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fef6c82491a1d873564b5b2bc67f94eda47112d9
tinygrad/test/test_ops.py
George Hotz fef6c82491 wow dilation support was simple
2022-06-15 11:38:23 -07:00

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import os
import torch
import numpy as np
import unittest
import timeit
import functools
from tinygrad.tensor import Tensor, Device
def helper_test_op(shps, torch_fxn, tinygrad_fxn, atol=1e-6, rtol=1e-3, grad_atol=1e-6, grad_rtol=1e-3, forward_only=False, vals=None, a=-0.5, b=20):
torch.manual_seed(0)
if shps is None:
ts = [torch.tensor(x, requires_grad=True) for x in vals]
else:
ts = [torch.tensor((np.random.random(size=x).astype(np.float32)+a)*b, requires_grad=True) for x in shps]
tst = [Tensor(x.detach().numpy()) for x in ts]
out = torch_fxn(*ts)
ret = tinygrad_fxn(*tst)
np.testing.assert_allclose(ret.cpu().data, out.detach().numpy(), atol=atol, rtol=rtol)
if not forward_only:
out.mean().backward()
ret.mean().backward()
for t, tt in zip(ts, tst):
np.testing.assert_allclose(t.grad, tt.cpu().grad.data, atol=grad_atol, rtol=grad_rtol)
# speed
torch_fp = timeit.Timer(functools.partial(torch_fxn, *ts)).timeit(5) * 1000/5
tinygrad_fp = timeit.Timer(functools.partial(tinygrad_fxn, *tst)).timeit(5) * 1000/5
if not forward_only:
torch_fbp = timeit.Timer(functools.partial(lambda f,x: f(*x).mean().backward(), torch_fxn, ts)).timeit(5) * 1000/5
tinygrad_fbp = timeit.Timer(functools.partial(lambda f,x: f(*x).mean().backward(), tinygrad_fxn, tst)).timeit(5) * 1000/5
else:
torch_fbp, tinygrad_fbp = np.nan, np.nan
print("testing %30r torch/tinygrad fp: %.2f / %.2f ms bp: %.2f / %.2f ms" % (shps, torch_fp, tinygrad_fp, torch_fbp-torch_fp, tinygrad_fbp-tinygrad_fp))
class TestOps(unittest.TestCase):
def test_add(self):
helper_test_op([(45,65), (45,65)], lambda x,y: x+y, Tensor.add)
def test_broadcasted_add(self):
helper_test_op([(45,65), (45,1)], lambda x,y: x+y, lambda x,y: x+y)
def test_sub(self):
helper_test_op([(45,65), (45,65)], lambda x,y: x-y, Tensor.sub)
def test_mul(self):
helper_test_op([(45,65), (45,65)], lambda x,y: x*y, Tensor.mul)
def test_div(self):
helper_test_op([(45,65), (45,65)], lambda x,y: x/y, Tensor.div)
def test_pow(self):
helper_test_op([(45,65), (45,65)], lambda x,y: x**y, Tensor.pow, a=0)
def test_sqrt(self):
helper_test_op([(45,65)], lambda x: x.sqrt(), Tensor.sqrt, a=0)
def test_relu(self):
helper_test_op([(45,65)], lambda x: x.relu(), Tensor.relu)
def test_leakyrelu(self):
helper_test_op([(45,65)], lambda x: torch.nn.functional.leaky_relu(x,0.01), Tensor.leakyrelu)
def test_abs(self):
helper_test_op([(45,65)], lambda x: torch.abs(x), Tensor.abs)
def test_log(self):
helper_test_op([(45,65)], lambda x: torch.log(x), Tensor.log)
def test_exp(self):
helper_test_op([(45,65)], lambda x: torch.exp(x), Tensor.exp)
def test_sign(self):
pass
#helper_test_op([(45,65)], lambda x: torch.sign(x), Tensor.sign) --> broken test
def test_sigmoid(self):
helper_test_op([(45,65)], lambda x: x.sigmoid(), Tensor.sigmoid)
def test_softplus(self):
helper_test_op([(45,65)], lambda x: torch.nn.functional.softplus(x), Tensor.softplus, atol=1e-6, grad_atol=1e-6)
def test_gelu(self):
pass
# fails?
#helper_test_op([(45,65)], lambda x: 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x))), Tensor.gelu)
def test_elu(self):
helper_test_op([(45,65)], lambda x: torch.nn.functional.elu(x), Tensor.elu)
def test_relu6(self):
helper_test_op([(45,65)], lambda x: torch.nn.functional.relu6(x), Tensor.relu6)
def test_hardswish(self):
helper_test_op([(45,65)], lambda x: torch.nn.functional.hardswish(x), Tensor.hardswish, atol=1e-6, grad_atol=1e-6)
def test_mish(self):
def _mish_pytorch(x):
return x*torch.tanh(torch.nn.functional.softplus(x))
helper_test_op([(45,65)], _mish_pytorch, Tensor.mish, atol=1e-4)
def test_dot(self):
helper_test_op([(45,65), (65,100)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
def test_broadcastdot(self):
helper_test_op([(10,45,65), (65,45)], lambda x,y: x @ y, Tensor.dot, atol=1e-4)
def test_multidot(self):
helper_test_op([(10,45,65), (10,65,45)], lambda x,y: x @ y, Tensor.dot, atol=1e-4)
helper_test_op([(3,3,45,65), (3,3,65,45)], lambda x,y: x @ y, Tensor.dot, atol=1e-4)
def test_sum(self):
helper_test_op([(45,3)], lambda x: x.sum(), Tensor.sum)
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=3), lambda x: Tensor.sum(x, axis=3))
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=(1,3)), lambda x: Tensor.sum(x, axis=(1,3)))
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=(0,2)), lambda x: Tensor.sum(x, axis=(0,2)))
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=(1,2)), lambda x: Tensor.sum(x, axis=(1,2)))
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=1), lambda x: Tensor.sum(x, axis=1))
def test_max(self):
helper_test_op([(45,3)], lambda x: x.max(), Tensor.max)
helper_test_op([(45,3)], lambda x: x.max().mul(0.5), lambda x: Tensor.max(x).mul(0.5))
#helper_test_op(None, lambda x: x.max().mul(0.5), lambda x: Tensor.max(x).mul(0.5),
# vals=[
# [[1.0,1.0,0.0,1.0]],
# ]) --> broken test
helper_test_op([(3,4,5,6)], lambda x: x.max(axis=1)[0], lambda x: Tensor.max(x, axis=1))
def test_mean_axis(self):
helper_test_op([(3,4,5,6)], lambda x: x.mean(axis=(1,2)), lambda x: Tensor.mean(x, axis=(1,2)))
def test_logsoftmax(self):
helper_test_op([(45,65)], lambda x: torch.nn.LogSoftmax(dim=1)(x), Tensor.logsoftmax, atol=1e-7, grad_atol=1e-7)
def test_tanh(self):
helper_test_op([(45,65)], lambda x: x.tanh(), Tensor.tanh, atol=1e-6, grad_atol=1e-6)
def test_topo_sort(self):
helper_test_op([(45,65)], lambda x: (x+x)*x, lambda x: x.add(x).mul(x), atol=1e-6, grad_atol=1e-6)
def test_scalar_mul(self):
helper_test_op([(45,65)], lambda x: x*2, lambda x: x*2)
def test_scalar_rmul(self):
helper_test_op([(45,65)], lambda x: 2*x, lambda x: 2*x)
def test_scalar_sub(self):
helper_test_op([(45,65)], lambda x: x-2, lambda x: x-2)
def test_scalar_rsub(self):
helper_test_op([(45,65)], lambda x: 2-x, lambda x: 2-x)
def test_broadcast_full(self):
for torch_op, tinygrad_op in [(torch.add, Tensor.add), (torch.sub, Tensor.sub), (torch.mul, Tensor.mul),
(torch.div, Tensor.div), (torch.pow, Tensor.pow)]:
for shapes in [((5,13,24,16), (5,1,24,1)), ((1,3,1,7,1), (2,1,5,1,8))]:
with self.subTest(op=torch_op.__name__, shapes=shapes):
helper_test_op(shapes, torch_op, tinygrad_op, a=-0.5 if tinygrad_op != Tensor.pow else 0.0)
def test_broadcast_partial(self):
for torch_op, tinygrad_op in [(torch.add, Tensor.add), (torch.sub, Tensor.sub), (torch.mul, Tensor.mul),
(torch.div, Tensor.div), (torch.pow, Tensor.pow)]:
for shapes in [((1,32,32,32), (1,32,1,1)), ((5,13,24,16,2), (1,13,24,1,1)),
((4,1), (4,5)), ((1,4), (5,4))]:
with self.subTest(op=torch_op.__name__, shapes=shapes):
# NOTE: ANE backwards?
helper_test_op(shapes, torch_op, tinygrad_op, a=-0.5 if tinygrad_op != Tensor.pow else 0.0)
def test_slice(self):
helper_test_op([(3,3,3,3)], lambda x: x[1:2], lambda x: x[1:2])
helper_test_op([(3,3,3,3)], lambda x: x[1:2, 1:2], lambda x: x[1:2, 1:2])
helper_test_op([(3,3,3,3)], lambda x: x[1:2, 1:2, 0:-1], lambda x: x[1:2, 1:2, 0:-1])
def test_pad2d(self):
helper_test_op([(3,3,3,3)], lambda x: torch.nn.functional.pad(x, (1,2,3,4)), lambda x: x.pad2d(padding=(1,2,3,4)))
def test_transpose(self):
helper_test_op([(3,3,3)], lambda x: x.transpose(1,2), lambda x: x.transpose(order=(0,2,1)))
helper_test_op([(3,3,3)], lambda x: x.transpose(0,2), lambda x: x.transpose(order=(2,1,0)))
helper_test_op([(1,2,3,4)], lambda x: x.movedim((3,0,2,1),(0,1,2,3)), lambda x: x.transpose(order=(3,0,2,1)))
helper_test_op([(3,4,5,6)], lambda x: x.movedim((3,2,1,0),(0,1,2,3)), lambda x: x.transpose(order=(3,2,1,0)))
def test_reshape(self):
helper_test_op([(4,3,6,6)], lambda x: torch.reshape(x, (-1,3,6,6)), lambda x: x.reshape(shape=(-1,3,6,6)))
helper_test_op([(4,3,6,6)], lambda x: torch.reshape(x, (-1,1,6,6)), lambda x: x.reshape(shape=(-1,1,6,6)))
def test_flip(self):
helper_test_op([(4,3,6,6)], lambda x: torch.flip(x, (0,)), lambda x: x.flip(axis=(0,)))
helper_test_op([(4,3,6,6)], lambda x: torch.flip(x, (0,1)), lambda x: x.flip(axis=(0,1)))
helper_test_op([(4,3,6,6)], lambda x: torch.flip(x, (0,1,3)), lambda x: x.flip(axis=(0,1,3)))
helper_test_op([(4,3,6,6)], lambda x: torch.flip(x, (3,)), lambda x: x.flip(axis=(3,)))
def test_flatten(self):
for axis in range(3):
helper_test_op([(4,3,6,6)], lambda x: torch.flatten(x, start_dim=axis), lambda x: x.flatten(axis))
def test_detach(self):
helper_test_op([(4,3,6,6)], lambda x: x.detach(), lambda x: x.detach(), forward_only=True)
def test_expand(self):
arg = (4,3,2,6)
helper_test_op([(4,3,1,6)], lambda x: x.expand(arg), lambda x: x.expand(shape=arg))
def test_simple_conv2d(self):
helper_test_op([(1,1,9,9), (1,1,3,3)],
lambda x,w: torch.nn.functional.conv2d(x,w).relu(),
lambda x,w: Tensor.conv2d(x,w).relu(), atol=1e-4, grad_rtol=1e-5)
def test_conv2d(self):
for bs in [1,8]:
for cin in [1,3]:
for groups in [1,3] if cin == 3 else [1]:
for H in [1,2,5]:
for W in [1,2,3,5]:
with self.subTest(batch_size=bs, channels=cin, groups=groups, height=H, width=W):
helper_test_op([(bs,cin,11,28), (6,cin//groups,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups).relu(),
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5)
def test_large_input_conv2d(self):
bs = 4
cin = 16
groups = 1
H = 5
W = 2
helper_test_op([(bs,cin,64,64), (6,cin//groups,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups).relu(),
# needed to relax tolerance on NVIDIA
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-3, grad_rtol=1e-5)
def test_simple_grouped_conv2d(self):
bs = 1
groups = 2
rcout = 1
cin = 2
helper_test_op([(bs,groups*cin,1,1), (groups*rcout,cin,1,1)],
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups).relu(),
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5)
def test_medium_grouped_conv2d(self):
bs = 1
groups = 2
rcout = 2
cin = 2
helper_test_op([(bs,groups*cin,1,1), (groups*rcout,cin,1,1)],
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups).relu(),
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5)
def test_grouped_conv2d(self):
bs = 4
groups = 5
rcout = 7
cin = 3
helper_test_op([(bs,groups*cin,5,5), (groups*rcout,cin,3,3)],
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups).relu(),
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5)
def test_fancy_conv2d(self):
bs = 2
cin = 3
cout = 1
groups = 3
H,W = 3,3
helper_test_op([(bs,cin,11,28), (groups*cout,cin//groups,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups).relu(),
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5)
def test_strided_conv2d(self):
bs = 4
cin = 3
H,W = 3,3
with self.subTest(stride := 2):
helper_test_op([(bs,cin,11,28), (4,cin,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,stride=2).relu(),
lambda x,w: Tensor.conv2d(x,w,stride=stride).relu(), atol=1e-4)
with self.subTest(stride := (2,1)):
helper_test_op([(bs,cin,11,28), (4,cin,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,stride=stride).relu(),
lambda x,w: Tensor.conv2d(x,w,stride=(2,1)).relu(), atol=1e-4)
def test_padded_conv2d(self):
bs = 4
cin = 3
H,W = 3,3
with self.subTest(padding := 2):
helper_test_op([(bs,cin,11,28), (4,cin,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,padding=padding).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=padding).relu(), atol=1e-4)
with self.subTest(padding := (2,1)):
helper_test_op([(bs,cin,11,28), (4,cin,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,padding=padding).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=padding).relu(), atol=1e-4)
def test_dilated_conv2d_forward(self):
bs = 4
cin = 3
H,W = 3,3
for d in [2, (2,1)]:
with self.subTest(dilation := d):
helper_test_op([(bs,cin,11,28), (4,cin,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,dilation=dilation).relu(),
lambda x,w: Tensor.conv2d(x,w,dilation=dilation).relu(), atol=1e-4, forward_only=True)
@unittest.skipUnless(Device.DEFAULT == Device.TORCH, "Not Implemented")
def test_dilated_conv2d(self):
bs = 4
cin = 3
H,W = 3,3
for d in [2, (2,1)]:
with self.subTest(dilation := d):
helper_test_op([(bs,cin,11,28), (4,cin,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,dilation=dilation).relu(),
lambda x,w: Tensor.conv2d(x,w,dilation=dilation).relu(), atol=1e-4)
def test_maxpool2d(self):
for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]:
with self.subTest(kernel_size=ksz):
helper_test_op([(32,2,110,28)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz),
# TODO: why is this tolerance so high?
lambda x: Tensor.max_pool2d(x, kernel_size=ksz), grad_atol=1e-4)
def test_avgpool2d(self):
shape = (32,2,111,28)
for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1), shape[2:]]:
with self.subTest(kernel_size=ksz):
helper_test_op([shape],
lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=ksz),
lambda x: Tensor.avg_pool2d(x, kernel_size=ksz), rtol=1e-5)
def test_cat(self):
for dim in range(-1, 2):
helper_test_op([(45,65), (45,65)], lambda x,y: torch.cat((x,y), dim), lambda x,y: x.cat(y, dim=dim))
def test_multicat(self):
for dim in range(-1, 2):
helper_test_op([(45,65), (45,65), (45,65)], lambda x,y,z: torch.cat((x,y,z), dim), lambda x,y,z: x.cat(y, z, dim=dim))
def test_clip(self):
helper_test_op([(45,65)], lambda x: x.clip(-2.3, 1.2), lambda x: x.clip(-2.3, 1.2))
if __name__ == '__main__':
np.random.seed(1337)
unittest.main(verbosity=2)
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