5f8b1699ae
### What this PR does / why we need it? **Problem Description:** The existing implementation for the w4a8-dynamic linear method only supports the old quantization format from msmodelslim. When attempting to load models quantized with the new version, vLLM encounters errors due to mismatched tensor shapes and unprocessed quantization parameters. Relavant issues: - https://github.com/vllm-project/vllm-ascend/issues/3192 - https://github.com/vllm-project/vllm-ascend/issues/3152 **Proposed Changes:** 1. Add support for w4a8 dynamic(new format) in AscendW4A8DynamicLinearMethod and TorchairAscendW4A8DynamicLinearMethod 2. Add unit tests and e2e tests for w4a8 dynamic new and old format models <details> <summary><b>details</b></summary> 1. **Support for new w4a8-dynamic format:** * Detects quantization format by reading the "version" field in quant_description to ensure backward compatibility. * Handles the new pre-packed weight format (`2x int4` in an `int8`), which has a halved dimension. It tells the vLLM loader how to unpack it using `_packed_dim` and `_packed_factor`. * Supports the new `scale_bias` parameter, setting its shape based on the layer type, as required by msmodelslim. For api consistency and future use, the `layer_type` parameter was also added to other quantization methods. * Updates the weight processing logic: new format weights are handled with `.view(torch.int32)` since they're pre-packed, while old ones are processed with `npu_convert_weight_to_int4pack`. 2. **New unit and E2E tests:** * Added unit tests that verify the logic for both the old and new formats. * Split the distributed E2E test to confirm that both old and new format models work correctly. </details> Theoretically, these changes will provide support for all common new version w4a8(dynamic) models from msmodelslim. ### Does this PR introduce _any_ user-facing change? no ### How was this patch tested? I implement relevant unit tests and e2e tests and test the changes with following commands: ```bash # unit tests python -m pytest tests/ut/quantization/test_w4a8_dynamic.py tests/ut/torchair/quantization/test_torchair_w4a8_dynamic.py -v # e2e tests pytest tests/e2e/singlecard/test_quantization.py -v -s pytest tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC_new_version -v -s pytest tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC_old_version -v -s pytest tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W4A8DYNAMIC -v -s ``` I also tested Hunyuan-1.8B-Instruct quantized with the new w4a8-dynamic format: ``` vllm serve ./models/Hunyuan-1.8B-Instruct-quantized --gpu-memory-utilization 0.96 --quantization ascend --max-model-len 9600 --seed 0 --max-num-batched-tokens 16384 ``` All tests mentioned passed locally. **NOTE: I use quantization model from my own repo in test_offline_inference_distributed.py**. Here is the description: [Anionex/Qwen3-1.7B-W4A8-V1](https://modelscope.cn/models/Anionex/Qwen3-1.7B-W4A8-V1/summary) (including quantization steps).This should be replaced by a model in vllm-ascend ci modelscope repo. Thanks for reading! - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 --------- Signed-off-by: Anionex <1005128408@qq.com>
302 lines
16 KiB
Python
302 lines
16 KiB
Python
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.quantization.w4a8_dynamic import (
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AscendW4A8DynamicFusedMoEMethod, AscendW4A8DynamicLinearMethod)
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class TestAscendW4A8DynamicLinearMethod(TestBase):
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@patch('vllm.distributed.get_tensor_model_parallel_world_size')
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@patch('vllm_ascend.quantization.w4a8_dynamic.get_current_vllm_config')
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def setUp(self, mock_get_current_vllm_config, mock_get_tp_world_size):
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mock_get_tp_world_size.return_value = 1
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mock_vllm_config = Mock()
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mock_vllm_config.quant_config = Mock(
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quant_description={"group_size": 256})
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mock_vllm_config.scheduler_config = Mock(max_num_batched_tokens=2048,
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max_model_len=2048,
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enable_chunked_prefill=False)
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mock_get_current_vllm_config.return_value = mock_vllm_config
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self.method = AscendW4A8DynamicLinearMethod()
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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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# new quant version weight
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self.method.new_quant_version = True
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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, (16, 8))
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self.assertEqual(weight["_packed_dim"], 0)
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self.assertEqual(weight["_packed_factor"], 2)
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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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# new quant version weight
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self.method.new_quant_version = True
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params = self.method.get_pergroup_param(8,
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32,
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torch.bfloat16,
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layer_type="column")
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self.assertEqual(params["scale_bias"].dtype, torch.float32)
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self.assertEqual(params["scale_bias"].shape, (32, 1))
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params = self.method.get_pergroup_param(8,
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32,
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torch.bfloat16,
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layer_type="row")
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self.assertEqual(params["scale_bias"].dtype, torch.float32)
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self.assertEqual(params["scale_bias"].shape, (32, 16))
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@patch('torch_npu.npu_convert_weight_to_int4pack')
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@patch('torch.Tensor.npu')
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def test_process_weights_after_loading(self, mock_npu,
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mock_npu_convert_weight):
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mock_npu.side_effect = lambda: torch.zeros(
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(1, 32), dtype=torch.float32)
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mock_npu_convert_weight.return_value = torch.zeros((32, 4),
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dtype=torch.int32)
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# old quant version weight
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layer = torch.nn.Module()
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layer.weight = torch.nn.Parameter(torch.zeros((32, 8),
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dtype=torch.int8),
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requires_grad=False)
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layer.weight_scale = torch.nn.Parameter(torch.ones(
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(32, 1), dtype=torch.float32),
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requires_grad=False)
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layer.weight_offset = torch.nn.Parameter(torch.empty_like(
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layer.weight_scale.data),
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requires_grad=False)
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layer.weight_scale_second = torch.nn.Parameter(torch.ones(
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(32, 1), dtype=torch.float32),
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requires_grad=False)
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layer.weight_offset_second = torch.nn.Parameter(torch.empty_like(
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layer.weight_scale_second.data),
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requires_grad=False)
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self.method.process_weights_after_loading(layer)
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self.assertTrue(hasattr(layer, "weight_scale_bias"))
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self.assertEqual(layer.weight_scale_bias.data.shape, (32, ))
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self.assertEqual(layer.weight_scale_bias.data.dtype, torch.float32)
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# new quant version weight
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self.method.new_quant_version = True
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new_layer = torch.nn.Module()
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new_layer.weight = torch.nn.Parameter(torch.zeros((16, 8),
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dtype=torch.int8),
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requires_grad=False)
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new_layer.weight_scale = torch.nn.Parameter(torch.ones(
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(32, 1), dtype=torch.float32),
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requires_grad=False)
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new_layer.weight_offset = torch.nn.Parameter(torch.empty_like(
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new_layer.weight_scale.data),
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requires_grad=False)
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new_layer.weight_scale_second = torch.nn.Parameter(torch.ones(
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(32, 1), dtype=torch.float32),
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requires_grad=False)
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new_layer.weight_offset_second = torch.nn.Parameter(
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torch.empty_like(new_layer.weight_scale_second.data),
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requires_grad=False)
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new_layer.scale_bias = torch.nn.Parameter(torch.zeros(
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(32, 1), dtype=torch.float32),
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requires_grad=False)
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self.method.process_weights_after_loading(new_layer)
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self.assertEqual(new_layer.scale_bias.data.shape, (32, ))
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self.assertTrue(hasattr(new_layer, "weight_scale_second"))
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self.assertEqual(new_layer.weight_scale_second.data.shape, (1, 32))
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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('vllm_ascend.quantization.w4a8_dynamic.get_ascend_config')
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@patch('vllm_ascend.quantization.w4a8_dynamic.get_current_vllm_config')
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@patch('vllm_ascend.quantization.w4a8_dynamic.get_ep_group')
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@patch('vllm_ascend.quantization.w4a8_dynamic.get_mc2_group')
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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_ep_group,
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get_current_vllm_config, mock_get_ascend_config):
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# Mock ascend config
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mock_ascend_config = Mock()
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mock_ascend_config.dynamic_eplb = 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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mock_vllm_config.scheduler_config = Mock(max_num_batched_tokens=2048,
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max_model_len=2048,
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enable_chunked_prefill=False)
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get_current_vllm_config.return_value = mock_vllm_config
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self.quant_method = AscendW4A8DynamicFusedMoEMethod()
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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.float32)
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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.float32)
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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.float32)
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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.float32)
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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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# per-channel weight
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self.quant_method.is_per_channel_weight = 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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pergroup_param = [
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"w13_weight_scale_second", "w13_weight_offset_second",
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"w2_weight_scale_second", "w2_weight_offset_second"
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]
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is_contains = any(key in param_dict for key in pergroup_param)
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self.assertFalse(is_contains)
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def build_layer(self,
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is_new_quant_version=True,
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is_per_channel_weight=False):
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layer = torch.nn.Module()
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if is_new_quant_version:
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layer.w13_weight = torch.nn.Parameter(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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requires_grad=False)
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layer.w2_weight = torch.nn.Parameter(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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requires_grad=False)
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w13_scale_bias = torch.zeros(
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(self.experts, 2 * self.input_size, 1), dtype=torch.float32)
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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((self.experts, self.output_size,
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16 // self.quant_method.tp_size),
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dtype=torch.float32)
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layer.w2_scale_bias = torch.nn.Parameter(w2_scale_bias,
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requires_grad=False)
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else:
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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.float32),
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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.float32),
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requires_grad=False)
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if not is_per_channel_weight:
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layer.w13_weight_scale_second = torch.nn.Parameter(
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torch.ones((self.experts, 2 * self.input_size,
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self.output_size // self.group_size),
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dtype=torch.float32),
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requires_grad=False)
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layer.w13_weight_offset_second = torch.nn.Parameter(
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torch.empty_like(layer.w13_weight_scale_second.data),
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requires_grad=False)
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layer.w2_weight_scale_second = torch.nn.Parameter(
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torch.ones((self.experts, self.output_size,
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self.input_size // self.group_size),
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dtype=torch.float32),
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requires_grad=False)
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layer.w2_weight_offset_second = torch.nn.Parameter(
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torch.empty_like(layer.w2_weight_scale_second.data),
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requires_grad=False)
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return layer
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@patch('torch_npu.npu_format_cast')
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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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mock_npu_format_cast):
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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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def func_by_args(weight, num_format):
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return weight
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mock_npu_format_cast.side_effect = func_by_args
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# old quant version weight
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layer = self.build_layer(is_new_quant_version=False)
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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 = self.build_layer(is_new_quant_version=True)
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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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self.assertFalse(hasattr(new_layer, "w13_weight_scale_second"))
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# per-channel weight
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self.quant_method.is_per_channel_weight = True
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per_channel_layer = self.build_layer(is_new_quant_version=True,
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is_per_channel_weight=True)
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self.quant_method.process_weights_after_loading(per_channel_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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