forked from EngineX-Cambricon/enginex-mlu370-vllm
65 lines
3.2 KiB
Python
65 lines
3.2 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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e2e_time_param_dict_list = [{"batch": 1, "seq_len": 5, "hidden_size": 1024,
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"act_mode": "gelu", "is_gated": False, "input_dtype": [torch.bfloat16]},
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{"batch": 16, "seq_len": 5, "hidden_size": 1024,
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"act_mode": "gelu", "is_gated": False, "input_dtype": [torch.bfloat16]},
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{"batch": 72, "seq_len": 5, "hidden_size": 1024,
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"act_mode": "gelu", "is_gated": False, "input_dtype": [torch.bfloat16]},
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{"batch": 1024, "seq_len": 5, "hidden_size": 1024,
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"act_mode": "gelu", "is_gated": False, "input_dtype": [torch.bfloat16]},
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{"batch": 4096, "seq_len": 5, "hidden_size": 1024,
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"act_mode": "gelu", "is_gated": False, "input_dtype": [torch.bfloat16]},
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{"batch": 8192, "seq_len": 5, "hidden_size": 1024,
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"act_mode": "gelu", "is_gated": False, "input_dtype": [torch.bfloat16]},
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{"batch": 32768, "seq_len": 5, "hidden_size": 1024,
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"act_mode": "gelu", "is_gated": False, "input_dtype": [torch.bfloat16]}]
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def main():
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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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device = 'mlu'
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titles = ["input_shape", "act_mode", "is_gated", "input_dtype", "hardware_time(us)", "e2e_latency(us)", "IO efficiency"]
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contents = []
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bd = get_band_width()
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for params_dict in e2e_time_param_dict_list:
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batch = params_dict["batch"]
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seq_len = params_dict["seq_len"]
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hidden_size = params_dict["hidden_size"]
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act_mode = params_dict["act_mode"]
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is_gated = params_dict["is_gated"]
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input_dtype_list = params_dict["input_dtype"]
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for dtype in input_dtype_list:
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if dtype == torch.bfloat16 and not torch_mlu.mlu.is_bf16_supported():
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dtype = torch.half
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input = torch.randn(batch, seq_len, hidden_size).to(device).to(dtype)
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hardware_time, e2e_time = benchmark_forward(tmo.active,
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input,
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act_mode,
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is_gated,
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repeats=args.repeat_times)
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io_bytes = input.element_size() * input.nelement() * (2 - 0.5 * is_gated)
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io_eff = io_bytes / hardware_time / bd
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content = [f"{batch,seq_len,hidden_size}", f"{act_mode}", f"{is_gated}", f"{dtype}", f"{hardware_time}", f"{e2e_time}", f"{io_eff}"]
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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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