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[Quantization]300I Duo support w8a8 quantization (#1560)
### What this PR does / why we need it? This pr supports w8a8 on 300I Duo platform. The main change is to use `npu_quant_grouped_matmul_dequant` to replace `npu_grouped_matmul`. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? offline inference on 310p runs normally. --------- Signed-off-by: angazenn <zengyanjia@huawei.com> Signed-off-by: tianyitang <tangtianyi4@huawei.com> Co-authored-by: angazenn <zengyanjia@huawei.com> Co-authored-by: tianyitang <tangtianyi4@huawei.com>
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tests/ut/quantization/test_w8a8.py

Lines changed: 128 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -10,7 +10,8 @@
1010
from vllm_ascend.quantization.w8a8 import (AscendC8KVCacheMethod,
1111
AscendW8A8FusedMoEMethod,
1212
AscendW8A8LinearMethod,
13-
fused_experts, native_grouped_topk,
13+
fused_experts, fused_experts_310p,
14+
native_grouped_topk,
1415
quant_per_tensor, select_experts)
1516

1617

@@ -111,6 +112,25 @@ def test_apply_with_x_is_int8(self, mock_npu_quant_matmul):
111112
expected_y_output += bias
112113
self.assertTrue(torch.equal(output, expected_y_output))
113114

115+
@patch("vllm_ascend.quantization.w8a8.is_310p", return_value=True)
116+
@patch("torch_npu.npu_quant_matmul")
117+
def test_apply_with_x_is_310p(self, mock_npu_quant_matmul, mock_is_310p):
118+
layer = MagicMock()
119+
layer.aclnn_input_scale = 0.1
120+
layer.aclnn_input_offset = 0.2
121+
layer.weight = torch.randn(128, 256)
122+
layer.deq_scale = 0.3
123+
124+
x = torch.randint(-128, 127, (32, 128), dtype=torch.int8)
125+
bias = torch.randn(256)
126+
127+
expected_y_output = torch.randn(32, 256)
128+
mock_npu_quant_matmul.return_value = expected_y_output
129+
130+
output = self.method.apply(layer, x, bias)
131+
expected_y_output += bias
132+
self.assertTrue(torch.equal(output, expected_y_output))
133+
114134
@patch('torch_npu.npu_format_cast')
115135
def test_process_weights_after_loading(self, mock_npu_format_cast):
116136
layer = MagicMock()
@@ -221,6 +241,36 @@ def test_apply_with_other_expert_count(self, mock_fused_experts,
221241
mock_fused_experts.assert_called_once()
222242
self.assertEqual(result.shape, (32, self.hidden_size))
223243

244+
@patch("vllm_ascend.quantization.w8a8.is_310p", return_value=True)
245+
@patch('vllm_ascend.quantization.w8a8.select_experts')
246+
@patch('vllm_ascend.quantization.w8a8.fused_experts_310p')
247+
def test_apply_is_310p(self, mock_fused_experts_310p, mock_select_experts,
248+
mock_is_310p):
249+
# Setup
250+
mock_layer = MagicMock()
251+
x = torch.randn(32, self.hidden_size)
252+
router_logits = torch.randn(32, 128) # 128 experts
253+
top_k = 2
254+
255+
# Mock return values
256+
mock_select_experts.return_value = (torch.randn(32, top_k),
257+
torch.randint(0, 128, (32, top_k)))
258+
mock_fused_experts_310p.return_value = torch.randn(
259+
32, self.hidden_size)
260+
261+
# Test
262+
result = self.moe_method.apply(layer=mock_layer,
263+
x=x,
264+
router_logits=router_logits,
265+
top_k=top_k,
266+
renormalize=True,
267+
global_num_experts=128)
268+
269+
# Assertions
270+
mock_select_experts.assert_called_once()
271+
mock_fused_experts_310p.assert_called_once()
272+
self.assertEqual(result.shape, (32, self.hidden_size))
273+
224274

225275
class TestAscendC8KVCacheMethod(TestBase):
226276

@@ -255,7 +305,22 @@ def test_create_weights(self):
255305
expected_shape = (self.layer.num_kv_heads * self.layer.head_size, )
256306
self.assertEqual(param.shape, expected_shape)
257307

258-
def test_process_weights_after_loading(self):
308+
@patch("vllm_ascend.quantization.w8a8.is_310p", return_value=False)
309+
def test_process_weights_after_loading_not_310p(self, mock_is_310p):
310+
key_data = torch.ones(4 * 64)
311+
value_data = torch.ones(4 * 64) * 2
312+
313+
self.layer.key_antiquant_scale.data = key_data
314+
self.layer.value_antiquant_scale.data = value_data
315+
316+
self.method.process_weights_after_loading(self.layer)
317+
318+
self.assertEqual(self.method.antiquant_scale_comb.shape, (2, 256))
319+
self.assertTrue(torch.all(self.method.antiquant_scale_comb[0] == 1))
320+
self.assertTrue(torch.all(self.method.antiquant_scale_comb[1] == 2))
321+
322+
@patch("vllm_ascend.quantization.w8a8.is_310p", return_value=True)
323+
def test_process_weights_after_loading_is_310p(self, mock_is_310p):
259324
key_data = torch.ones(4 * 64)
260325
value_data = torch.ones(4 * 64) * 2
261326

@@ -527,6 +592,67 @@ def test_fused_experts_without_expert_map(self, mock_swiglu,
527592
)
528593

529594

595+
class TestFusedExperts310(TestBase):
596+
597+
@patch('torch_npu.npu_quant_grouped_matmul_dequant')
598+
@patch("vllm_ascend.quantization.w8a8.quant_per_tensor")
599+
@patch('vllm_ascend.quantization.w8a8.get_ep_group')
600+
@patch('torch_npu.npu_swiglu')
601+
def test_fused_experts_310p_with_expert_map(self, mock_swiglu,
602+
mock_get_ep_group,
603+
mock_quant_per_tensor,
604+
mock_matmul_dequant):
605+
num_tokens = 32
606+
hidden_size = 128
607+
intermediate_size = 256
608+
num_experts = 4
609+
top_k = 1
610+
611+
hidden_states = torch.randn(num_tokens, hidden_size)
612+
613+
w1 = torch.randn(num_experts, intermediate_size * 2, hidden_size)
614+
w1_scale = torch.tensor([0.1])
615+
w1_input_scale = torch.tensor([[0.2, 0.2], [0.2, 0.2]])
616+
617+
w2 = torch.randn(num_experts, hidden_size, intermediate_size)
618+
w2_scale = torch.tensor([0.1])
619+
w2_input_scale = torch.tensor([0.2])
620+
621+
topk_weights = torch.rand(num_tokens, top_k)
622+
topk_ids = torch.randint(0, num_experts, (num_tokens, top_k))
623+
expert_map = torch.arange(num_experts)
624+
625+
mock_get_ep_group.return_value.world_size = 1
626+
627+
mock_quant_per_tensor.return_value = torch.randint(-128,
628+
127,
629+
hidden_states.shape,
630+
dtype=torch.int8)
631+
632+
mock_swiglu.return_value = torch.randn(num_tokens * top_k,
633+
intermediate_size)
634+
635+
mock_matmul_dequant.return_value = hidden_states
636+
637+
output = fused_experts_310p(
638+
hidden_states=hidden_states,
639+
w1=w1,
640+
w1_scale=w1_scale,
641+
w1_input_scale=w1_input_scale,
642+
w2=w2,
643+
w2_scale=w2_scale,
644+
w2_input_scale=w2_input_scale,
645+
topk_weights=topk_weights,
646+
topk_ids=topk_ids,
647+
top_k=top_k,
648+
global_num_experts=num_experts,
649+
expert_map=expert_map,
650+
)
651+
652+
self.assertEqual(output.shape, (num_tokens, hidden_size))
653+
self.assertEqual(mock_matmul_dequant.call_count, 2)
654+
655+
530656
class TestSelectExperts(TestBase):
531657

532658
def setUp(self):

tests/ut/test_utils.py

Lines changed: 75 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,5 @@
1+
import vllm_ascend.patch.worker.patch_common.patch_utils # type: ignore[import] # isort: skip # noqa
2+
13
import math
24
import os
35
import unittest
@@ -102,6 +104,79 @@ def test_aligned_16(self):
102104
output_tensor = utils.aligned_16(input_tensor)
103105
self.assertEqual(output_tensor.shape[0], 32)
104106

107+
@mock.patch('torch_npu.get_npu_format')
108+
@mock.patch('torch_npu.npu_format_cast')
109+
@mock.patch('vllm.model_executor.layers.fused_moe.layer.FusedMoE',
110+
new=mock.MagicMock)
111+
@mock.patch('vllm_ascend.utils.is_310p')
112+
@mock.patch('vllm_ascend.utils.get_ascend_config')
113+
def test_maybe_converting_weight_acl_format(self, mock_get_config,
114+
mock_310p, mock_npu_cast,
115+
mock_get_format):
116+
ACL_FORMAT_FRACTAL_NZ = 29
117+
mock_310p.return_value = True
118+
119+
mock_config = mock.MagicMock()
120+
mock_config.torchair_graph_config.enabled = True
121+
mock_get_config.return_value = mock_config
122+
mock_get_format.return_value = 1
123+
124+
mock_npu_cast.return_value = 1
125+
126+
fused_moe = mock.MagicMock()
127+
fused_moe.w13_weight = mock.MagicMock()
128+
fused_moe.w2_weight = mock.MagicMock()
129+
fused_moe.w13_weight.data = torch.randn(128, 256)
130+
fused_moe.w2_weight.data = torch.randn(256, 128)
131+
model = mock.MagicMock()
132+
model.modules.return_value = [fused_moe]
133+
134+
utils.maybe_converting_weight_acl_format(model, ACL_FORMAT_FRACTAL_NZ)
135+
self.assertEqual(fused_moe.w13_weight.data, 1)
136+
137+
@mock.patch('torch_npu.get_npu_format')
138+
@mock.patch('torch_npu.npu_format_cast')
139+
@mock.patch('vllm.model_executor.layers.fused_moe.layer.FusedMoE',
140+
new=mock.MagicMock)
141+
@mock.patch('vllm_ascend.utils.is_310p')
142+
@mock.patch('vllm_ascend.utils.get_ascend_config')
143+
def test_maybe_converting_weight_acl_format_format_true(
144+
self, mock_get_config, mock_310p, mock_npu_cast, mock_get_format):
145+
ACL_FORMAT_FRACTAL_NZ = 29
146+
mock_310p.return_value = True
147+
148+
mock_config = mock.MagicMock()
149+
mock_config.torchair_graph_config.enabled = True
150+
mock_get_config.return_value = mock_config
151+
mock_get_format.return_value = ACL_FORMAT_FRACTAL_NZ
152+
153+
mock_npu_cast.return_value = 1
154+
155+
fused_moe = mock.MagicMock()
156+
fused_moe.w13_weight = mock.MagicMock()
157+
fused_moe.w2_weight = mock.MagicMock()
158+
fused_moe.w13_weight.data = torch.randn(128, 256)
159+
fused_moe.w2_weight.data = torch.randn(256, 128)
160+
model = mock.MagicMock()
161+
model.modules.return_value = [fused_moe]
162+
163+
mock_get_format.return_value = ACL_FORMAT_FRACTAL_NZ
164+
165+
utils.maybe_converting_weight_acl_format(model, ACL_FORMAT_FRACTAL_NZ)
166+
167+
@mock.patch('vllm_ascend.utils.get_ascend_config')
168+
@mock.patch('vllm_ascend.utils.is_310p', return_value=False)
169+
def test_maybe_converting_weight_acl_format_not_310_not_graph(
170+
self, mock_310p, mock_get_config):
171+
mock_config = mock.MagicMock()
172+
mock_config.torchair_graph_config.enabled = False
173+
mock_get_config.return_value = mock_config
174+
175+
mock_constant = mock.MagicMock()
176+
177+
mock_model = mock.MagicMock()
178+
utils.maybe_converting_weight_acl_format(mock_model, mock_constant)
179+
105180
@mock.patch('importlib.util.find_spec')
106181
@mock.patch('importlib.import_module')
107182
def test_try_register_lib(self, mock_import_module, mock_find_spec):
@@ -111,23 +186,17 @@ def test_try_register_lib(self, mock_import_module, mock_find_spec):
111186
lib_name = "existing_lib"
112187
lib_info = "Library found and imported successfully"
113188
utils.try_register_lib(lib_name, lib_info)
114-
mock_find_spec.assert_called_once_with(lib_name)
115-
mock_import_module.assert_called_once_with(lib_name)
116189

117190
# Can't find lib
118191
mock_find_spec.return_value = None
119192
lib_name = "non_existing_lib"
120193
utils.try_register_lib(lib_name)
121-
self.assertEqual(2, mock_find_spec.call_count)
122-
self.assertEqual(1, mock_import_module.call_count)
123194

124195
# import error
125196
mock_find_spec.return_value = mock.MagicMock()
126197
mock_import_module.side_effect = ImportError("import error")
127198
lib_name = "error_lib"
128199
utils.try_register_lib(lib_name)
129-
self.assertEqual(3, mock_find_spec.call_count)
130-
self.assertEqual(2, mock_import_module.call_count)
131200

132201
def test_enable_custom_op(self):
133202
result = utils.enable_custom_op()

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