162 lines
4.9 KiB
Python
162 lines
4.9 KiB
Python
import unittest
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import numpy as np
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from tinygrad.tensor import Tensor, Device
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class TestConv(unittest.TestCase):
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def test_simple(self):
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x = Tensor.ones(1, 12, 128, 256).contiguous().realize()
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w = Tensor.ones(32, 12, 3, 3).contiguous().realize()
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ret = x.conv2d(w, stride=(2, 2), padding=(1, 1)).numpy()
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# it's not 108 around the padding
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assert (ret[:, :, 1:-1, 1:-1] == 108).all()
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assert ret[0, 0, 0, 0] == 48
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assert ret[0, 0, 0, 1] == 72
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def test_simple_rand(self):
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x = Tensor.rand(1, 12, 128, 256)
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w = Tensor.rand(32, 12, 3, 3)
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x.conv2d(w, stride=(2, 2), padding=(1, 1)).numpy()
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def test_many_simple(self):
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x = Tensor(np.arange(8 * 2 * 8).reshape(1, 8, 2, 8).astype(np.float32))
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# w = Tensor(np.arange(8*8*1*1).reshape(8,8,1,1).astype(np.float32))
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w = Tensor.eye(8).reshape((8, 8, 1, 1))
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ret = x.conv2d(w, stride=(1, 2), padding=(0, 0)).numpy()
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print(ret)
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def test_lazycache(self):
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Tensor.no_grad = True
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x = Tensor.rand(1, 32)
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y = Tensor.rand(32)
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out = (
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x
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+ y.reshape((1, 32, 1)).reshape((1, 32))
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+ y.reshape((1, 32, 1)).reshape((1, 32))
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)
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out.numpy()
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Tensor.no_grad = False
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def test_simple_biased(self):
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C = 8
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x = Tensor.rand(1, C, 5, 5)
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w = Tensor.eye(C).reshape((C, C, 1, 1))
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b = Tensor(np.arange(C).astype(np.float32))
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ret = Tensor.conv2d(x, w, b).relu().conv2d(w, b)
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print(ret.numpy())
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def test_two_binops_no_rerun(self):
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Tensor.no_grad = True
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x = Tensor.randn(1, 12, 128, 256)
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w = Tensor.randn(32, 12, 3, 3)
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out = x.conv2d(w, stride=(2, 2), padding=(1, 1))
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r1, r2 = out.relu(), (out - 1)
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np.testing.assert_allclose(r1.numpy(), np.maximum(out.numpy(), 0))
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np.testing.assert_allclose(r2.numpy(), out.numpy() - 1)
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Tensor.no_grad = False
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def test_two_overlapping_binops_no_rerun(self):
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Tensor.no_grad = True
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x = Tensor.randn(1, 12, 128, 256)
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w = Tensor.randn(32, 12, 3, 3)
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out = x.conv2d(w, stride=(2, 2), padding=(1, 1))
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r1, r2 = out.relu(), out.elu()
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np.testing.assert_allclose(r1.numpy(), np.maximum(out.numpy(), 0))
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np.testing.assert_allclose(
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r2.numpy(),
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np.where(out.numpy() > 0, out.numpy(), (np.exp(out.numpy()) - 1)),
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atol=1e-5,
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)
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Tensor.no_grad = False
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@unittest.skipIf(
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Device.DEFAULT != "TORCH", "Takes too long to compile for Compiled backends"
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)
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def test_two_overlapping_binops_no_rerun_wino(self):
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Tensor.no_grad = True
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old_wino = Tensor.wino
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Tensor.wino = True
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x = Tensor.randn(1, 4, 16, 16)
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w = Tensor.randn(6, 4, 3, 3)
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out = x.conv2d(w, padding=(1, 1))
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r1, r2 = out.relu(), out.elu()
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np.testing.assert_allclose(r1.numpy(), np.maximum(out.numpy(), 0))
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np.testing.assert_allclose(
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r2.numpy(),
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np.where(out.numpy() > 0, out.numpy(), (np.exp(out.numpy()) - 1)),
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atol=1e-5,
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)
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Tensor.wino = old_wino
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Tensor.no_grad = False
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def test_first_three(self):
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Tensor.no_grad = True
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x = Tensor.rand(1, 12, 128, 256)
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w = Tensor.rand(32, 12, 3, 3)
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x = x.conv2d(w, stride=(2, 2), padding=(1, 1)).elu()
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w = Tensor.rand(32, 1, 3, 3)
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x = x.conv2d(w, padding=(1, 1), groups=32).elu()
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w = Tensor.rand(16, 32, 1, 1)
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x = x.conv2d(w).elu()
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x = x.numpy()
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print(x.shape)
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Tensor.no_grad = False
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def test_elu(self):
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Tensor.no_grad = True
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x = Tensor.rand(1, 12, 128, 256)
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w = Tensor.rand(32, 12, 3, 3)
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x = x.conv2d(w, stride=(2, 2), padding=(1, 1))
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x = x.elu()
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w = Tensor.rand(32, 1, 3, 3)
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x = x.conv2d(w, padding=(1, 1), groups=32)
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x.numpy()
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Tensor.no_grad = False
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def test_reduce_relu(self):
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Tensor.no_grad = True
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x = Tensor.rand(1, 12, 128, 256)
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x = x.sum(keepdim=True).relu()
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x.numpy()
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Tensor.no_grad = False
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def test_bias(self):
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Tensor.no_grad = True
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from tinygrad.nn import Conv2d
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x = Tensor.rand(1, 12, 128, 256)
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c = Conv2d(12, 32, 3)
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x = c(x).relu()
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w = Tensor.uniform(32, 1, 3, 3)
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x = x.conv2d(w, groups=32)
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x.numpy()
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Tensor.no_grad = False
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def test_multiadd(self):
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w = Tensor.rand(32)
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x = Tensor.rand(32).relu()
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(w + x).numpy()
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def test_reorder(self):
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x = Tensor.rand(1, 12, 128, 256)
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w = Tensor.rand(12, 12, 3, 3)
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x = x.conv2d(w, padding=(1, 1))
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print(x.shape)
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x = x.reshape((1, 12, 256, 128))
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x += 1
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x += 1
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x = x.reshape((1, 12, 128, 256))
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x.numpy()
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if __name__ == "__main__":
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unittest.main()
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