88d03a783f
### What this PR does / why we need it? Refactor `vllm_ascend/ops/fused_moe` to replace scattered MoE business `**kwargs` with typed request objects and explicit stage boundaries. - Prepare, dispatch, MLP, and quant stages now have clearer ownership. - Main MoE path no longer depends on business `kwargs.get(...)` lookups. - Comm and dispatcher interfaces are request-only on the main path. - UTs can assert stage-level fields directly instead of inferring behavior indirectly. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? CI passed. --------- Signed-off-by: linfeng-yuan <1102311262@qq.com>
230 lines
11 KiB
Python
230 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.ascend_forward_context import MoECommType
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from vllm_ascend.quantization.methods.w8a8_dynamic import 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.methods.w8a8_dynamic.get_mc2_group")
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@patch("vllm_ascend.quantization.methods.w8a8_dynamic.get_ascend_config")
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@patch("vllm_ascend.quantization.methods.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.methods.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_ascend_config.multistream_overlap_gate = False
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mock_ascend_config.eplb_config = Mock(dynamic_eplb=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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@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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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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global_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("vllm_ascend.quantization.methods.w8a8_dynamic.get_flash_common3_context")
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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_overlap_gate_uses_fc3_context(
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self,
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mock_select_experts,
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mock_extra_ctx,
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mock_get_flash_common3_context,
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):
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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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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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self.quant_method.multistream_overlap_gate = True
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self.quant_method.in_dtype = torch.float32
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mock_get_flash_common3_context.return_value = Mock(topk_weights=topk_weights, topk_ids=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.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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global_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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mock_select_experts.assert_not_called()
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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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