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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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8
tests/e2e/singlecard/ops/test_fused_moe.py
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@@ -28,8 +28,7 @@ import torch
import torch_npu import torch_npu
from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.activation import SiluAndMul
from vllm_ascend.ops.moe.experts_selector import (check_npu_moe_gating_top_k, from vllm_ascend.ops.moe.experts_selector import select_experts
select_experts)
from vllm_ascend.ops.moe.moe_mlp import unified_apply_mlp from vllm_ascend.ops.moe.moe_mlp import unified_apply_mlp
from vllm_ascend.ops.moe.token_dispatcher import TokenDispatcherWithAllGather from vllm_ascend.ops.moe.token_dispatcher import TokenDispatcherWithAllGather
@@ -297,10 +296,7 @@ def test_select_experts(
e_score_correction_bias=e_score_correction_bias, e_score_correction_bias=e_score_correction_bias,
) )
call_moe_gatingtopk = check_npu_moe_gating_top_k( if use_grouped_topk:
hidden_states, topk, topk_group, num_expert_group, scoring_func,
custom_routing_function)
if not call_moe_gatingtopk and use_grouped_topk:
mock_native_grouped_topk.assert_called_once() mock_native_grouped_topk.assert_called_once()
else: else:
mock_native_grouped_topk.assert_not_called() mock_native_grouped_topk.assert_not_called()

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.hidden_states = torch.randn(self.num_tokens, self.hidden_size)
self.router_logits = torch.randn(self.num_tokens, self.num_experts) 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 = MagicMock()
self.mock_ctx.weight_prefetch_method = MagicMock() self.mock_ctx.weight_prefetch_method = MagicMock()
@@ -770,7 +762,7 @@ class TestSelectExperts(TestBase):
self.addCleanup(patcher.stop) self.addCleanup(patcher.stop)
patcher.start() 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): def test_softmax_scoring(self, mock_topk):
"""Test softmax scoring function""" """Test softmax scoring function"""
mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k), mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k),
@@ -797,14 +789,12 @@ class TestSelectExperts(TestBase):
def test_sigmoid_scoring(self): def test_sigmoid_scoring(self):
"""Test sigmoid scoring function""" """Test sigmoid scoring function"""
weights, ids = select_experts( weights, ids = select_experts(hidden_states=self.hidden_states,
hidden_states=self.hidden_states, router_logits=self.router_logits,
router_logits=self.router_logits, top_k=self.top_k,
top_k=self.top_k, use_grouped_topk=False,
use_grouped_topk=False, renormalize=False,
renormalize=False, scoring_func="sigmoid")
scoring_func="sigmoid",
custom_routing_function=self.mock_custom_routing)
self.assertEqual(weights.shape, (self.num_tokens, self.top_k)) self.assertEqual(weights.shape, (self.num_tokens, self.top_k))
self.assertEqual(ids.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): def test_custom_routing_function(self):
"""Test custom routing function""" """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( weights, ids = select_experts(
hidden_states=self.hidden_states, hidden_states=self.hidden_states,
router_logits=self.router_logits, router_logits=self.router_logits,
top_k=self.top_k, top_k=self.top_k,
use_grouped_topk=False, use_grouped_topk=False,
renormalize=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(weights.shape, (self.num_tokens, self.top_k))
self.assertEqual(ids.shape, (self.num_tokens, self.top_k)) self.assertEqual(ids.shape, (self.num_tokens, self.top_k))
self.assertEqual(ids.dtype, torch.int32) 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): def test_renormalize(self, mock_topk):
"""Test renormalization""" """Test renormalization"""
mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k), mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k),
@@ -902,13 +899,13 @@ class TestSelectExperts(TestBase):
sums = weights.sum(dim=-1) sums = weights.sum(dim=-1)
self.assertTrue(torch.allclose(sums, torch.ones_like(sums))) 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): def test_output_dtypes(self, mock_topk):
"""Test output dtypes""" """Test output dtypes"""
mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k), mock_topk.return_value = (torch.ones(self.num_tokens, self.top_k),
torch.zeros(self.num_tokens, torch.zeros(self.num_tokens,
self.top_k, self.top_k,
dtype=torch.int32), dtype=torch.long),
torch.arange(0, torch.arange(0,
self.num_tokens * self.top_k, self.num_tokens * self.top_k,
dtype=torch.int32).view( dtype=torch.int32).view(

1
vllm_ascend/ascend_forward_context.py
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@@ -96,7 +96,6 @@ def set_ascend_forward_context(
ep_size = (get_ep_group().world_size if ep_size = (get_ep_group().world_size if
vllm_config.parallel_config.enable_expert_parallel else 1) vllm_config.parallel_config.enable_expert_parallel else 1)
# fused_moe_state is used in torchair, it will be deleted along with torchair
is_deepseek_v3_r1 = hasattr( is_deepseek_v3_r1 = hasattr(
vllm_config.model_config.hf_config, 'n_routed_experts' vllm_config.model_config.hf_config, 'n_routed_experts'
) and vllm_config.model_config.hf_config.n_routed_experts == 256 ) and vllm_config.model_config.hf_config.n_routed_experts == 256

98
vllm_ascend/ops/moe/experts_selector.py
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@@ -20,6 +20,8 @@ import torch
import torch_npu import torch_npu
from vllm.forward_context import get_forward_context from vllm.forward_context import get_forward_context
from vllm_ascend.ascend_config import get_ascend_config
def select_experts(hidden_states: torch.Tensor, def select_experts(hidden_states: torch.Tensor,
router_logits: torch.Tensor, router_logits: torch.Tensor,
@@ -60,28 +62,21 @@ def select_experts(hidden_states: torch.Tensor,
if weight_prefetch_method: if weight_prefetch_method:
weight_prefetch_method.maybe_prefetch_moe_weight_preprocess( weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(
hidden_states, "gate_up") hidden_states, "gate_up")
is_support_npu_moe_gating_top_k = check_npu_moe_gating_top_k( topk_weights, topk_ids = _select_experts_with_fusion_ops(
hidden_states=hidden_states, hidden_states=hidden_states,
router_logits=router_logits,
top_k=top_k, top_k=top_k,
use_grouped_topk=use_grouped_topk,
topk_group=topk_group, topk_group=topk_group,
renormalize=renormalize,
e_score_correction_bias=e_score_correction_bias,
num_expert_group=num_expert_group, num_expert_group=num_expert_group,
custom_routing_function=custom_routing_function,
scoring_func=scoring_func, scoring_func=scoring_func,
custom_routing_function=custom_routing_function) routed_scaling_factor=routed_scaling_factor,
global_num_experts=global_num_experts)
if is_support_npu_moe_gating_top_k: if topk_weights is None:
topk_weights, topk_ids = _select_experts_with_fusion_ops(
hidden_states=hidden_states,
router_logits=router_logits,
top_k=top_k,
use_grouped_topk=use_grouped_topk,
topk_group=topk_group,
renormalize=renormalize,
e_score_correction_bias=e_score_correction_bias,
num_expert_group=num_expert_group,
scoring_func=scoring_func,
routed_scaling_factor=routed_scaling_factor,
global_num_experts=global_num_experts)
else:
topk_weights, topk_ids = _native_select_experts( topk_weights, topk_ids = _native_select_experts(
hidden_states=hidden_states, hidden_states=hidden_states,
router_logits=router_logits, router_logits=router_logits,
@@ -98,32 +93,6 @@ def select_experts(hidden_states: torch.Tensor,
return topk_weights, topk_ids return topk_weights, topk_ids
def check_npu_moe_gating_top_k(
hidden_states: torch.Tensor,
top_k: int,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
scoring_func: str = "softmax",
custom_routing_function: Optional[Callable] = None):
if custom_routing_function is not None:
return False
if scoring_func != "softmax" and scoring_func != "sigmoid":
return False
topk_group = topk_group if topk_group is not None else 1
num_expert_group = num_expert_group if num_expert_group is not None else 1
if not (num_expert_group > 0 and hidden_states.shape[-1] % num_expert_group
== 0 and hidden_states.shape[-1] // num_expert_group > 2):
return False
if topk_group < 1 or topk_group > num_expert_group:
return False
if top_k < 1 or \
top_k > (hidden_states.shape[-1] / (num_expert_group * topk_group)):
return False
if topk_group * hidden_states.shape[-1] / num_expert_group < top_k:
return False
return True
def _native_grouped_topk( def _native_grouped_topk(
topk_weights: torch.Tensor, topk_weights: torch.Tensor,
num_expert_group: Optional[int], num_expert_group: Optional[int],
@@ -202,31 +171,34 @@ def _select_experts_with_fusion_ops(
e_score_correction_bias: Optional[torch.Tensor], e_score_correction_bias: Optional[torch.Tensor],
topk_group: Optional[int], topk_group: Optional[int],
num_expert_group: Optional[int], num_expert_group: Optional[int],
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax", scoring_func: str = "softmax",
routed_scaling_factor=1.0, routed_scaling_factor=1.0,
global_num_experts: int = -1): global_num_experts: int = -1):
topk_group = topk_group if topk_group is not None else 1 topk_weights, topk_ids = None, None
num_expert_group = num_expert_group if num_expert_group is not None else 1 # NOTE: now npu_moe_gating_top_k can only support 'group_count=256' pattern
norm_type = 0 if scoring_func == "softmax" else 1 global_redundant_expert_num = get_ascend_config().init_redundancy_expert
if e_score_correction_bias is not None and \ is_deepseek_v3_r1 = global_num_experts - global_redundant_expert_num == 256
e_score_correction_bias.dtype != router_logits.dtype: if is_deepseek_v3_r1:
e_score_correction_bias = e_score_correction_bias.to( topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
router_logits.dtype) router_logits,
topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k( k=top_k, # topk currently 8
router_logits, bias=e_score_correction_bias,
k=top_k, k_group=topk_group, # fix: 4
bias=e_score_correction_bias, group_count=num_expert_group, # fix 8
k_group=topk_group, group_select_mode=
group_count=num_expert_group, 1, # 0: the maximum in the group; 1: topk2.sum(fix)
group_select_mode=1, # 0: the maximum in the group; 1: topk2.sum(fix) renorm=0, # 0: softmax->topk(fix); 1: topk->softmax
renorm=0, # 0: softmax->topk(fix); 1: topk->softmax norm_type=1, # 0: softmax; 1: sigmoid(fix)
norm_type=norm_type, # 0: softmax; 1: sigmoid # out_flag=False, # todo new api; should the third output be output
# out_flag=False, # todo new api; should the third output be output # y2_flag=False, # old api; should the third output be output
# y2_flag=False, # old api; should the third output be output routed_scaling_factor=1,
routed_scaling_factor=1, eps=float(1e-20))
eps=float(1e-20)) if not use_grouped_topk and custom_routing_function is None and scoring_func == "softmax":
if scoring_func == "softmax": topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k_softmax(
x=router_logits, finished=None, k=top_k)
topk_ids = topk_ids.to(torch.int32)
topk_weights = _renormalize_topk_weights(topk_weights, renormalize) topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
return topk_weights, topk_ids return topk_weights, topk_ids