,
clip_img=None,
effnet=None,
pixels=None,
sca=None,
crp=None,
return_dict=True,
):
if pixels is None:
pixels = sample.new_zeros(sample.size(0), 3, 8, 8)
# Process the conditioning embeddings
timestep_ratio_embed = self.get_timestep_ratio_embedding(timestep_ratio)
for c in self.config.timestep_conditioning_type:
if c == "sca":
cond = sca
elif c == "crp":
cond = crp
else:
cond = None
t_cond = cond or torch.zeros_like(timestep_ratio)
timestep_ratio_embed = torch.cat([timestep_ratio_embed, self.get_timestep_ratio_embedding(t_cond)], dim=1)
clip = self.get_clip_embeddings(clip_txt_pooled=clip_text_pooled, clip_txt=clip_text, clip_img=clip_img)
# Model Blocks
x = self.embedding(sample)
if hasattr(self, "effnet_mapper") and effnet is not None:
x = x + self.effnet_mapper(
nn.functional.interpolate(effnet, size=x.shape[-2:], mode="bilinear", align_corners=True)
)
if hasattr(self, "pixels_mapper"):
x = x + nn.functional.interpolate(
self.pixels_mapper(pixels), size=x.shape[-2:], mode="bilinear", align_corners=True
)
level_outputs = self._down_encode(x, timestep_ratio_embed, clip)
x = self._up_decode(level_outputs, timestep_ratio_embed, clip)
sample = self.clf(x)
if not return_dict:
return (sample,)
return StableCascadeUNetOutput(sample=sample)
|
diffusers/src/diffusers/models/unets/unet_stable_cascade.py/0
|
{
"file_path": "diffusers/src/diffusers/models/unets/unet_stable_cascade.py",
"repo_id": "diffusers",
"token_count": 14607
}
| 138
|
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_flax_available,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils import dummy_torch_and_transformers_objects # noqa F403
_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
else:
_import_structure["pipeline_stable_diffusion_3_controlnet"] = ["StableDiffusion3ControlNetPipeline"]
_import_structure["pipeline_stable_diffusion_3_controlnet_inpainting"] = [
"StableDiffusion3ControlNetInpaintingPipeline"
]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import *
else:
from .pipeline_stable_diffusion_3_controlnet import StableDiffusion3ControlNetPipeline
from .pipeline_stable_diffusion_3_controlnet_inpainting import StableDiffusion3ControlNetInpaintingPipeline
try:
if not (is_transformers_available() and is_flax_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
for name, value in _dummy_objects.items():
setattr(sys.modules[__name__], name, value)
|
diffusers/src/diffusers/pipelines/controlnet_sd3/__init__.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/controlnet_sd3/__init__.py",
"repo_id": "diffusers",
"token_count": 741
}
| 139
|
# 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.
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ....models import AutoencoderKL, UNet2DConditionModel
from ....schedulers import DDIMScheduler, DDPMScheduler
from ....utils.torch_utils import randn_tensor
from ...pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class AudioDiffusionPipeline(DiffusionPipeline):
"""
Pipeline for audio diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
vqae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
mel ([`Mel`]):
Transform audio into a spectrogram.
scheduler ([`DDIMScheduler`] or [`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`] or [`DDPMScheduler`].
"""
_optional_components = ["vqvae"]
def __init__(
self,
vqvae: AutoencoderKL,
unet: UNet2DConditionModel,
mel: Mel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)
def get_default_steps(self) -> int:
"""Returns default number of steps recommended for inference.
Returns:
`int`:
The number of steps.
"""
return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
audio_file: str = None,
raw_audio: np.ndarray = None,
slice: int = 0,
start_step: int = 0,
steps: int = None,
generator: torch.Generator = None,
mask_start_secs: float = 0,
mask_end_secs: float = 0,
step_generator: torch.Generator = None,
eta: float = 0,
noise: torch.Tensor = None,
encoding: torch.Tensor = None,
return_dict=True,
) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
"""
The call function to the pipeline for generation.
Args:
batch_size (`int`):
Number of samples to generate.
audio_file (`str`):
An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.
raw_audio (`np.ndarray`):
The raw audio file as a NumPy array.
slice (`int`):
Slice number of audio to convert.
start_step (int):
Step to start diffusion from.
steps (`int`):
Number of denoising steps (defaults to `50` for DDIM and `1000` for DDPM).
generator (`torch.Generator`):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
mask_start_secs (`float`):
Number of seconds of audio to mask (not generate) at start.
mask_end_secs (`float`):
Number of seconds of audio to mask (not generate) at end.
step_generator (`torch.Generator`):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) used to denoise.
None
eta (`float`):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
noise (`torch.Tensor`):
A noise tensor of shape `(batch_size, 1, height, width)` or `None`.
encoding (`torch.Tensor`):
A tensor for [`UNet2DConditionModel`] of shape `(batch_size, seq_length, cross_attention_dim)`.
return_dict (`bool`):
Whether or not to return a [`AudioPipelineOutput`], [`ImagePipelineOutput`] or a plain tuple.
Examples:
For audio diffusion:
```py
import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device)
output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=mel.get_sample_rate()))
```
For latent audio diffusion:
```py
import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device)
output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```
For other tasks like variation, inpainting, outpainting, etc:
```py
output = pipe(
raw_audio=output.audios[0, 0],
start_step=int(pipe.get_default_steps() / 2),
mask_start_secs=1,
mask_end_secs=1,
)
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```
Returns:
`List[PIL Image]`:
A list of Mel spectrograms (`float`, `List[np.ndarray]`) with the sample rate and raw audio.
"""
steps = steps or self.get_default_steps()
self.scheduler.set_timesteps(steps)
step_generator = step_generator or generator
# For backwards compatibility
if isinstance(self.unet.config.sample_size, int):
self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
noise = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
),
generator=generator,
device=self.device,
)
images = noise
mask = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(audio_file, raw_audio)
input_image = self.mel.audio_slice_to_image(slice)
input_image = np.frombuffer(input_image.tobytes(), dtype="uint8").reshape(
(input_image.height, input_image.width)
)
input_image = (input_image / 255) * 2 - 1
input_images = torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float).to(self.device)
if self.vqvae is not None:
input_images = self.vqvae.encode(torch.unsqueeze(input_images, 0)).latent_dist.sample(
generator=generator
)[0]
input_images = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1])
pixels_per_second = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
mask_start = int(mask_start_secs * pixels_per_second)
mask_end = int(mask_end_secs * pixels_per_second)
mask = self.scheduler.add_noise(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:]))
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
if isinstance(self.unet, UNet2DConditionModel):
model_output = self.unet(images, t, encoding)["sample"]
else:
model_output = self.unet(images, t)["sample"]
if isinstance(self.scheduler, DDIMScheduler):
images = self.scheduler.step(
model_output=model_output,
timestep=t,
sample=images,
eta=eta,
generator=step_generator,
)["prev_sample"]
else:
images = self.scheduler.step(
model_output=model_output,
timestep=t,
sample=images,
generator=step_generator,
)["prev_sample"]
if mask is not None:
if mask_start > 0:
images[:, :, :, :mask_start] = mask[:, step, :, :mask_start]
if mask_end > 0:
images[:, :, :, -mask_end:] = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
images = 1 / self.vqvae.config.scaling_factor * images
images = self.vqvae.decode(images)["sample"]
images = (images / 2 + 0.5).clamp(0, 1)
images = images.cpu().permute(0, 2, 3, 1).numpy()
images = (images * 255).round().astype("uint8")
images = list(
(Image.fromarray(_[:, :, 0]) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(_, mode="RGB").convert("L") for _ in images)
)
audios = [self.mel.image_to_audio(_) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images))
@torch.no_grad()
def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:
"""
Reverse the denoising step process to recover a noisy image from the generated image.
Args:
images (`List[PIL Image]`):
List of images to encode.
steps (`int`):
Number of encoding steps to perform (defaults to `50`).
Returns:
`np.ndarray`:
A noise tensor of shape `(batch_size, 1, height, width)`.
"""
# Only works with DDIM as this method is deterministic
assert isinstance(self.scheduler, DDIMScheduler)
self.scheduler.set_timesteps(steps)
sample = np.array(
[np.frombuffer(image.tobytes(), dtype="uint8").reshape((1, image.height, image.width)) for image in images]
)
sample = (sample / 255) * 2 - 1
sample = torch.Tensor(sample).to(self.device)
for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))):
prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
beta_prod_t = 1 - alpha_prod_t
model_output = self.unet(sample, t)["sample"]
pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * model_output
sample = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
sample = sample * alpha_prod_t ** (0.5) + beta_prod_t ** (0.5) * model_output
return sample
@staticmethod
def slerp(x0: torch.Tensor, x1: torch.Tensor, alpha: float) -> torch.Tensor:
"""Spherical Linear intERPolation.
Args:
x0 (`torch.Tensor`):
The first tensor to interpolate between.
x1 (`torch.Tensor`):
Second tensor to interpolate between.
alpha (`float`):
Interpolation between 0 and 1
Returns:
`torch.Tensor`:
The interpolated tensor.
"""
theta = acos(torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) / torch.norm(x1))
return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(alpha * theta) * x1 / sin(theta)
|
diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py",
"repo_id": "diffusers",
"token_count": 6240
}
| 140
|
# Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
# William Peebles and Saining Xie
#
# Copyright (c) 2021 OpenAI
# MIT License
#
# 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.
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, DiTTransformer2DModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class DiTPipeline(DiffusionPipeline):
r"""
Pipeline for image generation based on a Transformer backbone instead of a UNet.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
transformer ([`DiTTransformer2DModel`]):
A class conditioned `DiTTransformer2DModel` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
"""
model_cpu_offload_seq = "transformer->vae"
def __init__(
self,
transformer: DiTTransformer2DModel,
vae: AutoencoderKL,
scheduler: KarrasDiffusionSchedulers,
id2label: Optional[Dict[int, str]] = None,
):
super().__init__()
self.register_modules(transformer=transformer, vae=vae, scheduler=scheduler)
# create a imagenet -> id dictionary for easier use
self.labels = {}
if id2label is not None:
for key, value in id2label.items():
for label in value.split(","):
self.labels[label.lstrip().rstrip()] = int(key)
self.labels = dict(sorted(self.labels.items()))
def get_label_ids(self, label: Union[str, List[str]]) -> List[int]:
r"""
Map label strings from ImageNet to corresponding class ids.
Parameters:
label (`str` or `dict` of `str`):
Label strings to be mapped to class ids.
Returns:
`list` of `int`:
Class ids to be processed by pipeline.
"""
if not isinstance(label, list):
label = list(label)
for l in label:
if l not in self.labels:
raise ValueError(
f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."
)
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__(
self,
class_labels: List[int],
guidance_scale: float = 4.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
num_inference_steps: int = 50,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
The call function to the pipeline for generation.
Args:
class_labels (List[int]):
List of ImageNet class labels for the images to be generated.
guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
num_inference_steps (`int`, *optional*, defaults to 250):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
Examples:
```py
>>> from diffusers import DiTPipeline, DPMSolverMultistepScheduler
>>> import torch
>>> pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16)
>>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
>>> pipe = pipe.to("cuda")
>>> # pick words from Imagenet class labels
>>> pipe.labels # to print all available words
>>> # pick words that exist in ImageNet
>>> words = ["white shark", "umbrella"]
>>> class_ids = pipe.get_label_ids(words)
>>> generator = torch.manual_seed(33)
>>> output = pipe(class_labels=class_ids, num_inference_steps=25, generator=generator)
>>> image = output.images[0] # label 'white shark'
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
batch_size = len(class_labels)
latent_size = self.transformer.config.sample_size
latent_channels = self.transformer.config.in_channels
latents = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size),
generator=generator,
device=self._execution_device,
dtype=self.transformer.dtype,
)
latent_model_input = torch.cat([latents] * 2) if guidance_scale > 1 else latents
class_labels = torch.tensor(class_labels, device=self._execution_device).reshape(-1)
class_null = torch.tensor([1000] * batch_size, device=self._execution_device)
class_labels_input = torch.cat([class_labels, class_null], 0) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
if guidance_scale > 1:
half = latent_model_input[: len(latent_model_input) // 2]
latent_model_input = torch.cat([half, half], dim=0)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
timesteps = t
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
if isinstance(timesteps, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=latent_model_input.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(latent_model_input.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(latent_model_input.shape[0])
# predict noise model_output
noise_pred = self.transformer(
latent_model_input, timestep=timesteps, class_labels=class_labels_input
).sample
# perform guidance
if guidance_scale > 1:
eps, rest = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
eps = torch.cat([half_eps, half_eps], dim=0)
noise_pred = torch.cat([eps, rest], dim=1)
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
model_output, _ = torch.split(noise_pred, latent_channels, dim=1)
else:
model_output = noise_pred
# compute previous image: x_t -> x_t-1
latent_model_input = self.scheduler.step(model_output, t, latent_model_input).prev_sample
if guidance_scale > 1:
latents, _ = latent_model_input.chunk(2, dim=0)
else:
latents = latent_model_input
latents = 1 / self.vae.config.scaling_factor * latents
samples = self.vae.decode(latents).sample
samples = (samples / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
samples = samples.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
samples = self.numpy_to_pil(samples)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=samples)
|
diffusers/src/diffusers/pipelines/dit/pipeline_dit.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/dit/pipeline_dit.py",
"repo_id": "diffusers",
"token_count": 4219
}
| 141
|
# 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.
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
from ...models import PriorTransformer
from ...schedulers import UnCLIPScheduler
from ...utils import (
BaseOutput,
logging,
replace_example_docstring,
)
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save("cat.png")
```
"""
EXAMPLE_INTERPOLATE_DOC_STRING = """
Examples:
```py
>>> from diffusers import KandinskyPriorPipeline, KandinskyPipeline
>>> from diffusers.utils import load_image
>>> import PIL
>>> import torch
>>> from torchvision import transforms
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
... )
>>> pipe_prior.to("cuda")
>>> img1 = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... )
>>> img2 = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/starry_night.jpeg"
... )
>>> images_texts = ["a cat", img1, img2]
>>> weights = [0.3, 0.3, 0.4]
>>> image_emb, zero_image_emb = pipe_prior.interpolate(images_texts, weights)
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
>>> pipe.to("cuda")
>>> image = pipe(
... "",
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=150,
... ).images[0]
>>> image.save("starry_cat.png")
```
"""
@dataclass
class KandinskyPriorPipelineOutput(BaseOutput):
"""
Output class for KandinskyPriorPipeline.
Args:
image_embeds (`torch.Tensor`)
clip image embeddings for text prompt
negative_image_embeds (`List[PIL.Image.Image]` or `np.ndarray`)
clip image embeddings for unconditional tokens
"""
image_embeds: Union[torch.Tensor, np.ndarray]
negative_image_embeds: Union[torch.Tensor, np.ndarray]
class KandinskyPriorPipeline(DiffusionPipeline):
"""
Pipeline for generating image prior for Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
"""
_exclude_from_cpu_offload = ["prior"]
model_cpu_offload_seq = "text_encoder->prior"
def __init__(
self,
prior: PriorTransformer,
image_encoder: CLIPVisionModelWithProjection,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
scheduler: UnCLIPScheduler,
image_processor: CLIPImageProcessor,
):
super().__init__()
self.register_modules(
prior=prior,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
image_encoder=image_encoder,
image_processor=image_processor,
)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_INTERPOLATE_DOC_STRING)
def interpolate(
self,
images_and_prompts: List[Union[str, PIL.Image.Image, torch.Tensor]],
weights: List[float],
num_images_per_prompt: int = 1,
num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
negative_prior_prompt: Optional[str] = None,
negative_prompt: str = "",
guidance_scale: float = 4.0,
device=None,
):
"""
Function invoked when using the prior pipeline for interpolation.
Args:
images_and_prompts (`List[Union[str, PIL.Image.Image, torch.Tensor]]`):
list of prompts and images to guide the image generation.
weights: (`List[float]`):
list of weights for each condition in `images_and_prompts`
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
negative_prior_prompt (`str`, *optional*):
The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
`guidance_scale` is less than `1`).
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
`guidance_scale` is less than `1`).
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
Examples:
Returns:
[`KandinskyPriorPipelineOutput`] or `tuple`
"""
device = device or self.device
if len(images_and_prompts) != len(weights):
raise ValueError(
f"`images_and_prompts` contains {len(images_and_prompts)} items and `weights` contains {len(weights)} items - they should be lists of same length"
)
image_embeddings = []
for cond, weight in zip(images_and_prompts, weights):
if isinstance(cond, str):
image_emb = self(
cond,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
generator=generator,
latents=latents,
negative_prompt=negative_prior_prompt,
guidance_scale=guidance_scale,
).image_embeds
elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):
if isinstance(cond, PIL.Image.Image):
cond = (
self.image_processor(cond, return_tensors="pt")
.pixel_values[0]
.unsqueeze(0)
.to(dtype=self.image_encoder.dtype, device=device)
)
image_emb = self.image_encoder(cond)["image_embeds"]
else:
raise ValueError(
f"`images_and_prompts` can only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor` but is {type(cond)}"
)
image_embeddings.append(image_emb * weight)
image_emb = torch.cat(image_embeddings).sum(dim=0, keepdim=True)
out_zero = self(
negative_prompt,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
generator=generator,
latents=latents,
negative_prompt=negative_prior_prompt,
guidance_scale=guidance_scale,
)
zero_image_emb = out_zero.negative_image_embeds if negative_prompt == "" else out_zero.image_embeds
return KandinskyPriorPipelineOutput(image_embeds=image_emb, negative_image_embeds=zero_image_emb)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
def get_zero_embed(self, batch_size=1, device=None):
device = device or self.device
zero_img = torch.zeros(1, 3, self.image_encoder.config.image_size, self.image_encoder.config.image_size).to(
device=device, dtype=self.image_encoder.dtype
)
zero_image_emb = self.image_encoder(zero_img)["image_embeds"]
zero_image_emb = zero_image_emb.repeat(batch_size, 1)
return zero_image_emb
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
text_mask = text_inputs.attention_mask.bool().to(device)
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_encoder_output = self.text_encoder(text_input_ids.to(device))
prompt_embeds = text_encoder_output.text_embeds
text_encoder_hidden_states = text_encoder_output.last_hidden_state
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
uncond_text_mask = uncond_input.attention_mask.bool().to(device)
negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask])
return prompt_embeds, text_encoder_hidden_states, text_mask
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
guidance_scale: float = 4.0,
output_type: Optional[str] = "pt",
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
output_type (`str`, *optional*, defaults to `"pt"`):
The output format of the generate image. Choose between: `"np"` (`np.array`) or `"pt"`
(`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples:
Returns:
[`KandinskyPriorPipelineOutput`] or `tuple`
"""
if isinstance(prompt, str):
prompt = [prompt]
elif not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt]
elif not isinstance(negative_prompt, list) and negative_prompt is not None:
raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
# if the negative prompt is defined we double the batch size to
# directly retrieve the negative prompt embedding
if negative_prompt is not None:
prompt = prompt + negative_prompt
negative_prompt = 2 * negative_prompt
device = self._execution_device
batch_size = len(prompt)
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
# prior
self.scheduler.set_timesteps(num_inference_steps, device=device)
prior_timesteps_tensor = self.scheduler.timesteps
embedding_dim = self.prior.config.embedding_dim
latents = self.prepare_latents(
(batch_size, embedding_dim),
prompt_embeds.dtype,
device,
generator,
latents,
self.scheduler,
)
for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
predicted_image_embedding = self.prior(
latent_model_input,
timestep=t,
proj_embedding=prompt_embeds,
encoder_hidden_states=text_encoder_hidden_states,
attention_mask=text_mask,
).predicted_image_embedding
if do_classifier_free_guidance:
predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
predicted_image_embedding = predicted_image_embedding_uncond + guidance_scale * (
predicted_image_embedding_text - predicted_image_embedding_uncond
)
if i + 1 == prior_timesteps_tensor.shape[0]:
prev_timestep = None
else:
prev_timestep = prior_timesteps_tensor[i + 1]
latents = self.scheduler.step(
predicted_image_embedding,
timestep=t,
sample=latents,
generator=generator,
prev_timestep=prev_timestep,
).prev_sample
latents = self.prior.post_process_latents(latents)
image_embeddings = latents
# if negative prompt has been defined, we retrieve split the image embedding into two
if negative_prompt is None:
zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)
self.maybe_free_model_hooks()
else:
image_embeddings, zero_embeds = image_embeddings.chunk(2)
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.prior_hook.offload()
if output_type not in ["pt", "np"]:
raise ValueError(f"Only the output types `pt` and `np` are supported not output_type={output_type}")
if output_type == "np":
image_embeddings = image_embeddings.cpu().numpy()
zero_embeds = zero_embeds.cpu().numpy()
if not return_dict:
return (image_embeddings, zero_embeds)
return KandinskyPriorPipelineOutput(image_embeds=image_embeddings, negative_image_embeds=zero_embeds)
|
diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py",
"repo_id": "diffusers",
"token_count": 10612
}
| 142
|
# Copyright 2024 Stability AI, Kwai-Kolors Team 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.
import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from ...callbacks import MultiPipelineCallbacks, PipelineCallback
from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import IPAdapterMixin, StableDiffusionXLLoraLoaderMixin
from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import is_torch_xla_available, logging, replace_example_docstring
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from .pipeline_output import KolorsPipelineOutput
from .text_encoder import ChatGLMModel
from .tokenizer import ChatGLMTokenizer
if is_torch_xla_available():
import torch_xla.core.xla_model as xm
XLA_AVAILABLE = True
else:
XLA_AVAILABLE = False
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import KolorsPipeline
>>> pipe = KolorsPipeline.from_pretrained(
... "Kwai-Kolors/Kolors-diffusers", variant="fp16", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> prompt = (
... "A photo of a ladybug, macro, zoom, high quality, film, holding a wooden sign with the text 'KOLORS'"
... )
>>> image = pipe(prompt).images[0]
```
"""
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
scheduler,
num_inference_steps: Optional[int] = None,
device: Optional[Union[str, torch.device]] = None,
timesteps: Optional[List[int]] = None,
sigmas: Optional[List[float]] = None,
**kwargs,
):
"""
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
Args:
scheduler (`SchedulerMixin`):
The scheduler to get timesteps from.
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
must be `None`.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
timesteps (`List[int]`, *optional*):
Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
`num_inference_steps` and `sigmas` must be `None`.
sigmas (`List[float]`, *optional*):
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
`num_inference_steps` and `timesteps` must be `None`.
Returns:
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
second element is the number of inference steps.
"""
if timesteps is not None and sigmas is not None:
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
if timesteps is not None:
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accepts_timesteps:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" timestep schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
elif sigmas is not None:
accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accept_sigmas:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" sigmas schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
else:
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
class KolorsPipeline(DiffusionPipeline, StableDiffusionMixin, StableDiffusionXLLoraLoaderMixin, IPAdapterMixin):
r"""
Pipeline for text-to-image generation using Kolors.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
The pipeline also inherits the following loading methods:
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`ChatGLMModel`]):
Frozen text-encoder. Kolors uses [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b).
tokenizer (`ChatGLMTokenizer`):
Tokenizer of class
[ChatGLMTokenizer](https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"False"`):
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
`Kwai-Kolors/Kolors-diffusers`.
"""
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_optional_components = [
"image_encoder",
"feature_extractor",
]
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
"add_text_embeds",
"add_time_ids",
"negative_pooled_prompt_embeds",
"negative_add_time_ids",
]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: ChatGLMModel,
tokenizer: ChatGLMTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
image_encoder: CLIPVisionModelWithProjection = None,
feature_extractor: CLIPImageProcessor = None,
force_zeros_for_empty_prompt: bool = False,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
)
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
self.vae_scale_factor = (
2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.default_sample_size = self.unet.config.sample_size
def encode_prompt(
self,
prompt,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
do_classifier_free_guidance: bool = True,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
max_sequence_length: int = 256,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
"""
# from IPython import embed; embed(); exit()
device = device or self._execution_device
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# Define tokenizers and text encoders
tokenizers = [self.tokenizer]
text_encoders = [self.text_encoder]
if prompt_embeds is None:
prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
).to(device)
output = text_encoder(
input_ids=text_inputs["input_ids"],
attention_mask=text_inputs["attention_mask"],
position_ids=text_inputs["position_ids"],
output_hidden_states=True,
)
# [max_sequence_length, batch, hidden_size] -> [batch, max_sequence_length, hidden_size]
# clone to have a contiguous tensor
prompt_embeds = output.hidden_states[-2].permute(1, 0, 2).clone()
# [max_sequence_length, batch, hidden_size] -> [batch, hidden_size]
pooled_prompt_embeds = output.hidden_states[-1][-1, :, :].clone()
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = prompt_embeds_list[0]
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
elif do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
negative_prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
uncond_input = tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
).to(device)
output = text_encoder(
input_ids=uncond_input["input_ids"],
attention_mask=uncond_input["attention_mask"],
position_ids=uncond_input["position_ids"],
output_hidden_states=True,
)
# [max_sequence_length, batch, hidden_size] -> [batch, max_sequence_length, hidden_size]
# clone to have a contiguous tensor
negative_prompt_embeds = output.hidden_states[-2].permute(1, 0, 2).clone()
# [max_sequence_length, batch, hidden_size] -> [batch, hidden_size]
negative_pooled_prompt_embeds = output.hidden_states[-1][-1, :, :].clone()
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(
batch_size * num_images_per_prompt, seq_len, -1
)
negative_prompt_embeds_list.append(negative_prompt_embeds)
negative_prompt_embeds = negative_prompt_embeds_list[0]
bs_embed = pooled_prompt_embeds.shape[0]
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if do_classifier_free_guidance:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_enc_hidden_states = self.image_encoder(
torch.zeros_like(image), output_hidden_states=True
).hidden_states[-2]
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
num_images_per_prompt, dim=0
)
return image_enc_hidden_states, uncond_image_enc_hidden_states
else:
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
image_embeds = []
if do_classifier_free_guidance:
negative_image_embeds = []
if ip_adapter_image_embeds is None:
if not isinstance(ip_adapter_image, list):
ip_adapter_image = [ip_adapter_image]
if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
raise ValueError(
f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
)
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encode_image(
single_ip_adapter_image, device, 1, output_hidden_state
)
image_embeds.append(single_image_embeds[None, :])
if do_classifier_free_guidance:
negative_image_embeds.append(single_negative_image_embeds[None, :])
else:
for single_image_embeds in ip_adapter_image_embeds:
if do_classifier_free_guidance:
single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
negative_image_embeds.append(single_negative_image_embeds)
image_embeds.append(single_image_embeds)
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
single_image_embeds = single_image_embeds.to(device=device)
ip_adapter_image_embeds.append(single_image_embeds)
return ip_adapter_image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
num_inference_steps,
height,
width,
negative_prompt=None,
prompt_embeds=None,
pooled_prompt_embeds=None,
negative_prompt_embeds=None,
negative_pooled_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
callback_on_step_end_tensor_inputs=None,
max_sequence_length=None,
):
if not isinstance(num_inference_steps, int) or num_inference_steps <= 0:
raise ValueError(
f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
f" {type(num_inference_steps)}."
)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if prompt_embeds is not None and pooled_prompt_embeds is None:
raise ValueError(
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
)
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
)
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
raise ValueError(
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
)
if ip_adapter_image_embeds is not None:
if not isinstance(ip_adapter_image_embeds, list):
raise ValueError(
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
)
elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
raise ValueError(
f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
)
if max_sequence_length is not None and max_sequence_length > 256:
raise ValueError(f"`max_sequence_length` cannot be greater than 256 but is {max_sequence_length}")
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids
def _get_add_time_ids(
self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
):
add_time_ids = list(original_size + crops_coords_top_left + target_size)
passed_add_embed_dim = (
self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
)
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
if expected_add_embed_dim != passed_add_embed_dim:
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
)
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
return add_time_ids
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
def upcast_vae(self):
dtype = self.vae.dtype
self.vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
self.vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
XFormersAttnProcessor,
FusedAttnProcessor2_0,
),
)
# if xformers or torch_2_0 is used attention block does not need
# to be in float32 which can save lots of memory
if use_torch_2_0_or_xformers:
self.vae.post_quant_conv.to(dtype)
self.vae.decoder.conv_in.to(dtype)
self.vae.decoder.mid_block.to(dtype)
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
def get_guidance_scale_embedding(
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
) -> torch.Tensor:
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
w (`torch.Tensor`):
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
embedding_dim (`int`, *optional*, defaults to 512):
Dimension of the embeddings to generate.
dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
Data type of the generated embeddings.
Returns:
`torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1))
assert emb.shape == (w.shape[0], embedding_dim)
return emb
@property
def guidance_scale(self):
return self._guidance_scale
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def denoising_end(self):
return self._denoising_end
@property
def num_timesteps(self):
return self._num_timesteps
@property
def interrupt(self):
return self._interrupt
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
timesteps: List[int] = None,
sigmas: List[float] = None,
denoising_end: Optional[float] = None,
guidance_scale: float = 5.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
original_size: Optional[Tuple[int, int]] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
target_size: Optional[Tuple[int, int]] = None,
negative_original_size: Optional[Tuple[int, int]] = None,
negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
negative_target_size: Optional[Tuple[int, int]] = None,
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 256,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image. This is set to 1024 by default for the best results.
Anything below 512 pixels won't work well for
[Kwai-Kolors/Kolors-diffusers](https://huggingface.co/Kwai-Kolors/Kolors-diffusers) and checkpoints
that are not specifically fine-tuned on low resolutions.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image. This is set to 1024 by default for the best results.
Anything below 512 pixels won't work well for
[Kwai-Kolors/Kolors-diffusers](https://huggingface.co/Kwai-Kolors/Kolors-diffusers) and checkpoints
that are not specifically fine-tuned on low resolutions.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
passed will be used. Must be in descending order.
sigmas (`List[float]`, *optional*):
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
will be used.
denoising_end (`float`, *optional*):
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
completed before it is intentionally prematurely terminated. As a result, the returned sample will
still retain a substantial amount of noise as determined by the discrete timesteps selected by the
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
guidance_scale (`float`, *optional*, defaults to 5.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
provided, embeddings are computed from the `ip_adapter_image` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.kolors.KolorsPipelineOutput`] instead of a plain tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
`original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
For most cases, `target_size` should be set to the desired height and width of the generated image. If
not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
To negatively condition the generation process based on a specific image resolution. Part of SDXL's
micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
To negatively condition the generation process based on a target image resolution. It should be as same
as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
Examples:
Returns:
[`~pipelines.kolors.KolorsPipelineOutput`] or `tuple`: [`~pipelines.kolors.KolorsPipelineOutput`] if
`return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the
generated images.
"""
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
# 0. Default height and width to unet
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
original_size = original_size or (height, width)
target_size = target_size or (height, width)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
num_inference_steps,
height,
width,
negative_prompt,
prompt_embeds,
pooled_prompt_embeds,
negative_prompt_embeds,
negative_pooled_prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
callback_on_step_end_tensor_inputs,
max_sequence_length=max_sequence_length,
)
self._guidance_scale = guidance_scale
self._cross_attention_kwargs = cross_attention_kwargs
self._denoising_end = denoising_end
self._interrupt = False
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# 3. Encode input prompt
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler, num_inference_steps, device, timesteps, sigmas
)
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Prepare added time ids & embeddings
add_text_embeds = pooled_prompt_embeds
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
add_time_ids = self._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
if negative_original_size is not None and negative_target_size is not None:
negative_add_time_ids = self._get_add_time_ids(
negative_original_size,
negative_crops_coords_top_left,
negative_target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
else:
negative_add_time_ids = add_time_ids
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
prompt_embeds = prompt_embeds.to(device)
add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
image_embeds = self.prepare_ip_adapter_image_embeds(
ip_adapter_image,
ip_adapter_image_embeds,
device,
batch_size * num_images_per_prompt,
self.do_classifier_free_guidance,
)
# 8. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
# 8.1 Apply denoising_end
if (
self.denoising_end is not None
and isinstance(self.denoising_end, float)
and self.denoising_end > 0
and self.denoising_end < 1
):
discrete_timestep_cutoff = int(
round(
self.scheduler.config.num_train_timesteps
- (self.denoising_end * self.scheduler.config.num_train_timesteps)
)
)
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
timesteps = timesteps[:num_inference_steps]
# 9. Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
).to(device=device, dtype=latents.dtype)
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
added_cond_kwargs["image_embeds"] = image_embeds
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
latents = latents.to(latents_dtype)
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
negative_pooled_prompt_embeds = callback_outputs.pop(
"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
)
add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if XLA_AVAILABLE:
xm.mark_step()
if not output_type == "latent":
# make sure the VAE is in float32 mode, as it overflows in float16
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
elif latents.dtype != self.vae.dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
self.vae = self.vae.to(latents.dtype)
# unscale/denormalize the latents
latents = latents / self.vae.config.scaling_factor
image = self.vae.decode(latents, return_dict=False)[0]
# cast back to fp16 if needed
if needs_upcasting:
self.vae.to(dtype=torch.float16)
else:
image = latents
if not output_type == "latent":
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return KolorsPipelineOutput(images=image)
|
diffusers/src/diffusers/pipelines/kolors/pipeline_kolors.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/kolors/pipeline_kolors.py",
"repo_id": "diffusers",
"token_count": 24918
}
| 143
|
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...utils import BaseOutput
@dataclass
class LEditsPPDiffusionPipelineOutput(BaseOutput):
"""
Output class for LEdits++ Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
`None` if safety checking could not be performed.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
@dataclass
class LEditsPPInversionPipelineOutput(BaseOutput):
"""
Output class for LEdits++ Diffusion pipelines.
Args:
input_images (`List[PIL.Image.Image]` or `np.ndarray`)
List of the cropped and resized input images as PIL images of length `batch_size` or NumPy array of shape `
(batch_size, height, width, num_channels)`.
vae_reconstruction_images (`List[PIL.Image.Image]` or `np.ndarray`)
List of VAE reconstruction of all input images as PIL images of length `batch_size` or NumPy array of shape
` (batch_size, height, width, num_channels)`.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
vae_reconstruction_images: Union[List[PIL.Image.Image], np.ndarray]
|
diffusers/src/diffusers/pipelines/ledits_pp/pipeline_output.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/ledits_pp/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 613
}
| 144
|
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils import dummy_torch_and_transformers_objects # noqa F403
_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
else:
_import_structure["pipeline_pixart_alpha"] = ["PixArtAlphaPipeline"]
_import_structure["pipeline_pixart_sigma"] = ["PixArtSigmaPipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import *
else:
from .pipeline_pixart_alpha import (
ASPECT_RATIO_256_BIN,
ASPECT_RATIO_512_BIN,
ASPECT_RATIO_1024_BIN,
PixArtAlphaPipeline,
)
from .pipeline_pixart_sigma import ASPECT_RATIO_2048_BIN, PixArtSigmaPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
for name, value in _dummy_objects.items():
setattr(sys.modules[__name__], name, value)
|
diffusers/src/diffusers/pipelines/pixart_alpha/__init__.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/pixart_alpha/__init__.py",
"repo_id": "diffusers",
"token_count": 687
}
| 145
|
# 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.
from typing import Callable, Dict, List, Optional, Union
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from ...models import StableCascadeUNet
from ...schedulers import DDPMWuerstchenScheduler
from ...utils import is_torch_version, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline
from ..wuerstchen.modeling_paella_vq_model import PaellaVQModel
from .pipeline_stable_cascade import StableCascadeDecoderPipeline
from .pipeline_stable_cascade_prior import StableCascadePriorPipeline
TEXT2IMAGE_EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import StableCascadeCombinedPipeline
>>> pipe = StableCascadeCombinedPipeline.from_pretrained(
... "stabilityai/stable-cascade", variant="bf16", torch_dtype=torch.bfloat16
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "an image of a shiba inu, donning a spacesuit and helmet"
>>> images = pipe(prompt=prompt)
```
"""
class StableCascadeCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for text-to-image generation using Stable Cascade.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
tokenizer (`CLIPTokenizer`):
The decoder tokenizer to be used for text inputs.
text_encoder (`CLIPTextModel`):
The decoder text encoder to be used for text inputs.
decoder (`StableCascadeUNet`):
The decoder model to be used for decoder image generation pipeline.
scheduler (`DDPMWuerstchenScheduler`):
The scheduler to be used for decoder image generation pipeline.
vqgan (`PaellaVQModel`):
The VQGAN model to be used for decoder image generation pipeline.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `image_encoder`.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
prior_prior (`StableCascadeUNet`):
The prior model to be used for prior pipeline.
prior_scheduler (`DDPMWuerstchenScheduler`):
The scheduler to be used for prior pipeline.
"""
_load_connected_pipes = True
_optional_components = ["prior_feature_extractor", "prior_image_encoder"]
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModel,
decoder: StableCascadeUNet,
scheduler: DDPMWuerstchenScheduler,
vqgan: PaellaVQModel,
prior_prior: StableCascadeUNet,
prior_text_encoder: CLIPTextModel,
prior_tokenizer: CLIPTokenizer,
prior_scheduler: DDPMWuerstchenScheduler,
prior_feature_extractor: Optional[CLIPImageProcessor] = None,
prior_image_encoder: Optional[CLIPVisionModelWithProjection] = None,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
decoder=decoder,
scheduler=scheduler,
vqgan=vqgan,
prior_text_encoder=prior_text_encoder,
prior_tokenizer=prior_tokenizer,
prior_prior=prior_prior,
prior_scheduler=prior_scheduler,
prior_feature_extractor=prior_feature_extractor,
prior_image_encoder=prior_image_encoder,
)
self.prior_pipe = StableCascadePriorPipeline(
prior=prior_prior,
text_encoder=prior_text_encoder,
tokenizer=prior_tokenizer,
scheduler=prior_scheduler,
image_encoder=prior_image_encoder,
feature_extractor=prior_feature_extractor,
)
self.decoder_pipe = StableCascadeDecoderPipeline(
text_encoder=text_encoder,
tokenizer=tokenizer,
decoder=decoder,
scheduler=scheduler,
vqgan=vqgan,
)
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
self.prior_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device)
self.decoder_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device)
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗
Accelerate, significantly reducing memory usage. Models are moved to a `torch.device('meta')` and loaded on a
GPU only when their specific submodule's `forward` method is called. Offloading happens on a submodule basis.
Memory savings are higher than using `enable_model_cpu_offload`, but performance is lower.
"""
self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)
self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.set_progress_bar_config(**kwargs)
self.decoder_pipe.set_progress_bar_config(**kwargs)
@torch.no_grad()
@replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
images: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]] = None,
height: int = 512,
width: int = 512,
prior_num_inference_steps: int = 60,
prior_guidance_scale: float = 4.0,
num_inference_steps: int = 12,
decoder_guidance_scale: float = 0.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
prompt_embeds_pooled: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
prior_callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
prior_callback_on_step_end_tensor_inputs: List[str] = ["latents"],
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation for the prior and decoder.
images (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, *optional*):
The images to guide the image generation for the prior.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, text embeddings will be generated from `prompt` input argument.
prompt_embeds_pooled (`torch.Tensor`, *optional*):
Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.*
prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt`
input argument.
negative_prompt_embeds_pooled (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.*
prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt`
input argument.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`prior_guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting
`prior_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked
to the text `prompt`, usually at the expense of lower image quality.
prior_num_inference_steps (`Union[int, Dict[float, int]]`, *optional*, defaults to 60):
The number of prior denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. For more specific timestep spacing, you can pass customized
`prior_timesteps`
num_inference_steps (`int`, *optional*, defaults to 12):
The number of decoder denoising steps. More denoising steps usually lead to a higher quality image at
the expense of slower inference. For more specific timestep spacing, you can pass customized
`timesteps`
decoder_guidance_scale (`float`, *optional*, defaults to 0.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
prior_callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep:
int, callback_kwargs: Dict)`.
prior_callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `prior_callback_on_step_end` function. The tensors specified in the
list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in
the `._callback_tensor_inputs` attribute of your pipeline class.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,
otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.
"""
dtype = self.decoder_pipe.decoder.dtype
if is_torch_version("<", "2.2.0") and dtype == torch.bfloat16:
raise ValueError(
"`StableCascadeCombinedPipeline` requires torch>=2.2.0 when using `torch.bfloat16` dtype."
)
prior_outputs = self.prior_pipe(
prompt=prompt if prompt_embeds is None else None,
images=images,
height=height,
width=width,
num_inference_steps=prior_num_inference_steps,
guidance_scale=prior_guidance_scale,
negative_prompt=negative_prompt if negative_prompt_embeds is None else None,
prompt_embeds=prompt_embeds,
prompt_embeds_pooled=prompt_embeds_pooled,
negative_prompt_embeds=negative_prompt_embeds,
negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,
num_images_per_prompt=num_images_per_prompt,
generator=generator,
latents=latents,
output_type="pt",
return_dict=True,
callback_on_step_end=prior_callback_on_step_end,
callback_on_step_end_tensor_inputs=prior_callback_on_step_end_tensor_inputs,
)
image_embeddings = prior_outputs.image_embeddings
prompt_embeds = prior_outputs.get("prompt_embeds", None)
prompt_embeds_pooled = prior_outputs.get("prompt_embeds_pooled", None)
negative_prompt_embeds = prior_outputs.get("negative_prompt_embeds", None)
negative_prompt_embeds_pooled = prior_outputs.get("negative_prompt_embeds_pooled", None)
outputs = self.decoder_pipe(
image_embeddings=image_embeddings,
prompt=prompt if prompt_embeds is None else None,
num_inference_steps=num_inference_steps,
guidance_scale=decoder_guidance_scale,
negative_prompt=negative_prompt if negative_prompt_embeds is None else None,
prompt_embeds=prompt_embeds,
prompt_embeds_pooled=prompt_embeds_pooled,
negative_prompt_embeds=negative_prompt_embeds,
negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,
generator=generator,
output_type=output_type,
return_dict=return_dict,
callback_on_step_end=callback_on_step_end,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
)
return outputs
|
diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py",
"repo_id": "diffusers",
"token_count": 7377
}
| 146
|
# 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
from typing import Callable, List, Optional, Union
import PIL.Image
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import deprecate, logging
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class StableDiffusionImageVariationPipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline to generate image variations from an input image using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
# TODO: feature_extractor is required to encode images (if they are in PIL format),
# we should give a descriptive message if the pipeline doesn't have one.
_optional_components = ["safety_checker"]
model_cpu_offload_seq = "image_encoder->unet->vae"
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
image_encoder: CLIPVisionModelWithProjection,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
image_encoder=image_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeddings = self.image_encoder(image).image_embeds
image_embeddings = image_embeddings.unsqueeze(1)
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
negative_prompt_embeds = torch.zeros_like(image_embeddings)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
return image_embeddings
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, image, height, width, callback_steps):
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
f" {type(image)}"
)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation.
Args:
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):
Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
[`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
Examples:
```py
from diffusers import StableDiffusionImageVariationPipeline
from PIL import Image
from io import BytesIO
import requests
pipe = StableDiffusionImageVariationPipeline.from_pretrained(
"lambdalabs/sd-image-variations-diffusers", revision="v2.0"
)
pipe = pipe.to("cuda")
url = "https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
out = pipe(image, num_images_per_prompt=3, guidance_scale=15)
out["images"][0].save("result.jpg")
```
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(image, height, width, callback_steps)
# 2. Define call parameters
if isinstance(image, PIL.Image.Image):
batch_size = 1
elif isinstance(image, list):
batch_size = len(image)
else:
batch_size = image.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input image
image_embeddings = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
image_embeddings.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
self.maybe_free_model_hooks()
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
self.maybe_free_model_hooks()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py",
"repo_id": "diffusers",
"token_count": 9222
}
| 147
|
import inspect
import warnings
from typing import Callable, List, Optional, Union
import numpy as np
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from ...configuration_utils import FrozenDict
from ...image_processor import PipelineImageInput
from ...loaders import IPAdapterMixin
from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import deprecate, logging
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from . import StableDiffusionSafePipelineOutput
from .safety_checker import SafeStableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class StableDiffusionPipelineSafe(DiffusionPipeline, StableDiffusionMixin, IPAdapterMixin):
r"""
Pipeline based on the [`StableDiffusionPipeline`] for text-to-image generation using Safe Latent Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: SafeStableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
image_encoder: Optional[CLIPVisionModelWithProjection] = None,
requires_safety_checker: bool = True,
):
super().__init__()
safety_concept: Optional[str] = (
"an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity,"
" bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child"
" abuse, brutality, cruelty"
)
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
self._safety_text_concept = safety_concept
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.register_to_config(requires_safety_checker=requires_safety_checker)
@property
def safety_concept(self):
r"""
Getter method for the safety concept used with SLD
Returns:
`str`: The text describing the safety concept
"""
return self._safety_text_concept
@safety_concept.setter
def safety_concept(self, concept):
r"""
Setter method for the safety concept used with SLD
Args:
concept (`str`):
The text of the new safety concept
"""
self._safety_text_concept = concept
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
enable_safety_guidance,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
if not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# Encode the safety concept text
if enable_safety_guidance:
safety_concept_input = self.tokenizer(
[self._safety_text_concept],
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
safety_embeddings = self.text_encoder(safety_concept_input.input_ids.to(self.device))[0]
# duplicate safety embeddings for each generation per prompt, using mps friendly method
seq_len = safety_embeddings.shape[1]
safety_embeddings = safety_embeddings.repeat(batch_size, num_images_per_prompt, 1)
safety_embeddings = safety_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance + sld, we need to do three forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing three forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds, safety_embeddings])
else:
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def run_safety_checker(self, image, device, dtype, enable_safety_guidance):
if self.safety_checker is not None:
images = image.copy()
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
flagged_images = np.zeros((2, *image.shape[1:]))
if any(has_nsfw_concept):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned"
" instead."
f"{'You may look at this images in the `unsafe_images` variable of the output at your own discretion.' if enable_safety_guidance else 'Try again with a different prompt and/or seed.'}"
)
for idx, has_nsfw_concept in enumerate(has_nsfw_concept):
if has_nsfw_concept:
flagged_images[idx] = images[idx]
image[idx] = np.zeros(image[idx].shape) # black image
else:
has_nsfw_concept = None
flagged_images = None
return image, has_nsfw_concept, flagged_images
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def perform_safety_guidance(
self,
enable_safety_guidance,
safety_momentum,
noise_guidance,
noise_pred_out,
i,
sld_guidance_scale,
sld_warmup_steps,
sld_threshold,
sld_momentum_scale,
sld_mom_beta,
):
# Perform SLD guidance
if enable_safety_guidance:
if safety_momentum is None:
safety_momentum = torch.zeros_like(noise_guidance)
noise_pred_text, noise_pred_uncond = noise_pred_out[0], noise_pred_out[1]
noise_pred_safety_concept = noise_pred_out[2]
# Equation 6
scale = torch.clamp(torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0)
# Equation 6
safety_concept_scale = torch.where(
(noise_pred_text - noise_pred_safety_concept) >= sld_threshold, torch.zeros_like(scale), scale
)
# Equation 4
noise_guidance_safety = torch.mul((noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale)
# Equation 7
noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum
# Equation 8
safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety
if i >= sld_warmup_steps: # Warmup
# Equation 3
noise_guidance = noise_guidance - noise_guidance_safety
return noise_guidance, safety_momentum
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_enc_hidden_states = self.image_encoder(
torch.zeros_like(image), output_hidden_states=True
).hidden_states[-2]
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
num_images_per_prompt, dim=0
)
return image_enc_hidden_states, uncond_image_enc_hidden_states
else:
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
sld_guidance_scale: Optional[float] = 1000,
sld_warmup_steps: Optional[int] = 10,
sld_threshold: Optional[float] = 0.01,
sld_momentum_scale: Optional[float] = 0.3,
sld_mom_beta: Optional[float] = 0.4,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
ip_adapter_image: (`PipelineImageInput`, *optional*):
Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
sld_guidance_scale (`float`, *optional*, defaults to 1000):
If `sld_guidance_scale < 1`, safety guidance is disabled.
sld_warmup_steps (`int`, *optional*, defaults to 10):
Number of warmup steps for safety guidance. SLD is only be applied for diffusion steps greater than
`sld_warmup_steps`.
sld_threshold (`float`, *optional*, defaults to 0.01):
Threshold that separates the hyperplane between appropriate and inappropriate images.
sld_momentum_scale (`float`, *optional*, defaults to 0.3):
Scale of the SLD momentum to be added to the safety guidance at each diffusion step. If set to 0.0,
momentum is disabled. Momentum is built up during warmup for diffusion steps smaller than
`sld_warmup_steps`.
sld_mom_beta (`float`, *optional*, defaults to 0.4):
Defines how safety guidance momentum builds up. `sld_mom_beta` indicates how much of the previous
momentum is kept. Momentum is built up during warmup for diffusion steps smaller than
`sld_warmup_steps`.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
Examples:
```py
import torch
from diffusers import StableDiffusionPipelineSafe
from diffusers.pipelines.stable_diffusion_safe import SafetyConfig
pipeline = StableDiffusionPipelineSafe.from_pretrained(
"AIML-TUDA/stable-diffusion-safe", torch_dtype=torch.float16
).to("cuda")
prompt = "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c. leyendecker"
image = pipeline(prompt=prompt, **SafetyConfig.MEDIUM).images[0]
```
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
enable_safety_guidance = sld_guidance_scale > 1.0 and do_classifier_free_guidance
if not enable_safety_guidance:
warnings.warn("Safety checker disabled!")
if ip_adapter_image is not None:
output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
image_embeds, negative_image_embeds = self.encode_image(
ip_adapter_image, device, num_images_per_prompt, output_hidden_state
)
if do_classifier_free_guidance:
if enable_safety_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds, image_embeds])
else:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, enable_safety_guidance
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 6.1 Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
safety_momentum = None
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = (
torch.cat([latents] * (3 if enable_safety_guidance else 2))
if do_classifier_free_guidance
else latents
)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input, t, encoder_hidden_states=prompt_embeds, added_cond_kwargs=added_cond_kwargs
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_out = noise_pred.chunk((3 if enable_safety_guidance else 2))
noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
# default classifier free guidance
noise_guidance = noise_pred_text - noise_pred_uncond
# Perform SLD guidance
if enable_safety_guidance:
if safety_momentum is None:
safety_momentum = torch.zeros_like(noise_guidance)
noise_pred_safety_concept = noise_pred_out[2]
# Equation 6
scale = torch.clamp(
torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0
)
# Equation 6
safety_concept_scale = torch.where(
(noise_pred_text - noise_pred_safety_concept) >= sld_threshold,
torch.zeros_like(scale),
scale,
)
# Equation 4
noise_guidance_safety = torch.mul(
(noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale
)
# Equation 7
noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum
# Equation 8
safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety
if i >= sld_warmup_steps: # Warmup
# Equation 3
noise_guidance = noise_guidance - noise_guidance_safety
noise_pred = noise_pred_uncond + guidance_scale * noise_guidance
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
# 8. Post-processing
image = self.decode_latents(latents)
# 9. Run safety checker
image, has_nsfw_concept, flagged_images = self.run_safety_checker(
image, device, prompt_embeds.dtype, enable_safety_guidance
)
# 10. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if flagged_images is not None:
flagged_images = self.numpy_to_pil(flagged_images)
if not return_dict:
return (
image,
has_nsfw_concept,
self._safety_text_concept if enable_safety_guidance else None,
flagged_images,
)
return StableDiffusionSafePipelineOutput(
images=image,
nsfw_content_detected=has_nsfw_concept,
applied_safety_concept=self._safety_text_concept if enable_safety_guidance else None,
unsafe_images=flagged_images,
)
|
diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py",
"repo_id": "diffusers",
"token_count": 17431
}
| 148
|
# Copyright (c) 2022 Dominic Rampas MIT License
# 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.
from typing import Union
import torch
import torch.nn as nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.autoencoders.vae import DecoderOutput, VectorQuantizer
from ...models.modeling_utils import ModelMixin
from ...models.vq_model import VQEncoderOutput
from ...utils.accelerate_utils import apply_forward_hook
class MixingResidualBlock(nn.Module):
"""
Residual block with mixing used by Paella's VQ-VAE.
"""
def __init__(self, inp_channels, embed_dim):
super().__init__()
# depthwise
self.norm1 = nn.LayerNorm(inp_channels, elementwise_affine=False, eps=1e-6)
self.depthwise = nn.Sequential(
nn.ReplicationPad2d(1), nn.Conv2d(inp_channels, inp_channels, kernel_size=3, groups=inp_channels)
)
# channelwise
self.norm2 = nn.LayerNorm(inp_channels, elementwise_affine=False, eps=1e-6)
self.channelwise = nn.Sequential(
nn.Linear(inp_channels, embed_dim), nn.GELU(), nn.Linear(embed_dim, inp_channels)
)
self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True)
def forward(self, x):
mods = self.gammas
x_temp = self.norm1(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * (1 + mods[0]) + mods[1]
x = x + self.depthwise(x_temp) * mods[2]
x_temp = self.norm2(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * (1 + mods[3]) + mods[4]
x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5]
return x
class PaellaVQModel(ModelMixin, ConfigMixin):
r"""VQ-VAE model from Paella model.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the model (such as downloading or saving, etc.)
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
up_down_scale_factor (int, *optional*, defaults to 2): Up and Downscale factor of the input image.
levels (int, *optional*, defaults to 2): Number of levels in the model.
bottleneck_blocks (int, *optional*, defaults to 12): Number of bottleneck blocks in the model.
embed_dim (int, *optional*, defaults to 384): Number of hidden channels in the model.
latent_channels (int, *optional*, defaults to 4): Number of latent channels in the VQ-VAE model.
num_vq_embeddings (int, *optional*, defaults to 8192): Number of codebook vectors in the VQ-VAE.
scale_factor (float, *optional*, defaults to 0.3764): Scaling factor of the latent space.
"""
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
up_down_scale_factor: int = 2,
levels: int = 2,
bottleneck_blocks: int = 12,
embed_dim: int = 384,
latent_channels: int = 4,
num_vq_embeddings: int = 8192,
scale_factor: float = 0.3764,
):
super().__init__()
c_levels = [embed_dim // (2**i) for i in reversed(range(levels))]
# Encoder blocks
self.in_block = nn.Sequential(
nn.PixelUnshuffle(up_down_scale_factor),
nn.Conv2d(in_channels * up_down_scale_factor**2, c_levels[0], kernel_size=1),
)
down_blocks = []
for i in range(levels):
if i > 0:
down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1))
block = MixingResidualBlock(c_levels[i], c_levels[i] * 4)
down_blocks.append(block)
down_blocks.append(
nn.Sequential(
nn.Conv2d(c_levels[-1], latent_channels, kernel_size=1, bias=False),
nn.BatchNorm2d(latent_channels), # then normalize them to have mean 0 and std 1
)
)
self.down_blocks = nn.Sequential(*down_blocks)
# Vector Quantizer
self.vquantizer = VectorQuantizer(num_vq_embeddings, vq_embed_dim=latent_channels, legacy=False, beta=0.25)
# Decoder blocks
up_blocks = [nn.Sequential(nn.Conv2d(latent_channels, c_levels[-1], kernel_size=1))]
for i in range(levels):
for j in range(bottleneck_blocks if i == 0 else 1):
block = MixingResidualBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4)
up_blocks.append(block)
if i < levels - 1:
up_blocks.append(
nn.ConvTranspose2d(
c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, padding=1
)
)
self.up_blocks = nn.Sequential(*up_blocks)
self.out_block = nn.Sequential(
nn.Conv2d(c_levels[0], out_channels * up_down_scale_factor**2, kernel_size=1),
nn.PixelShuffle(up_down_scale_factor),
)
@apply_forward_hook
def encode(self, x: torch.Tensor, return_dict: bool = True) -> VQEncoderOutput:
h = self.in_block(x)
h = self.down_blocks(h)
if not return_dict:
return (h,)
return VQEncoderOutput(latents=h)
@apply_forward_hook
def decode(
self, h: torch.Tensor, force_not_quantize: bool = True, return_dict: bool = True
) -> Union[DecoderOutput, torch.Tensor]:
if not force_not_quantize:
quant, _, _ = self.vquantizer(h)
else:
quant = h
x = self.up_blocks(quant)
dec = self.out_block(x)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def forward(self, sample: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
r"""
Args:
sample (`torch.Tensor`): Input sample.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
"""
x = sample
h = self.encode(x).latents
dec = self.decode(h).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
|
diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py/0
|
{
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py",
"repo_id": "diffusers",
"token_count": 3040
}
| 149
|
# Copyright 2024 TSAIL Team 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.
# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver and https://github.com/NVlabs/edm
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_dpmsolver_sde import BrownianTreeNoiseSampler
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class CosineDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
"""
Implements a variant of `DPMSolverMultistepScheduler` with cosine schedule, proposed by Nichol and Dhariwal (2021).
This scheduler was used in Stable Audio Open [1].
[1] Evans, Parker, et al. "Stable Audio Open" https://arxiv.org/abs/2407.14358
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
sigma_min (`float`, *optional*, defaults to 0.3):
Minimum noise magnitude in the sigma schedule. This was set to 0.3 in Stable Audio Open [1].
sigma_max (`float`, *optional*, defaults to 500):
Maximum noise magnitude in the sigma schedule. This was set to 500 in Stable Audio Open [1].
sigma_data (`float`, *optional*, defaults to 1.0):
The standard deviation of the data distribution. This is set to 1.0 in Stable Audio Open [1].
sigma_schedule (`str`, *optional*, defaults to `exponential`):
Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper
(https://arxiv.org/abs/2206.00364). Other acceptable value is "exponential". The exponential schedule was
incorporated in this model: https://huggingface.co/stabilityai/cosxl.
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
solver_order (`int`, defaults to 2):
The DPMSolver order which can be `1` or `2`. It is recommended to use `solver_order=2`.
prediction_type (`str`, defaults to `v_prediction`, *optional*):
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
Video](https://imagen.research.google/video/paper.pdf) paper).
solver_type (`str`, defaults to `midpoint`):
Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the
sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.
lower_order_final (`bool`, defaults to `True`):
Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
euler_at_final (`bool`, defaults to `False`):
Whether to use Euler's method in the final step. It is a trade-off between numerical stability and detail
richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference
steps, but sometimes may result in blurring.
final_sigmas_type (`str`, defaults to `"zero"`):
The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final
sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.
"""
_compatibles = []
order = 1
@register_to_config
def __init__(
self,
sigma_min: float = 0.3,
sigma_max: float = 500,
sigma_data: float = 1.0,
sigma_schedule: str = "exponential",
num_train_timesteps: int = 1000,
solver_order: int = 2,
prediction_type: str = "v_prediction",
rho: float = 7.0,
solver_type: str = "midpoint",
lower_order_final: bool = True,
euler_at_final: bool = False,
final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min"
):
if solver_type not in ["midpoint", "heun"]:
if solver_type in ["logrho", "bh1", "bh2"]:
self.register_to_config(solver_type="midpoint")
else:
raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
ramp = torch.linspace(0, 1, num_train_timesteps)
if sigma_schedule == "karras":
sigmas = self._compute_karras_sigmas(ramp)
elif sigma_schedule == "exponential":
sigmas = self._compute_exponential_sigmas(ramp)
self.timesteps = self.precondition_noise(sigmas)
self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
# setable values
self.num_inference_steps = None
self.model_outputs = [None] * solver_order
self.lower_order_nums = 0
self._step_index = None
self._begin_index = None
self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
@property
def init_noise_sigma(self):
# standard deviation of the initial noise distribution
return (self.config.sigma_max**2 + 1) ** 0.5
@property
def step_index(self):
"""
The index counter for current timestep. It will increase 1 after each scheduler step.
"""
return self._step_index
@property
def begin_index(self):
"""
The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
"""
return self._begin_index
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
def set_begin_index(self, begin_index: int = 0):
"""
Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
Args:
begin_index (`int`):
The begin index for the scheduler.
"""
self._begin_index = begin_index
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.precondition_inputs
def precondition_inputs(self, sample, sigma):
c_in = 1 / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
scaled_sample = sample * c_in
return scaled_sample
def precondition_noise(self, sigma):
if not isinstance(sigma, torch.Tensor):
sigma = torch.tensor([sigma])
return sigma.atan() / math.pi * 2
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.precondition_outputs
def precondition_outputs(self, sample, model_output, sigma):
sigma_data = self.config.sigma_data
c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)
if self.config.prediction_type == "epsilon":
c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
elif self.config.prediction_type == "v_prediction":
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
else:
raise ValueError(f"Prediction type {self.config.prediction_type} is not supported.")
denoised = c_skip * sample + c_out * model_output
return denoised
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.scale_model_input
def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.
Args:
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
if self.step_index is None:
self._init_step_index(timestep)
sigma = self.sigmas[self.step_index]
sample = self.precondition_inputs(sample, sigma)
self.is_scale_input_called = True
return sample
def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None):
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
"""
self.num_inference_steps = num_inference_steps
ramp = torch.linspace(0, 1, self.num_inference_steps)
if self.config.sigma_schedule == "karras":
sigmas = self._compute_karras_sigmas(ramp)
elif self.config.sigma_schedule == "exponential":
sigmas = self._compute_exponential_sigmas(ramp)
sigmas = sigmas.to(dtype=torch.float32, device=device)
self.timesteps = self.precondition_noise(sigmas)
if self.config.final_sigmas_type == "sigma_min":
sigma_last = self.config.sigma_min
elif self.config.final_sigmas_type == "zero":
sigma_last = 0
else:
raise ValueError(
f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
)
self.sigmas = torch.cat([sigmas, torch.tensor([sigma_last], dtype=torch.float32, device=device)])
self.model_outputs = [
None,
] * self.config.solver_order
self.lower_order_nums = 0
# add an index counter for schedulers that allow duplicated timesteps
self._step_index = None
self._begin_index = None
self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
# if a noise sampler is used, reinitialise it
self.noise_sampler = None
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._compute_karras_sigmas
def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:
"""Constructs the noise schedule of Karras et al. (2022)."""
sigma_min = sigma_min or self.config.sigma_min
sigma_max = sigma_max or self.config.sigma_max
rho = self.config.rho
min_inv_rho = sigma_min ** (1 / rho)
max_inv_rho = sigma_max ** (1 / rho)
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._compute_exponential_sigmas
def _compute_exponential_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:
"""Implementation closely follows k-diffusion.
https://github.com/crowsonkb/k-diffusion/blob/6ab5146d4a5ef63901326489f31f1d8e7dd36b48/k_diffusion/sampling.py#L26
"""
sigma_min = sigma_min or self.config.sigma_min
sigma_max = sigma_max or self.config.sigma_max
sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), len(ramp)).exp().flip(0)
return sigmas
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
def _sigma_to_t(self, sigma, log_sigmas):
# get log sigma
log_sigma = np.log(np.maximum(sigma, 1e-10))
# get distribution
dists = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
high_idx = low_idx + 1
low = log_sigmas[low_idx]
high = log_sigmas[high_idx]
# interpolate sigmas
w = (low - log_sigma) / (low - high)
w = np.clip(w, 0, 1)
# transform interpolation to time range
t = (1 - w) * low_idx + w * high_idx
t = t.reshape(sigma.shape)
return t
def _sigma_to_alpha_sigma_t(self, sigma):
alpha_t = torch.tensor(1) # Inputs are pre-scaled before going into unet, so alpha_t = 1
sigma_t = sigma
return alpha_t, sigma_t
def convert_model_output(
self,
model_output: torch.Tensor,
sample: torch.Tensor = None,
) -> torch.Tensor:
"""
Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is
designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an
integral of the data prediction model.
<Tip>
The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise
prediction and data prediction models.
</Tip>
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
Returns:
`torch.Tensor`:
The converted model output.
"""
sigma = self.sigmas[self.step_index]
x0_pred = self.precondition_outputs(sample, model_output, sigma)
return x0_pred
def dpm_solver_first_order_update(
self,
model_output: torch.Tensor,
sample: torch.Tensor = None,
noise: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
One step for the first-order DPMSolver (equivalent to DDIM).
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
Returns:
`torch.Tensor`:
The sample tensor at the previous timestep.
"""
sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s)
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
lambda_s = torch.log(alpha_s) - torch.log(sigma_s)
h = lambda_t - lambda_s
assert noise is not None
x_t = (
(sigma_t / sigma_s * torch.exp(-h)) * sample
+ (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output
+ sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
)
return x_t
def multistep_dpm_solver_second_order_update(
self,
model_output_list: List[torch.Tensor],
sample: torch.Tensor = None,
noise: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
One step for the second-order multistep DPMSolver.
Args:
model_output_list (`List[torch.Tensor]`):
The direct outputs from learned diffusion model at current and latter timesteps.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
Returns:
`torch.Tensor`:
The sample tensor at the previous timestep.
"""
sigma_t, sigma_s0, sigma_s1 = (
self.sigmas[self.step_index + 1],
self.sigmas[self.step_index],
self.sigmas[self.step_index - 1],
)
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)
m0, m1 = model_output_list[-1], model_output_list[-2]
h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1
r0 = h_0 / h
D0, D1 = m0, (1.0 / r0) * (m0 - m1)
# sde-dpmsolver++
assert noise is not None
if self.config.solver_type == "midpoint":
x_t = (
(sigma_t / sigma_s0 * torch.exp(-h)) * sample
+ (alpha_t * (1 - torch.exp(-2.0 * h))) * D0
+ 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1
+ sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
)
elif self.config.solver_type == "heun":
x_t = (
(sigma_t / sigma_s0 * torch.exp(-h)) * sample
+ (alpha_t * (1 - torch.exp(-2.0 * h))) * D0
+ (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1
+ sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
)
return x_t
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
def index_for_timestep(self, timestep, schedule_timesteps=None):
if schedule_timesteps is None:
schedule_timesteps = self.timesteps
index_candidates = (schedule_timesteps == timestep).nonzero()
if len(index_candidates) == 0:
step_index = len(self.timesteps) - 1
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
elif len(index_candidates) > 1:
step_index = index_candidates[1].item()
else:
step_index = index_candidates[0].item()
return step_index
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index
def _init_step_index(self, timestep):
"""
Initialize the step_index counter for the scheduler.
"""
if self.begin_index is None:
if isinstance(timestep, torch.Tensor):
timestep = timestep.to(self.timesteps.device)
self._step_index = self.index_for_timestep(timestep)
else:
self._step_index = self._begin_index
def step(
self,
model_output: torch.Tensor,
timestep: Union[int, torch.Tensor],
sample: torch.Tensor,
generator=None,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
the multistep DPMSolver.
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`int`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
generator (`torch.Generator`, *optional*):
A random number generator.
return_dict (`bool`):
Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
if self.step_index is None:
self._init_step_index(timestep)
# Improve numerical stability for small number of steps
lower_order_final = (self.step_index == len(self.timesteps) - 1) and (
self.config.euler_at_final
or (self.config.lower_order_final and len(self.timesteps) < 15)
or self.config.final_sigmas_type == "zero"
)
lower_order_second = (
(self.step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15
)
model_output = self.convert_model_output(model_output, sample=sample)
for i in range(self.config.solver_order - 1):
self.model_outputs[i] = self.model_outputs[i + 1]
self.model_outputs[-1] = model_output
if self.noise_sampler is None:
seed = None
if generator is not None:
seed = (
[g.initial_seed() for g in generator] if isinstance(generator, list) else generator.initial_seed()
)
self.noise_sampler = BrownianTreeNoiseSampler(
model_output, sigma_min=self.config.sigma_min, sigma_max=self.config.sigma_max, seed=seed
)
noise = self.noise_sampler(self.sigmas[self.step_index], self.sigmas[self.step_index + 1]).to(
model_output.device
)
if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final:
prev_sample = self.dpm_solver_first_order_update(model_output, sample=sample, noise=noise)
elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:
prev_sample = self.multistep_dpm_solver_second_order_update(self.model_outputs, sample=sample, noise=noise)
if self.lower_order_nums < self.config.solver_order:
self.lower_order_nums += 1
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.Tensor,
) -> torch.Tensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
# mps does not support float64
schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
else:
schedule_timesteps = self.timesteps.to(original_samples.device)
timesteps = timesteps.to(original_samples.device)
# self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
if self.begin_index is None:
step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]
elif self.step_index is not None:
# add_noise is called after first denoising step (for inpainting)
step_indices = [self.step_index] * timesteps.shape[0]
else:
# add noise is called before first denoising step to create initial latent(img2img)
step_indices = [self.begin_index] * timesteps.shape[0]
sigma = sigmas[step_indices].flatten()
while len(sigma.shape) < len(original_samples.shape):
sigma = sigma.unsqueeze(-1)
noisy_samples = original_samples + noise * sigma
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps
|
diffusers/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py/0
|
{
"file_path": "diffusers/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py",
"repo_id": "diffusers",
"token_count": 10861
}
| 150
|
# Copyright 2024 Zhejiang University Team 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.
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
def betas_for_alpha_bar(
num_diffusion_timesteps,
max_beta=0.999,
alpha_transform_type="cosine",
):
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
(1-beta) over time from t = [0,1].
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
to that part of the diffusion process.
Args:
num_diffusion_timesteps (`int`): the number of betas to produce.
max_beta (`float`): the maximum beta to use; use values lower than 1 to
prevent singularities.
alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
Choose from `cosine` or `exp`
Returns:
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(t):
return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(t):
return math.exp(t * -12.0)
else:
raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}")
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
return torch.tensor(betas, dtype=torch.float32)
class PNDMScheduler(SchedulerMixin, ConfigMixin):
"""
`PNDMScheduler` uses pseudo numerical methods for diffusion models such as the Runge-Kutta and linear multi-step
method.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
beta_start (`float`, defaults to 0.0001):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
The final `beta` value.
beta_schedule (`str`, defaults to `"linear"`):
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
trained_betas (`np.ndarray`, *optional*):
Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
skip_prk_steps (`bool`, defaults to `False`):
Allows the scheduler to skip the Runge-Kutta steps defined in the original paper as being required before
PLMS steps.
set_alpha_to_one (`bool`, defaults to `False`):
Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
otherwise it uses the alpha value at step 0.
prediction_type (`str`, defaults to `epsilon`, *optional*):
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process)
or `v_prediction` (see section 2.4 of [Imagen Video](https://imagen.research.google/video/paper.pdf)
paper).
timestep_spacing (`str`, defaults to `"leading"`):
The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
steps_offset (`int`, defaults to 0):
An offset added to the inference steps, as required by some model families.
"""
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
skip_prk_steps: bool = False,
set_alpha_to_one: bool = False,
prediction_type: str = "epsilon",
timestep_spacing: str = "leading",
steps_offset: int = 0,
):
if trained_betas is not None:
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
elif beta_schedule == "linear":
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
self.pndm_order = 4
# running values
self.cur_model_output = 0
self.counter = 0
self.cur_sample = None
self.ets = []
# setable values
self.num_inference_steps = None
self._timesteps = np.arange(0, num_train_timesteps)[::-1].copy()
self.prk_timesteps = None
self.plms_timesteps = None
self.timesteps = None
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
"""
self.num_inference_steps = num_inference_steps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
self._timesteps = (
np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps).round().astype(np.int64)
)
elif self.config.timestep_spacing == "leading":
step_ratio = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
self._timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()
self._timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
step_ratio = self.config.num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
self._timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio))[::-1].astype(
np.int64
)
self._timesteps -= 1
else:
raise ValueError(
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
)
if self.config.skip_prk_steps:
# for some models like stable diffusion the prk steps can/should be skipped to
# produce better results. When using PNDM with `self.config.skip_prk_steps` the implementation
# is based on crowsonkb's PLMS sampler implementation: https://github.com/CompVis/latent-diffusion/pull/51
self.prk_timesteps = np.array([])
self.plms_timesteps = np.concatenate([self._timesteps[:-1], self._timesteps[-2:-1], self._timesteps[-1:]])[
::-1
].copy()
else:
prk_timesteps = np.array(self._timesteps[-self.pndm_order :]).repeat(2) + np.tile(
np.array([0, self.config.num_train_timesteps // num_inference_steps // 2]), self.pndm_order
)
self.prk_timesteps = (prk_timesteps[:-1].repeat(2)[1:-1])[::-1].copy()
self.plms_timesteps = self._timesteps[:-3][
::-1
].copy() # we copy to avoid having negative strides which are not supported by torch.from_numpy
timesteps = np.concatenate([self.prk_timesteps, self.plms_timesteps]).astype(np.int64)
self.timesteps = torch.from_numpy(timesteps).to(device)
self.ets = []
self.counter = 0
self.cur_model_output = 0
def step(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise), and calls [`~PNDMScheduler.step_prk`]
or [`~PNDMScheduler.step_plms`] depending on the internal variable `counter`.
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`int`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
return_dict (`bool`):
Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.counter < len(self.prk_timesteps) and not self.config.skip_prk_steps:
return self.step_prk(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)
else:
return self.step_plms(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)
def step_prk(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
the Runge-Kutta method. It performs four forward passes to approximate the solution to the differential
equation.
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`int`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
return_dict (`bool`):
Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
diff_to_prev = 0 if self.counter % 2 else self.config.num_train_timesteps // self.num_inference_steps // 2
prev_timestep = timestep - diff_to_prev
timestep = self.prk_timesteps[self.counter // 4 * 4]
if self.counter % 4 == 0:
self.cur_model_output += 1 / 6 * model_output
self.ets.append(model_output)
self.cur_sample = sample
elif (self.counter - 1) % 4 == 0:
self.cur_model_output += 1 / 3 * model_output
elif (self.counter - 2) % 4 == 0:
self.cur_model_output += 1 / 3 * model_output
elif (self.counter - 3) % 4 == 0:
model_output = self.cur_model_output + 1 / 6 * model_output
self.cur_model_output = 0
# cur_sample should not be `None`
cur_sample = self.cur_sample if self.cur_sample is not None else sample
prev_sample = self._get_prev_sample(cur_sample, timestep, prev_timestep, model_output)
self.counter += 1
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
def step_plms(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
the linear multistep method. It performs one forward pass multiple times to approximate the solution.
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`int`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
return_dict (`bool`):
Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
if not self.config.skip_prk_steps and len(self.ets) < 3:
raise ValueError(
f"{self.__class__} can only be run AFTER scheduler has been run "
"in 'prk' mode for at least 12 iterations "
"See: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_pndm.py "
"for more information."
)
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
if self.counter != 1:
self.ets = self.ets[-3:]
self.ets.append(model_output)
else:
prev_timestep = timestep
timestep = timestep + self.config.num_train_timesteps // self.num_inference_steps
if len(self.ets) == 1 and self.counter == 0:
model_output = model_output
self.cur_sample = sample
elif len(self.ets) == 1 and self.counter == 1:
model_output = (model_output + self.ets[-1]) / 2
sample = self.cur_sample
self.cur_sample = None
elif len(self.ets) == 2:
model_output = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets) == 3:
model_output = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
model_output = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
prev_sample = self._get_prev_sample(sample, timestep, prev_timestep, model_output)
self.counter += 1
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.Tensor`):
The input sample.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample
def _get_prev_sample(self, sample, timestep, prev_timestep, model_output):
# See formula (9) of PNDM paper https://arxiv.org/pdf/2202.09778.pdf
# this function computes x_(t−δ) using the formula of (9)
# Note that x_t needs to be added to both sides of the equation
# Notation (<variable name> -> <name in paper>
# alpha_prod_t -> α_t
# alpha_prod_t_prev -> α_(t−δ)
# beta_prod_t -> (1 - α_t)
# beta_prod_t_prev -> (1 - α_(t−δ))
# sample -> x_t
# model_output -> e_θ(x_t, t)
# prev_sample -> x_(t−δ)
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
if self.config.prediction_type == "v_prediction":
model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
elif self.config.prediction_type != "epsilon":
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `v_prediction`"
)
# corresponds to (α_(t−δ) - α_t) divided by
# denominator of x_t in formula (9) and plus 1
# Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) =
# sqrt(α_(t−δ)) / sqrt(α_t))
sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)
# corresponds to denominator of e_θ(x_t, t) in formula (9)
model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + (
alpha_prod_t * beta_prod_t * alpha_prod_t_prev
) ** (0.5)
# full formula (9)
prev_sample = (
sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff
)
return prev_sample
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
# Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
# for the subsequent add_noise calls
self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)
alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)
timesteps = timesteps.to(original_samples.device)
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps
|
diffusers/src/diffusers/schedulers/scheduling_pndm.py/0
|
{
"file_path": "diffusers/src/diffusers/schedulers/scheduling_pndm.py",
"repo_id": "diffusers",
"token_count": 9437
}
| 151
|
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def deprecate(*args, take_from: Optional[Union[Dict, Any]] = None, standard_warn=True, stacklevel=2):
from .. import __version__
deprecated_kwargs = take_from
values = ()
if not isinstance(args[0], tuple):
args = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__version__).base_version) >= version.parse(version_name):
raise ValueError(
f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
f" version {__version__} is >= {version_name}"
)
warning = None
if isinstance(deprecated_kwargs, dict) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(attribute),)
warning = f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
elif hasattr(deprecated_kwargs, attribute):
values += (getattr(deprecated_kwargs, attribute),)
warning = f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
elif deprecated_kwargs is None:
warning = f"`{attribute}` is deprecated and will be removed in version {version_name}."
if warning is not None:
warning = warning + " " if standard_warn else ""
warnings.warn(warning + message, FutureWarning, stacklevel=stacklevel)
if isinstance(deprecated_kwargs, dict) and len(deprecated_kwargs) > 0:
call_frame = inspect.getouterframes(inspect.currentframe())[1]
filename = call_frame.filename
line_number = call_frame.lineno
function = call_frame.function
key, value = next(iter(deprecated_kwargs.items()))
raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`")
if len(values) == 0:
return
elif len(values) == 1:
return values[0]
return values
|
diffusers/src/diffusers/utils/deprecation_utils.py/0
|
{
"file_path": "diffusers/src/diffusers/utils/deprecation_utils.py",
"repo_id": "diffusers",
"token_count": 793
}
| 152
|
import io
import random
import struct
import tempfile
from contextlib import contextmanager
from typing import List, Union
import numpy as np
import PIL.Image
import PIL.ImageOps
from .import_utils import BACKENDS_MAPPING, is_imageio_available, is_opencv_available
from .logging import get_logger
global_rng = random.Random()
logger = get_logger(__name__)
@contextmanager
def buffered_writer(raw_f):
f = io.BufferedWriter(raw_f)
yield f
f.flush()
def export_to_gif(image: List[PIL.Image.Image], output_gif_path: str = None, fps: int = 10) -> str:
if output_gif_path is None:
output_gif_path = tempfile.NamedTemporaryFile(suffix=".gif").name
image[0].save(
output_gif_path,
save_all=True,
append_images=image[1:],
optimize=False,
duration=1000 // fps,
loop=0,
)
return output_gif_path
def export_to_ply(mesh, output_ply_path: str = None):
"""
Write a PLY file for a mesh.
"""
if output_ply_path is None:
output_ply_path = tempfile.NamedTemporaryFile(suffix=".ply").name
coords = mesh.verts.detach().cpu().numpy()
faces = mesh.faces.cpu().numpy()
rgb = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in "RGB"], axis=1)
with buffered_writer(open(output_ply_path, "wb")) as f:
f.write(b"ply\n")
f.write(b"format binary_little_endian 1.0\n")
f.write(bytes(f"element vertex {len(coords)}\n", "ascii"))
f.write(b"property float x\n")
f.write(b"property float y\n")
f.write(b"property float z\n")
if rgb is not None:
f.write(b"property uchar red\n")
f.write(b"property uchar green\n")
f.write(b"property uchar blue\n")
if faces is not None:
f.write(bytes(f"element face {len(faces)}\n", "ascii"))
f.write(b"property list uchar int vertex_index\n")
f.write(b"end_header\n")
if rgb is not None:
rgb = (rgb * 255.499).round().astype(int)
vertices = [
(*coord, *rgb)
for coord, rgb in zip(
coords.tolist(),
rgb.tolist(),
)
]
format = struct.Struct("<3f3B")
for item in vertices:
f.write(format.pack(*item))
else:
format = struct.Struct("<3f")
for vertex in coords.tolist():
f.write(format.pack(*vertex))
if faces is not None:
format = struct.Struct("<B3I")
for tri in faces.tolist():
f.write(format.pack(len(tri), *tri))
return output_ply_path
def export_to_obj(mesh, output_obj_path: str = None):
if output_obj_path is None:
output_obj_path = tempfile.NamedTemporaryFile(suffix=".obj").name
verts = mesh.verts.detach().cpu().numpy()
faces = mesh.faces.cpu().numpy()
vertex_colors = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in "RGB"], axis=1)
vertices = [
"{} {} {} {} {} {}".format(*coord, *color) for coord, color in zip(verts.tolist(), vertex_colors.tolist())
]
faces = ["f {} {} {}".format(str(tri[0] + 1), str(tri[1] + 1), str(tri[2] + 1)) for tri in faces.tolist()]
combined_data = ["v " + vertex for vertex in vertices] + faces
with open(output_obj_path, "w") as f:
f.writelines("\n".join(combined_data))
def _legacy_export_to_video(
video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 10
):
if is_opencv_available():
import cv2
else:
raise ImportError(BACKENDS_MAPPING["opencv"][1].format("export_to_video"))
if output_video_path is None:
output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name
if isinstance(video_frames[0], np.ndarray):
video_frames = [(frame * 255).astype(np.uint8) for frame in video_frames]
elif isinstance(video_frames[0], PIL.Image.Image):
video_frames = [np.array(frame) for frame in video_frames]
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
h, w, c = video_frames[0].shape
video_writer = cv2.VideoWriter(output_video_path, fourcc, fps=fps, frameSize=(w, h))
for i in range(len(video_frames)):
img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)
video_writer.write(img)
return output_video_path
def export_to_video(
video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 10
) -> str:
# TODO: Dhruv. Remove by Diffusers release 0.33.0
# Added to prevent breaking existing code
if not is_imageio_available():
logger.warning(
(
"It is recommended to use `export_to_video` with `imageio` and `imageio-ffmpeg` as a backend. \n"
"These libraries are not present in your environment. Attempting to use legacy OpenCV backend to export video. \n"
"Support for the OpenCV backend will be deprecated in a future Diffusers version"
)
)
return _legacy_export_to_video(video_frames, output_video_path, fps)
if is_imageio_available():
import imageio
else:
raise ImportError(BACKENDS_MAPPING["imageio"][1].format("export_to_video"))
try:
imageio.plugins.ffmpeg.get_exe()
except AttributeError:
raise AttributeError(
(
"Found an existing imageio backend in your environment. Attempting to export video with imageio. \n"
"Unable to find a compatible ffmpeg installation in your environment to use with imageio. Please install via `pip install imageio-ffmpeg"
)
)
if output_video_path is None:
output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name
if isinstance(video_frames[0], np.ndarray):
video_frames = [(frame * 255).astype(np.uint8) for frame in video_frames]
elif isinstance(video_frames[0], PIL.Image.Image):
video_frames = [np.array(frame) for frame in video_frames]
with imageio.get_writer(output_video_path, fps=fps) as writer:
for frame in video_frames:
writer.append_data(frame)
return output_video_path
|
diffusers/src/diffusers/utils/export_utils.py/0
|
{
"file_path": "diffusers/src/diffusers/utils/export_utils.py",
"repo_id": "diffusers",
"token_count": 2769
}
| 153
|
# 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.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class CustomLocalPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
[`DDPMScheduler`], or [`DDIMScheduler`].
"""
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[torch.Generator] = None,
num_inference_steps: int = 50,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
eta (`float`, *optional*, defaults to 0.0):
The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
# Sample gaussian noise to begin loop
image = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
generator=generator,
)
image = image.to(self.device)
# set step values
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
# 1. predict noise model_output
model_output = self.unet(image, t).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
image = self.scheduler.step(model_output, t, image).prev_sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=image), "This is a local test"
|
diffusers/tests/fixtures/custom_pipeline/pipeline.py/0
|
{
"file_path": "diffusers/tests/fixtures/custom_pipeline/pipeline.py",
"repo_id": "diffusers",
"token_count": 1738
}
| 154
|
# coding=utf-8
# Copyright 2024 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 traceback
import unittest
import unittest.mock as mock
import uuid
from typing import Dict, List, Tuple
import numpy as np
import requests_mock
import torch
from accelerate.utils import compute_module_sizes
from huggingface_hub import ModelCard, delete_repo
from huggingface_hub.utils import is_jinja_available
from requests.exceptions import HTTPError
from diffusers.models import UNet2DConditionModel
from diffusers.models.attention_processor import (
AttnProcessor,
AttnProcessor2_0,
AttnProcessorNPU,
XFormersAttnProcessor,
)
from diffusers.training_utils import EMAModel
from diffusers.utils import SAFE_WEIGHTS_INDEX_NAME, is_torch_npu_available, is_xformers_available, logging
from diffusers.utils.hub_utils import _add_variant
from diffusers.utils.testing_utils import (
CaptureLogger,
get_python_version,
is_torch_compile,
require_torch_2,
require_torch_accelerator_with_training,
require_torch_gpu,
require_torch_multi_gpu,
run_test_in_subprocess,
torch_device,
)
from ..others.test_utils import TOKEN, USER, is_staging_test
def caculate_expected_num_shards(index_map_path):
with open(index_map_path) as f:
weight_map_dict = json.load(f)["weight_map"]
first_key = list(weight_map_dict.keys())[0]
weight_loc = weight_map_dict[first_key] # e.g., diffusion_pytorch_model-00001-of-00002.safetensors
expected_num_shards = int(weight_loc.split("-")[-1].split(".")[0])
return expected_num_shards
# Will be run via run_test_in_subprocess
def _test_from_save_pretrained_dynamo(in_queue, out_queue, timeout):
error = None
try:
init_dict, model_class = in_queue.get(timeout=timeout)
model = model_class(**init_dict)
model.to(torch_device)
model = torch.compile(model)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, safe_serialization=False)
new_model = model_class.from_pretrained(tmpdirname)
new_model.to(torch_device)
assert new_model.__class__ == model_class
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class ModelUtilsTest(unittest.TestCase):
def tearDown(self):
super().tearDown()
def test_accelerate_loading_error_message(self):
with self.assertRaises(ValueError) as error_context:
UNet2DConditionModel.from_pretrained("hf-internal-testing/stable-diffusion-broken", subfolder="unet")
# make sure that error message states what keys are missing
assert "conv_out.bias" in str(error_context.exception)
def test_cached_files_are_used_when_no_internet(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
orig_model = UNet2DConditionModel.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet"
)
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.request", return_value=response_mock):
# Download this model to make sure it's in the cache.
model = UNet2DConditionModel.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet", local_files_only=True
)
for p1, p2 in zip(orig_model.parameters(), model.parameters()):
if p1.data.ne(p2.data).sum() > 0:
assert False, "Parameters not the same!"
@unittest.skip("Flaky behaviour on CI. Re-enable after migrating to new runners")
@unittest.skipIf(torch_device == "mps", reason="Test not supported for MPS.")
def test_one_request_upon_cached(self):
use_safetensors = False
with tempfile.TemporaryDirectory() as tmpdirname:
with requests_mock.mock(real_http=True) as m:
UNet2DConditionModel.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch",
subfolder="unet",
cache_dir=tmpdirname,
use_safetensors=use_safetensors,
)
download_requests = [r.method for r in m.request_history]
assert (
download_requests.count("HEAD") == 3
), "3 HEAD requests one for config, one for model, and one for shard index file."
assert download_requests.count("GET") == 2, "2 GET requests one for config, one for model"
with requests_mock.mock(real_http=True) as m:
UNet2DConditionModel.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch",
subfolder="unet",
cache_dir=tmpdirname,
use_safetensors=use_safetensors,
)
cache_requests = [r.method for r in m.request_history]
assert (
"HEAD" == cache_requests[0] and len(cache_requests) == 2
), "We should call only `model_info` to check for commit hash and knowing if shard index is present."
def test_weight_overwrite(self):
with tempfile.TemporaryDirectory() as tmpdirname, self.assertRaises(ValueError) as error_context:
UNet2DConditionModel.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch",
subfolder="unet",
cache_dir=tmpdirname,
in_channels=9,
)
# make sure that error message states what keys are missing
assert "Cannot load" in str(error_context.exception)
with tempfile.TemporaryDirectory() as tmpdirname:
model = UNet2DConditionModel.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-torch",
subfolder="unet",
cache_dir=tmpdirname,
in_channels=9,
low_cpu_mem_usage=False,
ignore_mismatched_sizes=True,
)
assert model.config.in_channels == 9
class UNetTesterMixin:
def test_forward_signature(self):
init_dict, _ = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["sample", "timestep"]
self.assertListEqual(arg_names[:2], expected_arg_names)
def test_forward_with_norm_groups(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict["norm_num_groups"] = 16
init_dict["block_out_channels"] = (16, 32)
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
class ModelTesterMixin:
main_input_name = None # overwrite in model specific tester class
base_precision = 1e-3
forward_requires_fresh_args = False
model_split_percents = [0.5, 0.7, 0.9]
def check_device_map_is_respected(self, model, device_map):
for param_name, param in model.named_parameters():
# Find device in device_map
while len(param_name) > 0 and param_name not in device_map:
param_name = ".".join(param_name.split(".")[:-1])
if param_name not in device_map:
raise ValueError("device map is incomplete, it does not contain any device for `param_name`.")
param_device = device_map[param_name]
if param_device in ["cpu", "disk"]:
self.assertEqual(param.device, torch.device("meta"))
else:
self.assertEqual(param.device, torch.device(param_device))
def test_from_save_pretrained(self, expected_max_diff=5e-5):
if self.forward_requires_fresh_args:
model = self.model_class(**self.init_dict)
else:
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
if hasattr(model, "set_default_attn_processor"):
model.set_default_attn_processor()
model.to(torch_device)
model.eval()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, safe_serialization=False)
new_model = self.model_class.from_pretrained(tmpdirname)
if hasattr(new_model, "set_default_attn_processor"):
new_model.set_default_attn_processor()
new_model.to(torch_device)
with torch.no_grad():
if self.forward_requires_fresh_args:
image = model(**self.inputs_dict(0))
else:
image = model(**inputs_dict)
if isinstance(image, dict):
image = image.to_tuple()[0]
if self.forward_requires_fresh_args:
new_image = new_model(**self.inputs_dict(0))
else:
new_image = new_model(**inputs_dict)
if isinstance(new_image, dict):
new_image = new_image.to_tuple()[0]
max_diff = (image - new_image).abs().max().item()
self.assertLessEqual(max_diff, expected_max_diff, "Models give different forward passes")
def test_getattr_is_correct(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
# save some things to test
model.dummy_attribute = 5
model.register_to_config(test_attribute=5)
logger = logging.get_logger("diffusers.models.modeling_utils")
# 30 for warning
logger.setLevel(30)
with CaptureLogger(logger) as cap_logger:
assert hasattr(model, "dummy_attribute")
assert getattr(model, "dummy_attribute") == 5
assert model.dummy_attribute == 5
# no warning should be thrown
assert cap_logger.out == ""
logger = logging.get_logger("diffusers.models.modeling_utils")
# 30 for warning
logger.setLevel(30)
with CaptureLogger(logger) as cap_logger:
assert hasattr(model, "save_pretrained")
fn = model.save_pretrained
fn_1 = getattr(model, "save_pretrained")
assert fn == fn_1
# no warning should be thrown
assert cap_logger.out == ""
# warning should be thrown
with self.assertWarns(FutureWarning):
assert model.test_attribute == 5
with self.assertWarns(FutureWarning):
assert getattr(model, "test_attribute") == 5
with self.assertRaises(AttributeError) as error:
model.does_not_exist
assert str(error.exception) == f"'{type(model).__name__}' object has no attribute 'does_not_exist'"
@unittest.skipIf(
torch_device != "npu" or not is_torch_npu_available(),
reason="torch npu flash attention is only available with NPU and `torch_npu` installed",
)
def test_set_torch_npu_flash_attn_processor_determinism(self):
torch.use_deterministic_algorithms(False)
if self.forward_requires_fresh_args:
model = self.model_class(**self.init_dict)
else:
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
if not hasattr(model, "set_attn_processor"):
# If not has `set_attn_processor`, skip test
return
model.set_default_attn_processor()
assert all(type(proc) == AttnProcessorNPU for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output = model(**self.inputs_dict(0))[0]
else:
output = model(**inputs_dict)[0]
model.enable_npu_flash_attention()
assert all(type(proc) == AttnProcessorNPU for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output_2 = model(**self.inputs_dict(0))[0]
else:
output_2 = model(**inputs_dict)[0]
model.set_attn_processor(AttnProcessorNPU())
assert all(type(proc) == AttnProcessorNPU for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output_3 = model(**self.inputs_dict(0))[0]
else:
output_3 = model(**inputs_dict)[0]
torch.use_deterministic_algorithms(True)
assert torch.allclose(output, output_2, atol=self.base_precision)
assert torch.allclose(output, output_3, atol=self.base_precision)
assert torch.allclose(output_2, output_3, atol=self.base_precision)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_set_xformers_attn_processor_for_determinism(self):
torch.use_deterministic_algorithms(False)
if self.forward_requires_fresh_args:
model = self.model_class(**self.init_dict)
else:
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
if not hasattr(model, "set_attn_processor"):
# If not has `set_attn_processor`, skip test
return
if not hasattr(model, "set_default_attn_processor"):
# If not has `set_attn_processor`, skip test
return
model.set_default_attn_processor()
assert all(type(proc) == AttnProcessor for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output = model(**self.inputs_dict(0))[0]
else:
output = model(**inputs_dict)[0]
model.enable_xformers_memory_efficient_attention()
assert all(type(proc) == XFormersAttnProcessor for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output_2 = model(**self.inputs_dict(0))[0]
else:
output_2 = model(**inputs_dict)[0]
model.set_attn_processor(XFormersAttnProcessor())
assert all(type(proc) == XFormersAttnProcessor for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output_3 = model(**self.inputs_dict(0))[0]
else:
output_3 = model(**inputs_dict)[0]
torch.use_deterministic_algorithms(True)
assert torch.allclose(output, output_2, atol=self.base_precision)
assert torch.allclose(output, output_3, atol=self.base_precision)
assert torch.allclose(output_2, output_3, atol=self.base_precision)
@require_torch_gpu
def test_set_attn_processor_for_determinism(self):
torch.use_deterministic_algorithms(False)
if self.forward_requires_fresh_args:
model = self.model_class(**self.init_dict)
else:
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
if not hasattr(model, "set_attn_processor"):
# If not has `set_attn_processor`, skip test
return
assert all(type(proc) == AttnProcessor2_0 for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output_1 = model(**self.inputs_dict(0))[0]
else:
output_1 = model(**inputs_dict)[0]
model.set_default_attn_processor()
assert all(type(proc) == AttnProcessor for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output_2 = model(**self.inputs_dict(0))[0]
else:
output_2 = model(**inputs_dict)[0]
model.set_attn_processor(AttnProcessor2_0())
assert all(type(proc) == AttnProcessor2_0 for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output_4 = model(**self.inputs_dict(0))[0]
else:
output_4 = model(**inputs_dict)[0]
model.set_attn_processor(AttnProcessor())
assert all(type(proc) == AttnProcessor for proc in model.attn_processors.values())
with torch.no_grad():
if self.forward_requires_fresh_args:
output_5 = model(**self.inputs_dict(0))[0]
else:
output_5 = model(**inputs_dict)[0]
torch.use_deterministic_algorithms(True)
# make sure that outputs match
assert torch.allclose(output_2, output_1, atol=self.base_precision)
assert torch.allclose(output_2, output_4, atol=self.base_precision)
assert torch.allclose(output_2, output_5, atol=self.base_precision)
def test_from_save_pretrained_variant(self, expected_max_diff=5e-5):
if self.forward_requires_fresh_args:
model = self.model_class(**self.init_dict)
else:
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
if hasattr(model, "set_default_attn_processor"):
model.set_default_attn_processor()
model.to(torch_device)
model.eval()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, variant="fp16", safe_serialization=False)
new_model = self.model_class.from_pretrained(tmpdirname, variant="fp16")
if hasattr(new_model, "set_default_attn_processor"):
new_model.set_default_attn_processor()
# non-variant cannot be loaded
with self.assertRaises(OSError) as error_context:
self.model_class.from_pretrained(tmpdirname)
# make sure that error message states what keys are missing
assert "Error no file named diffusion_pytorch_model.bin found in directory" in str(error_context.exception)
new_model.to(torch_device)
with torch.no_grad():
if self.forward_requires_fresh_args:
image = model(**self.inputs_dict(0))
else:
image = model(**inputs_dict)
if isinstance(image, dict):
image = image.to_tuple()[0]
if self.forward_requires_fresh_args:
new_image = new_model(**self.inputs_dict(0))
else:
new_image = new_model(**inputs_dict)
if isinstance(new_image, dict):
new_image = new_image.to_tuple()[0]
max_diff = (image - new_image).abs().max().item()
self.assertLessEqual(max_diff, expected_max_diff, "Models give different forward passes")
@is_torch_compile
@require_torch_2
@unittest.skipIf(
get_python_version == (3, 12),
reason="Torch Dynamo isn't yet supported for Python 3.12.",
)
def test_from_save_pretrained_dynamo(self):
init_dict, _ = self.prepare_init_args_and_inputs_for_common()
inputs = [init_dict, self.model_class]
run_test_in_subprocess(test_case=self, target_func=_test_from_save_pretrained_dynamo, inputs=inputs)
def test_from_save_pretrained_dtype(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
for dtype in [torch.float32, torch.float16, torch.bfloat16]:
if torch_device == "mps" and dtype == torch.bfloat16:
continue
with tempfile.TemporaryDirectory() as tmpdirname:
model.to(dtype)
model.save_pretrained(tmpdirname, safe_serialization=False)
new_model = self.model_class.from_pretrained(tmpdirname, low_cpu_mem_usage=True, torch_dtype=dtype)
assert new_model.dtype == dtype
new_model = self.model_class.from_pretrained(tmpdirname, low_cpu_mem_usage=False, torch_dtype=dtype)
assert new_model.dtype == dtype
def test_determinism(self, expected_max_diff=1e-5):
if self.forward_requires_fresh_args:
model = self.model_class(**self.init_dict)
else:
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
if self.forward_requires_fresh_args:
first = model(**self.inputs_dict(0))
else:
first = model(**inputs_dict)
if isinstance(first, dict):
first = first.to_tuple()[0]
if self.forward_requires_fresh_args:
second = model(**self.inputs_dict(0))
else:
second = model(**inputs_dict)
if isinstance(second, dict):
second = second.to_tuple()[0]
out_1 = first.cpu().numpy()
out_2 = second.cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, expected_max_diff)
def test_output(self, expected_output_shape=None):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
self.assertIsNotNone(output)
# input & output have to have the same shape
input_tensor = inputs_dict[self.main_input_name]
if expected_output_shape is None:
expected_shape = input_tensor.shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
else:
self.assertEqual(output.shape, expected_output_shape, "Input and output shapes do not match")
def test_model_from_pretrained(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
# test if the model can be loaded from the config
# and has all the expected shape
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, safe_serialization=False)
new_model = self.model_class.from_pretrained(tmpdirname)
new_model.to(torch_device)
new_model.eval()
# check if all parameters shape are the same
for param_name in model.state_dict().keys():
param_1 = model.state_dict()[param_name]
param_2 = new_model.state_dict()[param_name]
self.assertEqual(param_1.shape, param_2.shape)
with torch.no_grad():
output_1 = model(**inputs_dict)
if isinstance(output_1, dict):
output_1 = output_1.to_tuple()[0]
output_2 = new_model(**inputs_dict)
if isinstance(output_2, dict):
output_2 = output_2.to_tuple()[0]
self.assertEqual(output_1.shape, output_2.shape)
@require_torch_accelerator_with_training
def test_training(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
input_tensor = inputs_dict[self.main_input_name]
noise = torch.randn((input_tensor.shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
@require_torch_accelerator_with_training
def test_ema_training(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
ema_model = EMAModel(model.parameters())
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
input_tensor = inputs_dict[self.main_input_name]
noise = torch.randn((input_tensor.shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
ema_model.step(model.parameters())
def test_outputs_equivalence(self):
def set_nan_tensor_to_zero(t):
# Temporary fallback until `aten::_index_put_impl_` is implemented in mps
# Track progress in https://github.com/pytorch/pytorch/issues/77764
device = t.device
if device.type == "mps":
t = t.to("cpu")
t[t != t] = 0
return t.to(device)
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
if self.forward_requires_fresh_args:
model = self.model_class(**self.init_dict)
else:
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
if self.forward_requires_fresh_args:
outputs_dict = model(**self.inputs_dict(0))
outputs_tuple = model(**self.inputs_dict(0), return_dict=False)
else:
outputs_dict = model(**inputs_dict)
outputs_tuple = model(**inputs_dict, return_dict=False)
recursive_check(outputs_tuple, outputs_dict)
@require_torch_accelerator_with_training
def test_enable_disable_gradient_checkpointing(self):
if not self.model_class._supports_gradient_checkpointing:
return # Skip test if model does not support gradient checkpointing
init_dict, _ = self.prepare_init_args_and_inputs_for_common()
# at init model should have gradient checkpointing disabled
model = self.model_class(**init_dict)
self.assertFalse(model.is_gradient_checkpointing)
# check enable works
model.enable_gradient_checkpointing()
self.assertTrue(model.is_gradient_checkpointing)
# check disable works
model.disable_gradient_checkpointing()
self.assertFalse(model.is_gradient_checkpointing)
def test_deprecated_kwargs(self):
has_kwarg_in_model_class = "kwargs" in inspect.signature(self.model_class.__init__).parameters
has_deprecated_kwarg = len(self.model_class._deprecated_kwargs) > 0
if has_kwarg_in_model_class and not has_deprecated_kwarg:
raise ValueError(
f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
" under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
" no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
" [<deprecated_argument>]`"
)
if not has_kwarg_in_model_class and has_deprecated_kwarg:
raise ValueError(
f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
" under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
" from `_deprecated_kwargs = [<deprecated_argument>]`"
)
@require_torch_gpu
def test_cpu_offload(self):
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict)
model_size = compute_module_sizes(model)[""]
# We test several splits of sizes to make sure it works.
max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents[1:]]
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir)
for max_size in max_gpu_sizes:
max_memory = {0: max_size, "cpu": model_size * 2}
new_model = self.model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
# Making sure part of the model will actually end up offloaded
self.assertSetEqual(set(new_model.hf_device_map.values()), {0, "cpu"})
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
torch.manual_seed(0)
new_output = new_model(**inputs_dict)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
@require_torch_gpu
def test_disk_offload_without_safetensors(self):
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict)
model_size = compute_module_sizes(model)[""]
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir, safe_serialization=False)
with self.assertRaises(ValueError):
max_size = int(self.model_split_percents[0] * model_size)
max_memory = {0: max_size, "cpu": max_size}
# This errors out because it's missing an offload folder
new_model = self.model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
max_size = int(self.model_split_percents[0] * model_size)
max_memory = {0: max_size, "cpu": max_size}
new_model = self.model_class.from_pretrained(
tmp_dir, device_map="auto", max_memory=max_memory, offload_folder=tmp_dir
)
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
torch.manual_seed(0)
new_output = new_model(**inputs_dict)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
@require_torch_gpu
def test_disk_offload_with_safetensors(self):
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict)
model_size = compute_module_sizes(model)[""]
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir)
max_size = int(self.model_split_percents[0] * model_size)
max_memory = {0: max_size, "cpu": max_size}
new_model = self.model_class.from_pretrained(
tmp_dir, device_map="auto", offload_folder=tmp_dir, max_memory=max_memory
)
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
torch.manual_seed(0)
new_output = new_model(**inputs_dict)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
@require_torch_multi_gpu
def test_model_parallelism(self):
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict)
model_size = compute_module_sizes(model)[""]
# We test several splits of sizes to make sure it works.
max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents[1:]]
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir)
for max_size in max_gpu_sizes:
max_memory = {0: max_size, 1: model_size * 2, "cpu": model_size * 2}
new_model = self.model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
# Making sure part of the model will actually end up offloaded
self.assertSetEqual(set(new_model.hf_device_map.values()), {0, 1})
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
torch.manual_seed(0)
new_output = new_model(**inputs_dict)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
@require_torch_gpu
def test_sharded_checkpoints(self):
torch.manual_seed(0)
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
model = model.to(torch_device)
base_output = model(**inputs_dict)
model_size = compute_module_sizes(model)[""]
max_shard_size = int((model_size * 0.75) / (2**10)) # Convert to KB as these test models are small.
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir, max_shard_size=f"{max_shard_size}KB")
self.assertTrue(os.path.exists(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
# Now check if the right number of shards exists. First, let's get the number of shards.
# Since this number can be dependent on the model being tested, it's important that we calculate it
# instead of hardcoding it.
expected_num_shards = caculate_expected_num_shards(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME))
actual_num_shards = len([file for file in os.listdir(tmp_dir) if file.endswith(".safetensors")])
self.assertTrue(actual_num_shards == expected_num_shards)
new_model = self.model_class.from_pretrained(tmp_dir).eval()
new_model = new_model.to(torch_device)
torch.manual_seed(0)
if "generator" in inputs_dict:
_, inputs_dict = self.prepare_init_args_and_inputs_for_common()
new_output = new_model(**inputs_dict)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
@require_torch_gpu
def test_sharded_checkpoints_with_variant(self):
torch.manual_seed(0)
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
model = model.to(torch_device)
base_output = model(**inputs_dict)
model_size = compute_module_sizes(model)[""]
max_shard_size = int((model_size * 0.75) / (2**10)) # Convert to KB as these test models are small.
variant = "fp16"
with tempfile.TemporaryDirectory() as tmp_dir:
# It doesn't matter if the actual model is in fp16 or not. Just adding the variant and
# testing if loading works with the variant when the checkpoint is sharded should be
# enough.
model.cpu().save_pretrained(tmp_dir, max_shard_size=f"{max_shard_size}KB", variant=variant)
index_filename = _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)
self.assertTrue(os.path.exists(os.path.join(tmp_dir, index_filename)))
# Now check if the right number of shards exists. First, let's get the number of shards.
# Since this number can be dependent on the model being tested, it's important that we calculate it
# instead of hardcoding it.
expected_num_shards = caculate_expected_num_shards(os.path.join(tmp_dir, index_filename))
actual_num_shards = len([file for file in os.listdir(tmp_dir) if file.endswith(".safetensors")])
self.assertTrue(actual_num_shards == expected_num_shards)
new_model = self.model_class.from_pretrained(tmp_dir, variant=variant).eval()
new_model = new_model.to(torch_device)
torch.manual_seed(0)
if "generator" in inputs_dict:
_, inputs_dict = self.prepare_init_args_and_inputs_for_common()
new_output = new_model(**inputs_dict)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
@require_torch_gpu
def test_sharded_checkpoints_device_map(self):
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict)
model_size = compute_module_sizes(model)[""]
max_shard_size = int((model_size * 0.75) / (2**10)) # Convert to KB as these test models are small.
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir, max_shard_size=f"{max_shard_size}KB")
self.assertTrue(os.path.exists(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
# Now check if the right number of shards exists. First, let's get the number of shards.
# Since this number can be dependent on the model being tested, it's important that we calculate it
# instead of hardcoding it.
expected_num_shards = caculate_expected_num_shards(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME))
actual_num_shards = len([file for file in os.listdir(tmp_dir) if file.endswith(".safetensors")])
self.assertTrue(actual_num_shards == expected_num_shards)
new_model = self.model_class.from_pretrained(tmp_dir, device_map="auto")
torch.manual_seed(0)
if "generator" in inputs_dict:
_, inputs_dict = self.prepare_init_args_and_inputs_for_common()
new_output = new_model(**inputs_dict)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
@is_staging_test
class ModelPushToHubTester(unittest.TestCase):
identifier = uuid.uuid4()
repo_id = f"test-model-{identifier}"
org_repo_id = f"valid_org/{repo_id}-org"
def test_push_to_hub(self):
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
model.push_to_hub(self.repo_id, token=TOKEN)
new_model = UNet2DConditionModel.from_pretrained(f"{USER}/{self.repo_id}")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=TOKEN, repo_id=self.repo_id)
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id=self.repo_id, push_to_hub=True, token=TOKEN)
new_model = UNet2DConditionModel.from_pretrained(f"{USER}/{self.repo_id}")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(self.repo_id, token=TOKEN)
def test_push_to_hub_in_organization(self):
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
model.push_to_hub(self.org_repo_id, token=TOKEN)
new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=TOKEN, repo_id=self.org_repo_id)
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, push_to_hub=True, token=TOKEN, repo_id=self.org_repo_id)
new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(self.org_repo_id, token=TOKEN)
@unittest.skipIf(
not is_jinja_available(),
reason="Model card tests cannot be performed without Jinja installed.",
)
def test_push_to_hub_library_name(self):
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
model.push_to_hub(self.repo_id, token=TOKEN)
model_card = ModelCard.load(f"{USER}/{self.repo_id}", token=TOKEN).data
assert model_card.library_name == "diffusers"
# Reset repo
delete_repo(self.repo_id, token=TOKEN)
|
diffusers/tests/models/test_modeling_common.py/0
|
{
"file_path": "diffusers/tests/models/test_modeling_common.py",
"repo_id": "diffusers",
"token_count": 20587
}
| 155
|
# coding=utf-8
# Copyright 2024 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 gc
import math
import unittest
import torch
from diffusers import UNet2DModel
from diffusers.utils import logging
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
require_torch_accelerator,
slow,
torch_all_close,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
logger = logging.get_logger(__name__)
enable_full_determinism()
class Unet2DModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = UNet2DModel
main_input_name = "sample"
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": (4, 8),
"norm_num_groups": 2,
"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
"up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
"attention_head_dim": 3,
"out_channels": 3,
"in_channels": 3,
"layers_per_block": 2,
"sample_size": 32,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_mid_block_attn_groups(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict["add_attention"] = True
init_dict["attn_norm_num_groups"] = 4
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
self.assertIsNotNone(
model.mid_block.attentions[0].group_norm, "Mid block Attention group norm should exist but does not."
)
self.assertEqual(
model.mid_block.attentions[0].group_norm.num_groups,
init_dict["attn_norm_num_groups"],
"Mid block Attention group norm does not have the expected number of groups.",
)
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
class UNetLDMModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = UNet2DModel
main_input_name = "sample"
@property
def dummy_input(self):
batch_size = 4
num_channels = 4
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
@property
def input_shape(self):
return (4, 32, 32)
@property
def output_shape(self):
return (4, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"sample_size": 32,
"in_channels": 4,
"out_channels": 4,
"layers_per_block": 2,
"block_out_channels": (32, 64),
"attention_head_dim": 32,
"down_block_types": ("DownBlock2D", "DownBlock2D"),
"up_block_types": ("UpBlock2D", "UpBlock2D"),
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input).sample
assert image is not None, "Make sure output is not None"
@require_torch_accelerator
def test_from_pretrained_accelerate(self):
model, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
model.to(torch_device)
image = model(**self.dummy_input).sample
assert image is not None, "Make sure output is not None"
@require_torch_accelerator
def test_from_pretrained_accelerate_wont_change_results(self):
# by default model loading will use accelerate as `low_cpu_mem_usage=True`
model_accelerate, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
model_accelerate.to(torch_device)
model_accelerate.eval()
noise = torch.randn(
1,
model_accelerate.config.in_channels,
model_accelerate.config.sample_size,
model_accelerate.config.sample_size,
generator=torch.manual_seed(0),
)
noise = noise.to(torch_device)
time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)
arr_accelerate = model_accelerate(noise, time_step)["sample"]
# two models don't need to stay in the device at the same time
del model_accelerate
torch.cuda.empty_cache()
gc.collect()
model_normal_load, _ = UNet2DModel.from_pretrained(
"fusing/unet-ldm-dummy-update", output_loading_info=True, low_cpu_mem_usage=False
)
model_normal_load.to(torch_device)
model_normal_load.eval()
arr_normal_load = model_normal_load(noise, time_step)["sample"]
assert torch_all_close(arr_accelerate, arr_normal_load, rtol=1e-3)
def test_output_pretrained(self):
model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
model.eval()
model.to(torch_device)
noise = torch.randn(
1,
model.config.in_channels,
model.config.sample_size,
model.config.sample_size,
generator=torch.manual_seed(0),
)
noise = noise.to(torch_device)
time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step).sample
output_slice = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
# fmt: on
self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-3))
class NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = UNet2DModel
main_input_name = "sample"
@property
def dummy_input(self, sizes=(32, 32)):
batch_size = 4
num_channels = 3
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [10]).to(dtype=torch.int32, device=torch_device)
return {"sample": noise, "timestep": time_step}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": [32, 64, 64, 64],
"in_channels": 3,
"layers_per_block": 1,
"out_channels": 3,
"time_embedding_type": "fourier",
"norm_eps": 1e-6,
"mid_block_scale_factor": math.sqrt(2.0),
"norm_num_groups": None,
"down_block_types": [
"SkipDownBlock2D",
"AttnSkipDownBlock2D",
"SkipDownBlock2D",
"SkipDownBlock2D",
],
"up_block_types": [
"SkipUpBlock2D",
"SkipUpBlock2D",
"AttnSkipUpBlock2D",
"SkipUpBlock2D",
],
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
@slow
def test_from_pretrained_hub(self):
model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
inputs = self.dummy_input
noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
inputs["sample"] = noise
image = model(**inputs)
assert image is not None, "Make sure output is not None"
@slow
def test_output_pretrained_ve_mid(self):
model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
model.to(torch_device)
batch_size = 4
num_channels = 3
sizes = (256, 256)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step).sample
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([-4836.2178, -6487.1470, -3816.8196, -7964.9302, -10966.3037, -20043.5957, 8137.0513, 2340.3328, 544.6056])
# fmt: on
self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_ve_large(self):
model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
model.to(torch_device)
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step).sample
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
# fmt: on
self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
def test_forward_with_norm_groups(self):
# not required for this model
pass
|
diffusers/tests/models/unets/test_models_unet_2d.py/0
|
{
"file_path": "diffusers/tests/models/unets/test_models_unet_2d.py",
"repo_id": "diffusers",
"token_count": 5099
}
| 156
|
import pickle as pkl
import unittest
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL.Image
from diffusers.utils.outputs import BaseOutput
from diffusers.utils.testing_utils import require_torch
@dataclass
class CustomOutput(BaseOutput):
images: Union[List[PIL.Image.Image], np.ndarray]
class ConfigTester(unittest.TestCase):
def test_outputs_single_attribute(self):
outputs = CustomOutput(images=np.random.rand(1, 3, 4, 4))
# check every way of getting the attribute
assert isinstance(outputs.images, np.ndarray)
assert outputs.images.shape == (1, 3, 4, 4)
assert isinstance(outputs["images"], np.ndarray)
assert outputs["images"].shape == (1, 3, 4, 4)
assert isinstance(outputs[0], np.ndarray)
assert outputs[0].shape == (1, 3, 4, 4)
# test with a non-tensor attribute
outputs = CustomOutput(images=[PIL.Image.new("RGB", (4, 4))])
# check every way of getting the attribute
assert isinstance(outputs.images, list)
assert isinstance(outputs.images[0], PIL.Image.Image)
assert isinstance(outputs["images"], list)
assert isinstance(outputs["images"][0], PIL.Image.Image)
assert isinstance(outputs[0], list)
assert isinstance(outputs[0][0], PIL.Image.Image)
def test_outputs_dict_init(self):
# test output reinitialization with a `dict` for compatibility with `accelerate`
outputs = CustomOutput({"images": np.random.rand(1, 3, 4, 4)})
# check every way of getting the attribute
assert isinstance(outputs.images, np.ndarray)
assert outputs.images.shape == (1, 3, 4, 4)
assert isinstance(outputs["images"], np.ndarray)
assert outputs["images"].shape == (1, 3, 4, 4)
assert isinstance(outputs[0], np.ndarray)
assert outputs[0].shape == (1, 3, 4, 4)
# test with a non-tensor attribute
outputs = CustomOutput({"images": [PIL.Image.new("RGB", (4, 4))]})
# check every way of getting the attribute
assert isinstance(outputs.images, list)
assert isinstance(outputs.images[0], PIL.Image.Image)
assert isinstance(outputs["images"], list)
assert isinstance(outputs["images"][0], PIL.Image.Image)
assert isinstance(outputs[0], list)
assert isinstance(outputs[0][0], PIL.Image.Image)
def test_outputs_serialization(self):
outputs_orig = CustomOutput(images=[PIL.Image.new("RGB", (4, 4))])
serialized = pkl.dumps(outputs_orig)
outputs_copy = pkl.loads(serialized)
# Check original and copy are equal
assert dir(outputs_orig) == dir(outputs_copy)
assert dict(outputs_orig) == dict(outputs_copy)
assert vars(outputs_orig) == vars(outputs_copy)
@require_torch
def test_torch_pytree(self):
# ensure torch.utils._pytree treats ModelOutput subclasses as nodes (and not leaves)
# this is important for DistributedDataParallel gradient synchronization with static_graph=True
import torch
import torch.utils._pytree
data = np.random.rand(1, 3, 4, 4)
x = CustomOutput(images=data)
self.assertFalse(torch.utils._pytree._is_leaf(x))
expected_flat_outs = [data]
expected_tree_spec = torch.utils._pytree.TreeSpec(CustomOutput, ["images"], [torch.utils._pytree.LeafSpec()])
actual_flat_outs, actual_tree_spec = torch.utils._pytree.tree_flatten(x)
self.assertEqual(expected_flat_outs, actual_flat_outs)
self.assertEqual(expected_tree_spec, actual_tree_spec)
unflattened_x = torch.utils._pytree.tree_unflatten(actual_flat_outs, actual_tree_spec)
self.assertEqual(x, unflattened_x)
|
diffusers/tests/others/test_outputs.py/0
|
{
"file_path": "diffusers/tests/others/test_outputs.py",
"repo_id": "diffusers",
"token_count": 1506
}
| 157
|
# coding=utf-8
# Copyright 2024 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 gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechT5HifiGan,
SpeechT5HifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, nightly, torch_device
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class AudioLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = AudioLDMPipeline
params = TEXT_TO_AUDIO_PARAMS
batch_params = TEXT_TO_AUDIO_BATCH_PARAMS
required_optional_params = frozenset(
[
"num_inference_steps",
"num_waveforms_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
]
)
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(8, 16),
layers_per_block=1,
norm_num_groups=8,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=(8, 16),
class_embed_type="simple_projection",
projection_class_embeddings_input_dim=8,
class_embeddings_concat=True,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[8, 16],
in_channels=1,
out_channels=1,
norm_num_groups=8,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = ClapTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=8,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=1,
num_hidden_layers=1,
pad_token_id=1,
vocab_size=1000,
projection_dim=8,
)
text_encoder = ClapTextModelWithProjection(text_encoder_config)
tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)
vocoder_config = SpeechT5HifiGanConfig(
model_in_dim=8,
sampling_rate=16000,
upsample_initial_channel=16,
upsample_rates=[2, 2],
upsample_kernel_sizes=[4, 4],
resblock_kernel_sizes=[3, 7],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],
normalize_before=False,
)
vocoder = SpeechT5HifiGan(vocoder_config)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"vocoder": vocoder,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
}
return inputs
def test_audioldm_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(**inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-2
def test_audioldm_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = audioldm_pipe.tokenizer(
prompt,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = audioldm_pipe.text_encoder(
text_inputs,
)
prompt_embeds = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
prompt_embeds = F.normalize(prompt_embeds, dim=-1)
inputs["prompt_embeds"] = prompt_embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm_negative_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
embeds = []
for p in [prompt, negative_prompt]:
text_inputs = audioldm_pipe.tokenizer(
p,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
text_embeds = audioldm_pipe.text_encoder(
text_inputs,
)
text_embeds = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
text_embeds = F.normalize(text_embeds, dim=-1)
embeds.append(text_embeds)
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "egg cracking"
output = audioldm_pipe(**inputs, negative_prompt=negative_prompt)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-2
def test_audioldm_num_waveforms_per_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = "A hammer hitting a wooden surface"
# test num_waveforms_per_prompt=1 (default)
audios = audioldm_pipe(prompt, num_inference_steps=2).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
batch_size = 2
audios = audioldm_pipe([prompt] * batch_size, num_inference_steps=2).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
num_waveforms_per_prompt = 2
audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
batch_size = 2
audios = audioldm_pipe(
[prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt
).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def test_audioldm_audio_length_in_s(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
vocoder_sampling_rate = audioldm_pipe.vocoder.config.sampling_rate
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(audio_length_in_s=0.016, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.016
output = audioldm_pipe(audio_length_in_s=0.032, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.032
def test_audioldm_vocoder_model_in_dim(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = ["hey"]
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
assert audio_shape == (1, 256)
config = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
audioldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical()
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
@nightly
class AudioLDMPipelineSlowTests(unittest.TestCase):
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def test_audioldm(self):
audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81920
audio_slice = audio[77230:77240]
expected_slice = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]
)
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1e-2
@nightly
class AudioLDMPipelineNightlyTests(unittest.TestCase):
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def test_audioldm_lms(self):
audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
audioldm_pipe.scheduler = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81920
audio_slice = audio[27780:27790]
expected_slice = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212])
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 3e-2
|
diffusers/tests/pipelines/audioldm/test_audioldm.py/0
|
{
"file_path": "diffusers/tests/pipelines/audioldm/test_audioldm.py",
"repo_id": "diffusers",
"token_count": 7644
}
| 158
|
# coding=utf-8
# Copyright 2024 Harutatsu Akiyama, Jinbin Bai, and 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 random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
AutoencoderKL,
ControlNetModel,
EulerDiscreteScheduler,
StableDiffusionXLControlNetInpaintPipeline,
UNet2DConditionModel,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
require_torch_gpu,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class ControlNetPipelineSDXLFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionXLControlNetInpaintPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = frozenset(IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"mask_image", "control_image"}))
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union(
{
"add_text_embeds",
"add_time_ids",
"mask",
"masked_image_latents",
}
)
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
conditioning_embedding_out_channels=(16, 32),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image_encoder_config = CLIPVisionConfig(
hidden_size=32,
image_size=224,
projection_dim=32,
intermediate_size=37,
num_attention_heads=4,
num_channels=3,
num_hidden_layers=5,
patch_size=14,
)
image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
feature_extractor = CLIPImageProcessor(
crop_size=224,
do_center_crop=True,
do_normalize=True,
do_resize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
resample=3,
size=224,
)
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"image_encoder": image_encoder,
"feature_extractor": feature_extractor,
}
return components
def get_dummy_inputs(self, device, seed=0, img_res=64):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
# Get random floats in [0, 1] as image
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
mask_image = torch.ones_like(image)
controlnet_embedder_scale_factor = 2
control_image = (
floats_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
rng=random.Random(seed),
)
.to(device)
.cpu()
)
control_image = control_image.cpu().permute(0, 2, 3, 1)[0]
# Convert image and mask_image to [0, 255]
image = 255 * image
mask_image = 255 * mask_image
control_image = 255 * control_image
# Convert to PIL image
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((img_res, img_res))
mask_image = Image.fromarray(np.uint8(mask_image)).convert("L").resize((img_res, img_res))
control_image = Image.fromarray(np.uint8(control_image)).convert("RGB").resize((img_res, img_res))
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
"image": init_image,
"mask_image": mask_image,
"control_image": control_image,
}
return inputs
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
inputs = self.get_dummy_inputs(torch_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
def test_stable_diffusion_xl_multi_prompts(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
# forward with single prompt
inputs = self.get_dummy_inputs(torch_device)
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = inputs["prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "different prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# manually set a negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same negative_prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = inputs["negative_prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = "different negative prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
def test_controlnet_sdxl_guess(self):
device = "cpu"
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["guess_mode"] = True
output = sd_pipe(**inputs)
image_slice = output.images[0, -3:, -3:, -1]
expected_slice = np.array([0.5460, 0.4943, 0.4635, 0.5832, 0.5366, 0.4815, 0.6034, 0.5741, 0.4341])
# make sure that it's equal
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4
# TODO(Patrick, Sayak) - skip for now as this requires more refiner tests
def test_save_load_optional_components(self):
pass
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=5e-1)
|
diffusers/tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py/0
|
{
"file_path": "diffusers/tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py",
"repo_id": "diffusers",
"token_count": 5955
}
| 159
|
# coding=utf-8
# Copyright 2024 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 gc
import tempfile
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, BertModel, T5EncoderModel
from diffusers import (
AutoencoderKL,
DDPMScheduler,
HunyuanDiT2DModel,
HunyuanDiTPipeline,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineTesterMixin,
check_qkv_fusion_matches_attn_procs_length,
check_qkv_fusion_processors_exist,
to_np,
)
enable_full_determinism()
class HunyuanDiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = HunyuanDiTPipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params
def get_dummy_components(self):
torch.manual_seed(0)
transformer = HunyuanDiT2DModel(
sample_size=16,
num_layers=2,
patch_size=2,
attention_head_dim=8,
num_attention_heads=3,
in_channels=4,
cross_attention_dim=32,
cross_attention_dim_t5=32,
pooled_projection_dim=16,
hidden_size=24,
activation_fn="gelu-approximate",
)
torch.manual_seed(0)
vae = AutoencoderKL()
scheduler = DDPMScheduler()
text_encoder = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel")
text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
components = {
"transformer": transformer.eval(),
"vae": vae.eval(),
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
"use_resolution_binning": False,
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 16, 16, 3))
expected_slice = np.array(
[0.56939435, 0.34541583, 0.35915792, 0.46489206, 0.38775963, 0.45004836, 0.5957267, 0.59481275, 0.33287364]
)
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_sequential_cpu_offload_forward_pass(self):
# TODO(YiYi) need to fix later
pass
def test_sequential_offload_forward_pass_twice(self):
# TODO(YiYi) need to fix later
pass
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-3,
)
def test_save_load_optional_components(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs["prompt"]
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
(
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
) = pipe.encode_prompt(prompt, device=torch_device, dtype=torch.float32, text_encoder_index=0)
(
prompt_embeds_2,
negative_prompt_embeds_2,
prompt_attention_mask_2,
negative_prompt_attention_mask_2,
) = pipe.encode_prompt(
prompt,
device=torch_device,
dtype=torch.float32,
text_encoder_index=1,
)
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"prompt_embeds_2": prompt_embeds_2,
"prompt_attention_mask_2": prompt_attention_mask_2,
"negative_prompt_embeds_2": negative_prompt_embeds_2,
"negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"prompt_embeds_2": prompt_embeds_2,
"prompt_attention_mask_2": prompt_attention_mask_2,
"negative_prompt_embeds_2": negative_prompt_embeds_2,
"negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1e-4)
def test_feed_forward_chunking(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice_no_chunking = image[0, -3:, -3:, -1]
pipe.transformer.enable_forward_chunking(chunk_size=1, dim=0)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice_chunking = image[0, -3:, -3:, -1]
max_diff = np.abs(to_np(image_slice_no_chunking) - to_np(image_slice_chunking)).max()
self.assertLess(max_diff, 1e-4)
def test_fused_qkv_projections(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["return_dict"] = False
image = pipe(**inputs)[0]
original_image_slice = image[0, -3:, -3:, -1]
pipe.transformer.fuse_qkv_projections()
# TODO (sayakpaul): will refactor this once `fuse_qkv_projections()` has been added
# to the pipeline level.
pipe.transformer.fuse_qkv_projections()
assert check_qkv_fusion_processors_exist(
pipe.transformer
), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
assert check_qkv_fusion_matches_attn_procs_length(
pipe.transformer, pipe.transformer.original_attn_processors
), "Something wrong with the attention processors concerning the fused QKV projections."
inputs = self.get_dummy_inputs(device)
inputs["return_dict"] = False
image_fused = pipe(**inputs)[0]
image_slice_fused = image_fused[0, -3:, -3:, -1]
pipe.transformer.unfuse_qkv_projections()
inputs = self.get_dummy_inputs(device)
inputs["return_dict"] = False
image_disabled = pipe(**inputs)[0]
image_slice_disabled = image_disabled[0, -3:, -3:, -1]
assert np.allclose(
original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2
), "Fusion of QKV projections shouldn't affect the outputs."
assert np.allclose(
image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2
), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
assert np.allclose(
original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
), "Original outputs should match when fused QKV projections are disabled."
@slow
@require_torch_gpu
class HunyuanDiTPipelineIntegrationTests(unittest.TestCase):
prompt = "一个宇航员在骑马"
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_hunyuan_dit_1024(self):
generator = torch.Generator("cpu").manual_seed(0)
pipe = HunyuanDiTPipeline.from_pretrained(
"XCLiu/HunyuanDiT-0523", revision="refs/pr/2", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
prompt = self.prompt
image = pipe(
prompt=prompt, height=1024, width=1024, generator=generator, num_inference_steps=2, output_type="np"
).images
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array(
[0.48388672, 0.33789062, 0.30737305, 0.47875977, 0.25097656, 0.30029297, 0.4440918, 0.26953125, 0.30078125]
)
max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
assert max_diff < 1e-3, f"Max diff is too high. got {image_slice.flatten()}"
|
diffusers/tests/pipelines/hunyuan_dit/test_hunyuan_dit.py/0
|
{
"file_path": "diffusers/tests/pipelines/hunyuan_dit/test_hunyuan_dit.py",
"repo_id": "diffusers",
"token_count": 5645
}
| 160
|
# coding=utf-8
# Copyright 2024 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 gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyV22InpaintPipeline,
KandinskyV22PriorPipeline,
UNet2DConditionModel,
VQModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
is_flaky,
load_image,
load_numpy,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class Dummies:
@property
def text_embedder_hidden_size(self):
return 32
@property
def time_input_dim(self):
return 32
@property
def block_out_channels_0(self):
return self.time_input_dim
@property
def time_embed_dim(self):
return self.time_input_dim * 4
@property
def cross_attention_dim(self):
return 32
@property
def dummy_unet(self):
torch.manual_seed(0)
model_kwargs = {
"in_channels": 9,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
model = UNet2DConditionModel(**model_kwargs)
return model
@property
def dummy_movq_kwargs(self):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def dummy_movq(self):
torch.manual_seed(0)
model = VQModel(**self.dummy_movq_kwargs)
return model
def get_dummy_components(self):
unet = self.dummy_unet
movq = self.dummy_movq
scheduler = DDIMScheduler(
num_train_timesteps=1000,
beta_schedule="linear",
beta_start=0.00085,
beta_end=0.012,
clip_sample=False,
set_alpha_to_one=False,
steps_offset=1,
prediction_type="epsilon",
thresholding=False,
)
components = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def get_dummy_inputs(self, device, seed=0):
image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed)).to(device)
negative_image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(
device
)
# create init_image
image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((256, 256))
# create mask
mask = np.zeros((64, 64), dtype=np.float32)
mask[:32, :32] = 1
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"image": init_image,
"mask_image": mask,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 2,
"guidance_scale": 4.0,
"output_type": "np",
}
return inputs
class KandinskyV22InpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = KandinskyV22InpaintPipeline
params = ["image_embeds", "negative_image_embeds", "image", "mask_image"]
batch_params = [
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
required_optional_params = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
test_xformers_attention = False
callback_cfg_params = ["image_embeds", "masked_image", "mask_image"]
def get_dummy_components(self):
dummies = Dummies()
return dummies.get_dummy_components()
def get_dummy_inputs(self, device, seed=0):
dummies = Dummies()
return dummies.get_dummy_inputs(device=device, seed=seed)
def test_kandinsky_inpaint(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.images
image_from_tuple = pipe(
**self.get_dummy_inputs(device),
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]
)
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=5e-1)
@is_flaky()
def test_model_cpu_offload_forward_pass(self):
super().test_inference_batch_single_identical(expected_max_diff=8e-4)
def test_save_load_optional_components(self):
super().test_save_load_optional_components(expected_max_difference=5e-4)
def test_sequential_cpu_offload_forward_pass(self):
super().test_sequential_cpu_offload_forward_pass(expected_max_diff=5e-4)
# override default test because we need to zero out mask too in order to make sure final latent is all zero
def test_callback_inputs(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_test(pipe, i, t, callback_kwargs):
missing_callback_inputs = set()
for v in pipe._callback_tensor_inputs:
if v not in callback_kwargs:
missing_callback_inputs.add(v)
self.assertTrue(
len(missing_callback_inputs) == 0, f"Missing callback tensor inputs: {missing_callback_inputs}"
)
last_i = pipe.num_timesteps - 1
if i == last_i:
callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
callback_kwargs["mask_image"] = torch.zeros_like(callback_kwargs["mask_image"])
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
inputs["callback_on_step_end"] = callback_inputs_test
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
assert output.abs().sum() == 0
@slow
@require_torch_gpu
class KandinskyV22InpaintPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_kandinsky_inpaint(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"
)
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"
)
mask = np.zeros((768, 768), dtype=np.float32)
mask[:250, 250:-250] = 1
prompt = "a hat"
pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
)
pipe_prior.to(torch_device)
pipeline = KandinskyV22InpaintPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder-inpaint", torch_dtype=torch.float16
)
pipeline = pipeline.to(torch_device)
pipeline.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
image_emb, zero_image_emb = pipe_prior(
prompt,
generator=generator,
num_inference_steps=2,
negative_prompt="",
).to_tuple()
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipeline(
image=init_image,
mask_image=mask,
image_embeds=image_emb,
negative_image_embeds=zero_image_emb,
generator=generator,
num_inference_steps=2,
height=768,
width=768,
output_type="np",
)
image = output.images[0]
assert image.shape == (768, 768, 3)
max_diff = numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten())
assert max_diff < 1e-4
|
diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_inpaint.py/0
|
{
"file_path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_inpaint.py",
"repo_id": "diffusers",
"token_count": 5553
}
| 161
|
# coding=utf-8
# Copyright 2024 Latte Team and 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 gc
import inspect
import tempfile
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import (
AutoencoderKL,
DDIMScheduler,
LattePipeline,
LatteTransformer3DModel,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, to_np
enable_full_determinism()
class LattePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = LattePipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params
def get_dummy_components(self):
torch.manual_seed(0)
transformer = LatteTransformer3DModel(
sample_size=8,
num_layers=1,
patch_size=2,
attention_head_dim=8,
num_attention_heads=3,
caption_channels=32,
in_channels=4,
cross_attention_dim=24,
out_channels=8,
attention_bias=True,
activation_fn="gelu-approximate",
num_embeds_ada_norm=1000,
norm_type="ada_norm_single",
norm_elementwise_affine=False,
norm_eps=1e-6,
)
torch.manual_seed(0)
vae = AutoencoderKL()
scheduler = DDIMScheduler()
text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
components = {
"transformer": transformer.eval(),
"vae": vae.eval(),
"scheduler": scheduler,
"text_encoder": text_encoder.eval(),
"tokenizer": tokenizer,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"negative_prompt": "low quality",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"height": 8,
"width": 8,
"video_length": 1,
"output_type": "pt",
"clean_caption": False,
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
video = pipe(**inputs).frames
generated_video = video[0]
self.assertEqual(generated_video.shape, (1, 3, 8, 8))
expected_video = torch.randn(1, 3, 8, 8)
max_diff = np.abs(generated_video - expected_video).max()
self.assertLessEqual(max_diff, 1e10)
def test_callback_inputs(self):
sig = inspect.signature(self.pipeline_class.__call__)
has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
has_callback_step_end = "callback_on_step_end" in sig.parameters
if not (has_callback_tensor_inputs and has_callback_step_end):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_subset(pipe, i, t, callback_kwargs):
# iterate over callback args
for tensor_name, tensor_value in callback_kwargs.items():
# check that we're only passing in allowed tensor inputs
assert tensor_name in pipe._callback_tensor_inputs
return callback_kwargs
def callback_inputs_all(pipe, i, t, callback_kwargs):
for tensor_name in pipe._callback_tensor_inputs:
assert tensor_name in callback_kwargs
# iterate over callback args
for tensor_name, tensor_value in callback_kwargs.items():
# check that we're only passing in allowed tensor inputs
assert tensor_name in pipe._callback_tensor_inputs
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
# Test passing in a subset
inputs["callback_on_step_end"] = callback_inputs_subset
inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
output = pipe(**inputs)[0]
# Test passing in a everything
inputs["callback_on_step_end"] = callback_inputs_all
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
output = pipe(**inputs)[0]
def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
is_last = i == (pipe.num_timesteps - 1)
if is_last:
callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
return callback_kwargs
inputs["callback_on_step_end"] = callback_inputs_change_tensor
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
output = pipe(**inputs)[0]
assert output.abs().sum() < 1e10
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3)
def test_attention_slicing_forward_pass(self):
pass
def test_save_load_optional_components(self):
if not hasattr(self.pipeline_class, "_optional_components"):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs["prompt"]
generator = inputs["generator"]
(
prompt_embeds,
negative_prompt_embeds,
) = pipe.encode_prompt(prompt)
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"negative_prompt": None,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"height": 8,
"width": 8,
"video_length": 1,
"mask_feature": False,
"output_type": "pt",
"clean_caption": False,
}
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir, safe_serialization=False)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
for component in pipe_loaded.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1.0)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
super()._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
@slow
@require_torch_gpu
class LattePipelineIntegrationTests(unittest.TestCase):
prompt = "A painting of a squirrel eating a burger."
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_latte(self):
generator = torch.Generator("cpu").manual_seed(0)
pipe = LattePipeline.from_pretrained("maxin-cn/Latte-1", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = self.prompt
videos = pipe(
prompt=prompt,
height=512,
width=512,
generator=generator,
num_inference_steps=2,
clean_caption=False,
).frames
video = videos[0]
expected_video = torch.randn(1, 512, 512, 3).numpy()
max_diff = numpy_cosine_similarity_distance(video.flatten(), expected_video)
assert max_diff < 1e-3, f"Max diff is too high. got {video.flatten()}"
|
diffusers/tests/pipelines/latte/test_latte.py/0
|
{
"file_path": "diffusers/tests/pipelines/latte/test_latte.py",
"repo_id": "diffusers",
"token_count": 4735
}
| 162
|
# coding=utf-8
# Copyright 2024 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 tempfile
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, BertModel, T5EncoderModel
from diffusers import (
AutoencoderKL,
DDPMScheduler,
HunyuanDiT2DModel,
HunyuanDiTPAGPipeline,
HunyuanDiTPipeline,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, to_np
enable_full_determinism()
class HunyuanDiTPAGPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = HunyuanDiTPAGPipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params
def get_dummy_components(self):
torch.manual_seed(0)
transformer = HunyuanDiT2DModel(
sample_size=16,
num_layers=2,
patch_size=2,
attention_head_dim=8,
num_attention_heads=3,
in_channels=4,
cross_attention_dim=32,
cross_attention_dim_t5=32,
pooled_projection_dim=16,
hidden_size=24,
activation_fn="gelu-approximate",
)
torch.manual_seed(0)
vae = AutoencoderKL()
scheduler = DDPMScheduler()
text_encoder = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel")
text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
components = {
"transformer": transformer.eval(),
"vae": vae.eval(),
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
"use_resolution_binning": False,
"pag_scale": 0.0,
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 16, 16, 3))
expected_slice = np.array(
[0.56939435, 0.34541583, 0.35915792, 0.46489206, 0.38775963, 0.45004836, 0.5957267, 0.59481275, 0.33287364]
)
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_sequential_cpu_offload_forward_pass(self):
# TODO(YiYi) need to fix later
pass
def test_sequential_offload_forward_pass_twice(self):
# TODO(YiYi) need to fix later
pass
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-3,
)
def test_save_load_optional_components(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs["prompt"]
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
(
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
) = pipe.encode_prompt(prompt, device=torch_device, dtype=torch.float32, text_encoder_index=0)
(
prompt_embeds_2,
negative_prompt_embeds_2,
prompt_attention_mask_2,
negative_prompt_attention_mask_2,
) = pipe.encode_prompt(
prompt,
device=torch_device,
dtype=torch.float32,
text_encoder_index=1,
)
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"prompt_embeds_2": prompt_embeds_2,
"prompt_attention_mask_2": prompt_attention_mask_2,
"negative_prompt_embeds_2": negative_prompt_embeds_2,
"negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"prompt_embeds_2": prompt_embeds_2,
"prompt_attention_mask_2": prompt_attention_mask_2,
"negative_prompt_embeds_2": negative_prompt_embeds_2,
"negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1e-4)
def test_feed_forward_chunking(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice_no_chunking = image[0, -3:, -3:, -1]
pipe.transformer.enable_forward_chunking(chunk_size=1, dim=0)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice_chunking = image[0, -3:, -3:, -1]
max_diff = np.abs(to_np(image_slice_no_chunking) - to_np(image_slice_chunking)).max()
self.assertLess(max_diff, 1e-4)
def test_fused_qkv_projections(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["return_dict"] = False
image = pipe(**inputs)[0]
original_image_slice = image[0, -3:, -3:, -1]
pipe.transformer.fuse_qkv_projections()
inputs = self.get_dummy_inputs(device)
inputs["return_dict"] = False
image_fused = pipe(**inputs)[0]
image_slice_fused = image_fused[0, -3:, -3:, -1]
pipe.transformer.unfuse_qkv_projections()
inputs = self.get_dummy_inputs(device)
inputs["return_dict"] = False
image_disabled = pipe(**inputs)[0]
image_slice_disabled = image_disabled[0, -3:, -3:, -1]
assert np.allclose(
original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2
), "Fusion of QKV projections shouldn't affect the outputs."
assert np.allclose(
image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2
), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
assert np.allclose(
original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
), "Original outputs should match when fused QKV projections are disabled."
def test_pag_disable_enable(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
# base pipeline (expect same output when pag is disabled)
pipe_sd = HunyuanDiTPipeline(**components)
pipe_sd = pipe_sd.to(device)
pipe_sd.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["pag_scale"]
assert (
"pag_scale" not in inspect.signature(pipe_sd.__call__).parameters
), f"`pag_scale` should not be a call parameter of the base pipeline {pipe_sd.__class__.__name__}."
out = pipe_sd(**inputs).images[0, -3:, -3:, -1]
components = self.get_dummy_components()
# pag disabled with pag_scale=0.0
pipe_pag = self.pipeline_class(**components)
pipe_pag = pipe_pag.to(device)
pipe_pag.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["pag_scale"] = 0.0
out_pag_disabled = pipe_pag(**inputs).images[0, -3:, -3:, -1]
# pag enabled
pipe_pag = self.pipeline_class(**components)
pipe_pag = pipe_pag.to(device)
pipe_pag.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["pag_scale"] = 3.0
out_pag_enabled = pipe_pag(**inputs).images[0, -3:, -3:, -1]
assert np.abs(out.flatten() - out_pag_disabled.flatten()).max() < 1e-3
assert np.abs(out.flatten() - out_pag_enabled.flatten()).max() > 1e-3
def test_pag_applied_layers(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
# base pipeline
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
all_self_attn_layers = [k for k in pipe.transformer.attn_processors.keys() if "attn1" in k]
original_attn_procs = pipe.transformer.attn_processors
pag_layers = ["blocks.0", "blocks.1"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert set(pipe.pag_attn_processors) == set(all_self_attn_layers)
# blocks.0
block_0_self_attn = ["blocks.0.attn1.processor"]
pipe.transformer.set_attn_processor(original_attn_procs.copy())
pag_layers = ["blocks.0"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert set(pipe.pag_attn_processors) == set(block_0_self_attn)
pipe.transformer.set_attn_processor(original_attn_procs.copy())
pag_layers = ["blocks.0.attn1"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert set(pipe.pag_attn_processors) == set(block_0_self_attn)
pipe.transformer.set_attn_processor(original_attn_procs.copy())
pag_layers = ["blocks.(0|1)"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert (len(pipe.pag_attn_processors)) == 2
pipe.transformer.set_attn_processor(original_attn_procs.copy())
pag_layers = ["blocks.0", r"blocks\.1"]
pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
assert len(pipe.pag_attn_processors) == 2
|
diffusers/tests/pipelines/pag/test_pag_hunyuan_dit.py/0
|
{
"file_path": "diffusers/tests/pipelines/pag/test_pag_hunyuan_dit.py",
"repo_id": "diffusers",
"token_count": 6402
}
| 163
|
# coding=utf-8
# Copyright 2024 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 gc
import tempfile
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PixArtSigmaPipeline,
PixArtTransformer2DModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineTesterMixin,
check_qkv_fusion_matches_attn_procs_length,
check_qkv_fusion_processors_exist,
to_np,
)
enable_full_determinism()
class PixArtSigmaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = PixArtSigmaPipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params
def get_dummy_components(self):
torch.manual_seed(0)
transformer = PixArtTransformer2DModel(
sample_size=8,
num_layers=2,
patch_size=2,
attention_head_dim=8,
num_attention_heads=3,
caption_channels=32,
in_channels=4,
cross_attention_dim=24,
out_channels=8,
attention_bias=True,
activation_fn="gelu-approximate",
num_embeds_ada_norm=1000,
norm_type="ada_norm_single",
norm_elementwise_affine=False,
norm_eps=1e-6,
)
torch.manual_seed(0)
vae = AutoencoderKL()
scheduler = DDIMScheduler()
text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
components = {
"transformer": transformer.eval(),
"vae": vae.eval(),
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"use_resolution_binning": False,
"output_type": "np",
}
return inputs
def test_sequential_cpu_offload_forward_pass(self):
# TODO(PVP, Sayak) need to fix later
return
def test_save_load_optional_components(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs["prompt"]
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = pipe.encode_prompt(prompt)
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt": None,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"negative_prompt": None,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": negative_prompt_attention_mask,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"use_resolution_binning": False,
}
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1e-4)
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 8, 8, 3))
expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.4830, 0.2583, 0.5331, 0.4852])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_inference_non_square_images(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs, height=32, width=48).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 32, 48, 3))
expected_slice = np.array([0.6493, 0.5370, 0.4081, 0.4762, 0.3695, 0.4711, 0.3026, 0.5218, 0.5263])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_inference_with_embeddings_and_multiple_images(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs["prompt"]
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
prompt_embeds, prompt_attn_mask, negative_prompt_embeds, neg_prompt_attn_mask = pipe.encode_prompt(prompt)
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attn_mask,
"negative_prompt": None,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": neg_prompt_attn_mask,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"num_images_per_prompt": 2,
"use_resolution_binning": False,
}
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
generator = inputs["generator"]
num_inference_steps = inputs["num_inference_steps"]
output_type = inputs["output_type"]
# inputs with prompt converted to embeddings
inputs = {
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attn_mask,
"negative_prompt": None,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_prompt_attention_mask": neg_prompt_attn_mask,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
"num_images_per_prompt": 2,
"use_resolution_binning": False,
}
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, 1e-4)
def test_inference_with_multiple_images_per_prompt(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["num_images_per_prompt"] = 2
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (2, 8, 8, 3))
expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.4830, 0.2583, 0.5331, 0.4852])
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=1e-3)
def test_fused_qkv_projections(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
original_image_slice = image[0, -3:, -3:, -1]
# TODO (sayakpaul): will refactor this once `fuse_qkv_projections()` has been added
# to the pipeline level.
pipe.transformer.fuse_qkv_projections()
assert check_qkv_fusion_processors_exist(
pipe.transformer
), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
assert check_qkv_fusion_matches_attn_procs_length(
pipe.transformer, pipe.transformer.original_attn_processors
), "Something wrong with the attention processors concerning the fused QKV projections."
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice_fused = image[0, -3:, -3:, -1]
pipe.transformer.unfuse_qkv_projections()
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice_disabled = image[0, -3:, -3:, -1]
assert np.allclose(
original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
), "Fusion of QKV projections shouldn't affect the outputs."
assert np.allclose(
image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
assert np.allclose(
original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
), "Original outputs should match when fused QKV projections are disabled."
@slow
@require_torch_gpu
class PixArtSigmaPipelineIntegrationTests(unittest.TestCase):
ckpt_id_1024 = "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
ckpt_id_512 = "PixArt-alpha/PixArt-Sigma-XL-2-512-MS"
prompt = "A small cactus with a happy face in the Sahara desert."
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_pixart_1024(self):
generator = torch.Generator("cpu").manual_seed(0)
pipe = PixArtSigmaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = self.prompt
image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.4517, 0.4446, 0.4375, 0.449, 0.4399, 0.4365, 0.4583, 0.4629, 0.4473])
max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
self.assertLessEqual(max_diff, 1e-4)
def test_pixart_512(self):
generator = torch.Generator("cpu").manual_seed(0)
transformer = PixArtTransformer2DModel.from_pretrained(
self.ckpt_id_512, subfolder="transformer", torch_dtype=torch.float16
)
pipe = PixArtSigmaPipeline.from_pretrained(
self.ckpt_id_1024, transformer=transformer, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
prompt = self.prompt
image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.0479, 0.0378, 0.0217, 0.0942, 0.064, 0.0791, 0.2073, 0.1975, 0.2017])
max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
self.assertLessEqual(max_diff, 1e-4)
def test_pixart_1024_without_resolution_binning(self):
generator = torch.manual_seed(0)
pipe = PixArtSigmaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt = self.prompt
height, width = 1024, 768
num_inference_steps = 2
image = pipe(
prompt,
height=height,
width=width,
generator=generator,
num_inference_steps=num_inference_steps,
output_type="np",
).images
image_slice = image[0, -3:, -3:, -1]
generator = torch.manual_seed(0)
no_res_bin_image = pipe(
prompt,
height=height,
width=width,
generator=generator,
num_inference_steps=num_inference_steps,
output_type="np",
use_resolution_binning=False,
).images
no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1]
assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4)
def test_pixart_512_without_resolution_binning(self):
generator = torch.manual_seed(0)
transformer = PixArtTransformer2DModel.from_pretrained(
self.ckpt_id_512, subfolder="transformer", torch_dtype=torch.float16
)
pipe = PixArtSigmaPipeline.from_pretrained(
self.ckpt_id_1024, transformer=transformer, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
prompt = self.prompt
height, width = 512, 768
num_inference_steps = 2
image = pipe(
prompt,
height=height,
width=width,
generator=generator,
num_inference_steps=num_inference_steps,
output_type="np",
).images
image_slice = image[0, -3:, -3:, -1]
generator = torch.manual_seed(0)
no_res_bin_image = pipe(
prompt,
height=height,
width=width,
generator=generator,
num_inference_steps=num_inference_steps,
output_type="np",
use_resolution_binning=False,
).images
no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1]
assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4)
|
diffusers/tests/pipelines/pixart_sigma/test_pixart.py/0
|
{
"file_path": "diffusers/tests/pipelines/pixart_sigma/test_pixart.py",
"repo_id": "diffusers",
"token_count": 8085
}
| 164
|
# coding=utf-8
# Copyright 2024 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 random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImg2ImgPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import (
floats_tensor,
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class OnnxStableDiffusionImg2ImgPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def get_dummy_inputs(self, seed=0):
image = floats_tensor((1, 3, 128, 128), rng=random.Random(seed))
generator = np.random.RandomState(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_pipeline_default_ddim(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087])
assert np.abs(image_slice - expected_slice).max() < 1e-1
def test_pipeline_pndm(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_lms(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
# warmup pass to apply optimizations
_ = pipe(**self.get_dummy_inputs())
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_euler_ancestral(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
def test_pipeline_dpm_multistep(self):
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs()
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase):
@property
def gpu_provider(self):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def gpu_options(self):
options = ort.SessionOptions()
options.enable_mem_pattern = False
return options
def test_inference_default_pndm(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg"
)
init_image = init_image.resize((768, 512))
# using the PNDM scheduler by default
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="onnx",
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=10,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
expected_slice = np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
def test_inference_k_lms(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg"
)
init_image = init_image.resize((768, 512))
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
)
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
revision="onnx",
scheduler=lms_scheduler,
safety_checker=None,
feature_extractor=None,
provider=self.gpu_provider,
sess_options=self.gpu_options,
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
generator = np.random.RandomState(0)
output = pipe(
prompt=prompt,
image=init_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=20,
generator=generator,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
expected_slice = np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
|
diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py/0
|
{
"file_path": "diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py",
"repo_id": "diffusers",
"token_count": 4266
}
| 165
|
# coding=utf-8
# Copyright 2024 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 gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet2DConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
slow,
torch_device,
)
enable_full_determinism()
class StableDiffusionUpscalePipelineFastTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def dummy_image(self):
batch_size = 1
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
return image
@property
def dummy_cond_unet_upscale(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
block_out_channels=(32, 32, 64),
layers_per_block=2,
sample_size=32,
in_channels=7,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
# SD2-specific config below
attention_head_dim=8,
use_linear_projection=True,
only_cross_attention=(True, True, False),
num_class_embeds=100,
)
return model
@property
def dummy_vae(self):
torch.manual_seed(0)
model = AutoencoderKL(
block_out_channels=[32, 32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
return model
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=512,
)
return CLIPTextModel(config)
def test_stable_diffusion_upscale(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet_upscale
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
vae = self.dummy_vae
text_encoder = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
)
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
expected_height_width = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_upscale_batch(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet_upscale
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
vae = self.dummy_vae
text_encoder = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
output = sd_pipe(
2 * [prompt],
image=2 * [low_res_image],
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
)
image = output.images
assert image.shape[0] == 2
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
num_images_per_prompt=2,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
)
image = output.images
assert image.shape[0] == 2
def test_stable_diffusion_upscale_prompt_embeds(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet_upscale
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
vae = self.dummy_vae
text_encoder = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
)
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
prompt_embeds, negative_prompt_embeds = sd_pipe.encode_prompt(prompt, device, 1, False)
if negative_prompt_embeds is not None:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
image_from_prompt_embeds = sd_pipe(
prompt_embeds=prompt_embeds,
image=[low_res_image],
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_prompt_embeds_slice = image_from_prompt_embeds[0, -3:, -3:, -1]
expected_height_width = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_prompt_embeds_slice.flatten() - expected_slice).max() < 1e-2
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
def test_stable_diffusion_upscale_fp16(self):
"""Test that stable diffusion upscale works with fp16"""
unet = self.dummy_cond_unet_upscale
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
vae = self.dummy_vae
text_encoder = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
# put models in fp16, except vae as it overflows in fp16
unet = unet.half()
text_encoder = text_encoder.half()
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = sd_pipe(
[prompt],
image=low_res_image,
generator=generator,
num_inference_steps=2,
output_type="np",
).images
expected_height_width = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
def test_stable_diffusion_upscale_from_save_pretrained(self):
pipes = []
device = "cpu" # ensure determinism for the device-dependent torch.Generator
low_res_scheduler = DDPMScheduler()
scheduler = DDIMScheduler(prediction_type="v_prediction")
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionUpscalePipeline(
unet=self.dummy_cond_unet_upscale,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
vae=self.dummy_vae,
text_encoder=self.dummy_text_encoder,
tokenizer=tokenizer,
max_noise_level=350,
)
sd_pipe = sd_pipe.to(device)
pipes.append(sd_pipe)
with tempfile.TemporaryDirectory() as tmpdirname:
sd_pipe.save_pretrained(tmpdirname)
sd_pipe = StableDiffusionUpscalePipeline.from_pretrained(tmpdirname).to(device)
pipes.append(sd_pipe)
prompt = "A painting of a squirrel eating a burger"
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
image_slices = []
for pipe in pipes:
generator = torch.Generator(device=device).manual_seed(0)
image = pipe(
[prompt],
image=low_res_image,
generator=generator,
guidance_scale=6.0,
noise_level=20,
num_inference_steps=2,
output_type="np",
).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
@slow
@require_torch_gpu
class StableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_diffusion_upscale_pipeline(self):
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat.npy"
)
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
prompt = "a cat sitting on a park bench"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
image=image,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 1e-3
def test_stable_diffusion_upscale_pipeline_fp16(self):
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
"/upsampled_cat_fp16.npy"
)
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipe = StableDiffusionUpscalePipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
prompt = "a cat sitting on a park bench"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
image=image,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 5e-1
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-upscale/low_res_cat.png"
)
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipe = StableDiffusionUpscalePipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
prompt = "a cat sitting on a park bench"
generator = torch.manual_seed(0)
_ = pipe(
prompt=prompt,
image=image,
generator=generator,
num_inference_steps=5,
output_type="np",
)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.9 GB is allocated
assert mem_bytes < 2.9 * 10**9
|
diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py/0
|
{
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py",
"repo_id": "diffusers",
"token_count": 8456
}
| 166
|
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, nightly, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class StableUnCLIPPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableUnCLIPPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
test_xformers_attention = False
def get_dummy_components(self):
embedder_hidden_size = 32
embedder_projection_dim = embedder_hidden_size
# prior components
torch.manual_seed(0)
prior_tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
prior_text_encoder = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=embedder_hidden_size,
projection_dim=embedder_projection_dim,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
)
torch.manual_seed(0)
prior = PriorTransformer(
num_attention_heads=2,
attention_head_dim=12,
embedding_dim=embedder_projection_dim,
num_layers=1,
)
torch.manual_seed(0)
prior_scheduler = DDPMScheduler(
variance_type="fixed_small_log",
prediction_type="sample",
num_train_timesteps=1000,
clip_sample=True,
clip_sample_range=5.0,
beta_schedule="squaredcos_cap_v2",
)
# regular denoising components
torch.manual_seed(0)
image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedder_hidden_size)
image_noising_scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2")
torch.manual_seed(0)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
text_encoder = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=embedder_hidden_size,
projection_dim=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
)
torch.manual_seed(0)
unet = UNet2DConditionModel(
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),
block_out_channels=(32, 64),
attention_head_dim=(2, 4),
class_embed_type="projection",
# The class embeddings are the noise augmented image embeddings.
# I.e. the image embeddings concated with the noised embeddings of the same dimension
projection_class_embeddings_input_dim=embedder_projection_dim * 2,
cross_attention_dim=embedder_hidden_size,
layers_per_block=1,
upcast_attention=True,
use_linear_projection=True,
)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_schedule="scaled_linear",
beta_start=0.00085,
beta_end=0.012,
prediction_type="v_prediction",
set_alpha_to_one=False,
steps_offset=1,
)
torch.manual_seed(0)
vae = AutoencoderKL()
components = {
# prior components
"prior_tokenizer": prior_tokenizer,
"prior_text_encoder": prior_text_encoder,
"prior": prior,
"prior_scheduler": prior_scheduler,
# image noising components
"image_normalizer": image_normalizer,
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"prior_num_inference_steps": 2,
"output_type": "np",
}
return inputs
# Overriding PipelineTesterMixin::test_attention_slicing_forward_pass
# because UnCLIP GPU undeterminism requires a looser check.
def test_attention_slicing_forward_pass(self):
test_max_difference = torch_device == "cpu"
self._test_attention_slicing_forward_pass(test_max_difference=test_max_difference)
# Overriding PipelineTesterMixin::test_inference_batch_single_identical
# because UnCLIP undeterminism requires a looser check.
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=1e-3)
@nightly
@require_torch_gpu
class StableUnCLIPPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_unclip(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"
)
pipe = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l", torch_dtype=torch.float16)
pipe.set_progress_bar_config(disable=None)
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipe("anime turle", generator=generator, output_type="np")
image = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(image, expected_image)
def test_stable_unclip_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l", torch_dtype=torch.float16)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_ = pipe(
"anime turtle",
prior_num_inference_steps=2,
num_inference_steps=2,
output_type="np",
)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
|
diffusers/tests/pipelines/stable_unclip/test_stable_unclip.py/0
|
{
"file_path": "diffusers/tests/pipelines/stable_unclip/test_stable_unclip.py",
"repo_id": "diffusers",
"token_count": 4023
}
| 167
|
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class PNDMSchedulerTest(SchedulerCommonTest):
scheduler_classes = (PNDMScheduler,)
forward_default_kwargs = (("num_inference_steps", 50),)
def get_scheduler_config(self, **kwargs):
config = {
"num_train_timesteps": 1000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**kwargs)
return config
def check_over_configs(self, time_step=0, **config):
kwargs = dict(self.forward_default_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
sample = self.dummy_sample
residual = 0.1 * sample
dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
scheduler_config = self.get_scheduler_config(**config)
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(num_inference_steps)
# copy over dummy past residuals
scheduler.ets = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(tmpdirname)
new_scheduler = scheduler_class.from_pretrained(tmpdirname)
new_scheduler.set_timesteps(num_inference_steps)
# copy over dummy past residuals
new_scheduler.ets = dummy_past_residuals[:]
output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
def test_from_save_pretrained(self):
pass
def check_over_forward(self, time_step=0, **forward_kwargs):
kwargs = dict(self.forward_default_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
sample = self.dummy_sample
residual = 0.1 * sample
dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(num_inference_steps)
# copy over dummy past residuals (must be after setting timesteps)
scheduler.ets = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(tmpdirname)
new_scheduler = scheduler_class.from_pretrained(tmpdirname)
# copy over dummy past residuals
new_scheduler.set_timesteps(num_inference_steps)
# copy over dummy past residual (must be after setting timesteps)
new_scheduler.ets = dummy_past_residuals[:]
output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
def full_loop(self, **config):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config(**config)
scheduler = scheduler_class(**scheduler_config)
num_inference_steps = 10
model = self.dummy_model()
sample = self.dummy_sample_deter
scheduler.set_timesteps(num_inference_steps)
for i, t in enumerate(scheduler.prk_timesteps):
residual = model(sample, t)
sample = scheduler.step_prk(residual, t, sample).prev_sample
for i, t in enumerate(scheduler.plms_timesteps):
residual = model(sample, t)
sample = scheduler.step_plms(residual, t, sample).prev_sample
return sample
def test_step_shape(self):
kwargs = dict(self.forward_default_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
for scheduler_class in self.scheduler_classes:
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
sample = self.dummy_sample
residual = 0.1 * sample
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
scheduler.set_timesteps(num_inference_steps)
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
kwargs["num_inference_steps"] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
scheduler.ets = dummy_past_residuals[:]
output_0 = scheduler.step_prk(residual, 0, sample, **kwargs).prev_sample
output_1 = scheduler.step_prk(residual, 1, sample, **kwargs).prev_sample
self.assertEqual(output_0.shape, sample.shape)
self.assertEqual(output_0.shape, output_1.shape)
output_0 = scheduler.step_plms(residual, 0, sample, **kwargs).prev_sample
output_1 = scheduler.step_plms(residual, 1, sample, **kwargs).prev_sample
self.assertEqual(output_0.shape, sample.shape)
self.assertEqual(output_0.shape, output_1.shape)
def test_timesteps(self):
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=timesteps)
def test_steps_offset(self):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=steps_offset)
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config(steps_offset=1)
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(10)
assert torch.equal(
scheduler.timesteps,
torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]
),
)
def test_betas(self):
for beta_start, beta_end in zip([0.0001, 0.001], [0.002, 0.02]):
self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
def test_schedules(self):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=schedule)
def test_prediction_type(self):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=prediction_type)
def test_time_indices(self):
for t in [1, 5, 10]:
self.check_over_forward(time_step=t)
def test_inference_steps(self):
for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]):
self.check_over_forward(num_inference_steps=num_inference_steps)
def test_pow_of_3_inference_steps(self):
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
num_inference_steps = 27
for scheduler_class in self.scheduler_classes:
sample = self.dummy_sample
residual = 0.1 * sample
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(num_inference_steps)
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2]):
sample = scheduler.step_prk(residual, t, sample).prev_sample
def test_inference_plms_no_past_residuals(self):
with self.assertRaises(ValueError):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
scheduler.step_plms(self.dummy_sample, 1, self.dummy_sample).prev_sample
def test_full_loop_no_noise(self):
sample = self.full_loop()
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 198.1318) < 1e-2
assert abs(result_mean.item() - 0.2580) < 1e-3
def test_full_loop_with_v_prediction(self):
sample = self.full_loop(prediction_type="v_prediction")
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 67.3986) < 1e-2
assert abs(result_mean.item() - 0.0878) < 1e-3
def test_full_loop_with_set_alpha_to_one(self):
# We specify different beta, so that the first alpha is 0.99
sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 230.0399) < 1e-2
assert abs(result_mean.item() - 0.2995) < 1e-3
def test_full_loop_with_no_set_alpha_to_one(self):
# We specify different beta, so that the first alpha is 0.99
sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 186.9482) < 1e-2
assert abs(result_mean.item() - 0.2434) < 1e-3
|
diffusers/tests/schedulers/test_scheduler_pndm.py/0
|
{
"file_path": "diffusers/tests/schedulers/test_scheduler_pndm.py",
"repo_id": "diffusers",
"token_count": 4654
}
| 168
|
# 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.
import argparse
import collections
import importlib.util
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
TRANSFORMERS_PATH = "src/diffusers"
PATH_TO_DOCS = "docs/source/en"
REPO_PATH = "."
def _find_text_in_file(filename, start_prompt, end_prompt):
"""
Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty
lines.
"""
with open(filename, "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
# Find the start prompt.
start_index = 0
while not lines[start_index].startswith(start_prompt):
start_index += 1
start_index += 1
end_index = start_index
while not lines[end_index].startswith(end_prompt):
end_index += 1
end_index -= 1
while len(lines[start_index]) <= 1:
start_index += 1
while len(lines[end_index]) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index]), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
ALLOWED_MODEL_SUFFIXES = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the diffusers module imported is the one in the repo.
spec = importlib.util.spec_from_file_location(
"diffusers",
os.path.join(TRANSFORMERS_PATH, "__init__.py"),
submodule_search_locations=[TRANSFORMERS_PATH],
)
diffusers_module = spec.loader.load_module()
# Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
def camel_case_split(identifier):
"""Split a camelcased `identifier` into words."""
matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier)
return [m.group(0) for m in matches]
def _center_text(text, width):
text_length = 2 if text == "✅" or text == "❌" else len(text)
left_indent = (width - text_length) // 2
right_indent = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def get_model_table_from_auto_modules():
"""Generates an up-to-date model table from the content of the auto modules."""
# Dictionary model names to config.
config_mapping_names = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
model_name_to_config = {
name: config_mapping_names[code]
for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
if code in config_mapping_names
}
model_name_to_prefix = {name: config.replace("ConfigMixin", "") for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
slow_tokenizers = collections.defaultdict(bool)
fast_tokenizers = collections.defaultdict(bool)
pt_models = collections.defaultdict(bool)
tf_models = collections.defaultdict(bool)
flax_models = collections.defaultdict(bool)
# Let's lookup through all diffusers object (once).
for attr_name in dir(diffusers_module):
lookup_dict = None
if attr_name.endswith("Tokenizer"):
lookup_dict = slow_tokenizers
attr_name = attr_name[:-9]
elif attr_name.endswith("TokenizerFast"):
lookup_dict = fast_tokenizers
attr_name = attr_name[:-13]
elif _re_tf_models.match(attr_name) is not None:
lookup_dict = tf_models
attr_name = _re_tf_models.match(attr_name).groups()[0]
elif _re_flax_models.match(attr_name) is not None:
lookup_dict = flax_models
attr_name = _re_flax_models.match(attr_name).groups()[0]
elif _re_pt_models.match(attr_name) is not None:
lookup_dict = pt_models
attr_name = _re_pt_models.match(attr_name).groups()[0]
if lookup_dict is not None:
while len(attr_name) > 0:
if attr_name in model_name_to_prefix.values():
lookup_dict[attr_name] = True
break
# Try again after removing the last word in the name
attr_name = "".join(camel_case_split(attr_name)[:-1])
# Let's build that table!
model_names = list(model_name_to_config.keys())
model_names.sort(key=str.lower)
columns = ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
widths = [len(c) + 2 for c in columns]
widths[0] = max([len(name) for name in model_names]) + 2
# Build the table per se
table = "|" + "|".join([_center_text(c, w) for c, w in zip(columns, widths)]) + "|\n"
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n"
check = {True: "✅", False: "❌"}
for name in model_names:
prefix = model_name_to_prefix[name]
line = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n"
return table
def check_model_table(overwrite=False):
"""Check the model table in the index.rst is consistent with the state of the lib and maybe `overwrite`."""
current_table, start_index, end_index, lines = _find_text_in_file(
filename=os.path.join(PATH_TO_DOCS, "index.md"),
start_prompt="<!--This table is updated automatically from the auto modules",
end_prompt="<!-- End table-->",
)
new_table = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(PATH_TO_DOCS, "index.md"), "w", encoding="utf-8", newline="\n") as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:])
else:
raise ValueError(
"The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
args = parser.parse_args()
check_model_table(args.fix_and_overwrite)
|
diffusers/utils/check_table.py/0
|
{
"file_path": "diffusers/utils/check_table.py",
"repo_id": "diffusers",
"token_count": 3011
}
| 169
|
"""
This script demonstrates the use of `LeRobotDataset` class for handling and processing robotic datasets from Hugging Face.
It illustrates how to load datasets, manipulate them, and apply transformations suitable for machine learning tasks in PyTorch.
Features included in this script:
- Loading a dataset and accessing its properties.
- Filtering data by episode number.
- Converting tensor data for visualization.
- Saving video files from dataset frames.
- Using advanced dataset features like timestamp-based frame selection.
- Demonstrating compatibility with PyTorch DataLoader for batch processing.
The script ends with examples of how to batch process data using PyTorch's DataLoader.
"""
from pathlib import Path
from pprint import pprint
import imageio
import torch
import lerobot
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
print("List of available datasets:")
pprint(lerobot.available_datasets)
# Let's take one for this example
repo_id = "lerobot/pusht"
# You can easily load a dataset from a Hugging Face repository
dataset = LeRobotDataset(repo_id)
# LeRobotDataset is actually a thin wrapper around an underlying Hugging Face dataset
# (see https://huggingface.co/docs/datasets/index for more information).
print(dataset)
print(dataset.hf_dataset)
# And provides additional utilities for robotics and compatibility with Pytorch
print(f"\naverage number of frames per episode: {dataset.num_samples / dataset.num_episodes:.3f}")
print(f"frames per second used during data collection: {dataset.fps=}")
print(f"keys to access images from cameras: {dataset.camera_keys=}\n")
# Access frame indexes associated to first episode
episode_index = 0
from_idx = dataset.episode_data_index["from"][episode_index].item()
to_idx = dataset.episode_data_index["to"][episode_index].item()
# LeRobot datasets actually subclass PyTorch datasets so you can do everything you know and love from working
# with the latter, like iterating through the dataset. Here we grab all the image frames.
frames = [dataset[idx]["observation.image"] for idx in range(from_idx, to_idx)]
# Video frames are now float32 in range [0,1] channel first (c,h,w) to follow pytorch convention. To visualize
# them, we convert to uint8 in range [0,255]
frames = [(frame * 255).type(torch.uint8) for frame in frames]
# and to channel last (h,w,c).
frames = [frame.permute((1, 2, 0)).numpy() for frame in frames]
# Finally, we save the frames to a mp4 video for visualization.
Path("outputs/examples/1_load_lerobot_dataset").mkdir(parents=True, exist_ok=True)
imageio.mimsave("outputs/examples/1_load_lerobot_dataset/episode_0.mp4", frames, fps=dataset.fps)
# For many machine learning applications we need to load the history of past observations or trajectories of
# future actions. Our datasets can load previous and future frames for each key/modality, using timestamps
# differences with the current loaded frame. For instance:
delta_timestamps = {
# loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame
"observation.image": [-1, -0.5, -0.20, 0],
# loads 8 state vectors: 1.5 seconds before, 1 second before, ... 20 ms, 10 ms, and current frame
"observation.state": [-1.5, -1, -0.5, -0.20, -0.10, -0.02, -0.01, 0],
# loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future
"action": [t / dataset.fps for t in range(64)],
}
dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
print(f"\n{dataset[0]['observation.image'].shape=}") # (4,c,h,w)
print(f"{dataset[0]['observation.state'].shape=}") # (8,c)
print(f"{dataset[0]['action'].shape=}\n") # (64,c)
# Finally, our datasets are fully compatible with PyTorch dataloaders and samplers because they are just
# PyTorch datasets.
dataloader = torch.utils.data.DataLoader(
dataset,
num_workers=0,
batch_size=32,
shuffle=True,
)
for batch in dataloader:
print(f"{batch['observation.image'].shape=}") # (32,4,c,h,w)
print(f"{batch['observation.state'].shape=}") # (32,8,c)
print(f"{batch['action'].shape=}") # (32,64,c)
break
|
lerobot/examples/1_load_lerobot_dataset.py/0
|
{
"file_path": "lerobot/examples/1_load_lerobot_dataset.py",
"repo_id": "lerobot",
"token_count": 1354
}
| 170
|
## Using / Updating `CODEBASE_VERSION` (for maintainers)
Since our dataset pushed to the hub are decoupled with the evolution of this repo, we ensure compatibility of
the datasets with our code, we use a `CODEBASE_VERSION` (defined in
lerobot/common/datasets/lerobot_dataset.py) variable.
For instance, [`lerobot/pusht`](https://huggingface.co/datasets/lerobot/pusht) has many versions to maintain backward compatibility between LeRobot codebase versions:
- [v1.0](https://huggingface.co/datasets/lerobot/pusht/tree/v1.0)
- [v1.1](https://huggingface.co/datasets/lerobot/pusht/tree/v1.1)
- [v1.2](https://huggingface.co/datasets/lerobot/pusht/tree/v1.2)
- [v1.3](https://huggingface.co/datasets/lerobot/pusht/tree/v1.3)
- [v1.4](https://huggingface.co/datasets/lerobot/pusht/tree/v1.4)
- [v1.5](https://huggingface.co/datasets/lerobot/pusht/tree/v1.5)
- [v1.6](https://huggingface.co/datasets/lerobot/pusht/tree/v1.6) <-- last version
- [main](https://huggingface.co/datasets/lerobot/pusht/tree/main) <-- points to the last version
Starting with v1.6, every dataset pushed to the hub or saved locally also have this version number in their
`info.json` metadata.
### Uploading a new dataset
If you are pushing a new dataset, you don't need to worry about any of the instructions below, nor to be
compatible with previous codebase versions. The `push_dataset_to_hub.py` script will automatically tag your
dataset with the current `CODEBASE_VERSION`.
### Updating an existing dataset
If you want to update an existing dataset, you need to change the `CODEBASE_VERSION` from `lerobot_dataset.py`
before running `push_dataset_to_hub.py`. This is especially useful if you introduce a breaking change
intentionally or not (i.e. something not backward compatible such as modifying the reward functions used,
deleting some frames at the end of an episode, etc.). That way, people running a previous version of the
codebase won't be affected by your change and backward compatibility is maintained.
However, you will need to update the version of ALL the other datasets so that they have the new
`CODEBASE_VERSION` as a branch in their hugging face dataset repository. Don't worry, there is an easy way
that doesn't require to run `push_dataset_to_hub.py`. You can just "branch-out" from the `main` branch on HF
dataset repo by running this script which corresponds to a `git checkout -b` (so no copy or upload needed):
```python
from huggingface_hub import HfApi
from lerobot import available_datasets
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
api = HfApi()
for repo_id in available_datasets:
dataset_info = api.list_repo_refs(repo_id, repo_type="dataset")
branches = [b.name for b in dataset_info.branches]
if CODEBASE_VERSION in branches:
print(f"{repo_id} already @{CODEBASE_VERSION}, skipping.")
continue
else:
# Now create a branch named after the new version by branching out from "main"
# which is expected to be the preceding version
api.create_branch(repo_id, repo_type="dataset", branch=CODEBASE_VERSION, revision="main")
print(f"{repo_id} successfully updated @{CODEBASE_VERSION}")
```
|
lerobot/lerobot/common/datasets/push_dataset_to_hub/CODEBASE_VERSION.md/0
|
{
"file_path": "lerobot/lerobot/common/datasets/push_dataset_to_hub/CODEBASE_VERSION.md",
"repo_id": "lerobot",
"token_count": 1078
}
| 171
|
#!/usr/bin/env python
# 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.
"""
Contains utilities to process raw data format from dora-record
"""
import re
import warnings
from pathlib import Path
import pandas as pd
import torch
from datasets import Dataset, Features, Image, Sequence, Value
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
from lerobot.common.datasets.utils import (
calculate_episode_data_index,
hf_transform_to_torch,
)
from lerobot.common.datasets.video_utils import VideoFrame
def check_format(raw_dir) -> bool:
assert raw_dir.exists()
leader_file = list(raw_dir.glob("*.parquet"))
if len(leader_file) == 0:
raise ValueError(f"Missing parquet files in '{raw_dir}'")
return True
def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episodes: list[int] | None = None):
# Load data stream that will be used as reference for the timestamps synchronization
reference_files = list(raw_dir.glob("observation.images.cam_*.parquet"))
if len(reference_files) == 0:
raise ValueError(f"Missing reference files for camera, starting with in '{raw_dir}'")
# select first camera in alphanumeric order
reference_key = sorted(reference_files)[0].stem
reference_df = pd.read_parquet(raw_dir / f"{reference_key}.parquet")
reference_df = reference_df[["timestamp_utc", reference_key]]
# Merge all data stream using nearest backward strategy
df = reference_df
for path in raw_dir.glob("*.parquet"):
key = path.stem # action or observation.state or ...
if key == reference_key:
continue
if "failed_episode_index" in key:
# TODO(rcadene): add support for removing episodes that are tagged as "failed"
continue
modality_df = pd.read_parquet(path)
modality_df = modality_df[["timestamp_utc", key]]
df = pd.merge_asof(
df,
modality_df,
on="timestamp_utc",
# "nearest" is the best option over "backward", since the latter can desynchronizes camera timestamps by
# matching timestamps that are too far appart, in order to fit the backward constraints. It's not the case for "nearest".
# However, note that "nearest" might synchronize the reference camera with other cameras on slightly future timestamps.
# are too far appart.
direction="nearest",
tolerance=pd.Timedelta(f"{1/fps} seconds"),
)
# Remove rows with episode_index -1 which indicates data that correspond to in-between episodes
df = df[df["episode_index"] != -1]
image_keys = [key for key in df if "observation.images." in key]
def get_episode_index(row):
episode_index_per_cam = {}
for key in image_keys:
path = row[key][0]["path"]
match = re.search(r"_(\d{6}).mp4", path)
if not match:
raise ValueError(path)
episode_index = int(match.group(1))
episode_index_per_cam[key] = episode_index
if len(set(episode_index_per_cam.values())) != 1:
raise ValueError(
f"All cameras are expected to belong to the same episode, but getting {episode_index_per_cam}"
)
return episode_index
df["episode_index"] = df.apply(get_episode_index, axis=1)
# dora only use arrays, so single values are encapsulated into a list
df["frame_index"] = df.groupby("episode_index").cumcount()
df = df.reset_index()
df["index"] = df.index
# set 'next.done' to True for the last frame of each episode
df["next.done"] = False
df.loc[df.groupby("episode_index").tail(1).index, "next.done"] = True
df["timestamp"] = df["timestamp_utc"].map(lambda x: x.timestamp())
# each episode starts with timestamp 0 to match the ones from the video
df["timestamp"] = df.groupby("episode_index")["timestamp"].transform(lambda x: x - x.iloc[0])
del df["timestamp_utc"]
# sanity check
has_nan = df.isna().any().any()
if has_nan:
raise ValueError("Dataset contains Nan values.")
# sanity check episode indices go from 0 to n-1
ep_ids = [ep_idx for ep_idx, _ in df.groupby("episode_index")]
expected_ep_ids = list(range(df["episode_index"].max() + 1))
if ep_ids != expected_ep_ids:
raise ValueError(f"Episodes indices go from {ep_ids} instead of {expected_ep_ids}")
# Create symlink to raw videos directory (that needs to be absolute not relative)
videos_dir.parent.mkdir(parents=True, exist_ok=True)
videos_dir.symlink_to((raw_dir / "videos").absolute())
# sanity check the video paths are well formated
for key in df:
if "observation.images." not in key:
continue
for ep_idx in ep_ids:
video_path = videos_dir / f"{key}_episode_{ep_idx:06d}.mp4"
if not video_path.exists():
raise ValueError(f"Video file not found in {video_path}")
data_dict = {}
for key in df:
# is video frame
if "observation.images." in key:
# we need `[0] because dora only use arrays, so single values are encapsulated into a list.
# it is the case for video_frame dictionary = [{"path": ..., "timestamp": ...}]
data_dict[key] = [video_frame[0] for video_frame in df[key].values]
# sanity check the video path is well formated
video_path = videos_dir.parent / data_dict[key][0]["path"]
if not video_path.exists():
raise ValueError(f"Video file not found in {video_path}")
# is number
elif df[key].iloc[0].ndim == 0 or df[key].iloc[0].shape[0] == 1:
data_dict[key] = torch.from_numpy(df[key].values)
# is vector
elif df[key].iloc[0].shape[0] > 1:
data_dict[key] = torch.stack([torch.from_numpy(x.copy()) for x in df[key].values])
else:
raise ValueError(key)
return data_dict
def to_hf_dataset(data_dict, video) -> Dataset:
features = {}
keys = [key for key in data_dict if "observation.images." in key]
for key in keys:
if video:
features[key] = VideoFrame()
else:
features[key] = Image()
features["observation.state"] = Sequence(
length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
)
if "observation.velocity" in data_dict:
features["observation.velocity"] = Sequence(
length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
)
if "observation.effort" in data_dict:
features["observation.effort"] = Sequence(
length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
)
features["action"] = Sequence(
length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
)
features["episode_index"] = Value(dtype="int64", id=None)
features["frame_index"] = Value(dtype="int64", id=None)
features["timestamp"] = Value(dtype="float32", id=None)
features["next.done"] = Value(dtype="bool", id=None)
features["index"] = Value(dtype="int64", id=None)
hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
hf_dataset.set_transform(hf_transform_to_torch)
return hf_dataset
def from_raw_to_lerobot_format(
raw_dir: Path,
videos_dir: Path,
fps: int | None = None,
video: bool = True,
episodes: list[int] | None = None,
encoding: dict | None = None,
):
# sanity check
check_format(raw_dir)
if fps is None:
fps = 30
else:
raise NotImplementedError()
if not video:
raise NotImplementedError()
if encoding is not None:
warnings.warn(
"Video encoding is currently done outside of LeRobot for the dora_parquet format.",
stacklevel=1,
)
data_df = load_from_raw(raw_dir, videos_dir, fps, episodes)
hf_dataset = to_hf_dataset(data_df, video)
episode_data_index = calculate_episode_data_index(hf_dataset)
info = {
"codebase_version": CODEBASE_VERSION,
"fps": fps,
"video": video,
}
if video:
info["encoding"] = "unknown"
return hf_dataset, episode_data_index, info
|
lerobot/lerobot/common/datasets/push_dataset_to_hub/dora_parquet_format.py/0
|
{
"file_path": "lerobot/lerobot/common/datasets/push_dataset_to_hub/dora_parquet_format.py",
"repo_id": "lerobot",
"token_count": 3602
}
| 172
|
#!/usr/bin/env python
# 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.
"""Borrowed from https://github.com/fyhMer/fowm/blob/main/src/logger.py
# TODO(rcadene, alexander-soare): clean this file
"""
import logging
import os
import re
from glob import glob
from pathlib import Path
import torch
from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
from omegaconf import DictConfig, OmegaConf
from termcolor import colored
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LRScheduler
from lerobot.common.policies.policy_protocol import Policy
from lerobot.common.utils.utils import get_global_random_state, set_global_random_state
def log_output_dir(out_dir):
logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}")
def cfg_to_group(cfg: DictConfig, return_list: bool = False) -> list[str] | str:
"""Return a group name for logging. Optionally returns group name as list."""
lst = [
f"policy:{cfg.policy.name}",
f"dataset:{cfg.dataset_repo_id}",
f"env:{cfg.env.name}",
f"seed:{cfg.seed}",
]
return lst if return_list else "-".join(lst)
def get_wandb_run_id_from_filesystem(checkpoint_dir: Path) -> str:
# Get the WandB run ID.
paths = glob(str(checkpoint_dir / "../wandb/latest-run/run-*"))
if len(paths) != 1:
raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.")
match = re.search(r"run-([^\.]+).wandb", paths[0].split("/")[-1])
if match is None:
raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.")
wandb_run_id = match.groups(0)[0]
return wandb_run_id
class Logger:
"""Primary logger object. Logs either locally or using wandb.
The logger creates the following directory structure:
provided_log_dir
├── .hydra # hydra's configuration cache
├── checkpoints
│ ├── specific_checkpoint_name
│ │ ├── pretrained_model # Hugging Face pretrained model directory
│ │ │ ├── ...
│ │ └── training_state.pth # optimizer, scheduler, and random states + training step
| ├── another_specific_checkpoint_name
│ │ ├── ...
| ├── ...
│ └── last # a softlink to the last logged checkpoint
"""
pretrained_model_dir_name = "pretrained_model"
training_state_file_name = "training_state.pth"
def __init__(self, cfg: DictConfig, log_dir: str, wandb_job_name: str | None = None):
"""
Args:
log_dir: The directory to save all logs and training outputs to.
job_name: The WandB job name.
"""
self._cfg = cfg
self.log_dir = Path(log_dir)
self.log_dir.mkdir(parents=True, exist_ok=True)
self.checkpoints_dir = self.get_checkpoints_dir(log_dir)
self.last_checkpoint_dir = self.get_last_checkpoint_dir(log_dir)
self.last_pretrained_model_dir = self.get_last_pretrained_model_dir(log_dir)
# Set up WandB.
self._group = cfg_to_group(cfg)
project = cfg.get("wandb", {}).get("project")
entity = cfg.get("wandb", {}).get("entity")
enable_wandb = cfg.get("wandb", {}).get("enable", False)
run_offline = not enable_wandb or not project
if run_offline:
logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))
self._wandb = None
else:
os.environ["WANDB_SILENT"] = "true"
import wandb
wandb_run_id = None
if cfg.resume:
wandb_run_id = get_wandb_run_id_from_filesystem(self.checkpoints_dir)
wandb.init(
id=wandb_run_id,
project=project,
entity=entity,
name=wandb_job_name,
notes=cfg.get("wandb", {}).get("notes"),
tags=cfg_to_group(cfg, return_list=True),
dir=log_dir,
config=OmegaConf.to_container(cfg, resolve=True),
# TODO(rcadene): try set to True
save_code=False,
# TODO(rcadene): split train and eval, and run async eval with job_type="eval"
job_type="train_eval",
resume="must" if cfg.resume else None,
)
print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"]))
logging.info(f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}")
self._wandb = wandb
@classmethod
def get_checkpoints_dir(cls, log_dir: str | Path) -> Path:
"""Given the log directory, get the sub-directory in which checkpoints will be saved."""
return Path(log_dir) / "checkpoints"
@classmethod
def get_last_checkpoint_dir(cls, log_dir: str | Path) -> Path:
"""Given the log directory, get the sub-directory in which the last checkpoint will be saved."""
return cls.get_checkpoints_dir(log_dir) / "last"
@classmethod
def get_last_pretrained_model_dir(cls, log_dir: str | Path) -> Path:
"""
Given the log directory, get the sub-directory in which the last checkpoint's pretrained weights will
be saved.
"""
return cls.get_last_checkpoint_dir(log_dir) / cls.pretrained_model_dir_name
def save_model(self, save_dir: Path, policy: Policy, wandb_artifact_name: str | None = None):
"""Save the weights of the Policy model using PyTorchModelHubMixin.
The weights are saved in a folder called "pretrained_model" under the checkpoint directory.
Optionally also upload the model to WandB.
"""
self.checkpoints_dir.mkdir(parents=True, exist_ok=True)
policy.save_pretrained(save_dir)
# Also save the full Hydra config for the env configuration.
OmegaConf.save(self._cfg, save_dir / "config.yaml")
if self._wandb and not self._cfg.wandb.disable_artifact:
# note wandb artifact does not accept ":" or "/" in its name
artifact = self._wandb.Artifact(wandb_artifact_name, type="model")
artifact.add_file(save_dir / SAFETENSORS_SINGLE_FILE)
self._wandb.log_artifact(artifact)
if self.last_checkpoint_dir.exists():
os.remove(self.last_checkpoint_dir)
def save_training_state(
self,
save_dir: Path,
train_step: int,
optimizer: Optimizer,
scheduler: LRScheduler | None,
):
"""Checkpoint the global training_step, optimizer state, scheduler state, and random state.
All of these are saved as "training_state.pth" under the checkpoint directory.
"""
training_state = {
"step": train_step,
"optimizer": optimizer.state_dict(),
**get_global_random_state(),
}
if scheduler is not None:
training_state["scheduler"] = scheduler.state_dict()
torch.save(training_state, save_dir / self.training_state_file_name)
def save_checkpont(
self,
train_step: int,
policy: Policy,
optimizer: Optimizer,
scheduler: LRScheduler | None,
identifier: str,
):
"""Checkpoint the model weights and the training state."""
checkpoint_dir = self.checkpoints_dir / str(identifier)
wandb_artifact_name = (
None
if self._wandb is None
else f"{self._group.replace(':', '_').replace('/', '_')}-{self._cfg.seed}-{identifier}"
)
self.save_model(
checkpoint_dir / self.pretrained_model_dir_name, policy, wandb_artifact_name=wandb_artifact_name
)
self.save_training_state(checkpoint_dir, train_step, optimizer, scheduler)
os.symlink(checkpoint_dir.absolute(), self.last_checkpoint_dir)
def load_last_training_state(self, optimizer: Optimizer, scheduler: LRScheduler | None) -> int:
"""
Given the last checkpoint in the logging directory, load the optimizer state, scheduler state, and
random state, and return the global training step.
"""
training_state = torch.load(self.last_checkpoint_dir / self.training_state_file_name)
optimizer.load_state_dict(training_state["optimizer"])
if scheduler is not None:
scheduler.load_state_dict(training_state["scheduler"])
elif "scheduler" in training_state:
raise ValueError(
"The checkpoint contains a scheduler state_dict, but no LRScheduler was provided."
)
# Small hack to get the expected keys: use `get_global_random_state`.
set_global_random_state({k: training_state[k] for k in get_global_random_state()})
return training_state["step"]
def log_dict(self, d, step, mode="train"):
assert mode in {"train", "eval"}
# TODO(alexander-soare): Add local text log.
if self._wandb is not None:
for k, v in d.items():
if not isinstance(v, (int, float, str)):
logging.warning(
f'WandB logging of key "{k}" was ignored as its type is not handled by this wrapper.'
)
continue
self._wandb.log({f"{mode}/{k}": v}, step=step)
def log_video(self, video_path: str, step: int, mode: str = "train"):
assert mode in {"train", "eval"}
assert self._wandb is not None
wandb_video = self._wandb.Video(video_path, fps=self._cfg.fps, format="mp4")
self._wandb.log({f"{mode}/video": wandb_video}, step=step)
|
lerobot/lerobot/common/logger.py/0
|
{
"file_path": "lerobot/lerobot/common/logger.py",
"repo_id": "lerobot",
"token_count": 4312
}
| 173
|
import enum
import time
import traceback
from copy import deepcopy
from pathlib import Path
import numpy as np
import tqdm
from dynamixel_sdk import (
COMM_SUCCESS,
DXL_HIBYTE,
DXL_HIWORD,
DXL_LOBYTE,
DXL_LOWORD,
GroupSyncRead,
GroupSyncWrite,
PacketHandler,
PortHandler,
)
from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
from lerobot.common.utils.utils import capture_timestamp_utc
PROTOCOL_VERSION = 2.0
BAUDRATE = 1_000_000
TIMEOUT_MS = 1000
MAX_ID_RANGE = 252
# https://emanual.robotis.com/docs/en/dxl/x/xl330-m077
# https://emanual.robotis.com/docs/en/dxl/x/xl330-m288
# https://emanual.robotis.com/docs/en/dxl/x/xl430-w250
# https://emanual.robotis.com/docs/en/dxl/x/xm430-w350
# https://emanual.robotis.com/docs/en/dxl/x/xm540-w270
# data_name: (address, size_byte)
X_SERIES_CONTROL_TABLE = {
"Model_Number": (0, 2),
"Model_Information": (2, 4),
"Firmware_Version": (6, 1),
"ID": (7, 1),
"Baud_Rate": (8, 1),
"Return_Delay_Time": (9, 1),
"Drive_Mode": (10, 1),
"Operating_Mode": (11, 1),
"Secondary_ID": (12, 1),
"Protocol_Type": (13, 1),
"Homing_Offset": (20, 4),
"Moving_Threshold": (24, 4),
"Temperature_Limit": (31, 1),
"Max_Voltage_Limit": (32, 2),
"Min_Voltage_Limit": (34, 2),
"PWM_Limit": (36, 2),
"Current_Limit": (38, 2),
"Acceleration_Limit": (40, 4),
"Velocity_Limit": (44, 4),
"Max_Position_Limit": (48, 4),
"Min_Position_Limit": (52, 4),
"Shutdown": (63, 1),
"Torque_Enable": (64, 1),
"LED": (65, 1),
"Status_Return_Level": (68, 1),
"Registered_Instruction": (69, 1),
"Hardware_Error_Status": (70, 1),
"Velocity_I_Gain": (76, 2),
"Velocity_P_Gain": (78, 2),
"Position_D_Gain": (80, 2),
"Position_I_Gain": (82, 2),
"Position_P_Gain": (84, 2),
"Feedforward_2nd_Gain": (88, 2),
"Feedforward_1st_Gain": (90, 2),
"Bus_Watchdog": (98, 1),
"Goal_PWM": (100, 2),
"Goal_Current": (102, 2),
"Goal_Velocity": (104, 4),
"Profile_Acceleration": (108, 4),
"Profile_Velocity": (112, 4),
"Goal_Position": (116, 4),
"Realtime_Tick": (120, 2),
"Moving": (122, 1),
"Moving_Status": (123, 1),
"Present_PWM": (124, 2),
"Present_Current": (126, 2),
"Present_Velocity": (128, 4),
"Present_Position": (132, 4),
"Velocity_Trajectory": (136, 4),
"Position_Trajectory": (140, 4),
"Present_Input_Voltage": (144, 2),
"Present_Temperature": (146, 1),
}
X_SERIES_BAUDRATE_TABLE = {
0: 9_600,
1: 57_600,
2: 115_200,
3: 1_000_000,
4: 2_000_000,
5: 3_000_000,
6: 4_000_000,
}
CALIBRATION_REQUIRED = ["Goal_Position", "Present_Position"]
CONVERT_UINT32_TO_INT32_REQUIRED = ["Goal_Position", "Present_Position"]
MODEL_CONTROL_TABLE = {
"x_series": X_SERIES_CONTROL_TABLE,
"xl330-m077": X_SERIES_CONTROL_TABLE,
"xl330-m288": X_SERIES_CONTROL_TABLE,
"xl430-w250": X_SERIES_CONTROL_TABLE,
"xm430-w350": X_SERIES_CONTROL_TABLE,
"xm540-w270": X_SERIES_CONTROL_TABLE,
}
MODEL_RESOLUTION = {
"x_series": 4096,
"xl330-m077": 4096,
"xl330-m288": 4096,
"xl430-w250": 4096,
"xm430-w350": 4096,
"xm540-w270": 4096,
}
MODEL_BAUDRATE_TABLE = {
"x_series": X_SERIES_BAUDRATE_TABLE,
"xl330-m077": X_SERIES_BAUDRATE_TABLE,
"xl330-m288": X_SERIES_BAUDRATE_TABLE,
"xl430-w250": X_SERIES_BAUDRATE_TABLE,
"xm430-w350": X_SERIES_BAUDRATE_TABLE,
"xm540-w270": X_SERIES_BAUDRATE_TABLE,
}
NUM_READ_RETRY = 10
NUM_WRITE_RETRY = 10
def convert_degrees_to_steps(degrees: float | np.ndarray, models: str | list[str]):
"""This function convert the degree range to the step range for indicating motors rotation.
It assums a motor achieves a full rotation by going from -180 degree position to +180.
The motor resolution (e.g. 4096) corresponds to the number of steps needed to achieve a full rotation.
"""
if isinstance(degrees, float):
degrees = np.array(degrees)
resolutions = [MODEL_RESOLUTION[model] for model in models]
steps = degrees / 180 * np.array(resolutions) / 2
steps = steps.astype(int)
return steps
def convert_to_bytes(value, bytes):
# Note: No need to convert back into unsigned int, since this byte preprocessing
# already handles it for us.
if bytes == 1:
data = [
DXL_LOBYTE(DXL_LOWORD(value)),
]
elif bytes == 2:
data = [
DXL_LOBYTE(DXL_LOWORD(value)),
DXL_HIBYTE(DXL_LOWORD(value)),
]
elif bytes == 4:
data = [
DXL_LOBYTE(DXL_LOWORD(value)),
DXL_HIBYTE(DXL_LOWORD(value)),
DXL_LOBYTE(DXL_HIWORD(value)),
DXL_HIBYTE(DXL_HIWORD(value)),
]
else:
raise NotImplementedError(
f"Value of the number of bytes to be sent is expected to be in [1, 2, 4], but "
f"{bytes} is provided instead."
)
return data
def get_group_sync_key(data_name, motor_names):
group_key = f"{data_name}_" + "_".join(motor_names)
return group_key
def get_result_name(fn_name, data_name, motor_names):
group_key = get_group_sync_key(data_name, motor_names)
rslt_name = f"{fn_name}_{group_key}"
return rslt_name
def get_queue_name(fn_name, data_name, motor_names):
group_key = get_group_sync_key(data_name, motor_names)
queue_name = f"{fn_name}_{group_key}"
return queue_name
def get_log_name(var_name, fn_name, data_name, motor_names):
group_key = get_group_sync_key(data_name, motor_names)
log_name = f"{var_name}_{fn_name}_{group_key}"
return log_name
def assert_same_address(model_ctrl_table, motor_models, data_name):
all_addr = []
all_bytes = []
for model in motor_models:
addr, bytes = model_ctrl_table[model][data_name]
all_addr.append(addr)
all_bytes.append(bytes)
if len(set(all_addr)) != 1:
raise NotImplementedError(
f"At least two motor models use a different address for `data_name`='{data_name}' ({list(zip(motor_models, all_addr, strict=False))}). Contact a LeRobot maintainer."
)
if len(set(all_bytes)) != 1:
raise NotImplementedError(
f"At least two motor models use a different bytes representation for `data_name`='{data_name}' ({list(zip(motor_models, all_bytes, strict=False))}). Contact a LeRobot maintainer."
)
def find_available_ports():
ports = []
for path in Path("/dev").glob("tty*"):
ports.append(str(path))
return ports
def find_port():
print("Finding all available ports for the DynamixelMotorsBus.")
ports_before = find_available_ports()
print(ports_before)
print("Remove the usb cable from your DynamixelMotorsBus and press Enter when done.")
input()
time.sleep(0.5)
ports_after = find_available_ports()
ports_diff = list(set(ports_before) - set(ports_after))
if len(ports_diff) == 1:
port = ports_diff[0]
print(f"The port of this DynamixelMotorsBus is '{port}'")
print("Reconnect the usb cable.")
elif len(ports_diff) == 0:
raise OSError(f"Could not detect the port. No difference was found ({ports_diff}).")
else:
raise OSError(f"Could not detect the port. More than one port was found ({ports_diff}).")
class TorqueMode(enum.Enum):
ENABLED = 1
DISABLED = 0
class OperatingMode(enum.Enum):
VELOCITY = 1
POSITION = 3
EXTENDED_POSITION = 4
CURRENT_CONTROLLED_POSITION = 5
PWM = 16
UNKNOWN = -1
class DriveMode(enum.Enum):
NON_INVERTED = 0
INVERTED = 1
class DynamixelMotorsBus:
# TODO(rcadene): Add a script to find the motor indices without DynamixelWizzard2
"""
The DynamixelMotorsBus class allows to efficiently read and write to the attached motors. It relies on
the python dynamixel sdk to communicate with the motors. For more info, see the [Dynamixel SDK Documentation](https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20).
A DynamixelMotorsBus instance requires a port (e.g. `DynamixelMotorsBus(port="/dev/tty.usbmodem575E0031751"`)).
To find the port, you can run our utility script:
```bash
python lerobot/common/robot_devices/motors/dynamixel.py
>>> Finding all available ports for the DynamixelMotorsBus.
>>> ['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
>>> Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
>>> The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751.
>>> Reconnect the usb cable.
```
Example of usage for 1 motor connected to the bus:
```python
motor_name = "gripper"
motor_index = 6
motor_model = "xl330-m288"
motors_bus = DynamixelMotorsBus(
port="/dev/tty.usbmodem575E0031751",
motors={motor_name: (motor_index, motor_model)},
)
motors_bus.connect()
position = motors_bus.read("Present_Position")
# move from a few motor steps as an example
few_steps = 30
motors_bus.write("Goal_Position", position + few_steps)
# when done, consider disconnecting
motors_bus.disconnect()
```
"""
def __init__(
self,
port: str,
motors: dict[str, tuple[int, str]],
extra_model_control_table: dict[str, list[tuple]] | None = None,
extra_model_resolution: dict[str, int] | None = None,
):
self.port = port
self.motors = motors
self.model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE)
if extra_model_control_table:
self.model_ctrl_table.update(extra_model_control_table)
self.model_resolution = deepcopy(MODEL_RESOLUTION)
if extra_model_resolution:
self.model_resolution.update(extra_model_resolution)
self.port_handler = None
self.packet_handler = None
self.calibration = None
self.is_connected = False
self.group_readers = {}
self.group_writers = {}
self.logs = {}
def connect(self):
if self.is_connected:
raise RobotDeviceAlreadyConnectedError(
f"DynamixelMotorsBus({self.port}) is already connected. Do not call `motors_bus.connect()` twice."
)
self.port_handler = PortHandler(self.port)
self.packet_handler = PacketHandler(PROTOCOL_VERSION)
try:
if not self.port_handler.openPort():
raise OSError(f"Failed to open port '{self.port}'.")
except Exception:
traceback.print_exc()
print(
"\nTry running `python lerobot/common/robot_devices/motors/dynamixel.py` to make sure you are using the correct port.\n"
)
raise
# Allow to read and write
self.is_connected = True
self.port_handler.setPacketTimeoutMillis(TIMEOUT_MS)
# Set expected baudrate for the bus
self.set_bus_baudrate(BAUDRATE)
if not self.are_motors_configured():
input(
"\n/!\\ A configuration issue has been detected with your motors: \n"
"If it's the first time that you use these motors, press enter to configure your motors... but before "
"verify that all the cables are connected the proper way. If you find an issue, before making a modification, "
"kill the python process, unplug the power cord to not damage the motors, rewire correctly, then plug the power "
"again and relaunch the script.\n"
)
print()
self.configure_motors()
def reconnect(self):
self.port_handler = PortHandler(self.port)
self.packet_handler = PacketHandler(PROTOCOL_VERSION)
if not self.port_handler.openPort():
raise OSError(f"Failed to open port '{self.port}'.")
self.is_connected = True
def are_motors_configured(self):
# Only check the motor indices and not baudrate, since if the motor baudrates are incorrect,
# a ConnectionError will be raised anyway.
try:
return (self.motor_indices == self.read("ID")).all()
except ConnectionError as e:
print(e)
return False
def configure_motors(self):
# TODO(rcadene): This script assumes motors follow the X_SERIES baudrates
# TODO(rcadene): Refactor this function with intermediate high-level functions
print("Scanning all baudrates and motor indices")
all_baudrates = set(X_SERIES_BAUDRATE_TABLE.values())
ids_per_baudrate = {}
for baudrate in all_baudrates:
self.set_bus_baudrate(baudrate)
present_ids = self.find_motor_indices()
if len(present_ids) > 0:
ids_per_baudrate[baudrate] = present_ids
print(f"Motor indices detected: {ids_per_baudrate}")
print()
possible_baudrates = list(ids_per_baudrate.keys())
possible_ids = list({idx for sublist in ids_per_baudrate.values() for idx in sublist})
untaken_ids = list(set(range(MAX_ID_RANGE)) - set(possible_ids) - set(self.motor_indices))
# Connect successively one motor to the chain and write a unique random index for each
for i in range(len(self.motors)):
self.disconnect()
input(
"1. Unplug the power cord\n"
"2. Plug/unplug minimal number of cables to only have the first "
f"{i+1} motor(s) ({self.motor_names[:i+1]}) connected.\n"
"3. Re-plug the power cord\n"
"Press Enter to continue..."
)
print()
self.reconnect()
if i > 0:
try:
self._read_with_motor_ids(self.motor_models, untaken_ids[:i], "ID")
except ConnectionError:
print(f"Failed to read from {untaken_ids[:i+1]}. Make sure the power cord is plugged in.")
input("Press Enter to continue...")
print()
self.reconnect()
print("Scanning possible baudrates and motor indices")
motor_found = False
for baudrate in possible_baudrates:
self.set_bus_baudrate(baudrate)
present_ids = self.find_motor_indices(possible_ids)
if len(present_ids) == 1:
present_idx = present_ids[0]
print(f"Detected motor with index {present_idx}")
if baudrate != BAUDRATE:
print(f"Setting its baudrate to {BAUDRATE}")
baudrate_idx = list(X_SERIES_BAUDRATE_TABLE.values()).index(BAUDRATE)
# The write can fail, so we allow retries
for _ in range(NUM_WRITE_RETRY):
self._write_with_motor_ids(
self.motor_models, present_idx, "Baud_Rate", baudrate_idx
)
time.sleep(0.5)
self.set_bus_baudrate(BAUDRATE)
try:
present_baudrate_idx = self._read_with_motor_ids(
self.motor_models, present_idx, "Baud_Rate"
)
except ConnectionError:
print("Failed to write baudrate. Retrying.")
self.set_bus_baudrate(baudrate)
continue
break
else:
raise
if present_baudrate_idx != baudrate_idx:
raise OSError("Failed to write baudrate.")
print(f"Setting its index to a temporary untaken index ({untaken_ids[i]})")
self._write_with_motor_ids(self.motor_models, present_idx, "ID", untaken_ids[i])
present_idx = self._read_with_motor_ids(self.motor_models, untaken_ids[i], "ID")
if present_idx != untaken_ids[i]:
raise OSError("Failed to write index.")
motor_found = True
break
elif len(present_ids) > 1:
raise OSError(f"More than one motor detected ({present_ids}), but only one was expected.")
if not motor_found:
raise OSError(
"No motor found, but one new motor expected. Verify power cord is plugged in and retry."
)
print()
print(f"Setting expected motor indices: {self.motor_indices}")
self.set_bus_baudrate(BAUDRATE)
self._write_with_motor_ids(
self.motor_models, untaken_ids[: len(self.motors)], "ID", self.motor_indices
)
print()
if (self.read("ID") != self.motor_indices).any():
raise OSError("Failed to write motors indices.")
print("Configuration is done!")
def find_motor_indices(self, possible_ids=None):
if possible_ids is None:
possible_ids = range(MAX_ID_RANGE)
indices = []
for idx in tqdm.tqdm(possible_ids):
try:
present_idx = self._read_with_motor_ids(self.motor_models, [idx], "ID")[0]
except ConnectionError:
continue
if idx != present_idx:
# sanity check
raise OSError(
"Motor index used to communicate through the bus is not the same as the one present in the motor memory. The motor memory might be damaged."
)
indices.append(idx)
return indices
def set_bus_baudrate(self, baudrate):
present_bus_baudrate = self.port_handler.getBaudRate()
if present_bus_baudrate != baudrate:
print(f"Setting bus baud rate to {baudrate}. Previously {present_bus_baudrate}.")
self.port_handler.setBaudRate(baudrate)
if self.port_handler.getBaudRate() != baudrate:
raise OSError("Failed to write bus baud rate.")
@property
def motor_names(self) -> list[str]:
return list(self.motors.keys())
@property
def motor_models(self) -> list[str]:
return [model for _, model in self.motors.values()]
@property
def motor_indices(self) -> list[int]:
return [idx for idx, _ in self.motors.values()]
def set_calibration(self, calibration: dict[str, tuple[int, bool]]):
self.calibration = calibration
def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
"""Convert from unsigned int32 joint position range [0, 2**32[ to the universal float32 nominal degree range ]-180.0, 180.0[ with
a "zero position" at 0 degree.
Note: We say "nominal degree range" since the motors can take values outside this range. For instance, 190 degrees, if the motor
rotate more than a half a turn from the zero position. However, most motors can't rotate more than 180 degrees and will stay in this range.
Joints values are original in [0, 2**32[ (unsigned int32). Each motor are expected to complete a full rotation
when given a goal position that is + or - their resolution. For instance, dynamixel xl330-m077 have a resolution of 4096, and
at any position in their original range, let's say the position 56734, they complete a full rotation clockwise by moving to 60830,
or anticlockwise by moving to 52638. The position in the original range is arbitrary and might change a lot between each motor.
To harmonize between motors of the same model, different robots, or even models of different brands, we propose to work
in the centered nominal degree range ]-180, 180[.
"""
if motor_names is None:
motor_names = self.motor_names
# Convert from unsigned int32 original range [0, 2**32[ to centered signed int32 range [-2**31, 2**31[
values = values.astype(np.int32)
for i, name in enumerate(motor_names):
homing_offset, drive_mode = self.calibration[name]
# Update direction of rotation of the motor to match between leader and follower. In fact, the motor of the leader for a given joint
# can be assembled in an opposite direction in term of rotation than the motor of the follower on the same joint.
if drive_mode:
values[i] *= -1
# Convert from range [-2**31, 2**31[ to nominal range ]-resolution, resolution[ (e.g. ]-2048, 2048[)
values[i] += homing_offset
# Convert from range ]-resolution, resolution[ to the universal float32 centered degree range ]-180, 180[
values = values.astype(np.float32)
for i, name in enumerate(motor_names):
_, model = self.motors[name]
resolution = self.model_resolution[model]
values[i] = values[i] / (resolution // 2) * 180
return values
def revert_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
"""Inverse of `apply_calibration`."""
if motor_names is None:
motor_names = self.motor_names
# Convert from the universal float32 centered degree range ]-180, 180[ to resolution range ]-resolution, resolution[
for i, name in enumerate(motor_names):
_, model = self.motors[name]
resolution = self.model_resolution[model]
values[i] = values[i] / 180 * (resolution // 2)
values = np.round(values).astype(np.int32)
# Convert from nominal range ]-resolution, resolution[ to centered signed int32 range [-2**31, 2**31[
for i, name in enumerate(motor_names):
homing_offset, drive_mode = self.calibration[name]
values[i] -= homing_offset
# Update direction of rotation of the motor that was matching between leader and follower to their original direction.
# In fact, the motor of the leader for a given joint can be assembled in an opposite direction in term of rotation
# than the motor of the follower on the same joint.
if drive_mode:
values[i] *= -1
return values
def _read_with_motor_ids(self, motor_models, motor_ids, data_name):
return_list = True
if not isinstance(motor_ids, list):
return_list = False
motor_ids = [motor_ids]
assert_same_address(self.model_ctrl_table, self.motor_models, data_name)
addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
group = GroupSyncRead(self.port_handler, self.packet_handler, addr, bytes)
for idx in motor_ids:
group.addParam(idx)
comm = group.txRxPacket()
if comm != COMM_SUCCESS:
raise ConnectionError(
f"Read failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
f"{self.packet_handler.getTxRxResult(comm)}"
)
values = []
for idx in motor_ids:
value = group.getData(idx, addr, bytes)
values.append(value)
if return_list:
return values
else:
return values[0]
def read(self, data_name, motor_names: str | list[str] | None = None):
if not self.is_connected:
raise RobotDeviceNotConnectedError(
f"DynamixelMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
)
start_time = time.perf_counter()
if motor_names is None:
motor_names = self.motor_names
if isinstance(motor_names, str):
motor_names = [motor_names]
motor_ids = []
models = []
for name in motor_names:
motor_idx, model = self.motors[name]
motor_ids.append(motor_idx)
models.append(model)
assert_same_address(self.model_ctrl_table, models, data_name)
addr, bytes = self.model_ctrl_table[model][data_name]
group_key = get_group_sync_key(data_name, motor_names)
if data_name not in self.group_readers:
# create new group reader
self.group_readers[group_key] = GroupSyncRead(self.port_handler, self.packet_handler, addr, bytes)
for idx in motor_ids:
self.group_readers[group_key].addParam(idx)
for _ in range(NUM_READ_RETRY):
comm = self.group_readers[group_key].txRxPacket()
if comm == COMM_SUCCESS:
break
if comm != COMM_SUCCESS:
raise ConnectionError(
f"Read failed due to communication error on port {self.port} for group_key {group_key}: "
f"{self.packet_handler.getTxRxResult(comm)}"
)
values = []
for idx in motor_ids:
value = self.group_readers[group_key].getData(idx, addr, bytes)
values.append(value)
values = np.array(values)
# Convert to signed int to use range [-2048, 2048] for our motor positions.
if data_name in CONVERT_UINT32_TO_INT32_REQUIRED:
values = values.astype(np.int32)
if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
values = self.apply_calibration(values, motor_names)
# We expect our motors to stay in a nominal range of [-180, 180] degrees
# which corresponds to a half turn rotation.
# However, some motors can turn a bit more, hence we extend the nominal range to [-270, 270]
# which is less than a full 360 degree rotation.
if not np.all((values > -270) & (values < 270)):
raise ValueError(
f"Wrong motor position range detected. "
f"Expected to be in [-270, +270] but in [{values.min()}, {values.max()}]. "
"This might be due to a cable connection issue creating an artificial 360 degrees jump in motor values. "
"You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
)
# log the number of seconds it took to read the data from the motors
delta_ts_name = get_log_name("delta_timestamp_s", "read", data_name, motor_names)
self.logs[delta_ts_name] = time.perf_counter() - start_time
# log the utc time at which the data was received
ts_utc_name = get_log_name("timestamp_utc", "read", data_name, motor_names)
self.logs[ts_utc_name] = capture_timestamp_utc()
return values
def _write_with_motor_ids(self, motor_models, motor_ids, data_name, values):
if not isinstance(motor_ids, list):
motor_ids = [motor_ids]
if not isinstance(values, list):
values = [values]
assert_same_address(self.model_ctrl_table, motor_models, data_name)
addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
group = GroupSyncWrite(self.port_handler, self.packet_handler, addr, bytes)
for idx, value in zip(motor_ids, values, strict=True):
data = convert_to_bytes(value, bytes)
group.addParam(idx, data)
comm = group.txPacket()
if comm != COMM_SUCCESS:
raise ConnectionError(
f"Write failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
f"{self.packet_handler.getTxRxResult(comm)}"
)
def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
if not self.is_connected:
raise RobotDeviceNotConnectedError(
f"DynamixelMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
)
start_time = time.perf_counter()
if motor_names is None:
motor_names = self.motor_names
if isinstance(motor_names, str):
motor_names = [motor_names]
if isinstance(values, (int, float, np.integer)):
values = [int(values)] * len(motor_names)
values = np.array(values)
motor_ids = []
models = []
for name in motor_names:
motor_idx, model = self.motors[name]
motor_ids.append(motor_idx)
models.append(model)
if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
values = self.revert_calibration(values, motor_names)
values = values.tolist()
assert_same_address(self.model_ctrl_table, models, data_name)
addr, bytes = self.model_ctrl_table[model][data_name]
group_key = get_group_sync_key(data_name, motor_names)
init_group = data_name not in self.group_readers
if init_group:
self.group_writers[group_key] = GroupSyncWrite(
self.port_handler, self.packet_handler, addr, bytes
)
for idx, value in zip(motor_ids, values, strict=True):
data = convert_to_bytes(value, bytes)
if init_group:
self.group_writers[group_key].addParam(idx, data)
else:
self.group_writers[group_key].changeParam(idx, data)
comm = self.group_writers[group_key].txPacket()
if comm != COMM_SUCCESS:
raise ConnectionError(
f"Write failed due to communication error on port {self.port} for group_key {group_key}: "
f"{self.packet_handler.getTxRxResult(comm)}"
)
# log the number of seconds it took to write the data to the motors
delta_ts_name = get_log_name("delta_timestamp_s", "write", data_name, motor_names)
self.logs[delta_ts_name] = time.perf_counter() - start_time
# TODO(rcadene): should we log the time before sending the write command?
# log the utc time when the write has been completed
ts_utc_name = get_log_name("timestamp_utc", "write", data_name, motor_names)
self.logs[ts_utc_name] = capture_timestamp_utc()
def disconnect(self):
if not self.is_connected:
raise RobotDeviceNotConnectedError(
f"DynamixelMotorsBus({self.port}) is not connected. Try running `motors_bus.connect()` first."
)
if self.port_handler is not None:
self.port_handler.closePort()
self.port_handler = None
self.packet_handler = None
self.group_readers = {}
self.group_writers = {}
self.is_connected = False
def __del__(self):
if getattr(self, "is_connected", False):
self.disconnect()
if __name__ == "__main__":
# Helper to find the usb port associated to all your DynamixelMotorsBus.
find_port()
|
lerobot/lerobot/common/robot_devices/motors/dynamixel.py/0
|
{
"file_path": "lerobot/lerobot/common/robot_devices/motors/dynamixel.py",
"repo_id": "lerobot",
"token_count": 14106
}
| 174
|
# @package _global_
seed: 1000
dataset_repo_id: lerobot/aloha_sim_insertion_human
override_dataset_stats:
observation.images.top:
# stats from imagenet, since we use a pretrained vision model
mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
training:
offline_steps: 100000
online_steps: 0
eval_freq: 20000
save_freq: 20000
save_checkpoint: true
batch_size: 8
lr: 1e-5
lr_backbone: 1e-5
weight_decay: 1e-4
grad_clip_norm: 10
online_steps_between_rollouts: 1
delta_timestamps:
action: "[i / ${fps} for i in range(${policy.chunk_size})]"
eval:
n_episodes: 50
batch_size: 50
# See `configuration_act.py` for more details.
policy:
name: act
# Input / output structure.
n_obs_steps: 1
chunk_size: 100 # chunk_size
n_action_steps: 100
input_shapes:
# TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
observation.images.top: [3, 480, 640]
observation.state: ["${env.state_dim}"]
output_shapes:
action: ["${env.action_dim}"]
# Normalization / Unnormalization
input_normalization_modes:
observation.images.top: mean_std
observation.state: mean_std
output_normalization_modes:
action: mean_std
# Architecture.
# Vision backbone.
vision_backbone: resnet18
pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
replace_final_stride_with_dilation: false
# Transformer layers.
pre_norm: false
dim_model: 512
n_heads: 8
dim_feedforward: 3200
feedforward_activation: relu
n_encoder_layers: 4
# Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
# that means only the first layer is used. Here we match the original implementation by setting this to 1.
# See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
n_decoder_layers: 1
# VAE.
use_vae: true
latent_dim: 32
n_vae_encoder_layers: 4
# Inference.
temporal_ensemble_coeff: null
# Training and loss computation.
dropout: 0.1
kl_weight: 10.0
|
lerobot/lerobot/configs/policy/act.yaml/0
|
{
"file_path": "lerobot/lerobot/configs/policy/act.yaml",
"repo_id": "lerobot",
"token_count": 808
}
| 175
|
#!/usr/bin/env python
# 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.
""" Visualize data of **all** frames of any episode of a dataset of type LeRobotDataset.
Note: The last frame of the episode doesnt always correspond to a final state.
That's because our datasets are composed of transition from state to state up to
the antepenultimate state associated to the ultimate action to arrive in the final state.
However, there might not be a transition from a final state to another state.
Note: This script aims to visualize the data used to train the neural networks.
~What you see is what you get~. When visualizing image modality, it is often expected to observe
lossly compression artifacts since these images have been decoded from compressed mp4 videos to
save disk space. The compression factor applied has been tuned to not affect success rate.
Examples:
- Visualize data stored on a local machine:
```
local$ python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht \
--episode-index 0
```
- Visualize data stored on a distant machine with a local viewer:
```
distant$ python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht \
--episode-index 0 \
--save 1 \
--output-dir path/to/directory
local$ scp distant:path/to/directory/lerobot_pusht_episode_0.rrd .
local$ rerun lerobot_pusht_episode_0.rrd
```
- Visualize data stored on a distant machine through streaming:
(You need to forward the websocket port to the distant machine, with
`ssh -L 9087:localhost:9087 username@remote-host`)
```
distant$ python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht \
--episode-index 0 \
--mode distant \
--ws-port 9087
local$ rerun ws://localhost:9087
```
"""
import argparse
import gc
import logging
import time
from pathlib import Path
from typing import Iterator
import numpy as np
import rerun as rr
import torch
import torch.utils.data
import tqdm
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
class EpisodeSampler(torch.utils.data.Sampler):
def __init__(self, dataset: LeRobotDataset, episode_index: int):
from_idx = dataset.episode_data_index["from"][episode_index].item()
to_idx = dataset.episode_data_index["to"][episode_index].item()
self.frame_ids = range(from_idx, to_idx)
def __iter__(self) -> Iterator:
return iter(self.frame_ids)
def __len__(self) -> int:
return len(self.frame_ids)
def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
assert chw_float32_torch.dtype == torch.float32
assert chw_float32_torch.ndim == 3
c, h, w = chw_float32_torch.shape
assert c < h and c < w, f"expect channel first images, but instead {chw_float32_torch.shape}"
hwc_uint8_numpy = (chw_float32_torch * 255).type(torch.uint8).permute(1, 2, 0).numpy()
return hwc_uint8_numpy
def visualize_dataset(
repo_id: str,
episode_index: int,
batch_size: int = 32,
num_workers: int = 0,
mode: str = "local",
web_port: int = 9090,
ws_port: int = 9087,
save: bool = False,
root: Path | None = None,
output_dir: Path | None = None,
) -> Path | None:
if save:
assert (
output_dir is not None
), "Set an output directory where to write .rrd files with `--output-dir path/to/directory`."
logging.info("Loading dataset")
dataset = LeRobotDataset(repo_id, root=root)
logging.info("Loading dataloader")
episode_sampler = EpisodeSampler(dataset, episode_index)
dataloader = torch.utils.data.DataLoader(
dataset,
num_workers=num_workers,
batch_size=batch_size,
sampler=episode_sampler,
)
logging.info("Starting Rerun")
if mode not in ["local", "distant"]:
raise ValueError(mode)
spawn_local_viewer = mode == "local" and not save
rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer)
# Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush
# when iterating on a dataloader with `num_workers` > 0
# TODO(rcadene): remove `gc.collect` when rerun version 0.16 is out, which includes a fix
gc.collect()
if mode == "distant":
rr.serve(open_browser=False, web_port=web_port, ws_port=ws_port)
logging.info("Logging to Rerun")
for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
# iterate over the batch
for i in range(len(batch["index"])):
rr.set_time_sequence("frame_index", batch["frame_index"][i].item())
rr.set_time_seconds("timestamp", batch["timestamp"][i].item())
# display each camera image
for key in dataset.camera_keys:
# TODO(rcadene): add `.compress()`? is it lossless?
rr.log(key, rr.Image(to_hwc_uint8_numpy(batch[key][i])))
# display each dimension of action space (e.g. actuators command)
if "action" in batch:
for dim_idx, val in enumerate(batch["action"][i]):
rr.log(f"action/{dim_idx}", rr.Scalar(val.item()))
# display each dimension of observed state space (e.g. agent position in joint space)
if "observation.state" in batch:
for dim_idx, val in enumerate(batch["observation.state"][i]):
rr.log(f"state/{dim_idx}", rr.Scalar(val.item()))
if "next.done" in batch:
rr.log("next.done", rr.Scalar(batch["next.done"][i].item()))
if "next.reward" in batch:
rr.log("next.reward", rr.Scalar(batch["next.reward"][i].item()))
if "next.success" in batch:
rr.log("next.success", rr.Scalar(batch["next.success"][i].item()))
if mode == "local" and save:
# save .rrd locally
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
repo_id_str = repo_id.replace("/", "_")
rrd_path = output_dir / f"{repo_id_str}_episode_{episode_index}.rrd"
rr.save(rrd_path)
return rrd_path
elif mode == "distant":
# stop the process from exiting since it is serving the websocket connection
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
print("Ctrl-C received. Exiting.")
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--repo-id",
type=str,
required=True,
help="Name of hugging face repositery containing a LeRobotDataset dataset (e.g. `lerobot/pusht`).",
)
parser.add_argument(
"--episode-index",
type=int,
required=True,
help="Episode to visualize.",
)
parser.add_argument(
"--root",
type=Path,
default=None,
help="Root directory for a dataset stored locally (e.g. `--root data`). By default, the dataset will be loaded from hugging face cache folder, or downloaded from the hub if available.",
)
parser.add_argument(
"--output-dir",
type=Path,
default=None,
help="Directory path to write a .rrd file when `--save 1` is set.",
)
parser.add_argument(
"--batch-size",
type=int,
default=32,
help="Batch size loaded by DataLoader.",
)
parser.add_argument(
"--num-workers",
type=int,
default=4,
help="Number of processes of Dataloader for loading the data.",
)
parser.add_argument(
"--mode",
type=str,
default="local",
help=(
"Mode of viewing between 'local' or 'distant'. "
"'local' requires data to be on a local machine. It spawns a viewer to visualize the data locally. "
"'distant' creates a server on the distant machine where the data is stored. "
"Visualize the data by connecting to the server with `rerun ws://localhost:PORT` on the local machine."
),
)
parser.add_argument(
"--web-port",
type=int,
default=9090,
help="Web port for rerun.io when `--mode distant` is set.",
)
parser.add_argument(
"--ws-port",
type=int,
default=9087,
help="Web socket port for rerun.io when `--mode distant` is set.",
)
parser.add_argument(
"--save",
type=int,
default=0,
help=(
"Save a .rrd file in the directory provided by `--output-dir`. "
"It also deactivates the spawning of a viewer. "
"Visualize the data by running `rerun path/to/file.rrd` on your local machine."
),
)
args = parser.parse_args()
visualize_dataset(**vars(args))
if __name__ == "__main__":
main()
|
lerobot/lerobot/scripts/visualize_dataset.py/0
|
{
"file_path": "lerobot/lerobot/scripts/visualize_dataset.py",
"repo_id": "lerobot",
"token_count": 3832
}
| 176
|
[tool.poetry]
name = "lerobot"
version = "0.1.0"
description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
authors = [
"Rémi Cadène <re.cadene@gmail.com>",
"Simon Alibert <alibert.sim@gmail.com>",
"Alexander Soare <alexander.soare159@gmail.com>",
"Quentin Gallouédec <quentin.gallouedec@ec-lyon.fr>",
"Adil Zouitine <adilzouitinegm@gmail.com>",
"Thomas Wolf <thomaswolfcontact@gmail.com>",
]
repository = "https://github.com/huggingface/lerobot"
readme = "README.md"
license = "Apache-2.0"
classifiers=[
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"Topic :: Software Development :: Build Tools",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3.10",
]
packages = [{include = "lerobot"}]
[tool.poetry.dependencies]
python = ">=3.10,<3.13"
termcolor = ">=2.4.0"
omegaconf = ">=2.3.0"
wandb = ">=0.16.3"
imageio = {extras = ["ffmpeg"], version = ">=2.34.0"}
gdown = ">=5.1.0"
hydra-core = ">=1.3.2"
einops = ">=0.8.0"
pymunk = ">=6.6.0"
zarr = ">=2.17.0"
numba = ">=0.59.0"
torch = ">=2.2.1"
opencv-python = ">=4.9.0"
diffusers = ">=0.27.2"
torchvision = ">=0.17.1"
h5py = ">=3.10.0"
huggingface-hub = {extras = ["hf-transfer", "cli"], version = ">=0.23.0"}
gymnasium = ">=0.29.1"
cmake = ">=3.29.0.1"
gym-dora = { git = "https://github.com/dora-rs/dora-lerobot.git", subdirectory = "gym_dora", optional = true }
gym-pusht = { version = ">=0.1.5", optional = true}
gym-xarm = { version = ">=0.1.1", optional = true}
gym-aloha = { version = ">=0.1.1", optional = true}
pre-commit = {version = ">=3.7.0", optional = true}
debugpy = {version = ">=1.8.1", optional = true}
pytest = {version = ">=8.1.0", optional = true}
pytest-cov = {version = ">=5.0.0", optional = true}
datasets = ">=2.19.0"
imagecodecs = { version = ">=2024.1.1", optional = true }
pyav = ">=12.0.5"
rerun-sdk = ">=0.15.1"
deepdiff = ">=7.0.1"
flask = ">=3.0.3"
pandas = {version = ">=2.2.2", optional = true}
scikit-image = {version = ">=0.23.2", optional = true}
dynamixel-sdk = {version = ">=3.7.31", optional = true}
pynput = {version = ">=1.7.7", optional = true}
# TODO(rcadene, salibert): 71.0.1 has a bug
setuptools = {version = "!=71.0.1", optional = true}
[tool.poetry.extras]
dora = ["gym-dora"]
pusht = ["gym-pusht"]
xarm = ["gym-xarm"]
aloha = ["gym-aloha"]
dev = ["pre-commit", "debugpy"]
test = ["pytest", "pytest-cov"]
umi = ["imagecodecs"]
video_benchmark = ["scikit-image", "pandas"]
koch = ["dynamixel-sdk", "pynput"]
[tool.ruff]
line-length = 110
target-version = "py310"
exclude = [
"tests/data",
".bzr",
".direnv",
".eggs",
".git",
".git-rewrite",
".hg",
".mypy_cache",
".nox",
".pants.d",
".pytype",
".ruff_cache",
".svn",
".tox",
".venv",
"__pypackages__",
"_build",
"buck-out",
"build",
"dist",
"node_modules",
"venv",
]
[tool.ruff.lint]
select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
|
lerobot/pyproject.toml/0
|
{
"file_path": "lerobot/pyproject.toml",
"repo_id": "lerobot",
"token_count": 1510
}
| 177
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version https://git-lfs.github.com/spec/v1
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|
lerobot/tests/data/lerobot/aloha_mobile_shrimp/meta_data/stats.safetensors/0
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{
"file_path": "lerobot/tests/data/lerobot/aloha_mobile_shrimp/meta_data/stats.safetensors",
"repo_id": "lerobot",
"token_count": 69
}
| 178
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"repo_id": "lerobot",
"token_count": 63
}
| 179
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lerobot/tests/data/lerobot/aloha_sim_insertion_scripted_image/train/data-00000-of-00001.arrow/0
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"repo_id": "lerobot",
"token_count": 69
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| 180
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|
lerobot/tests/data/lerobot/aloha_static_towel/train/state.json/0
|
{
"file_path": "lerobot/tests/data/lerobot/aloha_static_towel/train/state.json",
"repo_id": "lerobot",
"token_count": 63
}
| 181
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version https://git-lfs.github.com/spec/v1
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size 50824
|
lerobot/tests/data/lerobot/unitreeh1_two_robot_greeting/train/data-00000-of-00001.arrow/0
|
{
"file_path": "lerobot/tests/data/lerobot/unitreeh1_two_robot_greeting/train/data-00000-of-00001.arrow",
"repo_id": "lerobot",
"token_count": 64
}
| 182
|
#!/usr/bin/env python
# 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 datasets import Dataset
from lerobot.common.datasets.sampler import EpisodeAwareSampler
from lerobot.common.datasets.utils import (
calculate_episode_data_index,
hf_transform_to_torch,
)
def test_drop_n_first_frames():
dataset = Dataset.from_dict(
{
"timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
"index": [0, 1, 2, 3, 4, 5],
"episode_index": [0, 0, 1, 2, 2, 2],
},
)
dataset.set_transform(hf_transform_to_torch)
episode_data_index = calculate_episode_data_index(dataset)
sampler = EpisodeAwareSampler(episode_data_index, drop_n_first_frames=1)
assert sampler.indices == [1, 4, 5]
assert len(sampler) == 3
assert list(sampler) == [1, 4, 5]
def test_drop_n_last_frames():
dataset = Dataset.from_dict(
{
"timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
"index": [0, 1, 2, 3, 4, 5],
"episode_index": [0, 0, 1, 2, 2, 2],
},
)
dataset.set_transform(hf_transform_to_torch)
episode_data_index = calculate_episode_data_index(dataset)
sampler = EpisodeAwareSampler(episode_data_index, drop_n_last_frames=1)
assert sampler.indices == [0, 3, 4]
assert len(sampler) == 3
assert list(sampler) == [0, 3, 4]
def test_episode_indices_to_use():
dataset = Dataset.from_dict(
{
"timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
"index": [0, 1, 2, 3, 4, 5],
"episode_index": [0, 0, 1, 2, 2, 2],
},
)
dataset.set_transform(hf_transform_to_torch)
episode_data_index = calculate_episode_data_index(dataset)
sampler = EpisodeAwareSampler(episode_data_index, episode_indices_to_use=[0, 2])
assert sampler.indices == [0, 1, 3, 4, 5]
assert len(sampler) == 5
assert list(sampler) == [0, 1, 3, 4, 5]
def test_shuffle():
dataset = Dataset.from_dict(
{
"timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
"index": [0, 1, 2, 3, 4, 5],
"episode_index": [0, 0, 1, 2, 2, 2],
},
)
dataset.set_transform(hf_transform_to_torch)
episode_data_index = calculate_episode_data_index(dataset)
sampler = EpisodeAwareSampler(episode_data_index, shuffle=False)
assert sampler.indices == [0, 1, 2, 3, 4, 5]
assert len(sampler) == 6
assert list(sampler) == [0, 1, 2, 3, 4, 5]
sampler = EpisodeAwareSampler(episode_data_index, shuffle=True)
assert sampler.indices == [0, 1, 2, 3, 4, 5]
assert len(sampler) == 6
assert set(sampler) == {0, 1, 2, 3, 4, 5}
|
lerobot/tests/test_sampler.py/0
|
{
"file_path": "lerobot/tests/test_sampler.py",
"repo_id": "lerobot",
"token_count": 1409
}
| 183
|
# Inference tips
Parler-TTS benefits from a number of optimizations that can make the model up to 4x faster. Add to this the ability to stream audio as it's being generated, and you can achieve time-to-first audio in under 500ms on a modern GPU.
## 📖 Quick Index
* [Efficient Attention Implementation](#efficient-attention-implementations)
* [Compilation](#compilation)
* [Streaming](#streaming)
* [Batch generation](#batch-generation)
## Efficient Attention implementations
Parler-TTS supports [SDPA](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html) and [Flash Attention 2](https://github.com/Dao-AILab/flash-attention).
SDPA is used by default and speeds up generation time by up to 1.4x compared with eager attention.
To switch between attention implementations, simply specify `attn_implementation=attn_implementation` when loading the checkpoints:
```py
from parler_tts import ParlerTTSForConditionalGeneration
torch_device = "cuda:0" # use "mps" for Mac
torch_dtype = torch.bfloat16
model_name = "parler-tts/parler-tts-mini-v1"
attn_implementation = "eager" # "sdpa" or "flash_attention_2"
model = ParlerTTSForConditionalGeneration.from_pretrained(
model_name,
attn_implementation=attn_implementation
).to(torch_device, dtype=torch_dtype)
```
## Compilation
[Compiling](https://pytorch.org/docs/stable/generated/torch.compile.html) the forward method of Parler can speed up generation time by up to 4.5x.
As an indication, `mode=default` brings a speed-up of 1.4 times compared to no compilation, while `mode="reduce-overhead"` brings much faster generation, at the cost of a longer compilation time and the need to generate twice to see the benefits of compilation.
```py
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
torch_device = "cuda:0"
torch_dtype = torch.bfloat16
model_name = "parler-tts/parler-tts-mini-v1"
# need to set padding max length
max_length = 50
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = ParlerTTSForConditionalGeneration.from_pretrained(
model_name,
attn_implementation="eager"
).to(torch_device, dtype=torch_dtype)
# compile the forward pass
compile_mode = "default" # chose "reduce-overhead" for 3 to 4x speed-up
model.generation_config.cache_implementation = "static"
model.forward = torch.compile(model.forward, mode=compile_mode)
# warmup
inputs = tokenizer("This is for compilation", return_tensors="pt", padding="max_length", max_length=max_length).to(torch_device)
model_kwargs = {**inputs, "prompt_input_ids": inputs.input_ids, "prompt_attention_mask": inputs.attention_mask, }
n_steps = 1 if compile_mode == "default" else 2
for _ in range(n_steps):
_ = model.generate(**model_kwargs)
# now you can benefit from compilation speed-ups
...
```
## Streaming
### How Does It Work?
Parler-TTS is an auto-regressive transformer-based model, meaning generates audio codes (tokens) in a causal fashion.
At each decoding step, the model generates a new set of audio codes, conditional on the text input and all previous audio codes. From the
frame rate of the [DAC model](https://huggingface.co/parler-tts/dac_44khZ_8kbps) used to decode the generated codes to audio waveform, each set of generated audio codes corresponds to 0.011 seconds. This means we require a total of 1720 decoding steps to generate 20 seconds of audio.
Rather than waiting for the entire audio sequence to be generated, which would require the full 1720 decoding steps, we can start playing the audio after a specified number of decoding steps have been reached, a techinque known as [*streaming*](https://huggingface.co/docs/transformers/main/en/generation_strategies#streaming).
For example, after 86 steps we have the first second of audio ready, and so can play this without waiting for the remaining decoding steps to be complete. As we continue to generate with the Parler-TTS model, we append new chunks of generated audio to our output waveform on-the-fly. After the full 1720 decoding steps, the generated audio is complete, and is composed of 20 chunks of audio, each corresponding to 86 tokens.
This method of playing incremental generations reduces the latency of the Parler-TTS model from the total time to generate 1720 tokens, to the time taken to play the first chunk of audio (86 tokens). This can result in significant improvements to perceived latency, particularly when the chunk size is chosen to be small. In practice, the chunk size should be tuned to your device: using a smaller chunk size will mean that the first chunk is ready faster, but should not be chosen so small that the model generates slower than the time it takes to play the audio.
### How Can I Use It?
We've added [ParlerTTSStreamer](https://github.com/huggingface/parler-tts/blob/main/parler_tts/streamer.py) to the library. Don't hesitate to adapt it to your use-case.
Here's how to create a generator out of the streamer.
```py
import torch
from parler_tts import ParlerTTSForConditionalGeneration, ParlerTTSStreamer
from transformers import AutoTokenizer
from threading import Thread
torch_device = "cuda:0" # Use "mps" for Mac
torch_dtype = torch.bfloat16
model_name = "parler-tts/parler-tts-mini-v1"
# need to set padding max length
max_length = 50
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = ParlerTTSForConditionalGeneration.from_pretrained(
model_name,
).to(torch_device, dtype=torch_dtype)
sampling_rate = model.audio_encoder.config.sampling_rate
frame_rate = model.audio_encoder.config.frame_rate
def generate(text, description, play_steps_in_s=0.5):
play_steps = int(frame_rate * play_steps_in_s)
streamer = ParlerTTSStreamer(model, device=torch_device, play_steps=play_steps)
# tokenization
inputs = tokenizer(description, return_tensors="pt").to(torch_device)
prompt = tokenizer(text, return_tensors="pt").to(torch_device)
# create generation kwargs
generation_kwargs = dict(
input_ids=inputs.input_ids,
prompt_input_ids=prompt.input_ids,
attention_mask=inputs.attention_mask,
prompt_attention_mask=prompt.attention_mask,
streamer=streamer,
do_sample=True,
temperature=1.0,
min_new_tokens=10,
)
# initialize Thread
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
# iterate over chunks of audio
for new_audio in streamer:
if new_audio.shape[0] == 0:
break
print(f"Sample of length: {round(new_audio.shape[0] / sampling_rate, 4)} seconds")
yield sampling_rate, new_audio
# now you can do
text = "This is a test of the streamer class"
description = "Jon's talking really fast."
chunk_size_in_s = 0.5
for (sampling_rate, audio_chunk) in generate(text, description, chunk_size_in_s):
# You can do everything that you need with the chunk now
# For example: stream it, save it, play it.
print(audio_chunk.shape)
```
## Batch generation
Batching means combining operations for multiple samples to bring the overall time spent generating the samples lower than generating sample per sample.
Here is a quick example of how you can use it:
```py
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
import scipy
repo_id = "parler-tts/parler-tts-mini-v1"
model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(repo_id, padding_side="left")
feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
input_text = ["Hey, how are you doing?", "I'm not sure how to feel about it."]
description = 2 * ["A male speaker with a monotone and high-pitched voice is delivering his speech at a really low speed in a confined environment."]
inputs = tokenizer(description, return_tensors="pt", padding=True).to("cuda")
prompt = tokenizer(input_text, return_tensors="pt", padding=True).to("cuda")
set_seed(0)
generation = model.generate(
input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
prompt_input_ids=prompt.input_ids,
prompt_attention_mask=prompt.attention_mask,
do_sample=True,
return_dict_in_generate=True,
)
audio_1 = generation.sequences[0, :generation.audios_length[0]]
audio_2 = generation.sequences[1, :generation.audios_length[1]]
print(audio_1.shape, audio_2.shape)
scipy.io.wavfile.write("sample_out.wav", rate=feature_extractor.sampling_rate, data=audio_1.cpu().numpy().squeeze())
scipy.io.wavfile.write("sample_out_2.wav", rate=feature_extractor.sampling_rate, data=audio_2.cpu().numpy().squeeze())
```
|
parler-tts/INFERENCE.md/0
|
{
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"repo_id": "parler-tts",
"token_count": 2743
}
| 184
|
# coding=utf-8
# Copyright 2024 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.
""" Parler-TTS model configuration"""
from transformers import AutoConfig, logging
from transformers.configuration_utils import PretrainedConfig
logger = logging.get_logger(__name__)
MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/parler_tts-small": "https://huggingface.co/facebook/parler_tts-small/resolve/main/config.json",
# See all ParlerTTS models at https://huggingface.co/models?filter=parler_tts
}
class ParlerTTSDecoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`ParlerTTSDecoder`]. It is used to instantiate a
Parler-TTS decoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Parler-TTS
[facebook/parler_tts-small](https://huggingface.co/facebook/parler_tts-small) 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 2049):
Vocabulary size of the ParlerTTSDecoder model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`ParlerTTSDecoder`].
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of decoder layers.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer block.
num_key_value_heads (`int`, *optional*):
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 checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
num_cross_attention_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention in the cross-attention layers.
If it is not specified, will default to `num_key_value_heads`.
ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer block.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the decoder 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, text_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.
max_position_embeddings (`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).
initializer_factor (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(hidden_size).
use_cache (`bool`, *optional*, defaults to `True`):
Whether the model should return the last key/values attentions (not used by all models)
num_codebooks (`int`, *optional*, defaults to 4):
The number of parallel codebooks forwarded to the model.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether input and output word embeddings should be tied.
rope_embeddings (`bool`, *optional*, defaults to `False`):
Whether to use ROPE or absolute positional embeddings.
rope_theta (`float`, *optional*, defaults to 100000.0):
The base period of the RoPE embeddings.
cross_attention_implementation_strategy (`str`, *optional*):
If not specified, the cross-attention implementation will be the same as `_attn_implementation`. If `always_eager`, it will always be the eager implementation. If `always_sdpa`, it will always be the sdpa implementation.
"""
model_type = "parler_tts_decoder"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=2049, # vocab size = 2048 (encodec vocab size) + 1 (eos)
max_position_embeddings=2048,
num_hidden_layers=24,
ffn_dim=4096,
num_attention_heads=16,
num_key_value_heads=None,
num_cross_attention_key_value_heads=None,
layerdrop=0.0,
use_cache=True,
activation_function="gelu",
hidden_size=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
initializer_factor=0.02,
scale_embedding=False,
num_codebooks=4,
pad_token_id=2048,
bos_token_id=2049,
eos_token_id=2048,
tie_word_embeddings=False,
rope_embeddings=False,
rope_theta=10_000.0,
cross_attention_implementation_strategy=None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.ffn_dim = ffn_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
if num_cross_attention_key_value_heads is None:
num_cross_attention_key_value_heads = num_key_value_heads
self.num_cross_attention_key_value_heads = num_cross_attention_key_value_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.initializer_factor = initializer_factor
self.layerdrop = layerdrop
self.use_cache = use_cache
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.num_codebooks = num_codebooks
self.rope_embeddings = rope_embeddings
self.rope_theta = rope_theta
self.cross_attention_implementation_strategy = cross_attention_implementation_strategy
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
class ParlerTTSConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ParlerTTSModel`]. It is used to instantiate a
Parler-TTS model according to the specified arguments, defining the text encoder, audio encoder and Parler-TTS decoder
configs.
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 1024):
Vocabulary size of the prompt token ids. Defines the number of different tokens that can be
represented by the `prompt_inputs_ids`.
prompt_cross_attention (`bool`, *optional*, defaults to `False`):
Whether to use cross-attention conditioning for the prompt (as well as the description).
kwargs (*optional*):
Dictionary of keyword arguments. Notably:
- **text_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
defines the text encoder config.
- **audio_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
defines the audio encoder config.
- **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
the decoder config.
Example:
```python
>>> from transformers import (
... ParlerTTSConfig,
... ParlerTTSDecoderConfig,
... T5Config,
... EncodecConfig,
... ParlerTTSForConditionalGeneration,
... )
>>> # Initializing text encoder, audio encoder, and decoder model configurations
>>> text_encoder_config = T5Config()
>>> audio_encoder_config = EncodecConfig()
>>> decoder_config = ParlerTTSDecoderConfig()
>>> configuration = ParlerTTSConfig.from_sub_models_config(
... text_encoder_config, audio_encoder_config, decoder_config
... )
>>> # Initializing a ParlerTTSForConditionalGeneration (with random weights) from the facebook/parler_tts-small style configuration
>>> model = ParlerTTSForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> config_text_encoder = model.config.text_encoder
>>> config_audio_encoder = model.config.audio_encoder
>>> config_decoder = model.config.decoder
>>> # Saving the model, including its configuration
>>> model.save_pretrained("parler_tts-model")
>>> # loading model and config from pretrained folder
>>> parler_tts_config = ParlerTTSConfig.from_pretrained("parler_tts-model")
>>> model = ParlerTTSForConditionalGeneration.from_pretrained("parler_tts-model", config=parler_tts_config)
```"""
model_type = "parler_tts"
is_composition = True
def __init__(self, vocab_size=1024, prompt_cross_attention=False, **kwargs):
super().__init__(**kwargs)
if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config")
text_encoder_config = kwargs.pop("text_encoder")
text_encoder_model_type = text_encoder_config.pop("model_type")
audio_encoder_config = kwargs.pop("audio_encoder")
audio_encoder_model_type = audio_encoder_config.pop("model_type")
decoder_config = kwargs.pop("decoder")
self.vocab_size = vocab_size
self.prompt_cross_attention = prompt_cross_attention
self.text_encoder = AutoConfig.for_model(text_encoder_model_type, **text_encoder_config)
self.audio_encoder = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder_config)
self.decoder = ParlerTTSDecoderConfig(**decoder_config)
self.is_encoder_decoder = True
@classmethod
def from_sub_models_config(
cls,
text_encoder_config: PretrainedConfig,
audio_encoder_config: PretrainedConfig,
decoder_config: ParlerTTSDecoderConfig,
**kwargs,
):
r"""
Instantiate a [`ParlerTTSConfig`] (or a derived class) from text encoder, audio encoder and decoder
configurations.
Returns:
[`ParlerTTSConfig`]: An instance of a configuration object
"""
return cls(
text_encoder=text_encoder_config.to_dict(),
audio_encoder=audio_encoder_config.to_dict(),
decoder=decoder_config.to_dict(),
**kwargs,
)
@property
# This is a property because you might want to change the codec model on the fly
def sampling_rate(self):
return self.audio_encoder.sampling_rate
# Copy from musicgen
@property
def _attn_implementation(self):
# This property is made private for now (as it cannot be changed and a PreTrainedModel.use_attn_implementation method needs to be implemented.)
if hasattr(self, "_attn_implementation_internal"):
if self._attn_implementation_internal is None:
# `config.attn_implementation` should never be None, for backward compatibility.
return "eager"
else:
return self._attn_implementation_internal
else:
return "eager"
@_attn_implementation.setter
def _attn_implementation(self, value):
self._attn_implementation_internal = value
self.decoder._attn_implementation = value
|
parler-tts/parler_tts/configuration_parler_tts.py/0
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{
"file_path": "parler-tts/parler_tts/configuration_parler_tts.py",
"repo_id": "parler-tts",
"token_count": 5375
}
| 185
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<!--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.
⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# LoRA
LoRA is low-rank decomposition method to reduce the number of trainable parameters which speeds up finetuning large models and uses less memory. In PEFT, using LoRA is as easy as setting up a [`LoraConfig`] and wrapping it with [`get_peft_model`] to create a trainable [`PeftModel`].
This guide explores in more detail other options and features for using LoRA.
## Initialization
The initialization of LoRA weights is controlled by the parameter `init_lora_weights` in [`LoraConfig`]. By default, PEFT initializes LoRA weights with Kaiming-uniform for weight A and zeros for weight B resulting in an identity transform (same as the reference [implementation](https://github.com/microsoft/LoRA)).
It is also possible to pass `init_lora_weights="gaussian"`. As the name suggests, this initializes weight A with a Gaussian distribution and zeros for weight B (this is how [Diffusers](https://huggingface.co/docs/diffusers/index) initializes LoRA weights).
```py
from peft import LoraConfig
config = LoraConfig(init_lora_weights="gaussian", ...)
```
There is also an option to set `init_lora_weights=False` which is useful for debugging and testing. This should be the only time you use this option. When choosing this option, the LoRA weights are initialized such that they do *not* result in an identity transform.
```py
from peft import LoraConfig
config = LoraConfig(init_lora_weights=False, ...)
```
### PiSSA
[PiSSA](https://arxiv.org/abs/2404.02948) initializes the LoRA adapter using the principal singular values and singular vectors. This straightforward modification allows PiSSA to converge more rapidly than LoRA and ultimately attain superior performance. Moreover, PiSSA reduces the quantization error compared to QLoRA, leading to further enhancements.
Configure the initialization method to "pissa", which may take several minutes to execute SVD on the pre-trained model:
```python
from peft import LoraConfig
config = LoraConfig(init_lora_weights="pissa", ...)
```
Alternatively, execute fast SVD, which takes only a few seconds. The number of iterations determines the trade-off between the error and computation time:
```python
lora_config = LoraConfig(init_lora_weights="pissa_niter_[number of iters]", ...)
```
For detailed instruction on using PiSSA, please follow [these instructions](https://github.com/fxmeng/peft/tree/main/examples/pissa_finetuning).
### OLoRA
[OLoRA](https://arxiv.org/abs/2406.01775) utilizes QR decomposition to initialize the LoRA adapters. OLoRA translates the base weights of the model by a factor of their QR decompositions, i.e., it mutates the weights before performing any training on them. This approach significantly improves stability, accelerates convergence speed, and ultimately achieves superior performance.
You just need to pass a single additional option to use OLoRA:
```python
from peft import LoraConfig
config = LoraConfig(init_lora_weights="olora", ...)
```
For more advanced usage, please refer to our [documentation](https://github.com/huggingface/peft/tree/main/examples/olora_finetuning).
### LoftQ
#### Standard approach
When quantizing the base model for QLoRA training, consider using the [LoftQ initialization](https://arxiv.org/abs/2310.08659), which has been shown to improve performance when training quantized models. The idea is that the LoRA weights are initialized such that the quantization error is minimized. To use LoftQ, follow [these instructions](https://github.com/huggingface/peft/tree/main/examples/loftq_finetuning).
In general, for LoftQ to work best, it is recommended to target as many layers with LoRA as possible, since those not targeted cannot have LoftQ applied. This means that passing `LoraConfig(..., target_modules="all-linear")` will most likely give the best results. Also, you should use `nf4` as quant type in your quantization config when using 4bit quantization, i.e. `BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")`.
#### A more convenient way
An easier but more limited way to apply LoftQ initialization is to use the convenience function `replace_lora_weights_loftq`. This takes the quantized PEFT model as input and replaces the LoRA weights in-place with their LoftQ-initialized counterparts.
```python
from peft import replace_lora_weights_loftq
from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_4bit=True, ...)
base_model = AutoModelForCausalLM.from_pretrained(..., quantization_config=bnb_config)
# note: don't pass init_lora_weights="loftq" or loftq_config!
lora_config = LoraConfig(task_type="CAUSAL_LM")
peft_model = get_peft_model(base_model, lora_config)
replace_lora_weights_loftq(peft_model)
```
`replace_lora_weights_loftq` also allows you to pass a `callback` argument to give you more control over which layers should be modified or not, which empirically can improve the results quite a lot. To see a more elaborate example of this, check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/loftq_finetuning/LoftQ_weight_replacement.ipynb).
`replace_lora_weights_loftq` implements only one iteration step of LoftQ. This means that only the LoRA weights are updated, instead of iteratevily updating LoRA weights and quantized base model weights. This may lead to lower performance but has the advantage that we can use the original quantized weights derived from the base model, instead of having to keep an extra copy of modified quantized weights. Whether this tradeoff is worthwhile depends on the use case.
At the moment, `replace_lora_weights_loftq` has these additional limitations:
- Model files must be stored as a `safetensors` file.
- Only bitsandbytes 4bit quantization is supported.
<Tip>
Learn more about how PEFT works with quantization in the [Quantization](quantization) guide.
</Tip>
### Rank-stabilized LoRA
Another way to initialize [`LoraConfig`] is with the [rank-stabilized LoRA (rsLoRA)](https://huggingface.co/papers/2312.03732) method. The LoRA architecture scales each adapter during every forward pass by a fixed scalar which is set at initialization and depends on the rank `r`. The scalar is given by `lora_alpha/r` in the original implementation, but rsLoRA uses `lora_alpha/math.sqrt(r)` which stabilizes the adapters and increases the performance potential from using a higher `r`.
```py
from peft import LoraConfig
config = LoraConfig(use_rslora=True, ...)
```
### Weight-Decomposed Low-Rank Adaptation (DoRA)
This technique decomposes the updates of the weights into two parts, magnitude and direction. Direction is handled by normal LoRA, whereas the magnitude is handled by a separate learnable parameter. This can improve the performance of LoRA, especially at low ranks. For more information on DoRA, see https://arxiv.org/abs/2402.09353.
```py
from peft import LoraConfig
config = LoraConfig(use_dora=True, ...)
```
If parts of the model or the DoRA adapter are offloaded to CPU you can get a significant speedup at the cost of some temporary (ephemeral) VRAM overhead by using `ephemeral_gpu_offload=True` in `config.runtime_config`.
```py
from peft import LoraConfig, LoraRuntimeConfig
config = LoraConfig(use_dora=True, runtime_config=LoraRuntimeConfig(ephemeral_gpu_offload=True), ...)
```
A `PeftModel` with a DoRA adapter can also be loaded with `ephemeral_gpu_offload=True` flag using the `from_pretrained` method as well as the `load_adapter` method.
```py
from peft import PeftModel
model = PeftModel.from_pretrained(base_model, peft_model_id, ephemeral_gpu_offload=True)
```
#### Caveats
- DoRA only supports linear and Conv2d layers at the moment.
- DoRA introduces a bigger overhead than pure LoRA, so it is recommended to merge weights for inference, see [`LoraModel.merge_and_unload`].
- DoRA should work with weights quantized with bitsandbytes ("QDoRA"). However, issues have been reported when using QDoRA with DeepSpeed Zero2.
### QLoRA-style training
The default LoRA settings in PEFT add trainable weights to the query and value layers of each attention block. But [QLoRA](https://hf.co/papers/2305.14314), which adds trainable weights to all the linear layers of a transformer model, can provide performance equal to a fully finetuned model. To apply LoRA to all the linear layers, like in QLoRA, set `target_modules="all-linear"` (easier than specifying individual modules by name which can vary depending on the architecture).
```py
config = LoraConfig(target_modules="all-linear", ...)
```
### Memory efficient Layer Replication with LoRA
An approach used to improve the performance of models is to expand a model by duplicating layers in the model to build a larger model from a pretrained model of a given size. For example increasing a 7B model to a 10B model as described in the [SOLAR](https://arxiv.org/abs/2312.15166) paper. PEFT LoRA supports this kind of expansion in a memory efficient manner that supports further fine-tuning using LoRA adapters attached to the layers post replication of the layers. The replicated layers do not take additional memory as they share the underlying weights so the only additional memory required is the memory for the adapter weights. To use this feature you would create a config with the `layer_replication` argument.
```py
config = LoraConfig(layer_replication=[[0,4], [2,5]], ...)
```
Assuming the original model had 5 layers `[0, 1, 2 ,3, 4]`, this would create a model with 7 layers arranged as `[0, 1, 2, 3, 2, 3, 4]`. This follows the [mergekit](https://github.com/arcee-ai/mergekit) pass through merge convention where sequences of layers specified as start inclusive and end exclusive tuples are stacked to build the final model. Each layer in the final model gets its own distinct set of LoRA adapters.
[Fewshot-Metamath-OrcaVicuna-Mistral-10B](https://huggingface.co/abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B) is an example of a model trained using this method on Mistral-7B expanded to 10B. The
[adapter_config.json](https://huggingface.co/abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B/blob/main/adapter_config.json) shows a sample LoRA adapter config applying this method for fine-tuning.
## Optimizers
LoRA training can optionally include special purpose optimizers. Currently the only such optimizer is LoRA+.
### LoRA+ optimized LoRA
LoRA training can be optimized using [LoRA+](https://arxiv.org/abs/2402.12354), which uses different learning rates for the adapter matrices A and B, shown to increase finetuning speed by up to 2x and performance by 1-2%.
```py
from peft import LoraConfig, get_peft_model
from peft.optimizers import create_loraplus_optimizer
from transformers import Trainer
import bitsandbytes as bnb
base_model = ...
config = LoraConfig(...)
model = get_peft_model(base_model, config)
optimizer = create_loraplus_optimizer(
model=model,
optimizer_cls=bnb.optim.Adam8bit,
lr=5e-5,
loraplus_lr_ratio=16,
)
scheduler = None
...
trainer = Trainer(
...,
optimizers=(optimizer, scheduler),
)
```
## Merge LoRA weights into the base model
While LoRA is significantly smaller and faster to train, you may encounter latency issues during inference due to separately loading the base model and the LoRA adapter. To eliminate latency, use the [`~LoraModel.merge_and_unload`] function to merge the adapter weights with the base model. This allows you to use the newly merged model as a standalone model. The [`~LoraModel.merge_and_unload`] function doesn't keep the adapter weights in memory.
Below is a diagram that explains the intuition of LoRA adapter merging:
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_diagram.png"/>
</div>
We show in the snippets below how to run that using PEFT.
```py
from transformers import AutoModelForCausalLM
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id)
model.merge_and_unload()
```
If you need to keep a copy of the weights so you can unmerge the adapter later or delete and load different ones, you should use the [`~LoraModel.merge_adapter`] function instead. Now you have the option to use [`~LoraModel.unmerge_adapter`] to return the base model.
```py
from transformers import AutoModelForCausalLM
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id)
model.merge_adapter()
# unmerge the LoRA layers from the base model
model.unmerge_adapter()
```
The [`~LoraModel.add_weighted_adapter`] function is useful for merging multiple LoRAs into a new adapter based on a user provided weighting scheme in the `weights` parameter. Below is an end-to-end example.
First load the base model:
```python
from transformers import AutoModelForCausalLM
from peft import PeftModel
import torch
base_model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, device_map="auto"
)
```
Then we load the first adapter:
```python
peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id, adapter_name="sft")
```
Then load a different adapter and merge it with the first one:
```python
weighted_adapter_name = "sft-dpo"
model.load_adapter("alignment-handbook/zephyr-7b-dpo-lora", adapter_name="dpo")
model.add_weighted_adapter(
adapters=["sft", "dpo"],
weights=[0.7, 0.3],
adapter_name=weighted_adapter_name,
combination_type="linear"
)
model.set_adapter(weighted_adapter_name)
```
<Tip>
There are several supported methods for `combination_type`. Refer to the [documentation](../package_reference/lora#peft.LoraModel.add_weighted_adapter) for more details. Note that "svd" as the `combination_type` is not supported when using `torch.float16` or `torch.bfloat16` as the datatype.
</Tip>
Now, perform inference:
```python
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
prompt = "Hey, are you conscious? Can you talk to me?"
inputs = tokenizer(prompt, return_tensors="pt")
inputs = {k: v.to("cuda") for k, v in inputs.items()}
with torch.no_grad():
generate_ids = model.generate(**inputs, max_length=30)
outputs = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(outputs)
```
## Load adapters
Adapters can be loaded onto a pretrained model with [`~PeftModel.load_adapter`], which is useful for trying out different adapters whose weights aren't merged. Set the active adapter weights with the [`~LoraModel.set_adapter`] function.
```py
from transformers import AutoModelForCausalLM
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
model = PeftModel.from_pretrained(base_model, peft_model_id)
# load different adapter
model.load_adapter("alignment-handbook/zephyr-7b-dpo-lora", adapter_name="dpo")
# set adapter as active
model.set_adapter("dpo")
```
To return the base model, you could use [`~LoraModel.unload`] to unload all of the LoRA modules or [`~LoraModel.delete_adapter`] to delete the adapter entirely.
```py
# unload adapter
model.unload()
# delete adapter
model.delete_adapter("dpo")
```
## Inference with different LoRA adapters in the same batch
Normally, each inference batch has to use the same adapter(s) in PEFT. This can sometimes be annoying, because we may have batches that contain samples intended to be used with different LoRA adapters. For example, we could have a base model that works well in English and two more LoRA adapters, one for French and one for German. Usually, we would have to split our batches such that each batch only contains samples of one of the languages, we cannot combine different languages in the same batch.
Thankfully, it is possible to mix different LoRA adapters in the same batch using the `adapter_name` argument. Below, we show an example of how this works in practice. First, let's load the base model, English, and the two adapters, French and German, like this:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
model_id = ...
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# load the LoRA adapter for French
peft_model = PeftModel.from_pretrained(model, <path>, adapter_name="adapter_fr")
# next, load the LoRA adapter for German
peft_model.load_adapter(<path>, adapter_name="adapter_de")
```
Now, we want to generate text on a sample that contains all three languages: The first three samples are in English, the next three are in French, and the last three are in German. We can use the `adapter_names` argument to specify which adapter to use for each sample. Since our base model is used for English, we use the special string `"__base__"` for these samples. For the next three samples, we indicate the adapter name of the French LoRA fine-tune, in this case `"adapter_fr"`. For the last three samples, we indicate the adapter name of the German LoRA fine-tune, in this case `"adapter_de"`. This way, we can use the base model and the two adapters in a single batch.
```python
inputs = tokenizer(
[
"Hello, my dog is cute",
"Hello, my cat is awesome",
"Hello, my fish is great",
"Salut, mon chien est mignon",
"Salut, mon chat est génial",
"Salut, mon poisson est super",
"Hallo, mein Hund ist süß",
"Hallo, meine Katze ist toll",
"Hallo, mein Fisch ist großartig",
],
return_tensors="pt",
padding=True,
)
adapter_names = [
"__base__", "__base__", "__base__",
"adapter_fr", "adapter_fr", "adapter_fr",
"adapter_de", "adapter_de", "adapter_de",
]
output = peft_model.generate(**inputs, adapter_names=adapter_names, max_new_tokens=20)
```
Note that the order does not matter here, i.e. the samples in the batch don't need to be grouped by adapter as in the example above. We just need to ensure that the `adapter_names` argument is aligned correctly with the samples.
### Caveats
Using this features has some drawbacks, namely:
- It only works for inference, not for training.
- Disabling adapters using the `with model.disable_adapter()` context takes precedence over `adapter_names`.
- You cannot pass `adapter_names` when some adapter weights where merged with base weight using the `merge_adapter` method. Please unmerge all adapters first by calling `model.unmerge_adapter()`.
- For obvious reasons, this cannot be used after calling `merge_and_unload()`, since all the LoRA adapters will be merged into the base weights in this case.
- This feature does not currently work with DoRA, so set `use_dora=False` in your `LoraConfig` if you want to use it.
- There is an expected overhead for inference with `adapter_names`, especially if the amount of different adapters in the batch is high. This is because the batch size is effectively reduced to the number of samples per adapter. If runtime performance is your top priority, try the following:
- Increase the batch size.
- Try to avoid having a large number of different adapters in the same batch, prefer homogeneous batches. This can be achieved by buffering samples with the same adapter and only perform inference with a small handfull of different adapters.
- Take a look at alternative implementations such as [LoRAX](https://github.com/predibase/lorax), [punica](https://github.com/punica-ai/punica), or [S-LoRA](https://github.com/S-LoRA/S-LoRA), which are specialized to work with a large number of different adapters.
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|
{
"file_path": "peft/docs/source/developer_guides/lora.md",
"repo_id": "peft",
"token_count": 6115
}
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# VeRA: Vector-based Random Matrix Adaptation
[VeRA](https://huggingface.co/papers/2310.11454) is a parameter-efficient fine-tuning technique that is similar to LoRA but requires even fewer extra parameters while promising similar or even better performance. As such, it is particularly useful when the parameter budget is very limited, e.g. when scaling to very large models. The reduction of the count of trainable parameters is achieved by sharing the same low-rank matrices across all layers, and only training two additional vectors per layer.
When saving the adapter parameters, it's possible to eschew storing the low rank matrices by setting `save_projection=False` on the `VeraConfig`. In that case, these matrices will be restored based on the fixed random seed from the `projection_prng_key` argument. This cuts down on the size of the checkpoint, but we cannot guarantee reproducibility on all devices and for all future versions of PyTorch. If you want to ensure reproducibility, set `save_projection=True` (which is the default).
To handle different shapes of adapted layers, VeRA initializes shared A and B matrices with the largest required size for each dimension. During the forward pass, submatrices A and B for a given layer are sliced out from these shared matrices and used as described in the paper. For example, adapting two linear layers of shapes (100, 20) and (80, 50) will create A and B matrices of shapes (rank, 50) and (100, rank) respectively. Then, to adapt a layer of shape (100, 20), submatrices A and B of shapes (rank, 20) and (100, rank) will be extracted.
VeRA currently has the following constraints:
- Only `nn.Linear` layers are supported.
- Quantized layers are not supported.
If these constraints don't work for your use case, use LoRA instead.
The abstract from the paper is:
> Low-rank adapation (LoRA) is a popular method that reduces the number of trainable parameters when finetuning large language models, but still faces acute storage challenges when scaling to even larger models or deploying numerous per-user or per-task adapted models. In this work, we present Vector-based Random Matrix Adaptation (VeRA), which significantly reduces the number of trainable parameters compared to LoRA, yet maintains the same performance. It achieves this by using a single pair of low-rank matrices shared across all layers and learning small scaling vectors instead. We demonstrate its effectiveness on the GLUE and E2E benchmarks, image classification tasks, and show its application in instruction-tuning of 7B and 13B language models.
## VeRAConfig
[[autodoc]] tuners.vera.config.VeraConfig
## VeRAModel
[[autodoc]] tuners.vera.model.VeraModel
|
peft/docs/source/package_reference/vera.md/0
|
{
"file_path": "peft/docs/source/package_reference/vera.md",
"repo_id": "peft",
"token_count": 834
}
| 187
|
PEFT_TYPE="boft"
BLOCK_NUM=8
BLOCK_SIZE=0
N_BUTTERFLY_FACTOR=1
export DATASET_NAME="oftverse/control-celeba-hq"
export PROJECT_NAME="controlnet_${PEFT_TYPE}"
export RUN_NAME="${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export CONTROLNET_PATH=""
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="./output/${DATASET_NAME}/${RUN_NAME}"
accelerate launch train_controlnet.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--resume_from_checkpoint=$RESUME_PATH \
--controlnet_model_name_or_path=$CONTROLNET_PATH \
--output_dir=$OUTPUT_DIR \
--report_to="wandb" \
--dataset_name=$DATASET_NAME \
--resolution=512 \
--learning_rate=1e-5 \
--checkpointing_steps=500 \
--max_train_steps=50000 \
--validation_steps=5000 \
--num_validation_images=12 \
--train_batch_size=4 \
--dataloader_num_workers=2 \
--seed="0" \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--wandb_project_name=$PROJECT_NAME \
--wandb_run_name=$RUN_NAME \
--enable_xformers_memory_efficient_attention \
--use_boft \
--boft_block_num=$BLOCK_NUM \
--boft_block_size=$BLOCK_SIZE \
--boft_n_butterfly_factor=$N_BUTTERFLY_FACTOR \
--boft_dropout=0.1 \
--boft_bias="boft_only" \
|
peft/examples/boft_controlnet/train_controlnet.sh/0
|
{
"file_path": "peft/examples/boft_controlnet/train_controlnet.sh",
"repo_id": "peft",
"token_count": 557
}
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|
import argparse
import os
import warnings
from typing import Optional
from huggingface_hub import HfFolder, whoami
from transformers import PretrainedConfig
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a Dreambooth training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=True,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--validation_prompt",
nargs="+",
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=500,
help=(
"Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
# boft args
parser.add_argument("--use_boft", action="store_true", help="Whether to use BOFT for parameter efficient tuning")
parser.add_argument("--boft_block_num", type=int, default=4, help="The number of BOFT blocks")
parser.add_argument("--boft_block_size", type=int, default=0, help="The size of BOFT blocks")
parser.add_argument("--boft_n_butterfly_factor", type=int, default=2, help="The number of butterfly factors")
parser.add_argument("--boft_dropout", type=float, default=0.1, help="BOFT dropout, only used if use_boft is True")
parser.add_argument(
"--boft_bias",
type=str,
default="none",
help="Bias type for BOFT. Can be 'none', 'all' or 'boft_only', only used if use_boft is True",
)
parser.add_argument(
"--num_dataloader_workers", type=int, default=1, help="Num of workers for the training dataloader."
)
parser.add_argument(
"--no_tracemalloc",
default=False,
action="store_true",
help="Flag to stop memory allocation tracing during training. This could speed up training on Windows.",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="wandb",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--wandb_key",
type=str,
default=None,
help=("If report to option is set to wandb, api-key for wandb used for login to wandb "),
)
parser.add_argument(
"--wandb_project_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--wandb_run_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
# if args.dataset_name is None and args.train_data_dir is None:
# raise ValueError("Need either a dataset name or a training folder.")
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
|
peft/examples/boft_dreambooth/utils/args_loader.py/0
|
{
"file_path": "peft/examples/boft_dreambooth/utils/args_loader.py",
"repo_id": "peft",
"token_count": 5745
}
| 189
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<jupyter_start><jupyter_code>from transformers import AutoModelForSeq2SeqLM
from peft import PeftModel, PeftConfig
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers import default_data_collator, get_linear_schedule_with_warmup
from tqdm import tqdm
from datasets import load_dataset
dataset_name = "twitter_complaints"
text_column = "Tweet text"
label_column = "text_label"
batch_size = 8
peft_model_id = "smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM"
config = PeftConfig.from_pretrained(peft_model_id)
peft_model_id = "smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM"
max_memory = {0: "6GIB", 1: "0GIB", 2: "0GIB", 3: "0GIB", 4: "0GIB", "cpu": "30GB"}
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map="auto", max_memory=max_memory)
model = PeftModel.from_pretrained(model, peft_model_id, device_map="auto", max_memory=max_memory)
from datasets import load_dataset
dataset = load_dataset("ought/raft", dataset_name)
classes = [k.replace("_", " ") for k in dataset["train"].features["Label"].names]
print(classes)
dataset = dataset.map(
lambda x: {"text_label": [classes[label] for label in x["Label"]]},
batched=True,
num_proc=1,
)
print(dataset)
dataset["train"][0]
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
target_max_length = max([len(tokenizer(class_label)["input_ids"]) for class_label in classes])
def preprocess_function(examples):
inputs = examples[text_column]
targets = examples[label_column]
model_inputs = tokenizer(inputs, truncation=True)
labels = tokenizer(
targets, max_length=target_max_length, padding="max_length", truncation=True, return_tensors="pt"
)
labels = labels["input_ids"]
labels[labels == tokenizer.pad_token_id] = -100
model_inputs["labels"] = labels
return model_inputs
processed_datasets = dataset.map(
preprocess_function,
batched=True,
num_proc=1,
remove_columns=dataset["train"].column_names,
load_from_cache_file=True,
desc="Running tokenizer on dataset",
)
train_dataset = processed_datasets["train"]
eval_dataset = processed_datasets["train"]
test_dataset = processed_datasets["test"]
def collate_fn(examples):
return tokenizer.pad(examples, padding="longest", return_tensors="pt")
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True
)
eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True)
test_dataloader = DataLoader(test_dataset, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True)
model.eval()
i = 15
inputs = tokenizer(f'{text_column} : {dataset["test"][i]["Tweet text"]} Label : ', return_tensors="pt")
print(dataset["test"][i]["Tweet text"])
print(inputs)
with torch.no_grad():
outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=10)
print(outputs)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))
model.eval()
eval_preds = []
for _, batch in enumerate(tqdm(eval_dataloader)):
batch = {k: v.to("cuda") for k, v in batch.items() if k != "labels"}
with torch.no_grad():
outputs = model.generate(**batch, max_new_tokens=10)
preds = outputs.detach().cpu().numpy()
eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))
correct = 0
total = 0
for pred, true in zip(eval_preds, dataset["train"][label_column]):
if pred.strip() == true.strip():
correct += 1
total += 1
accuracy = correct / total * 100
print(f"{accuracy=}")
print(f"{eval_preds[:10]=}")
print(f"{dataset['train'][label_column][:10]=}")
model.eval()
test_preds = []
for _, batch in enumerate(tqdm(test_dataloader)):
batch = {k: v for k, v in batch.items() if k != "labels"}
with torch.no_grad():
outputs = model.generate(**batch, max_new_tokens=10)
preds = outputs.detach().cpu().numpy()
test_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))
if len(test_preds) > 100:
break
test_preds<jupyter_output><empty_output>
|
peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_big_model_inference.ipynb/0
|
{
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"repo_id": "peft",
"token_count": 1695
}
| 190
|
<!--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.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# DreamBooth fine-tuning with HRA
This guide demonstrates how to use Householder reflection adaptation (HRA) method, to fine-tune Dreambooth with `stabilityai/stable-diffusion-2-1` model.
HRA provides a new perspective connecting LoRA to OFT and achieves encouraging performance in various downstream tasks.
HRA adapts a pre-trained model by multiplying each frozen weight matrix with a chain of r learnable Householder reflections (HRs).
HRA can be interpreted as either an OFT adapter or an adaptive LoRA.
Consequently, it harnesses the advantages of both strategies, reducing parameters and computation costs while penalizing the loss of pre-training knowledge.
For further details on HRA, please consult the [original HRA paper](https://arxiv.org/abs/2405.17484).
In this guide we provide a Dreambooth fine-tuning script that is available in [PEFT's GitHub repo examples](https://github.com/huggingface/peft/tree/main/examples/hra_dreambooth). This implementation is adapted from [peft's boft_dreambooth](https://github.com/huggingface/peft/tree/main/examples/boft_dreambooth).
You can try it out and fine-tune on your custom images.
## Set up your environment
Start by cloning the PEFT repository:
```bash
git clone --recursive https://github.com/huggingface/peft
```
Navigate to the directory containing the training scripts for fine-tuning Dreambooth with HRA:
```bash
cd peft/examples/hra_dreambooth
```
Set up your environment: install PEFT, and all the required libraries. At the time of writing this guide we recommend installing PEFT from source. The following environment setup should work on A100 and H100:
```bash
conda create --name peft python=3.10
conda activate peft
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia
conda install xformers -c xformers
pip install -r requirements.txt
pip install git+https://github.com/huggingface/peft
```
## Download the data
[dreambooth](https://github.com/google/dreambooth) dataset should have been automatically cloned in the following structure when running the training script.
```
hra_dreambooth
├── data
│ └── dreambooth
│ └── dataset
│ ├── backpack
│ └── backpack_dog
│ ...
```
You can also put your custom images into `hra_dreambooth/data/dreambooth/dataset`.
## Fine-tune Dreambooth with HRA
```bash
class_idx=0
bash ./train_dreambooth.sh $class_idx
```
where the `$class_idx` corresponds to different subjects ranging from 0 to 29.
Launch the training script with `accelerate` and pass hyperparameters, as well as LoRa-specific arguments to it such as:
- `use_hra`: Enables HRA in the training script.
- `hra_r`: the number of HRs (i.e., r) across different layers, expressed in `int`.
As r increases, the number of trainable parameters increases, which generally leads to improved performance.
However, this also results in higher memory consumption and longer computation times.
Therefore, r is usually set to 8.
**Note**, please set r to an even number to avoid potential issues during initialization.
- `hra_apply_GS`: Applies Gram-Schmidt orthogonalization. Default is `false`.
- `hra_bias`: specify if the `bias` parameters should be trained. Can be `none`, `all` or `hra_only`.
If you are running this script on Windows, you may need to set the `--num_dataloader_workers` to 0.
To learn more about DreamBooth fine-tuning with prior-preserving loss, check out the [Diffusers documentation](https://huggingface.co/docs/diffusers/training/dreambooth#finetuning-with-priorpreserving-loss).
## Generate images with the fine-tuned model
To generate images with the fine-tuned model, simply run the jupyter notebook `dreambooth_inference.ipynb` for visualization with `jupyter notebook` under `./examples/hra_dreambooth`.
|
peft/examples/hra_dreambooth/README.md/0
|
{
"file_path": "peft/examples/hra_dreambooth/README.md",
"repo_id": "peft",
"token_count": 1328
}
| 191
|
import argparse
import gc
import json
import logging
import math
import os
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from random import randint
from typing import Any, Dict, List, Union
# datasets imports
import datasets
# metric imports
import evaluate
import numpy as np
import torch
import transformers
import wandb
# accelerate imports
from accelerate import Accelerator, dispatch_model
from accelerate.logging import get_logger
from datasets import Audio, DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset
# hf imports
from huggingface_hub import HfApi
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import (
BitsAndBytesConfig,
SchedulerType,
WhisperForConditionalGeneration,
WhisperProcessor,
get_scheduler,
set_seed,
)
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
# peft imports
from peft import AdaLoraConfig, LoraConfig, PeftModel, get_peft_model
logger = get_logger(__name__, log_level="INFO")
def parse_args():
parser = argparse.ArgumentParser(description="Whisper Fine-Tuning with AdaLora")
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
required=True,
)
parser.add_argument("--language", type=str, help="Language to use for training; e.g., 'Hindi' ", required=True)
parser.add_argument("--language_abbr", type=str, help="Language to use for training; e.g., 'hi' ", required=True)
parser.add_argument(
"--task", type=str, default="transcribe", help="Task to use for training; e.g., 'transcribe' ", required=False
)
parser.add_argument(
"--dataset_name",
type=str,
default="mozilla-foundation/common_voice_11_0",
help="Dataset to use for training; e.g., 'whisper' ",
required=False,
)
parser.add_argument(
"--dataset_in_streaming_mode",
action="store_true",
help="Whether to use streaming mode for the dataset.",
)
parser.add_argument(
"--do_lower_case", action="store_true", help="lowercase the transcribed text before tokenizing"
)
parser.add_argument(
"--do_remove_punctuation", action="store_true", help="remove punctuation from the transcribed text"
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--overwrite_cache", type=bool, default=False, help="Overwrite the cached training and evaluation sets"
)
parser.add_argument("--max_audio_input_length", type=float, default=30.0, help="Maximum audio length in seconds.")
parser.add_argument(
"--preprocessing_num_workers",
type=int,
default=None,
help="The number of processes to use for the preprocessing.",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=8,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=8,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--buffer_size",
type=int,
default=5000,
help="Number of samples to prefetch in the streaming mode.",
)
parser.add_argument(
"--dataloader_pin_memory",
action="store_true",
help="Whether or not to pin memory for the DataLoader.",
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help="Number of subprocesses to use for data loading.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="linear",
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--load_best_model",
action="store_true",
help="Whether to load the best model at the end of training",
)
parser.add_argument(
"--with_tracking",
action="store_true",
help="Whether to enable experiment trackers for logging.",
)
parser.add_argument(
"--report_to",
type=str,
default="all",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
"Only applicable when `--with_tracking` is passed."
),
)
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--logging_steps",
type=int,
default=100,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--evaluation_steps",
type=int,
default=500,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="If the training should continue from a checkpoint folder.",
)
# lora/adalora specific args
parser.add_argument(
"--use_peft",
action="store_true",
help="Whether to use PEFT",
)
parser.add_argument(
"--use_adalora",
action="store_true",
help="Whether to use AdaLoRA or LoRA. If set, uses AdaLoRA instead of the default LoRA.",
)
parser.add_argument(
"--init_r",
type=int,
default=12,
help="Initial AdaLoRA rank",
)
parser.add_argument(
"--target_r",
type=int,
default=4,
help="Target AdaLoRA rank",
)
parser.add_argument(
"--tinit",
type=int,
default=200,
help="number of warmup steps for AdaLoRA wherein no pruning is performed",
)
parser.add_argument(
"--tfinal",
type=int,
default=1000,
help=" fix the resulting budget distribution and fine-tune the model for tfinal steps when using AdaLoRA ",
)
parser.add_argument(
"--delta_t",
type=int,
default=10,
help="interval of steps for AdaLoRA to update rank",
)
parser.add_argument(
"--lora_alpha",
type=int,
default=32,
help="LORA alpha",
)
parser.add_argument(
"--r",
type=int,
default=8,
help="LORA rank",
)
parser.add_argument(
"--lora_dropout",
type=float,
default=0.1,
help="LORA dropout",
)
parser.add_argument(
"--orth_reg_weight",
type=float,
default=0.5,
help="Orthogonal regularization weight",
)
parser.add_argument(
"--debug_mode",
action="store_true",
help="Whether to use debug mode",
)
args = parser.parse_args()
if args.push_to_hub:
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
return args
def load_streaming_dataset(dataset_name, dataset_config_name, split, **kwargs):
if "+" in split:
# load multiple splits separated by the `+` symbol *with* streaming mode
dataset_splits = [
load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=True, **kwargs)
for split_name in split.split("+")
]
# interleave multiple splits to form one dataset
interleaved_dataset = interleave_datasets(dataset_splits)
return interleaved_dataset
else:
# load a single split *with* streaming mode
dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=True, **kwargs)
return dataset
def prepare_dataset_wrapper(do_lower_case, do_remove_punctuation, processor, normalizer):
def prepare_dataset(batch):
# load and (possibly) resample audio data to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = processor.feature_extractor(
audio["array"], sampling_rate=audio["sampling_rate"]
).input_features[0]
# compute input length of audio sample in seconds
batch["input_length"] = len(audio["array"]) / audio["sampling_rate"]
# optional pre-processing steps
transcription = batch["sentence"]
if do_lower_case:
transcription = transcription.lower()
if do_remove_punctuation:
transcription = normalizer(transcription).strip()
# encode target text to label ids
batch["labels"] = processor.tokenizer(transcription).input_ids
return batch
return prepare_dataset
def save_model_hook(models, weights, output_dir):
for model in models:
model.save_pretrained(output_dir)
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
while len(models) > 0:
model = models.pop()
# pop models so that they are not loaded again
PeftModel.from_pretrained(model.base_model.model, input_dir)
@dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
processor: Any
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lengths and need different padding methods
# first treat the audio inputs by simply returning torch tensors
input_features = [{"input_features": feature["input_features"]} for feature in features]
batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
# get the tokenized label sequences
label_features = [{"input_ids": feature["labels"]} for feature in features]
# pad the labels to max length
labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
# replace padding with -100 to ignore loss correctly
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
# if bos token is appended in previous tokenization step,
# cut bos token here as it's append later anyways
if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():
labels = labels[:, 1:]
batch["labels"] = labels
return batch
def get_audio_length_processor(max_input_length):
def is_audio_in_length_range(length):
return length < max_input_length
return is_audio_in_length_range
def evaluation_loop(model, eval_dataloader, processor, normalizer, metric, forced_decoder_ids, accelerator):
model.eval()
predictions = []
references = []
normalized_predictions = []
normalized_references = []
for _, batch in enumerate(tqdm(eval_dataloader)):
with torch.cuda.amp.autocast():
with torch.no_grad():
generated_tokens = (
model.generate(
input_features=batch["input_features"],
forced_decoder_ids=forced_decoder_ids,
max_new_tokens=255,
)
.cpu()
.numpy()
)
labels = batch["labels"].cpu().numpy()
labels = np.where(labels != -100, labels, processor.tokenizer.pad_token_id)
decoded_preds = processor.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
decoded_labels = processor.tokenizer.batch_decode(labels, skip_special_tokens=True)
predictions.extend(decoded_preds)
references.extend(decoded_labels)
normalized_predictions.extend([normalizer(pred).strip() for pred in decoded_preds])
normalized_references.extend([normalizer(label).strip() for label in decoded_labels])
del generated_tokens, labels, batch
gc.collect()
wer = 100 * metric.compute(predictions=predictions, references=references)
normalized_wer = 100 * metric.compute(predictions=normalized_predictions, references=normalized_references)
eval_metrics = {"eval/wer": wer, "eval/normalized_wer": normalized_wer}
if accelerator.get_tracker("wandb"):
sample_size = min(len(predictions), 256)
ids = [randint(0, len(predictions) - 1) for p in range(0, sample_size)]
sample_predictions = [predictions[i] for i in ids]
sample_references = [references[i] for i in ids]
sample_normalized_predictions = [normalized_predictions[i] for i in ids]
sample_normalized_references = [normalized_references[i] for i in ids]
table_rows = [
list(r)
for r in zip(
sample_predictions, sample_references, sample_normalized_predictions, sample_normalized_references
)
]
eval_metrics["eval_samples"] = wandb.Table(
columns=["predictions", "references", "normalized_predictions", "normalized_references"],
rows=table_rows,
)
return eval_metrics
def main():
args = parse_args()
accelerator_kwargs = {"gradient_accumulation_steps": args.gradient_accumulation_steps}
if args.with_tracking:
accelerator_kwargs["log_with"] = args.report_to
accelerator_kwargs["project_dir"] = args.output_dir
accelerator = Accelerator(**accelerator_kwargs)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
api = HfApi(token=args.hub_token)
# Create repo (repo_name from args or inferred)
repo_name = args.hub_model_id
if repo_name is None:
repo_name = Path(args.output_dir).absolute().name
repo_id = api.create_repo(repo_name, exist_ok=True).repo_id
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# load dataset either in streaming mode or not
processor = WhisperProcessor.from_pretrained(args.model_name_or_path, language=args.language, task=args.task)
normalizer = BasicTextNormalizer()
prepare_dataset = prepare_dataset_wrapper(args.do_lower_case, args.do_remove_punctuation, processor, normalizer)
is_audio_in_length_range = get_audio_length_processor(args.max_audio_input_length)
data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)
if args.dataset_in_streaming_mode:
raw_datasets = IterableDatasetDict()
loading_method = load_streaming_dataset
else:
raw_datasets = DatasetDict()
loading_method = load_dataset
if args.debug_mode:
train_split = "train[:100]"
test_split = "test[:10]"
else:
train_split = "train+validation"
test_split = "test"
raw_datasets["train"] = loading_method(
args.dataset_name, args.language_abbr, split=train_split, use_auth_token=True
)
raw_datasets["test"] = loading_method(args.dataset_name, args.language_abbr, split=test_split, use_auth_token=True)
raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000))
logger.info("Dataset loaded: %s", raw_datasets)
logger.info(f'{raw_datasets["train"][0]}')
vectorized_datasets = raw_datasets.map(
prepare_dataset,
remove_columns=list(next(iter(raw_datasets.values())).features),
num_proc=args.preprocessing_num_workers,
).with_format("torch")
if args.dataset_in_streaming_mode:
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(
buffer_size=args.buffer_size,
seed=args.seed,
)
# filter out audio files that are too long from the training set
is_audio_in_length_range = get_audio_length_processor(args.max_audio_input_length)
vectorized_datasets["train"] = vectorized_datasets["train"].filter(
is_audio_in_length_range, input_columns=["input_length"]
)
# get dataloaders
train_dataloader = DataLoader(
vectorized_datasets["train"],
batch_size=args.per_device_train_batch_size,
shuffle=True,
collate_fn=data_collator,
num_workers=args.dataloader_num_workers,
pin_memory=args.dataloader_pin_memory,
)
eval_dataloader = DataLoader(
vectorized_datasets["test"],
batch_size=args.per_device_eval_batch_size,
collate_fn=data_collator,
num_workers=args.dataloader_num_workers,
pin_memory=args.dataloader_pin_memory,
)
# metric
metric = evaluate.load("wer")
# model
model = WhisperForConditionalGeneration.from_pretrained(
args.model_name_or_path, quantization_config=BitsAndBytesConfig(load_in_8bit=True)
)
model.config.forced_decoder_ids = None
model.config.suppress_tokens = []
if len(set(model.hf_device_map.values()).intersection({"cpu", "disk"})) > 0:
raise ValueError("Training on CPU or disk is not supported.")
if len(set(model.hf_device_map.values())) > 1:
device_map = model.hf_device_map.copy()
# required because `labels` are on main execution device (0) while the output of `proj_out` is on other device.
# So, this leads to device mismatch error when calculation cross-entropy between logits and labels.
# Won't arise during inference as `labels` aren't supplied during that time
# instead of changing device of one of the tied modules, I have to do this for all tied modules
# else the execution device of remaining tied modules isn't changed
device_map["model.decoder.embed_tokens"] = model._hf_hook.execution_device
device_map["model.decoder.embed_positions"] = model._hf_hook.execution_device
device_map["proj_out"] = model._hf_hook.execution_device
dispatch_model(model, device_map=device_map)
# preparing peft model
if args.use_peft:
from peft import prepare_model_for_kbit_training
model = prepare_model_for_kbit_training(model)
# as Whisper model uses Conv layer in encoder, checkpointing disables grad computation
# to avoid this, make the inputs trainable
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
model.model.encoder.conv1.register_forward_hook(make_inputs_require_grad)
# wrapping model with adalora tuner
if args.use_adalora:
config = AdaLoraConfig(
init_r=args.init_r,
target_r=args.target_r,
beta1=0.85,
beta2=0.85,
tinit=args.tinit,
tfinal=args.tfinal,
deltaT=args.delta_t,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
target_modules=["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
orth_reg_weight=args.orth_reg_weight,
)
else:
config = LoraConfig(
r=args.r,
lora_alpha=args.lora_alpha,
target_modules=["q_proj", "v_proj"],
lora_dropout=args.lora_dropout,
)
model = get_peft_model(model, config)
model.print_trainable_parameters()
# optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
if args.max_train_steps is None:
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
else:
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# scheduler
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=args.max_train_steps,
)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)
accelerator.print(model)
# Note here that the max steps is adjusted by the accelerator's num_processes
args.max_train_steps = math.ceil(args.max_train_steps / accelerator.num_processes)
if args.use_peft and args.use_adalora:
model.base_model.peft_config["default"].total_step = args.max_train_steps
# model.base_model.peft_config.total_step = args.max_train_steps
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if args.with_tracking:
run_name = f"run-{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}"
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers(
"Whisper PEFT Fine-Tuning", config=experiment_config, init_kwargs={"wandb": {"name": run_name}}
)
# saving and loading checkpoints for resuming training
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
global_step = 0
starting_epoch = 0
best_metric = None
resume_step = 0
forced_decoder_ids = processor.get_decoder_prompt_ids(language=args.language, task=args.task)
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint)
path = os.path.basename(args.resume_from_checkpoint)
training_difference = os.path.splitext(path)[0]
global_step = resume_step = int(training_difference.replace("step_", ""))
starting_epoch = resume_step // len(train_dataloader)
resume_step -= starting_epoch * len(train_dataloader)
# We need to adjust the progress bar to the current step
progress_bar.update(resume_step)
for epoch in range(starting_epoch, args.num_train_epochs):
model.train()
if args.with_tracking:
total_loss = 0
running_loss = 0
for step, batch in enumerate(accelerator.skip_first_batches(train_dataloader, num_batches=resume_step)):
with accelerator.accumulate(model):
outputs = model(**batch)
loss = outputs.loss
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
# Update the importance of low-rank matrices
# and allocate the budget accordingly.
# This is only needed for AdaLora.
# Note that this requires parameter gradients.
# Hence being called before optimizer.zero_grad().
if args.use_peft and args.use_adalora:
model.update_and_allocate(global_step)
optimizer.zero_grad()
global_step += 1
progress_bar.update(1)
if args.with_tracking:
step_loss = accelerator.reduce(loss.detach().clone()).item()
total_loss += step_loss
running_loss += step_loss
if global_step % args.checkpointing_steps == 0:
output_dir = os.path.join(args.output_dir, f"step_{global_step}")
accelerator.save_state(output_dir)
if global_step % args.logging_steps == 0:
if args.with_tracking:
accelerator.log({"train/running_loss": running_loss / args.logging_steps}, step=global_step)
running_loss = 0
if global_step % args.evaluation_steps == 0:
eval_metrics = evaluation_loop(
model, eval_dataloader, processor, normalizer, metric, forced_decoder_ids, accelerator
)
if args.with_tracking:
logger.info(f"Step {global_step} eval metrics: {eval_metrics}")
accelerator.log(eval_metrics, step=global_step)
if best_metric is None or eval_metrics["eval/wer"] < best_metric:
best_metric = eval_metrics["eval/wer"]
accelerator.save_state(os.path.join(args.output_dir, "best_checkpoint"))
model.train()
if global_step >= args.max_train_steps:
break
if args.with_tracking:
train_epoch_loss = total_loss / (step + 1)
logger.info(f"Epoch {epoch} train loss: {train_epoch_loss}")
accelerator.log({"epoch/train_loss": train_epoch_loss}, step=epoch)
if args.push_to_hub and epoch <= args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir, is_main_process=accelerator.is_main_process)
# evaluate the model at the end of training
eval_metrics = evaluation_loop(
model, eval_dataloader, processor, normalizer, metric, forced_decoder_ids, accelerator
)
if args.with_tracking:
logger.info(f"Step {global_step} eval metrics: {eval_metrics}")
accelerator.log(eval_metrics, step=global_step)
if best_metric is None or eval_metrics["eval/wer"] < best_metric:
best_metric = eval_metrics["eval/wer"]
accelerator.save_state(os.path.join(args.output_dir, "best_checkpoint"))
if accelerator.is_main_process:
processor.tokenizer.save_pretrained(args.output_dir)
api.upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message=f"Training in progress epoch {epoch}",
run_as_future=True,
)
if args.load_best_model:
# load the best model
accelerator.load_state(os.path.join(args.output_dir, "best_checkpoint"))
model.resize_modules_by_rank_pattern(model.peft_config["default"].rank_pattern, "default")
eval_metrics = evaluation_loop(
model, eval_dataloader, processor, normalizer, metric, forced_decoder_ids, accelerator
)
if args.with_tracking:
best_metrics = {"best_" + k: v for k, v in eval_metrics.items()}
accelerator.log(best_metrics, step=global_step)
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir, is_main_process=accelerator.is_main_process)
if accelerator.is_main_process:
processor.tokenizer.save_pretrained(args.output_dir)
if args.push_to_hub:
api.upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
)
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
eval_metrics.pop("eval_samples")
json.dump(eval_metrics, f)
if __name__ == "__main__":
main()
|
peft/examples/int8_training/peft_adalora_whisper_large_training.py/0
|
{
"file_path": "peft/examples/int8_training/peft_adalora_whisper_large_training.py",
"repo_id": "peft",
"token_count": 13261
}
| 192
|
# 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.
from setuptools import find_packages, setup
VERSION = "0.12.1.dev0"
extras = {}
extras["quality"] = [
"black", # doc-builder has an implicit dependency on Black, see huggingface/doc-builder#434
"hf-doc-builder",
"ruff~=0.6.1",
]
extras["docs_specific"] = [
"black", # doc-builder has an implicit dependency on Black, see huggingface/doc-builder#434
"hf-doc-builder",
]
extras["dev"] = extras["quality"] + extras["docs_specific"]
extras["test"] = extras["dev"] + [
"pytest",
"pytest-cov",
"pytest-xdist",
"parameterized",
"datasets",
"diffusers<0.21.0",
"scipy",
]
setup(
name="peft",
version=VERSION,
description="Parameter-Efficient Fine-Tuning (PEFT)",
license_files=["LICENSE"],
long_description=open("README.md", encoding="utf-8").read(),
long_description_content_type="text/markdown",
keywords="deep learning",
license="Apache",
author="The HuggingFace team",
author_email="sourab@huggingface.co",
url="https://github.com/huggingface/peft",
package_dir={"": "src"},
packages=find_packages("src"),
package_data={"peft": ["py.typed", "tuners/boft/fbd/fbd_cuda.cpp", "tuners/boft/fbd/fbd_cuda_kernel.cu"]},
entry_points={},
python_requires=">=3.8.0",
install_requires=[
"numpy>=1.17",
"packaging>=20.0",
"psutil",
"pyyaml",
"torch>=1.13.0",
"transformers",
"tqdm",
"accelerate>=0.21.0",
"safetensors",
"huggingface_hub>=0.17.0",
],
extras_require=extras,
classifiers=[
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
)
# Release checklist
# 1. Change the version in __init__.py and setup.py to the release version, e.g. from "0.6.0.dev0" to "0.6.0"
# 2. Check if there are any deprecations that need to be addressed for this release by searching for "# TODO" in the code
# 3. Commit these changes with the message: "Release: VERSION", create a PR and merge it.
# 4. Add a tag in git to mark the release: "git tag -a VERSION -m 'Adds tag VERSION for pypi' "
# Push the tag to git:
# git push --tags origin main
# It is necessary to work on the original repository, not on a fork.
# 5. Run the following commands in the top-level directory:
# python setup.py bdist_wheel
# python setup.py sdist
# Ensure that you are on the clean and up-to-date main branch (git status --untracked-files=no should not list any
# files and show the main branch)
# 6. Upload the package to the pypi test server first:
# twine upload dist/* -r pypitest
# 7. Check that you can install it in a virtualenv by running:
# pip install -i https://testpypi.python.org/pypi --extra-index-url https://pypi.org/simple peft
# 8. Upload the final version to actual pypi:
# twine upload dist/* -r pypi
# 9. Add release notes to the tag on https://github.com/huggingface/peft/releases once everything is looking hunky-dory.
# Check the notes here: https://docs.google.com/document/d/1k-sOIfykuKjWcOIALqjhFKz4amFEp-myeJUJEzNgjoU/edit?usp=sharing
# 10. Update the version in __init__.py, setup.py to the bumped minor version + ".dev0" (e.g. from "0.6.0" to "0.7.0.dev0")
|
peft/setup.py/0
|
{
"file_path": "peft/setup.py",
"repo_id": "peft",
"token_count": 1584
}
| 193
|
# Copyright 2023-present 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 warnings
from dataclasses import dataclass, field
from typing import Optional
from peft.tuners.lora import LoraConfig
from peft.utils import PeftType
@dataclass
class AdaLoraConfig(LoraConfig):
"""
This is the configuration class to store the configuration of a [`~peft.AdaLora`].
Args:
target_r (`int`): The target average rank of incremental matrix.
init_r (`int`): The initial rank for each incremental matrix.
tinit (`int`): The steps of initial fine-tuning warmup.
tfinal (`int`): The step of final fine-tuning.
deltaT (`int`): The time internval between two budget allocations.
beta1 (`float`): The hyperparameter of EMA for sensitivity smoothing.
beta2 (`float`): The hyperparameter of EMA for undertainty quantification.
orth_reg_weight (`float`): The coefficient of orthogonal regularization.
total_step (`int`): The total training steps that should be specified before training.
rank_pattern (`list`): The allocated rank for each weight matrix by RankAllocator.
"""
target_r: int = field(default=8, metadata={"help": "Target Lora matrix dimension."})
init_r: int = field(default=12, metadata={"help": "Initial Lora matrix dimension."})
tinit: int = field(default=0, metadata={"help": "The steps of initial warmup."})
tfinal: int = field(default=0, metadata={"help": "The steps of final warmup."})
deltaT: int = field(default=1, metadata={"help": "Step interval of rank allocation."})
beta1: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."})
beta2: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."})
orth_reg_weight: float = field(default=0.5, metadata={"help": "The orthogonal regularization coefficient."})
total_step: Optional[int] = field(default=None, metadata={"help": "The total training steps."})
rank_pattern: Optional[dict] = field(default=None, metadata={"help": "The saved rank pattern."})
def __post_init__(self):
self.peft_type = PeftType.ADALORA
if self.use_dora:
raise ValueError(f"{self.peft_type} does not support DoRA.")
if self.loftq_config:
raise ValueError(f"{self.peft_type} does not support LOFTQ.")
self.target_modules = (
set(self.target_modules) if isinstance(self.target_modules, list) else self.target_modules
)
# if target_modules is a regex expression, then layers_to_transform should be None
if isinstance(self.target_modules, str) and self.layers_to_transform is not None:
raise ValueError("`layers_to_transform` cannot be used when `target_modules` is a str.")
# if target_modules is a regex expression, then layers_pattern should be None
if isinstance(self.target_modules, str) and self.layers_pattern is not None:
raise ValueError("`layers_pattern` cannot be used when `target_modules` is a str.")
# Check if 'r' has been set to a non-default value
if self.r != 8: # 8 is the default value for 'r' in LoraConfig
warnings.warn(
"Note that `r` is not used in AdaLora and will be ignored."
"If you intended to set the initial rank, use `init_r` instead."
)
|
peft/src/peft/tuners/adalora/config.py/0
|
{
"file_path": "peft/src/peft/tuners/adalora/config.py",
"repo_id": "peft",
"token_count": 1361
}
| 194
|
# Copyright 2024-present 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 __future__ import annotations
import warnings
from typing import Optional
from torch import nn
from torch.nn.modules import Module
from tqdm import tqdm
from peft.config import PeftConfig
from peft.tuners.tuners_utils import BaseTuner, _get_submodules, check_target_module_exists
from peft.utils import TRANSFORMERS_MODELS_TO_LNTUNING_TARGET_MODULES_MAPPING, ModulesToSaveWrapper
from .layer import LNTuningLayer
class LNTuningModel(BaseTuner):
"""
Creates LayerNorm tuning from a pretrained transformer model.
The method is described in detail in https://arxiv.org/abs/2312.11420.
Args:
model ([`torch.nn.Module`]): The model to be adapted.
config ([`LNTuningConfig`]): The configuration of the Lora model.
adapter_name (`str`): The name of the adapter, defaults to `"default"`.
Returns:
'torch.nn.Module': The adapted model with LayerNorm tuned on.
Example:
```py
>>> from transformers import AutoModelForCausalLM
>>> from peft import get_peft_model, TaskType, LNTuningConfig
>>> peft_config = LNTuningConfig(
... task_type=TaskType.CAUSAL_LM,
... )
>>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
>>> model = get_peft_model(model, peft_config)
>>> model.print_trainable_parameters()
```
**Attributes**:
- **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted.
- **peft_config** ([`LNTuningConfig`]): The configuration of the Lora model.
"""
prefix: str = "ln_tuning_"
def __init__(self, model, config, adapter_name) -> None:
# self.adapter_name = adapter_name
super().__init__(model, config, adapter_name)
def __getattr__(self, name: str):
"""Forward missing attributes to the wrapped module."""
try:
return super().__getattr__(name) # defer to nn.Module's logic
except AttributeError:
if name == "model": # see #1892: prevent infinite recursion if class is not initialized
raise
return getattr(self.model, name)
# TODO: here need to handle the modules_to_save rather than the target_modules
@staticmethod
def _prepare_adapter_config(peft_config: PeftConfig, model_config: dict) -> PeftConfig:
if peft_config.target_modules is None:
if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_LNTUNING_TARGET_MODULES_MAPPING:
raise ValueError("Please specify `target_modules` in `peft_config`")
peft_config.target_modules = set(
TRANSFORMERS_MODELS_TO_LNTUNING_TARGET_MODULES_MAPPING[model_config["model_type"]]
)
return peft_config
def _create_and_replace(
self,
peft_config: PeftConfig,
adapter_name: str,
target: Module,
target_name: str,
parent: Module,
current_key: str,
) -> None:
# replace the original module with a same new module
new_module = self._create_new_module(peft_config, target, adapter_name)
if adapter_name != self.active_adapter:
new_module.requires_grad_(False)
self._replace_module(parent, target_name, new_module, target)
def _create_new_module(
self,
peft_config: PeftConfig,
target: Module,
adapter_name: str,
) -> Module:
if not isinstance(target, LNTuningLayer):
new_module = LNTuningLayer(target, adapter_name)
else:
new_module = target
new_module.update_layer(target.base_layer, adapter_name)
return new_module
def _replace_module(self, parent: Module, child_name: str, new_module: Module, child: Module) -> None:
setattr(parent, child_name, new_module)
if hasattr(child, "base_layer"):
child = child.base_layer
if getattr(child, "state", None) is not None:
if hasattr(new_module, "base_layer"):
new_module.base_layer.state = child.state
else:
new_module.state = child.state
new_module.to(child.weight.device)
for name, module in new_module.named_modules():
weight = child.qweight if hasattr(child, "qweight") else child.weight
module.to(weight.device)
def _mark_only_adapters_as_trainable(self, model: Module):
for n, p in model.named_parameters():
if self.prefix not in n:
p.requires_grad = False
else:
p.requires_grad = True
def _check_target_module_exists(self, peft_config: PeftConfig, key: str) -> bool:
return check_target_module_exists(peft_config, key)
def _set_adapter_layers(self, enabled: bool) -> None:
for module in self.model.modules():
if isinstance(module, (LNTuningLayer, ModulesToSaveWrapper)):
module.enable_adapters(enabled)
def enable_adapter_layers(self) -> None:
"""Enable all adapters.
Call this if you have previously disabled all adapters and want to re-enable them.
"""
self._set_adapter_layers(enabled=True)
def disable_adapter_layers(self) -> None:
"""Disable all adapters.
When disabling all adapters, the model output corresponds to the output of the base model.
"""
self._set_adapter_layers(enabled=False)
def set_adapter(self, adapter_name: str) -> None:
for module in self.model.modules():
if isinstance(module, LNTuningLayer):
if module.merged:
warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.")
module.unmerge()
module.set_adapter(adapter_name)
self.active_adapter = adapter_name
def _unload_and_optionally_merge(
self,
merge=True,
progressbar: bool = False,
safe_merge: bool = False,
adapter_names: Optional[list[str]] = None,
):
self._unloading_checks(adapter_names)
key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
desc = "Unloading adapters " + ("and merging " if merge else "") + "model"
for key in tqdm(key_list, disable=not progressbar, desc=desc):
try:
parent, target, target_name = _get_submodules(self.model, key)
except AttributeError:
continue
if hasattr(target, "base_layer"):
if merge:
target.merge(adapter_names)
self._replace_module(parent, target_name, target.get_base_layer(), target)
return self.model
def unload(self):
return self._unload_and_optionally_merge(merge=False)
def merge_and_unload(
self, progressbar: bool = False, safe_merge: bool = False, adapter_names: Optional[list[str]] = None
) -> nn.Module:
return self._unload_and_optionally_merge(merge=True)
|
peft/src/peft/tuners/ln_tuning/model.py/0
|
{
"file_path": "peft/src/peft/tuners/ln_tuning/model.py",
"repo_id": "peft",
"token_count": 3180
}
| 195
|
# Copyright 2023-present 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 torch
from peft.tuners.lora.layer import LoraLayer
from peft.tuners.tuners_utils import BaseTunerLayer
from peft.utils import get_auto_gptq_quant_linear
class QuantLinear(torch.nn.Module, LoraLayer):
def __init__(
self,
base_layer,
adapter_name: str,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.0,
init_lora_weights: bool = True,
use_rslora: bool = False,
use_dora: bool = False,
**kwargs,
):
super().__init__()
LoraLayer.__init__(self, base_layer)
if use_dora:
raise ValueError(f"{self.__class__.__name__} does not support DoRA yet, please set it to False")
# self.base_layer and self.quant_linear_module are the same; we need the former for consistency and the latter
# for backwards compatibility
self.quant_linear_module = base_layer
self._active_adapter = adapter_name
self.update_layer(
adapter_name,
r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
init_lora_weights=init_lora_weights,
use_rslora=use_rslora,
use_dora=use_dora,
)
def forward(self, x: torch.Tensor):
# note: logic differs from default Linear because merging is not supported
result = self.quant_linear_module(x)
if self.disable_adapters:
return result
for active_adapter in self.active_adapters:
if active_adapter not in self.lora_A.keys():
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
x = x.to(lora_A.weight.dtype)
output = lora_B(lora_A(dropout(x)))
if requires_conversion:
output = output.to(expected_dtype)
output = output * scaling
result += output
return result
def __repr__(self) -> str:
rep = super().__repr__()
return "lora." + rep
# TODO: Check if it is better as suggested by users https://github.com/PanQiWei/AutoGPTQ/pull/102
# def reset_lora_parameters(self, adapter_name):
# if adapter_name in self.lora_A.keys():
# torch.nn.init.xavier_uniform_(self.lora_A[adapter_name].weight)
# torch.nn.init.zeros_(self.lora_B[adapter_name].weight)
def dispatch_gptq(
target: torch.nn.Module,
adapter_name: str,
**kwargs: Any,
) -> Optional[torch.nn.Module]:
new_module = None
if isinstance(target, BaseTunerLayer):
target_base_layer = target.get_base_layer()
else:
target_base_layer = target
gptq_quantization_config = kwargs.get("gptq_quantization_config", None)
AutoGPTQQuantLinear = get_auto_gptq_quant_linear(gptq_quantization_config)
if AutoGPTQQuantLinear is not None and isinstance(target_base_layer, AutoGPTQQuantLinear):
new_module = QuantLinear(target, adapter_name, **kwargs)
target.qweight = target_base_layer.qweight
return new_module
|
peft/src/peft/tuners/lora/gptq.py/0
|
{
"file_path": "peft/src/peft/tuners/lora/gptq.py",
"repo_id": "peft",
"token_count": 1708
}
| 196
|
# Copyright 2023-present 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 enum
from dataclasses import dataclass, field
from typing import Union
from peft.config import PromptLearningConfig
from peft.utils import PeftType
class PromptEncoderReparameterizationType(str, enum.Enum):
MLP = "MLP"
LSTM = "LSTM"
@dataclass
class PromptEncoderConfig(PromptLearningConfig):
"""
This is the configuration class to store the configuration of a [`PromptEncoder`].
Args:
encoder_reparameterization_type (Union[[`PromptEncoderReparameterizationType`], `str`]):
The type of reparameterization to use.
encoder_hidden_size (`int`): The hidden size of the prompt encoder.
encoder_num_layers (`int`): The number of layers of the prompt encoder.
encoder_dropout (`float`): The dropout probability of the prompt encoder.
"""
encoder_reparameterization_type: Union[str, PromptEncoderReparameterizationType] = field(
default=PromptEncoderReparameterizationType.MLP,
metadata={"help": "How to reparameterize the prompt encoder"},
)
encoder_hidden_size: int = field(
default=None,
metadata={"help": "The hidden size of the prompt encoder"},
)
encoder_num_layers: int = field(
default=2,
metadata={"help": "The number of layers of the prompt encoder"},
)
encoder_dropout: float = field(
default=0.0,
metadata={"help": "The dropout of the prompt encoder"},
)
def __post_init__(self):
self.peft_type = PeftType.P_TUNING
|
peft/src/peft/tuners/p_tuning/config.py/0
|
{
"file_path": "peft/src/peft/tuners/p_tuning/config.py",
"repo_id": "peft",
"token_count": 732
}
| 197
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# Copyright 2023-present 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 warnings
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.pytorch_utils import Conv1D
from peft.tuners.tuners_utils import BaseTunerLayer, check_adapters_to_merge
from peft.utils.other import transpose
from .._buffer_dict import BufferDict
class VeraLayer(BaseTunerLayer):
# List all names of layers that may contain adapter weights
adapter_layer_names = ("vera_lambda_b", "vera_lambda_d")
other_param_names = ("vera_A", "vera_B")
def __init__(self, base_layer: nn.Module, **kwargs):
self.base_layer = base_layer
self.r = {}
self.vera_dropout = nn.ModuleDict({})
# For storing vector scale
self.vera_lambda_b = nn.ParameterDict({})
self.vera_lambda_d = nn.ParameterDict({})
# Stores a reference to the vera_A/B BufferDict.
# Set to `None` otherwise to avoid computation with random weights
self.vera_A: Optional[BufferDict] = None
self.vera_B: Optional[BufferDict] = None
# Mark the weight as unmerged
self._disable_adapters = False
self.merged_adapters = []
base_layer = self.get_base_layer()
if isinstance(base_layer, nn.Linear):
in_features, out_features = base_layer.in_features, base_layer.out_features
elif isinstance(base_layer, Conv1D):
in_features, out_features = (
base_layer.weight.ds_shape if hasattr(base_layer.weight, "ds_shape") else base_layer.weight.shape
)
self.in_features = in_features
self.out_features = out_features
self.kwargs = kwargs
@property
def merged(self) -> bool:
return bool(self.merged_adapters)
def update_layer(
self,
adapter_name,
vera_A: BufferDict,
vera_B: BufferDict,
r,
vera_dropout,
init_weights,
d_initial: float = 0.1,
):
if r <= 0:
raise ValueError(f"`r` should be a positive integer value but the value passed is {r}")
self.r[adapter_name] = r
if vera_dropout > 0.0:
vera_dropout_layer = nn.Dropout(p=vera_dropout)
else:
vera_dropout_layer = nn.Identity()
self.vera_dropout.update(nn.ModuleDict({adapter_name: vera_dropout_layer}))
# Actual trainable parameters
self.vera_lambda_b[adapter_name] = nn.Parameter(torch.ones(self.out_features), requires_grad=True)
self.vera_lambda_d[adapter_name] = nn.Parameter(torch.randn(r), requires_grad=True)
# non trainable references to vera_A/B buffers
self.vera_A = vera_A
self.vera_B = vera_B
if adapter_name not in vera_A:
# This means that this is not the first VeRA adapter. We have to add an entry in the dict for this adapter.
if len(self.vera_A) < 1:
raise ValueError(
"The `vera_A` and `vera_B` buffers are empty. This should not happen. Please report this issue."
)
# we can take any of the existing adapter's parameters, as they should all be identical
vera_A_param = list(self.vera_A.values())[0]
vera_B_param = list(self.vera_B.values())[0]
error_tmpl = (
"{} has a size of {} but {} or greater is required; this probably happened because an additional VeRA "
"adapter was added after the first one with incompatible shapes."
)
# check input size
if vera_A_param.shape[1] < self.in_features:
raise ValueError(error_tmpl.format("vera_A", vera_A_param.shape[1], self.in_features))
# check output size
if vera_B_param.shape[0] < self.out_features:
raise ValueError(error_tmpl.format("vera_B", vera_B_param.shape[0], self.out_features))
# check r
error_tmpl = (
"{} has a size of {} but {} or greater is required; this probably happened because an additional VeRA "
"adapter with a lower rank was added after the first one; loading the adapters "
"in reverse order may solve this."
)
if vera_A_param.shape[0] < self.r[adapter_name]:
raise ValueError(error_tmpl.format("vera_A", vera_A_param.shape[0], self.r[adapter_name]))
if vera_B_param.shape[1] < self.r[adapter_name]:
raise ValueError(error_tmpl.format("vera_B", vera_B_param.shape[1], self.r[adapter_name]))
self.vera_A[adapter_name] = vera_A_param
self.vera_B[adapter_name] = vera_B_param
if init_weights:
self.reset_vera_parameters(adapter_name, d_initial=d_initial)
self._move_adapter_to_device_of_base_layer(adapter_name)
self.set_adapter(self.active_adapters)
def reset_vera_parameters(self, adapter_name, d_initial: float = 0.1):
if adapter_name in self.vera_lambda_d.keys():
with torch.no_grad():
nn.init.zeros_(self.vera_lambda_d[adapter_name]).fill_(d_initial)
nn.init.zeros_(self.vera_lambda_b[adapter_name])
class Linear(nn.Linear, VeraLayer):
# Vera implemented in a dense layer
def __init__(
self,
base_layer,
vera_A: BufferDict,
vera_B: BufferDict,
adapter_name: str,
r: int = 0,
vera_dropout: float = 0.0,
fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
is_target_conv_1d_layer: bool = False,
init_weights: bool = True,
d_initial: float = 0.1,
**kwargs,
) -> None:
# this gets the init from nn.Linear's super perspective, i.e. nn.Module.__init__, which should always be called
super(nn.Linear, self).__init__()
VeraLayer.__init__(self, base_layer, **kwargs)
self.fan_in_fan_out = fan_in_fan_out
self._active_adapter = adapter_name
self.update_layer(adapter_name, vera_A, vera_B, r, vera_dropout, init_weights, d_initial=d_initial)
self.is_target_conv_1d_layer = is_target_conv_1d_layer
def merge(self, safe_merge: bool = False, adapter_names: Optional[List[str]] = None) -> None:
"""
Merge the active adapter weights into the base weights
Args:
safe_merge (`bool`, *optional*):
If True, the merge operation will be performed in a copy of the original weights and check for NaNs
before merging the weights. This is useful if you want to check if the merge operation will produce
NaNs. Defaults to `False`.
adapter_names (`List[str]`, *optional*):
The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults
to `None`.
"""
adapter_names = check_adapters_to_merge(self, adapter_names)
if not adapter_names:
# no adapter to merge
return
for active_adapter in adapter_names:
if active_adapter in self.vera_lambda_d.keys():
base_layer = self.get_base_layer()
if safe_merge:
# Note that safe_merge will be slower than the normal merge
# because of the copy operation.
orig_weights = base_layer.weight.data.clone()
orig_weights += self.get_delta_weight(active_adapter)
if not torch.isfinite(orig_weights).all():
raise ValueError(
f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
)
base_layer.weight.data = orig_weights
else:
base_layer.weight.data += self.get_delta_weight(active_adapter)
self.merged_adapters.append(active_adapter)
def unmerge(self) -> None:
if not self.merged:
warnings.warn("Already unmerged. Nothing to do.")
return
while len(self.merged_adapters) > 0:
active_adapter = self.merged_adapters.pop()
if active_adapter in self.vera_lambda_d.keys():
self.get_base_layer().weight.data -= self.get_delta_weight(active_adapter)
def get_delta_weight(self, adapter) -> torch.Tensor:
"""
Compute the delta weight for the given adapter.
Args:
adapter (str):
The name of the adapter for which the delta weight should be computed.
"""
vera_A = self.vera_A[adapter]
vera_B = self.vera_B[adapter]
device = vera_B.device
dtype = vera_B.dtype
# In case users wants to merge the adapter weights that are in
# (b)float16 while being on CPU, we need to cast the weights to float32, perform the merge and then cast back to
# (b)float16 because some CPUs have slow bf16/fp16 matmuls.
cast_to_fp32 = device.type == "cpu" and (dtype == torch.float16 or dtype == torch.bfloat16)
lambda_d = self.vera_lambda_d[adapter]
lambda_b = self.vera_lambda_b[adapter]
if cast_to_fp32:
vera_A = vera_A.float()
vera_B = vera_B.float()
lambda_d = lambda_d.float()
lambda_b = lambda_b.float()
sliced_A = vera_A[:, : self.in_features]
sliced_B = vera_B[: self.out_features, :]
lambda_b = lambda_b.unsqueeze(-1)
lambda_d = lambda_d.unsqueeze(-1)
output_tensor = transpose((lambda_b * sliced_B) @ (lambda_d * sliced_A), self.fan_in_fan_out)
if cast_to_fp32:
output_tensor = output_tensor.to(dtype=dtype)
# cast back the weights
# TODO: why?
self.vera_lambda_d[adapter].data = lambda_d.to(dtype)
self.vera_lambda_b[adapter].data = lambda_b.to(dtype)
return output_tensor
def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
previous_dtype = x.dtype
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
result = self.base_layer(x, *args, **kwargs)
for active_adapter in self.active_adapters:
if active_adapter not in self.vera_lambda_d.keys():
continue
lambda_d = self.vera_lambda_d[active_adapter]
lambda_b = self.vera_lambda_b[active_adapter]
vera_A = self.vera_A[active_adapter]
vera_B = self.vera_B[active_adapter]
# As adapted layers may have different shapes and VeRA contains a single shared pair of A and B matrices,
# we initialize these matrices with the largest required size for each dimension.
# During the forward pass, required submatrices are sliced out from the shared vera_A and vera_B.
sliced_A = vera_A[:, : self.in_features]
sliced_B = vera_B[: self.out_features, :]
dropout = self.vera_dropout[active_adapter]
x = x.to(lambda_d.dtype)
result = result + lambda_b * F.linear(lambda_d * F.linear(dropout(x), sliced_A), sliced_B)
result = result.to(previous_dtype)
return result
def __repr__(self) -> str:
rep = super().__repr__()
return "vera." + rep
|
peft/src/peft/tuners/vera/layer.py/0
|
{
"file_path": "peft/src/peft/tuners/vera/layer.py",
"repo_id": "peft",
"token_count": 5620
}
| 198
|
# Copyright 2024-present 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 __future__ import annotations
import torch
from torch import nn
from peft.import_utils import is_bnb_available
from peft.optimizers import create_loraplus_optimizer
from .testing_utils import require_bitsandbytes
if is_bnb_available():
import bitsandbytes as bnb
class SimpleNet(nn.Module):
def __init__(self, bias=True):
super().__init__()
self.embedding = nn.Embedding(100, 20)
self.layer_norm = nn.LayerNorm(20)
self.lin0 = nn.Linear(20, 20, bias=bias)
self.relu = nn.ReLU()
self.lin1 = nn.Linear(20, 16, bias=bias)
def forward(self, X):
X = self.lin0(self.layer_norm(self.embedding(X)))
X = self.relu(X)
X = self.lin1(X)
return X
@require_bitsandbytes
def test_lora_plus_helper_sucess():
model = SimpleNet()
optimizer_cls = bnb.optim.Adam8bit
lr = 5e-5
optim_config = {
"eps": 1e-6,
"betas": (0.9, 0.999),
"loraplus_weight_decay": 0.0,
}
loraplus_lr_ratio = 1.2
loraplus_lr_embedding = 1e-6
optim = create_loraplus_optimizer(
model=model,
optimizer_cls=optimizer_cls,
lr=lr,
loraplus_lr_ratio=loraplus_lr_ratio,
loraplus_lr_embedding=loraplus_lr_embedding,
**optim_config,
)
assert optim is not None
assert len(optim.param_groups) == 4
assert optim.param_groups[0]["lr"] == lr
assert optim.param_groups[1]["lr"] == loraplus_lr_embedding
assert optim.param_groups[2]["lr"] == optim.param_groups[3]["lr"] == (lr * loraplus_lr_ratio)
@require_bitsandbytes
def test_lora_plus_optimizer_sucess():
"""
Test if the optimizer is correctly created and step function runs without any exception
"""
optimizer_cls = bnb.optim.Adam8bit
optim_config = {
"eps": 1e-6,
"betas": (0.9, 0.999),
"loraplus_weight_decay": 0.0,
}
model: SimpleNet = SimpleNet().cuda()
optim = create_loraplus_optimizer(
model=model,
optimizer_cls=optimizer_cls,
lr=5e-5,
loraplus_lr_ratio=1.2,
loraplus_lr_embedding=1e-6,
**optim_config,
)
loss = torch.nn.CrossEntropyLoss()
bnb.optim.GlobalOptimManager.get_instance().register_parameters(model.parameters())
x = torch.randint(100, (2, 4, 10)).cuda()
output = model(x).permute(0, 3, 1, 2)
label = torch.randint(16, (2, 4, 10)).cuda()
loss_value = loss(output, label)
loss_value.backward()
optim.step()
|
peft/tests/test_loraplus.py/0
|
{
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"repo_id": "peft",
"token_count": 1312
}
| 199
|
- sections:
- local: index
title: Home
- local: quickstart
title: Quickstart
- local: installation
title: Installation
- local: changes
title: Changelog
title: Get started
- sections:
- local: feature_extraction
title: Using Pretrained Models as Feature Extractors
- local: training_script
title: Training With The Official Training Script
- local: hf_hub
title: Share and Load Models from the 🤗 Hugging Face Hub
title: Tutorials
- sections:
- local: models
title: Model Summaries
- local: results
title: Results
- local: models/adversarial-inception-v3
title: Adversarial Inception v3
- local: models/advprop
title: AdvProp (EfficientNet)
- local: models/big-transfer
title: Big Transfer (BiT)
- local: models/csp-darknet
title: CSP-DarkNet
- local: models/csp-resnet
title: CSP-ResNet
- local: models/csp-resnext
title: CSP-ResNeXt
- local: models/densenet
title: DenseNet
- local: models/dla
title: Deep Layer Aggregation
- local: models/dpn
title: Dual Path Network (DPN)
- local: models/ecaresnet
title: ECA-ResNet
- local: models/efficientnet
title: EfficientNet
- local: models/efficientnet-pruned
title: EfficientNet (Knapsack Pruned)
- local: models/ensemble-adversarial
title: Ensemble Adversarial Inception ResNet v2
- local: models/ese-vovnet
title: ESE-VoVNet
- local: models/fbnet
title: FBNet
- local: models/gloun-inception-v3
title: (Gluon) Inception v3
- local: models/gloun-resnet
title: (Gluon) ResNet
- local: models/gloun-resnext
title: (Gluon) ResNeXt
- local: models/gloun-senet
title: (Gluon) SENet
- local: models/gloun-seresnext
title: (Gluon) SE-ResNeXt
- local: models/gloun-xception
title: (Gluon) Xception
- local: models/hrnet
title: HRNet
- local: models/ig-resnext
title: Instagram ResNeXt WSL
- local: models/inception-resnet-v2
title: Inception ResNet v2
- local: models/inception-v3
title: Inception v3
- local: models/inception-v4
title: Inception v4
- local: models/legacy-se-resnet
title: (Legacy) SE-ResNet
- local: models/legacy-se-resnext
title: (Legacy) SE-ResNeXt
- local: models/legacy-senet
title: (Legacy) SENet
- local: models/mixnet
title: MixNet
- local: models/mnasnet
title: MnasNet
- local: models/mobilenet-v2
title: MobileNet v2
- local: models/mobilenet-v3
title: MobileNet v3
- local: models/nasnet
title: NASNet
- local: models/noisy-student
title: Noisy Student (EfficientNet)
- local: models/pnasnet
title: PNASNet
- local: models/regnetx
title: RegNetX
- local: models/regnety
title: RegNetY
- local: models/res2net
title: Res2Net
- local: models/res2next
title: Res2NeXt
- local: models/resnest
title: ResNeSt
- local: models/resnet
title: ResNet
- local: models/resnet-d
title: ResNet-D
- local: models/resnext
title: ResNeXt
- local: models/rexnet
title: RexNet
- local: models/se-resnet
title: SE-ResNet
- local: models/selecsls
title: SelecSLS
- local: models/seresnext
title: SE-ResNeXt
- local: models/skresnet
title: SK-ResNet
- local: models/skresnext
title: SK-ResNeXt
- local: models/spnasnet
title: SPNASNet
- local: models/ssl-resnet
title: SSL ResNet
- local: models/swsl-resnet
title: SWSL ResNet
- local: models/swsl-resnext
title: SWSL ResNeXt
- local: models/tf-efficientnet
title: (Tensorflow) EfficientNet
- local: models/tf-efficientnet-condconv
title: (Tensorflow) EfficientNet CondConv
- local: models/tf-efficientnet-lite
title: (Tensorflow) EfficientNet Lite
- local: models/tf-inception-v3
title: (Tensorflow) Inception v3
- local: models/tf-mixnet
title: (Tensorflow) MixNet
- local: models/tf-mobilenet-v3
title: (Tensorflow) MobileNet v3
- local: models/tresnet
title: TResNet
- local: models/wide-resnet
title: Wide ResNet
- local: models/xception
title: Xception
title: Model Pages
isExpanded: false
- sections:
- local: reference/models
title: Models
- local: reference/data
title: Data
- local: reference/optimizers
title: Optimizers
- local: reference/schedulers
title: Learning Rate Schedulers
title: Reference
|
pytorch-image-models/hfdocs/source/_toctree.yml/0
|
{
"file_path": "pytorch-image-models/hfdocs/source/_toctree.yml",
"repo_id": "pytorch-image-models",
"token_count": 1701
}
| 200
|
# ECA-ResNet
An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) that reduces model complexity without dimensionality reduction.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('ecaresnet101d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `ecaresnet101d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('ecaresnet101d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{wang2020ecanet,
title={ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks},
author={Qilong Wang and Banggu Wu and Pengfei Zhu and Peihua Li and Wangmeng Zuo and Qinghua Hu},
year={2020},
eprint={1910.03151},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: ECAResNet
Paper:
Title: 'ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks'
URL: https://paperswithcode.com/paper/eca-net-efficient-channel-attention-for-deep
Models:
- Name: ecaresnet101d
In Collection: ECAResNet
Metadata:
FLOPs: 10377193728
Parameters: 44570000
File Size: 178815067
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Efficient Channel Attention
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x RTX 2080Ti GPUs
ID: ecaresnet101d
LR: 0.1
Epochs: 100
Layers: 101
Crop Pct: '0.875'
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1087
Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.18%
Top 5 Accuracy: 96.06%
- Name: ecaresnet101d_pruned
In Collection: ECAResNet
Metadata:
FLOPs: 4463972081
Parameters: 24880000
File Size: 99852736
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Efficient Channel Attention
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: ecaresnet101d_pruned
Layers: 101
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1097
Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.82%
Top 5 Accuracy: 95.64%
- Name: ecaresnet50d
In Collection: ECAResNet
Metadata:
FLOPs: 5591090432
Parameters: 25580000
File Size: 102579290
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Efficient Channel Attention
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x RTX 2080Ti GPUs
ID: ecaresnet50d
LR: 0.1
Epochs: 100
Layers: 50
Crop Pct: '0.875'
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1045
Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet50D_833caf58.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.61%
Top 5 Accuracy: 95.31%
- Name: ecaresnet50d_pruned
In Collection: ECAResNet
Metadata:
FLOPs: 3250730657
Parameters: 19940000
File Size: 79990436
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Efficient Channel Attention
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: ecaresnet50d_pruned
Layers: 50
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1055
Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.71%
Top 5 Accuracy: 94.88%
- Name: ecaresnetlight
In Collection: ECAResNet
Metadata:
FLOPs: 5276118784
Parameters: 30160000
File Size: 120956612
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Efficient Channel Attention
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: ecaresnetlight
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1077
Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNetLight_4f34b35b.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.46%
Top 5 Accuracy: 95.25%
-->
|
pytorch-image-models/hfdocs/source/models/ecaresnet.mdx/0
|
{
"file_path": "pytorch-image-models/hfdocs/source/models/ecaresnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3641
}
| 201
|
# Inception v4
**Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('inception_v4', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `inception_v4`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('inception_v4', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{szegedy2016inceptionv4,
title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi},
year={2016},
eprint={1602.07261},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Inception v4
Paper:
Title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on
Learning
URL: https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact
Models:
- Name: inception_v4
In Collection: Inception v4
Metadata:
FLOPs: 15806527936
Parameters: 42680000
File Size: 171082495
Architecture:
- Average Pooling
- Dropout
- Inception-A
- Inception-B
- Inception-C
- Reduction-A
- Reduction-B
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 20x NVIDIA Kepler GPUs
ID: inception_v4
LR: 0.045
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v4.py#L313
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/inceptionv4-8e4777a0.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 1.01%
Top 5 Accuracy: 16.85%
-->
|
pytorch-image-models/hfdocs/source/models/inception-v4.mdx/0
|
{
"file_path": "pytorch-image-models/hfdocs/source/models/inception-v4.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1625
}
| 202
|
# ResNet-D
**ResNet-D** is a modification on the [ResNet](https://paperswithcode.com/method/resnet) architecture that utilises an [average pooling](https://paperswithcode.com/method/average-pooling) tweak for downsampling. The motivation is that in the unmodified ResNet, the [1×1 convolution](https://paperswithcode.com/method/1x1-convolution) for the downsampling block ignores 3/4 of input feature maps, so this is modified so no information will be ignored
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('resnet101d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `resnet101d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('resnet101d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{he2018bag,
title={Bag of Tricks for Image Classification with Convolutional Neural Networks},
author={Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li},
year={2018},
eprint={1812.01187},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: ResNet-D
Paper:
Title: Bag of Tricks for Image Classification with Convolutional Neural Networks
URL: https://paperswithcode.com/paper/bag-of-tricks-for-image-classification-with
Models:
- Name: resnet101d
In Collection: ResNet-D
Metadata:
FLOPs: 13805639680
Parameters: 44570000
File Size: 178791263
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet101d
Crop Pct: '0.94'
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L716
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.31%
Top 5 Accuracy: 96.06%
- Name: resnet152d
In Collection: ResNet-D
Metadata:
FLOPs: 20155275264
Parameters: 60210000
File Size: 241596837
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet152d
Crop Pct: '0.94'
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L724
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.13%
Top 5 Accuracy: 96.35%
- Name: resnet18d
In Collection: ResNet-D
Metadata:
FLOPs: 2645205760
Parameters: 11710000
File Size: 46893231
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet18d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L649
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet18d_ra2-48a79e06.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 72.27%
Top 5 Accuracy: 90.69%
- Name: resnet200d
In Collection: ResNet-D
Metadata:
FLOPs: 26034378752
Parameters: 64690000
File Size: 259662933
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet200d
Crop Pct: '0.94'
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L749
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.24%
Top 5 Accuracy: 96.49%
- Name: resnet26d
In Collection: ResNet-D
Metadata:
FLOPs: 3335276032
Parameters: 16010000
File Size: 64209122
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet26d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L683
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.69%
Top 5 Accuracy: 93.15%
- Name: resnet34d
In Collection: ResNet-D
Metadata:
FLOPs: 5026601728
Parameters: 21820000
File Size: 87369807
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet34d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L666
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34d_ra2-f8dcfcaf.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.11%
Top 5 Accuracy: 93.38%
- Name: resnet50d
In Collection: ResNet-D
Metadata:
FLOPs: 5591002624
Parameters: 25580000
File Size: 102567109
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet50d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L699
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.55%
Top 5 Accuracy: 95.16%
-->
|
pytorch-image-models/hfdocs/source/models/resnet-d.mdx/0
|
{
"file_path": "pytorch-image-models/hfdocs/source/models/resnet-d.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3932
}
| 203
|
""" ONNX-runtime validation script
This script was created to verify accuracy and performance of exported ONNX
models running with the onnxruntime. It utilizes the PyTorch dataloader/processing
pipeline for a fair comparison against the originals.
Copyright 2020 Ross Wightman
"""
import argparse
import numpy as np
import onnxruntime
from timm.data import create_loader, resolve_data_config, create_dataset
from timm.utils import AverageMeter
import time
parser = argparse.ArgumentParser(description='ONNX Validation')
parser.add_argument('data', metavar='DIR',
help='path to dataset')
parser.add_argument('--onnx-input', default='', type=str, metavar='PATH',
help='path to onnx model/weights file')
parser.add_argument('--onnx-output-opt', default='', type=str, metavar='PATH',
help='path to output optimized onnx graph')
parser.add_argument('--profile', action='store_true', default=False,
help='Enable profiler output.')
parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
help='number of data loading workers (default: 2)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--img-size', default=None, type=int,
metavar='N', help='Input image dimension, uses model default if empty')
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
help='Override mean pixel value of dataset')
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
help='Override std deviation of of dataset')
parser.add_argument('--crop-pct', type=float, default=None, metavar='PCT',
help='Override default crop pct of 0.875')
parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
help='Image resize interpolation type (overrides model)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
def main():
args = parser.parse_args()
args.gpu_id = 0
# Set graph optimization level
sess_options = onnxruntime.SessionOptions()
sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
if args.profile:
sess_options.enable_profiling = True
if args.onnx_output_opt:
sess_options.optimized_model_filepath = args.onnx_output_opt
session = onnxruntime.InferenceSession(args.onnx_input, sess_options)
data_config = resolve_data_config(vars(args))
loader = create_loader(
create_dataset('', args.data),
input_size=data_config['input_size'],
batch_size=args.batch_size,
use_prefetcher=False,
interpolation=data_config['interpolation'],
mean=data_config['mean'],
std=data_config['std'],
num_workers=args.workers,
crop_pct=data_config['crop_pct']
)
input_name = session.get_inputs()[0].name
batch_time = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
for i, (input, target) in enumerate(loader):
# run the net and return prediction
output = session.run([], {input_name: input.data.numpy()})
output = output[0]
# measure accuracy and record loss
prec1, prec5 = accuracy_np(output, target.numpy())
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print(
f'Test: [{i}/{len(loader)}]\t'
f'Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {input.size(0) / batch_time.avg:.3f}/s, '
f'{100 * batch_time.avg / input.size(0):.3f} ms/sample) \t'
f'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
f'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'
)
print(f' * Prec@1 {top1.avg:.3f} ({100-top1.avg:.3f}) Prec@5 {top5.avg:.3f} ({100.-top5.avg:.3f})')
def accuracy_np(output, target):
max_indices = np.argsort(output, axis=1)[:, ::-1]
top5 = 100 * np.equal(max_indices[:, :5], target[:, np.newaxis]).sum(axis=1).mean()
top1 = 100 * np.equal(max_indices[:, 0], target).mean()
return top1, top5
if __name__ == '__main__':
main()
|
pytorch-image-models/onnx_validate.py/0
|
{
"file_path": "pytorch-image-models/onnx_validate.py",
"repo_id": "pytorch-image-models",
"token_count": 1960
}
| 204
|
"""Run tests for all models
Tests that run on CI should have a specific marker, e.g. @pytest.mark.base. This
marker is used to parallelize the CI runs, with one runner for each marker.
If new tests are added, ensure that they use one of the existing markers
(documented in pyproject.toml > pytest > markers) or that a new marker is added
for this set of tests. If using a new marker, adjust the test matrix in
.github/workflows/tests.yml to run tests with this new marker, otherwise the
tests will be skipped on CI.
"""
import pytest
import torch
import platform
import os
import fnmatch
_IS_MAC = platform.system() == 'Darwin'
try:
from torchvision.models.feature_extraction import create_feature_extractor, get_graph_node_names, NodePathTracer
has_fx_feature_extraction = True
except ImportError:
has_fx_feature_extraction = False
import timm
from timm import list_models, create_model, set_scriptable, get_pretrained_cfg_value
from timm.layers import Format, get_spatial_dim, get_channel_dim
from timm.models import get_notrace_modules, get_notrace_functions
import importlib
import os
torch_backend = os.environ.get('TORCH_BACKEND')
if torch_backend is not None:
importlib.import_module(torch_backend)
torch_device = os.environ.get('TORCH_DEVICE', 'cpu')
timeout = os.environ.get('TIMEOUT')
timeout120 = int(timeout) if timeout else 120
timeout300 = int(timeout) if timeout else 300
if hasattr(torch._C, '_jit_set_profiling_executor'):
# legacy executor is too slow to compile large models for unit tests
# no need for the fusion performance here
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(False)
# models with forward_intermediates() and support for FeatureGetterNet features_only wrapper
FEAT_INTER_FILTERS = [
'vision_transformer', 'vision_transformer_sam', 'vision_transformer_hybrid', 'vision_transformer_relpos',
'beit', 'mvitv2', 'eva', 'cait', 'xcit', 'volo', 'twins', 'deit', 'swin_transformer', 'swin_transformer_v2',
'swin_transformer_v2_cr', 'maxxvit', 'efficientnet', 'mobilenetv3', 'levit', 'efficientformer', 'resnet',
'regnet', 'byobnet', 'byoanet', 'mlp_mixer', 'hiera', 'fastvit', 'hieradet_sam2'
]
# transformer / hybrid models don't support full set of spatial / feature APIs and/or have spatial output.
NON_STD_FILTERS = [
'vit_*', 'tnt_*', 'pit_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
'convit_*', 'levit*', 'visformer*', 'deit*', 'xcit_*', 'crossvit_*', 'beit*',
'poolformer_*', 'volo_*', 'sequencer2d_*', 'mvitv2*', 'gcvit*', 'efficientformer*', 'sam_hiera*',
'eva_*', 'flexivit*', 'eva02*', 'samvit_*', 'efficientvit_m*', 'tiny_vit_*', 'hiera_*', 'vitamin*', 'test_vit*',
]
NUM_NON_STD = len(NON_STD_FILTERS)
# exclude models that cause specific test failures
if 'GITHUB_ACTIONS' in os.environ:
# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
EXCLUDE_FILTERS = [
'*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm',
'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*',
'*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', '*huge*', '*giant*', '*gigantic*',
'*enormous*', 'maxvit_xlarge*', 'regnet*1280', 'regnet*2560']
NON_STD_EXCLUDE_FILTERS = ['*huge*', '*giant*', '*gigantic*', '*enormous*']
else:
EXCLUDE_FILTERS = ['*enormous*']
NON_STD_EXCLUDE_FILTERS = ['*gigantic*', '*enormous*']
EXCLUDE_JIT_FILTERS = ['hiera_*']
TARGET_FWD_SIZE = MAX_FWD_SIZE = 384
TARGET_BWD_SIZE = 128
MAX_BWD_SIZE = 320
MAX_FWD_OUT_SIZE = 448
TARGET_JIT_SIZE = 128
MAX_JIT_SIZE = 320
TARGET_FFEAT_SIZE = 96
MAX_FFEAT_SIZE = 256
TARGET_FWD_FX_SIZE = 128
MAX_FWD_FX_SIZE = 256
TARGET_BWD_FX_SIZE = 128
MAX_BWD_FX_SIZE = 224
def _get_input_size(model=None, model_name='', target=None):
if model is None:
assert model_name, "One of model or model_name must be provided"
input_size = get_pretrained_cfg_value(model_name, 'input_size')
fixed_input_size = get_pretrained_cfg_value(model_name, 'fixed_input_size')
min_input_size = get_pretrained_cfg_value(model_name, 'min_input_size')
else:
default_cfg = model.default_cfg
input_size = default_cfg['input_size']
fixed_input_size = default_cfg.get('fixed_input_size', None)
min_input_size = default_cfg.get('min_input_size', None)
assert input_size is not None
if fixed_input_size:
return input_size
if min_input_size:
if target and max(input_size) > target:
input_size = min_input_size
else:
if target and max(input_size) > target:
input_size = tuple([min(x, target) for x in input_size])
return input_size
@pytest.mark.base
@pytest.mark.timeout(timeout120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
input_size = _get_input_size(model=model, target=TARGET_FWD_SIZE)
if max(input_size) > MAX_FWD_SIZE:
pytest.skip("Fixed input size model > limit.")
inputs = torch.randn((batch_size, *input_size))
inputs = inputs.to(torch_device)
model.to(torch_device)
outputs = model(inputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.base
@pytest.mark.timeout(timeout120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [2])
def test_model_backward(model_name, batch_size):
"""Run a single forward pass with each model"""
input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
if max(input_size) > MAX_BWD_SIZE:
pytest.skip("Fixed input size model > limit.")
model = create_model(model_name, pretrained=False, num_classes=42)
num_params = sum([x.numel() for x in model.parameters()])
model.train()
inputs = torch.randn((batch_size, *input_size))
inputs = inputs.to(torch_device)
model.to(torch_device)
outputs = model(inputs)
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
outputs.mean().backward()
for n, x in model.named_parameters():
assert x.grad is not None, f'No gradient for {n}'
num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
assert outputs.shape[-1] == 42
assert num_params == num_grad, 'Some parameters are missing gradients'
assert not torch.isnan(outputs).any(), 'Output included NaNs'
# models with extra conv/linear layers after pooling
EARLY_POOL_MODELS = (
timm.models.EfficientVit,
timm.models.EfficientVitLarge,
timm.models.HighPerfGpuNet,
timm.models.GhostNet,
timm.models.MetaNeXt, # InceptionNeXt
timm.models.MobileNetV3,
timm.models.RepGhostNet,
timm.models.VGG,
)
@pytest.mark.cfg
@pytest.mark.timeout(timeout300)
@pytest.mark.parametrize('model_name', list_models(
exclude_filters=EXCLUDE_FILTERS + NON_STD_FILTERS, include_tags=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
model.to(torch_device)
assert getattr(model, 'num_classes') >= 0
assert getattr(model, 'num_features') > 0
assert getattr(model, 'head_hidden_size') > 0
state_dict = model.state_dict()
cfg = model.default_cfg
pool_size = cfg['pool_size']
input_size = model.default_cfg['input_size']
output_fmt = getattr(model, 'output_fmt', 'NCHW')
spatial_axis = get_spatial_dim(output_fmt)
assert len(spatial_axis) == 2 # TODO add 1D sequence support
feat_axis = get_channel_dim(output_fmt)
if all([x <= MAX_FWD_OUT_SIZE for x in input_size]) and \
not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
# output sizes only checked if default res <= 448 * 448 to keep resource down
input_size = tuple([min(x, MAX_FWD_OUT_SIZE) for x in input_size])
input_tensor = torch.randn((batch_size, *input_size), device=torch_device)
# test forward_features (always unpooled) & forward_head w/ pre_logits
outputs = model.forward_features(input_tensor)
outputs_pre = model.forward_head(outputs, pre_logits=True)
assert outputs.shape[spatial_axis[0]] == pool_size[0], f'unpooled feature shape {outputs.shape} != config'
assert outputs.shape[spatial_axis[1]] == pool_size[1], f'unpooled feature shape {outputs.shape} != config'
assert outputs.shape[feat_axis] == model.num_features, f'unpooled feature dim {outputs.shape[feat_axis]} != model.num_features {model.num_features}'
assert outputs_pre.shape[1] == model.head_hidden_size, f'pre_logits feature dim {outputs_pre.shape[1]} != model.head_hidden_size {model.head_hidden_size}'
# test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
model.reset_classifier(0)
model.to(torch_device)
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 2
assert outputs.shape[1] == model.head_hidden_size, f'feature dim w/ removed classifier {outputs.shape[1]} != model.head_hidden_size {model.head_hidden_size}'
assert outputs.shape == outputs_pre.shape, f'output shape of pre_logits {outputs_pre.shape} does not match reset_head(0) {outputs.shape}'
# test model forward after removing pooling and classifier
if not isinstance(model, EARLY_POOL_MODELS):
model.reset_classifier(0, '') # reset classifier and disable global pooling
model.to(torch_device)
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 4
assert outputs.shape[spatial_axis[0]] == pool_size[0] and outputs.shape[spatial_axis[1]] == pool_size[1]
# test classifier + global pool deletion via __init__
if 'pruned' not in model_name and not isinstance(model, EARLY_POOL_MODELS):
model = create_model(model_name, pretrained=False, num_classes=0, global_pool='').eval()
model.to(torch_device)
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 4
assert outputs.shape[spatial_axis[0]] == pool_size[0] and outputs.shape[spatial_axis[1]] == pool_size[1]
# check classifier name matches default_cfg
if cfg.get('num_classes', None):
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
if isinstance(first_conv, str):
first_conv = (first_conv,)
assert isinstance(first_conv, (tuple, list))
for fc in first_conv:
assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
@pytest.mark.cfg
@pytest.mark.timeout(timeout300)
@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS, include_tags=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs_non_std(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
model.to(torch_device)
assert getattr(model, 'num_classes') >= 0
assert getattr(model, 'num_features') > 0
assert getattr(model, 'head_hidden_size') > 0
state_dict = model.state_dict()
cfg = model.default_cfg
input_size = _get_input_size(model=model)
if max(input_size) > 320: # FIXME const
pytest.skip("Fixed input size model > limit.")
input_tensor = torch.randn((batch_size, *input_size), device=torch_device)
feat_dim = getattr(model, 'feature_dim', None)
outputs = model.forward_features(input_tensor)
outputs_pre = model.forward_head(outputs, pre_logits=True)
if isinstance(outputs, (tuple, list)):
# cannot currently verify multi-tensor output.
pass
else:
if feat_dim is None:
feat_dim = -1 if outputs.ndim == 3 else 1
assert outputs.shape[feat_dim] == model.num_features
assert outputs_pre.shape[1] == model.head_hidden_size
# test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
model.reset_classifier(0)
model.to(torch_device)
outputs = model.forward(input_tensor)
if isinstance(outputs, (tuple, list)):
outputs = outputs[0]
if feat_dim is None:
feat_dim = -1 if outputs.ndim == 3 else 1
assert outputs.shape[feat_dim] == model.head_hidden_size, 'pooled num_features != config'
assert outputs.shape == outputs_pre.shape
model = create_model(model_name, pretrained=False, num_classes=0).eval()
model.to(torch_device)
outputs = model.forward(input_tensor)
if isinstance(outputs, (tuple, list)):
outputs = outputs[0]
if feat_dim is None:
feat_dim = -1 if outputs.ndim == 3 else 1
assert outputs.shape[feat_dim] == model.num_features
# check classifier name matches default_cfg
if cfg.get('num_classes', None):
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
if isinstance(first_conv, str):
first_conv = (first_conv,)
assert isinstance(first_conv, (tuple, list))
for fc in first_conv:
assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
if 'GITHUB_ACTIONS' not in os.environ:
@pytest.mark.timeout(240)
@pytest.mark.parametrize('model_name', list_models(pretrained=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_load_pretrained(model_name, batch_size):
"""Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
in_chans = 3 if 'pruned' in model_name else 1 # pruning not currently supported with in_chans change
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=0)
@pytest.mark.timeout(240)
@pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_features_pretrained(model_name, batch_size):
"""Create that pretrained weights load when features_only==True."""
create_model(model_name, pretrained=True, features_only=True)
@pytest.mark.torchscript
@pytest.mark.timeout(timeout120)
@pytest.mark.parametrize(
'model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_torchscript(model_name, batch_size):
"""Run a single forward pass with each model"""
input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
if max(input_size) > MAX_JIT_SIZE:
pytest.skip("Fixed input size model > limit.")
with set_scriptable(True):
model = create_model(model_name, pretrained=False)
model.eval()
model = torch.jit.script(model)
model.to(torch_device)
outputs = model(torch.randn((batch_size, *input_size)))
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
EXCLUDE_FEAT_FILTERS = [
'*pruned*', # hopefully fix at some point
] + NON_STD_FILTERS
if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d']
@pytest.mark.features
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_features(model_name, batch_size):
"""Run a single forward pass with each model in feature extraction mode"""
model = create_model(model_name, pretrained=False, features_only=True)
model.eval()
expected_channels = model.feature_info.channels()
expected_reduction = model.feature_info.reduction()
assert len(expected_channels) >= 3 # all models here should have at least 3 default feat levels
input_size = _get_input_size(model=model, target=TARGET_FFEAT_SIZE)
if max(input_size) > MAX_FFEAT_SIZE:
pytest.skip("Fixed input size model > limit.")
output_fmt = getattr(model, 'output_fmt', 'NCHW')
feat_axis = get_channel_dim(output_fmt)
spatial_axis = get_spatial_dim(output_fmt)
import math
outputs = model(torch.randn((batch_size, *input_size)))
assert len(expected_channels) == len(outputs)
spatial_size = input_size[-2:]
for e, r, o in zip(expected_channels, expected_reduction, outputs):
assert e == o.shape[feat_axis]
assert o.shape[spatial_axis[0]] <= math.ceil(spatial_size[0] / r) + 1
assert o.shape[spatial_axis[1]] <= math.ceil(spatial_size[1] / r) + 1
assert o.shape[0] == batch_size
assert not torch.isnan(o).any()
@pytest.mark.features
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(module=FEAT_INTER_FILTERS, exclude_filters=EXCLUDE_FILTERS + ['*pruned*']))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_intermediates_features(model_name, batch_size):
"""Run a single forward pass with each model in feature extraction mode"""
model = create_model(model_name, pretrained=False, features_only=True, feature_cls='getter')
model.eval()
expected_channels = model.feature_info.channels()
expected_reduction = model.feature_info.reduction()
input_size = _get_input_size(model=model, target=TARGET_FFEAT_SIZE)
if max(input_size) > MAX_FFEAT_SIZE:
pytest.skip("Fixed input size model > limit.")
output_fmt = getattr(model, 'output_fmt', 'NCHW')
feat_axis = get_channel_dim(output_fmt)
spatial_axis = get_spatial_dim(output_fmt)
import math
outputs = model(torch.randn((batch_size, *input_size)))
assert len(expected_channels) == len(outputs)
spatial_size = input_size[-2:]
for e, r, o in zip(expected_channels, expected_reduction, outputs):
print(o.shape)
assert e == o.shape[feat_axis]
assert o.shape[spatial_axis[0]] <= math.ceil(spatial_size[0] / r) + 1
assert o.shape[spatial_axis[1]] <= math.ceil(spatial_size[1] / r) + 1
assert o.shape[0] == batch_size
assert not torch.isnan(o).any()
@pytest.mark.features
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(module=FEAT_INTER_FILTERS, exclude_filters=EXCLUDE_FILTERS + ['*pruned*']))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_intermediates(model_name, batch_size):
"""Run a single forward pass with each model in feature extraction mode"""
model = create_model(model_name, pretrained=False)
model.eval()
feature_info = timm.models.FeatureInfo(model.feature_info, len(model.feature_info))
expected_channels = feature_info.channels()
expected_reduction = feature_info.reduction()
assert len(expected_channels) >= 3 # all models here should have at least 3 feature levels
input_size = _get_input_size(model=model, target=TARGET_FFEAT_SIZE)
if max(input_size) > MAX_FFEAT_SIZE:
pytest.skip("Fixed input size model > limit.")
output_fmt = 'NCHW' # NOTE output_fmt determined by forward_intermediates() arg, not model attribute
feat_axis = get_channel_dim(output_fmt)
spatial_axis = get_spatial_dim(output_fmt)
import math
output, intermediates = model.forward_intermediates(
torch.randn((batch_size, *input_size)),
output_fmt=output_fmt,
)
assert len(expected_channels) == len(intermediates)
spatial_size = input_size[-2:]
for e, r, o in zip(expected_channels, expected_reduction, intermediates):
assert e == o.shape[feat_axis]
assert o.shape[spatial_axis[0]] <= math.ceil(spatial_size[0] / r) + 1
assert o.shape[spatial_axis[1]] <= math.ceil(spatial_size[1] / r) + 1
assert o.shape[0] == batch_size
assert not torch.isnan(o).any()
def _create_fx_model(model, train=False):
# This block of code does a bit of juggling to handle any case where there are multiple outputs in train mode
# So we trace once and look at the graph, and get the indices of the nodes that lead into the original fx output
# node. Then we use those indices to select from train_nodes returned by torchvision get_graph_node_names
tracer_kwargs = dict(
leaf_modules=get_notrace_modules(),
autowrap_functions=get_notrace_functions(),
#enable_cpatching=True,
param_shapes_constant=True
)
train_nodes, eval_nodes = get_graph_node_names(model, tracer_kwargs=tracer_kwargs)
eval_return_nodes = [eval_nodes[-1]]
train_return_nodes = [train_nodes[-1]]
if train:
tracer = NodePathTracer(**tracer_kwargs)
graph = tracer.trace(model)
graph_nodes = list(reversed(graph.nodes))
output_node_names = [n.name for n in graph_nodes[0]._input_nodes.keys()]
graph_node_names = [n.name for n in graph_nodes]
output_node_indices = [-graph_node_names.index(node_name) for node_name in output_node_names]
train_return_nodes = [train_nodes[ix] for ix in output_node_indices]
fx_model = create_feature_extractor(
model,
train_return_nodes=train_return_nodes,
eval_return_nodes=eval_return_nodes,
tracer_kwargs=tracer_kwargs,
)
return fx_model
EXCLUDE_FX_FILTERS = ['vit_gi*', 'hiera*']
# not enough memory to run fx on more models than other tests
if 'GITHUB_ACTIONS' in os.environ:
EXCLUDE_FX_FILTERS += [
'beit_large*',
'mixer_l*',
'*nfnet_f2*',
'*resnext101_32x32d',
'resnetv2_152x2*',
'resmlp_big*',
'resnetrs270',
'swin_large*',
'vgg*',
'vit_large*',
'vit_base_patch8*',
'xcit_large*',
]
@pytest.mark.fxforward
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_fx(model_name, batch_size):
"""
Symbolically trace each model and run single forward pass through the resulting GraphModule
Also check that the output of a forward pass through the GraphModule is the same as that from the original Module
"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
model = create_model(model_name, pretrained=False)
model.eval()
input_size = _get_input_size(model=model, target=TARGET_FWD_FX_SIZE)
if max(input_size) > MAX_FWD_FX_SIZE:
pytest.skip("Fixed input size model > limit.")
with torch.no_grad():
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
model = _create_fx_model(model)
fx_outputs = tuple(model(inputs).values())
if isinstance(fx_outputs, tuple):
fx_outputs = torch.cat(fx_outputs)
assert torch.all(fx_outputs == outputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.fxbackward
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(
exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [2])
def test_model_backward_fx(model_name, batch_size):
"""Symbolically trace each model and run single backward pass through the resulting GraphModule"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE)
if max(input_size) > MAX_BWD_FX_SIZE:
pytest.skip("Fixed input size model > limit.")
model = create_model(model_name, pretrained=False, num_classes=42)
model.train()
num_params = sum([x.numel() for x in model.parameters()])
if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6:
pytest.skip("Skipping FX backward test on model with more than 100M params.")
model = _create_fx_model(model, train=True)
outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
outputs.mean().backward()
for n, x in model.named_parameters():
assert x.grad is not None, f'No gradient for {n}'
num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
assert outputs.shape[-1] == 42
assert num_params == num_grad, 'Some parameters are missing gradients'
assert not torch.isnan(outputs).any(), 'Output included NaNs'
if 'GITHUB_ACTIONS' not in os.environ:
# FIXME this test is causing GitHub actions to run out of RAM and abruptly kill the test process
# reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
EXCLUDE_FX_JIT_FILTERS = [
'deit_*_distilled_patch16_224',
'levit*',
'pit_*_distilled_224',
] + EXCLUDE_FX_FILTERS
@pytest.mark.timeout(120)
@pytest.mark.parametrize(
'model_name', list_models(
exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_fx_torchscript(model_name, batch_size):
"""Symbolically trace each model, script it, and run single forward pass"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
if max(input_size) > MAX_JIT_SIZE:
pytest.skip("Fixed input size model > limit.")
with set_scriptable(True):
model = create_model(model_name, pretrained=False)
model.eval()
model = torch.jit.script(_create_fx_model(model))
with torch.no_grad():
outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', ["regnetx_002"])
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_torchscript_with_features_fx(model_name, batch_size):
"""Create a model with feature extraction based on fx, script it, and run
a single forward pass"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
allowed_models = list_models(
exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS,
name_matches_cfg=True
)
assert model_name in allowed_models, f"{model_name=} not supported for this test"
input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
assert max(input_size) <= MAX_JIT_SIZE, "Fixed input size model > limit. Pick a different model to run this test"
with set_scriptable(True):
model = create_model(model_name, pretrained=False, features_only=True, feature_cfg={"feature_cls": "fx"})
model.eval()
model = torch.jit.script(model)
with torch.no_grad():
outputs = model(torch.randn((batch_size, *input_size)))
assert isinstance(outputs, list)
for tensor in outputs:
assert tensor.shape[0] == batch_size
assert not torch.isnan(tensor).any(), 'Output included NaNs'
|
pytorch-image-models/tests/test_models.py/0
|
{
"file_path": "pytorch-image-models/tests/test_models.py",
"repo_id": "pytorch-image-models",
"token_count": 11625
}
| 205
|
""" Loader Factory, Fast Collate, CUDA Prefetcher
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import random
from contextlib import suppress
from functools import partial
from itertools import repeat
from typing import Callable, Optional, Tuple, Union
import torch
import torch.utils.data
import numpy as np
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .dataset import IterableImageDataset, ImageDataset
from .distributed_sampler import OrderedDistributedSampler, RepeatAugSampler
from .random_erasing import RandomErasing
from .mixup import FastCollateMixup
from .transforms_factory import create_transform
_logger = logging.getLogger(__name__)
def fast_collate(batch):
""" A fast collation function optimized for uint8 images (np array or torch) and int64 targets (labels)"""
assert isinstance(batch[0], tuple)
batch_size = len(batch)
if isinstance(batch[0][0], tuple):
# This branch 'deinterleaves' and flattens tuples of input tensors into one tensor ordered by position
# such that all tuple of position n will end up in a torch.split(tensor, batch_size) in nth position
inner_tuple_size = len(batch[0][0])
flattened_batch_size = batch_size * inner_tuple_size
targets = torch.zeros(flattened_batch_size, dtype=torch.int64)
tensor = torch.zeros((flattened_batch_size, *batch[0][0][0].shape), dtype=torch.uint8)
for i in range(batch_size):
assert len(batch[i][0]) == inner_tuple_size # all input tensor tuples must be same length
for j in range(inner_tuple_size):
targets[i + j * batch_size] = batch[i][1]
tensor[i + j * batch_size] += torch.from_numpy(batch[i][0][j])
return tensor, targets
elif isinstance(batch[0][0], np.ndarray):
targets = torch.tensor([b[1] for b in batch], dtype=torch.int64)
assert len(targets) == batch_size
tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
for i in range(batch_size):
tensor[i] += torch.from_numpy(batch[i][0])
return tensor, targets
elif isinstance(batch[0][0], torch.Tensor):
targets = torch.tensor([b[1] for b in batch], dtype=torch.int64)
assert len(targets) == batch_size
tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
for i in range(batch_size):
tensor[i].copy_(batch[i][0])
return tensor, targets
else:
assert False
def adapt_to_chs(x, n):
if not isinstance(x, (tuple, list)):
x = tuple(repeat(x, n))
elif len(x) != n:
x_mean = np.mean(x).item()
x = (x_mean,) * n
_logger.warning(f'Pretrained mean/std different shape than model, using avg value {x}.')
else:
assert len(x) == n, 'normalization stats must match image channels'
return x
class PrefetchLoader:
def __init__(
self,
loader,
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
channels=3,
device=torch.device('cuda'),
img_dtype=torch.float32,
fp16=False,
re_prob=0.,
re_mode='const',
re_count=1,
re_num_splits=0):
mean = adapt_to_chs(mean, channels)
std = adapt_to_chs(std, channels)
normalization_shape = (1, channels, 1, 1)
self.loader = loader
self.device = device
if fp16:
# fp16 arg is deprecated, but will override dtype arg if set for bwd compat
img_dtype = torch.float16
self.img_dtype = img_dtype
self.mean = torch.tensor(
[x * 255 for x in mean], device=device, dtype=img_dtype).view(normalization_shape)
self.std = torch.tensor(
[x * 255 for x in std], device=device, dtype=img_dtype).view(normalization_shape)
if re_prob > 0.:
self.random_erasing = RandomErasing(
probability=re_prob,
mode=re_mode,
max_count=re_count,
num_splits=re_num_splits,
device=device,
)
else:
self.random_erasing = None
self.is_cuda = torch.cuda.is_available() and device.type == 'cuda'
def __iter__(self):
first = True
if self.is_cuda:
stream = torch.cuda.Stream()
stream_context = partial(torch.cuda.stream, stream=stream)
else:
stream = None
stream_context = suppress
for next_input, next_target in self.loader:
with stream_context():
next_input = next_input.to(device=self.device, non_blocking=True)
next_target = next_target.to(device=self.device, non_blocking=True)
next_input = next_input.to(self.img_dtype).sub_(self.mean).div_(self.std)
if self.random_erasing is not None:
next_input = self.random_erasing(next_input)
if not first:
yield input, target
else:
first = False
if stream is not None:
torch.cuda.current_stream().wait_stream(stream)
input = next_input
target = next_target
yield input, target
def __len__(self):
return len(self.loader)
@property
def sampler(self):
return self.loader.sampler
@property
def dataset(self):
return self.loader.dataset
@property
def mixup_enabled(self):
if isinstance(self.loader.collate_fn, FastCollateMixup):
return self.loader.collate_fn.mixup_enabled
else:
return False
@mixup_enabled.setter
def mixup_enabled(self, x):
if isinstance(self.loader.collate_fn, FastCollateMixup):
self.loader.collate_fn.mixup_enabled = x
def _worker_init(worker_id, worker_seeding='all'):
worker_info = torch.utils.data.get_worker_info()
assert worker_info.id == worker_id
if isinstance(worker_seeding, Callable):
seed = worker_seeding(worker_info)
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed % (2 ** 32 - 1))
else:
assert worker_seeding in ('all', 'part')
# random / torch seed already called in dataloader iter class w/ worker_info.seed
# to reproduce some old results (same seed + hparam combo), partial seeding is required (skip numpy re-seed)
if worker_seeding == 'all':
np.random.seed(worker_info.seed % (2 ** 32 - 1))
def create_loader(
dataset: Union[ImageDataset, IterableImageDataset],
input_size: Union[int, Tuple[int, int], Tuple[int, int, int]],
batch_size: int,
is_training: bool = False,
no_aug: bool = False,
re_prob: float = 0.,
re_mode: str = 'const',
re_count: int = 1,
re_split: bool = False,
train_crop_mode: Optional[str] = None,
scale: Optional[Tuple[float, float]] = None,
ratio: Optional[Tuple[float, float]] = None,
hflip: float = 0.5,
vflip: float = 0.,
color_jitter: float = 0.4,
color_jitter_prob: Optional[float] = None,
grayscale_prob: float = 0.,
gaussian_blur_prob: float = 0.,
auto_augment: Optional[str] = None,
num_aug_repeats: int = 0,
num_aug_splits: int = 0,
interpolation: str = 'bilinear',
mean: Tuple[float, ...] = IMAGENET_DEFAULT_MEAN,
std: Tuple[float, ...] = IMAGENET_DEFAULT_STD,
num_workers: int = 1,
distributed: bool = False,
crop_pct: Optional[float] = None,
crop_mode: Optional[str] = None,
crop_border_pixels: Optional[int] = None,
collate_fn: Optional[Callable] = None,
pin_memory: bool = False,
fp16: bool = False, # deprecated, use img_dtype
img_dtype: torch.dtype = torch.float32,
device: torch.device = torch.device('cuda'),
use_prefetcher: bool = True,
use_multi_epochs_loader: bool = False,
persistent_workers: bool = True,
worker_seeding: str = 'all',
tf_preprocessing: bool = False,
):
"""
Args:
dataset: The image dataset to load.
input_size: Target input size (channels, height, width) tuple or size scalar.
batch_size: Number of samples in a batch.
is_training: Return training (random) transforms.
no_aug: Disable augmentation for training (useful for debug).
re_prob: Random erasing probability.
re_mode: Random erasing fill mode.
re_count: Number of random erasing regions.
re_split: Control split of random erasing across batch size.
scale: Random resize scale range (crop area, < 1.0 => zoom in).
ratio: Random aspect ratio range (crop ratio for RRC, ratio adjustment factor for RKR).
hflip: Horizontal flip probability.
vflip: Vertical flip probability.
color_jitter: Random color jitter component factors (brightness, contrast, saturation, hue).
Scalar is applied as (scalar,) * 3 (no hue).
color_jitter_prob: Apply color jitter with this probability if not None (for SimlCLR-like aug
grayscale_prob: Probability of converting image to grayscale (for SimCLR-like aug).
gaussian_blur_prob: Probability of applying gaussian blur (for SimCLR-like aug).
auto_augment: Auto augment configuration string (see auto_augment.py).
num_aug_repeats: Enable special sampler to repeat same augmentation across distributed GPUs.
num_aug_splits: Enable mode where augmentations can be split across the batch.
interpolation: Image interpolation mode.
mean: Image normalization mean.
std: Image normalization standard deviation.
num_workers: Num worker processes per DataLoader.
distributed: Enable dataloading for distributed training.
crop_pct: Inference crop percentage (output size / resize size).
crop_mode: Inference crop mode. One of ['squash', 'border', 'center']. Defaults to 'center' when None.
crop_border_pixels: Inference crop border of specified # pixels around edge of original image.
collate_fn: Override default collate_fn.
pin_memory: Pin memory for device transfer.
fp16: Deprecated argument for half-precision input dtype. Use img_dtype.
img_dtype: Data type for input image.
device: Device to transfer inputs and targets to.
use_prefetcher: Use efficient pre-fetcher to load samples onto device.
use_multi_epochs_loader:
persistent_workers: Enable persistent worker processes.
worker_seeding: Control worker random seeding at init.
tf_preprocessing: Use TF 1.0 inference preprocessing for testing model ports.
Returns:
DataLoader
"""
re_num_splits = 0
if re_split:
# apply RE to second half of batch if no aug split otherwise line up with aug split
re_num_splits = num_aug_splits or 2
dataset.transform = create_transform(
input_size,
is_training=is_training,
no_aug=no_aug,
train_crop_mode=train_crop_mode,
scale=scale,
ratio=ratio,
hflip=hflip,
vflip=vflip,
color_jitter=color_jitter,
color_jitter_prob=color_jitter_prob,
grayscale_prob=grayscale_prob,
gaussian_blur_prob=gaussian_blur_prob,
auto_augment=auto_augment,
interpolation=interpolation,
mean=mean,
std=std,
crop_pct=crop_pct,
crop_mode=crop_mode,
crop_border_pixels=crop_border_pixels,
re_prob=re_prob,
re_mode=re_mode,
re_count=re_count,
re_num_splits=re_num_splits,
tf_preprocessing=tf_preprocessing,
use_prefetcher=use_prefetcher,
separate=num_aug_splits > 0,
)
if isinstance(dataset, IterableImageDataset):
# give Iterable datasets early knowledge of num_workers so that sample estimates
# are correct before worker processes are launched
dataset.set_loader_cfg(num_workers=num_workers)
sampler = None
if distributed and not isinstance(dataset, torch.utils.data.IterableDataset):
if is_training:
if num_aug_repeats:
sampler = RepeatAugSampler(dataset, num_repeats=num_aug_repeats)
else:
sampler = torch.utils.data.distributed.DistributedSampler(dataset)
else:
# This will add extra duplicate entries to result in equal num
# of samples per-process, will slightly alter validation results
sampler = OrderedDistributedSampler(dataset)
else:
assert num_aug_repeats == 0, "RepeatAugment not currently supported in non-distributed or IterableDataset use"
if collate_fn is None:
collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate
loader_class = torch.utils.data.DataLoader
if use_multi_epochs_loader:
loader_class = MultiEpochsDataLoader
loader_args = dict(
batch_size=batch_size,
shuffle=not isinstance(dataset, torch.utils.data.IterableDataset) and sampler is None and is_training,
num_workers=num_workers,
sampler=sampler,
collate_fn=collate_fn,
pin_memory=pin_memory,
drop_last=is_training,
worker_init_fn=partial(_worker_init, worker_seeding=worker_seeding),
persistent_workers=persistent_workers
)
try:
loader = loader_class(dataset, **loader_args)
except TypeError as e:
loader_args.pop('persistent_workers') # only in Pytorch 1.7+
loader = loader_class(dataset, **loader_args)
if use_prefetcher:
prefetch_re_prob = re_prob if is_training and not no_aug else 0.
loader = PrefetchLoader(
loader,
mean=mean,
std=std,
channels=input_size[0],
device=device,
fp16=fp16, # deprecated, use img_dtype
img_dtype=img_dtype,
re_prob=prefetch_re_prob,
re_mode=re_mode,
re_count=re_count,
re_num_splits=re_num_splits
)
return loader
class MultiEpochsDataLoader(torch.utils.data.DataLoader):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._DataLoader__initialized = False
if self.batch_sampler is None:
self.sampler = _RepeatSampler(self.sampler)
else:
self.batch_sampler = _RepeatSampler(self.batch_sampler)
self._DataLoader__initialized = True
self.iterator = super().__iter__()
def __len__(self):
return len(self.sampler) if self.batch_sampler is None else len(self.batch_sampler.sampler)
def __iter__(self):
for i in range(len(self)):
yield next(self.iterator)
class _RepeatSampler(object):
""" Sampler that repeats forever.
Args:
sampler (Sampler)
"""
def __init__(self, sampler):
self.sampler = sampler
def __iter__(self):
while True:
yield from iter(self.sampler)
|
pytorch-image-models/timm/data/loader.py/0
|
{
"file_path": "pytorch-image-models/timm/data/loader.py",
"repo_id": "pytorch-image-models",
"token_count": 6793
}
| 206
|
""" Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as np
import pkgutil
class RealLabelsImagenet:
def __init__(self, filenames, real_json=None, topk=(1, 5)):
if real_json is not None:
with open(real_json) as real_labels:
real_labels = json.load(real_labels)
else:
real_labels = json.loads(
pkgutil.get_data(__name__, os.path.join('_info', 'imagenet_real_labels.json')).decode('utf-8'))
real_labels = {f'ILSVRC2012_val_{i + 1:08d}.JPEG': labels for i, labels in enumerate(real_labels)}
self.real_labels = real_labels
self.filenames = filenames
assert len(self.filenames) == len(self.real_labels)
self.topk = topk
self.is_correct = {k: [] for k in topk}
self.sample_idx = 0
def add_result(self, output):
maxk = max(self.topk)
_, pred_batch = output.topk(maxk, 1, True, True)
pred_batch = pred_batch.cpu().numpy()
for pred in pred_batch:
filename = self.filenames[self.sample_idx]
filename = os.path.basename(filename)
if self.real_labels[filename]:
for k in self.topk:
self.is_correct[k].append(
any([p in self.real_labels[filename] for p in pred[:k]]))
self.sample_idx += 1
def get_accuracy(self, k=None):
if k is None:
return {k: float(np.mean(self.is_correct[k])) * 100 for k in self.topk}
else:
return float(np.mean(self.is_correct[k])) * 100
|
pytorch-image-models/timm/data/real_labels.py/0
|
{
"file_path": "pytorch-image-models/timm/data/real_labels.py",
"repo_id": "pytorch-image-models",
"token_count": 854
}
| 207
|
""" Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to have layers with no jit optimization (includes activations)
_NO_JIT = False
# Set to True if prefer to have activation layers with no jit optimization
# NOTE not currently used as no difference between no_jit and no_activation jit as only layers obeying
# the jit flags so far are activations. This will change as more layers are updated and/or added.
_NO_ACTIVATION_JIT = False
# Set to True if exporting a model with Same padding via ONNX
_EXPORTABLE = False
# Set to True if wanting to use torch.jit.script on a model
_SCRIPTABLE = False
# use torch.scaled_dot_product_attention where possible
_HAS_FUSED_ATTN = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if 'TIMM_FUSED_ATTN' in os.environ:
_USE_FUSED_ATTN = int(os.environ['TIMM_FUSED_ATTN'])
else:
_USE_FUSED_ATTN = 1 # 0 == off, 1 == on (for tested use), 2 == on (for experimental use)
def is_no_jit():
return _NO_JIT
class set_no_jit:
def __init__(self, mode: bool) -> None:
global _NO_JIT
self.prev = _NO_JIT
_NO_JIT = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _NO_JIT
_NO_JIT = self.prev
return False
def is_exportable():
return _EXPORTABLE
class set_exportable:
def __init__(self, mode: bool) -> None:
global _EXPORTABLE
self.prev = _EXPORTABLE
_EXPORTABLE = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _EXPORTABLE
_EXPORTABLE = self.prev
return False
def is_scriptable():
return _SCRIPTABLE
class set_scriptable:
def __init__(self, mode: bool) -> None:
global _SCRIPTABLE
self.prev = _SCRIPTABLE
_SCRIPTABLE = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _SCRIPTABLE
_SCRIPTABLE = self.prev
return False
class set_layer_config:
""" Layer config context manager that allows setting all layer config flags at once.
If a flag arg is None, it will not change the current value.
"""
def __init__(
self,
scriptable: Optional[bool] = None,
exportable: Optional[bool] = None,
no_jit: Optional[bool] = None,
no_activation_jit: Optional[bool] = None):
global _SCRIPTABLE
global _EXPORTABLE
global _NO_JIT
global _NO_ACTIVATION_JIT
self.prev = _SCRIPTABLE, _EXPORTABLE, _NO_JIT, _NO_ACTIVATION_JIT
if scriptable is not None:
_SCRIPTABLE = scriptable
if exportable is not None:
_EXPORTABLE = exportable
if no_jit is not None:
_NO_JIT = no_jit
if no_activation_jit is not None:
_NO_ACTIVATION_JIT = no_activation_jit
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _SCRIPTABLE
global _EXPORTABLE
global _NO_JIT
global _NO_ACTIVATION_JIT
_SCRIPTABLE, _EXPORTABLE, _NO_JIT, _NO_ACTIVATION_JIT = self.prev
return False
def use_fused_attn(experimental: bool = False) -> bool:
# NOTE: ONNX export cannot handle F.scaled_dot_product_attention as of pytorch 2.0
if not _HAS_FUSED_ATTN or _EXPORTABLE:
return False
if experimental:
return _USE_FUSED_ATTN > 1
return _USE_FUSED_ATTN > 0
def set_fused_attn(enable: bool = True, experimental: bool = False):
global _USE_FUSED_ATTN
if not _HAS_FUSED_ATTN:
warnings.warn('This version of pytorch does not have F.scaled_dot_product_attention, fused_attn flag ignored.')
return
if experimental and enable:
_USE_FUSED_ATTN = 2
elif enable:
_USE_FUSED_ATTN = 1
else:
_USE_FUSED_ATTN = 0
|
pytorch-image-models/timm/layers/config.py/0
|
{
"file_path": "pytorch-image-models/timm/layers/config.py",
"repo_id": "pytorch-image-models",
"token_count": 1787
}
| 208
|
from typing import Tuple
import torch
def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]:
"""generate N-D grid in dimension order.
The ndgrid function is like meshgrid except that the order of the first two input arguments are switched.
That is, the statement
[X1,X2,X3] = ndgrid(x1,x2,x3)
produces the same result as
[X2,X1,X3] = meshgrid(x2,x1,x3)
This naming is based on MATLAB, the purpose is to avoid confusion due to torch's change to make
torch.meshgrid behaviour move from matching ndgrid ('ij') indexing to numpy meshgrid defaults of ('xy').
"""
try:
return torch.meshgrid(*tensors, indexing='ij')
except TypeError:
# old PyTorch < 1.10 will follow this path as it does not have indexing arg,
# the old behaviour of meshgrid was 'ij'
return torch.meshgrid(*tensors)
def meshgrid(*tensors) -> Tuple[torch.Tensor, ...]:
"""generate N-D grid in spatial dim order.
The meshgrid function is similar to ndgrid except that the order of the
first two input and output arguments is switched.
That is, the statement
[X,Y,Z] = meshgrid(x,y,z)
produces the same result as
[Y,X,Z] = ndgrid(y,x,z)
Because of this, meshgrid is better suited to problems in two- or three-dimensional Cartesian space,
while ndgrid is better suited to multidimensional problems that aren't spatially based.
"""
# NOTE: this will throw in PyTorch < 1.10 as meshgrid did not support indexing arg or have
# capability of generating grid in xy order before then.
return torch.meshgrid(*tensors, indexing='xy')
|
pytorch-image-models/timm/layers/grid.py/0
|
{
"file_path": "pytorch-image-models/timm/layers/grid.py",
"repo_id": "pytorch-image-models",
"token_count": 565
}
| 209
|
""" Normalization + Activation Layers
Provides Norm+Act fns for standard PyTorch norm layers such as
* BatchNorm
* GroupNorm
* LayerNorm
This allows swapping with alternative layers that are natively both norm + act such as
* EvoNorm (evo_norm.py)
* FilterResponseNorm (filter_response_norm.py)
* InplaceABN (inplace_abn.py)
Hacked together by / Copyright 2022 Ross Wightman
"""
from typing import Union, List, Optional, Any
import torch
from torch import nn as nn
from torch.nn import functional as F
from torchvision.ops.misc import FrozenBatchNorm2d
from .create_act import create_act_layer
from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm
from .trace_utils import _assert
def _create_act(act_layer, act_kwargs=None, inplace=False, apply_act=True):
act_kwargs = act_kwargs or {}
act_kwargs.setdefault('inplace', inplace)
act = None
if apply_act:
act = create_act_layer(act_layer, **act_kwargs)
return nn.Identity() if act is None else act
class BatchNormAct2d(nn.BatchNorm2d):
"""BatchNorm + Activation
This module performs BatchNorm + Activation in a manner that will remain backwards
compatible with weights trained with separate bn, act. This is why we inherit from BN
instead of composing it as a .bn member.
"""
def __init__(
self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
device=None,
dtype=None,
):
try:
factory_kwargs = {'device': device, 'dtype': dtype}
super(BatchNormAct2d, self).__init__(
num_features,
eps=eps,
momentum=momentum,
affine=affine,
track_running_stats=track_running_stats,
**factory_kwargs,
)
except TypeError:
# NOTE for backwards compat with old PyTorch w/o factory device/dtype support
super(BatchNormAct2d, self).__init__(
num_features,
eps=eps,
momentum=momentum,
affine=affine,
track_running_stats=track_running_stats,
)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
def forward(self, x):
# cut & paste of torch.nn.BatchNorm2d.forward impl to avoid issues with torchscript and tracing
_assert(x.ndim == 4, f'expected 4D input (got {x.ndim}D input)')
# exponential_average_factor is set to self.momentum
# (when it is available) only so that it gets updated
# in ONNX graph when this node is exported to ONNX.
if self.momentum is None:
exponential_average_factor = 0.0
else:
exponential_average_factor = self.momentum
if self.training and self.track_running_stats:
# TODO: if statement only here to tell the jit to skip emitting this when it is None
if self.num_batches_tracked is not None: # type: ignore[has-type]
self.num_batches_tracked.add_(1) # type: ignore[has-type]
if self.momentum is None: # use cumulative moving average
exponential_average_factor = 1.0 / float(self.num_batches_tracked)
else: # use exponential moving average
exponential_average_factor = self.momentum
r"""
Decide whether the mini-batch stats should be used for normalization rather than the buffers.
Mini-batch stats are used in training mode, and in eval mode when buffers are None.
"""
if self.training:
bn_training = True
else:
bn_training = (self.running_mean is None) and (self.running_var is None)
r"""
Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be
passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are
used for normalization (i.e. in eval mode when buffers are not None).
"""
x = F.batch_norm(
x,
# If buffers are not to be tracked, ensure that they won't be updated
self.running_mean if not self.training or self.track_running_stats else None,
self.running_var if not self.training or self.track_running_stats else None,
self.weight,
self.bias,
bn_training,
exponential_average_factor,
self.eps,
)
x = self.drop(x)
x = self.act(x)
return x
class SyncBatchNormAct(nn.SyncBatchNorm):
# Thanks to Selim Seferbekov (https://github.com/rwightman/pytorch-image-models/issues/1254)
# This is a quick workaround to support SyncBatchNorm for timm BatchNormAct2d layers
# but ONLY when used in conjunction with the timm conversion function below.
# Do not create this module directly or use the PyTorch conversion function.
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = super().forward(x) # SyncBN doesn't work with torchscript anyways, so this is fine
if hasattr(self, "drop"):
x = self.drop(x)
if hasattr(self, "act"):
x = self.act(x)
return x
def convert_sync_batchnorm(module, process_group=None):
# convert both BatchNorm and BatchNormAct layers to Synchronized variants
module_output = module
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
if isinstance(module, BatchNormAct2d):
# convert timm norm + act layer
module_output = SyncBatchNormAct(
module.num_features,
module.eps,
module.momentum,
module.affine,
module.track_running_stats,
process_group=process_group,
)
# set act and drop attr from the original module
module_output.act = module.act
module_output.drop = module.drop
else:
# convert standard BatchNorm layers
module_output = torch.nn.SyncBatchNorm(
module.num_features,
module.eps,
module.momentum,
module.affine,
module.track_running_stats,
process_group,
)
if module.affine:
with torch.no_grad():
module_output.weight = module.weight
module_output.bias = module.bias
module_output.running_mean = module.running_mean
module_output.running_var = module.running_var
module_output.num_batches_tracked = module.num_batches_tracked
if hasattr(module, "qconfig"):
module_output.qconfig = module.qconfig
for name, child in module.named_children():
module_output.add_module(name, convert_sync_batchnorm(child, process_group))
del module
return module_output
class FrozenBatchNormAct2d(torch.nn.Module):
"""
BatchNormAct2d where the batch statistics and the affine parameters are fixed
Args:
num_features (int): Number of features ``C`` from an expected input of size ``(N, C, H, W)``
eps (float): a value added to the denominator for numerical stability. Default: 1e-5
"""
def __init__(
self,
num_features: int,
eps: float = 1e-5,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super().__init__()
self.eps = eps
self.register_buffer("weight", torch.ones(num_features))
self.register_buffer("bias", torch.zeros(num_features))
self.register_buffer("running_mean", torch.zeros(num_features))
self.register_buffer("running_var", torch.ones(num_features))
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
def _load_from_state_dict(
self,
state_dict: dict,
prefix: str,
local_metadata: dict,
strict: bool,
missing_keys: List[str],
unexpected_keys: List[str],
error_msgs: List[str],
):
num_batches_tracked_key = prefix + "num_batches_tracked"
if num_batches_tracked_key in state_dict:
del state_dict[num_batches_tracked_key]
super()._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# move reshapes to the beginning
# to make it fuser-friendly
w = self.weight.reshape(1, -1, 1, 1)
b = self.bias.reshape(1, -1, 1, 1)
rv = self.running_var.reshape(1, -1, 1, 1)
rm = self.running_mean.reshape(1, -1, 1, 1)
scale = w * (rv + self.eps).rsqrt()
bias = b - rm * scale
x = x * scale + bias
x = self.act(self.drop(x))
return x
def __repr__(self) -> str:
return f"{self.__class__.__name__}({self.weight.shape[0]}, eps={self.eps}, act={self.act})"
def freeze_batch_norm_2d(module):
"""
Converts all `BatchNorm2d` and `SyncBatchNorm` or `BatchNormAct2d` and `SyncBatchNormAct2d` layers
of provided module into `FrozenBatchNorm2d` or `FrozenBatchNormAct2d` respectively.
Args:
module (torch.nn.Module): Any PyTorch module.
Returns:
torch.nn.Module: Resulting module
Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
"""
res = module
if isinstance(module, (BatchNormAct2d, SyncBatchNormAct)):
res = FrozenBatchNormAct2d(module.num_features)
res.num_features = module.num_features
res.affine = module.affine
if module.affine:
res.weight.data = module.weight.data.clone().detach()
res.bias.data = module.bias.data.clone().detach()
res.running_mean.data = module.running_mean.data
res.running_var.data = module.running_var.data
res.eps = module.eps
res.drop = module.drop
res.act = module.act
elif isinstance(module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)):
res = FrozenBatchNorm2d(module.num_features)
res.num_features = module.num_features
res.affine = module.affine
if module.affine:
res.weight.data = module.weight.data.clone().detach()
res.bias.data = module.bias.data.clone().detach()
res.running_mean.data = module.running_mean.data
res.running_var.data = module.running_var.data
res.eps = module.eps
else:
for name, child in module.named_children():
new_child = freeze_batch_norm_2d(child)
if new_child is not child:
res.add_module(name, new_child)
return res
def unfreeze_batch_norm_2d(module):
"""
Converts all `FrozenBatchNorm2d` layers of provided module into `BatchNorm2d`. If `module` is itself and instance
of `FrozenBatchNorm2d`, it is converted into `BatchNorm2d` and returned. Otherwise, the module is walked
recursively and submodules are converted in place.
Args:
module (torch.nn.Module): Any PyTorch module.
Returns:
torch.nn.Module: Resulting module
Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
"""
res = module
if isinstance(module, FrozenBatchNormAct2d):
res = BatchNormAct2d(module.num_features)
if module.affine:
res.weight.data = module.weight.data.clone().detach()
res.bias.data = module.bias.data.clone().detach()
res.running_mean.data = module.running_mean.data
res.running_var.data = module.running_var.data
res.eps = module.eps
res.drop = module.drop
res.act = module.act
elif isinstance(module, FrozenBatchNorm2d):
res = torch.nn.BatchNorm2d(module.num_features)
if module.affine:
res.weight.data = module.weight.data.clone().detach()
res.bias.data = module.bias.data.clone().detach()
res.running_mean.data = module.running_mean.data
res.running_var.data = module.running_var.data
res.eps = module.eps
else:
for name, child in module.named_children():
new_child = unfreeze_batch_norm_2d(child)
if new_child is not child:
res.add_module(name, new_child)
return res
def _num_groups(num_channels, num_groups, group_size):
if group_size:
assert num_channels % group_size == 0
return num_channels // group_size
return num_groups
class GroupNormAct(nn.GroupNorm):
# NOTE num_channel and num_groups order flipped for easier layer swaps / binding of fixed args
def __init__(
self,
num_channels,
num_groups=32,
eps=1e-5,
affine=True,
group_size=None,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super(GroupNormAct, self).__init__(
_num_groups(num_channels, num_groups, group_size),
num_channels,
eps=eps,
affine=affine,
)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
self._fast_norm = is_fast_norm()
def forward(self, x):
if self._fast_norm:
x = fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
x = F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
x = self.drop(x)
x = self.act(x)
return x
class GroupNorm1Act(nn.GroupNorm):
def __init__(
self,
num_channels,
eps=1e-5,
affine=True,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super(GroupNorm1Act, self).__init__(1, num_channels, eps=eps, affine=affine)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
self._fast_norm = is_fast_norm()
def forward(self, x):
if self._fast_norm:
x = fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
x = F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
x = self.drop(x)
x = self.act(x)
return x
class LayerNormAct(nn.LayerNorm):
def __init__(
self,
normalization_shape: Union[int, List[int], torch.Size],
eps=1e-5,
affine=True,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super(LayerNormAct, self).__init__(normalization_shape, eps=eps, elementwise_affine=affine)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
self._fast_norm = is_fast_norm()
def forward(self, x):
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
x = self.drop(x)
x = self.act(x)
return x
class LayerNormAct2d(nn.LayerNorm):
def __init__(
self,
num_channels,
eps=1e-5,
affine=True,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super(LayerNormAct2d, self).__init__(num_channels, eps=eps, elementwise_affine=affine)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
self._fast_norm = is_fast_norm()
def forward(self, x):
x = x.permute(0, 2, 3, 1)
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
x = x.permute(0, 3, 1, 2)
x = self.drop(x)
x = self.act(x)
return x
|
pytorch-image-models/timm/layers/norm_act.py/0
|
{
"file_path": "pytorch-image-models/timm/layers/norm_act.py",
"repo_id": "pytorch-image-models",
"token_count": 7973
}
| 210
|
try:
from torch import _assert
except ImportError:
def _assert(condition: bool, message: str):
assert condition, message
def _float_to_int(x: float) -> int:
"""
Symbolic tracing helper to substitute for inbuilt `int`.
Hint: Inbuilt `int` can't accept an argument of type `Proxy`
"""
return int(x)
|
pytorch-image-models/timm/layers/trace_utils.py/0
|
{
"file_path": "pytorch-image-models/timm/layers/trace_utils.py",
"repo_id": "pytorch-image-models",
"token_count": 119
}
| 211
|
import hashlib
import json
import logging
import os
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Iterable, Optional, Union
import torch
from torch.hub import HASH_REGEX, download_url_to_file, urlparse
try:
from torch.hub import get_dir
except ImportError:
from torch.hub import _get_torch_home as get_dir
try:
import safetensors.torch
_has_safetensors = True
except ImportError:
_has_safetensors = False
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
from timm import __version__
from timm.models._pretrained import filter_pretrained_cfg
try:
from huggingface_hub import (
create_repo, get_hf_file_metadata,
hf_hub_download, hf_hub_url,
repo_type_and_id_from_hf_id, upload_folder)
from huggingface_hub.utils import EntryNotFoundError
hf_hub_download = partial(hf_hub_download, library_name="timm", library_version=__version__)
_has_hf_hub = True
except ImportError:
hf_hub_download = None
_has_hf_hub = False
_logger = logging.getLogger(__name__)
__all__ = ['get_cache_dir', 'download_cached_file', 'has_hf_hub', 'hf_split', 'load_model_config_from_hf',
'load_state_dict_from_hf', 'save_for_hf', 'push_to_hf_hub']
# Default name for a weights file hosted on the Huggingface Hub.
HF_WEIGHTS_NAME = "pytorch_model.bin" # default pytorch pkl
HF_SAFE_WEIGHTS_NAME = "model.safetensors" # safetensors version
HF_OPEN_CLIP_WEIGHTS_NAME = "open_clip_pytorch_model.bin" # default pytorch pkl
HF_OPEN_CLIP_SAFE_WEIGHTS_NAME = "open_clip_model.safetensors" # safetensors version
def get_cache_dir(child_dir=''):
"""
Returns the location of the directory where models are cached (and creates it if necessary).
"""
# Issue warning to move data if old env is set
if os.getenv('TORCH_MODEL_ZOO'):
_logger.warning('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead')
hub_dir = get_dir()
child_dir = () if not child_dir else (child_dir,)
model_dir = os.path.join(hub_dir, 'checkpoints', *child_dir)
os.makedirs(model_dir, exist_ok=True)
return model_dir
def download_cached_file(url, check_hash=True, progress=False):
if isinstance(url, (list, tuple)):
url, filename = url
else:
parts = urlparse(url)
filename = os.path.basename(parts.path)
cached_file = os.path.join(get_cache_dir(), filename)
if not os.path.exists(cached_file):
_logger.info('Downloading: "{}" to {}\n'.format(url, cached_file))
hash_prefix = None
if check_hash:
r = HASH_REGEX.search(filename) # r is Optional[Match[str]]
hash_prefix = r.group(1) if r else None
download_url_to_file(url, cached_file, hash_prefix, progress=progress)
return cached_file
def check_cached_file(url, check_hash=True):
if isinstance(url, (list, tuple)):
url, filename = url
else:
parts = urlparse(url)
filename = os.path.basename(parts.path)
cached_file = os.path.join(get_cache_dir(), filename)
if os.path.exists(cached_file):
if check_hash:
r = HASH_REGEX.search(filename) # r is Optional[Match[str]]
hash_prefix = r.group(1) if r else None
if hash_prefix:
with open(cached_file, 'rb') as f:
hd = hashlib.sha256(f.read()).hexdigest()
if hd[:len(hash_prefix)] != hash_prefix:
return False
return True
return False
def has_hf_hub(necessary=False):
if not _has_hf_hub and necessary:
# if no HF Hub module installed, and it is necessary to continue, raise error
raise RuntimeError(
'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
return _has_hf_hub
def hf_split(hf_id: str):
# FIXME I may change @ -> # and be parsed as fragment in a URI model name scheme
rev_split = hf_id.split('@')
assert 0 < len(rev_split) <= 2, 'hf_hub id should only contain one @ character to identify revision.'
hf_model_id = rev_split[0]
hf_revision = rev_split[-1] if len(rev_split) > 1 else None
return hf_model_id, hf_revision
def load_cfg_from_json(json_file: Union[str, os.PathLike]):
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
def download_from_hf(model_id: str, filename: str):
hf_model_id, hf_revision = hf_split(model_id)
return hf_hub_download(hf_model_id, filename, revision=hf_revision)
def load_model_config_from_hf(model_id: str):
assert has_hf_hub(True)
cached_file = download_from_hf(model_id, 'config.json')
hf_config = load_cfg_from_json(cached_file)
if 'pretrained_cfg' not in hf_config:
# old form, pull pretrain_cfg out of the base dict
pretrained_cfg = hf_config
hf_config = {}
hf_config['architecture'] = pretrained_cfg.pop('architecture')
hf_config['num_features'] = pretrained_cfg.pop('num_features', None)
if 'labels' in pretrained_cfg: # deprecated name for 'label_names'
pretrained_cfg['label_names'] = pretrained_cfg.pop('labels')
hf_config['pretrained_cfg'] = pretrained_cfg
# NOTE currently discarding parent config as only arch name and pretrained_cfg used in timm right now
pretrained_cfg = hf_config['pretrained_cfg']
pretrained_cfg['hf_hub_id'] = model_id # insert hf_hub id for pretrained weight load during model creation
pretrained_cfg['source'] = 'hf-hub'
# model should be created with base config num_classes if its exist
if 'num_classes' in hf_config:
pretrained_cfg['num_classes'] = hf_config['num_classes']
# label meta-data in base config overrides saved pretrained_cfg on load
if 'label_names' in hf_config:
pretrained_cfg['label_names'] = hf_config.pop('label_names')
if 'label_descriptions' in hf_config:
pretrained_cfg['label_descriptions'] = hf_config.pop('label_descriptions')
model_args = hf_config.get('model_args', {})
model_name = hf_config['architecture']
return pretrained_cfg, model_name, model_args
def load_state_dict_from_hf(
model_id: str,
filename: str = HF_WEIGHTS_NAME,
weights_only: bool = False,
):
assert has_hf_hub(True)
hf_model_id, hf_revision = hf_split(model_id)
# Look for .safetensors alternatives and load from it if it exists
if _has_safetensors:
for safe_filename in _get_safe_alternatives(filename):
try:
cached_safe_file = hf_hub_download(repo_id=hf_model_id, filename=safe_filename, revision=hf_revision)
_logger.info(
f"[{model_id}] Safe alternative available for '{filename}' "
f"(as '{safe_filename}'). Loading weights using safetensors.")
return safetensors.torch.load_file(cached_safe_file, device="cpu")
except EntryNotFoundError:
pass
# Otherwise, load using pytorch.load
cached_file = hf_hub_download(hf_model_id, filename=filename, revision=hf_revision)
_logger.debug(f"[{model_id}] Safe alternative not found for '{filename}'. Loading weights using default pytorch.")
try:
state_dict = torch.load(cached_file, map_location='cpu', weights_only=weights_only)
except TypeError:
state_dict = torch.load(cached_file, map_location='cpu')
return state_dict
def load_custom_from_hf(model_id: str, filename: str, model: torch.nn.Module):
assert has_hf_hub(True)
hf_model_id, hf_revision = hf_split(model_id)
cached_file = hf_hub_download(hf_model_id, filename=filename, revision=hf_revision)
return model.load_pretrained(cached_file)
def save_config_for_hf(
model,
config_path: str,
model_config: Optional[dict] = None,
model_args: Optional[dict] = None
):
model_config = model_config or {}
hf_config = {}
pretrained_cfg = filter_pretrained_cfg(model.pretrained_cfg, remove_source=True, remove_null=True)
# set some values at root config level
hf_config['architecture'] = pretrained_cfg.pop('architecture')
hf_config['num_classes'] = model_config.pop('num_classes', model.num_classes)
# NOTE these attr saved for informational purposes, do not impact model build
hf_config['num_features'] = model_config.pop('num_features', model.num_features)
global_pool_type = model_config.pop('global_pool', getattr(model, 'global_pool', None))
if isinstance(global_pool_type, str) and global_pool_type:
hf_config['global_pool'] = global_pool_type
# Save class label info
if 'labels' in model_config:
_logger.warning(
"'labels' as a config field for is deprecated. Please use 'label_names' and 'label_descriptions'."
" Renaming provided 'labels' field to 'label_names'.")
model_config.setdefault('label_names', model_config.pop('labels'))
label_names = model_config.pop('label_names', None)
if label_names:
assert isinstance(label_names, (dict, list, tuple))
# map label id (classifier index) -> unique label name (ie synset for ImageNet, MID for OpenImages)
# can be a dict id: name if there are id gaps, or tuple/list if no gaps.
hf_config['label_names'] = label_names
label_descriptions = model_config.pop('label_descriptions', None)
if label_descriptions:
assert isinstance(label_descriptions, dict)
# maps label names -> descriptions
hf_config['label_descriptions'] = label_descriptions
if model_args:
hf_config['model_args'] = model_args
hf_config['pretrained_cfg'] = pretrained_cfg
hf_config.update(model_config)
with config_path.open('w') as f:
json.dump(hf_config, f, indent=2)
def save_for_hf(
model,
save_directory: str,
model_config: Optional[dict] = None,
model_args: Optional[dict] = None,
safe_serialization: Union[bool, Literal["both"]] = False,
):
assert has_hf_hub(True)
save_directory = Path(save_directory)
save_directory.mkdir(exist_ok=True, parents=True)
# Save model weights, either safely (using safetensors), or using legacy pytorch approach or both.
tensors = model.state_dict()
if safe_serialization is True or safe_serialization == "both":
assert _has_safetensors, "`pip install safetensors` to use .safetensors"
safetensors.torch.save_file(tensors, save_directory / HF_SAFE_WEIGHTS_NAME)
if safe_serialization is False or safe_serialization == "both":
torch.save(tensors, save_directory / HF_WEIGHTS_NAME)
config_path = save_directory / 'config.json'
save_config_for_hf(
model,
config_path,
model_config=model_config,
model_args=model_args,
)
def push_to_hf_hub(
model: torch.nn.Module,
repo_id: str,
commit_message: str = 'Add model',
token: Optional[str] = None,
revision: Optional[str] = None,
private: bool = False,
create_pr: bool = False,
model_config: Optional[dict] = None,
model_card: Optional[dict] = None,
model_args: Optional[dict] = None,
safe_serialization: Union[bool, Literal["both"]] = 'both',
):
"""
Arguments:
(...)
safe_serialization (`bool` or `"both"`, *optional*, defaults to `False`):
Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
Can be set to `"both"` in order to push both safe and unsafe weights.
"""
# Create repo if it doesn't exist yet
repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True)
# Infer complete repo_id from repo_url
# Can be different from the input `repo_id` if repo_owner was implicit
_, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)
repo_id = f"{repo_owner}/{repo_name}"
# Check if README file already exist in repo
try:
get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision))
has_readme = True
except EntryNotFoundError:
has_readme = False
# Dump model and push to Hub
with TemporaryDirectory() as tmpdir:
# Save model weights and config.
save_for_hf(
model,
tmpdir,
model_config=model_config,
model_args=model_args,
safe_serialization=safe_serialization,
)
# Add readme if it does not exist
if not has_readme:
model_card = model_card or {}
model_name = repo_id.split('/')[-1]
readme_path = Path(tmpdir) / "README.md"
readme_text = generate_readme(model_card, model_name)
readme_path.write_text(readme_text)
# Upload model and return
return upload_folder(
repo_id=repo_id,
folder_path=tmpdir,
revision=revision,
create_pr=create_pr,
commit_message=commit_message,
)
def generate_readme(model_card: dict, model_name: str):
readme_text = "---\n"
readme_text += "tags:\n- image-classification\n- timm\n"
readme_text += "library_name: timm\n"
readme_text += f"license: {model_card.get('license', 'apache-2.0')}\n"
if 'details' in model_card and 'Dataset' in model_card['details']:
readme_text += 'datasets:\n'
if isinstance(model_card['details']['Dataset'], (tuple, list)):
for d in model_card['details']['Dataset']:
readme_text += f"- {d.lower()}\n"
else:
readme_text += f"- {model_card['details']['Dataset'].lower()}\n"
if 'Pretrain Dataset' in model_card['details']:
if isinstance(model_card['details']['Pretrain Dataset'], (tuple, list)):
for d in model_card['details']['Pretrain Dataset']:
readme_text += f"- {d.lower()}\n"
else:
readme_text += f"- {model_card['details']['Pretrain Dataset'].lower()}\n"
readme_text += "---\n"
readme_text += f"# Model card for {model_name}\n"
if 'description' in model_card:
readme_text += f"\n{model_card['description']}\n"
if 'details' in model_card:
readme_text += f"\n## Model Details\n"
for k, v in model_card['details'].items():
if isinstance(v, (list, tuple)):
readme_text += f"- **{k}:**\n"
for vi in v:
readme_text += f" - {vi}\n"
elif isinstance(v, dict):
readme_text += f"- **{k}:**\n"
for ki, vi in v.items():
readme_text += f" - {ki}: {vi}\n"
else:
readme_text += f"- **{k}:** {v}\n"
if 'usage' in model_card:
readme_text += f"\n## Model Usage\n"
readme_text += model_card['usage']
readme_text += '\n'
if 'comparison' in model_card:
readme_text += f"\n## Model Comparison\n"
readme_text += model_card['comparison']
readme_text += '\n'
if 'citation' in model_card:
readme_text += f"\n## Citation\n"
if not isinstance(model_card['citation'], (list, tuple)):
citations = [model_card['citation']]
else:
citations = model_card['citation']
for c in citations:
readme_text += f"```bibtex\n{c}\n```\n"
return readme_text
def _get_safe_alternatives(filename: str) -> Iterable[str]:
"""Returns potential safetensors alternatives for a given filename.
Use case:
When downloading a model from the Huggingface Hub, we first look if a .safetensors file exists and if yes, we use it.
Main use case is filename "pytorch_model.bin" => check for "model.safetensors" or "pytorch_model.safetensors".
"""
if filename == HF_WEIGHTS_NAME:
yield HF_SAFE_WEIGHTS_NAME
if filename == HF_OPEN_CLIP_WEIGHTS_NAME:
yield HF_OPEN_CLIP_SAFE_WEIGHTS_NAME
if filename not in (HF_WEIGHTS_NAME, HF_OPEN_CLIP_WEIGHTS_NAME) and filename.endswith(".bin"):
yield filename[:-4] + ".safetensors"
|
pytorch-image-models/timm/models/_hub.py/0
|
{
"file_path": "pytorch-image-models/timm/models/_hub.py",
"repo_id": "pytorch-image-models",
"token_count": 6957
}
| 212
|
""" ConvMixer
"""
from typing import Optional
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import SelectAdaptivePool2d
from ._registry import register_model, generate_default_cfgs
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
__all__ = ['ConvMixer']
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x):
return self.fn(x) + x
class ConvMixer(nn.Module):
def __init__(
self,
dim,
depth,
kernel_size=9,
patch_size=7,
in_chans=3,
num_classes=1000,
global_pool='avg',
drop_rate=0.,
act_layer=nn.GELU,
**kwargs,
):
super().__init__()
self.num_classes = num_classes
self.num_features = self.head_hidden_size = dim
self.grad_checkpointing = False
self.stem = nn.Sequential(
nn.Conv2d(in_chans, dim, kernel_size=patch_size, stride=patch_size),
act_layer(),
nn.BatchNorm2d(dim)
)
self.blocks = nn.Sequential(
*[nn.Sequential(
Residual(nn.Sequential(
nn.Conv2d(dim, dim, kernel_size, groups=dim, padding="same"),
act_layer(),
nn.BatchNorm2d(dim)
)),
nn.Conv2d(dim, dim, kernel_size=1),
act_layer(),
nn.BatchNorm2d(dim)
) for i in range(depth)]
)
self.pooling = SelectAdaptivePool2d(pool_type=global_pool, flatten=True)
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(dim, num_classes) if num_classes > 0 else nn.Identity()
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(stem=r'^stem', blocks=r'^blocks\.(\d+)')
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.num_classes = num_classes
if global_pool is not None:
self.pooling = SelectAdaptivePool2d(pool_type=global_pool, flatten=True)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.pooling(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_convmixer(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for ConvMixer models.')
return build_model_with_cfg(ConvMixer, variant, pretrained, **kwargs)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .96, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head',
'first_conv': 'stem.0',
**kwargs
}
default_cfgs = generate_default_cfgs({
'convmixer_1536_20.in1k': _cfg(hf_hub_id='timm/'),
'convmixer_768_32.in1k': _cfg(hf_hub_id='timm/'),
'convmixer_1024_20_ks9_p14.in1k': _cfg(hf_hub_id='timm/')
})
@register_model
def convmixer_1536_20(pretrained=False, **kwargs) -> ConvMixer:
model_args = dict(dim=1536, depth=20, kernel_size=9, patch_size=7, **kwargs)
return _create_convmixer('convmixer_1536_20', pretrained, **model_args)
@register_model
def convmixer_768_32(pretrained=False, **kwargs) -> ConvMixer:
model_args = dict(dim=768, depth=32, kernel_size=7, patch_size=7, act_layer=nn.ReLU, **kwargs)
return _create_convmixer('convmixer_768_32', pretrained, **model_args)
@register_model
def convmixer_1024_20_ks9_p14(pretrained=False, **kwargs) -> ConvMixer:
model_args = dict(dim=1024, depth=20, kernel_size=9, patch_size=14, **kwargs)
return _create_convmixer('convmixer_1024_20_ks9_p14', pretrained, **model_args)
|
pytorch-image-models/timm/models/convmixer.py/0
|
{
"file_path": "pytorch-image-models/timm/models/convmixer.py",
"repo_id": "pytorch-image-models",
"token_count": 2255
}
| 213
|
from ._factory import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)
|
pytorch-image-models/timm/models/factory.py/0
|
{
"file_path": "pytorch-image-models/timm/models/factory.py",
"repo_id": "pytorch-image-models",
"token_count": 43
}
| 214
|
""" Inception-V3
Originally from torchvision Inception3 model
Licensed BSD-Clause 3 https://github.com/pytorch/vision/blob/master/LICENSE
"""
from functools import partial
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import trunc_normal_, create_classifier, Linear, ConvNormAct
from ._builder import build_model_with_cfg
from ._builder import resolve_pretrained_cfg
from ._manipulate import flatten_modules
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
__all__ = ['InceptionV3'] # model_registry will add each entrypoint fn to this
class InceptionA(nn.Module):
def __init__(self, in_channels, pool_features, conv_block=None):
super(InceptionA, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch1x1 = conv_block(in_channels, 64, kernel_size=1)
self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)
self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)
self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionB(nn.Module):
def __init__(self, in_channels, conv_block=None):
super(InceptionB, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)
def _forward(self, x):
branch3x3 = self.branch3x3(x)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
outputs = [branch3x3, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionC(nn.Module):
def __init__(self, in_channels, channels_7x7, conv_block=None):
super(InceptionC, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch1x1 = conv_block(in_channels, 192, kernel_size=1)
c7 = channels_7x7
self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)
self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)
self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))
self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionD(nn.Module):
def __init__(self, in_channels, conv_block=None):
super(InceptionD, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)
self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)
self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)
self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))
self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))
self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)
def _forward(self, x):
branch3x3 = self.branch3x3_1(x)
branch3x3 = self.branch3x3_2(branch3x3)
branch7x7x3 = self.branch7x7x3_1(x)
branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
outputs = [branch3x3, branch7x7x3, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionE(nn.Module):
def __init__(self, in_channels, conv_block=None):
super(InceptionE, self).__init__()
conv_block = conv_block or ConvNormAct
self.branch1x1 = conv_block(in_channels, 320, kernel_size=1)
self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)
self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionAux(nn.Module):
def __init__(self, in_channels, num_classes, conv_block=None):
super(InceptionAux, self).__init__()
conv_block = conv_block or ConvNormAct
self.conv0 = conv_block(in_channels, 128, kernel_size=1)
self.conv1 = conv_block(128, 768, kernel_size=5)
self.conv1.stddev = 0.01
self.fc = Linear(768, num_classes)
self.fc.stddev = 0.001
def forward(self, x):
# N x 768 x 17 x 17
x = F.avg_pool2d(x, kernel_size=5, stride=3)
# N x 768 x 5 x 5
x = self.conv0(x)
# N x 128 x 5 x 5
x = self.conv1(x)
# N x 768 x 1 x 1
# Adaptive average pooling
x = F.adaptive_avg_pool2d(x, (1, 1))
# N x 768 x 1 x 1
x = torch.flatten(x, 1)
# N x 768
x = self.fc(x)
# N x 1000
return x
class InceptionV3(nn.Module):
"""Inception-V3
"""
aux_logits: torch.jit.Final[bool]
def __init__(
self,
num_classes=1000,
in_chans=3,
drop_rate=0.,
global_pool='avg',
aux_logits=False,
norm_layer='batchnorm2d',
norm_eps=1e-3,
act_layer='relu',
):
super(InceptionV3, self).__init__()
self.num_classes = num_classes
self.aux_logits = aux_logits
conv_block = partial(
ConvNormAct,
padding=0,
norm_layer=norm_layer,
act_layer=act_layer,
norm_kwargs=dict(eps=norm_eps),
act_kwargs=dict(inplace=True),
)
self.Conv2d_1a_3x3 = conv_block(in_chans, 32, kernel_size=3, stride=2)
self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
self.Pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
self.Pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
self.Mixed_5b = InceptionA(192, pool_features=32, conv_block=conv_block)
self.Mixed_5c = InceptionA(256, pool_features=64, conv_block=conv_block)
self.Mixed_5d = InceptionA(288, pool_features=64, conv_block=conv_block)
self.Mixed_6a = InceptionB(288, conv_block=conv_block)
self.Mixed_6b = InceptionC(768, channels_7x7=128, conv_block=conv_block)
self.Mixed_6c = InceptionC(768, channels_7x7=160, conv_block=conv_block)
self.Mixed_6d = InceptionC(768, channels_7x7=160, conv_block=conv_block)
self.Mixed_6e = InceptionC(768, channels_7x7=192, conv_block=conv_block)
if aux_logits:
self.AuxLogits = InceptionAux(768, num_classes, conv_block=conv_block)
else:
self.AuxLogits = None
self.Mixed_7a = InceptionD(768, conv_block=conv_block)
self.Mixed_7b = InceptionE(1280, conv_block=conv_block)
self.Mixed_7c = InceptionE(2048, conv_block=conv_block)
self.feature_info = [
dict(num_chs=64, reduction=2, module='Conv2d_2b_3x3'),
dict(num_chs=192, reduction=4, module='Conv2d_4a_3x3'),
dict(num_chs=288, reduction=8, module='Mixed_5d'),
dict(num_chs=768, reduction=16, module='Mixed_6e'),
dict(num_chs=2048, reduction=32, module='Mixed_7c'),
]
self.num_features = self.head_hidden_size = 2048
self.global_pool, self.head_drop, self.fc = create_classifier(
self.num_features,
self.num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
)
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
stddev = m.stddev if hasattr(m, 'stddev') else 0.1
trunc_normal_(m.weight, std=stddev)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def group_matcher(self, coarse=False):
module_map = {k: i for i, (k, _) in enumerate(flatten_modules(self.named_children(), prefix=()))}
module_map.pop(('fc',))
def _matcher(name):
if any([name.startswith(n) for n in ('Conv2d_1', 'Conv2d_2')]):
return 0
elif any([name.startswith(n) for n in ('Conv2d_3', 'Conv2d_4')]):
return 1
else:
for k in module_map.keys():
if k == tuple(name.split('.')[:len(k)]):
return module_map[k]
return float('inf')
return _matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.fc
def reset_classifier(self, num_classes: int, global_pool: str = 'avg'):
self.num_classes = num_classes
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_preaux(self, x):
x = self.Conv2d_1a_3x3(x) # N x 32 x 149 x 149
x = self.Conv2d_2a_3x3(x) # N x 32 x 147 x 147
x = self.Conv2d_2b_3x3(x) # N x 64 x 147 x 147
x = self.Pool1(x) # N x 64 x 73 x 73
x = self.Conv2d_3b_1x1(x) # N x 80 x 73 x 73
x = self.Conv2d_4a_3x3(x) # N x 192 x 71 x 71
x = self.Pool2(x) # N x 192 x 35 x 35
x = self.Mixed_5b(x) # N x 256 x 35 x 35
x = self.Mixed_5c(x) # N x 288 x 35 x 35
x = self.Mixed_5d(x) # N x 288 x 35 x 35
x = self.Mixed_6a(x) # N x 768 x 17 x 17
x = self.Mixed_6b(x) # N x 768 x 17 x 17
x = self.Mixed_6c(x) # N x 768 x 17 x 17
x = self.Mixed_6d(x) # N x 768 x 17 x 17
x = self.Mixed_6e(x) # N x 768 x 17 x 17
return x
def forward_postaux(self, x):
x = self.Mixed_7a(x) # N x 1280 x 8 x 8
x = self.Mixed_7b(x) # N x 2048 x 8 x 8
x = self.Mixed_7c(x) # N x 2048 x 8 x 8
return x
def forward_features(self, x):
x = self.forward_preaux(x)
if self.aux_logits:
aux = self.AuxLogits(x)
x = self.forward_postaux(x)
return x, aux
x = self.forward_postaux(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.head_drop(x)
if pre_logits:
return x
x = self.fc(x)
return x
def forward(self, x):
if self.aux_logits:
x, aux = self.forward_features(x)
x = self.forward_head(x)
return x, aux
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_inception_v3(variant, pretrained=False, **kwargs):
pretrained_cfg = resolve_pretrained_cfg(variant, pretrained_cfg=kwargs.pop('pretrained_cfg', None))
aux_logits = kwargs.get('aux_logits', False)
has_aux_logits = False
if pretrained_cfg:
# only torchvision pretrained weights have aux logits
has_aux_logits = pretrained_cfg.tag == 'tv_in1k'
if aux_logits:
assert not kwargs.pop('features_only', False)
load_strict = has_aux_logits
else:
load_strict = not has_aux_logits
return build_model_with_cfg(
InceptionV3,
variant,
pretrained,
pretrained_cfg=pretrained_cfg,
pretrained_strict=load_strict,
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'Conv2d_1a_3x3.conv', 'classifier': 'fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
# original PyTorch weights, ported from Tensorflow but modified
'inception_v3.tv_in1k': _cfg(
# NOTE checkpoint has aux logit layer weights
hf_hub_id='timm/',
url='https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth'),
# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
'inception_v3.tf_in1k': _cfg(hf_hub_id='timm/'),
# my port of Tensorflow adversarially trained Inception V3 from
# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
'inception_v3.tf_adv_in1k': _cfg(hf_hub_id='timm/'),
# from gluon pretrained models, best performing in terms of accuracy/loss metrics
# https://gluon-cv.mxnet.io/model_zoo/classification.html
'inception_v3.gluon_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, # also works well with inception defaults
std=IMAGENET_DEFAULT_STD, # also works well with inception defaults
)
})
@register_model
def inception_v3(pretrained=False, **kwargs) -> InceptionV3:
model = _create_inception_v3('inception_v3', pretrained=pretrained, **kwargs)
return model
register_model_deprecations(__name__, {
'tf_inception_v3': 'inception_v3.tf_in1k',
'adv_inception_v3': 'inception_v3.tf_adv_in1k',
'gluon_inception_v3': 'inception_v3.gluon_in1k',
})
|
pytorch-image-models/timm/models/inception_v3.py/0
|
{
"file_path": "pytorch-image-models/timm/models/inception_v3.py",
"repo_id": "pytorch-image-models",
"token_count": 8642
}
| 215
|
""" Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.13797},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Based on Apache 2.0 licensed code at https://github.com/whai362/PVT
Modifications and timm support by / Copyright 2022, Ross Wightman
"""
import math
from typing import Callable, List, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, to_2tuple, to_ntuple, trunc_normal_, LayerNorm, use_fused_attn
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['PyramidVisionTransformerV2']
class MlpWithDepthwiseConv(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.,
extra_relu=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.relu = nn.ReLU() if extra_relu else nn.Identity()
self.dwconv = nn.Conv2d(hidden_features, hidden_features, 3, 1, 1, bias=True, groups=hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x, feat_size: List[int]):
x = self.fc1(x)
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, feat_size[0], feat_size[1])
x = self.relu(x)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim,
num_heads=8,
sr_ratio=1,
linear_attn=False,
qkv_bias=True,
attn_drop=0.,
proj_drop=0.
):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.fused_attn = use_fused_attn()
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
if not linear_attn:
self.pool = None
if sr_ratio > 1:
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)
else:
self.sr = None
self.norm = None
self.act = None
else:
self.pool = nn.AdaptiveAvgPool2d(7)
self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1)
self.norm = nn.LayerNorm(dim)
self.act = nn.GELU()
def forward(self, x, feat_size: List[int]):
B, N, C = x.shape
H, W = feat_size
q = self.q(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
if self.pool is not None:
x = x.permute(0, 2, 1).reshape(B, C, H, W)
x = self.sr(self.pool(x)).reshape(B, C, -1).permute(0, 2, 1)
x = self.norm(x)
x = self.act(x)
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
else:
if self.sr is not None:
x = x.permute(0, 2, 1).reshape(B, C, H, W)
x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
x = self.norm(x)
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
else:
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k, v = kv.unbind(0)
if self.fused_attn:
x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
sr_ratio=1,
linear_attn=False,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
sr_ratio=sr_ratio,
linear_attn=linear_attn,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = MlpWithDepthwiseConv(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
extra_relu=linear_attn,
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x, feat_size: List[int]):
x = x + self.drop_path1(self.attn(self.norm1(x), feat_size))
x = x + self.drop_path2(self.mlp(self.norm2(x), feat_size))
return x
class OverlapPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
patch_size = to_2tuple(patch_size)
assert max(patch_size) > stride, "Set larger patch_size than stride"
self.patch_size = patch_size
self.proj = nn.Conv2d(
in_chans, embed_dim, patch_size,
stride=stride, padding=(patch_size[0] // 2, patch_size[1] // 2))
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
x = self.proj(x)
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
return x
class PyramidVisionTransformerStage(nn.Module):
def __init__(
self,
dim: int,
dim_out: int,
depth: int,
downsample: bool = True,
num_heads: int = 8,
sr_ratio: int = 1,
linear_attn: bool = False,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: Union[List[float], float] = 0.0,
norm_layer: Callable = LayerNorm,
):
super().__init__()
self.grad_checkpointing = False
if downsample:
self.downsample = OverlapPatchEmbed(
patch_size=3,
stride=2,
in_chans=dim,
embed_dim=dim_out,
)
else:
assert dim == dim_out
self.downsample = None
self.blocks = nn.ModuleList([Block(
dim=dim_out,
num_heads=num_heads,
sr_ratio=sr_ratio,
linear_attn=linear_attn,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
) for i in range(depth)])
self.norm = norm_layer(dim_out)
def forward(self, x):
# x is either B, C, H, W (if downsample) or B, H, W, C if not
if self.downsample is not None:
# input to downsample is B, C, H, W
x = self.downsample(x) # output B, H, W, C
B, H, W, C = x.shape
feat_size = (H, W)
x = x.reshape(B, -1, C)
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint.checkpoint(blk, x, feat_size)
else:
x = blk(x, feat_size)
x = self.norm(x)
x = x.reshape(B, feat_size[0], feat_size[1], -1).permute(0, 3, 1, 2).contiguous()
return x
class PyramidVisionTransformerV2(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
depths=(3, 4, 6, 3),
embed_dims=(64, 128, 256, 512),
num_heads=(1, 2, 4, 8),
sr_ratios=(8, 4, 2, 1),
mlp_ratios=(8., 8., 4., 4.),
qkv_bias=True,
linear=False,
drop_rate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=LayerNorm,
):
super().__init__()
self.num_classes = num_classes
assert global_pool in ('avg', '')
self.global_pool = global_pool
self.depths = depths
num_stages = len(depths)
mlp_ratios = to_ntuple(num_stages)(mlp_ratios)
num_heads = to_ntuple(num_stages)(num_heads)
sr_ratios = to_ntuple(num_stages)(sr_ratios)
assert(len(embed_dims)) == num_stages
self.feature_info = []
self.patch_embed = OverlapPatchEmbed(
patch_size=7,
stride=4,
in_chans=in_chans,
embed_dim=embed_dims[0],
)
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
cur = 0
prev_dim = embed_dims[0]
stages = []
for i in range(num_stages):
stages += [PyramidVisionTransformerStage(
dim=prev_dim,
dim_out=embed_dims[i],
depth=depths[i],
downsample=i > 0,
num_heads=num_heads[i],
sr_ratio=sr_ratios[i],
mlp_ratio=mlp_ratios[i],
linear_attn=linear,
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
)]
prev_dim = embed_dims[i]
cur += depths[i]
self.feature_info += [dict(num_chs=prev_dim, reduction=4 * 2**i, module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
# classification head
self.num_features = self.head_hidden_size = embed_dims[-1]
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def freeze_patch_emb(self):
self.patch_embed.requires_grad = False
@torch.jit.ignore
def no_weight_decay(self):
return {}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^patch_embed', # stem and embed
blocks=r'^stages\.(\d+)'
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.stages:
s.grad_checkpointing = enable
def get_classifier(self) -> nn.Module:
return self.head
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('avg', '')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.patch_embed(x)
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x.mean(dim=(-1, -2))
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
""" Remap original checkpoints -> timm """
if 'patch_embed.proj.weight' in state_dict:
return state_dict # non-original checkpoint, no remapping needed
out_dict = {}
import re
for k, v in state_dict.items():
if k.startswith('patch_embed'):
k = k.replace('patch_embed1', 'patch_embed')
k = k.replace('patch_embed2', 'stages.1.downsample')
k = k.replace('patch_embed3', 'stages.2.downsample')
k = k.replace('patch_embed4', 'stages.3.downsample')
k = k.replace('dwconv.dwconv', 'dwconv')
k = re.sub(r'block(\d+).(\d+)', lambda x: f'stages.{int(x.group(1)) - 1}.blocks.{x.group(2)}', k)
k = re.sub(r'^norm(\d+)', lambda x: f'stages.{int(x.group(1)) - 1}.norm', k)
out_dict[k] = v
return out_dict
def _create_pvt2(variant, pretrained=False, **kwargs):
default_out_indices = tuple(range(4))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
PyramidVisionTransformerV2,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs,
)
return model
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head', 'fixed_input_size': False,
**kwargs
}
default_cfgs = generate_default_cfgs({
'pvt_v2_b0.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b1.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b2.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b3.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b4.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b5.in1k': _cfg(hf_hub_id='timm/'),
'pvt_v2_b2_li.in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def pvt_v2_b0(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(2, 2, 2, 2), embed_dims=(32, 64, 160, 256), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b0', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b1(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(2, 2, 2, 2), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b2(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(3, 4, 6, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b3(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(3, 4, 18, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b3', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b4(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(depths=(3, 8, 27, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8))
return _create_pvt2('pvt_v2_b4', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b5(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(
depths=(3, 6, 40, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), mlp_ratios=(4, 4, 4, 4))
return _create_pvt2('pvt_v2_b5', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def pvt_v2_b2_li(pretrained=False, **kwargs) -> PyramidVisionTransformerV2:
model_args = dict(
depths=(3, 4, 6, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), linear=True)
return _create_pvt2('pvt_v2_b2_li', pretrained=pretrained, **dict(model_args, **kwargs))
|
pytorch-image-models/timm/models/pvt_v2.py/0
|
{
"file_path": "pytorch-image-models/timm/models/pvt_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 9064
}
| 216
|
""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
"""
# --------------------------------------------------------
# Swin Transformer V2
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import math
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert, ClassifierHead,\
resample_patch_embed, ndgrid, get_act_layer, LayerType
from ._builder import build_model_with_cfg
from ._features import feature_take_indices
from ._features_fx import register_notrace_function
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
__all__ = ['SwinTransformerV2'] # model_registry will add each entrypoint fn to this
_int_or_tuple_2_t = Union[int, Tuple[int, int]]
def window_partition(x: torch.Tensor, window_size: Tuple[int, int]) -> torch.Tensor:
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
return windows
@register_notrace_function # reason: int argument is a Proxy
def window_reverse(windows: torch.Tensor, window_size: Tuple[int, int], img_size: Tuple[int, int]) -> torch.Tensor:
"""
Args:
windows: (num_windows * B, window_size[0], window_size[1], C)
window_size (Tuple[int, int]): Window size
img_size (Tuple[int, int]): Image size
Returns:
x: (B, H, W, C)
"""
H, W = img_size
C = windows.shape[-1]
x = windows.view(-1, H // window_size[0], W // window_size[1], window_size[0], window_size[1], C)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
return x
class WindowAttention(nn.Module):
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
"""
def __init__(
self,
dim: int,
window_size: Tuple[int, int],
num_heads: int,
qkv_bias: bool = True,
qkv_bias_separate: bool = False,
attn_drop: float = 0.,
proj_drop: float = 0.,
pretrained_window_size: Tuple[int, int] = (0, 0),
) -> None:
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.pretrained_window_size = to_2tuple(pretrained_window_size)
self.num_heads = num_heads
self.qkv_bias_separate = qkv_bias_separate
self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
# mlp to generate continuous relative position bias
self.cpb_mlp = nn.Sequential(
nn.Linear(2, 512, bias=True),
nn.ReLU(inplace=True),
nn.Linear(512, num_heads, bias=False)
)
self.qkv = nn.Linear(dim, dim * 3, bias=False)
if qkv_bias:
self.q_bias = nn.Parameter(torch.zeros(dim))
self.register_buffer('k_bias', torch.zeros(dim), persistent=False)
self.v_bias = nn.Parameter(torch.zeros(dim))
else:
self.q_bias = None
self.k_bias = None
self.v_bias = None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.softmax = nn.Softmax(dim=-1)
self._make_pair_wise_relative_positions()
def _make_pair_wise_relative_positions(self):
# get relative_coords_table
relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0]).to(torch.float32)
relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1]).to(torch.float32)
relative_coords_table = torch.stack(ndgrid(relative_coords_h, relative_coords_w))
relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
if self.pretrained_window_size[0] > 0:
relative_coords_table[:, :, :, 0] /= (self.pretrained_window_size[0] - 1)
relative_coords_table[:, :, :, 1] /= (self.pretrained_window_size[1] - 1)
else:
relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
relative_coords_table *= 8 # normalize to -8, 8
relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
torch.abs(relative_coords_table) + 1.0) / math.log2(8)
self.register_buffer("relative_coords_table", relative_coords_table, persistent=False)
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(ndgrid(coords_h, coords_w)) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index, persistent=False)
def set_window_size(self, window_size: Tuple[int, int]) -> None:
"""Update window size & interpolate position embeddings
Args:
window_size (int): New window size
"""
window_size = to_2tuple(window_size)
if window_size != self.window_size:
self.window_size = window_size
self._make_pair_wise_relative_positions()
def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
if self.q_bias is None:
qkv = self.qkv(x)
else:
qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias))
if self.qkv_bias_separate:
qkv = self.qkv(x)
qkv += qkv_bias
else:
qkv = F.linear(x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
# cosine attention
attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
logit_scale = torch.clamp(self.logit_scale, max=math.log(1. / 0.01)).exp()
attn = attn * logit_scale
relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
num_win = mask.shape[0]
attn = attn.view(-1, num_win, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class SwinTransformerV2Block(nn.Module):
""" Swin Transformer Block.
"""
def __init__(
self,
dim: int,
input_resolution: _int_or_tuple_2_t,
num_heads: int,
window_size: _int_or_tuple_2_t = 7,
shift_size: _int_or_tuple_2_t = 0,
always_partition: bool = False,
dynamic_mask: bool = False,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
act_layer: LayerType = "gelu",
norm_layer: nn.Module = nn.LayerNorm,
pretrained_window_size: _int_or_tuple_2_t = 0,
):
"""
Args:
dim: Number of input channels.
input_resolution: Input resolution.
num_heads: Number of attention heads.
window_size: Window size.
shift_size: Shift size for SW-MSA.
always_partition: Always partition into full windows and shift
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: If True, add a learnable bias to query, key, value.
proj_drop: Dropout rate.
attn_drop: Attention dropout rate.
drop_path: Stochastic depth rate.
act_layer: Activation layer.
norm_layer: Normalization layer.
pretrained_window_size: Window size in pretraining.
"""
super().__init__()
self.dim = dim
self.input_resolution = to_2tuple(input_resolution)
self.num_heads = num_heads
self.target_shift_size = to_2tuple(shift_size) # store for later resize
self.always_partition = always_partition
self.dynamic_mask = dynamic_mask
self.window_size, self.shift_size = self._calc_window_shift(window_size, shift_size)
self.window_area = self.window_size[0] * self.window_size[1]
self.mlp_ratio = mlp_ratio
act_layer = get_act_layer(act_layer)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
pretrained_window_size=to_2tuple(pretrained_window_size),
)
self.norm1 = norm_layer(dim)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.norm2 = norm_layer(dim)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.register_buffer(
"attn_mask",
None if self.dynamic_mask else self.get_attn_mask(),
persistent=False,
)
def get_attn_mask(self, x: Optional[torch.Tensor] = None) -> Optional[torch.Tensor]:
if any(self.shift_size):
# calculate attention mask for SW-MSA
if x is None:
img_mask = torch.zeros((1, *self.input_resolution, 1)) # 1 H W 1
else:
img_mask = torch.zeros((1, x.shape[1], x.shape[2], 1), dtype=x.dtype, device=x.device) # 1 H W 1
cnt = 0
for h in (
(0, -self.window_size[0]),
(-self.window_size[0], -self.shift_size[0]),
(-self.shift_size[0], None),
):
for w in (
(0, -self.window_size[1]),
(-self.window_size[1], -self.shift_size[1]),
(-self.shift_size[1], None),
):
img_mask[:, h[0]:h[1], w[0]:w[1], :] = cnt
cnt += 1
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.window_area)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
else:
attn_mask = None
return attn_mask
def _calc_window_shift(
self,
target_window_size: _int_or_tuple_2_t,
target_shift_size: Optional[_int_or_tuple_2_t] = None,
) -> Tuple[Tuple[int, int], Tuple[int, int]]:
target_window_size = to_2tuple(target_window_size)
if target_shift_size is None:
# if passed value is None, recalculate from default window_size // 2 if it was active
target_shift_size = self.target_shift_size
if any(target_shift_size):
# if there was previously a non-zero shift, recalculate based on current window_size
target_shift_size = (target_window_size[0] // 2, target_window_size[1] // 2)
else:
target_shift_size = to_2tuple(target_shift_size)
if self.always_partition:
return target_window_size, target_shift_size
target_window_size = to_2tuple(target_window_size)
target_shift_size = to_2tuple(target_shift_size)
window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)]
return tuple(window_size), tuple(shift_size)
def set_input_size(
self,
feat_size: Tuple[int, int],
window_size: Tuple[int, int],
always_partition: Optional[bool] = None,
):
""" Updates the input resolution, window size.
Args:
feat_size (Tuple[int, int]): New input resolution
window_size (int): New window size
always_partition: Change always_partition attribute if not None
"""
# Update input resolution
self.input_resolution = feat_size
if always_partition is not None:
self.always_partition = always_partition
self.window_size, self.shift_size = self._calc_window_shift(to_2tuple(window_size))
self.window_area = self.window_size[0] * self.window_size[1]
self.attn.set_window_size(self.window_size)
self.register_buffer(
"attn_mask",
None if self.dynamic_mask else self.get_attn_mask(),
persistent=False,
)
def _attn(self, x: torch.Tensor) -> torch.Tensor:
B, H, W, C = x.shape
# cyclic shift
has_shift = any(self.shift_size)
if has_shift:
shifted_x = torch.roll(x, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2))
else:
shifted_x = x
pad_h = (self.window_size[0] - H % self.window_size[0]) % self.window_size[0]
pad_w = (self.window_size[1] - W % self.window_size[1]) % self.window_size[1]
shifted_x = torch.nn.functional.pad(shifted_x, (0, 0, 0, pad_w, 0, pad_h))
_, Hp, Wp, _ = shifted_x.shape
# partition windows
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.view(-1, self.window_area, C) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
if getattr(self, 'dynamic_mask', False):
attn_mask = self.get_attn_mask(shifted_x)
else:
attn_mask = self.attn_mask
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C)
shifted_x = window_reverse(attn_windows, self.window_size, (Hp, Wp)) # B H' W' C
shifted_x = shifted_x[:, :H, :W, :].contiguous()
# reverse cyclic shift
if has_shift:
x = torch.roll(shifted_x, shifts=self.shift_size, dims=(1, 2))
else:
x = shifted_x
return x
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, H, W, C = x.shape
x = x + self.drop_path1(self.norm1(self._attn(x)))
x = x.reshape(B, -1, C)
x = x + self.drop_path2(self.norm2(self.mlp(x)))
x = x.reshape(B, H, W, C)
return x
class PatchMerging(nn.Module):
""" Patch Merging Layer.
"""
def __init__(
self,
dim: int,
out_dim: Optional[int] = None,
norm_layer: nn.Module = nn.LayerNorm
):
"""
Args:
dim (int): Number of input channels.
out_dim (int): Number of output channels (or 2 * dim if None)
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
super().__init__()
self.dim = dim
self.out_dim = out_dim or 2 * dim
self.reduction = nn.Linear(4 * dim, self.out_dim, bias=False)
self.norm = norm_layer(self.out_dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, H, W, C = x.shape
pad_values = (0, 0, 0, H % 2, 0, W % 2)
x = nn.functional.pad(x, pad_values)
_, H, W, _ = x.shape
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3)
x = self.reduction(x)
x = self.norm(x)
return x
class SwinTransformerV2Stage(nn.Module):
""" A Swin Transformer V2 Stage.
"""
def __init__(
self,
dim: int,
out_dim: int,
input_resolution: _int_or_tuple_2_t,
depth: int,
num_heads: int,
window_size: _int_or_tuple_2_t,
always_partition: bool = False,
dynamic_mask: bool = False,
downsample: bool = False,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
act_layer: Union[str, Callable] = 'gelu',
norm_layer: nn.Module = nn.LayerNorm,
pretrained_window_size: _int_or_tuple_2_t = 0,
output_nchw: bool = False,
) -> None:
"""
Args:
dim: Number of input channels.
out_dim: Number of output channels.
input_resolution: Input resolution.
depth: Number of blocks.
num_heads: Number of attention heads.
window_size: Local window size.
always_partition: Always partition into full windows and shift
dynamic_mask: Create attention mask in forward based on current input size
downsample: Use downsample layer at start of the block.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: If True, add a learnable bias to query, key, value.
proj_drop: Projection dropout rate
attn_drop: Attention dropout rate.
drop_path: Stochastic depth rate.
act_layer: Activation layer type.
norm_layer: Normalization layer.
pretrained_window_size: Local window size in pretraining.
output_nchw: Output tensors on NCHW format instead of NHWC.
"""
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.output_resolution = tuple(i // 2 for i in input_resolution) if downsample else input_resolution
self.depth = depth
self.output_nchw = output_nchw
self.grad_checkpointing = False
window_size = to_2tuple(window_size)
shift_size = tuple([w // 2 for w in window_size])
# patch merging / downsample layer
if downsample:
self.downsample = PatchMerging(dim=dim, out_dim=out_dim, norm_layer=norm_layer)
else:
assert dim == out_dim
self.downsample = nn.Identity()
# build blocks
self.blocks = nn.ModuleList([
SwinTransformerV2Block(
dim=out_dim,
input_resolution=self.output_resolution,
num_heads=num_heads,
window_size=window_size,
shift_size=0 if (i % 2 == 0) else shift_size,
always_partition=always_partition,
dynamic_mask=dynamic_mask,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
act_layer=act_layer,
norm_layer=norm_layer,
pretrained_window_size=pretrained_window_size,
)
for i in range(depth)])
def set_input_size(
self,
feat_size: Tuple[int, int],
window_size: int,
always_partition: Optional[bool] = None,
):
""" Updates the resolution, window size and so the pair-wise relative positions.
Args:
feat_size: New input (feature) resolution
window_size: New window size
always_partition: Always partition / shift the window
"""
self.input_resolution = feat_size
if isinstance(self.downsample, nn.Identity):
self.output_resolution = feat_size
else:
assert isinstance(self.downsample, PatchMerging)
self.output_resolution = tuple(i // 2 for i in feat_size)
for block in self.blocks:
block.set_input_size(
feat_size=self.output_resolution,
window_size=window_size,
always_partition=always_partition,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.downsample(x)
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
return x
def _init_respostnorm(self) -> None:
for blk in self.blocks:
nn.init.constant_(blk.norm1.bias, 0)
nn.init.constant_(blk.norm1.weight, 0)
nn.init.constant_(blk.norm2.bias, 0)
nn.init.constant_(blk.norm2.weight, 0)
class SwinTransformerV2(nn.Module):
""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
"""
def __init__(
self,
img_size: _int_or_tuple_2_t = 224,
patch_size: int = 4,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = 'avg',
embed_dim: int = 96,
depths: Tuple[int, ...] = (2, 2, 6, 2),
num_heads: Tuple[int, ...] = (3, 6, 12, 24),
window_size: _int_or_tuple_2_t = 7,
always_partition: bool = False,
strict_img_size: bool = True,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.1,
act_layer: Union[str, Callable] = 'gelu',
norm_layer: Callable = nn.LayerNorm,
pretrained_window_sizes: Tuple[int, ...] = (0, 0, 0, 0),
**kwargs,
):
"""
Args:
img_size: Input image size.
patch_size: Patch size.
in_chans: Number of input image channels.
num_classes: Number of classes for classification head.
embed_dim: Patch embedding dimension.
depths: Depth of each Swin Transformer stage (layer).
num_heads: Number of attention heads in different layers.
window_size: Window size.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: If True, add a learnable bias to query, key, value.
drop_rate: Head dropout rate.
proj_drop_rate: Projection dropout rate.
attn_drop_rate: Attention dropout rate.
drop_path_rate: Stochastic depth rate.
norm_layer: Normalization layer.
act_layer: Activation layer type.
patch_norm: If True, add normalization after patch embedding.
pretrained_window_sizes: Pretrained window sizes of each layer.
output_fmt: Output tensor format if not None, otherwise output 'NHWC' by default.
"""
super().__init__()
self.num_classes = num_classes
assert global_pool in ('', 'avg')
self.global_pool = global_pool
self.output_fmt = 'NHWC'
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.num_features = self.head_hidden_size = int(embed_dim * 2 ** (self.num_layers - 1))
self.feature_info = []
if not isinstance(embed_dim, (tuple, list)):
embed_dim = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim[0],
norm_layer=norm_layer,
strict_img_size=strict_img_size,
output_fmt='NHWC',
)
grid_size = self.patch_embed.grid_size
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
layers = []
in_dim = embed_dim[0]
scale = 1
for i in range(self.num_layers):
out_dim = embed_dim[i]
layers += [SwinTransformerV2Stage(
dim=in_dim,
out_dim=out_dim,
input_resolution=(grid_size[0] // scale, grid_size[1] // scale),
depth=depths[i],
downsample=i > 0,
num_heads=num_heads[i],
window_size=window_size,
always_partition=always_partition,
dynamic_mask=not strict_img_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
act_layer=act_layer,
norm_layer=norm_layer,
pretrained_window_size=pretrained_window_sizes[i],
)]
in_dim = out_dim
if i > 0:
scale *= 2
self.feature_info += [dict(num_chs=out_dim, reduction=4 * scale, module=f'layers.{i}')]
self.layers = nn.Sequential(*layers)
self.norm = norm_layer(self.num_features)
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
input_fmt=self.output_fmt,
)
self.apply(self._init_weights)
for bly in self.layers:
bly._init_respostnorm()
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
def set_input_size(
self,
img_size: Optional[Tuple[int, int]] = None,
patch_size: Optional[Tuple[int, int]] = None,
window_size: Optional[Tuple[int, int]] = None,
window_ratio: Optional[int] = 8,
always_partition: Optional[bool] = None,
):
"""Updates the image resolution, window size, and so the pair-wise relative positions.
Args:
img_size (Optional[Tuple[int, int]]): New input resolution, if None current resolution is used
patch_size (Optional[Tuple[int, int]): New patch size, if None use current patch size
window_size (Optional[int]): New window size, if None based on new_img_size // window_div
window_ratio (int): divisor for calculating window size from patch grid size
always_partition: always partition / shift windows even if feat size is < window
"""
if img_size is not None or patch_size is not None:
self.patch_embed.set_input_size(img_size=img_size, patch_size=patch_size)
grid_size = self.patch_embed.grid_size
if window_size is None and window_ratio is not None:
window_size = tuple([s // window_ratio for s in grid_size])
for index, stage in enumerate(self.layers):
stage_scale = 2 ** max(index - 1, 0)
stage.set_input_size(
feat_size=(grid_size[0] // stage_scale, grid_size[1] // stage_scale),
window_size=window_size,
always_partition=always_partition,
)
@torch.jit.ignore
def no_weight_decay(self):
nod = set()
for n, m in self.named_modules():
if any([kw in n for kw in ("cpb_mlp", "logit_scale")]):
nod.add(n)
return nod
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^absolute_pos_embed|patch_embed', # stem and embed
blocks=r'^layers\.(\d+)' if coarse else [
(r'^layers\.(\d+).downsample', (0,)),
(r'^layers\.(\d+)\.\w+\.(\d+)', None),
(r'^norm', (99999,)),
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for l in self.layers:
l.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head.fc
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.num_classes = num_classes
self.head.reset(num_classes, global_pool)
def forward_intermediates(
self,
x: torch.Tensor,
indices: Optional[Union[int, List[int]]] = None,
norm: bool = False,
stop_early: bool = False,
output_fmt: str = 'NCHW',
intermediates_only: bool = False,
) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
""" Forward features that returns intermediates.
Args:
x: Input image tensor
indices: Take last n blocks if int, all if None, select matching indices if sequence
norm: Apply norm layer to compatible intermediates
stop_early: Stop iterating over blocks when last desired intermediate hit
output_fmt: Shape of intermediate feature outputs
intermediates_only: Only return intermediate features
Returns:
"""
assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
intermediates = []
take_indices, max_index = feature_take_indices(len(self.layers), indices)
# forward pass
x = self.patch_embed(x)
num_stages = len(self.layers)
if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
stages = self.layers
else:
stages = self.layers[:max_index + 1]
for i, stage in enumerate(stages):
x = stage(x)
if i in take_indices:
if norm and i == num_stages - 1:
x_inter = self.norm(x) # applying final norm last intermediate
else:
x_inter = x
x_inter = x_inter.permute(0, 3, 1, 2).contiguous()
intermediates.append(x_inter)
if intermediates_only:
return intermediates
x = self.norm(x)
return x, intermediates
def prune_intermediate_layers(
self,
indices: Union[int, List[int]] = 1,
prune_norm: bool = False,
prune_head: bool = True,
):
""" Prune layers not required for specified intermediates.
"""
take_indices, max_index = feature_take_indices(len(self.layers), indices)
self.layers = self.layers[:max_index + 1] # truncate blocks
if prune_norm:
self.norm = nn.Identity()
if prune_head:
self.reset_classifier(0, '')
return take_indices
def forward_features(self, x):
x = self.patch_embed(x)
x = self.layers(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=True) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
state_dict = state_dict.get('model', state_dict)
state_dict = state_dict.get('state_dict', state_dict)
native_checkpoint = 'head.fc.weight' in state_dict
out_dict = {}
import re
for k, v in state_dict.items():
if any([n in k for n in ('relative_position_index', 'relative_coords_table', 'attn_mask')]):
continue # skip buffers that should not be persistent
if 'patch_embed.proj.weight' in k:
_, _, H, W = model.patch_embed.proj.weight.shape
if v.shape[-2] != H or v.shape[-1] != W:
v = resample_patch_embed(
v,
(H, W),
interpolation='bicubic',
antialias=True,
verbose=True,
)
if not native_checkpoint:
# skip layer remapping for updated checkpoints
k = re.sub(r'layers.(\d+).downsample', lambda x: f'layers.{int(x.group(1)) + 1}.downsample', k)
k = k.replace('head.', 'head.fc.')
out_dict[k] = v
return out_dict
def _create_swin_transformer_v2(variant, pretrained=False, **kwargs):
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 1, 1))))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
SwinTransformerV2, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head.fc',
'license': 'mit', **kwargs
}
default_cfgs = generate_default_cfgs({
'swinv2_base_window12to16_192to256.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth',
),
'swinv2_base_window12to24_192to384.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
'swinv2_large_window12to16_192to256.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth',
),
'swinv2_large_window12to24_192to384.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
),
'swinv2_tiny_window8_256.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth',
),
'swinv2_tiny_window16_256.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth',
),
'swinv2_small_window8_256.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth',
),
'swinv2_small_window16_256.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth',
),
'swinv2_base_window8_256.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth',
),
'swinv2_base_window16_256.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth',
),
'swinv2_base_window12_192.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth',
num_classes=21841, input_size=(3, 192, 192), pool_size=(6, 6)
),
'swinv2_large_window12_192.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth',
num_classes=21841, input_size=(3, 192, 192), pool_size=(6, 6)
),
})
@register_model
def swinv2_tiny_window16_256(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(window_size=16, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer_v2(
'swinv2_tiny_window16_256', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_tiny_window8_256(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(window_size=8, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer_v2(
'swinv2_tiny_window8_256', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_small_window16_256(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(window_size=16, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer_v2(
'swinv2_small_window16_256', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_small_window8_256(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(window_size=8, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer_v2(
'swinv2_small_window8_256', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_base_window16_256(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
return _create_swin_transformer_v2(
'swinv2_base_window16_256', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_base_window8_256(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(window_size=8, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
return _create_swin_transformer_v2(
'swinv2_base_window8_256', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_base_window12_192(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
return _create_swin_transformer_v2(
'swinv2_base_window12_192', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_base_window12to16_192to256(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(
window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32),
pretrained_window_sizes=(12, 12, 12, 6))
return _create_swin_transformer_v2(
'swinv2_base_window12to16_192to256', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_base_window12to24_192to384(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(
window_size=24, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32),
pretrained_window_sizes=(12, 12, 12, 6))
return _create_swin_transformer_v2(
'swinv2_base_window12to24_192to384', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_large_window12_192(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48))
return _create_swin_transformer_v2(
'swinv2_large_window12_192', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_large_window12to16_192to256(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(
window_size=16, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48),
pretrained_window_sizes=(12, 12, 12, 6))
return _create_swin_transformer_v2(
'swinv2_large_window12to16_192to256', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swinv2_large_window12to24_192to384(pretrained=False, **kwargs) -> SwinTransformerV2:
"""
"""
model_args = dict(
window_size=24, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48),
pretrained_window_sizes=(12, 12, 12, 6))
return _create_swin_transformer_v2(
'swinv2_large_window12to24_192to384', pretrained=pretrained, **dict(model_args, **kwargs))
register_model_deprecations(__name__, {
'swinv2_base_window12_192_22k': 'swinv2_base_window12_192.ms_in22k',
'swinv2_base_window12to16_192to256_22kft1k': 'swinv2_base_window12to16_192to256.ms_in22k_ft_in1k',
'swinv2_base_window12to24_192to384_22kft1k': 'swinv2_base_window12to24_192to384.ms_in22k_ft_in1k',
'swinv2_large_window12_192_22k': 'swinv2_large_window12_192.ms_in22k',
'swinv2_large_window12to16_192to256_22kft1k': 'swinv2_large_window12to16_192to256.ms_in22k_ft_in1k',
'swinv2_large_window12to24_192to384_22kft1k': 'swinv2_large_window12to24_192to384.ms_in22k_ft_in1k',
})
|
pytorch-image-models/timm/models/swin_transformer_v2.py/0
|
{
"file_path": "pytorch-image-models/timm/models/swin_transformer_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 21255
}
| 217
|
"""Pytorch impl of Aligned Xception 41, 65, 71
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partial
from typing import List, Dict, Type, Optional
import torch
import torch.nn as nn
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import ClassifierHead, ConvNormAct, DropPath, PadType, create_conv2d, get_norm_act_layer
from timm.layers.helpers import to_3tuple
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs
__all__ = ['XceptionAligned']
class SeparableConv2d(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
):
super(SeparableConv2d, self).__init__()
self.kernel_size = kernel_size
self.dilation = dilation
# depthwise convolution
self.conv_dw = create_conv2d(
in_chs, in_chs, kernel_size, stride=stride,
padding=padding, dilation=dilation, depthwise=True)
self.bn_dw = norm_layer(in_chs)
self.act_dw = act_layer(inplace=True) if act_layer is not None else nn.Identity()
# pointwise convolution
self.conv_pw = create_conv2d(in_chs, out_chs, kernel_size=1)
self.bn_pw = norm_layer(out_chs)
self.act_pw = act_layer(inplace=True) if act_layer is not None else nn.Identity()
def forward(self, x):
x = self.conv_dw(x)
x = self.bn_dw(x)
x = self.act_dw(x)
x = self.conv_pw(x)
x = self.bn_pw(x)
x = self.act_pw(x)
return x
class PreSeparableConv2d(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
kernel_size: int = 3,
stride: int = 1,
dilation: int = 1,
padding: PadType = '',
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
first_act: bool = True,
):
super(PreSeparableConv2d, self).__init__()
norm_act_layer = get_norm_act_layer(norm_layer, act_layer=act_layer)
self.kernel_size = kernel_size
self.dilation = dilation
self.norm = norm_act_layer(in_chs, inplace=True) if first_act else nn.Identity()
# depthwise convolution
self.conv_dw = create_conv2d(
in_chs, in_chs, kernel_size, stride=stride,
padding=padding, dilation=dilation, depthwise=True)
# pointwise convolution
self.conv_pw = create_conv2d(in_chs, out_chs, kernel_size=1)
def forward(self, x):
x = self.norm(x)
x = self.conv_dw(x)
x = self.conv_pw(x)
return x
class XceptionModule(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
stride: int = 1,
dilation: int = 1,
pad_type: PadType = '',
start_with_relu: bool = True,
no_skip: bool = False,
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Optional[Type[nn.Module]] = None,
drop_path: Optional[nn.Module] = None
):
super(XceptionModule, self).__init__()
out_chs = to_3tuple(out_chs)
self.in_channels = in_chs
self.out_channels = out_chs[-1]
self.no_skip = no_skip
if not no_skip and (self.out_channels != self.in_channels or stride != 1):
self.shortcut = ConvNormAct(
in_chs, self.out_channels, 1, stride=stride, norm_layer=norm_layer, apply_act=False)
else:
self.shortcut = None
separable_act_layer = None if start_with_relu else act_layer
self.stack = nn.Sequential()
for i in range(3):
if start_with_relu:
self.stack.add_module(f'act{i + 1}', act_layer(inplace=i > 0))
self.stack.add_module(f'conv{i + 1}', SeparableConv2d(
in_chs, out_chs[i], 3, stride=stride if i == 2 else 1, dilation=dilation, padding=pad_type,
act_layer=separable_act_layer, norm_layer=norm_layer))
in_chs = out_chs[i]
self.drop_path = drop_path
def forward(self, x):
skip = x
x = self.stack(x)
if self.shortcut is not None:
skip = self.shortcut(skip)
if not self.no_skip:
if self.drop_path is not None:
x = self.drop_path(x)
x = x + skip
return x
class PreXceptionModule(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
stride: int = 1,
dilation: int = 1,
pad_type: PadType = '',
no_skip: bool = False,
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Optional[Type[nn.Module]] = None,
drop_path: Optional[nn.Module] = None
):
super(PreXceptionModule, self).__init__()
out_chs = to_3tuple(out_chs)
self.in_channels = in_chs
self.out_channels = out_chs[-1]
self.no_skip = no_skip
if not no_skip and (self.out_channels != self.in_channels or stride != 1):
self.shortcut = create_conv2d(in_chs, self.out_channels, 1, stride=stride)
else:
self.shortcut = nn.Identity()
self.norm = get_norm_act_layer(norm_layer, act_layer=act_layer)(in_chs, inplace=True)
self.stack = nn.Sequential()
for i in range(3):
self.stack.add_module(f'conv{i + 1}', PreSeparableConv2d(
in_chs,
out_chs[i],
3,
stride=stride if i == 2 else 1,
dilation=dilation,
padding=pad_type,
act_layer=act_layer,
norm_layer=norm_layer,
first_act=i > 0,
))
in_chs = out_chs[i]
self.drop_path = drop_path
def forward(self, x):
x = self.norm(x)
skip = x
x = self.stack(x)
if not self.no_skip:
if self.drop_path is not None:
x = self.drop_path(x)
x = x + self.shortcut(skip)
return x
class XceptionAligned(nn.Module):
"""Modified Aligned Xception
"""
def __init__(
self,
block_cfg: List[Dict],
num_classes: int = 1000,
in_chans: int = 3,
output_stride: int = 32,
preact: bool = False,
act_layer: Type[nn.Module] = nn.ReLU,
norm_layer: Type[nn.Module] = nn.BatchNorm2d,
drop_rate: float = 0.,
drop_path_rate: float = 0.,
global_pool: str = 'avg',
):
super(XceptionAligned, self).__init__()
assert output_stride in (8, 16, 32)
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
layer_args = dict(act_layer=act_layer, norm_layer=norm_layer)
self.stem = nn.Sequential(*[
ConvNormAct(in_chans, 32, kernel_size=3, stride=2, **layer_args),
create_conv2d(32, 64, kernel_size=3, stride=1) if preact else
ConvNormAct(32, 64, kernel_size=3, stride=1, **layer_args)
])
curr_dilation = 1
curr_stride = 2
self.feature_info = []
self.blocks = nn.Sequential()
module_fn = PreXceptionModule if preact else XceptionModule
net_num_blocks = len(block_cfg)
net_block_idx = 0
for i, b in enumerate(block_cfg):
block_dpr = drop_path_rate * net_block_idx / (net_num_blocks - 1) # stochastic depth linear decay rule
b['drop_path'] = DropPath(block_dpr) if block_dpr > 0. else None
b['dilation'] = curr_dilation
if b['stride'] > 1:
name = f'blocks.{i}.stack.conv2' if preact else f'blocks.{i}.stack.act3'
self.feature_info += [dict(num_chs=to_3tuple(b['out_chs'])[-2], reduction=curr_stride, module=name)]
next_stride = curr_stride * b['stride']
if next_stride > output_stride:
curr_dilation *= b['stride']
b['stride'] = 1
else:
curr_stride = next_stride
self.blocks.add_module(str(i), module_fn(**b, **layer_args))
self.num_features = self.blocks[-1].out_channels
net_block_idx += 1
self.feature_info += [dict(
num_chs=self.num_features, reduction=curr_stride, module='blocks.' + str(len(self.blocks) - 1))]
self.act = act_layer(inplace=True) if preact else nn.Identity()
self.head_hidden_size = self.num_features
self.head = ClassifierHead(
in_features=self.num_features,
num_classes=num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=r'^blocks\.(\d+)',
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head.fc
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.head.reset(num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
x = self.act(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _xception(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
XceptionAligned,
variant,
pretrained,
feature_cfg=dict(flatten_sequential=True, feature_cls='hook'),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (10, 10),
'crop_pct': 0.903, 'interpolation': 'bicubic',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'stem.0.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'xception65.ra3_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.94,
),
'xception41.tf_in1k': _cfg(hf_hub_id='timm/'),
'xception65.tf_in1k': _cfg(hf_hub_id='timm/'),
'xception71.tf_in1k': _cfg(hf_hub_id='timm/'),
'xception41p.ra3_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.94,
),
'xception65p.ra3_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.94,
),
})
@register_model
def xception41(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-41
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 8),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
]
model_args = dict(block_cfg=block_cfg, norm_layer=partial(nn.BatchNorm2d, eps=.001, momentum=.1))
return _xception('xception41', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def xception65(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-65
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
]
model_args = dict(block_cfg=block_cfg, norm_layer=partial(nn.BatchNorm2d, eps=.001, momentum=.1))
return _xception('xception65', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def xception71(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-71
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=1),
dict(in_chs=256, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=1),
dict(in_chs=728, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
]
model_args = dict(block_cfg=block_cfg, norm_layer=partial(nn.BatchNorm2d, eps=.001, momentum=.1))
return _xception('xception71', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def xception41p(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-41 w/ Pre-Act
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 8),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), no_skip=True, stride=1),
]
model_args = dict(block_cfg=block_cfg, preact=True, norm_layer=nn.BatchNorm2d)
return _xception('xception41p', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def xception65p(pretrained=False, **kwargs) -> XceptionAligned:
""" Modified Aligned Xception-65 w/ Pre-Act
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True),
]
model_args = dict(
block_cfg=block_cfg, preact=True, norm_layer=partial(nn.BatchNorm2d, eps=.001, momentum=.1))
return _xception('xception65p', pretrained=pretrained, **dict(model_args, **kwargs))
|
pytorch-image-models/timm/models/xception_aligned.py/0
|
{
"file_path": "pytorch-image-models/timm/models/xception_aligned.py",
"repo_id": "pytorch-image-models",
"token_count": 7763
}
| 218
|
""" Nvidia NovoGrad Optimizer.
Original impl by Nvidia from Jasper example:
- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
import torch
from torch.optim.optimizer import Optimizer
import math
class NvNovoGrad(Optimizer):
"""
Implements Novograd algorithm.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.95, 0.98))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
grad_averaging: gradient averaging
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False)
"""
def __init__(self, params, lr=1e-3, betas=(0.95, 0.98), eps=1e-8,
weight_decay=0, grad_averaging=False, amsgrad=False):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay,
grad_averaging=grad_averaging,
amsgrad=amsgrad)
super(NvNovoGrad, self).__init__(params, defaults)
def __setstate__(self, state):
super(NvNovoGrad, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
if grad.is_sparse:
raise RuntimeError('Sparse gradients are not supported.')
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
norm = torch.sum(torch.pow(grad, 2))
if exp_avg_sq == 0:
exp_avg_sq.copy_(norm)
else:
exp_avg_sq.mul_(beta2).add_(norm, alpha=1 - beta2)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
grad.div_(denom)
if group['weight_decay'] != 0:
grad.add_(p, alpha=group['weight_decay'])
if group['grad_averaging']:
grad.mul_(1 - beta1)
exp_avg.mul_(beta1).add_(grad)
p.add_(exp_avg, alpha=-group['lr'])
return loss
|
pytorch-image-models/timm/optim/nvnovograd.py/0
|
{
"file_path": "pytorch-image-models/timm/optim/nvnovograd.py",
"repo_id": "pytorch-image-models",
"token_count": 2415
}
| 219
|
""" Adaptive Gradient Clipping
An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021}
}
Code references:
* Official JAX impl (paper authors): https://github.com/deepmind/deepmind-research/tree/master/nfnets
* Phil Wang's PyTorch gist: https://gist.github.com/lucidrains/0d6560077edac419ab5d3aa29e674d5c
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
def unitwise_norm(x, norm_type=2.0):
if x.ndim <= 1:
return x.norm(norm_type)
else:
# works for nn.ConvNd and nn,Linear where output dim is first in the kernel/weight tensor
# might need special cases for other weights (possibly MHA) where this may not be true
return x.norm(norm_type, dim=tuple(range(1, x.ndim)), keepdim=True)
def adaptive_clip_grad(parameters, clip_factor=0.01, eps=1e-3, norm_type=2.0):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
for p in parameters:
if p.grad is None:
continue
p_data = p.detach()
g_data = p.grad.detach()
max_norm = unitwise_norm(p_data, norm_type=norm_type).clamp_(min=eps).mul_(clip_factor)
grad_norm = unitwise_norm(g_data, norm_type=norm_type)
clipped_grad = g_data * (max_norm / grad_norm.clamp(min=1e-6))
new_grads = torch.where(grad_norm < max_norm, g_data, clipped_grad)
p.grad.detach().copy_(new_grads)
|
pytorch-image-models/timm/utils/agc.py/0
|
{
"file_path": "pytorch-image-models/timm/utils/agc.py",
"repo_id": "pytorch-image-models",
"token_count": 661
}
| 220
|
__version__ = '1.0.9'
|
pytorch-image-models/timm/version.py/0
|
{
"file_path": "pytorch-image-models/timm/version.py",
"repo_id": "pytorch-image-models",
"token_count": 12
}
| 221
|
ARG CUDA_ARCH_LIST="75-real;80-real;86-real;89-real;90-real"
ARG OMPI_VERSION="4.1.6"
# Build dependencies resolver stage
FROM lukemathwalker/cargo-chef:latest AS chef
WORKDIR /usr/src/text-generation-inference/backends/trtllm
FROM chef AS planner
COPY . .
RUN cargo chef prepare --recipe-path recipe.json
# CUDA dependent dependencies resolver stage
FROM nvidia/cuda:12.5.1-cudnn-devel-ubuntu22.04 AS cuda-builder
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt,sharing=locked \
apt update && apt install -y \
build-essential \
cmake \
curl \
gcc \
g++ \
git \
git-lfs \
libssl-dev \
ninja-build \
pkg-config \
python3 \
python3-setuptools \
tar \
wget
ENV TGI_INSTALL_PREFIX=/usr/local/tgi
ENV TENSORRT_INSTALL_PREFIX=/usr/local/tensorrt
# Install OpenMPI
FROM cuda-builder AS mpi-builder
ARG OMPI_VERSION
ENV OMPI_TARBALL_FILENAME="openmpi-$OMPI_VERSION.tar.bz2"
RUN wget "https://download.open-mpi.org/release/open-mpi/v4.1/$OMPI_TARBALL_FILENAME" -P /opt/src && \
mkdir /usr/src/mpi && \
tar -xf "/opt/src/$OMPI_TARBALL_FILENAME" -C /usr/src/mpi --strip-components=1 && \
cd /usr/src/mpi && \
./configure --prefix=/usr/local/mpi --with-cuda=/usr/local/cuda && \
make -j all && \
make install && \
rm -rf "/opt/src/$OMPI_TARBALL_FILENAME"
# Install TensorRT
FROM cuda-builder AS trt-builder
COPY backends/trtllm/scripts/install_tensorrt.sh /opt/install_tensorrt.sh
RUN chmod +x /opt/install_tensorrt.sh && \
/opt/install_tensorrt.sh
# Build Backend
FROM cuda-builder AS tgi-builder
WORKDIR /usr/src/text-generation-inference
# Install Rust
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | bash -s -- -y && \
chmod -R a+w /root/.rustup && \
chmod -R a+w /root/.cargo
ENV PATH="/root/.cargo/bin:$PATH"
RUN cargo install cargo-chef
# Cache dependencies
COPY --from=planner /usr/src/text-generation-inference/backends/trtllm/recipe.json .
RUN cargo chef cook --release --recipe-path recipe.json
# Build actual TGI
ARG CUDA_ARCH_LIST
ENV CMAKE_PREFIX_PATH="/usr/local/mpi:/usr/local/tensorrt:$CMAKE_PREFIX_PATH"
ENV LD_LIBRARY_PATH="/usr/local/mpi/lib:$LD_LIBRARY_PATH"
ENV PKG_CONFIG_PATH="/usr/local/mpi/lib/pkgconfig:$PKG_CONFIG_PATH"
COPY . .
COPY --from=trt-builder /usr/local/tensorrt /usr/local/tensorrt
COPY --from=mpi-builder /usr/local/mpi /usr/local/mpi
RUN mkdir $TGI_INSTALL_PREFIX && mkdir "$TGI_INSTALL_PREFIX/include" && mkdir "$TGI_INSTALL_PREFIX/lib" && \
cd backends/trtllm && \
CMAKE_INSTALL_PREFIX=$TGI_INSTALL_PREFIX cargo build --release
FROM nvidia/cuda:12.5.1-cudnn-runtime-ubuntu22.04 AS runtime
WORKDIR /usr/local/tgi/bin
ENV LD_LIBRARY_PATH="/usr/local/tgi/lib:/usr/local/tensorrt/lib:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH"
COPY --from=mpi-builder /usr/local/mpi /usr/local/mpi
COPY --from=trt-builder /usr/local/tensorrt /usr/local/tensorrt
COPY --from=tgi-builder /usr/local/tgi /usr/local/tgi
COPY --from=tgi-builder /usr/src/text-generation-inference/target/release/text-generation-backends-trtllm /usr/local/tgi/bin/text-generation-launcher
FROM runtime
LABEL co.huggingface.vendor="Hugging Face Inc."
LABEL org.opencontainers.image.authors="hardware@hf.co"
ENTRYPOINT ["./text-generation-launcher"]
CMD ["--executor-worker", "/usr/local/tgi/bin/executorWorker"]
|
text-generation-inference/backends/trtllm/Dockerfile/0
|
{
"file_path": "text-generation-inference/backends/trtllm/Dockerfile",
"repo_id": "text-generation-inference",
"token_count": 1463
}
| 222
|
pub use backend::{GenerationContext, TensorRtLlmBackend};
mod backend;
pub mod errors;
#[cxx::bridge(namespace = "huggingface::tgi::backends")]
mod ffi {
/// Struct used as shared type between rust and C++ to represent the result
/// of a single decoding iteration
pub struct GenerationStep {
token_id: u32,
log_prob: f32,
is_final: bool,
has_error: bool,
error_msg: String,
}
extern "Rust" {
type GenerationContext;
}
unsafe extern "C++" {
include!("backends/trtllm/src/ffi.cpp");
/// Represent an instance of the underlying TensorRT-LLM backend
type TensorRtLlmBackendImpl;
/// Create an instance backed behind a std::unique_ptr to manage the lifespan of the backend
///
/// # Arguments
///
/// * `engine_folder`: Path to the folder containing all the TRTLLM engines
/// * `executor_worker`: Path to the TRTLLM executor worker
///
/// returns: <unknown>
///
/// # Examples
///
/// ```
///
/// ```
#[rust_name = "create_tensorrt_llm_backend"]
fn CreateTensorRtLlmBackend(
engine_folder: &str,
executor_worker: &str,
) -> UniquePtr<TensorRtLlmBackendImpl>;
// #[rust_name = "is_ready"]
// fn IsReady(self: &TensorRtLlmBackendImpl) -> bool;
#[rust_name = "num_responses_ready"]
fn NumResponsesReady(self: &TensorRtLlmBackendImpl) -> usize;
#[rust_name = "submit"]
fn Submit(
self: Pin<&mut TensorRtLlmBackendImpl>,
tokens: &[u32],
top_k: i32,
top_p: f32,
temperature: f32,
repetition_penalty: f32,
frequency_penalty: f32,
seed: u64,
) -> u64;
#[rust_name = "stream_tokens"]
unsafe fn StreamTokens(
self: Pin<&mut TensorRtLlmBackendImpl>,
request_id: u64,
ctx: *mut GenerationContext,
cb: unsafe fn(*mut GenerationContext, GenerationStep),
) -> usize;
// #[rust_name = "shutdown"]
// fn Shutdown(self: Pin<&mut TensorRtLlmBackendImpl>);
}
}
|
text-generation-inference/backends/trtllm/src/lib.rs/0
|
{
"file_path": "text-generation-inference/backends/trtllm/src/lib.rs",
"repo_id": "text-generation-inference",
"token_count": 1091
}
| 223
|
<div align="center">
# Text Generation Inference benchmarking tool
</div>
A lightweight benchmarking tool based inspired by [oha](https://github.com/hatoo/oha)
and powered by [tui](https://github.com/tui-rs-revival/ratatui).
## Install
```shell
make install-benchmark
```
## Run
First, start `text-generation-inference`:
```shell
text-generation-launcher --model-id bigscience/bloom-560m
```
Then run the benchmarking tool:
```shell
text-generation-benchmark --tokenizer-name bigscience/bloom-560m
```
|
text-generation-inference/benchmark/README.md/0
|
{
"file_path": "text-generation-inference/benchmark/README.md",
"repo_id": "text-generation-inference",
"token_count": 187
}
| 224
|
import pytest
from text_generation import (
InferenceAPIClient,
InferenceAPIAsyncClient,
Client,
AsyncClient,
)
from text_generation.errors import NotSupportedError, NotFoundError
from text_generation.inference_api import check_model_support, deployed_models
def test_check_model_support(flan_t5_xxl, unsupported_model, fake_model):
assert check_model_support(flan_t5_xxl)
assert not check_model_support(unsupported_model)
with pytest.raises(NotFoundError):
check_model_support(fake_model)
def test_deployed_models():
deployed_models()
def test_client(flan_t5_xxl):
client = InferenceAPIClient(flan_t5_xxl)
assert isinstance(client, Client)
def test_client_unsupported_model(unsupported_model):
with pytest.raises(NotSupportedError):
InferenceAPIClient(unsupported_model)
def test_async_client(flan_t5_xxl):
client = InferenceAPIAsyncClient(flan_t5_xxl)
assert isinstance(client, AsyncClient)
def test_async_client_unsupported_model(unsupported_model):
with pytest.raises(NotSupportedError):
InferenceAPIAsyncClient(unsupported_model)
|
text-generation-inference/clients/python/tests/test_inference_api.py/0
|
{
"file_path": "text-generation-inference/clients/python/tests/test_inference_api.py",
"repo_id": "text-generation-inference",
"token_count": 411
}
| 225
|
# Installation from source
<Tip warning={true}>
Installing TGI from source is not the recommended usage. We strongly recommend to use TGI through Docker, check the [Quick Tour](./quicktour), [Installation for Nvidia GPUs](./installation_nvidia) and [Installation for AMD GPUs](./installation_amd) to learn how to use TGI with Docker.
</Tip>
## Install CLI
You can use TGI command-line interface (CLI) to download weights, serve and quantize models, or get information on serving parameters.
To install the CLI, you need to first clone the TGI repository and then run `make`.
```bash
git clone https://github.com/huggingface/text-generation-inference.git && cd text-generation-inference
make install
```
If you would like to serve models with custom kernels, run
```bash
BUILD_EXTENSIONS=True make install
```
## Local Installation from Source
Before you start, you will need to setup your environment, and install Text Generation Inference. Text Generation Inference is tested on **Python 3.9+**.
Text Generation Inference is available on pypi, conda and GitHub.
To install and launch locally, first [install Rust](https://rustup.rs/) and create a Python virtual environment with at least
Python 3.9, e.g. using conda:
```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
conda create -n text-generation-inference python=3.9
conda activate text-generation-inference
```
You may also need to install Protoc.
On Linux:
```bash
PROTOC_ZIP=protoc-21.12-linux-x86_64.zip
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP
sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
sudo unzip -o $PROTOC_ZIP -d /usr/local 'include/*'
rm -f $PROTOC_ZIP
```
On MacOS, using Homebrew:
```bash
brew install protobuf
```
Then run to install Text Generation Inference:
```bash
git clone https://github.com/huggingface/text-generation-inference.git && cd text-generation-inference
BUILD_EXTENSIONS=True make install
```
<Tip warning={true}>
On some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:
```bash
sudo apt-get install libssl-dev gcc -y
```
</Tip>
Once installation is done, simply run:
```bash
make run-falcon-7b-instruct
```
This will serve Falcon 7B Instruct model from the port 8080, which we can query.
|
text-generation-inference/docs/source/installation.md/0
|
{
"file_path": "text-generation-inference/docs/source/installation.md",
"repo_id": "text-generation-inference",
"token_count": 727
}
| 226
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 4,
"prefill": [
{
"id": 100000,
"logprob": null,
"text": "<｜begin▁of▁sentence｜>"
},
{
"id": 3533,
"logprob": -9.625,
"text": "Test"
},
{
"id": 3102,
"logprob": -11.25,
"text": " request"
}
],
"seed": 0,
"tokens": [
{
"id": 2143,
"logprob": -1.828125,
"special": false,
"text": " sent"
},
{
"id": 10081,
"logprob": -0.41210938,
"special": false,
"text": " successfully"
},
{
"id": 13,
"logprob": 0.0,
"special": false,
"text": "."
},
{
"id": 100001,
"logprob": -0.16015625,
"special": true,
"text": "<｜end▁of▁sentence｜>"
}
],
"top_tokens": null
},
"generated_text": "Test request sent successfully."
}
|
text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2_all_params.json/0
|
{
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 618
}
| 227
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 30,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 5235,
"logprob": -10.0625,
"text": "info"
},
{
"id": 29901,
"logprob": -3.2324219,
"text": ":"
},
{
"id": 13260,
"logprob": -10.625,
"text": "dav"
},
{
"id": 333,
"logprob": -0.08276367,
"text": "id"
},
{
"id": 8753,
"logprob": -7.5273438,
"text": "hol"
},
{
"id": 17559,
"logprob": -3.8476562,
"text": "tz"
},
{
"id": 763,
"logprob": -10.140625,
"text": "like"
},
{
"id": 10697,
"logprob": -10.1953125,
"text": "trees"
},
{
"id": 322,
"logprob": -2.5742188,
"text": "and"
},
{
"id": 756,
"logprob": -7.4882812,
"text": "has"
},
{
"id": 1023,
"logprob": -5.0507812,
"text": "two"
},
{
"id": 274,
"logprob": -5.3164062,
"text": "c"
},
{
"id": 1446,
"logprob": -0.6694336,
"text": "ats"
},
{
"id": 29889,
"logprob": -0.9995117,
"text": "."
},
{
"id": 29871,
"logprob": -4.2421875,
"text": ""
}
],
"seed": null,
"tokens": [
{
"id": 6377,
"logprob": -0.14916992,
"special": false,
"text": "{\""
},
{
"id": 29888,
"logprob": -0.13598633,
"special": false,
"text": "f"
},
{
"id": 12935,
"logprob": -0.017669678,
"special": false,
"text": "irs"
},
{
"id": 29873,
"logprob": -0.00085639954,
"special": false,
"text": "t"
},
{
"id": 1170,
"logprob": -0.0054016113,
"special": false,
"text": "Name"
},
{
"id": 4710,
"logprob": -0.13549805,
"special": false,
"text": "\":\""
},
{
"id": 19504,
"logprob": -0.8852539,
"special": false,
"text": "David"
},
{
"id": 3284,
"logprob": -0.16394043,
"special": false,
"text": "\",\""
},
{
"id": 29882,
"logprob": -0.08862305,
"special": false,
"text": "h"
},
{
"id": 711,
"logprob": -0.66259766,
"special": false,
"text": "ob"
},
{
"id": 1609,
"logprob": -5.51939e-05,
"special": false,
"text": "by"
},
{
"id": 4710,
"logprob": -0.23120117,
"special": false,
"text": "\":\""
},
{
"id": 29911,
"logprob": -2.3730469,
"special": false,
"text": "T"
},
{
"id": 11003,
"logprob": -0.032104492,
"special": false,
"text": "rees"
},
{
"id": 3284,
"logprob": -0.22021484,
"special": false,
"text": "\",\""
},
{
"id": 4230,
"logprob": -0.06726074,
"special": false,
"text": "last"
},
{
"id": 1170,
"logprob": -0.003501892,
"special": false,
"text": "Name"
},
{
"id": 4710,
"logprob": -0.0045661926,
"special": false,
"text": "\":\""
},
{
"id": 29950,
"logprob": -0.12512207,
"special": false,
"text": "H"
},
{
"id": 14339,
"logprob": -0.009552002,
"special": false,
"text": "olt"
},
{
"id": 29920,
"logprob": -0.00042438507,
"special": false,
"text": "z"
},
{
"id": 3284,
"logprob": -0.11651611,
"special": false,
"text": "\",\""
},
{
"id": 29876,
"logprob": -0.29736328,
"special": false,
"text": "n"
},
{
"id": 398,
"logprob": -0.003030777,
"special": false,
"text": "um"
},
{
"id": 29907,
"logprob": -0.3774414,
"special": false,
"text": "C"
},
{
"id": 1446,
"logprob": -0.0003130436,
"special": false,
"text": "ats"
},
{
"id": 1115,
"logprob": -0.0021514893,
"special": false,
"text": "\":"
},
{
"id": 29906,
"logprob": -0.071899414,
"special": false,
"text": "2"
},
{
"id": 29913,
"logprob": -0.018997192,
"special": false,
"text": "}"
},
{
"id": 2,
"logprob": 0.0,
"special": true,
"text": "</s>"
}
],
"top_tokens": null
},
"generated_text": "{\"firstName\":\"David\",\"hobby\":\"Trees\",\"lastName\":\"Holtz\",\"numCats\":2}"
}
|
text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_json.json/0
|
{
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_json.json",
"repo_id": "text-generation-inference",
"token_count": 3397
}
| 228
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 2,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 54901,
"logprob": -0.84765625,
"special": false,
"text": "beach"
},
{
"id": 1,
"logprob": -0.008666992,
"special": true,
"text": "<eos>"
}
],
"top_tokens": null
},
"generated_text": "beach"
}
|
text-generation-inference/integration-tests/models/__snapshots__/test_flash_pali_gemma/test_flash_pali_gemma.json/0
|
{
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_pali_gemma/test_flash_pali_gemma.json",
"repo_id": "text-generation-inference",
"token_count": 266
}
| 229
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 2,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 3226,
"logprob": -8.9453125,
"text": " ge"
},
{
"id": 21017,
"logprob": -8.8515625,
"text": "ometric"
},
{
"id": 81,
"logprob": -0.21875,
"text": "_"
},
{
"id": 6009,
"logprob": -1.2773438,
"text": "mean"
},
{
"id": 26,
"logprob": -0.25195312,
"text": "("
},
{
"id": 62,
"logprob": -4.8203125,
"text": "L"
},
{
"id": 44,
"logprob": -3.7734375,
"text": ":"
},
{
"id": 1682,
"logprob": -0.8310547,
"text": " List"
},
{
"id": 77,
"logprob": -0.22766113,
"text": "["
},
{
"id": 1808,
"logprob": -0.46240234,
"text": "float"
},
{
"id": 10794,
"logprob": -3.0234375,
"text": "]):"
}
],
"seed": null,
"tokens": [
{
"id": 284,
"logprob": -0.04626465,
"special": false,
"text": "\n "
},
{
"id": 0,
"logprob": null,
"special": true,
"text": "<|endoftext|>"
}
],
"top_tokens": null
},
"generated_text": "\n "
}
|
text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq.json/0
|
{
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq.json",
"repo_id": "text-generation-inference",
"token_count": 994
}
| 230
|
{
"details": {
"finish_reason": "eos_token",
"generated_tokens": 7,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 1,
"logprob": -0.49658203,
"special": true,
"text": "<s>"
},
{
"id": 28705,
"logprob": -0.0016384125,
"special": false,
"text": " "
},
{
"id": 1,
"logprob": -1.4931641,
"special": true,
"text": "<s>"
},
{
"id": 28705,
"logprob": -0.00075769424,
"special": false,
"text": " "
},
{
"id": 28740,
"logprob": -0.25024414,
"special": false,
"text": "1"
},
{
"id": 28740,
"logprob": -0.2631836,
"special": false,
"text": "1"
},
{
"id": 2,
"logprob": -0.0003285408,
"special": true,
"text": "</s>"
}
]
},
"generated_text": " 11"
}
|
text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_with_dbpedia_adapter.json/0
|
{
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_with_dbpedia_adapter.json",
"repo_id": "text-generation-inference",
"token_count": 611
}
| 231
|
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.7890625,
"text": "Test"
},
{
"id": 2009,
"logprob": -9.625,
"text": "request"
}
],
"seed": 0,
"tokens": [
{
"id": 29899,
"logprob": -1.4980469,
"special": false,
"text": "-"
},
{
"id": 1454,
"logprob": -0.19433594,
"special": false,
"text": "for"
},
{
"id": 29899,
"logprob": 0.0,
"special": false,
"text": "-"
},
{
"id": 9342,
"logprob": 0.0,
"special": false,
"text": "comment"
},
{
"id": 29901,
"logprob": 0.0,
"special": false,
"text": ":"
},
{
"id": 396,
"logprob": -0.27392578,
"special": false,
"text": " #"
},
{
"id": 29906,
"logprob": -0.49389648,
"special": false,
"text": "2"
},
{
"id": 29900,
"logprob": -0.81103516,
"special": false,
"text": "0"
},
{
"id": 29896,
"logprob": 0.0,
"special": false,
"text": "1"
},
{
"id": 29955,
"logprob": -1.0800781,
"special": false,
"text": "7"
}
],
"top_tokens": null
},
"generated_text": "Test request-for-comment: #2017"
}
|
text-generation-inference/integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized_all_params.json/0
|
{
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1034
}
| 232
|
import pytest
@pytest.fixture(scope="module")
def flash_falcon_handle(launcher):
with launcher("tiiuae/falcon-7b", trust_remote_code=True) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_falcon(flash_falcon_handle):
await flash_falcon_handle.health(300)
return flash_falcon_handle.client
@pytest.mark.release
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_falcon(flash_falcon, response_snapshot):
response = await flash_falcon.generate(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_new_tokens=10,
decoder_input_details=True,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.release
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_falcon_all_params(flash_falcon, response_snapshot):
response = await flash_falcon.generate(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
stop_sequences=["test"],
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.release
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_falcon_load(flash_falcon, generate_load, response_snapshot):
responses = await generate_load(
flash_falcon,
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_new_tokens=10,
n=4,
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
|
text-generation-inference/integration-tests/models/test_flash_falcon.py/0
|
{
"file_path": "text-generation-inference/integration-tests/models/test_flash_falcon.py",
"repo_id": "text-generation-inference",
"token_count": 908
}
| 233
|
import pytest
import base64
@pytest.fixture(scope="module")
def flash_pali_gemma_handle(launcher):
with launcher(
"google/paligemma-3b-pt-224",
num_shard=1,
revision="float16",
max_input_length=4000,
max_total_tokens=4096,
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_pali_gemma(flash_pali_gemma_handle):
await flash_pali_gemma_handle.health(300)
return flash_pali_gemma_handle.client
def get_chicken():
with open("integration-tests/images/chicken_on_money.png", "rb") as image_file:
encoded_string = base64.b64encode(image_file.read())
return f"data:image/png;base64,{encoded_string.decode('utf-8')}"
def get_cow_beach():
with open("integration-tests/images/cow_beach.png", "rb") as image_file:
encoded_string = base64.b64encode(image_file.read())
return f"data:image/png;base64,{encoded_string.decode('utf-8')}"
@pytest.mark.release
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_pali_gemma(flash_pali_gemma, response_snapshot):
cow = get_cow_beach()
inputs = f"Where is the cow standing?\n"
response = await flash_pali_gemma.generate(inputs, max_new_tokens=20)
assert response.generated_text == "beach"
assert response == response_snapshot
@pytest.mark.release
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_pali_gemma_two_images(flash_pali_gemma, response_snapshot):
chicken = get_chicken()
cow_beach = get_cow_beach()
response = await flash_pali_gemma.generate(
f"caption\n",
max_new_tokens=20,
)
# Is PaliGemma not able to handle two separate images? At least we
# get output showing that both images are used.
assert (
response.generated_text == "image result for chicken on the beach"
), f"{repr(response.generated_text)}"
assert response == response_snapshot
|
text-generation-inference/integration-tests/models/test_flash_pali_gemma.py/0
|
{
"file_path": "text-generation-inference/integration-tests/models/test_flash_pali_gemma.py",
"repo_id": "text-generation-inference",
"token_count": 801
}
| 234
|
import pytest
@pytest.fixture(scope="module")
def neox_handle(launcher):
with launcher(
"stabilityai/stablelm-tuned-alpha-3b", num_shard=1, use_flash_attention=False
) as handle:
yield handle
@pytest.fixture(scope="module")
async def neox(neox_handle):
await neox_handle.health(300)
return neox_handle.client
@pytest.mark.release
@pytest.mark.skip
@pytest.mark.asyncio
async def test_neox(neox, response_snapshot):
response = await neox.generate(
"<|USER|>What's your mood today?<|ASSISTANT|>",
max_new_tokens=10,
decoder_input_details=True,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.release
@pytest.mark.skip
@pytest.mark.asyncio
async def test_neox_load(neox, generate_load, response_snapshot):
responses = await generate_load(
neox,
"<|USER|>What's your mood today?<|ASSISTANT|>",
max_new_tokens=10,
n=4,
)
generated_texts = [r.generated_text for r in responses]
assert len(generated_texts) == 4
assert generated_texts, all(
[text == generated_texts[0] for text in generated_texts]
)
assert responses == response_snapshot
|
text-generation-inference/integration-tests/models/test_neox.py/0
|
{
"file_path": "text-generation-inference/integration-tests/models/test_neox.py",
"repo_id": "text-generation-inference",
"token_count": 515
}
| 235
|
import json
import datasets
import tqdm
def main():
dataset = datasets.load_dataset("Open-Orca/OpenOrca", split="train")
# Select only the first 2k conversations that start with a human.
max = min(2000, len(dataset))
conversations = []
for item in tqdm.tqdm(dataset, total=max):
conversation = {
"conversations": [
{"from": "human", "value": item["question"]},
],
"id": item["id"],
}
conversations.append(conversation)
if len(conversations) >= max:
break
with open("./small.json", "w") as f:
json.dump(conversations, f, indent=4)
if __name__ == "__main__":
main()
|
text-generation-inference/load_tests/orca.py/0
|
{
"file_path": "text-generation-inference/load_tests/orca.py",
"repo_id": "text-generation-inference",
"token_count": 313
}
| 236
|
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