[Refactor] Remove unused dead code (#40640)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/layers/batch_invariant.py

@@ -39,7 +39,7 @@ def _matmul_launch_metadata(
 
 
 @triton.jit
-def _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS):
+def _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M):
     group_id = tile_id // num_pid_in_group
     first_pid_m = group_id * GROUP_SIZE_M
     group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
@@ -85,9 +85,7 @@ def matmul_kernel_persistent(
     num_pid_in_group = GROUP_SIZE_M * num_pid_n
 
     for tile_id in tl.range(start_pid, num_tiles, NUM_SMS, flatten=True):
-        pid_m, pid_n = _compute_pid(
-            tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
-        )
+        pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)
         start_m = pid_m * BLOCK_SIZE_M
         start_n = pid_n * BLOCK_SIZE_N
         offs_am = start_m + tl.arange(0, BLOCK_SIZE_M)
@@ -124,7 +122,7 @@ def matmul_kernel_persistent(
 
         tile_id_c += NUM_SMS
         pid_m, pid_n = _compute_pid(
-            tile_id_c, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
+            tile_id_c, num_pid_in_group, num_pid_m, GROUP_SIZE_M
         )
         offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
         offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)

vllm/v1/worker/dp_utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-
-import numpy as np
 import torch
 import torch.distributed as dist
 
@@ -167,7 +165,6 @@ def coordinate_batch_across_dp(
     parallel_config: ParallelConfig,
     num_tokens_padded: int | None = None,
     uniform_decode: bool | None = None,
-    num_scheduled_tokens_per_request: np.ndarray | None = None,
     cudagraph_mode: int = 0,
 ) -> tuple[bool, torch.Tensor | None, int]:
     """
@@ -182,8 +179,6 @@ def coordinate_batch_across_dp(
             TP, etc)
         uniform_decode: Only used if allow_microbatching is True. True if the batch
             only contains single token decodes
-        num_scheduled_tokens_per_request: Only used if allow_microbatching is True. The
-            number of tokens per request.
         cudagraph_mode: The cudagraph mode for this rank (0=NONE, 1=PIECEWISE, 2=FULL).
             DP padding is enabled when synced cudagraph mode across ranks is not NONE.
 

vllm/v1/worker/gpu/dp_utils.py

@@ -13,12 +13,6 @@ from vllm.v1.worker.gpu.cudagraph_utils import (
 )
 
 
-def make_num_tokens_across_dp(dp_size: int, num_tokens: int) -> torch.Tensor | None:
-    if dp_size == 1:
-        return None
-    return torch.full((dp_size,), num_tokens, dtype=torch.int32, device="cpu")
-
-
 def sync_cudagraph_and_dp_padding(
     cudagraph_manager: CudaGraphManager | None,
     desired_batch_desc: BatchExecutionDescriptor,

vllm/v1/worker/gpu_model_runner.py

@@ -3362,7 +3362,6 @@ class GPUModelRunner(
         logits: torch.Tensor | None,
         hidden_states: torch.Tensor,
         num_scheduled_tokens: int,
-        spec_decode_metadata: SpecDecodeMetadata | None,
     ) -> tuple[
         dict[str, int],
         LogprobsLists | None,
@@ -3630,7 +3629,6 @@ class GPUModelRunner(
                     allow_microbatching=allow_microbatching,
                     num_tokens_padded=num_tokens_padded,
                     uniform_decode=uniform_decode,
-                    num_scheduled_tokens_per_request=num_scheduled_tokens_np,
                     cudagraph_mode=cudagraph_mode.value,
                 )
             )
@@ -4308,7 +4306,6 @@ class GPUModelRunner(
                 logits,
                 hidden_states,
                 scheduler_output.total_num_scheduled_tokens,
-                spec_decode_metadata,
             )
 
         if propose_drafts_after_bookkeeping:
@@ -6540,7 +6537,6 @@ class GPUModelRunner(
 
     def _reshape_kv_cache_tensors(
         self,
-        kv_cache_config: KVCacheConfig,
         kv_cache_raw_tensors: dict[str, torch.Tensor],
         kernel_block_sizes: list[int],
     ) -> dict[str, torch.Tensor]:
@@ -6548,7 +6544,6 @@ class GPUModelRunner(
         Reshape the KV cache tensors to the desired shape and dtype.
 
         Args:
-            kv_cache_config: The KV cache config
             kv_cache_raw_tensors: The KV cache buffer of each layer, with
                 correct size but uninitialized shape.
             kernel_block_sizes: The kernel block sizes for each KV cache group.
@@ -6712,7 +6707,7 @@ class GPUModelRunner(
 
             # Change the memory buffer to the desired shape
             kv_caches = self._reshape_kv_cache_tensors(
-                kv_cache_config, kv_cache_raw_tensors, kernel_block_sizes
+                kv_cache_raw_tensors, kernel_block_sizes
             )
 
         # Set up cross-layer KV cache sharing