78fad4e348
### What this PR does / why we need it?
Refactor MLP weight prefetch to consistency with MoE Model's prefetching
in terms of code and usage.
Environments VLLM_ASCEND_ENABLE_PREFETCH_MLP,
VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE and
VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE is removed, usage as following:
--additional-config '{"weight_prefetch_config": { "enabled": true,
"prefetch_ratio": {"mlp": { "gate_up": 1.0, "down": 1.0} }}}'
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
95 lines
2.8 KiB
Python
95 lines
2.8 KiB
Python
#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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from unittest.mock import MagicMock, patch
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import pytest
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import torch
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from vllm.config import set_current_vllm_config
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from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
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from vllm_ascend.utils import AscendDeviceType
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from vllm_ascend.utils import is_310p as is_310p_hw
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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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@pytest.fixture
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def default_vllm_config():
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mock_config = MagicMock()
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mock_config.compilation_config.dispatch_forward_backend = "eager"
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mock_config.compilation_config.custom_ops = ["all"]
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with set_current_vllm_config(mock_config):
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yield mock_config
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@patch("torch_npu.npu_fast_gelu", side_effect=lambda x: x + 1)
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def test_QuickGELU_forward(mock_gelu, dummy_tensor, default_vllm_config):
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layer = QuickGELU()
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out = layer.forward(dummy_tensor)
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expected_out = dummy_tensor + 1
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assert torch.allclose(out, expected_out)
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mock_gelu.assert_called_once()
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@pytest.mark.skipif(is_310p_hw(), reason="non_310P device unittest case.")
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@patch("torch_npu.npu_swiglu", side_effect=lambda x: x + 1)
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def test_SiluAndMul_forward(
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mock_swiglu,
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dummy_tensor,
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default_vllm_config,
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):
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layer = SiluAndMul()
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out = layer.forward(dummy_tensor)
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expected_arg = dummy_tensor
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# assert mock_swiglu.call_count == 1
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mock_swiglu.assert_called_once()
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actual_arg = mock_swiglu.call_args[0][0]
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assert torch.allclose(actual_arg, expected_arg), "npu_swiglu called with unexpected input"
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expected_out = dummy_tensor + 1
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assert torch.allclose(out, expected_out)
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@pytest.mark.skipif(not is_310p_hw(), reason="310P device unittest case.")
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@patch("torch.nn.functional.silu", side_effect=lambda x: x + 1)
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def test_SiluAndMul_forward_310p(
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mock_silu,
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dummy_tensor,
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default_vllm_config,
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):
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layer = SiluAndMul()
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out = layer.forward(dummy_tensor)
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h = dummy_tensor.shape[-1] // 2
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expected_arg = dummy_tensor[..., :h]
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# assert mock_silu.call_count == 1
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mock_silu.assert_called_once()
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actual_arg = mock_silu.call_args[0][0]
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assert torch.allclose(actual_arg, expected_arg), "swiglu called with unexpected input"
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expected_out = (dummy_tensor[..., :h] + 1) * dummy_tensor[..., h:]
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assert torch.allclose(out, expected_out) |