mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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135 lines
4.1 KiB
Python
135 lines
4.1 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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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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"""
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import paddle
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from fastdeploy.config import FDConfig, GraphOptimizationConfig
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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from fastdeploy.model_executor.graph_optimization.decorator import (
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support_graph_optimization,
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)
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@support_graph_optimization
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class TestCase1SubLayer1(paddle.nn.Layer):
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"""Sub layer 1 of test case 1"""
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def __init__(self, fd_config: FDConfig, **kwargs):
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super().__init__()
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def forward(self, _, forward_meta: ForwardMeta):
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"""Sub layer1 forward pass"""
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output = paddle.add(forward_meta.input_ids, forward_meta.input_ids)
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print(" SubLayer1 Output: {output}")
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return output
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class TestCase1SubLayer2(paddle.nn.Layer):
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""" """
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def __init__(self, fd_config: FDConfig, **kwargs):
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super().__init__()
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def forward(self, _, forward_meta: ForwardMeta):
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"""Sub layer2 forward pass"""
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x = paddle.ones_like(forward_meta.input_ids)
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y = paddle.ones_like(forward_meta.input_ids)
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output = x + y
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print(" SubLayer2 Output: {output}")
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return output
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@support_graph_optimization
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class TestCase1SubLayer3(paddle.nn.Layer):
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""" """
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def __init__(self, fd_config: FDConfig, **kwargs):
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super().__init__()
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def forward(self, _, forward_meta: ForwardMeta):
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"""Sub layer3 forward pass"""
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output = paddle.add(forward_meta.input_ids, forward_meta.input_ids)
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print(" SubLayer3 Output: {output}")
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return output
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class TestModel1(paddle.nn.Layer):
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"""Tast Model"""
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def __init__(self, fd_config: FDConfig, **kwargs):
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super().__init__()
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self.fd_config = fd_config
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def forward(self, _, forward_meta: ForwardMeta):
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"""Test model for ward pass"""
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self.sublayer1 = TestCase1SubLayer1(self.fd_config)
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self.sublayer2 = TestCase1SubLayer2(self.fd_config)
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self.sublayer3 = TestCase1SubLayer3(self.fd_config)
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# sublayer1 use cuda graph
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sub_meta1 = forward_meta
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sublayer1_output = self.sublayer1(_=None, forward_meta=sub_meta1)
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# sublayer2 not use cuda garph
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sub_meta2 = ForwardMeta(input_ids=sublayer1_output)
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sublayer2_output = self.sublayer2(_=None, forward_meta=sub_meta2)
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# sublayer3 use cuda graph
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sub_meta3 = ForwardMeta(input_ids=sublayer2_output)
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sublayer3_output = self.sublayer3(_=None, forward_meta=sub_meta3)
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return sublayer3_output
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@support_graph_optimization
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class TestModel2(paddle.nn.Layer):
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"""Tast Model"""
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def __init__(self, fd_config: FDConfig, **kwargs):
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super().__init__()
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def forward(self, _, forward_meta: ForwardMeta):
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"""Test model for ward pass"""
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return forward_meta.input_ids + forward_meta.input_ids
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def run_test_case():
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"""Run test case"""
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# Set llm config1
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graph_opt_config = GraphOptimizationConfig()
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graph_opt_config.use_cudagraph = True
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graph_opt_config.cudagraph_capture_sizes = [1]
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fd_config = FDConfig(graph_opt_config=graph_opt_config)
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# Run Test Case1
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test_model1 = TestModel1(fd_config=fd_config)
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input_tensor1 = paddle.zeros([1, 8])
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forward_meta1 = ForwardMeta(input_ids=input_tensor1)
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output1 = test_model1(_=None, forward_meta=forward_meta1)
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print(output1)
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# Run Test Case2
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test_model2 = TestModel2(fd_config=fd_config)
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input_tensor2 = paddle.zeros([1, 8])
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forward_meta2 = ForwardMeta(input_ids=input_tensor2)
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output2 = test_model2(_=None, forward_meta=forward_meta2)
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print(output2)
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if __name__ == "__main__":
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run_test_case()
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