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>
272 lines
11 KiB
Python
272 lines
11 KiB
Python
from unittest.mock import MagicMock, patch
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import torch
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from vllm.model_executor.layers.fused_moe import FusedMoEConfig
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from tests.ut.base import TestBase
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from vllm_ascend.ops.fused_moe.moe_comm_method import (
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AllGatherCommImpl,
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AlltoAllCommImpl,
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MC2CommImpl,
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)
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from vllm_ascend.ops.fused_moe.moe_runtime_args import (
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MoEAllGatherCombineMetadata,
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MoEFusedExpertsInput,
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MoEPrepareOutput,
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MoEQuantParams,
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MoERoutingParams,
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MoEWeights,
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)
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from vllm_ascend.ops.fused_moe.token_dispatcher import MoETokenDispatchOutput
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from vllm_ascend.quantization.methods.base import QuantType
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class TestMoECommMethod(TestBase):
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def setUp(self):
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# Mock FusedMoEConfig
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self.moe_config = MagicMock(spec=FusedMoEConfig)
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self.moe_config.num_experts = 8
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self.moe_config.num_local_experts = 2
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self.moe_config.experts_per_token = 2
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self.moe_config.tp_group = MagicMock()
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self.moe_config.tp_group.device_group = MagicMock()
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self.moe_config.dp_size = 1
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self.moe_config.tp_size = 1
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self.moe_config.ep_size = 1
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self.moe_config.dp_group = MagicMock()
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self.moe_config.global_redundant_expert_num = 0
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@patch('vllm_ascend.ascend_forward_context.get_forward_context')
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@patch(
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"vllm_ascend.ops.fused_moe.moe_comm_method.PrepareAndFinalizeWithAllGather"
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)
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@patch(
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"vllm_ascend.ops.fused_moe.moe_comm_method.TokenDispatcherWithAllGather"
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)
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def test_all_gather_comm_impl(self, mock_token_dispatcher,
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mock_prepare_finalize,
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mock_get_forward_context):
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# Mock forward context
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mock_context = MagicMock()
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mock_context.moe_comm_method = "all_gather"
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mock_get_forward_context.return_value = mock_context
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# Mock prepare finalize
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mock_pf_instance = MagicMock()
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mock_pf_instance.prepare.return_value = MoEPrepareOutput(
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hidden_states=torch.randn(4, 8),
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router_logits=torch.randn(4, 2),
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mc2_mask=None,
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padded_hidden_states_shape=None)
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mock_pf_instance.finalize.return_value = torch.randn(4, 8)
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mock_prepare_finalize.return_value = mock_pf_instance
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# Mock token dispatcher
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mock_td_instance = MagicMock()
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mock_token_dispatcher.return_value = mock_td_instance
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# Create instance
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comm_impl = AllGatherCommImpl(self.moe_config)
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# Test prepare method
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hidden_states = torch.randn(3, 8)
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router_logits = torch.randn(3, 2)
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prepare_output = comm_impl.prepare(hidden_states, router_logits)
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h_out = prepare_output.hidden_states
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padded_hidden_states_shape = prepare_output.padded_hidden_states_shape
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# Verify prepare was called with correct arguments
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mock_pf_instance.prepare.assert_called_once_with(
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hidden_states, router_logits, False, False, QuantType.NONE)
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# Test finalize method
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comm_impl.finalize(h_out,
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reduce_results=True,
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padded_hidden_states_shape=padded_hidden_states_shape)
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mock_pf_instance.finalize.assert_called_once_with(h_out, True, None)
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@patch('vllm_ascend.ascend_forward_context.get_forward_context')
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@patch(
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"vllm_ascend.ops.fused_moe.moe_comm_method.PrepareAndFinalizeWithMC2")
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@patch("vllm_ascend.ops.fused_moe.moe_comm_method.TokenDispatcherWithMC2")
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def test_mc2_comm_impl(self, mock_token_dispatcher, mock_prepare_finalize,
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mock_get_forward_context):
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# Mock forward context
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mock_context = MagicMock()
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mock_context.moe_comm_method = "mc2"
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mock_get_forward_context.return_value = mock_context
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# Mock prepare finalize
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mock_pf_instance = MagicMock()
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mock_pf_instance.prepare.return_value = MoEPrepareOutput(
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hidden_states=torch.randn(4, 8),
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router_logits=torch.randn(4, 2),
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mc2_mask=torch.tensor([1, 0, 1, 0]),
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padded_hidden_states_shape=None)
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mock_pf_instance.finalize.return_value = torch.randn(4, 8)
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mock_prepare_finalize.return_value = mock_pf_instance
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# Mock token dispatcher
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mock_td_instance = MagicMock()
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mock_token_dispatcher.return_value = mock_td_instance
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# Create instance
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comm_impl = MC2CommImpl(self.moe_config)
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# Test prepare method
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hidden_states = torch.randn(3, 8)
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router_logits = torch.randn(3, 2)
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prepare_output = comm_impl.prepare(hidden_states, router_logits)
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h_out = prepare_output.hidden_states
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padded_hidden_states_shape = prepare_output.padded_hidden_states_shape
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# Verify prepare was called with correct arguments
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mock_pf_instance.prepare.assert_called_once_with(
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hidden_states, router_logits, False, False, QuantType.NONE)
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# Test finalize method
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comm_impl.finalize(h_out,
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reduce_results=True,
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padded_hidden_states_shape=padded_hidden_states_shape)
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mock_pf_instance.finalize.assert_called_once_with(h_out, True, None)
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@patch('vllm_ascend.ascend_forward_context.get_forward_context')
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@patch(
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"vllm_ascend.ops.fused_moe.moe_comm_method.PrepareAndFinalizeWithAll2All"
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)
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@patch(
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"vllm_ascend.ops.fused_moe.moe_comm_method.TokenDispatcherWithAll2AllV"
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)
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def test_alltoall_comm_impl(self, mock_token_dispatcher,
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mock_prepare_finalize,
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mock_get_forward_context):
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# Mock forward context
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mock_context = MagicMock()
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mock_context.moe_comm_method = "alltoall"
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mock_get_forward_context.return_value = mock_context
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# Mock prepare finalize
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mock_pf_instance = MagicMock()
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mock_pf_instance.prepare.return_value = MoEPrepareOutput(
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hidden_states=torch.randn(4, 8),
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router_logits=torch.randn(4, 2),
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mc2_mask=None,
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padded_hidden_states_shape=None)
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mock_pf_instance.finalize.return_value = torch.randn(4, 8)
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mock_prepare_finalize.return_value = mock_pf_instance
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# Mock token dispatcher
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mock_td_instance = MagicMock()
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mock_token_dispatcher.return_value = mock_td_instance
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# Create instance
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comm_impl = AlltoAllCommImpl(self.moe_config)
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# Test prepare method
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hidden_states = torch.randn(3, 8)
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router_logits = torch.randn(3, 2)
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_ = comm_impl.prepare(hidden_states, router_logits)
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# Verify prepare was called with correct arguments
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mock_pf_instance.prepare.assert_called_once_with(
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hidden_states, router_logits, False, False, QuantType.NONE)
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@patch('vllm_ascend.ascend_forward_context.get_forward_context')
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@patch(
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"vllm_ascend.ops.fused_moe.moe_comm_method.PrepareAndFinalizeWithAllGather"
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)
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@patch(
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"vllm_ascend.ops.fused_moe.moe_comm_method.TokenDispatcherWithAllGather"
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)
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@patch("vllm_ascend.ops.fused_moe.moe_comm_method.unified_apply_mlp")
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@patch("torch.npu.current_stream", MagicMock())
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def test_fused_experts_method(self, mock_unified_apply_mlp,
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mock_token_dispatcher, mock_prepare_finalize,
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mock_get_forward_context):
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# Mock forward context
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mock_context = MagicMock()
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mock_context.moe_comm_method = "all_gather"
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mock_get_forward_context.return_value = mock_context
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# Mock prepare finalize
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mock_pf_instance = MagicMock()
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mock_pf_instance.prepare.return_value = MoEPrepareOutput(
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hidden_states=torch.randn(4, 8),
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router_logits=torch.randn(4, 2),
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mc2_mask=None,
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padded_hidden_states_shape=None)
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mock_pf_instance.finalize.return_value = torch.randn(4, 8)
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mock_prepare_finalize.return_value = mock_pf_instance
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# Mock token dispatcher
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mock_td_instance = MagicMock()
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dispatch_topk_weights = torch.tensor([[0.5, 0.5], [0.3, 0.7], [0.8, 0.2], [0.6, 0.4]])
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mock_td_instance.token_dispatch.return_value = MoETokenDispatchOutput(
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hidden_states=torch.randn(6, 8),
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group_list=torch.tensor([2, 2, 2]),
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group_list_type=1,
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combine_metadata=MoEAllGatherCombineMetadata(
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topk_weights=dispatch_topk_weights,
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expanded_row_idx=torch.arange(8, dtype=torch.int32),
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restore_shape=torch.Size([4, 8]),
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))
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mock_td_instance.token_combine.return_value = torch.randn(4, 8)
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mock_token_dispatcher.return_value = mock_td_instance
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# Mock unified_apply_mlp
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mock_unified_apply_mlp.return_value = torch.randn(6, 8)
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# Create instance
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comm_impl = AllGatherCommImpl(self.moe_config)
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# Test fused_experts method
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hidden_states = torch.randn(4, 8).contiguous()
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w1 = torch.randn(16, 8).contiguous()
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w2 = torch.randn(16, 8).contiguous()
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topk_weights = dispatch_topk_weights
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topk_ids = torch.tensor([[0, 1], [1, 2], [2, 0], [1, 1]])
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# Make sure tensors are contiguous and have correct strides
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hidden_states = hidden_states.contiguous()
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w1 = w1.contiguous()
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w2 = w2.contiguous()
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result = comm_impl.fused_experts(fused_experts_input=MoEFusedExpertsInput(
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hidden_states=hidden_states,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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weights=MoEWeights(
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w1=[w1],
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w2=[w2],
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),
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routing=MoERoutingParams(
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expert_map=None,
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global_redundant_expert_num=0,
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mc2_mask=None,
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apply_router_weight_on_input=False,
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),
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activation="silu",
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need_trans=False,
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dynamic_eplb=False,
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quant=MoEQuantParams(),
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))
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# Verify result shape
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self.assertEqual(result.routed_out.shape, (4, 8))
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# Verify token_dispatch was called
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mock_td_instance.token_dispatch.assert_called_once()
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# Verify unified_apply_mlp was called
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mock_unified_apply_mlp.assert_called_once()
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mlp_compute_input = mock_unified_apply_mlp.call_args.kwargs["mlp_compute_input"]
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self.assertFalse(mlp_compute_input.fusion)
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self.assertFalse(mlp_compute_input.quant.is_mxfp)
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# Verify token_combine was called
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mock_td_instance.token_combine.assert_called_once_with(
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hidden_states=mock_unified_apply_mlp.return_value,
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combine_metadata=mock_td_instance.token_dispatch.return_value.combine_metadata,
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)
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