579 lines
21 KiB
Python
579 lines
21 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable, Set
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from typing import Optional, Union
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import torch
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from torch import nn
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from transformers import BertConfig
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from vllm.attention import Attention, AttentionType
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, PoolerConfig, VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.pooler import (ClassifierPooler,
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DispatchPooler, Pooler,
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PoolingMethod,
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PoolingParamsUpdate,
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PoolingType)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding)
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from vllm.model_executor.pooling_metadata import PoolingMetadata
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import PoolingTask
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from .interfaces import SupportsCrossEncoding, SupportsQuant, SupportsV0Only
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from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
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class BertEmbedding(nn.Module):
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def __init__(self, config: BertConfig):
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super().__init__()
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self.size = config.hidden_size
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self.word_embeddings = VocabParallelEmbedding(config.vocab_size,
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config.hidden_size)
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self.position_embeddings = VocabParallelEmbedding(
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config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = VocabParallelEmbedding(
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config.type_vocab_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.register_buffer(
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"position_ids",
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torch.arange(config.max_position_embeddings).unsqueeze(0),
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)
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self.position_embedding_type = config.position_embedding_type
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if self.position_embedding_type != "absolute":
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raise ValueError("Only 'absolute' position_embedding_type" +
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" is supported")
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def forward(
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self,
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input_ids: torch.Tensor,
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seq_lens: torch.Tensor,
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position_ids: torch.Tensor,
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token_type_ids: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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input_shape = input_ids.size()
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# Input embeddings.
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inputs_embeds = self.word_embeddings(input_ids)
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# Position embeddings.
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position_embeddings = self.position_embeddings(position_ids)
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if token_type_ids is None:
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token_type_ids = torch.zeros(input_shape,
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dtype=torch.long,
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device=inputs_embeds.device)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings + position_embeddings
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embeddings = self.LayerNorm(embeddings)
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return embeddings
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class BertPooler(Pooler):
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def __init__(self, config: BertConfig):
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super().__init__()
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self.pooling = PoolingMethod.from_pooling_type(PoolingType.CLS)
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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def get_supported_tasks(self) -> Set[PoolingTask]:
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return self.pooling.get_supported_tasks()
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def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
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return self.pooling.get_pooling_updates(task)
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def _head(self, pooled_output: torch.Tensor):
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pooled_output = self.dense(pooled_output)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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def forward(
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self,
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hidden_states: Union[torch.Tensor, list[torch.Tensor]],
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pooling_metadata: PoolingMetadata,
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) -> Union[torch.Tensor, list[torch.Tensor]]:
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pooled_output = self.pooling(hidden_states, pooling_metadata)
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if isinstance(pooled_output, list):
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pooled_output = [self._head(output) for output in pooled_output]
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else:
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pooled_output = self._head(pooled_output)
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return pooled_output
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@support_torch_compile
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class BertEncoder(nn.Module):
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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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self.layer = nn.ModuleList([
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BertLayer(config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.layer.{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.Tensor,
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) -> torch.Tensor:
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for layer in self.layer:
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hidden_states = layer(hidden_states)
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return hidden_states
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class BertLayer(nn.Module):
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def __init__(self,
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config: BertConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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super().__init__()
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self.attention = BertAttention(
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hidden_size=config.hidden_size,
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num_attention_heads=config.num_attention_heads,
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layer_norm_eps=config.layer_norm_eps,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attention")
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self.intermediate = BertIntermediate(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.intermediate")
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self.output = BertOutput(hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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layer_norm_eps=config.layer_norm_eps,
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quant_config=quant_config,
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prefix=f"{prefix}.output")
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def forward(self, hidden_states: torch.Tensor):
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attn_output = self.attention(hidden_states)
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intermediate_output = self.intermediate(attn_output)
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output = self.output(intermediate_output, attn_output)
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return output
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class BertAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_attention_heads: int,
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layer_norm_eps: float,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.self = BertSelfAttention(hidden_size=hidden_size,
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num_attention_heads=num_attention_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.output")
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self.output = BertSelfOutput(hidden_size=hidden_size,
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layer_norm_eps=layer_norm_eps,
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quant_config=quant_config,
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prefix=f"{prefix}.output")
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def forward(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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self_output = self.self(hidden_states)
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return self.output(self_output, hidden_states)
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class BertSelfAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_attention_heads: int,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = self.total_num_heads
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self.head_dim = self.hidden_size // self.total_num_heads
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assert self.head_dim * self.total_num_heads == self.hidden_size
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(
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hidden_size=self.hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj")
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self.attn = Attention(num_heads=self.num_heads,
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head_size=self.head_dim,
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scale=self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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attn_type=AttentionType.ENCODER_ONLY)
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def forward(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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output = self.attn(q, k, v)
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return output
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class BertSelfOutput(nn.Module):
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def __init__(self,
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hidden_size: int,
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layer_norm_eps: float,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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super().__init__()
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self.dense = RowParallelLinear(input_size=hidden_size,
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output_size=hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.dense")
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self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor,
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input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.dense(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 BertIntermediate(nn.Module):
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def __init__(self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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super().__init__()
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self.dense = ColumnParallelLinear(input_size=hidden_size,
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output_size=intermediate_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.dense")
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self.intermediate_act_fn = get_act_fn(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 BertOutput(nn.Module):
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def __init__(self,
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hidden_size: int,
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intermediate_size: int,
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layer_norm_eps: float,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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super().__init__()
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self.dense = RowParallelLinear(input_size=intermediate_size,
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output_size=hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.dense")
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self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor,
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input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.dense(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 BertModel(nn.Module, SupportsQuant):
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is_pooling_model = True
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packed_modules_mapping = {"qkv_proj": ["query", "key", "value"]}
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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embedding_class: type[nn.Module] = BertEmbedding,
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) -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.embeddings = embedding_class(config)
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self.encoder = BertEncoder(vllm_config=vllm_config,
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prefix=f"{prefix}.encoder")
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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token_type_ids: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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attn_metadata = get_forward_context().attn_metadata
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assert hasattr(attn_metadata, "seq_lens_tensor")
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hidden_states = self.embeddings(
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input_ids=input_ids,
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seq_lens=attn_metadata.seq_lens_tensor,
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position_ids=position_ids,
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token_type_ids=token_type_ids)
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return self.encoder(hidden_states)
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def _load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "query", "q"),
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("qkv_proj", "key", "k"),
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("qkv_proj", "value", "v"),
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]
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loaded_stacked_params = []
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other_weights = []
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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if name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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loaded_stacked_params.append(name)
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break
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else:
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if name in params_dict:
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other_weights.append((name, loaded_weight))
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return other_weights, loaded_stacked_params
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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other_weights, loaded_stacked_params = self._load_weights(weights)
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loader = AutoWeightsLoader(self, skip_prefixes=["pooler."])
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loaded_params = loader.load_weights(other_weights)
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loaded_params.update(loaded_stacked_params)
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return loaded_params
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class BertPoolingModel(BertModel):
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is_pooling_model = True
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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embedding_class: type[nn.Module] = BertEmbedding,
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) -> None:
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super().__init__(
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vllm_config=vllm_config,
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prefix=prefix,
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embedding_class=embedding_class,
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)
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config = vllm_config.model_config.hf_config
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self.pooler = BertPooler(config)
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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other_weights, loaded_stacked_params = self._load_weights(weights)
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loader = AutoWeightsLoader(self)
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loaded_params = loader.load_weights(other_weights)
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loaded_params.update(loaded_stacked_params)
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return loaded_params
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class BertEmbeddingModel(nn.Module, SupportsV0Only, SupportsQuant):
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"""A model that uses Bert to provide embedding functionalities.
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This class encapsulates the BertModel and provides an interface for
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embedding operations and customized pooling functions.
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Attributes:
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model: An instance of BertModel used for forward operations.
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_pooler: An instance of Pooler used for pooling operations.
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"""
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is_pooling_model = True
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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pooler_config = vllm_config.model_config.pooler_config
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assert pooler_config is not None
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self.model = self._build_model(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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self.pooler = self._build_pooler(pooler_config)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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return self.model(input_ids=input_ids,
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position_ids=positions,
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inputs_embeds=inputs_embeds,
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intermediate_tensors=intermediate_tensors)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
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weights_list = list(weights)
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has_model_prefix = any(
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name.startswith("model.") for name, _ in weights_list)
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if not has_model_prefix:
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mapper = WeightsMapper(orig_to_new_prefix={"": "model."})
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loader = AutoWeightsLoader(self, skip_prefixes=["lm_head."])
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return loader.load_weights(weights_list, mapper=mapper)
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def _build_model(self,
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vllm_config: VllmConfig,
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prefix: str = "") -> BertModel:
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return BertModel(vllm_config=vllm_config,
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prefix=prefix,
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embedding_class=BertEmbedding)
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def _build_pooler(self, pooler_config: PoolerConfig) -> Pooler:
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return DispatchPooler({
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"encode":
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Pooler.for_encode(pooler_config),
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"embed":
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Pooler.for_embed(
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pooler_config,
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default_pooling_type=PoolingType.CLS,
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|
),
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|
})
|
|
|
|
|
|
class BertForSequenceClassification(nn.Module, SupportsV0Only,
|
|
SupportsCrossEncoding, SupportsQuant):
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|
"""A model that uses Bert to provide embedding functionalities.
|
|
|
|
This class encapsulates the BertModel and provides an interface for
|
|
embedding operations and customized pooling functions.
|
|
|
|
Attributes:
|
|
model: An instance of BertModel used for forward operations.
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|
_pooler: An instance of Pooler used for pooling operations.
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|
"""
|
|
|
|
is_pooling_model = True
|
|
|
|
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
|
|
|
|
self.num_labels = config.num_labels
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|
self.bert = BertPoolingModel(vllm_config=vllm_config,
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|
prefix=maybe_prefix(prefix, "bert"),
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|
embedding_class=BertEmbedding)
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|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
pooler_config = vllm_config.model_config.pooler_config
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|
assert pooler_config is not None
|
|
|
|
self.pooler = DispatchPooler({
|
|
"encode":
|
|
Pooler.for_encode(pooler_config),
|
|
"classify":
|
|
ClassifierPooler(
|
|
pooling=self.bert.pooler,
|
|
classifier=self.classifier,
|
|
act_fn=ClassifierPooler.act_fn_for_seq_cls(
|
|
vllm_config.model_config),
|
|
),
|
|
"score":
|
|
ClassifierPooler(
|
|
pooling=self.bert.pooler,
|
|
classifier=self.classifier,
|
|
act_fn=ClassifierPooler.act_fn_for_cross_encoder(
|
|
vllm_config.model_config),
|
|
),
|
|
})
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
|
loader = AutoWeightsLoader(self)
|
|
loaded_params = loader.load_weights(weights)
|
|
return loaded_params
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor],
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
return self.bert(input_ids=input_ids,
|
|
position_ids=positions,
|
|
inputs_embeds=inputs_embeds,
|
|
intermediate_tensors=intermediate_tensors,
|
|
token_type_ids=token_type_ids)
|