56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.layernorm import RMSNorm
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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 ParallelLMHead
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from vllm.model_executor.models.deepseek_mtp import \
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DeepSeekMultiTokenPredictorLayer
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from vllm.model_executor.models.deepseek_v2 import DeepseekV2DecoderLayer
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from vllm.model_executor.models.utils import maybe_prefix
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class SharedHead(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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prefix: str,
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quant_config: QuantizationConfig = None,
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) -> None:
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super().__init__()
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "head"),
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return self.norm(hidden_states)
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def predictor_init(self, vllm_config: VllmConfig, prefix: str) -> None:
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nn.Module.__init__(self)
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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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self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.eh_proj = nn.Linear(config.hidden_size * 2,
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config.hidden_size,
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bias=False)
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# We don't need topk_indices_buffer in Ascend
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topk_indices_buffer = None
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self.shared_head = SharedHead(config=config,
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prefix=prefix,
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quant_config=quant_config)
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self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix,
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topk_indices_buffer)
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DeepSeekMultiTokenPredictorLayer.__init__ = predictor_init
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