Merge branch 'vllm-project:main' into main

Author: fems14

.gemini/styleguide.md

@@ -1,5 +1,14 @@
 # Pull Request Summary Style Guide
 
+## Output Instructions
+
+**IMPORTANT**: When doing PR review, you MUST output them in markdown code blocks so users can easily copy them:
+
+1. **PR Title**: Output the generated title in a code block with triple backticks
+2. **PR Summary**: Output the generated summary in a markdown code block with triple backticks
+
+This allows users to directly copy the content without manual formatting.
+
 ## Pull Request Summary Format
 
 The summary should follow the format:
@@ -37,7 +46,7 @@ The summary should also refresh the Pull Request Title to follow the format:
     [Branch][Module][Action] Pull Request Title
     ```
 
-- Branch: The branch name where the PR is based.
+- Branch: The branch name where the PR is based. If the base branch is main, this prefix can be omitted.
 - Module: The module or component being changed. It includes but is not limited to the following:
     - [Attention]
     - [Ops]
@@ -49,3 +58,33 @@ The summary should also refresh the Pull Request Title to follow the format:
     - [BugFix]
     - [Feature]
     - [Misc]
+
+## Example Output Format
+
+When providing a PR review, format your response like this:
+
+**Suggested PR Title:**
+
+```markdown
+[Branch][Module][Action] Your generated title here
+```
+
+**Suggested PR Summary:**
+
+```markdown
+### What this PR does / why we need it?
+
+Your analysis of what the PR does and why it's needed.
+
+Fixes #issue_number
+
+### Does this PR introduce _any_ user-facing change?
+
+Your assessment of user-facing changes.
+
+### How was this patch tested?
+
+Your description of testing approach.
+```
+
+And please print your review suggestion in markdown format.

tests/e2e/singlecard/test_aclgraph_accuracy.py

@@ -44,29 +44,6 @@
     ],
 )
 
-CASE_QWEN_FULL = LLMTestCase(
-    model="Qwen/Qwen3-0.6B",
-    prompts=PROMPTS_SHORT,
-    golden_answers=[
-        " Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I want to know if there are any",
-        ' the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president',
-        ' Paris. The capital of France is also the capital of the Republic of France. The capital of France is also the capital of the European Union. The capital of',
-        ' not just a technological frontier but a profound transformation of how we live, work, and interact with the world. As we stand at the intersection of artificial intelligence and'
-    ],
-)
-
-CASE_DS_FULL = LLMTestCase(
-    model="vllm-ascend/DeepSeek-V2-Lite-W8A8",
-    quantization="ascend",
-    prompts=PROMPTS_SHORT,
-    golden_answers=[
-        '\nI am a 20 year old female, and I have been suffering from depression for 3 years now. I have been on medication for 2',
-        ' a man who has been in the public eye for decades. He has been a senator, a governor, and a businessman. He has also been married to the',
-        ' Paris, which is also the largest city in the country. The city is located on the River Seine and is known for its beautiful architecture, museums, and art',
-        ' here, and it’s not what you think.\nThe future of AI is here, and it’s not what you think.\nThe future of'
-    ],
-)
-
 CASE_QWEN_FULL_DECODE_ONLY = LLMTestCase(
     model="Qwen/Qwen3-0.6B",
     prompts=PROMPTS_LONG,
@@ -117,23 +94,6 @@ def test_piecewise_res_consistency(cur_case: LLMTestCase):
                   sampling_params=cur_case.sampling_params,
                   golden_answers=cur_case.golden_answers)
 
-@pytest.mark.parametrize(
-    "cur_case", [CASE_QWEN_FULL, CASE_DS_FULL])
-def test_full_res_consistency(cur_case: LLMTestCase, monkeypatch):
-    monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False)
-    runner_kwargs = {
-        "model_name": cur_case.model,
-        "max_model_len": 1024,
-        "compilation_config": {
-            "cudagraph_capture_sizes": [4, 8, 32, 64],
-            "cudagraph_mode": "FULL_DECODE_ONLY"
-        },
-        "quantization": cur_case.quantization,
-    }
-    gen_and_valid(runner_kwargs=runner_kwargs,
-                  prompts=cur_case.prompts,
-                  sampling_params=cur_case.sampling_params,
-                  golden_answers=cur_case.golden_answers)
 
 @pytest.mark.parametrize(
     "cur_case", [CASE_QWEN_FULL_DECODE_ONLY, CASE_DS_FULL_DECODE_ONLY])

vllm_ascend/worker/model_runner_v1.py

@@ -206,13 +206,6 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
         vllm_config.scheduler_config.max_num_batched_tokens += max_pcp_pad_tokens
         with _torch_cuda_wrapper():
             super().__init__(vllm_config, device)
-
-        # NOTE: For FULL mode we change +1 to +2 to reserve extra space for padding.
-        # See _pad_query_start_loc_for_fia.
-        self.query_start_loc = self._make_buffer(
-            self.max_num_reqs + 2, dtype=torch.int32  # type: ignore[has-type]
-        )
-
         vllm_config.scheduler_config.max_num_batched_tokens -= max_pcp_pad_tokens
         self.max_num_tokens = self.scheduler_config.max_num_batched_tokens
         self.max_num_reqs = self.scheduler_config.max_num_seqs
@@ -516,36 +509,6 @@ def get_model(self) -> nn.Module:
             return self.model.unwrap()
         return self.model
 
-    def _pad_query_start_loc_for_fia(
-        self, num_tokens_padded: int, num_reqs_padded: int, num_reqs: int
-    ) -> int:
-        """
-        This function is only designed to satisfied the constraint that when the layout is TND,
-        the first dimension of `hidden_states` must equal the last element of `actual_seq_lengths_q`.
-        """
-
-        if num_tokens_padded == num_reqs_padded * self.uniform_decode_query_len:
-            # Uniform-batch case: num_reqs must be no greater than num_reqs_padded
-            assert num_reqs <= num_reqs_padded
-
-            last_loc = self.query_start_loc.np[num_reqs]
-            self.query_start_loc.np[num_reqs + 1 : num_reqs_padded + 1] = (
-                self.arange_np[1 : num_reqs_padded + 1 - num_reqs]
-                * self.uniform_decode_query_len
-                + last_loc
-            )
-        else:
-            # Mixed-batch case: num_reqs must equal num_reqs_padded
-            assert num_reqs == num_reqs_padded
-
-            # Insert a dummy request instead of setting query_start_loc[num_reqs] = num_tokens_padded directly
-            self.query_start_loc.np[num_reqs_padded + 1] = num_tokens_padded
-            num_reqs_padded = num_reqs_padded + 1
-
-        self.query_start_loc.copy_to_gpu()
-
-        return num_reqs_padded
-
     def _prepare_inputs(
         self,
         scheduler_output: "SchedulerOutput",
@@ -707,6 +670,10 @@ def _prepare_inputs(
 
         self.query_start_loc.np[0] = 0
         self.query_start_loc.np[1:num_reqs + 1] = cu_num_tokens
+        # NOTE: Due to the FIA operator limitation, here we pad so that hidden_states.shape[0]
+        # and self.query_start_loc[num_reqs_padded] are equal
+        self.query_start_loc.np[num_reqs + 1:] = (self.arange_np[1:self.max_num_reqs + 1 - num_reqs]
+                                                  * self.uniform_decode_query_len + cu_num_tokens[-1])
         self.query_start_loc.copy_to_gpu()
 
         self.seq_lens.np[:num_reqs] = (
@@ -1188,7 +1155,6 @@ def execute_model(
                     scheduler_output,
                     num_scheduled_tokens_np,
                 )
-
                 num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
                 if self.pcp_size > 1:
                     num_tokens_unpadded = self.pcp_manager.total_num_sampled_tokens_pcp
@@ -1243,11 +1209,6 @@ def execute_model(
                 use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
                 ubatch_slices_attn = ubatch_slices_padded if pad_attn else ubatch_slices
 
-                if cudagraph_mode != CUDAGraphMode.NONE:
-                    num_reqs_padded = self._pad_query_start_loc_for_fia(
-                        num_tokens_padded, num_reqs_padded, num_reqs
-                    )
-
                 (attn_metadata, spec_decode_common_attn_metadata) = (
                     self._build_attention_metadata(
                         num_tokens=num_tokens_unpadded,
@@ -1382,6 +1343,7 @@ def execute_model(
                 assert broadcasted is not None
                 logits = broadcasted["logits"]
 
+
             # Apply structured output bitmasks if present
             self.execute_model_state = ExecuteModelState(
                 scheduler_output,
@@ -1981,13 +1943,6 @@ def _get_block_table_and_slot_mapping(kv_cache_gid: int):
         self.long_seq_metadata = _get_pcp_metadata(num_tokens)
         block_table_gid_0, slot_mapping_gid_0 = _get_block_table_and_slot_mapping(0)
 
-        actual_last_loc = self.query_start_loc.np[num_reqs_padded]
-        error_msg = (
-            f"Due to FIA kernel constraints, when the layout is TND, "
-            f"the first dimension of `hidden_states` ({num_tokens_padded}) "
-            f"must equal the last element of `actual_seq_lengths_q` ({actual_last_loc})."
-        )
-        assert self.query_start_loc.np[num_reqs_padded] == num_tokens_padded, error_msg
         cm_base = AscendCommonAttentionMetadata(
             query_start_loc=self.query_start_loc.gpu[: num_reqs_padded + 1],
             query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs_padded + 1],
@@ -2245,15 +2200,9 @@ def _dummy_run(
             self.seq_lens.np[:num_reqs_padded] = seq_lens
             self.seq_lens.np[num_reqs_padded:] = 0
             self.seq_lens.copy_to_gpu()
-
             cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
             self.query_start_loc.np[1 : num_reqs_padded + 1] = cum_num_tokens
             self.query_start_loc.copy_to_gpu()
-
-            num_reqs_padded = self._pad_query_start_loc_for_fia(
-                num_tokens_padded, num_reqs_padded, num_reqs
-            )
-
             pad_attn = cudagraph_runtime_mode == CUDAGraphMode.FULL
             attn_metadata, _ = self._build_attention_metadata(
                 num_tokens=num_tokens_unpadded,