mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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2e9e53ff7e
* remove max_num_batched_tokens in parallel config * remove max_num_seqs * update test case * fix test * fix --------- Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
138 lines
4.8 KiB
Python
138 lines
4.8 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 unittest
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import paddle
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from fastdeploy.config import (
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CacheConfig,
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FDConfig,
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GraphOptimizationConfig,
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ParallelConfig,
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SchedulerConfig,
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)
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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, ids_remove_padding, 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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return output
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def forward_correct(self, ids_remove_padding, forward_meta: ForwardMeta):
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"""Sub layer1 Correct forward pass"""
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output = paddle.add(forward_meta.input_ids, forward_meta.input_ids)
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return output
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class TestModel1(paddle.nn.Layer):
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"""Test 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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self.sublayer1 = TestCase1SubLayer1(self.fd_config)
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sublayer1_copy = TestCase1SubLayer1(self.fd_config)
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self.sublayer2 = sublayer1_copy
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def forward(self, ids_remove_padding, forward_meta: ForwardMeta):
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"""Test model forward pass"""
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# sublayer1 use cuda graph
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sub_meta1 = forward_meta
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sublayer1_output = self.sublayer1(ids_remove_padding=ids_remove_padding, forward_meta=sub_meta1)
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# sublayer2 use cuda graph
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sub_meta2 = ForwardMeta(
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input_ids=sublayer1_output, ids_remove_padding=sublayer1_output, step_use_cudagraph=True
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)
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sublayer2_output = self.sublayer2(ids_remove_padding=sublayer1_output, forward_meta=sub_meta2)
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return sublayer2_output
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def forward_correct(self, ids_remove_padding, forward_meta: ForwardMeta):
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"""Test model Correct forward pass"""
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# sublayer1 not use cuda graph
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sub_meta1 = forward_meta
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sublayer1_output = self.sublayer1.forward_correct(
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ids_remove_padding=ids_remove_padding, forward_meta=sub_meta1
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)
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# sublayer2 not use cuda graph
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sub_meta2 = ForwardMeta(input_ids=sublayer1_output, ids_remove_padding=sublayer1_output)
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sublayer2_output = self.sublayer2.forward_correct(ids_remove_padding=sublayer1_output, forward_meta=sub_meta2)
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return sublayer2_output
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class TestCUDAGrpahSpecDecode(unittest.TestCase):
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"""
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Test CUDAGraph Memory change
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"""
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def test_cuda_graph_spec_decode(self):
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"""Run test case"""
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graph_opt_config = GraphOptimizationConfig(args={})
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graph_opt_config.use_cudagraph = True
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scheduler_config = SchedulerConfig(args={})
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scheduler_config.max_num_seqs = 1
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cache_config = CacheConfig({})
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parallel_config = ParallelConfig(args={})
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# Initialize cuda graph capture list
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graph_opt_config._set_cudagraph_sizes(max_num_seqs=scheduler_config.max_num_seqs)
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graph_opt_config.init_with_cudagrpah_size(max_capture_size=scheduler_config.max_num_seqs)
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fd_config = FDConfig(
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graph_opt_config=graph_opt_config,
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scheduler_config=scheduler_config,
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cache_config=cache_config,
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parallel_config=parallel_config,
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test_mode=True,
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)
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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.ones([1, 32768])
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forward_meta1 = ForwardMeta(input_ids=input_tensor1, ids_remove_padding=input_tensor1, step_use_cudagraph=True)
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# Trigger Capture
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_ = test_model1(ids_remove_padding=input_tensor1, forward_meta=forward_meta1)
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# Replay
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_ = test_model1(ids_remove_padding=input_tensor1, forward_meta=forward_meta1)
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output1 = test_model1(ids_remove_padding=input_tensor1, forward_meta=forward_meta1)
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# Correct output
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output1_correct = test_model1.forward_correct(ids_remove_padding=input_tensor1, forward_meta=forward_meta1)
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assert (output1 == output1_correct).all()
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if __name__ == "__main__":
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unittest.main()
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