67 lines
3.0 KiB
Python
67 lines
3.0 KiB
Python
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from unittest.mock import Mock, patch
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import torch
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from tests.ut.base import TestBase
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from vllm_ascend._310p.quantization.methods.w8a8_dynamic import AscendW8A8DynamicFusedMoEMethod310
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class TestAscendW8A8FusedMoEMethod310(TestBase):
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num_experts = 8
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hidden_size = 128
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intermediate_size = 128
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@patch("vllm_ascend._310p.quantization.methods.w8a8_dynamic.get_ep_group")
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def setUp(self, mock_get_ep_group):
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with patch(
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"vllm_ascend._310p.quantization.methods.w8a8_dynamic.get_current_vllm_config"
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) as mock_get_current_vllm_config:
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mock_vllm_config = Mock()
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mock_vllm_config.quant_config = Mock(quant_description={"group_size": 0})
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mock_vllm_config.scheduler_config = Mock(
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max_num_batched_tokens=2048, max_model_len=2048, enable_chunked_prefill=False
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)
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mock_get_current_vllm_config.return_value = mock_vllm_config
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mock_ep_group = Mock()
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mock_get_ep_group.return_value = mock_ep_group
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mock_ascend_config = Mock()
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mock_ascend_config.enable_chunked_prefill = False
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self.quant_method = AscendW8A8DynamicFusedMoEMethod310()
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def test_get_weight_310(self):
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param_dict = self.quant_method.get_weight(
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self.num_experts, self.intermediate_size, self.hidden_size, torch.float16
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)
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self.assertEqual(param_dict["w13_weight"].dtype, torch.int8)
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self.assertEqual(
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param_dict["w13_weight"].shape, (self.num_experts, 2 * self.intermediate_size, self.hidden_size)
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)
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self.assertEqual(param_dict["w2_weight"].dtype, torch.int8)
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self.assertEqual(param_dict["w2_weight"].shape, (self.num_experts, self.hidden_size, self.intermediate_size))
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def test_get_dynamic_quant_param_310(self):
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param_dict = self.quant_method.get_dynamic_quant_param(
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self.num_experts, self.intermediate_size, self.hidden_size, torch.float16
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)
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self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.float32)
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self.assertEqual(param_dict["w13_weight_scale"].shape, (self.num_experts, 2 * self.intermediate_size, 1))
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self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.float32)
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self.assertEqual(param_dict["w2_weight_scale"].shape, (self.num_experts, self.hidden_size, 1))
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