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"""Tests for c++ MLP"""
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import unittest
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from time import time
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import numpy as np
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import torch
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from torch import nn
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from apex.mlp import MLP
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batch_size = 1024
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mlp_sizes = [480, 1024, 1024, 512, 256, 1]
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num_iters = 10
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class TestMLP(unittest.TestCase):
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    def test_creation(self):
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        MLP(mlp_sizes)
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    def test_numeric(self):
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        mlp = MLP(mlp_sizes).cuda()
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        mlp_layers = []
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        for i in range(mlp.num_layers):
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            linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])
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            mlp.weights[i].data.copy_(linear.weight)
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            mlp.biases[i].data.copy_(linear.bias)
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            mlp_layers.append(linear)
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            mlp_layers.append(nn.ReLU(inplace=True))
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        ref_mlp = nn.Sequential(*mlp_layers).cuda()
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        test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_()
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        ref_input = test_input.clone().detach().requires_grad_()
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        mlp_out = mlp(test_input)
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        ref_out = ref_mlp(ref_input)
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        np.testing.assert_allclose(
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            mlp_out.detach().cpu().numpy(),
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            ref_out.detach().cpu().numpy(),
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            atol=1e-7, rtol=1e-5)
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        # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
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        mlp_out.mean().mul(10.).backward()
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        ref_out.mean().mul(10.).backward()
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        np.testing.assert_allclose(
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            test_input.grad.detach().cpu().numpy(),
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            ref_input.grad.detach().cpu().numpy(),
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            atol=0, rtol=1e-5)
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        np.testing.assert_allclose(
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            mlp.biases[0].grad.detach().cpu().numpy(),
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            ref_mlp[0].bias.grad.detach().cpu().numpy(),
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            atol=1e-7, rtol=1e-5)
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    def test_no_bias(self):
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        for use_activation in ['none', 'relu', 'sigmoid']:
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            mlp = MLP(mlp_sizes, bias=False, activation=use_activation).cuda()
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            mlp_layers = []
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            for i in range(mlp.num_layers):
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                linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1], bias=False)
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                mlp.weights[i].data.copy_(linear.weight)
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                mlp_layers.append(linear)
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                if use_activation == 'relu':
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                    mlp_layers.append(nn.ReLU(inplace=True))
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                if use_activation == 'sigmoid':
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                    mlp_layers.append(nn.Sigmoid())
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            ref_mlp = nn.Sequential(*mlp_layers).cuda()
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            test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_()
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            ref_input = test_input.clone().detach().requires_grad_()
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            mlp_out = mlp(test_input)
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            ref_out = ref_mlp(ref_input)
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            np.testing.assert_allclose(
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                mlp_out.detach().cpu().numpy(),
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                ref_out.detach().cpu().numpy(),
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                atol=1e-7, rtol=1e-5)
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            # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
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            mlp_out.mean().mul(10.).backward()
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            ref_out.mean().mul(10.).backward()
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            np.testing.assert_allclose(
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                test_input.grad.detach().cpu().numpy(),
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                ref_input.grad.detach().cpu().numpy(),
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                atol=0, rtol=100)
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            np.testing.assert_allclose(
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                mlp.weights[0].grad.detach().cpu().numpy(),
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                ref_mlp[0].weight.grad.detach().cpu().numpy(),
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                atol=1e-7, rtol=100)
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    def test_with_bias(self):
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        for use_activation in ['none', 'relu', 'sigmoid']:
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            mlp = MLP(mlp_sizes, bias=True, activation=use_activation).cuda()
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            mlp_layers = []
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            for i in range(mlp.num_layers):
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                linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1], bias=True)
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                mlp.weights[i].data.copy_(linear.weight)
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                mlp.biases[i].data.copy_(linear.bias)
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                mlp_layers.append(linear)
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                if use_activation == 'relu':
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                    mlp_layers.append(nn.ReLU(inplace=True))
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                if use_activation == 'sigmoid':
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                    mlp_layers.append(nn.Sigmoid())
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            ref_mlp = nn.Sequential(*mlp_layers).cuda()
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            test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_()
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            ref_input = test_input.clone().detach().requires_grad_()
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            mlp_out = mlp(test_input)
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            ref_out = ref_mlp(ref_input)
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            np.testing.assert_allclose(
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                mlp_out.detach().cpu().numpy(),
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                ref_out.detach().cpu().numpy(),
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                atol=1e-7, rtol=1e-5)
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            # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
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            mlp_out.mean().mul(10.).backward()
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            ref_out.mean().mul(10.).backward()
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            np.testing.assert_allclose(
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                test_input.grad.detach().cpu().numpy(),
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                ref_input.grad.detach().cpu().numpy(),
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                atol=0, rtol=1)
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            np.testing.assert_allclose(
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                mlp.weights[0].grad.detach().cpu().numpy(),
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                ref_mlp[0].weight.grad.detach().cpu().numpy(),
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                atol=1e-7, rtol=1)
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            np.testing.assert_allclose(
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                mlp.biases[0].grad.detach().cpu().numpy(),
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                ref_mlp[0].bias.grad.detach().cpu().numpy(),
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                atol=1e-7, rtol=1e-5)
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    def test_no_grad(self):
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        mlp = MLP(mlp_sizes).cuda()
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        mlp_layers = []
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        for i in range(mlp.num_layers):
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            linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])
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            mlp.weights[i].data.copy_(linear.weight)
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            mlp.biases[i].data.copy_(linear.bias)
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            mlp_layers.append(linear)
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            mlp_layers.append(nn.ReLU(inplace=True))
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        ref_mlp = nn.Sequential(*mlp_layers).cuda()
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        test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.)
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        ref_input = test_input.clone().detach()
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        mlp_out = mlp(test_input)
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        ref_out = ref_mlp(ref_input)
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        np.testing.assert_allclose(
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            mlp_out.detach().cpu().numpy(),
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            ref_out.detach().cpu().numpy(),
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            atol=1e-7, rtol=1e-5)
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        # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
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        mlp_out.mean().mul(10.).backward()
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        ref_out.mean().mul(10.).backward()
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        np.testing.assert_allclose(
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            mlp.weights[0].grad.detach().cpu().numpy(),
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            ref_mlp[0].weight.grad.detach().cpu().numpy(),
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            atol=1e-7, rtol=1e-5)
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    def test_performance_half(self):
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        mlp = MLP(mlp_sizes).cuda().half()
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        mlp_layers = []
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        for i in range(mlp.num_layers):
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            linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])
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            mlp.weights[i].data.copy_(linear.weight)
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            mlp.biases[i].data.copy_(linear.bias)
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            mlp_layers.append(linear)
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            mlp_layers.append(nn.ReLU(inplace=True))
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        ref_mlp = nn.Sequential(*mlp_layers).cuda().half()
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        test_input = torch.empty(
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            batch_size, mlp_sizes[0], device="cuda", dtype=torch.half).fill_(10.).requires_grad_()
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        ref_input = torch.empty(
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            batch_size, mlp_sizes[0], device="cuda", dtype=torch.half).fill_(10.).requires_grad_()
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        # Warm up GPU
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        for _ in range(100):
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            ref_out = ref_mlp(ref_input)
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            ref_loss = ref_out.mean()
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            ref_mlp.zero_grad()
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            ref_loss.backward()
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            mlp_out = mlp(test_input)
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            test_loss = mlp_out.mean()
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            mlp.zero_grad()
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            test_loss.backward()
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        torch.cuda.profiler.start()
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        torch.cuda.synchronize()
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        start_time = time()
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        for _ in range(num_iters):
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            ref_out = ref_mlp(ref_input)
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            ref_loss = ref_out.mean()
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            ref_mlp.zero_grad()
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            ref_loss.backward()
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        torch.cuda.synchronize()
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        stop_time = time()
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        print(F"\nPytorch MLP time {(stop_time - start_time) * 1000. / num_iters:.4f} ms")
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        torch.cuda.synchronize()
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        start_time = time()
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        for _ in range(num_iters):
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            mlp_out = mlp(test_input)
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            test_loss = mlp_out.mean()
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            mlp.zero_grad()
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            test_loss.backward()
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        torch.cuda.synchronize()
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        stop_time = time()
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        print(F"C++ MLP time {(stop_time - start_time) * 1000. / num_iters:.4f} ms")
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        torch.cuda.profiler.stop()
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if __name__ == '__main__':
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    unittest.main()
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