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[main] [bugfix] Fix misjudging quantized/unquantized scenarios (#2627)

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### What this PR does / why we need it?
In a mixed-precision scenario, quant_config is not None, but MoE needs
to perform unquantized computation; however, quantized computation is
currently being used. Therefore, we put the with_quant logic into
forward, avoid misjudging in mix-precision scenarios.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
e2e & ut

- vLLM version: v0.10.1.1
- vLLM main:
98ac0cb32d

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
This commit is contained in:
weichen
2025-08-29 16:20:22 +08:00
committed by GitHub
parent aadc75c247
commit 52aff9e229
7 changed files with 62 additions and 65 deletions
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42
tests/ut/ops/test_fused_ops.py
View File

@@ -543,7 +543,6 @@ class TestUnifiedApplyMLP(TestBase):
mock_get_forward_context):
mock_forward_context = MagicMock()
mock_forward_context.with_quant = True
mock_forward_context.fused_moe_state = FusedMoEState.MC2
mock_get_forward_context.return_value = mock_forward_context
@@ -587,10 +586,10 @@ class TestUnifiedApplyMLP(TestBase):
group_list_type=1,
w1_scale_bias=None,
w2_scale_bias=None,
topk_scales=None)
topk_scales=None,
with_quant=True)
mock_get_forward_context.assert_called()
self.assertTrue(mock_forward_context.with_quant)
self.assertEqual(mock_forward_context.fused_moe_state,
FusedMoEState.MC2)
@@ -602,19 +601,15 @@ class TestUnifiedApplyMLP(TestBase):
self.assertEqual(result.dtype, torch.bfloat16)
@patch('vllm_ascend.ops.fused_moe.get_forward_context')
@patch('vllm_ascend.ops.fused_moe.is_310p')
@patch('torch_npu.npu_grouped_matmul')
@patch('torch_npu.npu_swiglu')
@patch('torch_npu.npu_dynamic_quant')
def test_unified_apply_mlp_without_quantization(
self, mock_npu_dynamic_quant, mock_npu_swiglu,
mock_npu_grouped_matmul, mock_is_310p, mock_get_forward_context):
mock_forward_context = MagicMock()
mock_forward_context.with_quant = False
mock_get_forward_context.return_value = mock_forward_context
def test_unified_apply_mlp_without_quantization(self,
mock_npu_dynamic_quant,
mock_npu_swiglu,
mock_npu_grouped_matmul,
mock_is_310p):
mock_is_310p.return_value = False
mock_npu_grouped_matmul.side_effect = [[
@@ -639,10 +634,8 @@ class TestUnifiedApplyMLP(TestBase):
group_list_type=1,
w1_scale_bias=None,
w2_scale_bias=None,
topk_scales=topk_scales)
mock_get_forward_context.assert_called()
self.assertFalse(mock_forward_context.with_quant)
topk_scales=topk_scales,
with_quant=False)
self.assertEqual(mock_npu_grouped_matmul.call_count, 2)
mock_npu_swiglu.assert_called_once()
@@ -698,10 +691,10 @@ class TestUnifiedApplyMLP(TestBase):
group_list_type=1,
w1_scale_bias=w1_scale_bias,
w2_scale_bias=w2_scale_bias,
topk_scales=None)
topk_scales=None,
with_quant=True)
mock_get_forward_context.assert_called()
self.assertTrue(mock_forward_context.with_quant)
self.assertEqual(mock_npu_grouped_matmul.call_count, 2)
mock_npu_swiglu.assert_called_once()
@@ -710,19 +703,13 @@ class TestUnifiedApplyMLP(TestBase):
self.assertEqual(result.shape, hidden_states.shape)
self.assertEqual(result.dtype, torch.bfloat16)
@patch('vllm_ascend.ops.fused_moe.get_forward_context')
@patch('vllm_ascend.ops.fused_moe.is_310p')
@patch('torch_npu.npu_grouped_matmul')
@patch('torch_npu.npu_swiglu')
@patch('torch_npu.npu_dynamic_quant')
def test_unified_apply_mlp_without_quantization_310p(
self, mock_npu_dynamic_quant, mock_npu_swiglu,
mock_npu_grouped_matmul, mock_is_310p, mock_get_forward_context):
mock_forward_context = MagicMock()
mock_forward_context.with_quant = False
mock_get_forward_context.return_value = mock_forward_context
mock_npu_grouped_matmul, mock_is_310p):
mock_is_310p.return_value = True
mock_gmm1_out = torch.randn(10, 40, dtype=torch.float16)
@@ -750,10 +737,9 @@ class TestUnifiedApplyMLP(TestBase):
group_list_type=1,
w1_scale_bias=None,
w2_scale_bias=None,
topk_scales=topk_scales)
topk_scales=topk_scales,
with_quant=False)
mock_get_forward_context.assert_called()
self.assertFalse(mock_forward_context.with_quant)
mock_is_310p.assert_called_once()
self.assertEqual(mock_npu_grouped_matmul.call_count, 2)

13
tests/ut/ops/test_token_dispatcher.py
View File

@@ -263,7 +263,6 @@ class TestTokenDispatcherWithAllGather(TestBase):
"max_num_tokens": 100,
"ep_size": 2,
"num_experts": 128,
"with_quant": True,
}
self.dispatcher_quant = TokenDispatcherWithAllGather(**kwargs)
@@ -460,8 +459,7 @@ class TestTokenDispatcherWithAll2AllV(TestBase):
def test_token_dispatch_with_quant(self):
self.dispatcher = TokenDispatcherWithAll2AllV(top_k=2,
num_experts=4,
num_local_experts=2,
with_quant=True)
num_local_experts=2)
hidden_states = torch.randn(8, 16)
topk_weights = torch.rand(8, 4)
@@ -476,7 +474,8 @@ class TestTokenDispatcherWithAll2AllV(TestBase):
topk_weights=topk_weights,
topk_ids=topk_ids,
row_idx=self.row_idx,
expert_map=expert_map)
expert_map=expert_map,
with_quant=True)
self.assertIsNotNone(result["hidden_states"])
self.assertIsNotNone(result["group_list"])
@@ -486,8 +485,7 @@ class TestTokenDispatcherWithAll2AllV(TestBase):
def test_token_dispatch_with_quant_no_active_tokens(self):
self.dispatcher = TokenDispatcherWithAll2AllV(top_k=2,
num_experts=4,
num_local_experts=2,
with_quant=True)
num_local_experts=2)
self.mock_repeat_interleave.return_value = torch.tensor(
[], dtype=torch.long)
@@ -505,7 +503,8 @@ class TestTokenDispatcherWithAll2AllV(TestBase):
topk_weights=topk_weights,
topk_ids=topk_ids,
row_idx=self.row_idx,
expert_map=expert_map)
expert_map=expert_map,
with_quant=True)
self.assertIsNotNone(result["hidden_states"])
self.assertIsNotNone(result["group_list"])