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()