5df2ddd8db
### What this PR does / why we need it? cherry-pick from https://github.com/vllm-project/vllm-ascend/pull/7736 **Error information** When the quantized weights in CompressedTensors format of the kimi-k2 model are used, the following error is reported: `AttributeError: 'AscendCompressedTensorsConfig' obiect has no attribute 'enabling_fa_quant'` **Error Cause** Currently, FA3 quantization supports only the weights of modelslim quantization. The added methods are not defined in AscendCompressedTensorsConfig. **Solution** Before invoking related methods, check whether the FA3 feature is enabled. Additionally, the unused `get_scaled_act_names` method and its corresponding unit test have been removed. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Existing unit tests were updated by removing a deprecated test case, and the refactored logic was reviewed for correctness. Signed-off-by: Wang Kunpeng <1289706727@qq.com>
256 lines
11 KiB
Python
256 lines
11 KiB
Python
import json
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import os
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import tempfile
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from unittest.mock import MagicMock, patch
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
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from vllm.model_executor.layers.linear import LinearBase
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from tests.ut.base import TestBase
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from vllm_ascend.ops.linear import AscendUnquantizedLinearMethod
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from vllm_ascend.quantization.modelslim_config import (
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MODELSLIM_CONFIG_FILENAME,
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AscendModelSlimConfig,
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)
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from vllm_ascend.utils import ASCEND_QUANTIZATION_METHOD
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from vllm.model_executor.layers.attention import Attention
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class TestAscendModelSlimConfig(TestBase):
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def setUp(self):
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self.sample_config = {
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"weight": "INT8",
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"fa_quant_type": "C8",
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"layers.1.fa_k.scale": "C8",
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"layer1.weight": "INT8",
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"layer2.weight": "FLOAT",
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"fused_layer.weight": "FLOAT",
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"fused_layer.shard1.weight": "FLOAT",
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"fused_layer.shard2.weight": "FLOAT",
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"shard1.weight": "FLOAT",
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"shard2.weight": "FLOAT",
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}
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self.ascend_config = AscendModelSlimConfig(self.sample_config)
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self.ascend_config.packed_modules_mapping = None
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def test_init(self):
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self.assertEqual(self.ascend_config.quant_description,
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self.sample_config)
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def test_repr(self):
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repr_str = repr(self.ascend_config)
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self.assertTrue(repr_str.startswith("AscendModelSlimConfig:\n"))
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def test_get_name(self):
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self.assertEqual(AscendModelSlimConfig.get_name(),
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ASCEND_QUANTIZATION_METHOD)
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def test_get_supported_act_dtypes(self):
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supported_dtypes = AscendModelSlimConfig.get_supported_act_dtypes()
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self.assertEqual(len(supported_dtypes), 3)
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def test_get_min_capability(self):
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with self.assertRaises(NotImplementedError):
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AscendModelSlimConfig.get_min_capability()
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def test_get_config_filenames(self):
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filenames = AscendModelSlimConfig.get_config_filenames()
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self.assertEqual(filenames, [])
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def test_from_config(self):
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config = AscendModelSlimConfig.from_config(self.sample_config)
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self.assertIsInstance(config, AscendModelSlimConfig)
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self.assertEqual(config.quant_description, self.sample_config)
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@patch('torch.npu.is_available')
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def test_override_quantization_method(self, mock_is_available):
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# Test when NPU is available
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mock_is_available.return_value = True
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result = AscendModelSlimConfig.override_quantization_method(None, None)
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self.assertIsNone(result)
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hf_quant_cfg = {"quant_method": ""}
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result = AscendModelSlimConfig.override_quantization_method(
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hf_quant_cfg, None)
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self.assertEqual(result, "ascend")
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# Test when NPU is not available
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mock_is_available.return_value = False
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result = AscendModelSlimConfig.override_quantization_method(None, None)
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self.assertIsNone(result)
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hf_quant_cfg = {"quant_method": ""}
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result = AscendModelSlimConfig.override_quantization_method(
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hf_quant_cfg, None)
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self.assertIsNone(result)
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def test_get_quant_method_for_linear(self):
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mock_config = MagicMock()
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mock_config.model_config.hf_config.model_type = None
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linear_layer = MagicMock(spec=LinearBase)
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# Test skipped layer
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with patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config), \
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patch.object(self.ascend_config, \
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'is_layer_skipped_ascend',
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return_value=True):
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method = self.ascend_config.get_quant_method(linear_layer, ".attn")
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self.assertIsInstance(method, AscendUnquantizedLinearMethod)
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# Test quantized layer
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mock_scheme = MagicMock()
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with patch.object(self.ascend_config, 'is_layer_skipped_ascend', return_value=False), \
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patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config), \
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patch("vllm_ascend.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme), \
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patch('vllm_ascend.quantization.method_adapters.AscendLinearMethod', return_value=MagicMock()) as mock_ascend_linear:
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method = self.ascend_config.get_quant_method(linear_layer, ".attn")
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self.assertIs(method, mock_ascend_linear.return_value)
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mock_ascend_linear.assert_called_once_with(mock_scheme)
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def test_get_quant_method_for_attention(self):
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attention_layer = MagicMock(spec=Attention)
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mock_config = MagicMock()
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mock_config.model_config.hf_config.model_type = None
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mock_scheme = MagicMock()
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with patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config), \
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patch("vllm_ascend.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme), \
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patch('vllm_ascend.quantization.method_adapters.AscendKVCacheMethod', \
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return_value=MagicMock()) as mock_ascend_kvcache:
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# Test with fa_quant_type
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method = self.ascend_config.get_quant_method(
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attention_layer, ".attn")
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self.assertIs(method, None)
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method = self.ascend_config.get_quant_method(
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attention_layer, "layers.1.attn")
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self.assertIs(method, mock_ascend_kvcache.return_value)
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def test_get_quant_method_for_fused_moe(self):
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fused_moe_layer = MagicMock(spec=FusedMoE)
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fused_moe_layer.moe = MagicMock(spec=FusedMoEConfig)
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fused_moe_layer.moe_config = MagicMock(spec=FusedMoEConfig)
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mock_config = MagicMock()
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mock_config.model_config.hf_config.model_type = None
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# Test skipped layer
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with patch.object(self.ascend_config, 'is_layer_skipped_ascend', return_value=True), \
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patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config), \
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patch('vllm_ascend.ops.fused_moe.fused_moe.AscendUnquantizedFusedMoEMethod', return_value=MagicMock()) as mock_ascend_moe:
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method = self.ascend_config.get_quant_method(
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fused_moe_layer, "moe_layer")
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self.assertIs(method, mock_ascend_moe.return_value)
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# Test quantized layer
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mock_scheme = MagicMock()
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with patch.object(self.ascend_config, 'is_layer_skipped_ascend', return_value=False), \
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patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config), \
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patch("vllm_ascend.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme), \
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patch('vllm_ascend.quantization.method_adapters.AscendFusedMoEMethod', return_value=MagicMock()) as mock_ascend_moe:
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method = self.ascend_config.get_quant_method(
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fused_moe_layer, "moe_layer")
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self.assertIs(method, mock_ascend_moe.return_value)
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def test_is_layer_skipped_ascend(self):
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# Test non-fused layer that should be quantized
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self.assertFalse(self.ascend_config.is_layer_skipped_ascend("layer1"))
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# Test non-fused layer that should be skipped
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self.assertTrue(self.ascend_config.is_layer_skipped_ascend("layer2"))
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# Test fused layer
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fused_mapping = {"fused_layer": ["shard1", "shard2"]}
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self.assertTrue(
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self.ascend_config.is_layer_skipped_ascend("fused_layer",
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fused_mapping))
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# Test inconsistent fused layer shards
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bad_config = {"shard1.weight": "FLOAT", "shard2.weight": "INT8"}
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config = AscendModelSlimConfig(bad_config)
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with self.assertRaises(ValueError):
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config.is_layer_skipped_ascend("fused_layer", fused_mapping)
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def test_init_with_none_config(self):
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config = AscendModelSlimConfig(None)
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self.assertEqual(config.quant_description, {})
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def test_init_with_default_config(self):
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config = AscendModelSlimConfig()
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self.assertEqual(config.quant_description, {})
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def test_maybe_update_config_already_populated(self):
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# When quant_description is already populated, should be a no-op
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self.assertTrue(len(self.ascend_config.quant_description) > 0)
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self.ascend_config.maybe_update_config("/some/model/path")
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# quant_description should remain unchanged
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self.assertEqual(self.ascend_config.quant_description,
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self.sample_config)
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def test_maybe_update_config_loads_from_file(self):
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config = AscendModelSlimConfig()
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self.assertEqual(config.quant_description, {})
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quant_data = {"layer1.weight": "INT8", "layer2.weight": "FLOAT"}
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with tempfile.TemporaryDirectory() as tmpdir:
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config_path = os.path.join(tmpdir, MODELSLIM_CONFIG_FILENAME)
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with open(config_path, "w") as f:
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json.dump(quant_data, f)
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config.maybe_update_config(tmpdir)
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self.assertEqual(config.quant_description, quant_data)
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def test_maybe_update_config_raises_when_file_missing(self):
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config = AscendModelSlimConfig()
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with tempfile.TemporaryDirectory() as tmpdir:
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with self.assertRaises(ValueError) as ctx:
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config.maybe_update_config(tmpdir)
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error_msg = str(ctx.exception)
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self.assertIn("ModelSlim Quantization Config Not Found", error_msg)
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self.assertIn(MODELSLIM_CONFIG_FILENAME, error_msg)
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def test_maybe_update_config_raises_with_json_files_listed(self):
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config = AscendModelSlimConfig()
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with tempfile.TemporaryDirectory() as tmpdir:
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# Create a dummy json file that is NOT the config file
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dummy_path = os.path.join(tmpdir, "config.json")
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with open(dummy_path, "w") as f:
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json.dump({"dummy": True}, f)
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with self.assertRaises(ValueError) as ctx:
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config.maybe_update_config(tmpdir)
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error_msg = str(ctx.exception)
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self.assertIn("config.json", error_msg)
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def test_maybe_update_config_non_directory_raises(self):
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config = AscendModelSlimConfig()
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with self.assertRaises(ValueError) as ctx:
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config.maybe_update_config("not_a_real_directory_path")
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error_msg = str(ctx.exception)
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self.assertIn("ModelSlim Quantization Config Not Found", error_msg)
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def test_apply_extra_quant_adaptations_shared_head(self):
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config = AscendModelSlimConfig()
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config.quant_description = {
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"model.layers.0.shared_head.weight": "INT8",
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}
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config._apply_extra_quant_adaptations()
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self.assertIn("model.layers.0.weight", config.quant_description)
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self.assertEqual(config.quant_description["model.layers.0.weight"],
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"INT8")
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def test_apply_extra_quant_adaptations_weight_packed(self):
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config = AscendModelSlimConfig()
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config.quant_description = {
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"model.layers.0.weight_packed": "INT8",
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}
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config._apply_extra_quant_adaptations()
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self.assertIn("model.layers.0.weight", config.quant_description)
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self.assertEqual(config.quant_description["model.layers.0.weight"],
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"INT8")
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