fix pagedattention to support fullgraph. (#3436)
### What this PR does / why we need it? Calculate in advance the workspace memory size needed for the PagedAttention operator to avoid deadlocks during resource cleanup. This PR requires torch_npu version 0920 or newer. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 Signed-off-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com> Co-authored-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
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XiaoxinWang
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tests/e2e/multicard/test_full_graph_mode.py
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tests/e2e/multicard/test_full_graph_mode.py
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#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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"""Compare the short outputs of HF and vLLM when using greedy sampling.
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Run `pytest tests/e2e/multicard/test_qwen3_moe.py`.
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"""
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import os
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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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from tests.e2e.model_utils import check_outputs_equal
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def test_models_distributed_Qwen3_MOE_TP2_WITH_FULLGRAPH():
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if 'HCCL_OP_EXPANSION_MODE' in os.environ:
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del os.environ['HCCL_OP_EXPANSION_MODE']
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prompts = [
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"Hello, my name is", "The president of the United States is",
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"The capital of France is", "The future of AI is"
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]
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model = "Qwen/Qwen3-30B-A3B"
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sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
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with VllmRunner(model,
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max_model_len=1024,
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tensor_parallel_size=2,
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enforce_eager=False,
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compilation_config={"cudagraph_mode":
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"FULL_DECODE_ONLY"}) as runner:
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vllm_fullgraph_outputs = runner.model.generate(prompts,
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sampling_params)
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with VllmRunner(
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model,
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max_model_len=1024,
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enforce_eager=True,
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) as runner:
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vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
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vllm_fullgraph_outputs_list = []
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for output in vllm_fullgraph_outputs:
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vllm_fullgraph_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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vllm_eager_outputs_list = []
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for output in vllm_eager_outputs:
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vllm_eager_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs_list,
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outputs_1_lst=vllm_fullgraph_outputs_list,
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name_0="vllm_eager_outputs",
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name_1="vllm_fullgraph_outputs",
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)
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@@ -405,6 +405,113 @@ class TestAscendAttentionBackendImpl(TestBase):
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mock_paged_attention.assert_called_once()
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assert output.shape == (10, 8 * 64)
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@patch('vllm_ascend.attention.attention_v1.get_forward_context')
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@patch('vllm_ascend.attention.attention_v1.get_graph_params')
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@patch('torch_npu._npu_reshape_and_cache')
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@patch('torch_npu._npu_paged_attention')
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@patch('torch.npu.graph_task_group_end')
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@patch('torch.npu.graph_task_group_begin')
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@patch('torch.npu.ExternalEvent')
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@patch('torch_npu.npu.current_stream')
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@patch('vllm_ascend.attention.attention_v1.weak_ref_tensors')
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def test_paged_attention_with_existing_workspace(
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self,
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mock_get_forward_context,
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mock_get_graph_params,
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mock_npu_reshape_and_cache,
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mock_paged_attention,
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mock_graph_begin,
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mock_graph_end,
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mock_external_event_class,
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mock_current_stream,
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mock_weak_ref_tensors,
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):
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graph_params = MagicMock()
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attn_metadata = MagicMock()
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num_tokens = 10
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graph_params.workspaces = {num_tokens: 10}
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graph_params.events = {num_tokens: []}
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graph_params.attn_params = {num_tokens: []}
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graph_params.handles = {num_tokens: []}
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query = torch.randn(2, 5, 8) # [batch_size, seq_len, hidden_size]
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key_cache = MagicMock()
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value_cache = MagicMock()
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num_kv_heads = 4
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num_heads = 8
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scale = 0.1
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output = torch.randn(2, 5, 8)
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self_obj = MagicMock()
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self_obj.key_cache = key_cache
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self_obj.value_cache = value_cache
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self_obj.num_kv_heads = num_kv_heads
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self_obj.num_heads = num_heads
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self_obj.scale = scale
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mock_stream = MagicMock()
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mock_current_stream.return_value = mock_stream
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mock_event_instance = MagicMock()
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mock_external_event_class.return_value = mock_event_instance
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mock_handle = MagicMock()
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mock_graph_end.return_value = mock_handle
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workspace = graph_params.workspaces.get(num_tokens)
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self.assertEqual(workspace, 10)
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weak_ref_tensors = MagicMock(side_effect=lambda x: x)
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# 2. Handle graph capturing mode
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stream = mock_current_stream()
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event = mock_external_event_class()
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event.wait(stream)
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event.reset(stream)
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graph_params.events[num_tokens].append(event)
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graph_params.attn_params[num_tokens].append((
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weak_ref_tensors(query),
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weak_ref_tensors(self_obj.key_cache),
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weak_ref_tensors(self_obj.value_cache),
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self_obj.num_kv_heads,
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self_obj.num_heads,
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self_obj.scale,
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weak_ref_tensors(attn_metadata.block_tables),
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attn_metadata.seq_lens,
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output,
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))
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mock_event_instance.wait.assert_called_once_with(mock_stream)
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mock_event_instance.reset.assert_called_once_with(mock_stream)
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self.assertEqual(len(graph_params.events[num_tokens]), 1)
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self.assertEqual(len(graph_params.attn_params[num_tokens]), 1)
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query = torch.randn(10, 8 * 64)
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key = torch.randn(10, 8 * 64)
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value = torch.randn(10, 8 * 64)
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kv_cache = torch.empty(2, 5, 128, 8, 64)
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metadata = self.attn_metadata
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metadata.attn_state = AscendAttentionState.DecodeOnly
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metadata.seq_lens = torch.tensor([10])
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metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
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metadata.num_actual_tokens = 10
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metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
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layer = self.layer_no_quant
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mock_get_forward_context.return_value = MagicMock(capturing=True)
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mock_get_graph_params.return_value = graph_params
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output = self.impl.forward(layer,
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query,
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key,
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value,
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kv_cache,
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metadata,
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trace_flag=False)
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mock_paged_attention.assert_called_once()
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self.assertEqual(len(graph_params.handles[num_tokens]), 0)
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@patch('torch_npu._npu_reshape_and_cache')
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@patch('torch_npu.npu_fused_infer_attention_score')
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def test_forward_decode_only_swa(self, mock_fused_infer_attention_score,
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