206 lines
9.3 KiB
Python
206 lines
9.3 KiB
Python
from unittest.mock import MagicMock, Mock, patch
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import torch
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from tests.ut.base import TestBase
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from tests.ut.quantization.conftest_quantization import (
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create_linear_layer,
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create_mock_ascend_config,
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create_mock_vllm_config,
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create_moe_layer,
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)
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from vllm_ascend.ascend_forward_context import MoECommType
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from vllm_ascend.quantization.methods.w8a8_dynamic import (
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AscendW8A8DynamicFusedMoEMethod,
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AscendW8A8DynamicLinearMethod,
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)
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class TestAscendW8A8DynamicLinearMethod(TestBase):
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def setUp(self):
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self.method = AscendW8A8DynamicLinearMethod()
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def test_get_weight_various_sizes(self):
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sizes = [(64, 128), (256, 512), (1024, 2048)]
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for input_size, output_size in sizes:
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weight = self.method.get_weight(input_size, output_size, torch.bfloat16)
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self.assertEqual(weight["weight"].dtype, torch.int8)
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self.assertEqual(weight["weight"].shape, (output_size, input_size))
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def test_get_perchannel_param_dtype_variations(self):
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dtypes = [torch.bfloat16, torch.float16]
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for dtype in dtypes:
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params = self.method.get_perchannel_param(128, dtype)
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self.assertEqual(params["weight_scale"].dtype, dtype)
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self.assertEqual(params["weight_offset"].dtype, dtype)
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self.assertEqual(params["weight_scale"].shape, (128, 1))
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self.assertEqual(params["weight_offset"].shape, (128, 1))
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@patch("torch_npu.npu_quant_matmul")
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@patch("torch_npu.npu_dynamic_quant")
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def test_apply_3d_input_with_squeeze(self, mock_dyn_quant, mock_matmul):
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mock_dyn_quant.return_value = (
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torch.randint(-128, 127, (32, 1, 128), dtype=torch.int8),
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torch.randn(32, 1, dtype=torch.float32),
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)
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mock_matmul.return_value = torch.randn(32, 1, 256)
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layer = MagicMock()
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layer.weight = torch.randint(-128, 127, (128, 256), dtype=torch.int8)
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layer.weight_scale = torch.randn(256, dtype=torch.float32)
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x = torch.randn(32, 1, 128, dtype=torch.bfloat16)
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output = self.method.apply(layer, x)
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mock_dyn_quant.assert_called_once()
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mock_matmul.assert_called_once()
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self.assertEqual(output.shape, (32, 1, 1, 256))
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def test_process_weights_after_loading(self):
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layer = MagicMock()
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layer.weight.data = torch.randint(-128, 127, (128, 256), dtype=torch.int8)
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layer.weight_scale.data = torch.randn(256, 1, dtype=torch.bfloat16)
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layer.weight_offset.data = torch.randn(256, 1, dtype=torch.bfloat16)
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with patch("vllm_ascend.quantization.methods.w8a8_dynamic.maybe_trans_nz", side_effect=lambda x: x):
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self.method.process_weights_after_loading(layer)
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self.assertEqual(layer.weight_scale_fp32.dtype, torch.float32)
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self.assertEqual(layer.weight_scale.data.shape, (256,))
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self.assertEqual(layer.weight_offset.data.shape, (256,))
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self.assertEqual(layer.weight.data.shape, (256, 128))
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class TestAscendW8A8DynamicLinearMethodWithNpu(TestBase):
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def setUp(self):
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self.method = AscendW8A8DynamicLinearMethod()
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self.mock_get_config = patch("vllm_ascend.utils.get_ascend_config")
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mock_config = self.mock_get_config.start()
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mock_ascend_config = MagicMock()
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mock_ascend_config.weight_nz_mode = 0
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mock_config.return_value = mock_ascend_config
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def tearDown(self):
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self.mock_get_config.stop()
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def test_apply_with_npu(self):
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input_size, output_size = 128, 256
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params_dtype = torch.bfloat16
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layer = create_linear_layer(self.method, input_size, output_size, params_dtype)
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self.method.process_weights_after_loading(layer)
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x = torch.randn(32, input_size, dtype=params_dtype).npu()
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bias = torch.randn(output_size, dtype=torch.float32).npu()
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output = self.method.apply(layer, x, bias)
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self.assertEqual(output.shape, (32, output_size))
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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.methods.w8a8_dynamic.get_mc2_group")
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@patch("vllm_ascend.quantization.methods.w8a8_dynamic.get_ascend_config")
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def setUp(self, mock_ascend, mock_mc2, mock_rank):
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with patch("vllm_ascend.quantization.methods.w8a8_dynamic.get_current_vllm_config") as mock_vllm:
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mock_vllm.return_value = create_mock_vllm_config()
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mock_ascend.return_value = create_mock_ascend_config()
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mock_mc2.return_value = MagicMock(
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device_group=Mock(
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_get_backend=Mock(return_value=Mock(get_hccl_comm_name=Mock(return_value="test_comm")))
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)
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)
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mock_rank.return_value = 0
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self.quant_method = AscendW8A8DynamicFusedMoEMethod()
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def test_get_weight_various_expert_counts(self):
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expert_counts = [4, 8, 16, 32]
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for num_experts in expert_counts:
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param_dict = self.quant_method.get_weight(
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num_experts, self.intermediate_size, self.hidden_size, torch.bfloat16
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)
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self.assertEqual(param_dict["w13_weight"].shape[0], num_experts)
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self.assertEqual(param_dict["w2_weight"].shape[0], num_experts)
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def test_get_dynamic_quant_param_various_sizes(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, torch.bfloat16
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)
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self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.bfloat16)
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self.assertEqual(param_dict["w13_weight_offset"].shape, (self.num_experts, 2 * self.intermediate_size, 1))
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self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.bfloat16)
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self.assertEqual(param_dict["w2_weight_offset"].shape, (self.num_experts, self.hidden_size, 1))
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@patch("vllm_ascend.quantization.methods.w8a8_dynamic._EXTRA_CTX")
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@patch("vllm_ascend.quantization.methods.w8a8_dynamic.select_experts")
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def test_apply_uses_explicit_dispatch_and_mlp_args(self, mock_select_experts, mock_extra_ctx):
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tokens = 4
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hidden_size = self.hidden_size
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layer = torch.nn.Module()
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layer.w13_weight = torch.randint(
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-8,
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8,
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(self.num_experts, 2 * self.intermediate_size, hidden_size),
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dtype=torch.int8,
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)
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layer.w2_weight = torch.randint(
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-8,
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8,
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(self.num_experts, hidden_size, self.intermediate_size),
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dtype=torch.int8,
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)
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layer.w13_weight_scale_fp32 = torch.ones(self.num_experts, 2 * self.intermediate_size, dtype=torch.float32)
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layer.w2_weight_scale = torch.ones(self.num_experts, hidden_size, dtype=torch.float32)
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layer.swiglu_limit = 1000000
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x = torch.randn(tokens, hidden_size, dtype=torch.float32)
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router_logits = torch.randn(tokens, self.num_experts, dtype=torch.float32)
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topk_weights = torch.randn(tokens, 2, dtype=torch.float32)
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topk_ids = torch.randint(0, self.num_experts, (tokens, 2), dtype=torch.int64)
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mc2_mask = torch.tensor([1, 0, 1, 0], dtype=torch.bool)
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pertoken_scale = torch.randn(tokens, dtype=torch.float32)
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mock_select_experts.return_value = (topk_weights, topk_ids)
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mock_comm = Mock()
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mock_comm.fused_experts.return_value = torch.randn(tokens, hidden_size, dtype=torch.float32)
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mock_extra_ctx.moe_comm_method = mock_comm
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mock_extra_ctx.moe_comm_type = MoECommType.ALLGATHER
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self.quant_method.multistream_overlap_gate = False
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self.quant_method.in_dtype = torch.float32
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self.quant_method.apply(
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layer=layer,
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x=x,
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router_logits=router_logits,
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top_k=2,
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renormalize=True,
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num_experts=self.num_experts,
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activation="gelu",
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apply_router_weight_on_input=True,
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mc2_mask=mc2_mask,
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pertoken_scale=pertoken_scale,
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)
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fused_experts_input = mock_comm.fused_experts.call_args.kwargs["fused_experts_input"]
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self.assertEqual(fused_experts_input.activation, "gelu")
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self.assertTrue(fused_experts_input.routing.apply_router_weight_on_input)
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self.assertIs(fused_experts_input.routing.mc2_mask, mc2_mask)
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self.assertIs(fused_experts_input.routing.pertoken_scale, pertoken_scale)
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self.assertIs(fused_experts_input.topk_weights, topk_weights)
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self.assertIs(fused_experts_input.topk_ids, topk_ids)
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@patch("torch_npu.npu_format_cast")
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@patch("vllm_ascend.quantization.methods.w8a8_dynamic.get_ascend_config")
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def test_process_weights_after_loading(self, mock_get_config, mock_format_cast):
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mock_config = MagicMock()
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mock_config.enable_fused_mc2 = 1
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mock_get_config.return_value = mock_config
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self.quant_method.dynamic_eplb = True
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mock_format_cast.return_value = torch.randint(
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-8, 8, (self.num_experts, self.hidden_size, 2 * self.intermediate_size), dtype=torch.int8
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)
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layer = create_moe_layer(
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num_experts=self.num_experts, hidden_size=self.hidden_size, intermediate_size=self.intermediate_size
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)
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self.quant_method.process_weights_after_loading(layer)
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self.assertTrue(hasattr(layer, "w13_weight_list"))
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self.assertFalse(hasattr(layer, "w13_weight_scale_fp32"))
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