94 lines
5.8 KiB
Python
94 lines
5.8 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 numpy as np
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e2e_time_param_dict_list = [{"token_num": 1, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
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{"token_num": 16, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
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{"token_num": 32, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
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{"token_num": 64, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
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{"token_num": 128, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
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{"token_num": 512, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
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{"token_num": 1024, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
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{"token_num": 4096, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
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{"token_num": 8192, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
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{"token_num": 32768, "hidden_size": 4096, "expert_num": 32, "topk": 5,
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"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]}]
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def gen_tensor(token_num, hidden_size, expert_num, topk, start_expert_id, expert_size, dtype):
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input = torch.randn(token_num, hidden_size).to(dtype).to('mlu')
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gather_idx = torch.randint(low=0, high=token_num, size=(token_num * topk,)).to(torch.int32).to('mlu')
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cusum_token_count, _ = generate_token_count(expert_num, token_num * topk)
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cusum_token_count = cusum_token_count.to('mlu')
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use_all_experts = expert_num == expert_size
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if use_all_experts:
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cusum_token_count = None
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real_token_count = token_num * topk
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else:
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real_token_count = cusum_token_count[start_expert_id+expert_size] - cusum_token_count[start_expert_id]
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return input, gather_idx, cusum_token_count, real_token_count
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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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titles = ["token_num", "hidden_size", "expert_num", "topk", "start_expert_id", "expert_size", "input_dtype",
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"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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token_num = params_dict["token_num"]
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hidden_size = params_dict["hidden_size"]
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expert_num = params_dict["expert_num"]
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topk = params_dict["topk"]
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start_expert_id = params_dict["start_expert_id"]
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expert_size = params_dict["expert_size"]
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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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continue
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input, gather_idx, cusum_token_count, real_token_count = \
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gen_tensor(token_num, hidden_size, expert_num,topk, start_expert_id, expert_size, dtype)
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hardware_time, e2e_time = benchmark_forward(tmo.moe_expand_input,
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input,
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gather_idx,
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cusum_token_count,
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start_expert_id,
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expert_size,
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repeats=args.repeat_times)
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io_bytes = input.element_size() * input.nelement() + \
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gather_idx.element_size() * gather_idx.nelement() + \
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(cusum_token_count.element_size() * cusum_token_count.nelement() if cusum_token_count is not None else 0) + \
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real_token_count * input.element_size()
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io_eff = io_bytes / hardware_time / bd
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content = [f"{token_num}", f"{hidden_size}", f"{expert_num}", f"{topk}", f"{start_expert_id}", f"{expert_size}",
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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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