first commit

vllm_br/model_executor/models/deepseek_mtp.py

@@ -0,0 +1,99 @@
+################################################################################
+# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+################################################################################
+
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from fastcore.basics import patch_to
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.models.deepseek_mtp import (
+    DeepSeekMultiTokenPredictor, DeepSeekMultiTokenPredictorLayer, SharedHead)
+from vllm.model_executor.models.deepseek_v2 import DeepseekV2DecoderLayer
+
+# from vllm.model_executor.sampling_metadata import SamplingMetadata
+# from vllm.model_executor.layers.sampler import get_sampler
+
+
+@patch_to(DeepSeekMultiTokenPredictorLayer)
+def __init__(self, vllm_config: VllmConfig, prefix: str) -> None:
+    super(DeepSeekMultiTokenPredictorLayer, self).__init__()
+
+    config = vllm_config.model_config.hf_config
+    quant_config = vllm_config.quant_config
+
+    self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+    self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+    self.eh_proj = nn.Linear(config.hidden_size * 2,
+                             config.hidden_size,
+                             bias=False)
+
+    self.is_v32 = hasattr(config, "index_topk")
+    if self.is_v32:
+        topk_tokens = config.index_topk
+        topk_indices_buffer = torch.empty(
+            vllm_config.scheduler_config.max_num_batched_tokens,
+            topk_tokens,
+            dtype=torch.int32,
+            device="cuda")
+    else:
+        topk_indices_buffer = None
+    self.shared_head = SharedHead(config=config, quant_config=quant_config)
+    self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix,
+                                            topk_indices_buffer)
+
+
+@patch_to(DeepSeekMultiTokenPredictorLayer)
+def forward(
+    self,
+    input_ids: torch.Tensor,
+    positions: torch.Tensor,
+    previous_hidden_states: torch.Tensor,
+    inputs_embeds: Optional[torch.Tensor] = None,
+    spec_step_index: int = 0,
+) -> torch.Tensor:
+    assert inputs_embeds is not None
+    # masking inputs at position 0, as not needed by MTP
+    inputs_embeds = torch.where((positions == 0).unsqueeze(-1),
+                                torch.zeros_like(inputs_embeds), inputs_embeds)
+    inputs_embeds = self.enorm(inputs_embeds.unsqueeze(0))
+    previous_hidden_states = self.hnorm(previous_hidden_states.unsqueeze(0))
+
+    fused_hidden_states = torch.cat([inputs_embeds, previous_hidden_states],
+                                    dim=-1)
+    hidden_states = self.eh_proj(fused_hidden_states)
+    hidden_states, residual = self.mtp_block(positions=positions,
+                                             hidden_states=hidden_states,
+                                             residual=None)
+    hidden_states = residual + hidden_states
+    return hidden_states.squeeze(0)
+
+
+@patch_to(DeepSeekMultiTokenPredictor)
+def compute_logits(
+    self,
+    hidden_states: torch.Tensor,
+    spec_step_idx: int = 0,
+) -> torch.Tensor:
+    current_step_idx = (spec_step_idx % self.num_mtp_layers)
+    mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
+    logits = self.logits_processor(
+        mtp_layer.shared_head.head,
+        mtp_layer.shared_head(
+            hidden_states.unsqueeze(0)).squeeze(0).contiguous())
+    return logits