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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import math
from collections import OrderedDict
from collections.abc import Iterable, Mapping, Sequence
from typing import Literal, Optional, TypedDict, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from transformers import BartTokenizer, BatchFeature, PretrainedConfig
from vllm.config import VllmConfig
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.bart import (BartDecoder, BartEncoder,
BartParallelLMHead,
BartScaledWordEmbedding)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
MultiModalKwargs)
from vllm.multimodal.parse import MultiModalDataItems
from vllm.multimodal.processing import (BaseProcessingInfo,
EncDecMultiModalProcessor,
PromptIndexTargets, PromptInsertion,
PromptUpdate)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from .interfaces import (MultiModalEmbeddings, SupportsMultiModal,
SupportsV0Only)
from .utils import AutoWeightsLoader, flatten_bn, merge_multimodal_embeddings
class Florence2ImagePixelInputs(TypedDict):
type: Literal["pixel_values"]
data: torch.Tensor
"""Shape: (batch_size, num_channel, height, width)"""
# ViT implementation are all copied from
# https://huggingface.co/microsoft/Florence-2-base/blob/main/modeling_florence2.py
class LearnedAbsolutePositionEmbedding2D(nn.Module):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, embedding_dim=256, num_pos=50):
super().__init__()
self.row_embeddings = nn.Embedding(num_pos, embedding_dim // 2)
self.column_embeddings = nn.Embedding(
num_pos, embedding_dim - (embedding_dim // 2))
def forward(self, pixel_values):
"""
pixel_values: (batch_size, height, width, num_channels)
returns: (batch_size, height, width, embedding_dim * 2)
"""
if len(pixel_values.shape) != 4:
raise ValueError('pixel_values must be a 4D tensor')
height, width = pixel_values.shape[1:3]
width_values = torch.arange(width, device=pixel_values.device)
height_values = torch.arange(height, device=pixel_values.device)
x_emb = self.column_embeddings(width_values)
y_emb = self.row_embeddings(height_values)
# (height, width, embedding_dim * 2)
pos = torch.cat([
x_emb.unsqueeze(0).repeat(height, 1, 1),
y_emb.unsqueeze(1).repeat(1, width, 1)
],
dim=-1)
# (embedding_dim * 2, height, width)
pos = pos.permute(2, 0, 1)
pos = pos.unsqueeze(0)
# (batch_size, embedding_dim * 2, height, width)
pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
# (batch_size, height, width, embedding_dim * 2)
pos = pos.permute(0, 2, 3, 1)
return pos
class PositionalEmbeddingCosine1D(nn.Module):
"""
This class implements a very simple positional encoding. It follows closely
the encoder from the link below:
https://pytorch.org/tutorials/beginner/translation_transformer.html
Args:
embed_dim: The dimension of the embeddings.
dropout_prob: The dropout probability.
max_seq_len: The maximum length to precompute the positional encodings.
"""
def __init__(self, embed_dim: int = 512, max_seq_len: int = 1024) -> None:
super().__init__()
self.embed_dim = embed_dim
self.max_seq_len = max_seq_len
# Generate the sinusoidal arrays.
factor = math.log(10000)
denominator = torch.exp(-factor * torch.arange(0, self.embed_dim, 2) /
self.embed_dim)
# Matrix where rows correspond to a positional embedding as a function
# of the position index (i.e., the row index).
frequencies = \
torch.arange(0, self.max_seq_len) \
.reshape(self.max_seq_len, 1) * denominator
pos_idx_to_embed = torch.zeros((self.max_seq_len, self.embed_dim))
# Populate uneven entries.
pos_idx_to_embed[:, 0::2] = torch.sin(frequencies)
pos_idx_to_embed[:, 1::2] = torch.cos(frequencies)
# Save the positional embeddings in a constant buffer.
# self.register_buffer("pos_idx_to_embed", pos_idx_to_embed)
self.pos_idx_to_embed = nn.Parameter(pos_idx_to_embed,
requires_grad=False)
def forward(self, seq_embeds: torch.Tensor) -> torch.Tensor:
"""
Args:
seq_embeds: The sequence embeddings in order. Allowed size:
1. [T, D], where T is the length of the sequence, and D is the
frame embedding dimension.
2. [B, T, D], where B is the batch size and T and D are the
same as above.
Returns a tensor of with the same dimensions as the input: i.e.,
[1, T, D] or [T, D].
"""
shape_len = len(seq_embeds.shape)
assert 2 <= shape_len <= 3
len_seq = seq_embeds.size(-2)
assert len_seq <= self.max_seq_len
pos_embeds = self.pos_idx_to_embed[0:seq_embeds.size(-2), :]
# Adapt pre-computed positional embeddings to the input.
if shape_len == 3:
pos_embeds = pos_embeds.view(
(1, pos_embeds.size(0), pos_embeds.size(1)))
return pos_embeds
class MySequential(nn.Sequential):
def forward(self, *inputs):
for module in self._modules.values():
if isinstance(inputs, tuple):
inputs = module(*inputs)
else:
inputs = module(inputs)
return inputs
class PreNorm(nn.Module):
def __init__(self, norm, fn):
super().__init__()
self.norm = norm
self.fn = fn
def forward(self, x, *args, **kwargs):
shortcut = x
if self.norm is not None:
x, size = self.fn(self.norm(x), *args, **kwargs)
else:
x, size = self.fn(x, *args, **kwargs)
x = shortcut + x
return x, size
class Mlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.net = nn.Sequential(
OrderedDict([("fc1", nn.Linear(in_features, hidden_features)),
("act", act_layer()),
("fc2", nn.Linear(hidden_features, out_features))]))
def forward(self, x, size):
return self.net(x), size
class DepthWiseConv2d(nn.Module):
def __init__(
self,
dim_in,
kernel_size,
padding,
stride,
bias=True,
):
super().__init__()
self.dw = nn.Conv2d(dim_in,
dim_in,
kernel_size=kernel_size,
padding=padding,
groups=dim_in,
stride=stride,
bias=bias)
def forward(self, x, size):
B, N, C = x.shape
H, W = size
assert N == H * W
x = self.dw(x.transpose(1, 2).view(B, C, H, W))
size = (x.size(-2), x.size(-1))
x = x.flatten(2).transpose(1, 2)
return x, size
class ConvEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self,
patch_size=7,
in_chans=3,
embed_dim=64,
stride=4,
padding=2,
norm_layer=None,
pre_norm=True):
super().__init__()
self.patch_size = patch_size
self.proj = nn.Conv2d(in_chans,
embed_dim,
kernel_size=patch_size,
stride=stride,
padding=padding)
dim_norm = in_chans if pre_norm else embed_dim
self.norm = norm_layer(dim_norm) if norm_layer else None
self.pre_norm = pre_norm
def forward(self, x, size):
H, W = size
if len(x.size()) == 3:
if self.norm and self.pre_norm:
x = self.norm(x)
x = rearrange(x, 'b (h w) c -> b c h w', h=H, w=W)
x = self.proj(x)
_, _, H, W = x.shape
x = rearrange(x, 'b c h w -> b (h w) c')
if self.norm and not self.pre_norm:
x = self.norm(x)
return x, (H, W)
class ChannelAttention(nn.Module):
def __init__(self, dim, groups=8, qkv_bias=True):
super().__init__()
self.groups = groups
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
def forward(self, x, size):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.groups,
C // self.groups).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * (float(N)**-0.5)
attention = q.transpose(-1, -2) @ k
attention = attention.softmax(dim=-1)
x = (attention @ v.transpose(-1, -2)).transpose(-1, -2)
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
return x, size
class ChannelBlock(nn.Module):
def __init__(self,
dim,
groups,
mlp_ratio=4.,
qkv_bias=True,
drop_path_rate=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
conv_at_attn=True,
conv_at_ffn=True):
super().__init__()
self.conv1 = PreNorm(None, DepthWiseConv2d(
dim, 3, 1, 1)) if conv_at_attn else None
self.channel_attn = PreNorm(
norm_layer(dim),
ChannelAttention(dim, groups=groups, qkv_bias=qkv_bias),
)
self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1,
1)) if conv_at_ffn else None
self.ffn = PreNorm(
norm_layer(dim),
Mlp(in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer),
)
def forward(self, x, size):
if self.conv1:
x, size = self.conv1(x, size)
x, size = self.channel_attn(x, size)
if self.conv2:
x, size = self.conv2(x, size)
x, size = self.ffn(x, size)
return x, size
def window_partition(x, window_size: int):
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size,
C)
windows = x.permute(0, 1, 3, 2, 4,
5).contiguous().view(-1, window_size, window_size, C)
return windows
def window_reverse(windows, batch_size: int, window_size: int, H: int, W: int):
B = batch_size
x = windows.view(B, H // window_size, W // window_size, window_size,
window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
class WindowAttention(nn.Module):
def __init__(self, dim, num_heads, window_size, qkv_bias=True):
super().__init__()
self.dim = dim
self.window_size = window_size
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = float(head_dim)**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, size):
H, W = size
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
x = x.view(B, H, W, C)
pad_l = pad_t = 0
pad_r = (self.window_size - W % self.window_size) % self.window_size
pad_b = (self.window_size - H % self.window_size) % self.window_size
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
_, Hp, Wp, _ = x.shape
x = window_partition(x, self.window_size)
x = x.view(-1, self.window_size * self.window_size, C)
# W-MSA/SW-MSA
# attn_windows = self.attn(x_windows)
B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
attn = self.softmax(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
# merge windows
x = x.view(-1, self.window_size, self.window_size, C)
x = window_reverse(x, B, self.window_size, Hp, Wp)
if pad_r > 0 or pad_b > 0:
x = x[:, :H, :W, :].contiguous()
x = x.view(B, H * W, C)
return x, size
class SpatialBlock(nn.Module):
def __init__(self,
dim,
num_heads,
window_size,
mlp_ratio=4.,
qkv_bias=True,
drop_path_rate=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
conv_at_attn=True,
conv_at_ffn=True):
super().__init__()
self.conv1 = PreNorm(None, DepthWiseConv2d(
dim, 3, 1, 1)) if conv_at_attn else None
self.window_attn = PreNorm(
norm_layer(dim),
WindowAttention(dim, num_heads, window_size, qkv_bias=qkv_bias),
)
self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1,
1)) if conv_at_ffn else None
self.ffn = PreNorm(
norm_layer(dim),
Mlp(in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer),
)
def forward(self, x, size):
if self.conv1:
x, size = self.conv1(x, size)
x, size = self.window_attn(x, size)
if self.conv2:
x, size = self.conv2(x, size)
x, size = self.ffn(x, size)
return x, size
class DaViT(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
depths=(1, 1, 3, 1),
patch_size=(7, 2, 2, 2),
patch_stride=(4, 2, 2, 2),
patch_padding=(3, 0, 0, 0),
patch_prenorm=(False, False, False, False),
embed_dims=(64, 128, 192, 256),
num_heads=(3, 6, 12, 24),
num_groups=(3, 6, 12, 24),
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
enable_checkpoint=False,
conv_at_attn=True,
conv_at_ffn=True,
):
super().__init__()
self.num_classes = num_classes
self.embed_dims = embed_dims
self.num_heads = num_heads
self.num_groups = num_groups
self.num_stages = len(self.embed_dims)
self.enable_checkpoint = enable_checkpoint
assert self.num_stages == len(self.num_heads) == len(self.num_groups)
num_stages = len(embed_dims)
dpr = [
x.item() for x in torch.linspace(0, drop_path_rate,
sum(depths) * 2)
]
depth_offset = 0
convs = []
blocks = []
for i in range(num_stages):
conv_embed = ConvEmbed(
patch_size=patch_size[i],
stride=patch_stride[i],
padding=patch_padding[i],
in_chans=in_chans if i == 0 else self.embed_dims[i - 1],
embed_dim=self.embed_dims[i],
norm_layer=norm_layer,
pre_norm=patch_prenorm[i])
convs.append(conv_embed)
block = MySequential(*[
MySequential(
OrderedDict([('spatial_block',
SpatialBlock(
embed_dims[i],
num_heads[i],
window_size,
drop_path_rate=dpr[depth_offset + j * 2],
qkv_bias=qkv_bias,
mlp_ratio=mlp_ratio,
conv_at_attn=conv_at_attn,
conv_at_ffn=conv_at_ffn,
)),
('channel_block',
ChannelBlock(
embed_dims[i],
num_groups[i],
drop_path_rate=dpr[depth_offset + j * 2 +
1],
qkv_bias=qkv_bias,
mlp_ratio=mlp_ratio,
conv_at_attn=conv_at_attn,
conv_at_ffn=conv_at_ffn,
))])) for j in range(depths[i])
])
blocks.append(block)
depth_offset += depths[i] * 2
self.convs = nn.ModuleList(convs)
self.blocks = nn.ModuleList(blocks)
self.avgpool = nn.AdaptiveAvgPool1d(1)
@property
def dim_out(self):
return self.embed_dims[-1]
def forward_features_unpool(self, x):
"""
forward until avg pooling
Args:
x (_type_): input image tensor
"""
input_size = (x.size(2), x.size(3))
for conv, block in zip(self.convs, self.blocks):
x, input_size = conv(x, input_size)
x, input_size = block(x, input_size)
return x
def forward_features(self, x):
x = self.forward_features_unpool(x)
# (batch_size, num_tokens, token_dim)
x = self.avgpool(x.transpose(1, 2))
# (batch_size, 1, num_tokens)
x = torch.flatten(x, 1)
x = self.norms(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
@classmethod
def from_config(cls, config):
return cls(
depths=config.depths,
embed_dims=config.dim_embed,
num_heads=config.num_heads,
num_groups=config.num_groups,
patch_size=config.patch_size,
patch_stride=config.patch_stride,
patch_padding=config.patch_padding,
patch_prenorm=config.patch_prenorm,
drop_path_rate=config.drop_path_rate,
window_size=config.window_size,
)
# Language backbone and processor implementation
class Florence2LanguageModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.vocab_size = config.vocab_size
self.shared = BartScaledWordEmbedding(self.vocab_size, config.d_model)
self.encoder = BartEncoder(config,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.encoder")
self.decoder = BartDecoder(config,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.decoder")
if self.config.tie_word_embeddings:
self.encoder.embed_tokens.weight = self.shared.weight
self.decoder.embed_tokens.weight = self.shared.weight
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
encoder_input_ids: torch.Tensor,
encoder_positions: torch.Tensor,
inputs_embeds: Optional[torch.Tensor] = None,
) -> torch.Tensor:
r"""
Args:
input_ids
Indices of *decoder* input sequence tokens in the vocabulary.
Padding will be ignored by default should you
provide it.
positions
Positions of *decoder* input sequence tokens.
encoder_input_ids
Indices of *encoder* input sequence tokens in the vocabulary.
encoder_positions:
Positions of *encoder* input sequence tokens.
Returns:
Model output torch.Tensor
"""
encoder_hidden_states = None
if inputs_embeds is not None or encoder_input_ids.numel() > 0:
# Run encoder attention if a non-zero number of encoder tokens
# are provided as input
encoder_hidden_states = self.encoder(input_ids=encoder_input_ids,
positions=encoder_positions,
inputs_embeds=inputs_embeds)
# decoder outputs consists of
# (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
decoder_input_ids=input_ids,
decoder_positions=positions,
encoder_hidden_states=encoder_hidden_states)
return decoder_outputs
class Florence2LanguageForConditionalGeneration(nn.Module, SupportsV0Only):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
self.config = config
self.model = Florence2LanguageModel(vllm_config=vllm_config,
prefix=f"{prefix}.model")
embed_scale = math.sqrt(
config.d_model) if config.scale_embedding else 1.0
self.vocab_size = config.vocab_size
self.lm_head = BartParallelLMHead(self.vocab_size,
config.d_model,
embed_scale=embed_scale)
self.logits_processor = LogitsProcessor(self.vocab_size,
config.vocab_size)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
encoder_input_ids: torch.Tensor,
encoder_positions: torch.Tensor,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
r"""
Args:
input_ids
torch.Tensor of *decoder* input token ids.
positions
torch.Tensor of *decoder* position indices.
encoder_input_ids
torch.Tensor of *encoder* input token ids.
encoder_positions
torch.Tensor of *encoder* position indices
Returns:
Output torch.Tensor
"""
return self.model(input_ids,
positions,
encoder_input_ids,
encoder_positions,
inputs_embeds=inputs_embeds)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.encoder.embed_tokens(input_ids)
def compute_logits(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[torch.Tensor]:
logits = self.logits_processor(self.lm_head, hidden_states,
sampling_metadata)
return logits
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("qkv_proj", "q_proj", "q"),
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
]
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
for (param_name, weight_name, shard_id) in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
if "final_logits_bias" in name:
continue
if self.config.tie_word_embeddings and "embed_tokens" in name:
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
class Florence2ProcessingInfo(BaseProcessingInfo):
def get_hf_config(self):
return self.ctx.get_hf_config()
def get_hf_processor(self):
return self.ctx.get_hf_processor()
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
return {"image": 1}
def get_num_image_tokens(self) -> int:
processor_config = self.ctx.get_hf_image_processor_config()
return processor_config["image_seq_length"]
class Florence2DummyInputsBuilder(
BaseDummyInputsBuilder[Florence2ProcessingInfo]):
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
return ""
def get_dummy_mm_data(
self,
seq_len: int,
mm_counts: Mapping[str, int],
) -> MultiModalDataDict:
num_images = mm_counts.get("image", 0)
target_width = target_height = self.info.get_hf_config().projection_dim
return {
"image":
self._get_dummy_images(width=target_width,
height=target_height,
num_images=num_images)
}
class Florence2MultiModalProcessor(
EncDecMultiModalProcessor[Florence2ProcessingInfo]):
def _hf_processor_applies_updates(
self,
prompt_text: str,
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
) -> bool:
return False
def create_encoder_prompt(
self,
prompt: Union[str, list[int]],
mm_data: MultiModalDataDict,
) -> Union[str, list[int]]:
return prompt
def create_decoder_prompt(
self,
prompt: Union[str, list[int]],
mm_data: MultiModalDataDict,
) -> Union[str, list[int]]:
return [self.info.get_hf_config().eos_token_id]
def _apply_hf_processor_tokens_only(
self,
prompt_tokens: list[int],
) -> list[int]:
hf_processor = self.info.get_hf_processor()
tokenizer: BartTokenizer = hf_processor.tokenizer
prompt_text = tokenizer.decode(prompt_tokens)
# convert task tokens to prompt
prompt_text = hf_processor._construct_prompts([prompt_text])[0]
prompt_tokens = tokenizer.encode(prompt_text, add_special_tokens=False)
return prompt_tokens
def _call_hf_processor(
self,
prompt: str,
mm_data: Mapping[str, object],
mm_kwargs: Mapping[str, object],
) -> BatchFeature:
if mm_data:
processed_outputs = super()._call_hf_processor(
prompt, mm_data, mm_kwargs)
else:
hf_processor = self.info.get_hf_processor()
tokenizer = hf_processor.tokenizer
prompt = hf_processor._construct_prompts([prompt])[0]
processed_outputs = tokenizer(prompt,
add_special_tokens=True,
return_tensors="pt")
return processed_outputs
def _get_mm_fields_config(
self,
hf_inputs: BatchFeature,
hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
return dict(pixel_values=MultiModalFieldConfig.batched("image"))
def _get_prompt_updates(
self,
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]:
hf_config = self.info.get_hf_config()
pad_token_id = hf_config.pad_token_id
num_image_tokens = self.info.get_num_image_tokens()
image_tokens = [pad_token_id] * num_image_tokens
return [
PromptInsertion(
modality="image",
target=PromptIndexTargets.start(),
insertion=image_tokens,
)
]
@MULTIMODAL_REGISTRY.register_processor(
Florence2MultiModalProcessor,
info=Florence2ProcessingInfo,
dummy_inputs=Florence2DummyInputsBuilder)
class Florence2ForConditionalGeneration(nn.Module, SupportsMultiModal,
SupportsV0Only):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
processor_config = vllm_config.model_config.hf_image_processor_config
self.config = config
self.vision_config = config.vision_config
self.processor_config = processor_config
assert config.vision_config.model_type == 'davit', (
'only DaViT is supported for now')
self.vision_tower = DaViT.from_config(config=config.vision_config)
self._build_image_projection_layers(config)
self.language_model = Florence2LanguageForConditionalGeneration(
vllm_config=vllm_config.with_hf_config(config.text_config),
prefix=f"{prefix}.language_model",
)
self.pad_token_id = config.pad_token_id
def _build_image_projection_layers(self, config: PretrainedConfig):
image_dim_out = config.vision_config.dim_embed[-1]
dim_projection = config.vision_config.projection_dim
self.image_projection = nn.Parameter(
torch.empty(image_dim_out, dim_projection))
self.image_proj_norm = nn.LayerNorm(dim_projection)
image_pos_embed_config = config.vision_config.image_pos_embed
if image_pos_embed_config['type'] == 'learned_abs_2d':
self.image_pos_embed = LearnedAbsolutePositionEmbedding2D(
embedding_dim=image_dim_out,
num_pos=image_pos_embed_config['max_pos_embeddings'])
else:
raise NotImplementedError("Florence2 only supports learned_abs_2d "
"as image position embedding.")
self.image_feature_source = config.vision_config.image_feature_source
# temporal embedding
visual_temporal_embedding_config = (
self.vision_config.visual_temporal_embedding)
if visual_temporal_embedding_config['type'] == 'COSINE':
self.visual_temporal_embed = PositionalEmbeddingCosine1D(
embed_dim=image_dim_out,
max_seq_len=visual_temporal_embedding_config[
'max_temporal_embeddings'])
else:
raise NotImplementedError(
'Florence2 only supports COSINE as temporal embedding.')
def _validate_pixel_values(
self, data: Union[torch.Tensor, list[torch.Tensor]]
) -> Union[torch.Tensor, list[torch.Tensor]]:
size = self.processor_config["size"]
h, w = size["height"], size["width"]
expected_dims = (3, h, w)
def _validate_shape(d: torch.Tensor):
actual_dims = tuple(d.shape)
if actual_dims != expected_dims:
expected_expr = tuple(*map(str, expected_dims))
raise ValueError(
"The expected shape of pixel values per batch "
f"is {expected_expr}. You supplied {tuple(d.shape)}.")
for d in data:
_validate_shape(d)
return data
def _parse_and_validate_image_input(self, **kwargs: object):
pixel_values: Optional[Union[list[list[torch.Tensor]],
list[torch.Tensor],
torch.Tensor]] = kwargs.pop(
"pixel_values", None)
image_embeds: Optional[Union[list[list[torch.Tensor]],
list[torch.Tensor],
torch.Tensor]] = kwargs.pop(
"image_embeds", None)
if pixel_values is None and image_embeds is None:
return None
if pixel_values is not None and image_embeds is not None:
raise ValueError(
"Both pixel values and image embeds are provided.")
if pixel_values is not None:
return Florence2ImagePixelInputs(
type="pixel_values",
data=self._validate_pixel_values(
flatten_bn(pixel_values, concat=True)),
)
if image_embeds is not None:
raise NotImplementedError
raise AssertionError("This line should be unreachable.")
def _encode_image(self, pixel_values: torch.Tensor) -> torch.Tensor:
dtype = next(self.vision_tower.parameters()).dtype
pixel_values = pixel_values.to(dtype)
batch_size, T = pixel_values.size(0), 1
x = self.vision_tower.forward_features_unpool(pixel_values)
if self.image_pos_embed is not None:
x = x.view(batch_size * T, -1, x.shape[-1])
num_tokens = x.shape[-2]
h, w = int(num_tokens**0.5), int(num_tokens**0.5)
assert h * w == num_tokens, (
'only support square feature maps for now')
x = x.view(batch_size * T, h, w, x.shape[-1])
pos_embed = self.image_pos_embed(x)
x = x + pos_embed
x = x.view(batch_size, T * h * w, x.shape[-1])
if self.visual_temporal_embed is not None:
visual_temporal_embed = self.visual_temporal_embed(
x.view(batch_size, T, -1, x.shape[-1])[:, :, 0])
x = x.view(batch_size, T, -1,
x.shape[-1]) + visual_temporal_embed.view(
1, T, 1, x.shape[-1])
x_feat_dict = {}
spatial_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=2)
x_feat_dict['spatial_avg_pool'] = spatial_avg_pool_x
temporal_avg_pool_x = x.view(batch_size, T, -1,
x.shape[-1]).mean(dim=1)
x_feat_dict['temporal_avg_pool'] = temporal_avg_pool_x
x = x.view(batch_size, T, -1, x.shape[-1])[:, -1]
x_feat_dict['last_frame'] = x
new_x = []
for _image_feature_source in self.image_feature_source:
if _image_feature_source not in x_feat_dict:
raise ValueError('invalid image feature source: {}'.format(
_image_feature_source))
new_x.append(x_feat_dict[_image_feature_source])
x = torch.cat(new_x, dim=1)
x = x @ self.image_projection
x = self.image_proj_norm(x)
return x
def _process_image_input(
self, image_input: Florence2ImagePixelInputs) -> torch.Tensor:
assert image_input["type"] == "pixel_values"
pixel_values = image_input["data"]
return self._encode_image(pixel_values)
def get_language_model(self) -> torch.nn.Module:
return self.language_model
def get_multimodal_embeddings(
self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
image_input = self._parse_and_validate_image_input(**kwargs)
if image_input is None:
return None
vision_embeddings = self._process_image_input(image_input)
return vision_embeddings
def get_input_embeddings(
self,
input_ids: torch.Tensor,
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
) -> torch.Tensor:
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
if multimodal_embeddings is not None:
inputs_embeds = merge_multimodal_embeddings(
input_ids, inputs_embeds, multimodal_embeddings,
self.pad_token_id)
return inputs_embeds
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
*,
encoder_input_ids: torch.Tensor,
encoder_positions: torch.Tensor,
**kwargs,
) -> torch.Tensor:
r"""
Args:
input_ids
torch.Tensor of *decoder* input token ids.
positions
torch.Tensor of *decoder* position indices.
encoder_input_ids
torch.Tensor of *encoder* input token ids.
encoder_positions
torch.Tensor of *encoder* position indices
Returns:
Output torch.Tensor
"""
vision_embeddings = self.get_multimodal_embeddings(**kwargs)
if encoder_input_ids.numel() > 0 or vision_embeddings is not None:
inputs_embeds = self.get_input_embeddings(encoder_input_ids,
vision_embeddings)
else:
inputs_embeds = None
hidden_states = self.language_model(input_ids,
positions,
encoder_input_ids,
encoder_positions,
inputs_embeds=inputs_embeds)
return hidden_states
def compute_logits(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[torch.Tensor]:
return self.language_model.compute_logits(hidden_states,
sampling_metadata)
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(self)
return loader.load_weights(weights)
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