cc23067f1e
### What this PR does / why we need it?
Now `VLLM_ASCEND_ENABLE_NZ` will have three options:
0: disable nz;
1: only quant case enable nz;
2: enable nz as long as possible;
And `VLLM_ASCEND_ENABLE_NZ`=1 by default.
All cases are shown in the table below:
| | W4A4 | W4A8 | W8A8 | fp16/bf16 | fp32 |
|---|---|---|---|---|---|
| trans nz | can't support nz | trans nz by default | trans nz by
default | trans nz when VLLM_ASCEND_ENABLE_NZ is 2 | can't support nz |
| transpose | only support not transpose case | only support transpose
case | only support transpose case | linear: only support not transpose
case<br>gmm: only support transpose case | same to fp16/bf16 |
Some exceptional cases:
1. MLAPO op need to do some additional processing on the weights,
including trans nz. If use MLAPO op, some weight will be transformed to
nz forcely;
2. MLA/SFA's weight `W_UV` will be used by op
`torch.ops._C_ascend.batch_matmul_transpose`, and this op can't support
nz currently;
### Does this PR introduce _any_ user-facing change?
Now fp16/bf16 weight will not trans nz by default.
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: zzzzwwjj <1183291235@qq.com>
222 lines
8.7 KiB
Python
222 lines
8.7 KiB
Python
import os
|
|
from unittest.mock import MagicMock, patch
|
|
|
|
import torch
|
|
|
|
from tests.ut.base import TestBase
|
|
from vllm_ascend.quantization.w8a8 import (AscendW8A8LinearMethod,
|
|
quant_per_tensor)
|
|
from vllm_ascend.utils import AscendDeviceType
|
|
|
|
|
|
class TestQuantPerTensor(TestBase):
|
|
|
|
@patch("torch_npu.npu_quantize")
|
|
def test_quant_per_tensor(self, mock_npu_quantize):
|
|
in_tensor = torch.randn(32, 128)
|
|
input_scale = torch.tensor(0.1)
|
|
input_offset = torch.tensor(0)
|
|
|
|
expected_output = torch.randint(-128, 127, (32, 128), dtype=torch.int8)
|
|
mock_npu_quantize.return_value = expected_output
|
|
|
|
output = quant_per_tensor(in_tensor, input_scale, input_offset)
|
|
|
|
mock_npu_quantize.assert_called_once_with(
|
|
in_tensor,
|
|
input_scale,
|
|
input_offset,
|
|
torch.qint8,
|
|
-1,
|
|
False,
|
|
)
|
|
|
|
self.assertTrue(torch.equal(output, expected_output))
|
|
|
|
|
|
class TestAscendW8A8LinearMethod(TestBase):
|
|
|
|
def setUp(self):
|
|
self.method = AscendW8A8LinearMethod()
|
|
|
|
def test_get_weight(self):
|
|
weight = self.method.get_weight(10, 20)
|
|
self.assertEqual(weight['weight'].dtype, torch.int8)
|
|
self.assertEqual(weight['weight'].shape, (20, 10))
|
|
|
|
def test_get_pertensor_param(self):
|
|
params = self.method.get_pertensor_param(torch.bfloat16)
|
|
self.assertEqual(params['input_scale'].dtype, torch.bfloat16)
|
|
self.assertEqual(params['input_offset'].dtype, torch.int8)
|
|
self.assertEqual(params['input_scale'].shape, (1, ))
|
|
self.assertEqual(params['input_offset'].shape, (1, ))
|
|
|
|
def test_get_perchannel_param(self):
|
|
params = self.method.get_perchannel_param(10, torch.bfloat16)
|
|
|
|
self.assertEqual(params['quant_bias'].dtype, torch.int32)
|
|
self.assertEqual(params['deq_scale'].dtype, torch.float32)
|
|
self.assertEqual(params['weight_scale'].dtype, torch.bfloat16)
|
|
self.assertEqual(params['weight_offset'].dtype, torch.bfloat16)
|
|
self.assertEqual(params['quant_bias'].shape, (10, ))
|
|
self.assertEqual(params['deq_scale'].shape, (10, ))
|
|
self.assertEqual(params['weight_scale'].shape, (10, 1))
|
|
self.assertEqual(params['weight_offset'].shape, (10, 1))
|
|
|
|
@patch("vllm_ascend.quantization.w8a8.get_forward_context")
|
|
@patch("torch.ops.vllm.quantize")
|
|
@patch("torch_npu.npu_quant_matmul")
|
|
def test_apply_with_x_not_int8(self, mock_npu_quant_matmul, mock_quantize,
|
|
mock_get_forward_context):
|
|
layer = MagicMock()
|
|
layer.aclnn_input_scale = 0.1
|
|
layer.aclnn_input_offset = 0.2
|
|
layer.weight = torch.randn(128, 256)
|
|
layer.deq_scale = 0.3
|
|
|
|
mock_forward_context = MagicMock()
|
|
mock_get_forward_context.return_value = mock_forward_context
|
|
mock_weight_prefetch_method = MagicMock()
|
|
mock_forward_context.weight_prefetch_method = mock_weight_prefetch_method
|
|
|
|
x = torch.randn(32, 128)
|
|
bias = torch.randn(256)
|
|
mock_quantize.return_value = torch.randint(-128,
|
|
127,
|
|
x.shape,
|
|
dtype=torch.int8)
|
|
|
|
expected_y_output = torch.randn(32, 256)
|
|
mock_npu_quant_matmul.return_value = expected_y_output
|
|
|
|
output = self.method.apply(layer, x, bias)
|
|
|
|
expected_y_output += bias
|
|
self.assertTrue(torch.equal(output, expected_y_output))
|
|
|
|
@patch("torch_npu.npu_quant_matmul")
|
|
def test_apply_with_x_is_int8(self, mock_npu_quant_matmul):
|
|
layer = MagicMock()
|
|
layer.aclnn_input_scale = 0.1
|
|
layer.aclnn_input_offset = 0.2
|
|
layer.weight = torch.randn(128, 256)
|
|
layer.deq_scale = 0.3
|
|
|
|
x = torch.randint(-128, 127, (32, 128), dtype=torch.int8)
|
|
bias = torch.randn(256)
|
|
|
|
expected_y_output = torch.randn(32, 256)
|
|
mock_npu_quant_matmul.return_value = expected_y_output
|
|
|
|
output = self.method.apply(layer, x, bias)
|
|
expected_y_output += bias
|
|
self.assertTrue(torch.equal(output, expected_y_output))
|
|
|
|
@patch('vllm_ascend.utils.get_ascend_device_type',
|
|
return_value=AscendDeviceType._310P)
|
|
@patch("torch_npu.npu_quant_matmul")
|
|
def test_apply_with_x_is_310p(self, mock_npu_quant_matmul,
|
|
mock_soc_version):
|
|
layer = MagicMock()
|
|
layer.aclnn_input_scale = 0.1
|
|
layer.aclnn_input_offset = 0.2
|
|
layer.weight = torch.randn(128, 256)
|
|
layer.deq_scale = 0.3
|
|
|
|
x = torch.randint(-128, 127, (32, 128), dtype=torch.int8)
|
|
bias = torch.randn(256)
|
|
|
|
expected_y_output = torch.randn(32, 256)
|
|
mock_npu_quant_matmul.return_value = expected_y_output
|
|
|
|
output = self.method.apply(layer, x, bias)
|
|
expected_y_output += bias
|
|
self.assertTrue(torch.equal(output, expected_y_output))
|
|
|
|
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "0"})
|
|
@patch('torch_npu.npu_format_cast')
|
|
def test_process_weights_after_loading_with_nz0(self,
|
|
mock_npu_format_cast):
|
|
layer = MagicMock()
|
|
|
|
layer.weight.data = torch.randint(-127,
|
|
128, (128, 256),
|
|
dtype=torch.int8)
|
|
layer.input_scale.data = torch.tensor([0.1])
|
|
layer.input_offset.data = torch.tensor([0])
|
|
layer.deq_scale = torch.tensor([0.5])
|
|
layer.weight_scale.data = torch.randn(128, 1)
|
|
layer.weight_offset.data = torch.randn(128, 1)
|
|
|
|
mock_npu_format_cast.return_value = MagicMock
|
|
self.method.process_weights_after_loading(layer)
|
|
|
|
expected_offset = torch.tensor([0]).repeat(256).to(torch.int8)
|
|
self.assertTrue(
|
|
torch.equal(layer.aclnn_input_offset.data, expected_offset))
|
|
self.assertFalse(layer.aclnn_input_offset.requires_grad)
|
|
|
|
self.assertFalse(layer.deq_scale.requires_grad)
|
|
|
|
self.assertEqual(layer.weight_scale.data.shape, (128, ))
|
|
self.assertEqual(layer.weight_offset.data.shape, (128, ))
|
|
mock_npu_format_cast.assert_not_called()
|
|
|
|
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "1"})
|
|
@patch('torch_npu.npu_format_cast')
|
|
def test_process_weights_after_loading_with_nz1(self,
|
|
mock_npu_format_cast):
|
|
layer = MagicMock()
|
|
|
|
layer.weight.data = torch.randint(-127,
|
|
128, (128, 256),
|
|
dtype=torch.int8)
|
|
layer.input_scale.data = torch.tensor([0.1])
|
|
layer.input_offset.data = torch.tensor([0])
|
|
layer.deq_scale = torch.tensor([0.5])
|
|
layer.weight_scale.data = torch.randn(128, 1)
|
|
layer.weight_offset.data = torch.randn(128, 1)
|
|
|
|
mock_npu_format_cast.return_value = MagicMock
|
|
self.method.process_weights_after_loading(layer)
|
|
|
|
expected_offset = torch.tensor([0]).repeat(256).to(torch.int8)
|
|
self.assertTrue(
|
|
torch.equal(layer.aclnn_input_offset.data, expected_offset))
|
|
self.assertFalse(layer.aclnn_input_offset.requires_grad)
|
|
|
|
self.assertFalse(layer.deq_scale.requires_grad)
|
|
|
|
self.assertEqual(layer.weight_scale.data.shape, (128, ))
|
|
self.assertEqual(layer.weight_offset.data.shape, (128, ))
|
|
mock_npu_format_cast.assert_called_once()
|
|
|
|
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "2"})
|
|
@patch('torch_npu.npu_format_cast')
|
|
def test_process_weights_after_loading_with_nz2(self,
|
|
mock_npu_format_cast):
|
|
layer = MagicMock()
|
|
|
|
layer.weight.data = torch.randint(-127,
|
|
128, (128, 256),
|
|
dtype=torch.int8)
|
|
layer.input_scale.data = torch.tensor([0.1])
|
|
layer.input_offset.data = torch.tensor([0])
|
|
layer.deq_scale = torch.tensor([0.5])
|
|
layer.weight_scale.data = torch.randn(128, 1)
|
|
layer.weight_offset.data = torch.randn(128, 1)
|
|
|
|
mock_npu_format_cast.return_value = MagicMock
|
|
self.method.process_weights_after_loading(layer)
|
|
|
|
expected_offset = torch.tensor([0]).repeat(256).to(torch.int8)
|
|
self.assertTrue(
|
|
torch.equal(layer.aclnn_input_offset.data, expected_offset))
|
|
self.assertFalse(layer.aclnn_input_offset.requires_grad)
|
|
|
|
self.assertFalse(layer.deq_scale.requires_grad)
|
|
|
|
self.assertEqual(layer.weight_scale.data.shape, (128, ))
|
|
self.assertEqual(layer.weight_offset.data.shape, (128, ))
|
|
mock_npu_format_cast.assert_called_once()
|