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| from caffe2.python import workspace, core, lstm_benchmark, utils |
| from copy import copy |
|
|
| @utils.debug |
| def Compare(args): |
| results = [] |
| num_iters = 1000 |
| args.gpu = True |
| with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, 0)): |
| for batch_size in [64, 128, 256]: |
| for seq_length in [20, 100]: |
| for hidden_dim in [40, 100, 400, 800]: |
| args.batch_size = batch_size |
| args.seq_length = seq_length |
| args.hidden_dim = hidden_dim |
| args.data_size = batch_size * seq_length * num_iters |
| args.iters_to_report = num_iters // 3 |
|
|
| args.implementation = 'own' |
| t_own = lstm_benchmark.Benchmark(args) |
| workspace.ResetWorkspace() |
| args.implementation = 'cudnn' |
| t_cudnn = lstm_benchmark.Benchmark(args) |
| workspace.ResetWorkspace() |
| results.append((copy(args), float(t_own), float(t_cudnn))) |
| print(args) |
| print("t_cudnn / t_own: {}".format(t_cudnn / t_own)) |
|
|
| for args, t_own, t_cudnn in results: |
| print("{}: cudnn time: {}, own time: {}, ratio: {}".format( |
| str(args), t_cudnn, t_own, t_cudnn / t_own)) |
|
|
| ratio_sum = 0 |
| for args, t_own, t_cudnn in results: |
| ratio = float(t_cudnn) / t_own |
| ratio_sum += ratio |
| print("hidden_dim: {}, seq_lengths: {}, batch_size: {}, num_layers: {}:" |
| " cudnn time: {}, own time: {}, ratio: {}".format( |
| args.hidden_dim, args.seq_length, args.batch_size, |
| args.num_layers, t_cudnn, t_own, ratio)) |
|
|
| print("Ratio average: {}".format(ratio_sum / len(results))) |
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|
| if __name__ == '__main__': |
| args = lstm_benchmark.GetArgumentParser().parse_args() |
|
|
| workspace.GlobalInit([ |
| 'caffe2', |
| '--caffe2_log_level=0', |
| '--caffe2_print_blob_sizes_at_exit=0', |
| '--caffe2_gpu_memory_tracking=1']) |
|
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| Compare(args) |
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|
