From cda9e995e298d2d0ec95bdebba86fc0079a849e5 Mon Sep 17 00:00:00 2001
From: Kimish Patel <kimishpatel@fb.com>
Date: Mon, 20 May 2019 07:31:52 -0700
Subject: [PATCH] Benchmark repeat op. (#20016)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20016

PT's repeat op benchmark

Reviewed By: zheng-xq

Differential Revision: D15166941

fbshipit-source-id: b1ed7af790460456210b60bfb4e44a08657e9612
---
 .../ops/repeat_benchmark.py                   | 58 +++++++++++++++++++
 1 file changed, 58 insertions(+)
 create mode 100644 benchmarks/operator_benchmark/ops/repeat_benchmark.py

diff --git a/benchmarks/operator_benchmark/ops/repeat_benchmark.py b/benchmarks/operator_benchmark/ops/repeat_benchmark.py
new file mode 100644
index 000000000000..f9e53886f1d0
--- /dev/null
+++ b/benchmarks/operator_benchmark/ops/repeat_benchmark.py
@@ -0,0 +1,58 @@
+from __future__ import absolute_import, division, print_function, unicode_literals
+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))