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import numpy as np
import torch
import time
"""Microbenchmarks for Tensor repeat operator. Supports PyTorch."""
input_shapes = (
(4, 4, 1),
(16, 1, 32),
(64, 64, 1, 1),
(8, 256, 128),
(1, 64, 128, 32),
(512, 512),
)
repeats = (
(1, 1, 1, 64),
(1, 4, 1, 2),
(1, 2, 2, 15),
(1, 1, 3, 2),
(128, 1, 8, 1),
(1, 1, 2, 16),
)
NUM_WARMUP_ITERS = 5
NUM_BENCHMARK_ITERS = 10
DTYPE_TO_BYTES = {'float' : 4}
def generate_data_for_repeat():
input_tensors = [torch.randn(*input_shape) for input_shape in input_shapes]
total_num_elements = 0
for input_tensor, repeat in zip(input_tensors, repeats):
total_num_elements += input_tensor.numel()
total_num_elements += input_tensor.numel() * np.prod(repeat)
return input_tensors, (total_num_elements * DTYPE_TO_BYTES['float'])
input_tensors, total_bytes = generate_data_for_repeat()
BYTES_TO_MB = (1. / 1000. / 1000.)
def pt_repeat(input_tensor, repeat):
return input_tensor.repeat(repeat)
def pt_repeat_n_times(niters):
for _ in range(niters):
for input_tensor, repeat in zip(input_tensors, repeats):
pt_repeat(input_tensor, repeat)
if __name__ == "__main__":
# Warm up runs.
pt_repeat_n_times(NUM_WARMUP_ITERS)
s = time.time()
pt_repeat_n_times(NUM_BENCHMARK_ITERS)
total_time_s = (time.time() - s)
total_time_per_iter_s = total_time_s / NUM_BENCHMARK_ITERS
achieved_bandwidth = (total_bytes * BYTES_TO_MB) / total_time_per_iter_s
print("Time:{} Achieved Bandwidth:{} MB/s".format(total_time_per_iter_s, achieved_bandwidth))
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