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131 lines
4.4 KiB
Python
131 lines
4.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapted from
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# https://huggingface.co/Qwen/Qwen2.5-Math-RM-72B/blob/main/modeling_qwen2_rm.py
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# Copyright 2024 The Qwen team.
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# Copyright 2023 The vLLM team.
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"""Inference-only Qwen2-RM model compatible with HuggingFace weights."""
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from typing import Iterable, Optional, Set, Tuple, Union
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import torch
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from torch import nn
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.pooler import Pooler, PoolingType, SimplePooler
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from vllm.model_executor.pooling_metadata import PoolingMetadata
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from vllm.sequence import IntermediateTensors, PoolerOutput
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from .interfaces import SupportsLoRA, SupportsPP, SupportsV0Only
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from .qwen2 import Qwen2Model
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from .utils import AutoWeightsLoader, maybe_prefix
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class ReLU(nn.Module):
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def __init__(self):
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super().__init__()
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self.activation = nn.ReLU()
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def forward(self, input):
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input, _ = input
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return self.activation(input)
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class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
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SupportsV0Only):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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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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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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self.config = config
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self.lora_config = lora_config
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self.quant_config = quant_config
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self.model = Qwen2Model(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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self.score = nn.Sequential(
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ColumnParallelLinear(config.hidden_size,
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config.hidden_size,
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quant_config=quant_config),
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ReLU(),
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RowParallelLinear(config.hidden_size,
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config.num_labels,
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quant_config=quant_config),
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)
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self._pooler: SimplePooler
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_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: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, intermediate_tensors,
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inputs_embeds)
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logits, _ = self.score(hidden_states)
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return logits
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def pooler(
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self,
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hidden_states: torch.Tensor,
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pooling_metadata: PoolingMetadata,
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) -> Optional[PoolerOutput]:
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return self._pooler(hidden_states, pooling_metadata)
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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loader = AutoWeightsLoader(self,
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ignore_unexpected_prefixes=["lm_head."])
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return loader.load_weights(weights)
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class Qwen2ForRewardModel(Qwen2RewardBaseModel):
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def __init__(self, *, vllm_config, prefix=""):
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vllm_config.model_config.hf_config.num_labels = 1
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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pooler_config = vllm_config.model_config.pooler_config
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self._pooler = Pooler.from_config_with_defaults(
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pooler_config,
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pooling_type=PoolingType.ALL,
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normalize=False,
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softmax=False)
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class Qwen2ForProcessRewardModel(Qwen2RewardBaseModel):
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def __init__(self, *, vllm_config, prefix=""):
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vllm_config.model_config.hf_config.num_labels = 2
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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pooler_config = vllm_config.model_config.pooler_config
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self._pooler = Pooler.from_config_with_defaults(
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pooler_config,
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pooling_type=PoolingType.STEP,
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normalize=False,
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softmax=True,
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step_tag_id=151651,
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)
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