696 lines
25 KiB
Python
696 lines
25 KiB
Python
from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
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import torch
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import torch.nn as nn
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from transformers import (Blip2Config, Blip2QFormerConfig, Blip2VisionConfig,
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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, MultiModalConfig
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from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.opt import OPTModel
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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.sequence import IntermediateTensors, SamplerOutput, SequenceData
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from .blip import (BlipVisionModel, dummy_image_for_blip,
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get_max_blip_image_tokens)
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from .interfaces import SupportsVision
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from .utils import merge_vision_embeddings
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_KEYS_TO_MODIFY_MAPPING = {
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"language_model.lm_head": "lm_head",
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"language_model.model": "language_model",
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}
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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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BLIP2_IMAGE_TOKEN = "<image>"
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BLIP2_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_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, 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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def get_blip2_image_feature_size(hf_config: Blip2Config) -> int:
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return hf_config.num_query_tokens
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def get_max_blip2_image_tokens(ctx: InputContext):
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hf_config = ctx.get_hf_config(Blip2Config)
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vision_config = hf_config.vision_config
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if isinstance(vision_config, Blip2VisionConfig):
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return get_max_blip_image_tokens(vision_config)
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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def dummy_data_for_blip2(ctx: InputContext, seq_len: int):
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hf_config = ctx.get_hf_config(Blip2Config)
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vision_config = hf_config.vision_config
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image_feature_size = get_blip2_image_feature_size(hf_config)
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token_ids = [BLIP2_IMAGE_TOKEN_ID] * image_feature_size
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token_ids += [0] * (seq_len - image_feature_size)
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seq_data = SequenceData(token_ids)
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if isinstance(vision_config, Blip2VisionConfig):
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mm_data = dummy_image_for_blip(vision_config)
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return seq_data, mm_data
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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def input_processor_for_blip2(ctx: InputContext, llm_inputs: LLMInputs):
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multi_modal_data = llm_inputs.get("multi_modal_data")
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if multi_modal_data is None or "image" not in multi_modal_data:
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return llm_inputs
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hf_config = ctx.get_hf_config(Blip2Config)
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image_feature_size = get_blip2_image_feature_size(hf_config)
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# The original model places image tokens at the front
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# https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/models/blip_2/modeling_blip_2.py#L1514
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new_token_ids = [BLIP2_IMAGE_TOKEN_ID] * image_feature_size
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new_token_ids += llm_inputs["prompt_token_ids"]
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new_prompt = llm_inputs.get("prompt")
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if new_prompt is not None:
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new_prompt = BLIP2_IMAGE_TOKEN * image_feature_size + new_prompt
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return LLMInputs(prompt_token_ids=new_token_ids,
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prompt=new_prompt,
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multi_modal_data=multi_modal_data)
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@MULTIMODAL_REGISTRY.register_image_input_mapper()
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@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_blip2_image_tokens)
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@INPUT_REGISTRY.register_dummy_data(dummy_data_for_blip2)
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@INPUT_REGISTRY.register_input_processor(input_processor_for_blip2)
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class Blip2ForConditionalGeneration(nn.Module, SupportsVision):
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def __init__(self,
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config: Blip2Config,
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multimodal_config: MultiModalConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None) -> None:
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super().__init__()
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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)
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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.quant_config = quant_config
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self.language_model = OPTModel(config.text_config, cache_config,
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quant_config)
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self.unpadded_vocab_size = config.text_config.vocab_size
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size)
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self.sampler = Sampler()
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def get_lm_head(self):
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return self.language_model.decoder.embed_tokens
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def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
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h = w = self.config.vision_config.image_size
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expected_dims = (3, h, w)
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actual_dims = tuple(data.shape[1:])
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if actual_dims != expected_dims:
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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)}")
|
|
|
|
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)}")
|
|
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 forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
**kwargs: object,
|
|
) -> SamplerOutput:
|
|
"""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`
|
|
"""
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
|
|
if image_input is not None:
|
|
vision_embeddings = self._process_image_input(image_input)
|
|
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
|
|
|
|
inputs_embeds = merge_vision_embeddings(input_ids, inputs_embeds,
|
|
vision_embeddings,
|
|
BLIP2_IMAGE_TOKEN_ID)
|
|
|
|
input_ids = None
|
|
else:
|
|
inputs_embeds = None
|
|
|
|
hidden_states = self.language_model(input_ids,
|
|
positions,
|
|
kv_caches,
|
|
attn_metadata,
|
|
inputs_embeds=inputs_embeds)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
|
logits = self.logits_processor(self.get_lm_head(), hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
# only doing this for language model part for now.
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
|
|
for name, loaded_weight in weights:
|
|
if "lm_head.weight" in name:
|
|
continue
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
|
|
if key_to_modify in name:
|
|
name = name.replace(key_to_modify, new_key)
|
|
use_default_weight_loading = False
|
|
if "vision" in name:
|
|
if self.vision_model is not None:
|
|
# We only do sharding for language model and
|
|
# not vision model for now.
|
|
use_default_weight_loading = True
|
|
else:
|
|
for (param_name, weight_name,
|
|
shard_id) in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
param = params_dict[name.replace(weight_name, param_name)]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
use_default_weight_loading = True
|
|
if use_default_weight_loading:
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|