Revert D15435461: [pytorch][PR] PyTorch ThroughputBenchmark

Differential Revision:
D15435461

Original commit changeset: db08829dc3f4

fbshipit-source-id: 72a0eac1658b2d3f885bc9a21c49fcc23030ae3e

test/test_throughput_benchmark.py

@@ -1,79 +0,0 @@
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import torch
-from torch.utils import ThroughputBenchmark
-from torch.testing import assert_allclose
-
-from common_utils import run_tests, TestCase
-
-class TwoLayerNet(torch.jit.ScriptModule):
-    def __init__(self, D_in, H, D_out):
-        super(TwoLayerNet, self).__init__()
-        self.linear1 = torch.nn.Linear(D_in, H)
-        self.linear2 = torch.nn.Linear(2 * H, D_out)
-
-    @torch.jit.script_method
-    def forward(self, x1, x2):
-        h1_relu = self.linear1(x1).clamp(min=0)
-        h2_relu = self.linear1(x2).clamp(min=0)
-        cat = torch.cat((h1_relu, h2_relu), 1)
-        y_pred = self.linear2(cat)
-        return y_pred
-
-class TwoLayerNetModule(torch.nn.Module):
-    def __init__(self, D_in, H, D_out):
-        super(TwoLayerNetModule, self).__init__()
-        self.linear1 = torch.nn.Linear(D_in, H)
-        self.linear2 = torch.nn.Linear(2 * H, D_out)
-
-    def forward(self, x1, x2):
-        h1_relu = self.linear1(x1).clamp(min=0)
-        h2_relu = self.linear1(x2).clamp(min=0)
-        cat = torch.cat((h1_relu, h2_relu), 1)
-        y_pred = self.linear2(cat)
-        return y_pred
-
-class TestThroughputBenchmark(TestCase):
-    def linear_test(self, Module):
-        D_in = 10
-        H = 5
-        D_out = 15
-        B = 8
-        NUM_INPUTS = 2
-
-        module = Module(D_in, H, D_out)
-
-        inputs = []
-
-        for i in range(NUM_INPUTS):
-            inputs.append([torch.randn(B, D_in), torch.randn(B, D_in)])
-        bench = ThroughputBenchmark(module)
-
-        for input in inputs:
-            # can do both args and kwargs here
-            bench.add_input(input[0], x2=input[1])
-
-        for i in range(NUM_INPUTS):
-            # or just unpack the list of inputs
-            module_result = module(*inputs[i])
-            bench_result = bench.run_once(*inputs[i])
-            assert_allclose(bench_result, module_result)
-
-        stats = bench.benchmark(
-            num_calling_threads=4,
-            num_warmup_iters=100,
-            num_iters=1000,
-        )
-
-        print("Avg latency (ms): {}".format(stats.latency_avg_ms))
-        print("Number of iterations: {}".format(stats.num_iters))
-
-
-    def test_script_module(self):
-        self.linear_test(TwoLayerNet)
-
-    def test_module(self):
-        self.linear_test(TwoLayerNetModule)
-
-if __name__ == '__main__':
-    run_tests()