90 lines
5.8 KiB
Python
90 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 benchmark_forward, save_to_csv
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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": 1024, "hidden_size": 1600, "inner_size": 6400,
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"gated_ffn": False, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 2048, "inner_size": 8192,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 4096, "inner_size": 11008,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 4096, "inner_size": 14336,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 4096, "inner_size": 16384,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 5120, "inner_size": 13824,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 5120, "inner_size": 27392,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 6656, "inner_size": 17920,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 8192, "inner_size": 22016,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 8192, "inner_size": 24576,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 8192, "inner_size": 28672,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 8192, "inner_size": 49152,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 12288, "inner_size": 32768,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, torch.bfloat16]},
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{"batch": 1, "seq_len": 1024, "hidden_size": 14336, "inner_size": 57344,
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"gated_ffn": True, "act_mode": "gelu", "input_dtype": [torch.float16, 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 = ["batch", "seq_len", "hidden_size", "inner_size", "gated_ffn", "act_mode", "input_dtype", "hardware_time(us)", "e2e_latency(us)"]
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contents = []
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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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inner_size = params_dict["inner_size"]
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gated_ffn = params_dict["gated_ffn"]
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act_mode = params_dict["act_mode"]
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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 = torch.randn(batch, seq_len, hidden_size).to(device).to(dtype)
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up_proj_weight = torch.randn(inner_size, hidden_size).to(device).to(dtype)
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up_proj_bias = torch.randn(inner_size).to(device).to(dtype)
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down_proj_weight = torch.randn(hidden_size, inner_size).to(device).to(dtype)
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down_proj_bias = torch.randn(hidden_size).to(device).to(dtype)
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gate_up_proj_weight, gate_up_proj_bias = None, None
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if gated_ffn:
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gate_up_proj_weight = torch.randn(inner_size, hidden_size).to(device).to(dtype)
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gate_up_proj_bias = torch.randn(inner_size).to(device).to(dtype)
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hardware_time, e2e_time = benchmark_forward(tmo.ffn,
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input,
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up_proj_weight,
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up_proj_bias,
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down_proj_weight,
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down_proj_bias,
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gate_up_proj_weight,
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gate_up_proj_bias,
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act_mode,
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repeats=args.repeat_times)
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content = [f"{batch}", f"{seq_len}", f"{hidden_size}", f"{inner_size}", f"{gated_ffn}", f"{act_mode}", f"{dtype}", f"{hardware_time}", f"{e2e_time}"]
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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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