Sync from v0.13
This commit is contained in:
925
vllm/model_executor/models/bert.py
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925
vllm/model_executor/models/bert.py
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# 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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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.layers.encoder_only_attention import EncoderOnlyAttention
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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.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.pooler import (
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ClassifierPooler,
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DispatchPooler,
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Pooler,
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PoolingMethod,
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PoolingParamsUpdate,
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PoolingType,
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)
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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 VocabParallelEmbedding
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import PoolingTask
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from vllm.v1.pool.metadata import PoolingMetadata
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from .interfaces import SupportsCrossEncoding, SupportsQuant
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from .interfaces_base import attn_type, default_pooling_type
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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(
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config.vocab_size, config.hidden_size
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)
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self.position_embeddings = VocabParallelEmbedding(
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config.max_position_embeddings, config.hidden_size
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)
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self.token_type_embeddings = VocabParallelEmbedding(
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config.type_vocab_size, config.hidden_size
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)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, 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(
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"Only 'absolute' position_embedding_type" + " is supported"
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)
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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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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor:
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token_type_ids = _decode_token_type_ids(input_ids)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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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: torch.Tensor | list[torch.Tensor],
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pooling_metadata: PoolingMetadata,
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) -> 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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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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[
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BertLayer(
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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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)
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for layer_idx in range(config.num_hidden_layers)
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]
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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__(
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self,
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config: BertConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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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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)
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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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)
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self.output = BertOutput(
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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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)
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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: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.self = BertSelfAttention(
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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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)
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self.output = BertSelfOutput(
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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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)
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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: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = 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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)
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self.attn = EncoderOnlyAttention(
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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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)
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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__(
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self,
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hidden_size: int,
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layer_norm_eps: float,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.dense = RowParallelLinear(
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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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)
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self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
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def forward(
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self, hidden_states: torch.Tensor, 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.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__(
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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: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.dense = ColumnParallelLinear(
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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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)
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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__(
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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: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.dense = RowParallelLinear(
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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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)
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self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
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def forward(
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self, hidden_states: torch.Tensor, 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.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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@support_torch_compile
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@default_pooling_type("CLS")
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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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self.config = vllm_config.model_config.hf_config
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self.embeddings = embedding_class(self.config)
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self.encoder = BertEncoder(vllm_config=vllm_config, prefix=f"{prefix}.encoder")
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embeddings.word_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
|
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) -> torch.Tensor:
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hidden_states = self.embeddings(
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input_ids=input_ids,
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position_ids=positions,
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inputs_embeds=inputs_embeds,
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)
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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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|
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return other_weights, loaded_stacked_params
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|
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def load_weights(self, weights: Iterable[tuple[str, 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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|
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|
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class BertPoolingModel(BertModel):
|
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is_pooling_model = True
|
||||
|
||||
def __init__(
|
||||
self,
|
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*,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
embedding_class: type[nn.Module] = BertEmbedding,
|
||||
) -> None:
|
||||
super().__init__(
|
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vllm_config=vllm_config,
|
||||
prefix=prefix,
|
||||
embedding_class=embedding_class,
|
||||
)
|
||||
|
||||
config = vllm_config.model_config.hf_config
|
||||
self.pooler = BertPooler(config)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
other_weights, loaded_stacked_params = self._load_weights(weights)
|
||||
|
||||
loader = AutoWeightsLoader(self)
|
||||
loaded_params = loader.load_weights(other_weights)
|
||||
loaded_params.update(loaded_stacked_params)
|
||||
return loaded_params
|
||||
|
||||
|
||||
@default_pooling_type("CLS")
|
||||
class BertEmbeddingModel(nn.Module, SupportsQuant):
|
||||
"""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.
|
||||
_pooler: An instance of Pooler used for pooling operations.
|
||||
"""
|
||||
|
||||
is_pooling_model = True
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
|
||||
pooler_config = vllm_config.model_config.pooler_config
|
||||
assert pooler_config is not None
|
||||
|
||||
self.model = self._build_model(
|
||||
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
||||
)
|
||||
self.pooler = self._build_pooler(pooler_config)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.embed_input_ids(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
return self.model(
|
||||
input_ids=input_ids,
|
||||
positions=positions,
|
||||
inputs_embeds=inputs_embeds,
|
||||
intermediate_tensors=intermediate_tensors,
|
||||
)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
||||
weights_list = list(weights)
|
||||
|
||||
has_model_prefix = any(name.startswith("model.") for name, _ in weights_list)
|
||||
if not has_model_prefix:
|
||||
mapper = WeightsMapper(orig_to_new_prefix={"": "model."})
|
||||
|
||||
loader = AutoWeightsLoader(self, skip_prefixes=["lm_head."])
|
||||
return loader.load_weights(weights_list, mapper=mapper)
|
||||
|
||||
def _build_model(self, vllm_config: VllmConfig, prefix: str = "") -> BertModel:
|
||||
return BertModel(
|
||||
vllm_config=vllm_config, prefix=prefix, embedding_class=BertEmbedding
|
||||
)
|
||||
|
||||
def _build_pooler(self, pooler_config: PoolerConfig) -> Pooler:
|
||||
return DispatchPooler(
|
||||
{
|
||||
"token_embed": Pooler.for_token_embed(pooler_config),
|
||||
"embed": Pooler.for_embed(pooler_config),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
# Here we encode the token type ids together with the input ids.
|
||||
# Since we use int 32 for the input IDs and the vocabulary size
|
||||
# is way lower than 2**31, there is room to encode additional
|
||||
# bits. At the same time, for cross-encoder use cases, the
|
||||
# token type ids are only 0 or 1, requiring only 1 bit.
|
||||
# This means that we can store the token type ids in the 31st
|
||||
# bit. We void the 32nd bit because that would produce a negative
|
||||
# number, which could be used to signal other things.
|
||||
#
|
||||
# The reason for all of this is that all the tensors that are
|
||||
# passed as input to the forward function of a module marked
|
||||
# with @support_torch_compile have to be persistent. So to
|
||||
# avoid adding more persistent tensors in the model runner, we
|
||||
# encode more information in the same persistent tensor.
|
||||
#
|
||||
# Since the *ForClassification module is outside of the BertModel
|
||||
# which is compiled, we can do the encoding here and then separate
|
||||
# the information again in the Embedding layer. Since with bit masks
|
||||
# we can do this entirely with torch operations and without branching,
|
||||
# it works with torch compile.
|
||||
|
||||
TOKEN_TYPE_SHIFT = 30
|
||||
|
||||
|
||||
def _encode_token_type_ids(
|
||||
input_ids: torch.Tensor, token_type_ids: torch.Tensor
|
||||
) -> None:
|
||||
# input_ids can be padded to the right
|
||||
input_ids[: token_type_ids.shape[0]].bitwise_or_(token_type_ids << TOKEN_TYPE_SHIFT)
|
||||
|
||||
|
||||
def _decode_token_type_ids(input_ids: torch.Tensor) -> torch.Tensor:
|
||||
ids_mask = (
|
||||
torch.ones_like(input_ids, dtype=torch.int32, device=input_ids.device)
|
||||
<< TOKEN_TYPE_SHIFT
|
||||
)
|
||||
tokens_mask = ids_mask.bitwise_not()
|
||||
|
||||
token_type_ids = input_ids.bitwise_and(ids_mask) >> TOKEN_TYPE_SHIFT
|
||||
|
||||
input_ids.bitwise_and_(tokens_mask)
|
||||
|
||||
return token_type_ids
|
||||
|
||||
|
||||
class BertMLMHead(nn.Module):
|
||||
def __init__(
|
||||
self, hidden_size: int, vocab_size: int, layer_norm_eps: float = 1e-12
|
||||
):
|
||||
super().__init__()
|
||||
self.dense = nn.Linear(hidden_size, hidden_size)
|
||||
self.activation = nn.GELU()
|
||||
self.layer_norm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
|
||||
self.decoder = nn.Linear(hidden_size, vocab_size, bias=True)
|
||||
|
||||
def tie_weights_with_embeddings(self, embeddings_weight: torch.Tensor):
|
||||
self.decoder.weight = embeddings_weight
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
x = self.dense(hidden_states)
|
||||
x = self.activation(x)
|
||||
x = self.layer_norm(x)
|
||||
logits = self.decoder(x)
|
||||
return logits
|
||||
|
||||
|
||||
class SPLADESparsePooler(Pooler):
|
||||
"""
|
||||
SPLADE sparse pooling:
|
||||
logits = mlm_head(hidden_states)
|
||||
-> log1p(relu(logits))
|
||||
-> (max|sum over L)
|
||||
-> [V]
|
||||
|
||||
Padding is masked with an attention mask,
|
||||
[CLS]/[SEP] is removed (selected),
|
||||
and then pooled.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
mlm_head: nn.Module,
|
||||
cls_token_id: int | None = 101,
|
||||
sep_token_id: int | None = 102,
|
||||
pooling: str = "max",
|
||||
remove_cls_sep: bool = True,
|
||||
):
|
||||
super().__init__()
|
||||
assert pooling in ("max", "sum")
|
||||
self.mlm_head = mlm_head
|
||||
self.cls_token_id = cls_token_id
|
||||
self.sep_token_id = sep_token_id
|
||||
self.pooling = pooling
|
||||
self.remove_cls_sep = remove_cls_sep
|
||||
|
||||
def get_supported_tasks(self) -> Set[PoolingTask]:
|
||||
return {"embed"}
|
||||
|
||||
def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
|
||||
return PoolingParamsUpdate(requires_token_ids=True)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
pooling_metadata: PoolingMetadata,
|
||||
) -> torch.Tensor:
|
||||
assert isinstance(hidden_states, torch.Tensor) and hidden_states.dim() == 2
|
||||
|
||||
lens_tensor: torch.Tensor = pooling_metadata.prompt_lens
|
||||
lens: list[int] = lens_tensor.tolist()
|
||||
B: int = len(lens)
|
||||
|
||||
token_ids = pooling_metadata.prompt_token_ids
|
||||
offset = 0
|
||||
pooled_list: list[torch.Tensor] = []
|
||||
|
||||
for i in range(B):
|
||||
L = int(lens[i])
|
||||
hs = hidden_states[offset : offset + L]
|
||||
|
||||
start_idx = 0
|
||||
end_idx = L
|
||||
if self.remove_cls_sep and token_ids is not None:
|
||||
if (
|
||||
self.cls_token_id is not None
|
||||
and token_ids[i, 0].item() == self.cls_token_id
|
||||
):
|
||||
start_idx = 1
|
||||
if (
|
||||
self.sep_token_id is not None
|
||||
and token_ids[i, L - 1].item() == self.sep_token_id
|
||||
):
|
||||
end_idx = max(start_idx, L - 1)
|
||||
|
||||
if end_idx <= start_idx:
|
||||
V = int(self.mlm_head.decoder.out_features)
|
||||
pooled_list.append(hs.new_zeros((V,)))
|
||||
offset += L
|
||||
continue
|
||||
|
||||
logits_i = self.mlm_head(hs[start_idx:end_idx])
|
||||
scores_i = torch.log1p(torch.relu(logits_i))
|
||||
|
||||
if self.pooling == "sum":
|
||||
pooled_i = scores_i.sum(dim=0)
|
||||
else: # "max"
|
||||
pooled_i = scores_i.max(dim=0).values
|
||||
|
||||
pooled_list.append(pooled_i.contiguous())
|
||||
offset += L
|
||||
|
||||
return torch.stack(pooled_list, dim=0).contiguous()
|
||||
|
||||
|
||||
@default_pooling_type("CLS")
|
||||
class BertSpladeSparseEmbeddingModel(BertEmbeddingModel):
|
||||
"""
|
||||
BertEmbeddingModel + SPLADE sparse embedding.
|
||||
- Make logits by self.mlm_head
|
||||
- pooler: SPLADESparsePooler(mlm_head...)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, *, vllm_config: VllmConfig, prefix: str = "", splade_pooling: str = "max"
|
||||
):
|
||||
super().__init__(vllm_config=vllm_config, prefix=prefix)
|
||||
cfg = vllm_config.model_config.hf_config
|
||||
|
||||
# MLM head
|
||||
self.mlm_head = BertMLMHead(
|
||||
hidden_size=cfg.hidden_size,
|
||||
vocab_size=cfg.vocab_size,
|
||||
layer_norm_eps=getattr(cfg, "layer_norm_eps", 1e-12),
|
||||
)
|
||||
|
||||
self._splade_pooling = splade_pooling
|
||||
pooler_config = vllm_config.model_config.pooler_config
|
||||
assert pooler_config is not None
|
||||
self.pooler = self._build_pooler(pooler_config)
|
||||
|
||||
def _build_pooler(self, pooler_config: PoolerConfig) -> Pooler:
|
||||
cfg = self.model.config
|
||||
|
||||
if not hasattr(self, "mlm_head"):
|
||||
self.mlm_head = BertMLMHead(
|
||||
hidden_size=cfg.hidden_size,
|
||||
vocab_size=cfg.vocab_size,
|
||||
layer_norm_eps=getattr(cfg, "layer_norm_eps", 1e-12),
|
||||
)
|
||||
|
||||
pooling_mode = getattr(self, "_splade_pooling", "max")
|
||||
|
||||
cls_id = getattr(cfg, "cls_token_id", None)
|
||||
sep_id = getattr(cfg, "sep_token_id", None)
|
||||
|
||||
return DispatchPooler(
|
||||
{
|
||||
"token_embed": Pooler.for_token_embed(pooler_config),
|
||||
"embed": SPLADESparsePooler(
|
||||
mlm_head=self.mlm_head,
|
||||
cls_token_id=cls_id,
|
||||
sep_token_id=sep_id,
|
||||
pooling=pooling_mode, # "max" or "sum"
|
||||
remove_cls_sep=True,
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
||||
if not hasattr(self, "mlm_head"):
|
||||
cfg = self.model.config
|
||||
self.mlm_head = BertMLMHead(
|
||||
hidden_size=cfg.hidden_size,
|
||||
vocab_size=cfg.vocab_size,
|
||||
layer_norm_eps=getattr(cfg, "layer_norm_eps", 1e-12),
|
||||
)
|
||||
|
||||
def _strip(name: str) -> str:
|
||||
for p in ("model.", "bert."):
|
||||
if name.startswith(p):
|
||||
name = name[len(p) :]
|
||||
return name
|
||||
|
||||
weights_list = list(weights)
|
||||
model_side: list[tuple[str, torch.Tensor]] = []
|
||||
mlm_side: list[tuple[str, torch.Tensor]] = []
|
||||
|
||||
for k, w in weights_list:
|
||||
name = _strip(k)
|
||||
if name.startswith("cls.predictions."):
|
||||
mlm_side.append((name, w))
|
||||
else:
|
||||
model_side.append((name, w))
|
||||
|
||||
loaded: set[str] = set()
|
||||
loaded_model = self.model.load_weights(model_side)
|
||||
loaded.update({"model." + n for n in loaded_model})
|
||||
|
||||
if mlm_side:
|
||||
name_map = {
|
||||
"cls.predictions.transform.dense.weight": "mlm_head.dense.weight",
|
||||
"cls.predictions.transform.dense.bias": "mlm_head.dense.bias",
|
||||
("cls.predictions.transform.LayerNorm.weight"): (
|
||||
"mlm_head.layer_norm.weight"
|
||||
),
|
||||
("cls.predictions.transform.LayerNorm.bias"): (
|
||||
"mlm_head.layer_norm.bias"
|
||||
),
|
||||
"cls.predictions.decoder.weight": "mlm_head.decoder.weight",
|
||||
"cls.predictions.decoder.bias": "mlm_head.decoder.bias",
|
||||
}
|
||||
remapped = [(name_map[n], w) for n, w in mlm_side if n in name_map]
|
||||
if remapped:
|
||||
loaded_mlm = AutoWeightsLoader(self).load_weights(remapped)
|
||||
loaded.update(loaded_mlm)
|
||||
|
||||
return loaded
|
||||
|
||||
|
||||
@default_pooling_type("CLS")
|
||||
class BertForSequenceClassification(nn.Module, SupportsCrossEncoding, SupportsQuant):
|
||||
"""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.
|
||||
_pooler: An instance of Pooler used for pooling operations.
|
||||
"""
|
||||
|
||||
is_pooling_model = True
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
|
||||
self.num_labels = config.num_labels
|
||||
self.bert = BertPoolingModel(
|
||||
vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "bert"),
|
||||
embedding_class=BertEmbedding,
|
||||
)
|
||||
self.classifier = nn.Linear(
|
||||
config.hidden_size,
|
||||
config.num_labels,
|
||||
dtype=vllm_config.model_config.head_dtype,
|
||||
)
|
||||
|
||||
pooler_config = vllm_config.model_config.pooler_config
|
||||
assert pooler_config is not None
|
||||
|
||||
self.pooler = DispatchPooler(
|
||||
{
|
||||
"token_classify": Pooler.for_token_classify(
|
||||
pooler_config, classifier=self.classifier
|
||||
),
|
||||
"classify": ClassifierPooler(
|
||||
pooling=self.bert.pooler,
|
||||
classifier=self.classifier,
|
||||
act_fn="classify",
|
||||
),
|
||||
"score": ClassifierPooler(
|
||||
pooling=self.bert.pooler, classifier=self.classifier, act_fn="score"
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.bert.embed_input_ids(input_ids)
|
||||
|
||||
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: torch.Tensor | None,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
token_type_ids: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
if token_type_ids is not None:
|
||||
assert self.bert.config.vocab_size < (1 << TOKEN_TYPE_SHIFT)
|
||||
assert input_ids is not None
|
||||
_encode_token_type_ids(input_ids, token_type_ids)
|
||||
|
||||
return self.bert(
|
||||
input_ids=input_ids,
|
||||
positions=positions,
|
||||
inputs_embeds=inputs_embeds,
|
||||
intermediate_tensors=intermediate_tensors,
|
||||
)
|
||||
|
||||
|
||||
@attn_type("encoder_only")
|
||||
@default_pooling_type("ALL")
|
||||
class BertForTokenClassification(nn.Module):
|
||||
is_pooling_model = True
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
self.head_dtype = vllm_config.model_config.head_dtype
|
||||
self.num_labels = config.num_labels
|
||||
self.bert = BertModel(
|
||||
vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "bert"),
|
||||
embedding_class=BertEmbedding,
|
||||
)
|
||||
self.classifier = nn.Linear(
|
||||
config.hidden_size, config.num_labels, dtype=self.head_dtype
|
||||
)
|
||||
|
||||
pooler_config = vllm_config.model_config.pooler_config
|
||||
assert pooler_config is not None
|
||||
|
||||
self.pooler = DispatchPooler(
|
||||
{
|
||||
"token_classify": Pooler.for_token_classify(
|
||||
pooler_config=pooler_config
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.bert.embed_input_ids(input_ids)
|
||||
|
||||
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: torch.Tensor | None,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
token_type_ids: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
if token_type_ids is not None:
|
||||
assert self.bert.config.vocab_size < (1 << TOKEN_TYPE_SHIFT)
|
||||
assert input_ids is not None
|
||||
_encode_token_type_ids(input_ids, token_type_ids)
|
||||
|
||||
hidden_states = self.bert(
|
||||
input_ids=input_ids,
|
||||
positions=positions,
|
||||
inputs_embeds=inputs_embeds,
|
||||
intermediate_tensors=intermediate_tensors,
|
||||
)
|
||||
|
||||
hidden_states = hidden_states.to(self.head_dtype)
|
||||
return self.classifier(hidden_states)
|
||||
Reference in New Issue
Block a user