722 lines
25 KiB
Python
722 lines
25 KiB
Python
from functools import cached_property
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from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
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TypedDict, Union)
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import torch
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import torch.nn as nn
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from transformers import (BatchFeature, Blip2Config, Blip2QFormerConfig,
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apply_chunking_to_forward)
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, VllmConfig
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
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MultiModalInputsV2, MultiModalKwargs,
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NestedTensors, PlaceholderRange)
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from vllm.multimodal.parse import MultiModalDataItems
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo, PromptReplacement)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
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from vllm.sequence import IntermediateTensors
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from .blip import BlipVisionModel
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from .interfaces import SupportsMultiModal, SupportsPP
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from .utils import (AutoWeightsLoader, init_vllm_registered_model,
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maybe_prefix, merge_multimodal_embeddings)
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# We use this internally as placeholders since there is no image token
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# defined on the HuggingFace repo
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_IMAGE_TOKEN_ID = 50265
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class Blip2ImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: torch.Tensor
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"""Shape: `(batch_size * num_images, num_channels, height, width)`"""
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class Blip2ImageEmbeddingInputs(TypedDict):
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type: Literal["image_embeds"]
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data: torch.Tensor
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"""Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
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`hidden_size` must match the hidden size of language model backbone.
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"""
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Blip2ImageInputs = Union[Blip2ImagePixelInputs, Blip2ImageEmbeddingInputs]
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class Blip2QFormerMultiHeadAttention(nn.Module):
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def __init__(
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self,
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config: Blip2QFormerConfig,
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*,
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quant_config: Optional[QuantizationConfig],
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cache_config: Optional[CacheConfig],
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is_cross_attention: bool = False,
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) -> None:
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super().__init__()
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self.config = config
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if config.hidden_size % config.num_attention_heads != 0:
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of "
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f"the number of attention heads ({config.num_attention_heads})"
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = (config.hidden_size //
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config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.scaling = self.attention_head_size**-0.5
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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if is_cross_attention:
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kv_hidden_size = config.encoder_hidden_size
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else:
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kv_hidden_size = config.hidden_size
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self.key = nn.Linear(kv_hidden_size, self.all_head_size)
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self.value = nn.Linear(kv_hidden_size, self.all_head_size)
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self.position_embedding_type = getattr(config,
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"position_embedding_type",
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"absolute")
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if self.position_embedding_type != "absolute":
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raise NotImplementedError("Unsupported position_embedding_type: "
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f"{self.position_embedding_type}")
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x):
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x = x.view(*x.size()[:-1], self.num_attention_heads,
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self.attention_head_size)
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return x.permute(0, 2, 1, 3)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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):
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is_cross_attention = encoder_hidden_states is not None
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if is_cross_attention:
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key_layer = self.transpose_for_scores(
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self.key(encoder_hidden_states))
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value_layer = self.transpose_for_scores(
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self.value(encoder_hidden_states))
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else:
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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mixed_query_layer = self.query(hidden_states)
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query_layer = self.transpose_for_scores(mixed_query_layer)
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attention_scores = torch.matmul(query_layer,
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key_layer.transpose(-1, -2))
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attention_probs = torch.softmax(attention_scores * self.scaling,
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dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs_dropped = self.dropout(attention_probs)
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context_layer = torch.matmul(attention_probs_dropped, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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context_layer = context_layer.view(*context_layer.size()[:-2],
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self.all_head_size)
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return context_layer
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class Blip2QFormerSelfOutput(nn.Module):
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def __init__(self, config: Blip2QFormerConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(
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self,
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hidden_states: torch.Tensor,
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input_tensor: torch.Tensor,
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) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class Blip2QFormerAttention(nn.Module):
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def __init__(
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self,
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config: Blip2QFormerConfig,
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*,
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quant_config: Optional[QuantizationConfig],
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cache_config: Optional[CacheConfig],
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is_cross_attention: bool = False,
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) -> None:
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super().__init__()
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self.attention = Blip2QFormerMultiHeadAttention(
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config,
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quant_config=quant_config,
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cache_config=cache_config,
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is_cross_attention=is_cross_attention,
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)
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self.output = Blip2QFormerSelfOutput(config)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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) -> Tuple[torch.Tensor]:
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self_output = self.attention(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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)
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attention_output = self.output(self_output, hidden_states)
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return attention_output
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class Blip2QFormerIntermediate(nn.Module):
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def __init__(self, config: Blip2QFormerConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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self.intermediate_act_fn = get_act_fn(config.hidden_act)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class Blip2QFormerOutput(nn.Module):
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def __init__(self, config: Blip2QFormerConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(
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self,
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hidden_states: torch.Tensor,
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input_tensor: torch.Tensor,
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) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class Blip2QFormerLayer(nn.Module):
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def __init__(
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self,
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config: Blip2QFormerConfig,
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*,
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quant_config: Optional[QuantizationConfig],
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cache_config: Optional[CacheConfig],
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layer_idx: int,
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) -> None:
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = Blip2QFormerAttention(config,
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quant_config=quant_config,
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cache_config=cache_config)
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self.layer_idx = layer_idx
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if layer_idx % config.cross_attention_frequency == 0:
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self.crossattention = Blip2QFormerAttention(
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config,
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quant_config=quant_config,
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cache_config=cache_config,
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is_cross_attention=True)
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self.has_cross_attention = True
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else:
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self.has_cross_attention = False
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self.intermediate_query = Blip2QFormerIntermediate(config)
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self.output_query = Blip2QFormerOutput(config)
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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encoder_hidden_states: torch.FloatTensor,
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query_length: int,
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):
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attention_output = self.attention(hidden_states)
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if query_length > 0:
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query_attention_output = attention_output[:, :query_length, :]
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if self.has_cross_attention:
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query_attention_output = self.crossattention(
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query_attention_output,
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encoder_hidden_states=encoder_hidden_states,
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)
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layer_output = apply_chunking_to_forward(
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self.feed_forward_chunk_query,
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self.chunk_size_feed_forward,
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self.seq_len_dim,
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query_attention_output,
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)
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if attention_output.shape[1] > query_length:
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layer_output_text = apply_chunking_to_forward(
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self.feed_forward_chunk,
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self.chunk_size_feed_forward,
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self.seq_len_dim,
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attention_output[:, query_length:, :],
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)
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layer_output = torch.cat([layer_output, layer_output_text],
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dim=1)
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else:
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layer_output = apply_chunking_to_forward(
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self.feed_forward_chunk,
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self.chunk_size_feed_forward,
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self.seq_len_dim,
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attention_output,
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)
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return layer_output
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def feed_forward_chunk(self,
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attention_output: torch.Tensor) -> torch.Tensor:
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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return layer_output
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def feed_forward_chunk_query(
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self, attention_output: torch.Tensor) -> torch.Tensor:
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intermediate_output = self.intermediate_query(attention_output)
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layer_output = self.output_query(intermediate_output, attention_output)
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return layer_output
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class Blip2QFormerEncoder(nn.Module):
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def __init__(
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self,
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config: Blip2QFormerConfig,
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*,
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quant_config: Optional[QuantizationConfig],
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cache_config: Optional[CacheConfig],
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) -> None:
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([
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Blip2QFormerLayer(config,
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quant_config=quant_config,
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cache_config=cache_config,
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layer_idx=layer_idx)
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for layer_idx in range(config.num_hidden_layers)
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])
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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encoder_hidden_states: torch.FloatTensor,
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query_length: int,
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) -> torch.Tensor:
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for i in range(self.config.num_hidden_layers):
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layer_module = self.layer[i]
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hidden_states = layer_module(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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query_length=query_length,
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)
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return hidden_states
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# Adapted from https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/models/blip_2/modeling_blip_2.py#L1025
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class Blip2QFormerModel(nn.Module):
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def __init__(
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self,
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config: Blip2QFormerConfig,
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*,
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quant_config: Optional[QuantizationConfig],
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cache_config: Optional[CacheConfig],
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) -> None:
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super().__init__()
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self.config = config
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self.layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.encoder = Blip2QFormerEncoder(config,
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quant_config=quant_config,
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cache_config=cache_config)
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def forward(
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self,
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query_embeds: torch.FloatTensor,
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encoder_hidden_states: torch.FloatTensor,
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) -> torch.Tensor:
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query_length = query_embeds.shape[1]
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embedding_output = self.layernorm(query_embeds)
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embedding_output = self.dropout(embedding_output)
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sequence_output = self.encoder(
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embedding_output,
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encoder_hidden_states=encoder_hidden_states,
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query_length=query_length,
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)
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return sequence_output
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class Blip2ProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(Blip2Config)
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"image": 1}
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def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
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return {"image": self.get_num_image_tokens()}
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def get_num_image_tokens(self) -> int:
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hf_config = self.get_hf_config()
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return hf_config.num_query_tokens
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class Blip2DummyInputsBuilder(BaseDummyInputsBuilder[Blip2ProcessingInfo]):
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def get_dummy_processor_inputs(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> ProcessorInputs:
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hf_config = self.info.get_hf_config()
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vision_config = hf_config.vision_config
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max_image_size = vision_config.image_size
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num_images = mm_counts.get("image", 0)
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mm_data = {
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"image":
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self._get_dummy_images(width=max_image_size,
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height=max_image_size,
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num_images=num_images)
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}
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return ProcessorInputs(
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prompt_text="",
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mm_data=mm_data,
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)
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class Blip2MultiModalProcessor(BaseMultiModalProcessor[Blip2ProcessingInfo]):
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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return dict(
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pixel_values=MultiModalFieldConfig.batched("image"),
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image_embeds=MultiModalFieldConfig.batched("image"),
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)
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def _get_prompt_replacements(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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out_mm_kwargs: MultiModalKwargs,
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) -> list[PromptReplacement]:
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num_image_tokens = self.info.get_num_image_tokens()
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return [
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PromptReplacement(
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modality="image",
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target="</s>",
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replacement="<image>" * num_image_tokens + "</s>",
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)
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]
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def apply(
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self,
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prompt_text: str,
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mm_data: MultiModalDataDict,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> MultiModalInputsV2:
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result = super().apply(prompt_text, mm_data, hf_processor_mm_kwargs)
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# Only <image> tokens should be considered as placeholders,
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# so we ignore the trailing bos_token
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result["mm_placeholders"] = {
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modality: [
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PlaceholderRange(offset=p["offset"], length=p["length"] - 1)
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for p in ps
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]
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for modality, ps in result["mm_placeholders"].items()
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}
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return result
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@MULTIMODAL_REGISTRY.register_processor(Blip2MultiModalProcessor,
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info=Blip2ProcessingInfo,
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dummy_inputs=Blip2DummyInputsBuilder)
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class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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multimodal_config = vllm_config.model_config.multimodal_config
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self.config = config
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self.multimodal_config = multimodal_config
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# TODO: Optionally initializes this for supporting embeddings.
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self.vision_model = BlipVisionModel(config.vision_config, quant_config)
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self.query_tokens = nn.Parameter(
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torch.zeros(1, config.num_query_tokens,
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config.qformer_config.hidden_size))
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self.qformer = Blip2QFormerModel(config.qformer_config,
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cache_config=cache_config,
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quant_config=quant_config)
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self.language_projection = nn.Linear(
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config.qformer_config.hidden_size,
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config.text_config.hidden_size,
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bias=True,
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)
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self.language_model = init_vllm_registered_model(
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|
vllm_config=vllm_config,
|
|
hf_config=config.text_config,
|
|
prefix=maybe_prefix(prefix, "language_model"),
|
|
)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.language_model.make_empty_intermediate_tensors)
|
|
|
|
@cached_property
|
|
def sampler(self):
|
|
if hasattr(self.language_model, "sampler"):
|
|
return self.language_model.sampler
|
|
|
|
return get_sampler()
|
|
|
|
def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
|
|
h = w = self.config.vision_config.image_size
|
|
expected_dims = (3, h, w)
|
|
actual_dims = tuple(data.shape[1:])
|
|
|
|
if actual_dims != expected_dims:
|
|
expected_expr = ("batch_size", *map(str, expected_dims))
|
|
raise ValueError(
|
|
f"The expected shape of pixel values is {expected_expr}. "
|
|
f"You supplied {tuple(data.shape)}.")
|
|
|
|
return data
|
|
|
|
def _parse_and_validate_image_input(
|
|
self, **kwargs: object) -> Optional[Blip2ImageInputs]:
|
|
pixel_values = kwargs.pop("pixel_values", None)
|
|
image_embeds = kwargs.pop("image_embeds", None)
|
|
|
|
if pixel_values is None and image_embeds is None:
|
|
return None
|
|
|
|
if pixel_values is not None:
|
|
if not isinstance(pixel_values, torch.Tensor):
|
|
raise ValueError("Incorrect type of pixel values. "
|
|
f"Got type: {type(pixel_values)}")
|
|
|
|
# Remove the N dimension until multiple images are supported.
|
|
pixel_values = pixel_values.squeeze(1)
|
|
|
|
return Blip2ImagePixelInputs(
|
|
type="pixel_values",
|
|
data=self._validate_pixel_values(pixel_values),
|
|
)
|
|
|
|
if image_embeds is not None:
|
|
if not isinstance(image_embeds, torch.Tensor):
|
|
raise ValueError("Incorrect type of image embeddings. "
|
|
f"Got type: {type(image_embeds)}")
|
|
|
|
# Remove the N dimension until multiple images are supported.
|
|
image_embeds = image_embeds.squeeze(1)
|
|
|
|
return Blip2ImageEmbeddingInputs(
|
|
type="image_embeds",
|
|
data=image_embeds,
|
|
)
|
|
|
|
raise AssertionError("This line should be unreachable.")
|
|
|
|
def _image_pixels_to_features(self, vision_model: BlipVisionModel,
|
|
pixel_values: torch.Tensor) -> torch.Tensor:
|
|
|
|
# NOTE: we skip the step to select the vision feature layer since
|
|
# this is already done inside the vision tower
|
|
image_features = vision_model(pixel_values)
|
|
|
|
return image_features
|
|
|
|
def _process_image_pixels(self,
|
|
inputs: Blip2ImagePixelInputs) -> torch.Tensor:
|
|
assert self.vision_model is not None
|
|
|
|
pixel_values = inputs["data"]
|
|
|
|
return self._image_pixels_to_features(self.vision_model, pixel_values)
|
|
|
|
def _process_image_input(self,
|
|
image_input: Blip2ImageInputs) -> torch.Tensor:
|
|
|
|
if image_input["type"] == "image_embeds":
|
|
return image_input["data"]
|
|
|
|
assert self.vision_model is not None
|
|
image_features = self._process_image_pixels(image_input)
|
|
|
|
query_tokens = self.query_tokens.expand(image_features.shape[0], -1,
|
|
-1)
|
|
query_output = self.qformer(
|
|
query_embeds=query_tokens,
|
|
encoder_hidden_states=image_features,
|
|
)
|
|
|
|
return self.language_projection(query_output)
|
|
|
|
def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
|
|
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[NestedTensors] = 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,
|
|
_IMAGE_TOKEN_ID)
|
|
return inputs_embeds
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object,
|
|
) -> Union[SamplerOutput, IntermediateTensors]:
|
|
"""Run forward pass for BLIP-2.
|
|
|
|
One key thing to understand is the `input_ids` already accounts for the
|
|
positions of the to-be-inserted image embeddings.
|
|
|
|
Concretely, consider a text prompt:
|
|
`"Question: What's the content of the image? Answer:"`.
|
|
|
|
Tokenizer outputs:
|
|
`[2, 45641, 35, 653, 18, 5, 1383, 9, 5, 2274, 116, 31652, 35]`.
|
|
|
|
To reserve space in KV cache, we have to insert placeholder tokens
|
|
before they are inputted to the model, so the input processor prepends
|
|
dummy tokens (denoted as `50265`), resulting in:
|
|
`[50265, ..., 50265, 2, 45641, 35, ..., 31652, 35]`.
|
|
|
|
We insert 32 tokens since it corresponds to the number of query
|
|
embeddings outputted by the Q-Former and inputted to the language model.
|
|
|
|
This way, the `positions` and `attn_metadata` are consistent
|
|
with the `input_ids`.
|
|
|
|
Args:
|
|
input_ids: Flattened (concatenated) input_ids corresponding to a
|
|
batch.
|
|
pixel_values: The pixels in each input image.
|
|
|
|
See also:
|
|
:class:`Blip2ImageInputs`
|
|
"""
|
|
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
# NOTE: In v1, inputs_embeds is always generated at model runner, this
|
|
# condition is for v0 compatibility.
|
|
elif inputs_embeds is None:
|
|
vision_embeddings = self.get_multimodal_embeddings(**kwargs)
|
|
inputs_embeds = self.get_input_embeddings(input_ids,
|
|
vision_embeddings)
|
|
input_ids = None
|
|
|
|
hidden_states = self.language_model.model(input_ids,
|
|
positions,
|
|
kv_caches,
|
|
attn_metadata,
|
|
intermediate_tensors,
|
|
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 sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
return self.language_model.sample(logits, sampling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|