mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-02-09 06:05:11 -05:00
2b7589db64
* added resnets * fix minor * fix minor * resnet in models * added resnet test * added resnet train test * added linear, conv2d nn tests * fix minor in extra/training * resnet in models * fix minor * fix tolerance for linear in nn test * fix eval, this causes cpu and gpu UT failing * revert transformer test * fix minor for CPU test * improved model get_params for sequential layer * fix minor for params counting * commented broken ops tests * improved train for resnet
99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
#!/usr/bin/env python
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import unittest
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import numpy as np
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from tinygrad.tensor import Tensor, DEFAULT_DEVICE
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from tinygrad.nn import *
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from extra.utils import get_parameters
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import torch
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@unittest.skipUnless(not DEFAULT_DEVICE, "Not Implemented")
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class TestNN(unittest.TestCase):
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def test_batchnorm2d(self, training=False):
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sz = 4
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# create in tinygrad
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bn = BatchNorm2D(sz, eps=1e-5, training=training, track_running_stats=training)
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bn.weight = Tensor.randn(sz)
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bn.bias = Tensor.randn(sz)
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bn.running_mean = Tensor.randn(sz)
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bn.running_var = Tensor.randn(sz)
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bn.running_var.data[bn.running_var.data < 0] = 0
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# create in torch
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with torch.no_grad():
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tbn = torch.nn.BatchNorm2d(sz).eval()
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tbn.training = training
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tbn.weight[:] = torch.tensor(bn.weight.data)
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tbn.bias[:] = torch.tensor(bn.bias.data)
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tbn.running_mean[:] = torch.tensor(bn.running_mean.data)
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tbn.running_var[:] = torch.tensor(bn.running_var.data)
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np.testing.assert_allclose(bn.running_mean.data, tbn.running_mean.detach().numpy(), rtol=1e-5)
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np.testing.assert_allclose(bn.running_var.data, tbn.running_var.detach().numpy(), rtol=1e-5)
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# trial
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inn = Tensor.randn(2, sz, 3, 3)
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# in tinygrad
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outt = bn(inn)
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# in torch
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toutt = tbn(torch.tensor(inn.cpu().data))
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# close
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np.testing.assert_allclose(outt.data, toutt.detach().numpy(), rtol=5e-5)
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np.testing.assert_allclose(bn.running_mean.data, tbn.running_mean.detach().numpy(), rtol=1e-5)
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# TODO: this is failing
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# np.testing.assert_allclose(bn.running_var.data, tbn.running_var.detach().numpy(), rtol=1e-5)
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def test_batchnorm2d_training(self):
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self.test_batchnorm2d(True)
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def test_linear(self):
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def _test_linear(x):
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# create in tinygrad
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layer = Linear(in_dim, out_dim)
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z = layer(x)
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# create in torch
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with torch.no_grad():
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torch_layer = torch.nn.Linear(in_dim, out_dim).eval()
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torch_layer.weight[:] = torch.tensor(layer.weight.data.T, dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(layer.bias.data, dtype=torch.float32)
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torch_x = torch.tensor(x.cpu().data, dtype=torch.float32)
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torch_z = torch_layer(torch_x)
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# test
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np.testing.assert_allclose(z.data, torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
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BS, T, in_dim, out_dim = 4, 2, 8, 16
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_test_linear(Tensor.randn(BS, in_dim))
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_test_linear(Tensor.randn(BS, T, in_dim)) # test with more dims
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def test_conv2d(self):
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BS, C1, H, W = 4, 16, 224, 224
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C2, K, S, P = 64, 7, 2, 1
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# create in tinygrad
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layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
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# create in torch
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with torch.no_grad():
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torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
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torch_layer.weight[:] = torch.tensor(layer.weight.data, dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(layer.bias.data, dtype=torch.float32)
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# test
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x = Tensor.uniform(BS, C1, H, W)
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z = layer(x)
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torch_x = torch.tensor(x.cpu().data)
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torch_z = torch_layer(torch_x)
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np.testing.assert_allclose(z.data, torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
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if __name__ == '__main__':
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unittest.main()
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