mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-04-29 03:00:14 -04:00
5d212864b5
* Add ResNet inference test and cannon * Test with ResNet50 * test_car works with resnet fix * Add KiTS19 dataset * KiTS19: Implement iterate * No batch load for this dataset * Save results on iterate * Implement dice score * Add data prep and eval functions * Resolve shape issue * Conversion works but wrong values * Segfaults when load_from_pretrained is called * Fix segfault and assign properly * Final result generated, though very slow * Store and load final result to save time * Fix typo in finalize * Score computes * More bug fixes, dice score is very low * Working broken code * Assign output values to result * Getting a much higher score now * Fix dataset preprocessing * Mean DICE score of 88.5 * Ugh, typo * Attempt to reimplement model * Rename layers * Tiny model works, kinda * Accuracy? gone * Implement InstanceNorm and match torch * Test instance norm 2d and 3d * Combined input block with downsample block * Tiny model works, support strided convtranspose * Commands to download dataset * Clean up a bit * unet3d_v2 -> unet3d * Remove duplicated code * Oops, put tests back
244 lines
8.2 KiB
Python
Executable File
244 lines
8.2 KiB
Python
Executable File
#!/usr/bin/env python
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import unittest
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import numpy as np
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from tinygrad.jit import TinyJit
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from tinygrad.tensor import Tensor, Device
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from tinygrad.nn import BatchNorm2d, Conv2d, ConvTranspose2d, Linear, GroupNorm, LayerNorm, LayerNorm2d, Embedding, InstanceNorm
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import torch
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class TestNN(unittest.TestCase):
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def test_batchnorm2d(self, training=False):
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szs = [4, 8, 16, 32]
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for sz in szs:
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# create in tinygrad
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Tensor.training = training
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bn = BatchNorm2d(sz, eps=1e-5, 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.numpy()[bn.running_var.numpy() < 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.numpy())
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tbn.bias[:] = torch.tensor(bn.bias.numpy())
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tbn.running_mean[:] = torch.tensor(bn.running_mean.numpy())
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tbn.running_var[:] = torch.tensor(bn.running_var.numpy())
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np.testing.assert_allclose(bn.running_mean.numpy(), tbn.running_mean.detach().numpy(), rtol=1e-5, atol=1e-6)
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np.testing.assert_allclose(bn.running_var.numpy(), tbn.running_var.detach().numpy(), rtol=1e-5, atol=1e-6)
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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().numpy()))
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# close
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np.testing.assert_allclose(outt.numpy(), toutt.detach().numpy(), rtol=5e-4, atol=1e-6)
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np.testing.assert_allclose(bn.running_mean.numpy(), tbn.running_mean.detach().numpy(), rtol=1e-5, atol=1e-6)
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np.testing.assert_allclose(bn.running_var.numpy(), 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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model = Linear(in_dim, out_dim)
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z = model(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(model.weight.numpy(), dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(model.bias.numpy(), dtype=torch.float32)
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torch_x = torch.tensor(x.cpu().numpy(), 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.numpy(), 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.numpy(), dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), 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().numpy())
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torch_z = torch_layer(torch_x)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
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def test_conv_transpose2d(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 = ConvTranspose2d(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.ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
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torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), 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().numpy())
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torch_z = torch_layer(torch_x)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
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def test_groupnorm(self):
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BS, H, W, C, G = 20, 10, 10, 6, 3
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# create in tinygrad
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layer = GroupNorm(G, C)
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# create in torch
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with torch.no_grad():
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torch_layer = torch.nn.GroupNorm(G, C).eval()
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torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
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# test
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x = Tensor.randn(BS, C, H, W)
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z = layer(x)
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torch_x = torch.tensor(x.cpu().numpy())
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torch_z = torch_layer(torch_x)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)
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def test_layernorm(self):
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N, C, H, W = 20, 5, 10, 10
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# create in tinygrad
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layer = LayerNorm([H, W])
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# create in torch
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with torch.no_grad():
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torch_layer = torch.nn.LayerNorm([H, W]).eval()
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torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
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# test
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x = Tensor.randn(N, C, H, W)
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z = layer(x)
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torch_x = torch.tensor(x.cpu().numpy())
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torch_z = torch_layer(torch_x)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)
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def test_layernorm_2d(self):
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N, C, H, W = 20, 5, 10, 10
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# create in tinygrad
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layer = LayerNorm2d(C)
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# create in torch
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with torch.no_grad():
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torch_layer = torch.nn.LayerNorm([C]).eval()
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torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
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# test
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x = Tensor.randn(N, C, H, W)
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z = layer(x)
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torch_x = torch.tensor(x.cpu().numpy())
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torch_z = torch_layer(torch_x.permute(0,2,3,1)).permute(0,3,1,2)
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def test_instancenorm_2d(self):
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N, C, H, W = 20, 5, 10, 10
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# create in tinygrad
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layer = InstanceNorm(C)
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# create in torch
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with torch.no_grad():
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torch_layer = torch.nn.InstanceNorm2d(C, affine=True).eval()
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torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
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# test
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x = Tensor.randn(N, C, H, W)
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z = layer(x)
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torch_x = torch.tensor(x.cpu().numpy())
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torch_z = torch_layer(torch_x)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)
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def test_instancenorm_3d(self):
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N, C, D, H, W = 20, 5, 3, 10, 10
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# create in tinygrad
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layer = InstanceNorm(C)
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# create in torch
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with torch.no_grad():
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torch_layer = torch.nn.InstanceNorm3d(C, affine=True).eval()
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torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
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torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
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# test
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x = Tensor.randn(N, C, D, H, W)
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z = layer(x)
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torch_x = torch.tensor(x.cpu().numpy())
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torch_z = torch_layer(torch_x)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)
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def test_embedding(self):
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B, T, C, VS = 4, 10, 20, 28
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# create in tinygrad
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layer = Embedding(VS, C)
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with torch.no_grad():
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torch_layer = torch.nn.Embedding(VS, C).eval()
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torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
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# test
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x = Tensor(np.random.randint(0, VS, (B, T)).astype(np.float32))
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z = layer(x)
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torch_x = torch.tensor(x.cpu().numpy().astype(np.int32))
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torch_z = torch_layer(torch_x)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)
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# test with jit enabled
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@TinyJit
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def layer_jit(x):
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return layer(x).realize()
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for _ in range(3):
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x = Tensor(np.random.randint(0, VS, (B, T)).astype(np.float32))
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z = layer_jit(x)
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torch_x = torch.tensor(x.cpu().numpy().astype(np.int32))
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torch_z = torch_layer(torch_x)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)
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if __name__ == '__main__':
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unittest.main()
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