ce5872705e
### What this PR does / why we need it?
Adds W4A16 quantization method for the Kimi-K2-Thinking model and
updates relevant modules to support the new quantization method.
- Implements complete W4A16 quantization method including weight
packing/unpacking, per-group quantization parameter generation,
post-processing logic and MoE method application.
- Adds parameters `use_int4_w4a16`, `w1_offset` and `w2_offset`, adjusts
`with_quant` conditional logic to support W4A16 matrix multiplication.
- Adds `packed_modules_model_mapping` for Kimi-K2-Thinking model and
processing logic for `weight_packed` field.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
Signed-off-by: Ruri <33858552+zhoux77899@users.noreply.github.com>
Signed-off-by: Ruri <zhouxiang100@huawei.com>
270 lines
11 KiB
Python
270 lines
11 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.w4a16 import (AscendW4A16FusedMoEMethod,
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pack_to_int32, unpack_from_int32)
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class TestUnpackFromInt32(TestBase):
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def test_unpack_from_int32_packed_dim_1(self):
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weight = torch.tensor([[305419896, -1420531520]], dtype=torch.int32)
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shape = torch.Size([1, 8])
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num_bits = 4
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result = unpack_from_int32(weight, shape, num_bits, packed_dim=1)
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self.assertEqual(result.dtype, torch.int8)
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self.assertEqual(result.shape, shape)
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def test_unpack_from_int32_packed_dim_0(self):
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weight = torch.tensor([[305419896], [-1420531520]], dtype=torch.int32)
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shape = torch.Size([8, 1])
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num_bits = 4
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result = unpack_from_int32(weight, shape, num_bits, packed_dim=0)
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self.assertEqual(result.dtype, torch.int8)
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self.assertEqual(result.shape, shape)
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def test_unpack_from_int32_assertions(self):
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with self.assertRaises(AssertionError):
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weight = torch.tensor([[1, 2]], dtype=torch.int64)
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unpack_from_int32(weight, torch.Size([8, 1]), 4)
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with self.assertRaises(AssertionError):
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weight = torch.tensor([[1, 2]], dtype=torch.int32)
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unpack_from_int32(weight, torch.Size([8, 1]), 16)
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class TestPackToInt32(TestBase):
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@patch(
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"vllm_ascend.quantization.w4a16.torch_npu.npu_convert_weight_to_int4pack"
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)
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def test_pack_to_int32_int8(self, mock_npu_convert_weight_to_int4pack):
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mock_npu_convert_weight_to_int4pack.return_value = torch.zeros(
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(2, 4), dtype=torch.int32)
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weight = torch.zeros((2, 8, 16), dtype=torch.int8)
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result = pack_to_int32(weight)
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self.assertEqual(result.dtype, torch.int32)
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mock_npu_convert_weight_to_int4pack.assert_not_called()
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self.assertEqual(result.shape, torch.Size([2, 8, 4]))
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@patch(
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"vllm_ascend.quantization.w4a16.torch_npu.npu_convert_weight_to_int4pack"
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)
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def test_pack_to_int32_int32(self, mock_npu_convert_weight_to_int4pack):
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def mock_convert_weight(weight):
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return weight
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mock_npu_convert_weight_to_int4pack.side_effect = mock_convert_weight
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weight = torch.zeros((2, 8, 8), dtype=torch.int32)
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result = pack_to_int32(weight)
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self.assertEqual(result.dtype, torch.int32)
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self.assertEqual(result.shape, weight.shape)
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def test_pack_to_int32_assertion_dim(self):
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with self.assertRaises(AssertionError):
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weight = torch.zeros((8, 8), dtype=torch.int8)
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pack_to_int32(weight)
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def test_pack_to_int32_assertion_dtype(self):
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with self.assertRaises(AssertionError):
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weight = torch.zeros((2, 8, 8), dtype=torch.float32)
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pack_to_int32(weight)
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def test_pack_to_int32_assertion_divisible(self):
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with self.assertRaises(AssertionError):
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weight = torch.zeros((2, 8, 7), dtype=torch.int32)
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pack_to_int32(weight)
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with self.assertRaises(AssertionError):
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weight = torch.zeros((2, 8, 7), dtype=torch.int8)
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pack_to_int32(weight)
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class TestAscendW4A16FusedMoEMethod(TestBase):
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experts = 8
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input_size = 32
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output_size = 128
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group_size = 32
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@patch("vllm_ascend.quantization.w4a16.get_ascend_config")
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@patch("vllm_ascend.quantization.w4a16.get_current_vllm_config")
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def setUp(self, mock_get_current_vllm_config, mock_get_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_ascend_config.expert_map_record_path = None
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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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})
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mock_get_current_vllm_config.return_value = mock_vllm_config
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self.quant_method = AscendW4A16FusedMoEMethod()
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def test_init(self):
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self.assertTrue(self.quant_method.transpose_weight)
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self.assertEqual(self.quant_method.num_bits, 4)
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self.assertEqual(self.quant_method.pack_factor, 8)
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self.assertEqual(self.quant_method.group_size, self.group_size)
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self.assertFalse(self.quant_method.dynamic_eplb)
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def test_get_weight(self):
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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_packed"].dtype, torch.int32)
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expected_w13_shape = (self.experts, 2 * self.input_size,
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self.output_size //
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self.quant_method.pack_factor)
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self.assertEqual(param_dict["w13_weight_packed"].shape,
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expected_w13_shape)
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self.assertEqual(param_dict["w2_weight_packed"].dtype, torch.int32)
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expected_w2_shape = (self.experts, self.output_size,
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self.input_size // self.quant_method.pack_factor)
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self.assertEqual(param_dict["w2_weight_packed"].shape,
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expected_w2_shape)
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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.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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expected_w13_scale_shape = (self.experts, 2 * self.input_size,
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self.output_size // self.group_size)
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self.assertEqual(param_dict["w13_weight_scale"].shape,
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expected_w13_scale_shape)
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self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.bfloat16)
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expected_w2_scale_shape = (self.experts, self.output_size,
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self.input_size // self.group_size)
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self.assertEqual(param_dict["w2_weight_scale"].shape,
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expected_w2_scale_shape)
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self.assertEqual(param_dict["w13_weight_shape"].dtype, torch.int32)
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self.assertEqual(param_dict["w13_weight_shape"].shape,
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(self.experts, 2))
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self.assertEqual(param_dict["w2_weight_shape"].dtype, torch.int32)
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self.assertEqual(param_dict["w2_weight_shape"].shape,
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(self.experts, 2))
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self.assertEqual(param_dict["w13_weight_offset"].dtype, torch.bfloat16)
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self.assertEqual(param_dict["w13_weight_offset"].shape,
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expected_w13_scale_shape)
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self.assertEqual(param_dict["w2_weight_offset"].dtype, torch.bfloat16)
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self.assertEqual(param_dict["w2_weight_offset"].shape,
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expected_w2_scale_shape)
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def build_layer(self):
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"""Build a mock layer for testing"""
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layer = torch.nn.Module()
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w13_shape = (self.experts, 2 * self.input_size,
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self.output_size // self.quant_method.pack_factor)
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w2_shape = (self.experts, self.output_size,
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self.input_size // self.quant_method.pack_factor)
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layer.w13_weight_packed = torch.nn.Parameter(torch.randint(
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-100, 100, w13_shape, dtype=torch.int32),
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requires_grad=False)
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layer.w2_weight_packed = torch.nn.Parameter(torch.randint(
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-100, 100, w2_shape, dtype=torch.int32),
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requires_grad=False)
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w13_scale_shape = (self.experts, 2 * self.input_size,
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self.output_size // self.group_size)
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w2_scale_shape = (self.experts, self.output_size,
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self.input_size // self.group_size)
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layer.w13_weight_scale = torch.nn.Parameter(torch.ones(
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w13_scale_shape, 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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w2_scale_shape, dtype=torch.bfloat16),
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requires_grad=False)
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layer.w13_weight_offset = torch.nn.Parameter(torch.zeros(
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w13_scale_shape, dtype=torch.bfloat16),
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requires_grad=False)
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layer.w2_weight_offset = torch.nn.Parameter(torch.zeros(
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w2_scale_shape, dtype=torch.bfloat16),
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requires_grad=False)
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layer.w13_weight_shape = torch.nn.Parameter(torch.tensor(
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[[2 * self.input_size, self.output_size]] * self.experts,
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dtype=torch.int32),
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requires_grad=False)
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layer.w2_weight_shape = torch.nn.Parameter(torch.tensor(
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[[self.output_size, self.input_size]] * self.experts,
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dtype=torch.int32),
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requires_grad=False)
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return layer
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@patch(
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"vllm_ascend.quantization.w4a16.torch_npu.npu_convert_weight_to_int4pack"
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)
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def test_process_weights_after_loading_with_transpose(
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self, mock_npu_convert_weight_to_int4pack):
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def mock_convert_weight(weight):
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new_shape = list(weight.shape)
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new_shape[-1] = new_shape[-1] // 8
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return torch.zeros(new_shape, dtype=torch.int32)
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mock_npu_convert_weight_to_int4pack.side_effect = mock_convert_weight
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layer = self.build_layer()
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self.quant_method.transpose_weight = True
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self.quant_method.process_weights_after_loading(layer)
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self.assertEqual(layer.w13_weight_packed.data.shape,
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torch.Size([8, 128, 8]))
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self.assertEqual(layer.w2_weight_packed.data.shape,
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torch.Size([8, 32, 16]))
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self.assertEqual(layer.w13_weight_scale.data.shape,
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torch.Size([8, 4, 64]))
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self.assertEqual(layer.w2_weight_scale.data.shape,
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torch.Size([8, 1, 128]))
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self.assertEqual(layer.w13_weight_offset.data.shape,
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torch.Size([8, 4, 64]))
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self.assertEqual(layer.w2_weight_offset.data.shape,
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torch.Size([8, 1, 128]))
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self.assertTrue(layer.w13_weight_scale.data.is_contiguous())
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self.assertTrue(layer.w2_weight_scale.data.is_contiguous())
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self.assertTrue(layer.w13_weight_offset.data.is_contiguous())
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self.assertTrue(layer.w2_weight_offset.data.is_contiguous())
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def test_process_weights_after_loading_without_transpose(self):
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layer = self.build_layer()
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self.quant_method.transpose_weight = False
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original_w13_data = layer.w13_weight_packed.data.clone()
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original_w2_data = layer.w2_weight_packed.data.clone()
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self.quant_method.process_weights_after_loading(layer)
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self.assertTrue(
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torch.equal(layer.w13_weight_packed.data, original_w13_data))
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self.assertTrue(
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torch.equal(layer.w2_weight_packed.data, original_w2_data))
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