Revert PTA upgrade PR (#3352)
we notice that torch npu 0919 doesn't work. This PR revert related change which rely on 0919 version. Revert PR: #3295 #3205 #3102 Related: #3353 - vLLM version: v0.11.0
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wangxiyuan
@@ -1,103 +0,0 @@
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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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('Solve the following math problem step by step.'
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'The last line of your response should be of the form Answer: '
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'$Answer (without quotes) where $Answer is the answer to the problem.\n\n'
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'In triangle $ABC$, $\\sin \\angle A = \\frac{4}{5}$ and $\\angle A < 90^\\circ$. Let $D$'
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'be a point outside triangle $ABC$ such that $\\angle BAD = \\angle DAC$,'
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'$\\angle BDC = 90^\\circ$. Suppose $AD = 1$ and $\\frac{BD}{CD} = \\frac{3}{2}$.'
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'If $AB + AC$ can be expressed in the form $\\frac{a\\sqrt{b}}{c}$,'
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'where $a, b, c$ are pairwise relatively prime integers, find $a + b + c$.'
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),
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('Solve the following math problem step by step.'
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'The last line of your response should be of the form Answer: '
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'$Answer (without quotes) where $Answer is the answer to the problem.\n\n'
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'Let $ABCD$ be a unit square in the plane. Points $X$ and $Y$ are chosen'
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'independently and uniformly at random on the perimeter of $ABCD$.'
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'If the expected value of the area of triangle $\\triangle AXY$'
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'can be expressed as $\\frac{m}{n}$, for relatively prime positive'
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'integers $m$ and $n$, compute $m+n$.'),
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('Solve the following math problem step by step.'
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'The last line of your response should be of the form Answer: '
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'$Answer (without quotes) where $Answer is the answer to the problem.\n\n'
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'Let $a, b, c$ be distinct numbers such that the equations $x^2 + ax + 1 = 0$'
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'and $x^2 + bx + c = 0$ have a common real root, and the equations $x^2 + x + a = 0$'
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'and $x^2 + cx + b = 0$ also have a common real root.'
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'Compute the sum $a + b + c$.')
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]
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model = "Qwen/Qwen3-30B-A3B"
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sampling_params = SamplingParams(max_tokens=5,
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n=1,
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temperature=0.0,
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top_p=1.0,
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top_k=1)
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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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gpu_memory_utilization=0.95,
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compilation_config={
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"cudagraph_capture_sizes":
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[4, 8, 12, 16, 24, 32, 40, 48],
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"cudagraph_mode": "FULL_DECODE_ONLY"
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}) 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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tensor_parallel_size=2,
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enforce_eager=True,
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gpu_memory_utilization=0.95,
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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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@@ -405,109 +405,6 @@ 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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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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):
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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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# 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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query,
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self_obj.key_cache,
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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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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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@@ -24,7 +24,7 @@ def mock_add_rms_norm(x, residual, weight, eps):
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def mock_add_rms_norm_quant(x, residual, weight, quant_scale, quant_offset,
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beta, epsilon):
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epsilon):
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x_out = 2 * x
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residual_out = 2 * residual
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x_out_quant = x_out.to(torch.int8)
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@@ -94,7 +94,7 @@ class TestAscendRMSNorm(PytestBase):
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mock_model_instance = mocker.MagicMock()
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mock_forward_context.model_instance = mock_model_instance
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mock_model_instance.model.layers = [
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mocker.MagicMock() for _ in range(3)
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mocker.MagicMock() for _ in range(2)
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]
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mock_layer_0 = mock_model_instance.model.layers[0]
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@@ -124,7 +124,7 @@ class TestAscendRMSNorm(PytestBase):
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mock_forward_context.addrmsnorm_quant_fusion_enabled = True
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mock_forward_context.prefetch_mlp_enabled = False
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mock_forward_context.layer_idx = 0
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mock_forward_context.num_hidden_layers = 3
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mock_forward_context.num_hidden_layers = 2
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mock_forward_context.fusion_linear = "gate_up_dense"
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# Ensure fusion and layer_idx increment are handled correctly
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@@ -144,32 +144,18 @@ class TestAscendRMSNorm(PytestBase):
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assert mock_forward_context.fusion_linear == "gate_up_dense"
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assert mock_forward_context.layer_idx == 1
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mock_forward_context.fusion_linear = "gate_moe"
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 3
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assert mock_forward_context.fusion_linear == "qkv_moe"
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assert mock_forward_context.fusion_linear == "qkv_dense"
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assert mock_forward_context.layer_idx == 2
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 4
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assert mock_forward_context.fusion_linear == "gate_moe"
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assert mock_forward_context.fusion_linear == "qkv_dense"
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assert mock_forward_context.layer_idx == 2
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# last layer returned directly
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 5
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assert mock_forward_context.fusion_linear == "qkv_moe"
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assert mock_forward_context.layer_idx == 3
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 6
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assert mock_forward_context.fusion_linear == "qkv_moe"
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assert mock_forward_context.layer_idx == 3
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if __name__ == '__main__':
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unittest.main()
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@@ -1,6 +1,5 @@
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from unittest.mock import MagicMock, patch
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import pytest
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import torch
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from tests.ut.base import TestBase
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@@ -17,10 +16,6 @@ class TestAscendW8A8FusedMoEMethod(TestBase):
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self.hidden_size,
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dtype=torch.bfloat16)
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@pytest.mark.skipif(
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True,
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reason="fix me",
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)
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@patch("torch.distributed.all_to_all_single")
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@patch("torch_npu.npu_moe_re_routing")
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@patch("torch_npu.npu_grouped_matmul")
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