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133 lines
4.5 KiB
Python
133 lines
4.5 KiB
Python
# coding=utf-8
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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, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from transformers import Qwen2Config
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, LoRAConfig
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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
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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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 SupportsPP
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from .qwen2 import Qwen2Model
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from .utils import group_weights_with_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 Qwen2ForRewardModel(nn.Module, SupportsPP):
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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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# LoRA specific attributes
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supported_lora_modules = [
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"qkv_proj",
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"o_proj",
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"gate_up_proj",
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"down_proj",
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]
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embedding_modules = {}
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embedding_padding_modules = []
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def __init__(
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self,
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config: Qwen2Config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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lora_config: Optional[LoRAConfig] = None,
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) -> None:
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# TODO (@robertgshaw2): see if this can be moved out
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if (cache_config.sliding_window is not None
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and hasattr(config, "max_window_layers")):
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raise ValueError("Sliding window for some but all layers is not "
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"supported. This model uses sliding window "
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"but `max_window_layers` = %s is less than "
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"`num_hidden_layers` = %s. Please open an issue "
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"to discuss this feature." % (
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config.max_window_layers,
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config.num_hidden_layers,
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))
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super().__init__()
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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(config, cache_config, quant_config)
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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, 1,
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quant_config=quant_config),
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)
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self._pooler = Pooler(pooling_type=PoolingType.ALL, normalize=False)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, kv_caches,
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attn_metadata, intermediate_tensors)
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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, torch.Tensor]]):
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weights_group = group_weights_with_prefix(weights)
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self.model.load_weights(weights_group["model"])
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score_dict = dict(self.score.named_parameters())
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for name, loaded_weight in weights_group["score"]:
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param = score_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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