60 lines
2.9 KiB
Python
60 lines
2.9 KiB
Python
import torch
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import torch_mlu
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import torch_mlu_ops as tmo
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from common import *
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import argparse
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from tabulate import tabulate
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import os
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import random
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e2e_time_param_dict_list = [{"batch": 1, "seq_len": 2048, "hidden_size": 8192, "expert_num": 32, "input_dtype": torch.bfloat16},
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{"batch": 1, "seq_len": 4096, "hidden_size": 8192, "expert_num": 32, "input_dtype": torch.bfloat16},
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{"batch": 1, "seq_len": 32768, "hidden_size": 8192, "expert_num": 32, "input_dtype": torch.bfloat16},
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{"batch": 16, "seq_len": 1, "hidden_size": 8192, "expert_num": 32, "input_dtype": torch.bfloat16},
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{"batch": 128, "seq_len": 1, "hidden_size": 8192, "expert_num": 32, "input_dtype": torch.bfloat16},
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{"batch": 512, "seq_len": 1, "hidden_size": 8192, "expert_num": 32, "input_dtype": torch.bfloat16}]
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def main():
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if 'MLU3' in torch.mlu.get_device_name():
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exit()
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parser = argparse.ArgumentParser()
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parser.add_argument('--repeat_times', type=int, default=10, help='repeat times for testing')
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parser.add_argument('--csv', action='store_true', help='write the report data to csv')
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parser.add_argument('-o', type=str, help='specify the output folder name under --csv mode')
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args = parser.parse_args()
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titles = ["batch", "seq_len", "hidden_size", "expert_num", "input_dtype", "hardware_time(us)",
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"e2e_latency(us)", "IO efficiency"]
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contents = []
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bandwidth = get_band_width()
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for param_dict in e2e_time_param_dict_list:
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batch = param_dict["batch"]
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seq_len = param_dict["seq_len"]
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hidden_size = param_dict["hidden_size"]
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expert_num = param_dict["expert_num"]
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input_dtype = param_dict["input_dtype"]
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if input_dtype == torch.bfloat16 and not torch_mlu.mlu.is_bf16_supported():
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input_dtype = torch.half
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input = torch.randn(batch, seq_len, hidden_size, dtype=input_dtype, device="mlu")
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weight = torch.randn(expert_num, hidden_size, dtype=torch.float32, device="mlu")
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hardware_time, e2e_time = benchmark_forward(tmo.moe_cast_gating,
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input,
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weight)
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io_bytes = batch * seq_len * hidden_size * input.element_size() + \
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expert_num * hidden_size * weight.element_size() + batch * seq_len * expert_num * weight.element_size()
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io_coeff = io_bytes / hardware_time / bandwidth
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content = [f"{batch}", f"{seq_len}", f"{hidden_size}", f"{expert_num}", f"{input_dtype}",
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f"{hardware_time}", f"{e2e_time}", f"{io_coeff}"]
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contents.append(content)
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table = [titles] + contents
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print(tabulate(table, headers="firstrow", tablefmt="grid"))
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if args.csv:
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current_file_path = __file__
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_, file_name = os.path.split(current_file_path)
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save_to_csv(table, args.o, file_name)
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if __name__=="__main__":
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main()
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