Merge branch 'v0.9.2-dev-wm' into 'v0.9.2-dev'

[feat]支持mtp模型full_cuda_graph

See merge request dcutoolkit/deeplearing/vllm!172

vllm/model_executor/models/deepseek_mtp.py

@@ -150,7 +150,7 @@ class DeepSeekMultiTokenPredictor(nn.Module):
                                        sampling_metadata)
         return logits
 
-#@support_torch_compile
+@support_torch_compile
 class DeepSeekMTP(nn.Module, SupportsPP):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):

vllm/v1/attention/backends/mla/common.py

@@ -647,13 +647,13 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
                 rarange = np.repeat(query_lens, query_lens) - arange - 1
 
                 repeats = torch.from_numpy(query_lens).pin_memory().to(
-                    block_table_tensor.device, non_blocking=True)
+                    block_table_tensor.device, non_blocking=True).contiguous()
                 decode_block_table_tensor = torch.repeat_interleave(
                     block_table_tensor[:self._num_decodes, ...],
-                    repeats, dim=0)
-                decode_seq_lens = torch.repeat_interleave(seq_lens[:self._num_decodes], repeats, dim=0)
+                    repeats, dim=0).contiguous()
+                decode_seq_lens = torch.repeat_interleave(seq_lens[:self._num_decodes], repeats, dim=0).contiguous()
                 seq_lens_minus = torch.from_numpy(rarange).to(torch.int32).pin_memory().to(
-                    seq_lens.device, non_blocking=True)
+                    seq_lens.device, non_blocking=True).contiguous()
                 decode_seq_lens = decode_seq_lens - seq_lens_minus
 
                 if self.spec_decode_block_table_tensor is not None:

vllm/v1/spec_decode/eagle.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any, Optional
+import numpy as np
+
 import torch
 import torch.nn as nn
 
@@ -56,6 +59,9 @@ class EagleProposer:
         self.use_cuda_graph = (self.vllm_config.compilation_config.level
                                == CompilationLevel.PIECEWISE and
                                not self.vllm_config.model_config.enforce_eager)
+        self.use_full_cuda_graph = (
+            self.use_cuda_graph
+            and vllm_config.compilation_config.full_cuda_graph)
         self.cudagraph_batch_sizes = list(
             reversed(
                 self.vllm_config.compilation_config.cudagraph_capture_sizes))
@@ -71,6 +77,9 @@ class EagleProposer:
             (self.max_num_tokens, self.hidden_size),
             dtype=self.dtype,
             device=device)
+
+        # attention metadata captured in full cudagraph mode
+        self.attn_metadata_cudagraph = None
         # We need +1 here because the arange is used to set query_start_loc,
         # which has one more element than batch_size.
         self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs +
@@ -157,7 +166,7 @@ class EagleProposer:
             # FIXME: need to consider multiple kv_cache_groups
             attn_metadata = self.runner.attn_metadata_builders[0].build(
                 common_prefix_len=0,
-                common_attn_metadata=common_attn_metadata,
+                common_attn_metadata=common_attn_metadata
             )
         else:
             raise ValueError(f"Unsupported method: {self.method}")
@@ -176,6 +185,38 @@ class EagleProposer:
         self.positions[:num_tokens] = target_positions
         self.hidden_states[:num_tokens] = target_hidden_states
 
+        if (self.use_full_cuda_graph
+                and num_tokens <= self.cudagraph_batch_sizes[-1]):
+            assert self.attn_metadata_cudagraph
+            if self.method in ["eagle", "eagle3"]:
+                self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
+                    attn_metadata.seq_lens)
+                self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
+                    attn_metadata.slot_mapping)
+                self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
+                    attn_metadata.query_start_loc)
+                self.attn_metadata_cudagraph.block_table[:batch_size] = (
+                    attn_metadata.block_table)
+            elif self.method == "deepseek_mtp":
+                self.attn_metadata_cudagraph.num_actual_tokens = (
+                    attn_metadata.num_actual_tokens)
+                self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
+                    attn_metadata.query_start_loc)
+                self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
+                    attn_metadata.slot_mapping)
+                self.attn_metadata_cudagraph.num_decodes = (
+                    attn_metadata.num_decodes)
+                self.attn_metadata_cudagraph.num_decode_tokens = (
+                    attn_metadata.num_decode_tokens)
+                self.attn_metadata_cudagraph.num_prefills = (
+                    attn_metadata.num_prefills)
+
+                if attn_metadata.decode is not None:
+                    self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
+                        attn_metadata.decode.block_table)
+                    self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
+                        attn_metadata.decode.seq_lens)
+
         with set_forward_context(per_layer_attn_metadata,
                                  self.vllm_config,
                                  num_tokens=num_input_tokens):
@@ -190,6 +231,7 @@ class EagleProposer:
                 last_hidden_states, hidden_states = ret_hidden_states
         sample_hidden_states = last_hidden_states[last_token_indices]
         logits = self.model.compute_logits(sample_hidden_states, None)
+
         draft_token_ids = logits.argmax(dim=-1)
 
         # Early exit if there is only one draft token to be generated.
@@ -227,10 +269,10 @@ class EagleProposer:
             block_table = self.runner.attn_metadata_builders[0].block_table.get_device_tensor()[:batch_size, ...]
             attn_metadata.decode = self.runner.attn_metadata_builders[0]._build_decode(
                 block_table_tensor=block_table,
-                seq_lens=(seq_lens + 1),
+                seq_lens=seq_lens,
             )
 
-        for _ in range(self.num_speculative_tokens - 1):
+        for i in range(self.num_speculative_tokens - 1):
             # Update the inputs.
             # cast to int32 is crucial when eagle model is compiled.
             # tensor.argmax() returns int64 by default.
@@ -282,6 +324,43 @@ class EagleProposer:
             self.positions[:batch_size] = clamped_positions
             self.hidden_states[:batch_size] = hidden_states
 
+            if (self.use_full_cuda_graph
+                    and batch_size <= self.cudagraph_batch_sizes[-1]):
+                assert self.attn_metadata_cudagraph
+                if self.method in ["eagle", "eagle3"]:
+                    self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
+                        attn_metadata.seq_lens)
+                    self.attn_metadata_cudagraph.slot_mapping[:batch_size] = (
+                        attn_metadata.slot_mapping)
+                    if i == 0:
+                        self.attn_metadata_cudagraph.query_start_loc[:batch_size +
+                                                                    1] = (
+                                                                        attn_metadata
+                                                                        .
+                                                                        query_start_loc
+                                                                    )
+                        self.attn_metadata_cudagraph.block_table[:batch_size] = (
+                            attn_metadata.block_table)
+                elif self.method == "deepseek_mtp":
+                    self.attn_metadata_cudagraph.num_actual_tokens = (
+                        attn_metadata.num_actual_tokens)
+                    self.attn_metadata_cudagraph.slot_mapping[:attn_metadata.num_decode_tokens] = (
+                        attn_metadata.slot_mapping)
+                    self.attn_metadata_cudagraph.num_decodes = (
+                        attn_metadata.num_decodes)
+                    self.attn_metadata_cudagraph.num_decode_tokens = (
+                        attn_metadata.num_decode_tokens)
+                    self.attn_metadata_cudagraph.num_prefills = (
+                        attn_metadata.num_prefills)
+                    self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
+                        attn_metadata.decode.seq_lens)
+
+                    if i == 0:
+                        self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
+                            attn_metadata.query_start_loc)
+                        self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
+                            attn_metadata.decode.block_table)
+
             # Run the model.
             with set_forward_context(per_layer_attn_metadata,
                                      self.vllm_config,
@@ -307,6 +386,7 @@ class EagleProposer:
 
         # [batch_size, num_speculative_tokens]
         draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
+
         return draft_token_ids
 
     @staticmethod
@@ -342,7 +422,7 @@ class EagleProposer:
         )
         batch_size = num_rejected_tokens.shape[0]
         BLOCK_SIZE = 1024
-        prepare_eagle_input_kernel[(batch_size,)](
+        prepare_eagle_input_kernel[(batch_size, )](
             token_indices,
             cu_target_query_lens,
             cu_num_tokens,
@@ -404,8 +484,13 @@ class EagleProposer:
     def dummy_run(
         self,
         num_tokens: int,
+        attn_metadata: Optional[dict[str, Any]] = None,
     ) -> None:
-        with set_forward_context(None, self.vllm_config,
+        if attn_metadata is not None and self.attn_metadata_cudagraph is None:
+            self.attn_metadata_cudagraph = attn_metadata[
+                self.attn_layer_names[0]]
+        with set_forward_context(attn_metadata,
+                                 self.vllm_config,
                                  num_tokens=num_tokens):
             self.model(
                 self.input_ids[:num_tokens],

vllm/v1/worker/gpu_model_runner.py

@@ -2083,7 +2083,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
 
             if self.speculative_config and self.speculative_config.use_eagle():
                 assert isinstance(self.drafter, EagleProposer)
-                self.drafter.dummy_run(num_tokens)
+                self.drafter.dummy_run(num_tokens, attn_metadata)
 
         # This is necessary to avoid blocking DP.
         # For dummy runs, we typically skip EPLB since we don't have any real