[WIP] Run eagle with full cudagraph

examples/offline_inference/eagle.py

@@ -48,6 +48,7 @@ def parse_args():
     parser.add_argument("--enable_chunked_prefill", action="store_true")
     parser.add_argument("--max_num_batched_tokens", type=int, default=2048)
     parser.add_argument("--temp", type=float, default=0)
+    parser.add_argument("--compilation_config", type=str, default="")
     return parser.parse_args()
 
 
@@ -94,6 +95,9 @@ def main():
             "max_model_len": max_model_len,
         },
         disable_log_stats=False,
+        compilation_config=(
+            json.loads(args.compilation_config) if args.compilation_config else None
+        ),
     )
 
     sampling_params = SamplingParams(temperature=args.temp, max_tokens=256)

vllm/v1/spec_decode/eagle.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any, Optional
+
 import torch
 import torch.nn as nn
 
@@ -74,6 +76,7 @@ def __init__(
                                    1,
                                    device=device,
                                    dtype=torch.int32)
+        self.draft_attn_metadata = None
 
     def propose(
         self,
@@ -169,6 +172,13 @@ def propose(
         self.positions[:num_tokens] = target_positions
         self.hidden_states[:num_tokens] = target_hidden_states
 
+        # copy attention metadata for full cudagraph mode
+        if self.draft_attn_metadata is not None and num_tokens <= self.cudagraph_batch_sizes[-1]:
+            self.draft_attn_metadata.seq_lens[:attn_metadata.seq_lens.shape[0]].copy_(attn_metadata.seq_lens.clone())
+            self.draft_attn_metadata.slot_mapping[:attn_metadata.slot_mapping.shape[0]].copy_(attn_metadata.slot_mapping.clone())
+            self.draft_attn_metadata.query_start_loc[:attn_metadata.query_start_loc.shape[0]].copy_(attn_metadata.query_start_loc.clone())
+            self.draft_attn_metadata.block_table[:attn_metadata.block_table.shape[0]].copy_(attn_metadata.block_table.clone())
+
         with set_forward_context(per_layer_attn_metadata,
                                  self.vllm_config,
                                  num_tokens=num_input_tokens):
@@ -254,6 +264,13 @@ def propose(
             self.positions[:batch_size] = clamped_positions
             self.hidden_states[:batch_size] = hidden_states
 
+            # copy attention metadata for full cudagraph mode
+            if self.draft_attn_metadata is not None:
+                self.draft_attn_metadata.seq_lens[:attn_metadata.seq_lens.shape[0]].copy_(attn_metadata.seq_lens.clone())
+                self.draft_attn_metadata.slot_mapping[:attn_metadata.slot_mapping.shape[0]].copy_(attn_metadata.slot_mapping.clone())
+                self.draft_attn_metadata.query_start_loc[:attn_metadata.query_start_loc.shape[0]].copy_(attn_metadata.query_start_loc.clone())
+                self.draft_attn_metadata.block_table[:attn_metadata.block_table.shape[0]].copy_(attn_metadata.block_table.clone())
+
             # Run the model.
             with set_forward_context(per_layer_attn_metadata,
                                      self.vllm_config,
@@ -369,8 +386,13 @@ def load_model(self, target_model: nn.Module) -> None:
     def dummy_run(
         self,
         num_tokens: int,
+        attn_metadata: Optional[dict[str, Any]],
     ) -> None:
-        with set_forward_context(None, self.vllm_config,
+        if attn_metadata is not None and self.draft_attn_metadata is None:
+            attn_metadata[self.attn_layer_names[0]].scheduler_metadata = None
+            self.draft_attn_metadata = attn_metadata[self.attn_layer_names[0]] # assume only one draft layer
+        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

@@ -1860,7 +1860,7 @@ def maybe_randomize_inputs(self, input_ids: torch.Tensor):
         Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
         This is to help balance expert-selection
          - during profile_run
-         - during DP rank dummy run 
+         - during DP rank dummy run
         """
         dp_size = self.vllm_config.parallel_config.data_parallel_size
         randomize_inputs = envs.VLLM_RANDOMIZE_DP_DUMMY_INPUTS and dp_size > 1
@@ -1982,7 +1982,7 @@ def _dummy_run(
 
             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)
 
         logit_indices = np.cumsum(num_scheduled_tokens) - 1
         return hidden_states, hidden_states[logit_indices]