qlinear operator level benchmark (#22914)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22914

Adding op level benchmarking for qlinear operator

Reviewed By: mingzhe09088

Differential Revision: D16285204

fbshipit-source-id: 99b734ddfa0af6aada820cac7b2f38ef7a5868cb

benchmarks/operator_benchmark/pt/linear_test.py

@@ -9,7 +9,7 @@ import torch
 import torch.nn as nn
 
 
-"""Microbenchmarks for batchnorm operator."""
+"""Microbenchmarks for Linear operator."""
 
 configs = op_bench.config_list(
     attrs=[

benchmarks/operator_benchmark/pt/qlinear_test.py

@@ -0,0 +1,62 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+
+import operator_benchmark as op_bench
+import torch
+import torch.nn.quantized as nnq
+
+
+"""
+Microbenchmarks for Quantized Linear operators.
+"""
+
+# Configs for qlinear
+qlinear_configs = op_bench.config_list(
+    attrs=[
+        [1024, 1024, 1024],
+        [64, 800, 320],
+        [64, 768, 512],
+        [16, 256, 512],
+        [128, 128, 128],
+        [256, 512, 256],
+        [6400, 15, 141],
+        [6400, 8, 141],
+        [16, 211, 2504],
+        [16, 369, 1434],
+        [1, 1024, 3496],
+        [16, 256, 512],
+        [1, 1600, 3456],
+    ],
+    attr_names=["N", "OUT", "IN"],  # M, N, K
+    tags=["short"],
+)
+
+
+class QLinearBenchmark(op_bench.TorchBenchmarkBase):
+    def init(self, N, IN, OUT):
+        scale = 1.0 / 255
+        zero_point = 0
+        X = torch.randn(N, IN, dtype=torch.float32)
+        qX = torch.quantize_linear(X, scale=scale, zero_point=zero_point, dtype=torch.quint8)
+        W = torch.randn(OUT, IN, dtype=torch.float32)
+        qW = torch.quantize_linear(W, scale=scale, zero_point=0, dtype=torch.qint8)
+
+        self.input = qX
+        self.qlinear = nnq.Linear(IN, OUT)
+        self.qlinear.weight = qW
+        self.qlinear.scale = scale
+        self.qlinear.zero_point = zero_point
+        self.set_module_name("QLinear")
+
+    def forward(self):
+        return self.qlinear(self.input)
+
+
+op_bench.generate_pt_test(qlinear_configs, QLinearBenchmark)
+
+
+if __name__ == "__main__":
+    op_bench.benchmark_runner.main()