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Revert "[refactor]support gatingtopk operator generalization (#4356)" (#4873)

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This reverts commit c4a11a745a.

ops npu_gating_top_k caused Qwen3-30B precision problem, so revert it.

Signed-off-by: 1092626063 <1092626063@qq.com>
This commit is contained in:
1092626063
2025-12-10 15:45:20 +08:00
committed by GitHub
parent 9a144bc7be
commit ceadc2788d
4 changed files with 56 additions and 92 deletions
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41
tests/ut/quantization/test_w8a8.py
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@@ -753,14 +753,6 @@ 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()
@@ -770,7 +762,7 @@ class TestSelectExperts(TestBase):
self.addCleanup(patcher.stop)
patcher.start()
@patch('torch_npu.npu_moe_gating_top_k')
@patch('torch_npu.npu_moe_gating_top_k_softmax')
def test_softmax_scoring(self, mock_topk):
"""Test softmax scoring function"""
mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k),
@@ -797,14 +789,12 @@ 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",
custom_routing_function=self.mock_custom_routing)
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")
self.assertEqual(weights.shape, (self.num_tokens, self.top_k))
self.assertEqual(ids.shape, (self.num_tokens, self.top_k))
@@ -863,20 +853,27 @@ 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=self.mock_custom_routing)
custom_routing_function=mock_custom_routing)
self.mock_custom_routing.assert_called_once()
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')
@patch('torch_npu.npu_moe_gating_top_k_softmax')
def test_renormalize(self, mock_topk):
"""Test renormalization"""
mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k),
@@ -902,13 +899,13 @@ 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')
@patch('torch_npu.npu_moe_gating_top_k_softmax')
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.int32),
dtype=torch.long),
torch.arange(0,
self.num_tokens * self.top_k,
dtype=torch.int32).view(