diff --git a/doc/fluid/advanced_guide/performance_improving/device_switching/device_switching.md b/doc/fluid/advanced_guide/performance_improving/device_switching/device_switching.md index f4f4b9f44b69c527f3b13d975a6e01146798449d..c20f1abf113a51632d20eb1c2340b85cd3d67aa3 100644 --- a/doc/fluid/advanced_guide/performance_improving/device_switching/device_switching.md +++ b/doc/fluid/advanced_guide/performance_improving/device_switching/device_switching.md @@ -59,19 +59,21 @@ result = exe.run(fluid.default_main_program(), fetch_list=[avg_cost]) ```python import paddle.fluid as fluid +import paddle.fluid.compiler as compiler import paddle.fluid.profiler as profiler data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32') data2 = fluid.layers.fill_constant(shape=[1, 3, 5, 5], value=0.5, dtype='float32') -shape = fluid.layers.shape(data2) -shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4]) -out = fluid.layers.crop_tensor(data1, shape=shape) -place = fluid.CUDAPlace(0) +shape = fluid.layers.shape(data2) +shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4]) +out = fluid.layers.crop_tensor(data1, shape=shape) +place = fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) +compiled_prog = compiler.CompiledProgram(fluid.default_main_program()) with profiler.profiler('All', 'total') as prof: for i in range(10): - result = exe.run(fetch_list=[out]) + result = exe.run(program=compiled_prog, fetch_list=[out]) ``` 在程序运行结束后，将会自动地打印出profile report。在下面的profile report中，可以看到 `GpuMemCpy Summary`中给出了2项数据传输的调用耗时。在OP执行过程中，如果输入Tensor所在的设备与OP执行的设备不同，就会发生`GpuMemcpySync`，通常我们可以直接优化的就是这一项。进一步分析，可以看到`slice`和`crop_tensor`执行中都发生了`GpuMemcpySync`。尽管我们在程序中设置了GPU模式运行，但是框架中有些OP，例如shape，会将输出结果放在CPU上。 @@ -82,35 +84,34 @@ with profiler.profiler('All', 'total') as prof: Note! This Report merge all thread info into one. Place: All Time unit: ms -Sorted by event first end time in descending order in the same thread +Sorted by total time in descending order in the same thread -Total time: 24.0922 - Computation time Total: 3.60143 Ratio: 14.9485% - Framework overhead Total: 20.4908 Ratio: 85.0515% +Total time: 26.6328 + Computation time Total: 13.3133 Ratio: 49.9884% + Framework overhead Total: 13.3195 Ratio: 50.0116% ------------------------- GpuMemCpy Summary ------------------------- -GpuMemcpy Calls: 30 Total: 1.44377 Ratio: 5.99267% - GpuMemcpyAsync Calls: 10 Total: 0.459803 Ratio: 1.90851% - GpuMemcpySync Calls: 20 Total: 0.983967 Ratio: 4.08416% +GpuMemcpy Calls: 30 Total: 1.47508 Ratio: 5.5386% + GpuMemcpyAsync Calls: 10 Total: 0.443514 Ratio: 1.66529% + GpuMemcpySync Calls: 20 Total: 1.03157 Ratio: 3.87331% ------------------------- Event Summary ------------------------- Event Calls Total CPU Time (Ratio) GPU Time (Ratio) Min. Max. Ave. Ratio. -fill_constant 20 2.03147 1.995597 (0.982342) 0.035872 (0.017658) 0.064199 0.379822 0.101573 0.0843204 -shape 10 0.466503 0.466503 (1.000000) 0.000000 (0.000000) 0.021165 0.207393 0.0466503 0.0193632 -eager_deletion 30 0.28398 0.283980 (1.000000) 0.000000 (0.000000) 0.004668 0.028065 0.009466 0.0117872 -slice 10 1.53533 1.505664 (0.980679) 0.029664 (0.019321) 0.1312 0.259446 0.153533 0.0637271 - GpuMemcpySync:CPU->GPU 10 0.41714 0.408532 (0.979364) 0.008608 (0.020636) 0.038545 0.054022 0.041714 0.0173143 -crop_tensor 10 1.49584 1.438558 (0.961707) 0.057280 (0.038293) 0.129106 0.246395 0.149584 0.0620879 - GpuMemcpySync:GPU->CPU 10 0.566827 0.543787 (0.959353) 0.023040 (0.040647) 0.047598 0.097705 0.0566827 0.0235274 -Fetch 10 0.921333 0.897141 (0.973742) 0.024192 (0.026258) 0.077059 0.177223 0.0921333 0.0382419 - GpuMemcpyAsync:GPU->CPU 10 0.459803 0.435611 (0.947386) 0.024192 (0.052614) 0.039321 0.073849 0.0459803 0.0190851 -ParallelExecutor::Run 10 17.3578 17.345797 (0.999309) 0.012000 (0.000691) 0.705361 10.3389 1.73578 0.720472 - InitLocalVars 1 0.084954 0.084954 (1.000000) 0.000000 (0.000000) 0.084954 0.084954 0.084954 0.0035262 - ScopeBufferedMonitor::pre_local_exec_scopes_process 10 0.040771 0.040771 (1.000000) 0.000000 (0.000000) 0.003653 0.00543 0.0040771 0.00169229 - FastThreadedSSAGraphExecutorPrepare 10 8.64291 8.630914 (0.998612) 0.012000 (0.001388) 0.033383 8.29818 0.864291 0.358743 - ScopeBufferedMonitor::post_local_exec_scopes_process 10 0.252618 0.252618 (1.000000) 0.000000 (0.000000) 0.022696 0.041439 0.0252618 0.0104854 +FastThreadedSSAGraphExecutorPrepare 10 9.16493 9.152509 (0.998645) 0.012417 (0.001355) 0.025192 8.85968 0.916493 0.344122 +shape 10 8.33057 8.330568 (1.000000) 0.000000 (0.000000) 0.030711 7.99849 0.833057 0.312793 +fill_constant 20 4.06097 4.024522 (0.991025) 0.036449 (0.008975) 0.075087 0.888959 0.203049 0.15248 +slice 10 1.78033 1.750439 (0.983212) 0.029888 (0.016788) 0.148503 0.290851 0.178033 0.0668471 + GpuMemcpySync:CPU->GPU 10 0.45524 0.446312 (0.980388) 0.008928 (0.019612) 0.039089 0.060694 0.045524 0.0170932 +crop_tensor 10 1.67658 1.620542 (0.966578) 0.056034 (0.033422) 0.143906 0.258776 0.167658 0.0629515 + GpuMemcpySync:GPU->CPU 10 0.57633 0.552906 (0.959357) 0.023424 (0.040643) 0.050657 0.076322 0.057633 0.0216398 +Fetch 10 0.919361 0.895201 (0.973721) 0.024160 (0.026279) 0.082935 0.138122 0.0919361 0.0345199 + GpuMemcpyAsync:GPU->CPU 10 0.443514 0.419354 (0.945526) 0.024160 (0.054474) 0.040639 0.059673 0.0443514 0.0166529 +ScopeBufferedMonitor::post_local_exec_scopes_process 10 0.341999 0.341999 (1.000000) 0.000000 (0.000000) 0.028436 0.057134 0.0341999 0.0128413 +eager_deletion 30 0.287236 0.287236 (1.000000) 0.000000 (0.000000) 0.005452 0.022696 0.00957453 0.010785 +ScopeBufferedMonitor::pre_local_exec_scopes_process 10 0.047864 0.047864 (1.000000) 0.000000 (0.000000) 0.003668 0.011592 0.0047864 0.00179718 +InitLocalVars 1 0.022981 0.022981 (1.000000) 0.000000 (0.000000) 0.022981 0.022981 0.022981 0.000862883 ``` ### 通过log查看发生数据传输的具体位置 @@ -138,6 +139,7 @@ I0406 14:56:23.287473 17516 operator.cc:180] CUDAPlace(0) Op(crop_tensor), input ```python import paddle.fluid as fluid +import paddle.fluid.compiler as compiler import paddle.fluid.profiler as profiler data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32') @@ -146,13 +148,13 @@ shape = fluid.layers.shape(data2) with fluid.device_guard("cpu"): shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4]) out = fluid.layers.crop_tensor(data1, shape=shape) - -place = fluid.CUDAPlace(0) +place = fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) +compiled_prog = compiler.CompiledProgram(fluid.default_main_program()) with profiler.profiler('All', 'total') as prof: for i in range(10): - result = exe.run(fetch_list=[out]) + result = exe.run(program=compiled_prog, fetch_list=[out]) ``` 再次观察profile report中`GpuMemCpy Summary`的内容，可以看到`GpuMemCpySync`已经被消除。在实际的模型中，若`GpuMemCpySync` 调用耗时占比较大，并且可以通过设置`device_guard`避免，那么就能够带来一定的性能提升。