1bbb20ea13
### What this PR does / why we need it?
Flashcomm_v1 optim in Qwen Dense Models.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.1.1
- vLLM main:
5e537f45b4
Co-authored-by: 1024daniel <xxltju324@gmail.com>
85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
from unittest.mock import patch
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import pytest
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import torch
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from vllm.model_executor.layers.layernorm import RMSNorm
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@pytest.fixture
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def dummy_tensor():
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return torch.randn(4, 8, dtype=torch.float16)
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def mock_maybe_chunk_residual(x, residual):
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if x.size(0) != residual.size(0):
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return residual[:4]
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return residual
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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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@pytest.mark.parametrize("is_310p_return", [True, False])
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@pytest.mark.parametrize("residual",
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[None, torch.randn(4, 8, dtype=torch.float32)])
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@patch("torch_npu.npu_rms_norm", side_effect=mock_rms_norm)
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@patch("torch_npu.npu_add_rms_norm", side_effect=mock_add_rms_norm)
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@patch("torch.ops.vllm.maybe_chunk_residual",
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side_effect=mock_maybe_chunk_residual)
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def test_RMSNorm_forward(mock_maybe_chunk_residual, mock_add_rmsnorm,
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mock_rmsnorm, is_310p_return, residual, dummy_tensor):
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with patch("vllm_ascend.utils.is_310p", return_value=is_310p_return):
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layer = RMSNorm(hidden_size=8, eps=1e-05)
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if residual is not None:
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out_x, out_residual = layer.forward_oot(dummy_tensor, residual)
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if is_310p_return:
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expected_arg_x = dummy_tensor + residual.to(dummy_tensor.dtype)
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expected_out_x = expected_arg_x + 1
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expected_out_residual = expected_arg_x.to(residual.dtype)
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mock_rmsnorm.assert_called_once()
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assert torch.allclose(out_x, expected_out_x)
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assert torch.allclose(out_residual, expected_out_residual)
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else:
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expected_out_x = 2 * dummy_tensor
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expected_out_residual = 2 * residual
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mock_add_rmsnorm.assert_called_once()
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assert torch.allclose(out_x, expected_out_x)
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assert torch.allclose(out_residual, expected_out_residual)
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else:
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out_x = layer.forward(dummy_tensor, residual)
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expected_out_x = dummy_tensor + 1
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mock_rmsnorm.assert_called_once()
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assert torch.allclose(out_x, expected_out_x)
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@patch("vllm_ascend.utils.is_310p", return_value=False)
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@patch("torch_npu.npu_add_rms_norm", side_effect=mock_add_rms_norm)
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@patch("torch.ops.vllm.maybe_chunk_residual",
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side_effect=mock_maybe_chunk_residual)
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def test_RMSNorm_forward_with_flashcomm_v1(mock_maybe_chunk_residual,
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mock_add_rms_norm, mock_is310p):
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x = torch.randn(4, 512, dtype=torch.bfloat16)
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residual = torch.randn(16, 512, dtype=torch.bfloat16)
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layer = RMSNorm(hidden_size=512, eps=1e-05)
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out_x, out_residual = layer.forward_oot(x, residual)
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expected_out_x = 2 * x
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expected_out_residual = 2 * residual[:4]
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mock_maybe_chunk_residual.assert_called_once()
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mock_add_rms_norm.assert_called_once()
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assert out_residual.size(0) == 4
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assert torch.allclose(out_x, expected_out_x)
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assert torch.allclose(out_residual, expected_out_residual)
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