052e472453
### What this PR does / why we need it?
Currently, `torch_npu.npu_grouped_matmul_swiglu_quant` can only support
weight nz, so we need to trans w13_weight, w2_weight to nz forcely.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: zzzzwwjj <1183291235@qq.com>
107 lines
4.8 KiB
Python
107 lines
4.8 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.w8a8_dynamic import \
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AscendW8A8DynamicFusedMoEMethod
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class TestAscendW8A8FusedMoEMethod(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("torch.distributed.get_rank")
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@patch("vllm_ascend.quantization.w8a8_dynamic.get_mc2_group")
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@patch("vllm_ascend.quantization.w8a8_dynamic.get_ascend_config")
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@patch("vllm_ascend.quantization.w8a8_dynamic.get_ep_group")
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def setUp(self, mock_get_ep_group, mock_get_ascend_config,
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mock_get_mc2_group, mock_get_rank):
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with patch(
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'vllm_ascend.quantization.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(
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quant_description={"group_size": 256})
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mock_vllm_config.scheduler_config = Mock(
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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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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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mock_get_ascend_config.return_value = mock_ascend_config
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mock_mc2_group = Mock(device_group=0)
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mock_get_mc2_group.return_value = mock_mc2_group
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mock_rank = Mock()
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mock_get_rank.return_value = mock_rank
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self.quant_method = AscendW8A8DynamicFusedMoEMethod()
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def test_get_weight(self):
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param_dict = self.quant_method.get_weight(self.num_experts,
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self.intermediate_size,
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self.hidden_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(
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param_dict["w13_weight"].shape,
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(self.num_experts, 2 * self.intermediate_size, self.hidden_size))
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def test_get_dynamic_quant_param(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,
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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.num_experts, 2 * self.intermediate_size, 1))
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def build_layer(self):
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layer = torch.nn.Module()
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layer.w13_weight = torch.nn.Parameter(torch.empty(
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self.num_experts,
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2 * self.intermediate_size,
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self.hidden_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.empty(
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self.num_experts,
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self.hidden_size,
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self.intermediate_size,
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dtype=torch.int8),
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requires_grad=False)
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w13_weight_scale = torch.zeros(
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(self.num_experts, 2 * self.intermediate_size, 1),
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dtype=torch.float32)
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layer.w13_weight_scale = torch.nn.Parameter(w13_weight_scale,
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requires_grad=False)
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w13_weight_offset = torch.zeros(
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(self.num_experts, 2 * self.intermediate_size, 1),
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dtype=torch.float32)
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layer.w13_weight_offset = torch.nn.Parameter(w13_weight_offset,
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requires_grad=False)
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w2_weight_scale = torch.zeros((self.num_experts, self.hidden_size, 1),
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dtype=torch.float32)
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layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
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requires_grad=False)
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w2_weight_offset = torch.zeros((self.num_experts, self.hidden_size, 1),
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dtype=torch.float32)
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layer.w2_weight_offset = torch.nn.Parameter(w2_weight_offset,
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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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def test_process_weights_after_loading(self, mock_npu_format_cast):
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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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new_layer = self.build_layer()
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self.quant_method.process_weights_after_loading(new_layer)
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mock_npu_format_cast.assert_called()
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