100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
################################################################################
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# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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################################################################################
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from typing import Optional
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import torch
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import torch.nn as nn
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from fastcore.basics import patch_to
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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.models.deepseek_mtp import (
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DeepSeekMultiTokenPredictor, DeepSeekMultiTokenPredictorLayer, SharedHead)
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from vllm.model_executor.models.deepseek_v2 import DeepseekV2DecoderLayer
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# from vllm.model_executor.sampling_metadata import SamplingMetadata
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# from vllm.model_executor.layers.sampler import get_sampler
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@patch_to(DeepSeekMultiTokenPredictorLayer)
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def __init__(self, vllm_config: VllmConfig, prefix: str) -> None:
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super(DeepSeekMultiTokenPredictorLayer, self).__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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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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self.is_v32 = hasattr(config, "index_topk")
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if self.is_v32:
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topk_tokens = config.index_topk
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topk_indices_buffer = torch.empty(
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vllm_config.scheduler_config.max_num_batched_tokens,
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topk_tokens,
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dtype=torch.int32,
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device="cuda")
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else:
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topk_indices_buffer = None
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self.shared_head = SharedHead(config=config, 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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@patch_to(DeepSeekMultiTokenPredictorLayer)
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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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previous_hidden_states: torch.Tensor,
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inputs_embeds: Optional[torch.Tensor] = None,
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spec_step_index: int = 0,
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) -> torch.Tensor:
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assert inputs_embeds is not None
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# masking inputs at position 0, as not needed by MTP
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inputs_embeds = torch.where((positions == 0).unsqueeze(-1),
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torch.zeros_like(inputs_embeds), inputs_embeds)
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inputs_embeds = self.enorm(inputs_embeds.unsqueeze(0))
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previous_hidden_states = self.hnorm(previous_hidden_states.unsqueeze(0))
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fused_hidden_states = torch.cat([inputs_embeds, previous_hidden_states],
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dim=-1)
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hidden_states = self.eh_proj(fused_hidden_states)
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hidden_states, residual = self.mtp_block(positions=positions,
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hidden_states=hidden_states,
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residual=None)
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hidden_states = residual + hidden_states
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return hidden_states.squeeze(0)
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@patch_to(DeepSeekMultiTokenPredictor)
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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current_step_idx = (spec_step_idx % self.num_mtp_layers)
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mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
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logits = self.logits_processor(
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mtp_layer.shared_head.head,
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mtp_layer.shared_head(
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hidden_states.unsqueeze(0)).squeeze(0).contiguous())
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return logits
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