143e1f46d0
### What this PR does / why we need it? Support shared expert DP for deepseek_mtp feature. `shared_expert_dp` requires `SP==True`, with corresponding parameter restrictions. Previously, due to the coupling between `shared_expert_dp` and torchair, and the removal of `deepseek_mtp` in vllm_ascend, shared expert dp of deepseek_mtp was temporarily removed. Currently, by performing the `reduce_scatter` on the input of deepssek_mtp in `mtp_proposer.py`, we ensure that it matches the dimensions of `input_embedding`, and then perform the `all_gather` on the output of mtp. ### How was this patch tested? baseline: <img width="1184" height="692" alt="image" src="https://github.com/user-attachments/assets/9680d53a-7b1d-481a-accc-b8f3dae2b9e3" /> enable shared_expert_dp and multistream_overlap_shared_expert: <img width="1167" height="687" alt="image" src="https://github.com/user-attachments/assets/2531d06b-dfda-4e24-8628-6f4b0f677ddc" /> TPOT: 48ms -> 45.4ms Average TPS per rank: 117.6 -> 126.1 - vLLM version: v0.11.2 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2 --------- Signed-off-by: chenmenglong <chenmenglong1@huawei.com> Signed-off-by: zengran <zengran2@huawei.com> Co-authored-by: zengran <zengran2@huawei.com>
164 lines
6.5 KiB
Python
164 lines
6.5 KiB
Python
import unittest
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from unittest.mock import patch
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import pytest
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import torch
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from pytest_mock import MockerFixture
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from vllm.model_executor.layers.layernorm import RMSNorm
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from tests.ut.base import PytestBase
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from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
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from vllm_ascend.utils import AscendDeviceType
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def mock_rms_norm(x, weight, eps):
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return x + 1, None
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def mock_add_rms_norm(x, residual, weight, eps):
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return 2 * x, None, 2 * residual
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def mock_add_rms_norm_quant_with_bias(x, residual, weight, quant_scale,
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quant_offset, beta, epsilon):
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x_out = 2 * x
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residual_out = 2 * residual
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x_out_quant = x_out.to(torch.int8)
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residual_out_quant = residual_out.to(torch.int8)
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return x_out_quant, None, residual_out_quant
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class TestAscendRMSNorm(PytestBase):
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@pytest.fixture(autouse=True)
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def context(self, mocker: MockerFixture):
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mocker.patch("torch_npu.npu_rms_norm", side_effect=mock_rms_norm)
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mocker.patch("torch_npu.npu_add_rms_norm",
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side_effect=mock_add_rms_norm)
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mocker.patch("torch_npu.npu_add_rms_norm_quant",
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side_effect=mock_add_rms_norm_quant_with_bias)
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mocker.patch("torch.ops.vllm.maybe_wait_prefetch_done",
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side_effect=lambda x: None)
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# Test case for the most common and basic scenario
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@pytest.mark.parametrize(
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"residual", [None, torch.randn(4, 8, dtype=torch.float16)])
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@patch("torch.ops.vllm.maybe_chunk_residual")
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def test_forward_oot_basic(self, mock_maybe_chunk_residual, residual):
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mock_maybe_chunk_residual.side_effect = lambda x, residual: residual
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layer = RMSNorm(hidden_size=8, eps=1e-05)
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x = torch.randn(4, 8, dtype=torch.float16)
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if residual is not None:
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x_out, residual_out = layer.forward_oot(x, residual)
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x_out_expected = 2 * x
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residual_out_expected = 2 * residual
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assert torch.allclose(x_out, x_out_expected)
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assert torch.allclose(residual_out, residual_out_expected)
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else:
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x_out = layer.forward(x, residual)
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x_out_expected = x + 1
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assert torch.allclose(x_out, x_out_expected)
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# Test case for addrmsnorm + w8a8 quant fusion
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def test_forward_oot_with_quant_fusion(self, mocker: MockerFixture):
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mock_soc_version = mocker.patch(
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"vllm_ascend.utils.get_ascend_device_type")
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mock_soc_version.return_value = AscendDeviceType._910_93
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mock_get_forward_context = mocker.patch(
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"vllm_ascend.ops.layernorm.get_forward_context")
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# Simulating a scenario with quant_fusion enabled
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mock_forward_context = mocker.MagicMock()
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mock_model_instance = mocker.MagicMock()
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mock_forward_context.model_instance = mock_model_instance
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num_hidden_layers = 3
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mock_model_instance.model.layers = [
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mocker.MagicMock() for _ in range(num_hidden_layers)
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]
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mock_layer_0 = mock_model_instance.model.layers[0]
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mock_layer_0.self_attn.qkv_proj = mocker.MagicMock()
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mock_layer_0.mlp.gate_up_proj = mocker.MagicMock()
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mock_layer_1 = mock_model_instance.model.layers[1]
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mock_layer_1.self_attn.qkv_proj = mocker.MagicMock()
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mock_layer_1.mlp.gate_up_proj = mocker.MagicMock()
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mock_quant_method_0_qkv = mocker.MagicMock()
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mock_quant_method_0_qkv.quant_method = AscendW8A8LinearMethod()
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mock_quant_method_0_gate_up = mocker.MagicMock()
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mock_quant_method_0_gate_up.quant_method = AscendW8A8LinearMethod()
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mock_layer_0.self_attn.qkv_proj.quant_method = mock_quant_method_0_qkv
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mock_layer_0.mlp.gate_up_proj.quant_method = mock_quant_method_0_gate_up
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mock_quant_method_1_qkv = mocker.MagicMock()
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mock_quant_method_1_qkv.quant_method = AscendW8A8LinearMethod()
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mock_quant_method_1_gate_up = mocker.MagicMock()
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mock_quant_method_1_gate_up.quant_method = AscendW8A8LinearMethod()
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mock_layer_1.self_attn.qkv_proj.quant_method = mock_quant_method_1_qkv
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mock_layer_1.mlp.gate_up_proj.quant_method = mock_quant_method_1_gate_up
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mock_get_forward_context.return_value = mock_forward_context
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mock_forward_context.addrmsnorm_quant_fusion_enabled = True
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mock_forward_context.prefetch_mlp_enabled = False
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mock_forward_context.layer_idx = 0
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mock_forward_context.num_hidden_layers = num_hidden_layers
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mock_forward_context.fusion_linear = "gate_up_dense"
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mock_forward_context.weight_prefetch_method = None
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mocker.patch("torch.ops.vllm.maybe_chunk_residual",
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lambda x, residual: residual)
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# Ensure fusion and layer_idx increment are handled correctly
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x = torch.randn(4, 8, dtype=torch.float16)
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residual = torch.randn(4, 8, dtype=torch.float16)
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layer = RMSNorm(hidden_size=8, eps=1e-05)
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 2
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assert mock_forward_context.fusion_linear == "qkv_dense"
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assert mock_forward_context.layer_idx == 1
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 4
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assert mock_forward_context.fusion_linear == "gate_up_dense"
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assert mock_forward_context.layer_idx == 1
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mock_forward_context.fusion_linear = "gate_moe"
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 5
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fusion_linear_expected = "qkv_moe"
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assert mock_forward_context.fusion_linear == fusion_linear_expected
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assert mock_forward_context.layer_idx == 2
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 6
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fusion_linear_expected = "gate_moe"
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assert mock_forward_context.fusion_linear == fusion_linear_expected
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assert mock_forward_context.layer_idx == 2
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# last layer returned directly
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 7
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assert mock_forward_context.fusion_linear == "qkv_moe"
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assert mock_forward_context.layer_idx == 3
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x_out, residual_out = layer.forward_oot(x, residual)
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assert mock_get_forward_context.call_count == 8
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assert mock_forward_context.fusion_linear == "qkv_moe"
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assert mock_forward_context.layer_idx == 3
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if __name__ == '__main__':
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unittest.main()
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