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[cherry-pick][refactor]support gatingtopk operator generalization (#4050)

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### What this PR does / why we need it?
pick from : https://github.com/vllm-project/vllm-ascend/pull/2958
Past：
npu_moe_gating_top_k can only support 'group_count=256' pattern

Now：
1、npu_moe_gating_top_k support all size of group_count
2、the functionality of `torch_npu.npu_moe_gating_top_k_softmax` are
included in `torch_npu.npu_moe_gating_top_k`

CANN: depends on 8.3.RC1

Performance：
1. GLM4.5-w8a8, TPS improve 6%
2. Qwen3, the same as before


Signed-off-by: 1092626063 <1092626063@qq.com>
This commit is contained in:
1092626063
2025-11-19 10:39:28 +08:00
committed by GitHub
parent ddf3e75800
commit c87a77e8b4
3 changed files with 74 additions and 69 deletions
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63
tests/ut/quantization/test_w8a8.py
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@@ -754,6 +754,14 @@ class TestSelectExperts(TestBase):
self.hidden_states = torch.randn(self.num_tokens, self.hidden_size)
self.router_logits = torch.randn(self.num_tokens, self.num_experts)
"""Mock custom routing"""
self.mock_custom_routing = MagicMock()
self.mock_custom_routing.return_value = (torch.ones(
self.num_tokens, self.top_k),
torch.zeros(
self.num_tokens,
self.top_k,
dtype=torch.int32))
self.mock_ctx = MagicMock()
self.mock_ctx.weight_prefetch_method = MagicMock()
@@ -763,7 +771,7 @@ class TestSelectExperts(TestBase):
self.addCleanup(patcher.stop)
patcher.start()
@patch('torch_npu.npu_moe_gating_top_k_softmax')
@patch('torch_npu.npu_moe_gating_top_k')
def test_softmax_scoring(self, mock_topk):
"""Test softmax scoring function"""
mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k),
@@ -790,12 +798,14 @@ class TestSelectExperts(TestBase):
def test_sigmoid_scoring(self):
"""Test sigmoid scoring function"""
weights, ids = select_experts(hidden_states=self.hidden_states,
router_logits=self.router_logits,
top_k=self.top_k,
use_grouped_topk=False,
renormalize=False,
scoring_func="sigmoid")
weights, ids = select_experts(
hidden_states=self.hidden_states,
router_logits=self.router_logits,
top_k=self.top_k,
use_grouped_topk=False,
renormalize=False,
scoring_func="sigmoid",
custom_routing_function=self.mock_custom_routing)
self.assertEqual(weights.shape, (self.num_tokens, self.top_k))
self.assertEqual(ids.shape, (self.num_tokens, self.top_k))
@@ -808,7 +818,8 @@ class TestSelectExperts(TestBase):
top_k=self.top_k,
use_grouped_topk=False,
renormalize=False,
scoring_func="invalid_func")
scoring_func="invalid_func",
custom_routing_function=self.mock_custom_routing)
@patch('torch.topk')
def test_grouped_topk(self, mock_topk):
@@ -818,13 +829,15 @@ class TestSelectExperts(TestBase):
self.top_k,
dtype=torch.long))
weights, ids = select_experts(hidden_states=self.hidden_states,
router_logits=self.router_logits,
top_k=self.top_k,
use_grouped_topk=True,
renormalize=False,
topk_group=4,
num_expert_group=2)
weights, ids = select_experts(
hidden_states=self.hidden_states,
router_logits=self.router_logits,
top_k=self.top_k,
use_grouped_topk=True,
renormalize=False,
topk_group=4,
num_expert_group=2,
custom_routing_function=self.mock_custom_routing)
mock_topk.assert_called()
self.assertEqual(weights.shape, (self.num_tokens, self.top_k))
@@ -846,7 +859,8 @@ class TestSelectExperts(TestBase):
renormalize=False,
topk_group=4,
num_expert_group=2,
e_score_correction_bias=e_score_correction_bias)
e_score_correction_bias=e_score_correction_bias,
custom_routing_function=self.mock_custom_routing)
mock_grouped_topk.assert_called_once()
self.assertEqual(weights.shape, (self.num_tokens, self.top_k))
@@ -854,27 +868,20 @@ class TestSelectExperts(TestBase):
def test_custom_routing_function(self):
"""Test custom routing function"""
mock_custom_routing = MagicMock()
mock_custom_routing.return_value = (torch.ones(self.num_tokens,
self.top_k),
torch.zeros(self.num_tokens,
self.top_k,
dtype=torch.int32))
weights, ids = select_experts(
hidden_states=self.hidden_states,
router_logits=self.router_logits,
top_k=self.top_k,
use_grouped_topk=False,
renormalize=False,
custom_routing_function=mock_custom_routing)
custom_routing_function=self.mock_custom_routing)
mock_custom_routing.assert_called_once()
self.mock_custom_routing.assert_called_once()
self.assertEqual(weights.shape, (self.num_tokens, self.top_k))
self.assertEqual(ids.shape, (self.num_tokens, self.top_k))
self.assertEqual(ids.dtype, torch.int32)
@patch('torch_npu.npu_moe_gating_top_k_softmax')
@patch('torch_npu.npu_moe_gating_top_k')
def test_renormalize(self, mock_topk):
"""Test renormalization"""
mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k),
@@ -900,13 +907,13 @@ class TestSelectExperts(TestBase):
sums = weights.sum(dim=-1)
self.assertTrue(torch.allclose(sums, torch.ones_like(sums)))
@patch('torch_npu.npu_moe_gating_top_k_softmax')
@patch('torch_npu.npu_moe_gating_top_k')
def test_output_dtypes(self, mock_topk):
"""Test output dtypes"""
mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k),
torch.zeros(self.num_tokens,
self.top_k,
dtype=torch.long),
dtype=torch.int32),
torch.arange(0,
self.num_tokens * self.top_k,
dtype=torch.int32).view(