444 lines
14 KiB
Python
Executable File
444 lines
14 KiB
Python
Executable File
#!/usr/bin/env python
|
|
import unittest
|
|
import numpy as np
|
|
from tinygrad.helpers import CI
|
|
from tinygrad.jit import TinyJit
|
|
from tinygrad.tensor import Tensor, Device
|
|
from tinygrad.nn import (
|
|
BatchNorm2d,
|
|
Conv1d,
|
|
ConvTranspose1d,
|
|
Conv2d,
|
|
ConvTranspose2d,
|
|
Linear,
|
|
GroupNorm,
|
|
LayerNorm,
|
|
LayerNorm2d,
|
|
Embedding,
|
|
InstanceNorm,
|
|
)
|
|
import torch
|
|
import pytest
|
|
|
|
pytestmark = [pytest.mark.exclude_cuda]
|
|
|
|
|
|
class TestNN(unittest.TestCase):
|
|
def test_sparse_cat_cross_entropy(self):
|
|
input = torch.randn(3, 5)
|
|
target = torch.empty(3, dtype=torch.long).random_(5)
|
|
loss_fun = torch.nn.CrossEntropyLoss(reduction="mean")
|
|
loss = loss_fun(input, target)
|
|
|
|
input_tiny = Tensor(input.detach().numpy())
|
|
target_tiny = Tensor(target.detach().numpy())
|
|
loss_tiny = input_tiny.sparse_categorical_crossentropy(target_tiny)
|
|
|
|
np.testing.assert_allclose(
|
|
loss_tiny.numpy(), loss.detach().numpy(), atol=1e-5, rtol=1e-6
|
|
)
|
|
|
|
def test_batchnorm2d(self, training=False):
|
|
szs = [4, 8, 16, 32]
|
|
for sz in szs:
|
|
# create in tinygrad
|
|
Tensor.training = training
|
|
bn = BatchNorm2d(sz, eps=1e-5, track_running_stats=training)
|
|
bn.weight = Tensor.randn(sz)
|
|
bn.bias = Tensor.randn(sz)
|
|
bn.running_mean = Tensor.randn(sz)
|
|
bn.running_var = Tensor.randn(sz)
|
|
bn.running_var.numpy()[bn.running_var.numpy() < 0] = 0
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
tbn = torch.nn.BatchNorm2d(sz).eval()
|
|
tbn.training = training
|
|
tbn.weight[:] = torch.tensor(bn.weight.numpy())
|
|
tbn.bias[:] = torch.tensor(bn.bias.numpy())
|
|
tbn.running_mean[:] = torch.tensor(bn.running_mean.numpy())
|
|
tbn.running_var[:] = torch.tensor(bn.running_var.numpy())
|
|
|
|
np.testing.assert_allclose(
|
|
bn.running_mean.numpy(),
|
|
tbn.running_mean.detach().numpy(),
|
|
rtol=1e-5,
|
|
atol=1e-6,
|
|
)
|
|
np.testing.assert_allclose(
|
|
bn.running_var.numpy(),
|
|
tbn.running_var.detach().numpy(),
|
|
rtol=1e-5,
|
|
atol=1e-6,
|
|
)
|
|
|
|
# trial
|
|
inn = Tensor.randn(2, sz, 3, 3)
|
|
|
|
# in tinygrad
|
|
outt = bn(inn)
|
|
|
|
# in torch
|
|
toutt = tbn(torch.tensor(inn.numpy()))
|
|
|
|
# close
|
|
np.testing.assert_allclose(
|
|
outt.numpy(), toutt.detach().numpy(), rtol=5e-4, atol=1e-6
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
bn.running_mean.numpy(),
|
|
tbn.running_mean.detach().numpy(),
|
|
rtol=1e-5,
|
|
atol=1e-6,
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
bn.running_var.numpy(),
|
|
tbn.running_var.detach().numpy(),
|
|
rtol=1e-5,
|
|
atol=1e-6,
|
|
)
|
|
|
|
def test_batchnorm2d_training(self):
|
|
self.test_batchnorm2d(True)
|
|
|
|
def test_linear(self):
|
|
def _test_linear(x):
|
|
# create in tinygrad
|
|
model = Linear(in_dim, out_dim)
|
|
z = model(x)
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.Linear(in_dim, out_dim).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
model.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(
|
|
model.bias.numpy(), dtype=torch.float32
|
|
)
|
|
torch_x = torch.tensor(x.numpy(), dtype=torch.float32)
|
|
torch_z = torch_layer(torch_x)
|
|
|
|
# test
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5
|
|
)
|
|
|
|
BS, T, in_dim, out_dim = 4, 2, 8, 16
|
|
_test_linear(Tensor.randn(BS, in_dim))
|
|
_test_linear(Tensor.randn(BS, T, in_dim)) # test with more dims
|
|
|
|
def test_conv1d(self):
|
|
BS, C1, W = 4, 16, 224 // 4
|
|
C2, K, S, P = 64, 7, 2, 1
|
|
|
|
# create in tinygrad
|
|
layer = Conv1d(C1, C2, kernel_size=K, stride=S, padding=P)
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.Conv1d(
|
|
C1, C2, kernel_size=K, stride=S, padding=P
|
|
).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
|
|
# test
|
|
x = Tensor.uniform(BS, C1, W)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy())
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5
|
|
)
|
|
|
|
def test_conv2d(self):
|
|
BS, C1, H, W = 4, 16, 224 // 4, 224 // 4
|
|
C2, K, S, P = 64, 7, 2, 1
|
|
|
|
# create in tinygrad
|
|
layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.Conv2d(
|
|
C1, C2, kernel_size=K, stride=S, padding=P
|
|
).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
|
|
# test
|
|
x = Tensor.uniform(BS, C1, H, W)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy())
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5
|
|
)
|
|
|
|
@unittest.skipIf(
|
|
Device.DEFAULT != "TORCH", "Takes too long to compile for Compiled backends"
|
|
)
|
|
def test_conv2d_winograd(self):
|
|
BS, C1, H, W = 2, 8, 16, 16
|
|
C2, K, S, P = 8, 3, 1, 1
|
|
|
|
old_wino = Tensor.wino
|
|
Tensor.wino = True
|
|
|
|
# create in tinygrad
|
|
layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
|
|
layer.weight.requires_grad = True
|
|
layer.bias.requires_grad = True
|
|
|
|
# create in torch
|
|
torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
|
|
torch_layer.weight = torch.nn.Parameter(
|
|
torch.tensor(layer.weight.numpy(), dtype=torch.float32)
|
|
)
|
|
torch_layer.bias = torch.nn.Parameter(
|
|
torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
)
|
|
|
|
# test
|
|
x = Tensor.uniform(BS, C1, H, W, requires_grad=True)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy(), requires_grad=True)
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5
|
|
)
|
|
|
|
m = z.mean()
|
|
m.backward()
|
|
gw = layer.weight.grad.realize()
|
|
gb = layer.bias.grad.realize()
|
|
gx = x.grad.realize()
|
|
|
|
torch_z.mean().backward()
|
|
np.testing.assert_allclose(
|
|
gw.numpy(), torch_layer.weight.grad.numpy(), atol=5e-4, rtol=1e-5
|
|
)
|
|
np.testing.assert_allclose(
|
|
gb.numpy(), torch_layer.bias.grad.numpy(), atol=5e-4, rtol=1e-5
|
|
)
|
|
np.testing.assert_allclose(
|
|
gx.numpy(), torch_x.grad.numpy(), atol=5e-4, rtol=1e-5
|
|
)
|
|
|
|
Tensor.wino = old_wino
|
|
|
|
@unittest.skipIf(CI and Device.DEFAULT == "WEBGPU", "runs out of memory in CI")
|
|
def test_conv_transpose1d(self):
|
|
BS, C1, W = 4, 16, 224 // 4
|
|
C2, K, S, P = 64, 7, 2, 1
|
|
|
|
# create in tinygrad
|
|
layer = ConvTranspose1d(C1, C2, kernel_size=K, stride=S, padding=P)
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.ConvTranspose1d(
|
|
C1, C2, kernel_size=K, stride=S, padding=P
|
|
).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
|
|
# test
|
|
x = Tensor.uniform(BS, C1, W)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy())
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5
|
|
)
|
|
|
|
@unittest.skipIf(CI and Device.DEFAULT == "WEBGPU", "runs out of memory in CI")
|
|
def test_conv_transpose2d(self):
|
|
BS, C1, H, W = 4, 16, 224 // 4, 224 // 4
|
|
C2, K, S, P = 64, 7, 2, 1
|
|
|
|
# create in tinygrad
|
|
layer = ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P)
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.ConvTranspose2d(
|
|
C1, C2, kernel_size=K, stride=S, padding=P
|
|
).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
|
|
# test
|
|
x = Tensor.uniform(BS, C1, H, W)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy())
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5
|
|
)
|
|
|
|
def test_groupnorm(self):
|
|
BS, H, W, C, G = 20, 10, 10, 6, 3
|
|
|
|
# create in tinygrad
|
|
layer = GroupNorm(G, C)
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.GroupNorm(G, C).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
|
|
# test
|
|
x = Tensor.randn(BS, C, H, W)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy())
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3
|
|
)
|
|
|
|
def test_layernorm(self):
|
|
N, C, H, W = 20, 5, 10, 10
|
|
|
|
# create in tinygrad
|
|
layer = LayerNorm([H, W])
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.LayerNorm([H, W]).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
|
|
# test
|
|
x = Tensor.randn(N, C, H, W)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy())
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3
|
|
)
|
|
|
|
def test_layernorm_2d(self):
|
|
N, C, H, W = 20, 5, 10, 10
|
|
|
|
# create in tinygrad
|
|
layer = LayerNorm2d(C)
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.LayerNorm([C]).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
|
|
# test
|
|
x = Tensor.randn(N, C, H, W)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy())
|
|
torch_z = torch_layer(torch_x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3
|
|
)
|
|
|
|
def test_instancenorm_2d(self):
|
|
N, C, H, W = 20, 5, 10, 10
|
|
|
|
# create in tinygrad
|
|
layer = InstanceNorm(C)
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.InstanceNorm2d(C, affine=True).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
|
|
# test
|
|
x = Tensor.randn(N, C, H, W)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy())
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3
|
|
)
|
|
|
|
def test_instancenorm_3d(self):
|
|
N, C, D, H, W = 20, 5, 3, 10, 10
|
|
|
|
# create in tinygrad
|
|
layer = InstanceNorm(C)
|
|
|
|
# create in torch
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.InstanceNorm3d(C, affine=True).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
|
|
|
|
# test
|
|
x = Tensor.randn(N, C, D, H, W)
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy())
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3
|
|
)
|
|
|
|
def test_embedding(self):
|
|
B, T, C, VS = 4, 10, 20, 28
|
|
|
|
# create in tinygrad
|
|
layer = Embedding(VS, C)
|
|
|
|
with torch.no_grad():
|
|
torch_layer = torch.nn.Embedding(VS, C).eval()
|
|
torch_layer.weight[:] = torch.tensor(
|
|
layer.weight.numpy(), dtype=torch.float32
|
|
)
|
|
|
|
# test
|
|
x = Tensor(np.random.randint(0, VS, (B, T)).astype(np.float32))
|
|
z = layer(x)
|
|
torch_x = torch.tensor(x.numpy().astype(np.int32))
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8
|
|
)
|
|
|
|
# test with jit enabled
|
|
@TinyJit
|
|
def layer_jit(x):
|
|
return layer(x).realize()
|
|
|
|
for _ in range(3):
|
|
x = Tensor(np.random.randint(0, VS, (B, T)).astype(np.float32))
|
|
z = layer_jit(x)
|
|
torch_x = torch.tensor(x.numpy().astype(np.int32))
|
|
torch_z = torch_layer(torch_x)
|
|
np.testing.assert_allclose(
|
|
z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|