[3/N][refactor] refactoer quantization (#2504)
### What this PR does / why we need it? Move torchair related qunatization section into torchair dir to make the code clear. Next step we'll remove all torchair related code outside of torchair quantization. ### Does this PR introduce _any_ user-facing change? NO ### How was this patch tested? vLLM version: main vLLM main:ab9f2cfd19- vLLM version: v0.10.1.1 - vLLM main:959783fb99Signed-off-by: hust17yixuan <303660421@qq.com>
This commit is contained in:
Wang Yixuan
176
tests/ut/torchair/quantization/test_torchair_w4a8_dynamic.py
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176
tests/ut/torchair/quantization/test_torchair_w4a8_dynamic.py
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import copy
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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.torchair.quantization.torchair_w4a8_dynamic import (
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TorchairAscendW4A8DynamicFusedMoEMethod,
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TorchairAscendW4A8DynamicLinearMethod)
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class TestAscendW4A8DynamicLinearMethod(TestBase):
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def setUp(self):
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self.method = TorchairAscendW4A8DynamicLinearMethod()
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self.method.group_size = 8
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def test_get_weight(self):
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weight = self.method.get_weight(8, 32, torch.bfloat16)
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self.assertEqual(weight["weight"].dtype, torch.int8)
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self.assertEqual(weight["weight"].shape, (32, 8))
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def test_get_pergroup_param(self):
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params = self.method.get_pergroup_param(8, 32, torch.bfloat16)
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self.assertEqual(params["weight_scale"].dtype, torch.bfloat16)
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self.assertEqual(params["weight_scale"].shape, (32, 1))
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self.assertEqual(params["weight_offset"].dtype, torch.bfloat16)
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self.assertEqual(params["weight_offset"].shape, (32, 1))
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self.assertEqual(params["weight_scale_second"].dtype, torch.bfloat16)
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self.assertEqual(params["weight_scale_second"].shape, (32, 1))
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self.assertEqual(params["weight_offset_second"].dtype, torch.bfloat16)
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self.assertEqual(params["weight_offset_second"].shape, (32, 1))
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class TestAscendW4A8DynamicFusedMoEMethod(TestBase):
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experts = 8
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input_size = 16
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output_size = 56
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group_size = 2
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@patch(
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'vllm_ascend.torchair.quantization.torchair_w4a8_dynamic.get_current_vllm_config'
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)
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@patch(
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'vllm_ascend.torchair.quantization.torchair_w4a8_dynamic.get_ep_group')
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@patch("vllm_ascend.ascend_config.get_ascend_config")
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@patch(
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'vllm_ascend.torchair.quantization.torchair_w4a8_dynamic.get_mc2_group'
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)
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@patch('torch.distributed.get_rank', return_value=0)
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def setUp(self, mock_get_rank, mock_get_mc2_group, mock_get_ascend_config,
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mock_get_ep_group, get_current_vllm_config):
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mock_ascend_config = Mock()
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mock_ascend_config.torchair_graph_config = Mock(enabled=False)
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mock_get_ascend_config.return_value = mock_ascend_config
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mock_vllm_config = Mock()
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mock_vllm_config.quant_config = Mock(quant_description={
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"group_size": self.group_size,
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"version": "0.0.0"
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})
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mock_vllm_config.parallel_config = Mock(enable_expert_parallel=True)
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get_current_vllm_config.return_value = mock_vllm_config
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self.quant_method = TorchairAscendW4A8DynamicFusedMoEMethod()
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def test_get_weight(self):
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# old quant version w4a8 weight
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param_dict = self.quant_method.get_weight(self.experts,
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self.input_size,
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self.output_size,
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torch.bfloat16)
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self.assertEqual(param_dict["w13_weight"].dtype, torch.int8)
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self.assertEqual(param_dict["w13_weight"].shape,
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(self.experts, 2 * self.input_size, self.output_size))
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# new quant version weight
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self.quant_method.new_quant_version = True
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param_dict = self.quant_method.get_weight(self.experts,
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self.input_size,
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self.output_size,
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torch.bfloat16)
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self.assertEqual(param_dict["w13_weight"].dtype, torch.int8)
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self.assertEqual(param_dict["w13_weight"].shape,
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(self.experts, self.input_size, self.output_size))
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def test_get_dynamic_quant_param(self):
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# old quant version weight
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param_dict = self.quant_method.get_dynamic_quant_param(
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self.experts, self.input_size, self.output_size, torch.bfloat16)
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self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.bfloat16)
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self.assertEqual(param_dict["w13_weight_scale"].shape,
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(self.experts, 2 * self.input_size, 1))
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self.assertEqual(param_dict["w13_weight_scale_second"].dtype,
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torch.bfloat16)
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self.assertEqual(param_dict["w13_weight_scale_second"].shape,
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(self.experts, 2 * self.input_size,
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self.output_size // self.group_size))
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self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.bfloat16)
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self.assertEqual(param_dict["w2_weight_scale"].shape,
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(self.experts, self.output_size, 1))
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self.assertEqual(param_dict["w2_weight_scale_second"].dtype,
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torch.bfloat16)
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self.assertEqual(param_dict["w2_weight_scale_second"].shape,
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(self.experts, self.output_size,
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self.input_size // self.group_size))
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# new quant version weight
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self.quant_method.new_quant_version = True
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param_dict = self.quant_method.get_dynamic_quant_param(
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self.experts, self.input_size, self.output_size, torch.bfloat16)
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self.assertEqual(param_dict["w2_scale_bias"].dtype, torch.float32)
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self.assertEqual(
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param_dict["w2_scale_bias"].shape,
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(self.experts, self.output_size, 16 // self.quant_method.tp_size))
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@patch('torch_npu.npu_quantize')
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@patch('torch.Tensor.npu')
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def test_process_weights_after_loading(self, mock_npu, mock_npu_quantize):
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# old quant version weight
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layer = torch.nn.Module()
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layer.w13_weight = torch.nn.Parameter(torch.zeros(
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(self.experts, 2 * self.input_size, self.output_size),
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dtype=torch.int8),
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requires_grad=False)
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layer.w2_weight = torch.nn.Parameter(torch.zeros(
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(self.experts, self.output_size, self.input_size),
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dtype=torch.int8),
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requires_grad=False)
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layer.w13_weight_scale = torch.nn.Parameter(torch.ones(
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(self.experts, 2 * self.input_size, 1), dtype=torch.bfloat16),
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requires_grad=False)
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layer.w13_weight_scale_second = torch.nn.Parameter(torch.ones(
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(self.experts, 2 * self.input_size,
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self.output_size // self.group_size),
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dtype=torch.bfloat16),
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requires_grad=False)
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layer.w2_weight_scale = torch.nn.Parameter(torch.ones(
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(self.experts, self.output_size, 1), dtype=torch.bfloat16),
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requires_grad=False)
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layer.w2_weight_scale_second = torch.nn.Parameter(torch.ones(
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(self.experts, self.output_size,
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self.input_size // self.group_size),
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dtype=torch.bfloat16),
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requires_grad=False)
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new_layer = copy.deepcopy(layer)
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mock_npu.return_value = torch.Tensor()
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mock_npu_quantize.return_value = torch.Tensor()
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self.quant_method.process_weights_after_loading(layer)
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self.assertTrue(hasattr(layer, "w13_scale_bias"))
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self.assertEqual(layer.w13_scale_bias.data.shape,
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(self.experts, 2 * self.input_size))
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self.assertEqual(layer.w13_scale_bias.data.dtype, torch.float32)
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self.assertTrue(hasattr(layer, "w2_scale_bias"))
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self.assertEqual(layer.w2_scale_bias.data.shape,
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(self.experts, self.output_size))
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self.assertEqual(layer.w2_scale_bias.data.dtype, torch.float32)
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# new quant version weight
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self.quant_method.new_quant_version = True
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new_layer.w13_weight.data = torch.zeros(
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(self.experts, self.input_size, self.output_size),
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dtype=torch.int8)
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new_layer.w2_weight.data = torch.zeros(
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(self.experts, self.output_size // 2, self.input_size),
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dtype=torch.int8)
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w13_scale_bias = torch.zeros((self.experts, 2 * self.input_size, 1),
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dtype=torch.float32)
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new_layer.w13_scale_bias = torch.nn.Parameter(w13_scale_bias,
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requires_grad=False)
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w2_scale_bias = torch.zeros(
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(self.experts, self.output_size, 16 // self.quant_method.tp_size),
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dtype=torch.float32)
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new_layer.w2_scale_bias = torch.nn.Parameter(w2_scale_bias,
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requires_grad=False)
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self.quant_method.process_weights_after_loading(new_layer)
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self.assertEqual(new_layer.w13_scale_bias.data.shape,
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(self.experts, 2 * self.input_size))
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self.assertEqual(new_layer.w2_scale_bias.data.shape,
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(self.experts, self.output_size))
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75
tests/ut/torchair/quantization/test_torchair_w8a8_dynamic.py
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75
tests/ut/torchair/quantization/test_torchair_w8a8_dynamic.py
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@@ -0,0 +1,75 @@
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from unittest.mock import MagicMock, patch
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import torch
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from tests.ut.base import TestBase
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from vllm_ascend.torchair.quantization.torchair_w8a8_dynamic import \
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torchair_fused_experts_with_all2all
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class TestAscendW8A8FusedMoEMethod(TestBase):
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def setUp(self):
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self.hidden_size = 128
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self.num_tokens = 128
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self.placeholder = torch.randn(self.num_tokens,
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self.hidden_size,
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dtype=torch.bfloat16)
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@patch("torch.distributed.all_to_all_single")
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@patch("torch_npu.npu_moe_re_routing")
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@patch("torch_npu.npu_grouped_matmul")
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@patch("torch_npu.npu_swiglu")
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@patch("torch_npu.npu_dynamic_quant")
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@patch("torch_npu.npu_moe_finalize_routing")
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@patch("torch_npu.npu_moe_init_routing")
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def test_torchair_fused_experts_with_all2all(
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self, mock_moe_init_routing, mock_moe_finalize_routing,
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mock_dynamic_quant, mock_swiglu, mock_grouped_matmul,
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mock_moe_re_routing, mock_all_to_all_single):
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expert_map = MagicMock()
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ep_group = MagicMock()
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placeholder_int8 = torch.randint(0,
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100,
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(self.num_tokens, self.hidden_size),
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dtype=torch.int8)
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placeholder_ones = torch.ones(self.num_tokens, dtype=torch.int32)
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mock_all_to_all_single.side_effect = lambda output, input, *args, **kwargs: output.copy_(
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input)
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mock_moe_init_routing.return_value = (
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placeholder_int8,
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placeholder_ones,
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placeholder_ones,
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)
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mock_moe_re_routing.return_value = (placeholder_int8, self.placeholder,
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torch.randint(0,
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100,
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(self.num_tokens, ),
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dtype=torch.int32),
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self.placeholder)
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mock_grouped_matmul.return_value = self.placeholder
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mock_swiglu.return_value = self.placeholder
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mock_dynamic_quant.return_value = (
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placeholder_int8,
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torch.randn(self.num_tokens),
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)
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mock_moe_finalize_routing.return_value = self.placeholder
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result = torchair_fused_experts_with_all2all(
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hidden_states=self.placeholder,
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w1=self.placeholder,
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w1_scale=self.placeholder,
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w2=self.placeholder,
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w2_scale=self.placeholder,
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topk_weights=self.placeholder,
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topk_ids=self.placeholder,
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top_k=8,
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expert_map=expert_map,
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ep_group=ep_group,
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log2phy=None,
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global_redundant_expert_num=256,
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)
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self.assertIsNotNone(result)
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self.assertEqual(result.dtype, torch.bfloat16)
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self.assertEqual(result.shape, (128, 128))
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