remove seq_lens_tensor of v1 fa

vllm/v1/attention/backends/flash_attn.py

@@ -123,7 +123,6 @@ class FlashAttentionMetadata:
     query_start_loc: torch.Tensor
     max_seq_len: int
     seq_lens: torch.Tensor
-    # seq_lens_tensor: torch.Tensor
     block_table: torch.Tensor
     slot_mapping: torch.Tensor
 
@@ -228,7 +227,6 @@ class FlashAttentionMetadataBuilder(
         max_seq_len = int(self.runner.seq_lens_np[:num_reqs].max())
         query_start_loc = common_attn_metadata.query_start_loc
         seq_lens = common_attn_metadata.seq_lens
-        # seq_lens_tensor = common_attn_metadata.seq_lens_tensor
         block_table = self.block_table
         block_table_tensor = block_table.get_device_tensor()[:num_reqs]
 
@@ -391,7 +389,6 @@ class FlashAttentionMetadataBuilder(
             query_start_loc=query_start_loc,
             max_seq_len=max_seq_len,
             seq_lens=seq_lens,
-            # seq_lens_tensor=seq_lens_tensor,
             block_table=block_table_tensor,
             slot_mapping=slot_mapping,
             use_cascade=use_cascade,
@@ -623,53 +620,6 @@ class FlashAttentionImpl(AttentionImpl):
                     # num_splits=attn_metadata.max_num_splits,
                     is_prefix_cache=False,
                 )
-                
-                # if num_actual_tokens > 1:
-                #     vllm_flash_attn_varlen_func(
-                #         q=query[:num_actual_tokens],
-                #         k=key_cache,
-                #         v=value_cache,
-                #         out=output[:num_actual_tokens],
-                #         cu_seqlens_q=cu_seqlens_q,
-                #         max_seqlen_q=max_seqlen_q,
-                #         seqused_k=seqused_k,
-                #         max_seqlen_k=max_seqlen_k,
-                #         softmax_scale=self.scale,
-                #         causal=True,
-                #         alibi_slopes=self.alibi_slopes,
-                #         window_size=self.sliding_window,
-                #         block_table=block_table,
-                #         softcap=self.logits_soft_cap,
-                #         scheduler_metadata=scheduler_metadata,
-                #         # fa_version=self.vllm_flash_attn_version,
-                #         # q_descale=layer._q_scale.expand(descale_shape),
-                #         # k_descale=layer._k_scale.expand(descale_shape),
-                #         # v_descale=layer._v_scale.expand(descale_shape),
-                #         # num_splits=attn_metadata.max_num_splits,
-                #         is_prefix_cache=False,
-                #     )
-                # else:
-                #     from flash_attn import vllm_flash_attn_with_kvcache
-                #     if envs.VLLM_USE_PA_PRINT_PARAM:
-                #         print("PA SIZE:")
-                #         print(f"q.shape = {query[:num_actual_tokens].unsqueeze(1).shape}, key_cache.shape = {key_cache.shape}, value_cache.shape = {value_cache.shape}, kv_cache_dtype = {self.kv_cache_dtype}")
-                #         print(f"cache_seqlens.shape = {attn_metadata.seq_lens_tensor.shape}, block_tables.shape = {block_table.shape}")
-                #         print(f"softmax_scale = {self.scale:.3f}, window_size = {self.sliding_window}, softcap = {self.logits_soft_cap}, alibi_slopes = {self.alibi_slopes}")
-                       
-                #     output[:num_actual_tokens] = vllm_flash_attn_with_kvcache(
-                #         q=query[:num_actual_tokens].unsqueeze(1),
-                #         k_cache=key_cache,
-                #         v_cache=value_cache,
-                #         cache_seqlens=attn_metadata.seq_lens_tensor,
-                #         softmax_scale=self.scale,
-                #         causal=True,
-                #         alibi_slopes=self.alibi_slopes,
-                #         window_size=self.sliding_window,
-                #         block_table=block_table,
-                #         softcap=self.logits_soft_cap,
-                #         # k_scale=layer._k_scale.expand(descale_shape),
-                #         # v_scale=layer._v_scale.expand(descale_shape),
-                #     ).squeeze(1) 
             return output
 
         assert not use_local_attn, (

vllm/v1/attention/backends/utils.py

@@ -4,7 +4,7 @@ import abc
 import functools
 from abc import abstractmethod
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, ClassVar, Generic, TypeVar, Optional
+from typing import TYPE_CHECKING, ClassVar, Generic, TypeVar
 
 import numpy as np
 import torch
@@ -35,8 +35,6 @@ class CommonAttentionMetadata:
     seq_lens: torch.Tensor
     """(batch_size,), the length of each request including both computed tokens
     and newly scheduled tokens"""
-    # seq_lens_tensor: torch.Tensor
-    # """seq_lens stored as a tensor."""
     num_reqs: int
     """Number of requests"""
     num_actual_tokens: int

vllm/v1/worker/gpu_model_runner.py

@@ -248,7 +248,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         self.seq_lens = torch.zeros(self.max_num_reqs,
                                     dtype=torch.int32,
                                     device=self.device)
-        # self.seq_lens_tensor = torch.zeros_like(self.seq_lens)
         self.slot_mapping = torch.zeros(self.max_num_tokens,
                                         dtype=torch.int64,
                                         device=self.device)
@@ -702,7 +701,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
 
         query_start_loc = self.query_start_loc[:num_reqs + 1]
         seq_lens = self.seq_lens[:num_reqs]
-        # seq_lens_tensor = self.seq_lens_tensor[:num_reqs]
 
         common_attn_metadata = CommonAttentionMetadata(
             query_start_loc=query_start_loc,
@@ -2033,7 +2031,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             self.seq_lens[:num_reqs].copy_(self.seq_lens_cpu[:num_reqs],
                                            non_blocking=True)
             seq_lens = self.seq_lens[:num_reqs]
-            # seq_lens_tensor = self.seq_lens_tensor[:num_reqs]
 
             num_speculative_tokens = 0 if self.speculative_config is None else self.speculative_config.num_lookahead_slots
             common_attn_metadata = CommonAttentionMetadata(