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>
125 lines
3.9 KiB
Python
125 lines
3.9 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 os
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os.environ["FLAGS_cuda_graph_blacklist"] = "pd_op.matmul,pd_op.transpose"
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import unittest
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import paddle
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import paddle.nn as nn
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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 Attention(nn.Layer):
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def __init__(self, fd_config: FDConfig) -> None:
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super().__init__()
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self.embed_tokens = nn.Embedding(num_embeddings=100, embedding_dim=32)
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self.qkv_proj = nn.Linear(32, 64)
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self.attn = nn.MultiHeadAttention(embed_dim=64, num_heads=1)
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self.o_proj = nn.Linear(64, 32)
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def forward(
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self,
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ids_remove_padding,
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forward_meta: ForwardMeta,
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):
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hidden_states = self.embed_tokens(forward_meta.ids_remove_padding)
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qkv_out = self.qkv_proj(hidden_states)
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attn_out = self.attn(qkv_out)
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output = self.o_proj(attn_out)
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return output
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def forward_dynamic(
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self,
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ids_remove_padding,
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forward_meta: ForwardMeta,
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):
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hidden_states = self.embed_tokens(forward_meta.ids_remove_padding)
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qkv_out = self.qkv_proj(hidden_states)
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attn_out = self.attn(qkv_out)
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output = self.o_proj(attn_out)
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return output
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class TestModel(nn.Layer):
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def __init__(self, fd_config: FDConfig, **kwargs):
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super().__init__()
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self.model = Attention(fd_config)
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def forward(self, ids_remove_padding, forward_meta: ForwardMeta):
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return self.model(ids_remove_padding=ids_remove_padding, forward_meta=forward_meta)
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def forward_correct(self, ids_remove_padding, forward_meta: ForwardMeta):
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return self.model.forward_dynamic(ids_remove_padding=ids_remove_padding, forward_meta=forward_meta)
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class TestStaticGraphCUDAGraphSplit(unittest.TestCase):
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def test(self):
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"""Run test case"""
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# Set FastDeploy config
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graph_opt_config = GraphOptimizationConfig({"use_cudagraph": True, "graph_opt_level": 1})
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scheduler_config = SchedulerConfig({"max_num_seqs": 1})
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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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cache_config = CacheConfig({})
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parallel_config = ParallelConfig(args={})
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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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test_model1 = TestModel(fd_config=fd_config)
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x = paddle.randint(32, shape=[1, 8])
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forward_meta1 = ForwardMeta(input_ids=x, ids_remove_padding=x, step_use_cudagraph=True)
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# Trigger Capture
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_ = test_model1(x, forward_meta=forward_meta1)
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# Replay
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_ = test_model1(x, forward_meta=forward_meta1)
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output1 = test_model1(x, forward_meta=forward_meta1)
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# Correct output
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output1_correct = test_model1.forward_correct(x, 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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