110 lines
3.8 KiB
Python
110 lines
3.8 KiB
Python
# 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 Kakao Corp. (Kanana-X Team)
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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-Classification model compatible with HF weights."""
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from typing import Iterable, List, Optional, Tuple
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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, PoolerConfig
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from vllm.model_executor.layers.linear import RowParallelLinear
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from vllm.model_executor.layers.pooler import Pooler, PoolingType
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.models.qwen2 import Qwen2Model
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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 .utils import AutoWeightsLoader
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class Qwen2ForSequenceClassification(nn.Module):
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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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pooler_config: Optional[PoolerConfig] = 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` = {} is less than "
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"`num_hidden_layers` = {}. Please open an issue "
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"to discuss this feature.".format(
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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 = RowParallelLinear(config.hidden_size,
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config.num_labels,
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quant_config=quant_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.LAST,
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normalize=False,
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softmax=True)
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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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) -> torch.Tensor:
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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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loader = AutoWeightsLoader(self,
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ignore_unexpected_prefixes=["lm_head."])
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loader.load_weights(weights)
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