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tinygrad/test/test_ops.py

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import time, math, unittest
import numpy as np
import torch
from tinygrad.helpers import getenv, IMAGE, DEBUG, CI
from tinygrad import Tensor, Device, dtypes
if CI:
import warnings
warnings.filterwarnings("ignore", message="Non-empty compiler output encountered")
FORWARD_ONLY = getenv("FORWARD_ONLY", 0)
PRINT_TENSORS = getenv("PRINT_TENSORS", 0)
def helper_test_op(shps, torch_fxn, tinygrad_fxn=None, atol=1e-6, rtol=1e-3, grad_atol=1e-4, grad_rtol=1e-3,
forward_only=False, vals=None, low=-1.5, high=1.5):
if tinygrad_fxn is None: tinygrad_fxn = torch_fxn
ts, tst = prepare_test_op(low, high, shps, vals, forward_only)
st = time.monotonic()
out = torch_fxn(*ts)
torch_fp = time.monotonic() - st
# move inputs to a different device, test the device of intermediate tensors are correct
if mt:=getenv("MOVE_TENSOR", ""):
for t in tst: t.to_(mt)
st = time.monotonic()
ret = tinygrad_fxn(*tst).realize()
tinygrad_fp = time.monotonic() - st
def compare(s, tinygrad_output, torch_output, atol, rtol):
if PRINT_TENSORS: print(s, tinygrad_output, torch_output)
try:
assert tinygrad_output.shape == torch_output.shape, f"shape mismatch: tinygrad={tinygrad_output.shape} | torch={torch_output.shape}"
assert tinygrad_output.dtype == torch_output.dtype, f"dtype mismatch: tinygrad={tinygrad_output.dtype} | torch={torch_output.dtype}"
np.testing.assert_allclose(tinygrad_output, torch_output, atol=atol, rtol=rtol)
except Exception as e:
raise Exception(f"{s} failed shape {tinygrad_output.shape}: {e}")
if DEBUG >= 6:
np.set_printoptions(linewidth=200, suppress=True)
print(ret.numpy())
print(out.detach().numpy())
compare("forward pass", ret.numpy(), out.detach().numpy(), atol=atol, rtol=rtol)
torch_fbp, tinygrad_fbp = np.nan, np.nan
if not forward_only and not FORWARD_ONLY:
st = time.monotonic()
(out+1).square().mean().backward()
torch_fbp = time.monotonic() - st
st = time.monotonic()
(ret+1).square().mean().backward()
for tt in tst: tt.grad.realize()
tinygrad_fbp = time.monotonic() - st
for i, (t, tt) in enumerate(zip(ts, tst)):
compare(f"backward pass tensor {i}", tt.grad.numpy(), t.grad.detach().numpy(), atol=grad_atol, rtol=grad_rtol)
if not CI:
print("\ntesting %40r torch/tinygrad fp: %.2f / %.2f ms bp: %.2f / %.2f ms " % \
(shps, torch_fp*1000, tinygrad_fp*1000, torch_fbp*1000, tinygrad_fbp*1000), end="")
def prepare_test_op(low, high, shps, vals, forward_only=False):
if shps is None:
ts = [torch.tensor(x, requires_grad=(not forward_only)) for x in vals]
else:
np.random.seed(0)
np_data = [np.random.uniform(low=low, high=high, size=size).astype(dtypes.default_float.np) for size in shps]
ts = [torch.tensor(data, requires_grad=(not forward_only)) for data in np_data]
tst = [Tensor(x.detach().numpy(), requires_grad=(not forward_only and not FORWARD_ONLY)) for x in ts]
return ts, tst
class TestOps(unittest.TestCase):
def helper_test_exception(self, shps, torch_fxn, tinygrad_fxn, expected, exact=False, vals=None, low=-1.5, high=1.5):
if getenv("CUDACPU"): self.skipTest('helper_test_exception fails in CUDACPU')
ts, tst = prepare_test_op(low, high, shps, vals)
with self.assertRaises(expected) as torch_cm:
torch_fxn(*ts)
with self.assertRaises(expected) as tinygrad_cm:
tinygrad_fxn(*tst)
if exact: self.assertEqual(str(torch_cm.exception), str(tinygrad_cm.exception))
if not CI: print("\ntesting %40r torch/tinygrad exception: %s / %s" % (shps, torch_cm.exception, tinygrad_cm.exception), end="")
def test_full_like(self):
a = Tensor([[1,2,3],[4,5,6]], dtype=dtypes.float32)
b = torch.tensor([[1,2,3],[4,5,6]], dtype=torch.float32)
helper_test_op([], lambda: torch.full_like(b, 4), lambda: Tensor.full_like(a, 4), forward_only=True)
a = Tensor([[1,2,3],[4,5,6]], dtype=dtypes.int32)
b = torch.tensor([[1,2,3],[4,5,6]], dtype=torch.int32)
helper_test_op([], lambda: torch.full_like(b, 4), lambda: Tensor.full_like(a, 4), forward_only=True)
def test_full(self):
helper_test_op([], lambda: torch.full((45,65), 4, dtype=torch.int32), lambda: Tensor.full((45,65), 4), forward_only=True)
def test_zeros(self):
helper_test_op([], lambda: torch.zeros(45,65), lambda: Tensor.zeros(45,65), forward_only=True)
helper_test_op([], lambda: torch.zeros([45,65]), lambda: Tensor.zeros([45,65]), forward_only=True)
helper_test_op([], lambda: torch.zeros([]), lambda: Tensor.zeros([]), forward_only=True)
def test_zeros_like(self):
a = Tensor([[1,2,3],[4,5,6]], dtype=dtypes.float32)
b = torch.tensor([[1,2,3],[4,5,6]], dtype=torch.float32)
helper_test_op([], lambda: torch.zeros_like(b), lambda: Tensor.zeros_like(a), forward_only=True)
a = Tensor([[1,2,3],[4,5,6]], dtype=dtypes.int32)
b = torch.tensor([[1,2,3],[4,5,6]], dtype=torch.int32)
helper_test_op([], lambda: torch.zeros_like(b), lambda: Tensor.zeros_like(a), forward_only=True)
def test_empty_0(self):
helper_test_op([], lambda: torch.empty(45,65)*0/0, lambda: Tensor.empty(45,65)*0/0, forward_only=True)
def test_ones(self):
helper_test_op([], lambda: torch.ones(45,65), lambda: Tensor.ones(45,65), forward_only=True)
helper_test_op([], lambda: torch.ones([45,65]), lambda: Tensor.ones([45,65]), forward_only=True)
helper_test_op([], lambda: torch.ones([]), lambda: Tensor.ones([]), forward_only=True)
def test_ones_like(self):
a = Tensor([[1,2,3],[4,5,6]], dtype=dtypes.float32)
b = torch.tensor([[1,2,3],[4,5,6]], dtype=torch.float32)
helper_test_op([], lambda: torch.ones_like(b), lambda: Tensor.ones_like(a), forward_only=True)
a = Tensor([[1,2,3],[4,5,6]], dtype=dtypes.int32)
b = torch.tensor([[1,2,3],[4,5,6]], dtype=torch.int32)
helper_test_op([], lambda: torch.ones_like(b), lambda: Tensor.ones_like(a), forward_only=True)
def test_eye(self):
helper_test_op([], lambda: torch.eye(10), lambda: Tensor.eye(10), forward_only=True)
helper_test_op([], lambda: torch.eye(1), lambda: Tensor.eye(1), forward_only=True)
helper_test_op([], lambda: torch.eye(0), lambda: Tensor.eye(0), forward_only=True)
@unittest.skipIf(Device.DEFAULT=="RHIP", "broken in HIP CI")
def test_split(self):
def tensor(s): return torch.arange(math.prod(s), dtype=torch.int32).reshape(s), Tensor.arange(math.prod(s)).reshape(s)
test_cases = [
(tensor((10,)), 5, {}),
(tensor((10,)), [1,4,5], {}),
(tensor((10,)), 3, {}),
(tensor((3,4,)), 1, {}),
(tensor((3,4,)), 1, {'dim':1}),
(tensor((4,4,)), [2,2], {}),
(tensor((4,4,)), [2,2], {'dim':1}),
(tensor((10000,)), 2500, {}),
]
for (tor, ten), sizes, args in test_cases:
tor_splits, ten_splits = tor.split(sizes, **args), ten.split(sizes, **args)
assert len(tor_splits) == len(ten_splits)
for tor_chunk, ten_chunk in zip(tor_splits, ten_splits):
helper_test_op([], lambda: tor_chunk, lambda: ten_chunk, forward_only=True)
def test_chunk(self):
tor = torch.arange(13, dtype=torch.int32).repeat(8, 1).chunk(6, 1)
ten = Tensor.arange(13).repeat((8, 1)).chunk(6, 1)
assert len(tor) == len(ten)
for i in range(len(tor)):
helper_test_op([], lambda: tor[i], lambda: ten[i], forward_only=True)
tor = torch.arange(13, dtype=torch.int32).repeat(8, 1).chunk(6, 0)
ten = Tensor.arange(13).repeat((8, 1)).chunk(6, 0)
assert len(tor) == len(ten)
for i in range(len(tor)):
helper_test_op([], lambda: tor[i], lambda: ten[i], forward_only=True)
tor = torch.arange(13, dtype=torch.int32).repeat(8, 1).chunk(3, -1)
ten = Tensor.arange(13).repeat((8, 1)).chunk(3, -1)
assert len(tor) == len(ten)
for i in range(len(tor)):
helper_test_op([], lambda: tor[i], lambda: ten[i], forward_only=True)
tor = torch.arange(13, dtype=torch.int32).repeat(8, 3, 3).chunk(3, -2)
ten = Tensor.arange(13).repeat((8, 3, 3)).chunk(3, -2)
assert len(tor) == len(ten)
for i in range(len(tor)):
helper_test_op([], lambda: tor[i], lambda: ten[i], forward_only=True)
def test_arange(self):
helper_test_op([], lambda: torch.arange(10, dtype=torch.int32), lambda: Tensor.arange(10), forward_only=True)
helper_test_op([], lambda: torch.arange(5, 10, 3, dtype=torch.int32), lambda: Tensor.arange(5, 10, 3), forward_only=True)
helper_test_op([], lambda: torch.arange(10, 5, -3, dtype=torch.int32), lambda: Tensor.arange(10, 5, -3), forward_only=True)
helper_test_op([], lambda: torch.arange(11, 5, -3, dtype=torch.int32), lambda: Tensor.arange(11, 5, -3), forward_only=True)
helper_test_op([], lambda: torch.arange(1, 78, 2, dtype=torch.int32), lambda: Tensor.arange(1, 78, 2), forward_only=True)
helper_test_op([], lambda: torch.arange(5.5, 175.5, 2.5), lambda: Tensor.arange(5.5, 175.5, 2.5), forward_only=True)
helper_test_op([], lambda: torch.arange(-30.2, -0.3, 0.75), lambda: Tensor.arange(-30.2, -0.3, 0.75), forward_only=True)
helper_test_op([], lambda: torch.arange(-50.3, -380.2, -2.25), lambda: Tensor.arange(-50.3, -380.2, -2.25), forward_only=True)
def test_arange_big(self):
helper_test_op([], lambda: torch.arange(256, dtype=torch.int32), lambda: Tensor.arange(256), forward_only=True)
def test_sum_fake(self):
helper_test_op([(256, 1)], lambda x: x.sum(axis=1))
def test_sum_collapse(self):
helper_test_op([], lambda: torch.ones(256,256).sum(axis=1), lambda: Tensor.ones(256,256).sum(axis=1), forward_only=True)
def test_sum_collapse_neg(self):
helper_test_op([], lambda: (-torch.ones(3,3)).sum(axis=1), lambda: (-Tensor.ones(3,3)).sum(axis=1), forward_only=True)
def test_sum_pad_collapse(self):
helper_test_op([], lambda: torch.nn.functional.pad(torch.ones(256,256), pad=(0,64,0,0)).sum(axis=1),
lambda: Tensor.ones(256,256).pad(((0,0), (0,64))).sum(axis=1), forward_only=True)
# this is more complex and won't fold for a while
def test_sum_cat_collapse(self):
helper_test_op([], lambda: torch.cat([torch.ones(256,256), torch.zeros(256,64)], dim=1).sum(axis=1),
lambda: Tensor.cat(Tensor.ones(256,256), Tensor.zeros(256,64), dim=1).sum(axis=1), forward_only=True)
def test_max_dont_collapse(self):
helper_test_op([], lambda: torch.ones(256,256).max(1)[0], lambda: Tensor.ones(256,256).max(1), forward_only=True)
def test_where(self):
helper_test_op(
[(100,)],
lambda x: torch.where(x > 0.5, 4, 2).type(torch.int32),
lambda x: (x > 0.5).where(4, 2), forward_only=True)
for shps in [[(8,),(1,),(1,)], [(10,10),(10,),(10,)], [(100,)]*3, [(10,10)]*3]:
helper_test_op(
shps,
lambda x, a, b: torch.where(x > 0.5, a, b),
lambda x, a, b: (x > 0.5).where(a, b), forward_only=True)
def test_where_permute(self):
helper_test_op(
[(5, 5)],
lambda x: torch.where(x > 0.5, 4, 2).type(torch.int32).permute((1, 0)),
lambda x: (x > 0.5).where(4, 2).permute((1, 0)), forward_only=True)
def _test_cmp(self, fxn, reverse=True):
for shps in [[(3, 4, 5), (3, 4, 5)], [(3, 4, 5), (5,)], [(5,), (3, 4, 5)]]:
helper_test_op(shps, fxn, fxn, forward_only=True)
helper_test_op(None, fxn, fxn, forward_only=True, vals=[[0.,1,2], [2.,1,0]])
helper_test_op(None, lambda x,y: fxn(x,2), lambda x,y: fxn(x,2), forward_only=True, vals=[[0.,1,2], [2.,1,0]])
if Device.DEFAULT != "WEBGPU": # bool is not HOST_SHARABLE, so it cannot be used as a storage buffer type
helper_test_op(None, fxn, fxn, forward_only=True, vals=[[True, True, False], [False,True,False]])
if reverse: helper_test_op(None, lambda x,y: fxn(2,y), lambda x,y: fxn(2,y), forward_only=True, vals=[[0.,1,2], [2.,1,0]])
def test_cmp_eq(self): self._test_cmp(lambda x,y: x==y, reverse=False)
def test_cmp_gt(self): self._test_cmp(lambda x,y: x>y)
def test_cmp_ge(self): self._test_cmp(lambda x,y: x>=y)
def test_cmp_lt(self): self._test_cmp(lambda x,y: x<y)
def test_cmp_le(self): self._test_cmp(lambda x,y: x<=y)
def test_cmp_eq_backwards(self):
t1 = torch.ones(4, requires_grad=True)
t2 = torch.ones(4, requires_grad=True)
self.assertRaises(RuntimeError, (t1 == t2).sum().backward)
tt1 = Tensor.ones(4, requires_grad=True)
tt2 = Tensor.ones(4, requires_grad=True)
self.assertRaises(RuntimeError, (tt1 == tt2).sum().backward)
tt = Tensor.randn(4, requires_grad=True)
(tt*(tt == 0)).sum().backward()
t = torch.tensor(tt.numpy(), requires_grad=True)
(t*(t == 0)).sum().backward()
np.testing.assert_allclose(t.grad.numpy(), tt.grad.numpy(), rtol=1e-5)
def test_cmp_lt_backwards(self):
t1 = torch.ones(4, requires_grad=True)
t2 = torch.ones(4, requires_grad=True)
self.assertRaises(RuntimeError, (t1 < t2).sum().backward)
tt1 = Tensor.ones(4, requires_grad=True)
tt2 = Tensor.ones(4, requires_grad=True)
self.assertRaises(RuntimeError, (tt1 < tt2).sum().backward)
tt = Tensor.randn(4, requires_grad=True)
(tt*(tt < 0)).sum().backward()
t = torch.tensor(tt.numpy(), requires_grad=True)
(t*(t < 0)).sum().backward()
np.testing.assert_allclose(t.grad.numpy(), tt.grad.numpy(), rtol=1e-5)
def test_trunc(self):
helper_test_op([(45,35)], lambda x: x.trunc(), forward_only=True)
helper_test_op(None, lambda x: x.trunc(), vals=[[1.499, 1.5, 1.501, 1.0, 2.1, 0.0, -5.0, -2.499, -2.5, -2.501]], forward_only=True)
def test_floor(self):
helper_test_op([(45,35)], lambda x: x.floor(), forward_only=True)
helper_test_op(None, lambda x: x.floor(), vals=[[1.499, 1.5, 1.501, 1.0, 2.1, 0.0, -5.0, -2.499, -2.5, -2.501]], forward_only=True)
def test_ceil(self):
helper_test_op([(45,35)], lambda x: x.ceil(), forward_only=True)
helper_test_op(None, lambda x: x.ceil(), vals=[[1.499, 1.5, 1.501, 1.0, 2.1, 0.0, -5.0, -2.499, -2.5, -2.501]], forward_only=True)
def test_round(self):
helper_test_op([(45,35)], lambda x: x.round(), forward_only=True)
helper_test_op(None, lambda x: x.round(), vals=[[1.499, 1.5, 1.501, 1.0, 2.1, 0.0, -5.0, -2.499, -2.5, -2.501]], forward_only=True)
helper_test_op(None, lambda x: x.round(), vals=[[2.5, -1.5]], forward_only=True)
def test_lerp(self):
helper_test_op([(45,35), (45,35), (45,35)], lambda x,y,z: x.lerp(y,z))
helper_test_op(None, lambda x,y,z: x.lerp(y,z), vals=[[1.,2.,3.], [4.,5.,6.], 0.5])
def test_tril(self):
helper_test_op([(3,3)], lambda x: x.tril())
helper_test_op([(3,3)], lambda x: x.tril(1))
helper_test_op([(3,3)], lambda x: x.tril(-1))
helper_test_op([(5,3,3)], lambda x: x.tril())
helper_test_op([(5,0,3)], lambda x: x.tril())
helper_test_op([(5,3,3)], lambda x: x.tril(1))
def test_triu(self):
helper_test_op([(3,3)], lambda x: x.triu())
helper_test_op([(3,3)], lambda x: x.triu(1))
helper_test_op([(3,3)], lambda x: x.triu(-1))
helper_test_op([(5,3,3)], lambda x: x.triu())
helper_test_op([(5,0,3)], lambda x: x.triu())
helper_test_op([(5,3,3)], lambda x: x.triu(1))
def test_maximum(self):
helper_test_op([(45,65), (45,65)], torch.maximum, Tensor.maximum)
helper_test_op([(), ()], torch.maximum, Tensor.maximum)
helper_test_op(None, torch.maximum, Tensor.maximum, vals=[[1., 0., 3., -4.], 3.])
helper_test_op(None, torch.maximum, Tensor.maximum, vals=[[1., 0., 3., -4.], [-1., -2., 3., 0.]])
helper_test_op(None, torch.maximum, Tensor.maximum, vals=[[True, False, False], True], forward_only=True)
helper_test_op(None, torch.maximum, Tensor.maximum, vals=[[True, False, False], [True, True, False]], forward_only=True)
def test_minimum(self):
helper_test_op([(45,65), (45,65)], torch.minimum, Tensor.minimum)
helper_test_op([(), ()], torch.minimum, Tensor.minimum)
helper_test_op(None, torch.minimum, Tensor.minimum, vals=[[1., 0., 3., -4.], 3.])
helper_test_op(None, torch.minimum, Tensor.minimum, vals=[[1., 0., 3., -4.], [-1., -2., 3., 0.]])
helper_test_op(None, torch.minimum, Tensor.minimum, vals=[[True, False, False], True], forward_only=True)
helper_test_op(None, torch.minimum, Tensor.minimum, vals=[[True, False, False], [True, True, False]], forward_only=True)
def test_tiny_add(self):
helper_test_op([(3), (3)], lambda x,y: x+y, Tensor.add, forward_only=True)
def test_add(self):
helper_test_op([(45,68), (45,68)], lambda x,y: x+y, Tensor.add)
helper_test_op([(45,68), (45,68)], lambda x,y: x+y)
helper_test_op([(), ()], lambda x,y: x+y)
def test_add3(self):
helper_test_op([(45,65), (45,65), (45,65)], lambda x,y,z: x+y+z)
def test_broadcasted_add(self):
helper_test_op([(45,65), (45,1)], lambda x,y: x+y)
helper_test_op([(45,65), ()], lambda x,y: x+y)
def test_broadcasted_add_2(self):
helper_test_op([(45,65), (65,)], lambda x,y: x+y)
def test_sub(self):
helper_test_op([(45,65), (45,65)], lambda x,y: x-y, Tensor.sub)
helper_test_op([(45,65), (45,65)], lambda x,y: x-y)
helper_test_op([(), ()], lambda x,y: x-y)
def test_scalar_sub(self):
helper_test_op([(45,65)], lambda x: x-2)
helper_test_op([()], lambda x: x-2)
def test_scalar_rsub(self):
helper_test_op([(45,65)], lambda x: 2-x)
helper_test_op([()], lambda x: 2-x)
def test_neg(self):
helper_test_op([(45,65)], lambda x: -x)
helper_test_op([(45,65)], lambda x: x.neg())
helper_test_op([()], lambda x: x.neg())
def test_logical_not(self):
helper_test_op(None, torch.logical_not, Tensor.logical_not, vals=[[True, False, True]], forward_only=True)
helper_test_op(None, torch.logical_not, Tensor.logical_not, vals=[[1.,2.,0.,0.5]], forward_only=True)
def test_mul(self):
helper_test_op([(64,64), (64,64)], lambda x,y: x*y, Tensor.mul)
helper_test_op([(64,64), (64,64)], lambda x,y: x*y)
helper_test_op([(), ()], lambda x,y: x*y)
def test_scalar_mul(self):
helper_test_op([(45,65)], lambda x: x*2)
helper_test_op([(45,65)], lambda x: x*-1)
helper_test_op([(45,65)], lambda x: 255*x)
helper_test_op([(45,65)], lambda x: 2*x)
helper_test_op([()], lambda x: x*2)
helper_test_op([()], lambda x: 2*x)
def test_div(self):
helper_test_op([(45,65), (45,65)], lambda x,y: x/y, Tensor.div)
helper_test_op([(45,65), (45,65)], lambda x,y: x/y)
helper_test_op([(), ()], lambda x,y: x/y)
def test_div_int(self):
helper_test_op(None, lambda x,y: x/y, Tensor.div, forward_only=True, vals=np.array([[5, 6, 7],[1, 2, 3]], dtype=np.int32))
helper_test_op(None, lambda x: x/2, lambda x: x/2, forward_only=True, vals=np.array([[3, 4, 5]], dtype=np.int32))
def test_scalar_div(self):
helper_test_op([(45,65)], lambda x: x/255)
helper_test_op([(45,65)], lambda x: x/1)
helper_test_op([(45,65)], lambda x: 1/x)
helper_test_op([(45,65)], lambda x: x/2)
helper_test_op([(45,65)], lambda x: 2/x)
helper_test_op([()], lambda x: x/2)
helper_test_op([()], lambda x: 2/x)
def test_mul_naninf(self):
helper_test_op([(45,65)], lambda x: x*math.inf)
helper_test_op([(45,65)], lambda x: x*-math.inf)
helper_test_op([(45,65)], lambda x: x*math.nan)
def test_div_naninf(self):
helper_test_op([(45,65)], lambda x: x/math.inf)
helper_test_op([(45,65)], lambda x: x/-math.inf)
helper_test_op([(45,65)], lambda x: x/math.nan)
helper_test_op([(45,65)], lambda x: math.inf/x)
helper_test_op([(45,65)], lambda x: (-math.inf)/x)
helper_test_op([(45,65)], lambda x: math.nan/x)
def test_pow_full(self):
helper_test_op([(45,65), (45,65)], lambda x,y: x**y)
helper_test_op([(45,65), (45,65)], lambda x,y: x.pow(y))
def test_pow(self):
helper_test_op([(45,65)], lambda x: x**0)
helper_test_op([(45,65)], lambda x: x**1)
helper_test_op([(45,65)], lambda x: x**2)
helper_test_op([(45,65)], lambda x: x**3)
helper_test_op([(45,65)], lambda x: x**-2)
helper_test_op([()], lambda x: x**2)
helper_test_op([()], lambda x: x**-2)
# Regression tests for https://github.com/tinygrad/tinygrad/issues/1151
helper_test_op([(45,65)], lambda x: x**3, low=-30, high=-27)
helper_test_op([()], lambda x: x**3, low=-30, high=-27)
# Regression tests for https://github.com/tinygrad/tinygrad/issues/1251
helper_test_op([(45,65)], lambda x: x**0.2, low=-30, high=-27)
helper_test_op([(45,65)], lambda x: x**1.2, low=-30, high=-27)
helper_test_op([()], lambda x: x**0.2, low=-30, high=-27)
helper_test_op([()], lambda x: x**1.2, low=-30, high=-27)
a, b = Tensor([0.0], requires_grad=True), torch.tensor([0.0], requires_grad=True)
helper_test_op([], lambda: b**1.1, lambda: a**1.1)
def test_pow_const(self):
helper_test_op([(45,65)], lambda x: x**1.0)
helper_test_op([(45,65)], lambda x: x**-1.0)
helper_test_op([(45,65)], lambda x: 1.0**x)
helper_test_op([(45,65)], lambda x: x**2.0)
helper_test_op([(45,65)], lambda x: 2.0**x)
helper_test_op([()], lambda x: x**2.0)
helper_test_op([()], lambda x: 2.0**x)
# TODO: fix 0**x and 0**0 == 1
# helper_test_op(None, lambda x: 0**x, vals=[[-2.,-1,0,1,2,3]])
# TODO: fix backward, should be nan
helper_test_op(None, lambda x: (-2)**x, vals=[[-2.,-1,0,1,2,3]], forward_only=True)
def test_sqrt(self):
helper_test_op([(45,65)], lambda x: x.sqrt())
helper_test_op([()], lambda x: x.sqrt())
def test_rsqrt(self):
helper_test_op([(45,65)], lambda x: x.rsqrt())
helper_test_op([()], lambda x: x.rsqrt())
def test_xor(self):
tor = torch.tensor([[1,-8,1],[32,1,6]], dtype=torch.int)
ten = Tensor([[1,-8,1],[32,1,6]], dtype=dtypes.int32)
helper_test_op([], lambda: tor^tor, lambda: ten^ten, forward_only=True)
helper_test_op([], lambda: tor^0x1337, lambda: ten^0x1337, forward_only=True)
helper_test_op([], lambda: 0x1337^tor, lambda: 0x1337^ten, forward_only=True)
def test_sin(self):
helper_test_op([(45,65)], lambda x: x.sin())
helper_test_op([()], lambda x: x.sin())
def test_cos(self):
helper_test_op([(45,65)], lambda x: x.cos())
helper_test_op([()], lambda x: x.cos())
def test_tan(self):
helper_test_op([(45,65)], lambda x: x.tan())
helper_test_op([()], lambda x: x.tan())
def test_relu(self):
helper_test_op([(64,64)], lambda x: x.relu())
helper_test_op([()], lambda x: x.relu())
def test_relu_exact(self):
helper_test_op(None, lambda x: x.relu(), vals=[[-1.,0,1]])
def test_relu_maximum_exact(self):
helper_test_op(None, lambda x: torch.maximum(x, torch.zeros_like(x, requires_grad=False)), lambda x: Tensor.maximum(x, 0), vals=[[-1.,0,1]])
def test_leakyrelu(self):
helper_test_op([(45,65)], lambda x: torch.nn.functional.leaky_relu(x,0.01), Tensor.leakyrelu)
helper_test_op([()], lambda x: torch.nn.functional.leaky_relu(x,0.01), Tensor.leakyrelu)
def test_celu(self):
for val in range(1, 5):
helper_test_op([(45,65)], lambda x: torch.nn.functional.celu(x,val), lambda x: x.celu(val))
helper_test_op([()], lambda x: torch.nn.functional.celu(x,val), lambda x: x.celu(val))
def test_abs(self):
helper_test_op([(45,65)], torch.abs, Tensor.abs)
helper_test_op([()], torch.abs, Tensor.abs)
def test_log(self):
helper_test_op([(45,65)], torch.log, Tensor.log)
helper_test_op([()], torch.log, Tensor.log)
def test_log2(self):
helper_test_op([(45,65)], torch.log2, Tensor.log2)
helper_test_op([()], torch.log2, Tensor.log2)
def test_exp(self):
helper_test_op([(45,65)], torch.exp, Tensor.exp)
helper_test_op([()], torch.exp, Tensor.exp)
def test_exp2(self):
helper_test_op([(45,65)], torch.exp2, Tensor.exp2)
helper_test_op([()], torch.exp2, Tensor.exp2)
def test_sign(self):
helper_test_op([(45,65)], torch.sign, Tensor.sign)
helper_test_op([()], torch.sign, Tensor.sign)
def test_softsign(self):
helper_test_op([(45,65)], torch.nn.functional.softsign, Tensor.softsign)
helper_test_op([()], torch.nn.functional.softsign, Tensor.softsign)
def test_sigmoid(self):
helper_test_op([(45,65)], torch.sigmoid, Tensor.sigmoid)
helper_test_op([(45,65)], torch.sigmoid, Tensor.sigmoid, low=300, high=303)
helper_test_op([(45,65)], torch.sigmoid, Tensor.sigmoid, low=-300, high=-297)
helper_test_op([()], torch.sigmoid, Tensor.sigmoid)
def test_softplus(self):
helper_test_op([(45,65)], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6)
helper_test_op([()], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6)
def test_gelu(self):
helper_test_op([(45,65)], lambda x: torch.nn.functional.gelu(x, approximate="tanh"), Tensor.gelu)
helper_test_op([(45,65)], lambda x: torch.nn.functional.gelu(x, approximate="tanh"), Tensor.gelu, low=300, high=303)
helper_test_op([(45,65)], lambda x: torch.nn.functional.gelu(x, approximate="tanh"), Tensor.gelu, low=-300, high=-297)
def test_quick_gelu(self):
helper_test_op([(45,65)], lambda x: x * torch.sigmoid(1.702 * x), Tensor.quick_gelu)
helper_test_op([(45,65)], lambda x: x * torch.sigmoid(1.702 * x), Tensor.quick_gelu, low=300, high=303)
helper_test_op([(45,65)], lambda x: x * torch.sigmoid(1.702 * x), Tensor.quick_gelu, low=-300, high=-297)
helper_test_op([()], lambda x: x * torch.sigmoid(1.702 * x), Tensor.quick_gelu)
def test_elu(self):
helper_test_op([(45,65)], torch.nn.functional.elu, Tensor.elu)
helper_test_op([(45,65)], lambda x: torch.nn.functional.elu(x, alpha=0.1), lambda x: Tensor.elu(x, alpha=0.1))
helper_test_op([()], torch.nn.functional.elu, Tensor.elu)
def test_relu6(self):
helper_test_op([(45,65)], torch.nn.functional.relu6, Tensor.relu6)
helper_test_op([()], torch.nn.functional.relu6, Tensor.relu6)
def test_hardswish(self):
helper_test_op([(45,65)], torch.nn.functional.hardswish, Tensor.hardswish, grad_atol=1e-6)
helper_test_op([()], torch.nn.functional.hardswish, Tensor.hardswish, grad_atol=1e-6)
def test_mish(self):
helper_test_op([(45,65)], torch.nn.functional.mish, Tensor.mish)
helper_test_op([()], torch.nn.functional.mish, Tensor.mish)
def test_multinomial(self):
# NOTE: this is random, so it has a very large atol
helper_test_op([(1000,)], lambda x: torch.multinomial(x.clip(0,1), num_samples=1).type(torch.int32),
lambda x: Tensor.multinomial(x.clip(0,1)), forward_only=True, atol=1000.)
def test_small_cumsum(self):
helper_test_op([(10)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
def test_simple_cumsum(self):
helper_test_op([(512)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
helper_test_op([(1022)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
def test_cumsum(self):
helper_test_op([(20)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
helper_test_op([(20,30)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
helper_test_op([(20,30)], lambda x: torch.cumsum(x, dim=1), lambda x: Tensor.cumsum(x, axis=1))
helper_test_op([(20,30,40)], lambda x: torch.cumsum(x, dim=2), lambda x: Tensor.cumsum(x, axis=2))
helper_test_op([(20,30,40)], lambda x: torch.cumsum(x, dim=-1), lambda x: Tensor.cumsum(x, axis=-1))
def test_cumsum_zero_axis(self):
helper_test_op([(2,0,4)], lambda x: torch.cumsum(x, dim=1), lambda x: Tensor.cumsum(x, axis=1))
helper_test_op([(0,3)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
helper_test_op([(2,3,0)], lambda x: torch.cumsum(x, dim=2), lambda x: Tensor.cumsum(x, axis=2))
def test_argmax(self):
# check if it returns the first index for multiple occurences
self.assertEqual(torch.tensor([2,2]).argmax().numpy(), Tensor([2,2]).argmax().numpy())
np.testing.assert_equal(Tensor([2,2]).argmax().numpy(), np.array(0))
np.testing.assert_equal(Tensor([1,2,2]).argmax().numpy(), np.array(1))
helper_test_op([(10,20)], lambda x: x.argmax().type(torch.int32), lambda x: x.argmax(), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmax(0, False).type(torch.int32), lambda x: x.argmax(0, False), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmax(1, False).type(torch.int32), lambda x: x.argmax(1, False), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmax(1, True).type(torch.int32), lambda x: x.argmax(1, True), forward_only=True)
def test_argmin(self):
# check if it returns the first index for multiple occurences
self.assertEqual(torch.tensor([2, 2]).argmin().numpy(), Tensor([2, 2]).argmin().numpy())
np.testing.assert_equal(Tensor([2,2]).argmin().numpy(), np.array(0))
np.testing.assert_equal(Tensor([3,2,2]).argmin().numpy(), np.array(1))
helper_test_op([(10,20)], lambda x: x.argmin().type(torch.int32), lambda x: x.argmin(), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmin(0, False).type(torch.int32), lambda x: x.argmin(0, False), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmin(1, False).type(torch.int32), lambda x: x.argmin(1, False), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmin(1, True).type(torch.int32), lambda x: x.argmin(1, True), forward_only=True)
def test_einsum(self):
# matrix transpose
helper_test_op([(150,150)], lambda a: torch.einsum('ij->ji', a), lambda a: Tensor.einsum('ij->ji', a))
helper_test_op([(150,150)], lambda a: torch.einsum('ij -> ji', a), lambda a: Tensor.einsum('ij -> ji', a))
helper_test_op([(150,150)], lambda a: torch.einsum('ji', a), lambda a: Tensor.einsum('ji', a))
helper_test_op([(20,30,40)], lambda a: torch.einsum('jki', a), lambda a: Tensor.einsum('jki', a))
helper_test_op([(20,30,40)], lambda a: torch.einsum('dog', a), lambda a: Tensor.einsum('dog', a))
# sum all elements
helper_test_op([(20,30,40)], lambda a: torch.einsum('ijk->', a), lambda a: Tensor.einsum('ijk->', a))
# column sum
helper_test_op([(50,50)], lambda a: torch.einsum('ij->j', a), lambda a: Tensor.einsum('ij->j', a))
# row sum
helper_test_op([(15,15)], lambda a: torch.einsum('ij->i', a), lambda a: Tensor.einsum('ij->i', a))
# matrix-vector multiplication
helper_test_op([(15,20), (20,)], lambda a,b: torch.einsum('ik,k->i', a,b), lambda a,b: Tensor.einsum('ik,k->i', a, b))
# matrix-matrix multiplication
helper_test_op([(15,20), (20,30)], lambda a,b: torch.einsum('ik,kj->ij', a,b), lambda a,b: Tensor.einsum('ik,kj->ij', a, b))
# matrix-matrix multiplication, different letter order
helper_test_op([(15,20), (20,30)], lambda a,b: torch.einsum('jk,ki->ji', a,b), lambda a,b: Tensor.einsum('jk,ki->ji', a, b))
# dot product
helper_test_op([(30),(30)], lambda a,b: torch.einsum('i,i->i', [a,b]), lambda a,b: Tensor.einsum('i,i->i', [a,b]))
# hadamard product
helper_test_op([(30,40),(30,40)], lambda a,b: torch.einsum('ij,ij->ij', a,b), lambda a,b: Tensor.einsum('ij,ij->ij', a,b))
# outer product
helper_test_op([(15,), (15,)], lambda a,b: torch.einsum('i,j->ij', a,b), lambda a,b: Tensor.einsum('i,j->ij',a,b))
# batch matrix multiplication
helper_test_op([(10,20,30),(10,30,40)], lambda a,b: torch.einsum('ijk,ikl->ijl', [a, b]), lambda a,b: Tensor.einsum('ijk,ikl->ijl', [a, b]))
# batch matrix multiplication, result permuted
helper_test_op([(10,20,25),(10,25,32)], lambda a,b: torch.einsum('ijk,ikl->jil', [a, b]), lambda a,b: Tensor.einsum('ijk,ikl->jil', [a, b]))
# batch matrix multiplication, result & input permuted
helper_test_op([(20,10,25),(10,25,32)], lambda a,b: torch.einsum('jik,ikl->jil', [a, b]), lambda a,b: Tensor.einsum('jik,ikl->jil', [a, b]))
# batch matrix multiplication, result with different letters
helper_test_op([(10,20,30),(10,30,40)], lambda a,b: torch.einsum('ijk,ika->ija', [a, b]), lambda a,b: Tensor.einsum('ijk,ika->ija', [a, b]))
# tensor contraction
helper_test_op([(3,5,8,10),(11,13,5,16,8)], lambda a,b: torch.einsum('pqrs,tuqvr->pstuv', a,b),
lambda a,b: Tensor.einsum('pqrs,tuqvr->pstuv', a,b), atol=1e-5)
# tensor contraction, input permuted
helper_test_op([(3,8,10,5),(11,5,13,16,8)], lambda a,b: torch.einsum('prsq,tquvr->pstuv', a,b),
lambda a,b: Tensor.einsum('prsq,tquvr->pstuv', a,b), atol=1e-5)
# tensor contraction, result with different letters
helper_test_op([(3,5,8,10),(11,13,5,16,8)], lambda a,b: torch.einsum('zqrs,tuqvr->zstuv', a,b),
lambda a,b: Tensor.einsum('zqrs,tuqvr->zstuv', a,b), atol=1e-5)
# bilinear transformation
helper_test_op([(2,3),(5,3,7),(2,7)], lambda a,b,c: torch.einsum('ik,jkl,il->ij', [a,b,c]), lambda a,b,c: Tensor.einsum('ik,jkl,il->ij', [a,b,c]))
def test_einsum_shape_check(self):
a = Tensor.zeros(3,8,10,5)
b = Tensor.zeros(11,5,13,16,8)
with self.assertRaises(AssertionError):
Tensor.einsum('pqrs,tuqvr->pstuv',a,b)
def test_einsum_arity_check1(self):
a = Tensor.zeros(10,15)
b = Tensor.zeros(15,20)
c = Tensor.zeros(20,10)
with self.assertRaises(AssertionError):
Tensor.einsum('ij,jk->ij', a,b,c)
def test_einsum_arity_check2(self):
a = Tensor.zeros(10,10)
with self.assertRaises(AssertionError):
Tensor.einsum('ij,jk->ij', a)
@unittest.skipIf(IMAGE>0, "no 1d dot for images")
def test_dot_1d(self):
helper_test_op([(65), (65)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
helper_test_op([(65), (65,45)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
helper_test_op([(45,65), (65)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
helper_test_op([(8,45,65), (65)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
helper_test_op([(65), (8,65,45)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
self.helper_test_exception([(4), (1,2)], lambda x, y: x.matmul(y), Tensor.dot, expected=(RuntimeError, AssertionError))
self.helper_test_exception([(2,1), (4)], lambda x, y: x.matmul(y), Tensor.dot, expected=(RuntimeError, AssertionError))
self.helper_test_exception([(1), (4)], lambda x, y: x.matmul(y), Tensor.dot, expected=(RuntimeError, AssertionError))
def test_dot(self):
helper_test_op([(45,65), (65,100)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
helper_test_op([(8,45,65), (8,65,100)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
self.helper_test_exception([(2, 4), (1, 3)], lambda x, y: x.matmul(y), Tensor.dot, expected=(RuntimeError, AssertionError))
self.helper_test_exception([(2, 1), (4, 3)], lambda x, y: x.matmul(y), Tensor.dot, expected=(RuntimeError, AssertionError))
with self.assertRaises(AssertionError):
a = Tensor(3.14)
a.matmul(a)
def test_mulacc_with_zero_strides(self):
helper_test_op(
[],
lambda: torch.tensor(1.0).reshape((1,1,1)).expand(2,4,3).mul(torch.tensor(1.0).reshape((1,1,1)).expand(2,4,3)).sum(-1),
lambda: Tensor(1.0).reshape((1,1,1)).expand(2,4,3).mul(Tensor(1.0).reshape((1,1,1)).expand(2,4,3)).sum(-1),
forward_only=True
)
a = [[1.,1.,1.,1.], [1.,1.,1.,1.]]
b = [1.,1.,1.,1.]
helper_test_op(
[],
lambda: torch.tensor(a).reshape((2,4,1)).expand(2,4,3).mul(torch.tensor(b).reshape((1,4,1)).expand(2,4,3)).sum([0,2]),
lambda: Tensor(a).reshape((2,4,1)).expand(2,4,3).mul(Tensor(b).reshape((1,4,1)).expand(2,4,3)).sum([0,2]),
forward_only=True
)
helper_test_op(
[],
lambda: torch.ones((1,2)).matmul(torch.ones((2,3))), lambda: Tensor.ones((1,2)).dot(Tensor.ones((2,3))),
forward_only=True
)
def test_matmul_simple(self):
helper_test_op([(4), (4,4)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
def test_matmul(self):
helper_test_op([(64), (64,99)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
@unittest.skipIf(IMAGE>0, "no batched matmul on images")
def test_matmul_batched(self):
helper_test_op([(3), (1,3,3,5)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
@unittest.skipIf(IMAGE>0, "no batched matmul on images")
def test_matmul_batched_vector(self):
helper_test_op([(4,3), (1,3,3,5)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
def test_small_gemm(self):
helper_test_op([(8,8), (8,8)], lambda x,y: x.matmul(y), lambda x,y: x@y, atol=1e-3)
def test_small_gemm_range(self):
helper_test_op(None, lambda x,y: x.matmul(y), lambda x,y: x@y, atol=1e-3, vals=[np.arange(0,64,dtype=np.float32).reshape(8,8),
np.arange(64,128,dtype=np.float32).reshape(8,8)])
def test_small_gemm_eye(self):
helper_test_op(None, lambda x,y: x.matmul(y), lambda x,y: x@y, atol=1e-3, vals=[np.eye(8).astype(np.float32), np.eye(8).astype(np.float32)])
def test_gemm(self):
helper_test_op([(64,64), (64,64)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-3)
def test_big_gemm(self):
helper_test_op([(256,256), (256,256)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-3)
@unittest.skipIf(IMAGE>0, "no 0 in shape matmul on images")
def test_gemm_with_zeros_shape(self):
helper_test_op([(8,8), (8,0)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
helper_test_op([(0,8), (8,8)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
helper_test_op([(0,8), (8,0)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
helper_test_op([(8,0), (0,8)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
helper_test_op([(0,0), (0,0)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
helper_test_op([(0), (0,8)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
helper_test_op([(0), (0)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
def test_broadcastdot(self):
helper_test_op([(10,45,65), (65,45)], lambda x,y: x @ y, Tensor.dot, atol=1e-4)
with self.assertRaises(AssertionError):
a = Tensor(3.14)
b = Tensor.ones(3,3)
a @ b
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_simple(self):
helper_test_op(None, lambda x: x.sum(), vals=[[1.,1.]])
def test_sum_full(self):
helper_test_op([(16384)], lambda x: x.sum())
def test_sum_relu(self):
helper_test_op([(3,4,5)], lambda x: x.relu().sum().relu())
def test_sum(self):
helper_test_op([(45,3)], lambda x: x.sum())
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=3))
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=(1,3)))
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=(0,2)))
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=(1,2)))
helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=1))
helper_test_op([()], lambda x: x.sum(), Tensor.sum)
def test_sum_with_zeros_shape(self):
helper_test_op([(4, 0)], lambda x: x.sum(axis=(0,)))
helper_test_op([(4, 0)], lambda x: x.sum(axis=(1,)))
helper_test_op([(4, 0)], lambda x: x.sum(axis=(0,1)))
def test_min(self):
helper_test_op([(3,3)], lambda x: x.min())
helper_test_op([(45,3)], lambda x: x.min())
helper_test_op([(45,3)], lambda x: x.min().mul(0.5))
helper_test_op([()], lambda x: x.min())
def test_max(self):
helper_test_op([(45,3)], lambda x: x.max())
helper_test_op([(45,3)], lambda x: x.max().mul(0.5))
helper_test_op(None, lambda x: x.max().mul(0.5), vals=[[[1.0,1.0,0.0,1.0]],])
helper_test_op([(3,4,5,6)], lambda x: x.max(axis=1)[0], lambda x: x.max(axis=1))
helper_test_op([()], lambda x: x.max())
def test_mean(self):
helper_test_op([(3,4,5,6)], lambda x: x.mean())
helper_test_op([()], lambda x: x.mean())
def test_mean_axis(self):
helper_test_op([(3,4,5,6)], lambda x: x.mean(axis=(1,2)))
def test_mean_zero_axis(self):
helper_test_op([(1,0,3,0,5)], lambda x: x.mean(axis=(1,3)))
def test_var(self):
helper_test_op([(15, 25, 35)], lambda x: x.var())
helper_test_op([(15, 25, 35)], lambda x: x.var(correction=0))
helper_test_op([(15, 25, 35)], lambda x: x.var(correction=5))
# TODO: fix this
# helper_test_op([(10, 2)], lambda x: x.var(correction=50))
def test_var_axis(self):
helper_test_op([(15, 25, 35)], lambda x: x.var(0))
helper_test_op([(15, 25, 35)], lambda x: x.var(2))
helper_test_op([(15, 25, 35)], lambda x: x.var([1, 2]))
helper_test_op([(15, 25, 35)], lambda x: x.var(0, correction=0))
helper_test_op([(15, 25, 35)], lambda x: x.var(2, correction=0))
helper_test_op([(15, 25, 35)], lambda x: x.var([1, 2], correction=0))
def test_var_zero_axis(self):
helper_test_op([(1,0,3,0,5)], lambda x: x.var(axis=(1,3)))
helper_test_op([(1,0,3,0,5)], lambda x: x.var(axis=(1,3), correction=0))
helper_test_op([(1,0,3,0,5)], lambda x: x.var(axis=(1,3), correction=5))
def test_var_keepdim(self):
helper_test_op([(15, 25, 35)], lambda x: x.var(keepdim=True))
helper_test_op([(15, 25, 35)], lambda x: x.var(0, keepdim=True, correction=0))
def test_std(self):
helper_test_op([(15, 25, 35)], lambda x: x.std())
helper_test_op([(15, 25, 35)], lambda x: x.std(correction=0))
helper_test_op([(15, 25, 35)], lambda x: x.std(correction=5))
def test_std_axis(self):
helper_test_op([(15, 25, 35)], lambda x: x.std(0))
helper_test_op([(15, 25, 35)], lambda x: x.std(2))
helper_test_op([(15, 25, 35)], lambda x: x.std([1, 2]))
helper_test_op([(15, 25, 35)], lambda x: x.std(0, correction=0))
helper_test_op([(15, 25, 35)], lambda x: x.std(2, correction=0))
helper_test_op([(15, 25, 35)], lambda x: x.std([1, 2], correction=0))
def test_std_zero_axis(self):
helper_test_op([(1,0,3,0,5)], lambda x: x.std(axis=(1,3)))
helper_test_op([(1,0,3,0,5)], lambda x: x.std(axis=(1,3), correction=0))
helper_test_op([(1,0,3,0,5)], lambda x: x.std(axis=(1,3), correction=5))
def test_std_keepdim(self):
helper_test_op([(15, 25, 35)], lambda x: x.std(keepdim=True))
helper_test_op([(15, 25, 35)], lambda x: x.std(0, keepdim=True, correction=0))
def test_softmax(self):
# exceed per kernel buffer limit with backward
forward_only = (Device.DEFAULT == "WEBGPU")
helper_test_op([(45,65)], torch.nn.Softmax(dim=1), Tensor.softmax, atol=1e-7, grad_atol=1e-7, forward_only=forward_only)
helper_test_op([(45)], torch.nn.Softmax(dim=0), Tensor.softmax, atol=1e-7, grad_atol=1e-7, forward_only=forward_only)
helper_test_op([()], torch.nn.Softmax(dim=0), Tensor.softmax, atol=1e-7, grad_atol=1e-7, forward_only=forward_only)
helper_test_op([()], torch.nn.Softmax(dim=-1), Tensor.softmax, atol=1e-7, grad_atol=1e-7, forward_only=forward_only)
def test_softmax_other_axis(self):
helper_test_op([(10,10,10)], lambda x: x.softmax(0), atol=1e-7, grad_atol=1e-7)
helper_test_op([(10,10,10)], lambda x: x.softmax(1), atol=1e-7, grad_atol=1e-7)
helper_test_op([(10,10,10)], lambda x: x.softmax(2), atol=1e-7, grad_atol=1e-7)
def test_softmax_argmax(self):
helper_test_op([(45,65)], lambda x: x.softmax(0).argmax().type(torch.int32),
lambda x: x.softmax(0).argmax(), forward_only=True, atol=1e-7, grad_atol=1e-7)
helper_test_op([(45,65)], lambda x: x.softmax(1).argmax().type(torch.int32),
lambda x: x.softmax(1).argmax(), forward_only=True, atol=1e-7, grad_atol=1e-7)
def test_log_softmax(self):
helper_test_op([(45,65)], torch.nn.LogSoftmax(dim=1), Tensor.log_softmax, atol=1e-7, grad_atol=1e-7)
helper_test_op([(45)], torch.nn.LogSoftmax(dim=0), Tensor.log_softmax, atol=1e-7, grad_atol=1e-7)
helper_test_op([()], torch.nn.LogSoftmax(dim=0), Tensor.log_softmax, atol=1e-7, grad_atol=1e-7)
helper_test_op([()], torch.nn.LogSoftmax(dim=-1), Tensor.log_softmax, atol=1e-7, grad_atol=1e-7)
def test_log_softmax_other_axis(self):
helper_test_op([(10,10,10)], lambda x: x.log_softmax(0), atol=1e-7, grad_atol=1e-7)
helper_test_op([(10,10,10)], lambda x: x.log_softmax(1), atol=1e-7, grad_atol=1e-7)
helper_test_op([(10,10,10)], lambda x: x.log_softmax(2), atol=1e-7, grad_atol=1e-7)
def test_sinh(self):
helper_test_op([(45,65)], lambda x: x.sinh(), grad_atol=1e-6)
# TODO: backward nan instead of inf
helper_test_op([(45,65)], lambda x: x.sinh(), grad_atol=1e-6, low=-300, high=-297, forward_only=True)
helper_test_op([(45,65)], lambda x: x.sinh(), grad_atol=1e-6, low=300, high=303, forward_only=True)
def test_cosh(self):
helper_test_op([(45,65)], lambda x: x.cosh(), grad_atol=1e-6)
# TODO: backward nan instead of inf
helper_test_op([(45,65)], lambda x: x.cosh(), grad_atol=1e-6, low=-300, high=-297, forward_only=True)
helper_test_op([(45,65)], lambda x: x.cosh(), grad_atol=1e-6, low=300, high=303, forward_only=True)
def test_tanh(self):
helper_test_op([(45,65)], lambda x: x.tanh(), grad_atol=1e-6)
helper_test_op([(45,65)], lambda x: x.tanh(), grad_atol=1e-6, low=-300, high=-297)
helper_test_op([(45,65)], lambda x: x.tanh(), grad_atol=1e-6, low=300, high=303)
def test_hardtanh(self):
for val in range(10, 30, 5):
helper_test_op([(45,65)], lambda x: torch.nn.functional.hardtanh(x, -val, val), lambda x: x.hardtanh(-val, val), grad_atol=1e-6)
helper_test_op([()], lambda x: torch.nn.functional.hardtanh(x, -val, val), lambda x: x.hardtanh(-val, val), grad_atol=1e-6)
def test_asinh(self):
helper_test_op([(45,65)], lambda x: x.asinh(), grad_atol=1e-6)
# NOTE: this one has larger atol
helper_test_op([(45,65)], lambda x: x.asinh(), atol=1e-2, grad_atol=1e-6, low=-300, high=-297)
helper_test_op([(45,65)], lambda x: x.asinh(), grad_atol=1e-6, low=300, high=303)
def test_acosh(self):
helper_test_op([(45,65)], lambda x: x.acosh(), grad_atol=1e-6)
helper_test_op([(45,65)], lambda x: x.acosh(), grad_atol=1e-6, low=-300, high=-297)
helper_test_op([(45,65)], lambda x: x.acosh(), grad_atol=1e-6, low=300, high=303)
def test_atanh(self):
helper_test_op([(45,65)], lambda x: x.atanh(), grad_atol=1e-6)
helper_test_op([(45,65)], lambda x: x.atanh(), grad_atol=1e-6, low=-300, high=-297)
helper_test_op([(45,65)], lambda x: x.atanh(), grad_atol=1e-6, low=300, high=303)
def test_topo_sort(self):
helper_test_op([(45,65)], lambda x: (x+x)*x, grad_atol=1e-6)
helper_test_op([()], lambda x: (x+x)*x, grad_atol=1e-6)
def test_flip_eye_crash(self):
helper_test_op([], lambda: (torch.eye(10)@torch.eye(10).flip(0)),
lambda: (Tensor.eye(10)@Tensor.eye(10).flip(0)), forward_only=True)
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):
if tinygrad_op != Tensor.pow:
helper_test_op(shapes, torch_op, tinygrad_op)
else:
helper_test_op(shapes, torch_op, tinygrad_op, low=0, high=3)
def test_broadcast_simple(self):
helper_test_op([(45,65), (45,1)], lambda x,y: x/y)
helper_test_op([(45,65), ()], lambda x,y: x/y)
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?
if tinygrad_op != Tensor.pow:
helper_test_op(shapes, torch_op, tinygrad_op)
else:
helper_test_op(shapes, torch_op, tinygrad_op, low=0, high=3)
def test_slice_in_bounds_1dim(self):
helper_test_op([(3)], lambda x: x[1:3])
helper_test_op([(3)], lambda x: x[0:2])
helper_test_op([(3)], lambda x: x[-2:2])
def test_slice_on_0dim_tensor(self):
helper_test_op([()], lambda x: x[None])
with self.assertRaises(IndexError):
a = Tensor(3.14)
a[0]
def test_slice_int_indexing(self):
helper_test_op([(3)], lambda x: x[0])
helper_test_op([(3)], lambda x: x[2])
helper_test_op([(3)], lambda x: x[-1])
helper_test_op([(3)], lambda x: x[-3])
helper_test_op([(10,10)], lambda x: x[1])
helper_test_op([(3,3,3)], lambda x: x[1,1,1])
def test_slice_in_bounds_multidim(self):
helper_test_op([(3,3,3)], lambda x: x[1:2])
helper_test_op([(3,3,3)], lambda x: x[1:2, 2])
helper_test_op([(3,3,3)], lambda x: x[1:2, 1:2])
helper_test_op([(3,3,3)], lambda x: x[1:2, 1:2, 0:-1])
def test_slice_with_none(self):
helper_test_op([(3,3,3)], lambda x: x[None])
helper_test_op([(3,3,3)], lambda x: x[1:2, None])
helper_test_op([(3,3,3)], lambda x: x[1:2, None, 1:2])
helper_test_op([(3,3,3)], lambda x: x[1:2, 1:2, None, -1])
helper_test_op([(3,3,3)], lambda x: x[None, None, 1, None, 2, 0:2])
def test_slice_with_const_tensor(self):
t = Tensor.zeros(1, dtype=dtypes.int)
helper_test_op([(3,3,3)], lambda x: x[:, [0], :], lambda x: x[:, t, :])
helper_test_op([(3,3,3)], lambda x: x[:, [0], :], lambda x: x[:, t.contiguous(), :])
def test_slice_one_endpoint_out_of_bounds(self):
helper_test_op([(3,3,3)], lambda x: x[0:4])
helper_test_op([(3,3,3)], lambda x: x[-6:4])
helper_test_op([(3,3,3)], lambda x: x[1:50])
helper_test_op([(3,3,3)], lambda x: x[1:50, 1:2, -1])
def test_slice_stride_gt_one(self):
helper_test_op([(7,5,10)], lambda x: x[::2, ::3, ::4])
helper_test_op([(7,5,10)], lambda x: x[1:5:2, ::3, ::4])
helper_test_op([(7,5,10)], lambda x: x[1:5:2, 3, ::4])
helper_test_op([(7,5,10)], lambda x: x[1:5:2, None, None, 3, None, ::4])
def test_slice_negative_strides(self):
# Torch doesn't support slicing with negative steps
a = np.random.randn(10, 10, 10).astype(np.float32)
t = Tensor(a)
np.testing.assert_allclose(a[::-1], t[::-1].numpy())
np.testing.assert_allclose(a[::-2], t[::-2].numpy())
np.testing.assert_allclose(a[:, 2:0:-1], t[:, 2:0:-1].numpy())
np.testing.assert_allclose(a[:, 2:0:-1, 3:1:-2], t[:, 2:0:-1, 3:1:-2].numpy())
np.testing.assert_allclose(a[4:0:-3, 2:0:-1, -1:-5:-2], t[4:0:-3, 2:0:-1, -1:-5:-2].numpy())
np.testing.assert_allclose(a[2:5:-1, :, :], t[2:5:-1, :, :].numpy()) # shape = (0, 10, 10)
np.testing.assert_allclose(a[:, 2:5:-1, :], t[:, 2:5:-1, :].numpy()) # shape = (0, 10, 10)
np.testing.assert_allclose(a[:, :, 2:5:-1], t[:, :, 2:5:-1].numpy()) # shape = (0, 10, 10)
def test_slice_both_endpoints_out_of_bounds(self):
helper_test_op([(3,3,3)], lambda x: x[5:10])
helper_test_op([(3,3,3)], lambda x: x[-15:-7])
def test_slice_start_gt_end(self):
helper_test_op([(3,3,3)], lambda x: x[-2:2])
helper_test_op([(3,3,3)], lambda x: x[-2:-5])
def test_slice_empty(self):
helper_test_op([(10,10)], lambda x: x[1:1])
def test_slice_zero_in_shape(self):
helper_test_op([(10,10)], lambda x: x[1:1]) # x.shape = (0, 10)
helper_test_op([(3,3,3)], lambda x: x[-2:-5]) # x.shape = (0, 3, 3)
def test_slice_errors(self):
a = Tensor.ones(4, 3)
b = Tensor(2)
with self.assertRaises(IndexError): a[1, 77, 77, 77] # IndexError: (finds too many indices before the out of bounds)
with self.assertRaises(IndexError): a[1, 3] # IndexError: (out of bounds).
with self.assertRaises(IndexError): a[1, -4]
with self.assertRaises(IndexError): a[..., ...] # IndexError: only single ellipsis
with self.assertRaises(ValueError): a[::0, 1] # no 0 strides
with self.assertRaises(IndexError): b[:] # slice cannot be applied to a 0-dim tensor
def test_slice_ellipsis(self):
helper_test_op([(3,3,3,3)], lambda x: x[..., 0])
helper_test_op([(3,3,3,3)], lambda x: x[0, ...])
helper_test_op([(3,3,3,3)], lambda x: x[0, ..., 0])
helper_test_op([(3,3,3,3)], lambda x: x[0:3, ..., 2:3])
helper_test_op([(3,3,3,3)], lambda x: x[None, 0:3, ..., 0, None])
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)))
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)))
helper_test_op([(3,3,3,3)], lambda x: torch.nn.functional.pad(x, (1,2,3,4), value=5), lambda x: x.pad2d(padding=(1,2,3,4),value=5))
helper_test_op([(3,3,3,3)], lambda x: torch.nn.functional.pad(x, (-1,2,-3,4), value=5), lambda x: x.pad2d(padding=(-1,2,-3,4),value=5))
def test_pad(self):
helper_test_op([(3,3)], lambda x: torch.nn.functional.pad(x, (1,2,3,4)),lambda x: x.pad(((3,4),(1,2))))
helper_test_op([(3,3)], lambda x: torch.nn.functional.pad(x, (1,2,3,4), value=5), lambda x: x.pad(((3,4), (1,2)), value=5))
helper_test_op([(3,3)], lambda x: torch.nn.functional.pad(x, (1,2,3,4), value=math.inf), lambda x: x.pad(((3,4), (1,2)), value=math.inf))
helper_test_op([(3,3)], lambda x: torch.nn.functional.pad(x, (1,2,3,4), value=-math.inf), lambda x: x.pad(((3,4), (1,2)), value=-math.inf))
helper_test_op([(3,3)], lambda x: torch.nn.functional.pad(x, (0,0,3,4), value=1), lambda x: x.pad(((3,4), None), value=1))
helper_test_op([(3,3)], lambda x: torch.nn.functional.pad(x, (0,0,0,0), value=1), lambda x: x.pad((None, None), value=1))
@unittest.skipIf(Device.DEFAULT == "WEBGL", "incorrect result")
def test_pad_slice(self):
for value in 0., 3.456:
helper_test_op([(1)], lambda x: torch.nn.functional.pad(x,(1,0), value=value)[0], lambda x: x.pad(((1,0),), value=value)[0])
helper_test_op([(4)], lambda x: torch.nn.functional.pad(x,(1,0), value=value)[0], lambda x: x.pad(((1,0),), value=value)[0])
helper_test_op([(4)], lambda x: torch.nn.functional.pad(x,(3,0), value=value)[0:1], lambda x: x.pad(((3,0),), value=value)[0:1])
helper_test_op([(4)], lambda x: torch.nn.functional.pad(x,(0,3), value=value)[6], lambda x: x.pad(((0,3),), value=value)[6])
helper_test_op([(4)], lambda x: torch.nn.functional.pad(x,(0,3), value=value)[4:6], lambda x: x.pad(((0,3),), value=value)[4:6])
helper_test_op([(5,5)], lambda x: torch.nn.functional.pad(x,(0,0,1,0), value=value)[0], lambda x: x.pad(((1,0),(0,0)), value=value)[0])
helper_test_op([(2,2)], lambda x: torch.nn.functional.pad(x,(0,1,0,0), value=value)[0,2], lambda x: x.pad(((0,0),(0,1)), value=value)[0,2])
helper_test_op([(4,4)], lambda x: torch.nn.functional.pad(x,(0,0,1,0), value=value)[0,2], lambda x: x.pad(((1,0),(0,0)), value=value)[0,2])
helper_test_op([(4,4)], lambda x: torch.nn.functional.pad(x,(0,0,0,2), value=value)[5], lambda x: x.pad(((0,2),(0,0)), value=value)[5])
helper_test_op([(4,4)], lambda x: torch.nn.functional.pad(x,(0,0,0,2), value=value)[3:5], lambda x: x.pad(((0,2),(0,0)), value=value)[3:5])
helper_test_op([(4,4)], lambda x: torch.nn.functional.pad(x,(3,0,0,0), value=value)[1,0], lambda x: x.pad(((0,0),(3,0)), value=value)[1,0])
helper_test_op([(4,4)], lambda x: torch.nn.functional.pad(x,(3,0,0,0), value=value)[1,0:4], lambda x: x.pad(((0,0),(3,0)), value=value)[1,0:4])
helper_test_op([(4,4)], lambda x: torch.nn.functional.pad(x,(3,4,1,2), value=value)[0], lambda x: x.pad(((1,2),(3,4)), value=value)[0])
helper_test_op([(4,4)], lambda x: torch.nn.functional.pad(x,(3,4,1,2), value=value)[:,1], lambda x: x.pad(((1,2),(3,4)), value=value)[:,1])
helper_test_op([(4,4)], lambda x: torch.nn.functional.pad(x,(3,4,1,2), value=value)[:,4], lambda x: x.pad(((1,2),(3,4)), value=value)[:,4])
helper_test_op([(3,3)], lambda x: torch.nn.functional.pad(x,(0,3,0,0), value=value)[:,4:6], lambda x: x.pad(((0,0),(0,3)), value=value)[:,4:6])
helper_test_op([(3,3)], lambda x: torch.nn.functional.pad(x,(0,1,3,2), value=value)[0:2,:], lambda x: x.pad(((3,2),(0,1)), value=value)[0:2,:])
helper_test_op([(3,3,3)], lambda x: torch.nn.functional.pad(x,(1,1,0,1,3,2), value=value)[0:2,:,:],
lambda x: x.pad(((3,2),(0,1),(1,1)), value=value)[0:2,:,:])
helper_test_op([(3,3,3)], lambda x: torch.nn.functional.pad(x,(1,1,0,1,3,2), value=value)[2:4,:,:],
lambda x: x.pad(((3,2),(0,1),(1,1)), value=value)[2:4,:,:])
def test_stack_slice(self):
helper_test_op([(4)], lambda x: torch.stack([x for i in range(3)])[0,:], lambda x: Tensor.stack([x for i in range(3)])[0,:])
helper_test_op([(5)], lambda x: torch.stack([x for i in range(3)])[0,0], lambda x: Tensor.stack([x for i in range(3)])[0,0])
helper_test_op([(4,4)], lambda x: torch.stack([x for i in range(4)])[3], lambda x: Tensor.stack([x for i in range(4)])[3])
def test_transpose(self):
helper_test_op([(3,3)], lambda x: x.T)
helper_test_op([(3,3,3)], lambda x: x.transpose(1,2))
helper_test_op([(3,3,3)], lambda x: x.transpose(0,2))
helper_test_op([(1,2,3,4)], lambda x: x.permute((3,0,2,1)))
helper_test_op([(3,4,5,6)], lambda x: x.permute((3,2,1,0)))
helper_test_op([()], lambda x: x.permute(()))
def test_reshape(self):
helper_test_op([(4,3,6,6)], lambda x: x.reshape((-1,3,6,6)))
helper_test_op([(4,3,6,6)], lambda x: x.reshape((-1,1,6,6)))
helper_test_op([()], lambda x: x.reshape([]))
helper_test_op([(1,)], lambda x: x.reshape([]))
helper_test_op([()], lambda x: x.reshape([1]))
helper_test_op([()], lambda x: x.reshape([1, 1, 1]))
with self.assertRaises(ValueError):
x = Tensor.ones((4,3,6,6))
x.reshape([])
def test_flip(self):
helper_test_op([(4,3,6,6)], lambda x: x.flip((0,)))
helper_test_op([(4,3,6,6)], lambda x: x.flip((0,1)))
helper_test_op([(4,3,6,6)], lambda x: x.flip((0,1,3)))
helper_test_op([(4,3,6,6)], lambda x: x.flip((3,)))
helper_test_op([(4,3,6,6)], lambda x: x.flip((0,1,3)).flip(0))
helper_test_op([(4,3,6,6)], lambda x: x.flip((-1,)))
helper_test_op([()], lambda x: x.flip(()))
helper_test_op([(1,)], lambda x: x.flip(()))
helper_test_op([(4, 3, 6, 6)], lambda x: x.flip(()))
def test_squeeze(self):
helper_test_op([(1,3,6,6)], lambda x: x.squeeze(0))
helper_test_op([(4,3,1,6)], lambda x: x.squeeze(1))
helper_test_op([(4,3,6,6)], lambda x: x.squeeze(3))
self.helper_test_exception([(4,3,6,6)], lambda x: torch.squeeze(x, 50), lambda x: x.squeeze(dim=50), expected=IndexError)
self.helper_test_exception([(4,3,6,6)], lambda x: torch.squeeze(x, -50), lambda x: x.squeeze(dim=-50), expected=IndexError)
helper_test_op([(4,3,6,1)], lambda x: x.squeeze(-1))
helper_test_op([(4,3,6,6)], lambda x: x.squeeze())
helper_test_op([(1,3,6,6)], lambda x: x.squeeze())
helper_test_op([(2,3,1)], lambda x: x.squeeze())
helper_test_op([()], lambda x: x.squeeze(-1))
helper_test_op([()], lambda x: x.squeeze(0))
helper_test_op([()], lambda x: x.squeeze())
self.helper_test_exception([()], lambda x: torch.squeeze(x, 10), lambda x: x.squeeze(dim=10), expected=IndexError)
self.helper_test_exception([()], lambda x: torch.squeeze(x, 1), lambda x: x.squeeze(dim=1), expected=IndexError)
self.helper_test_exception([()], lambda x: torch.squeeze(x, -2), lambda x: x.squeeze(dim=-2), expected=IndexError)
def test_unsqueeze(self):
helper_test_op([(4,3,6,6)], lambda x: x.unsqueeze(0))
helper_test_op([(4,3,6,6)], lambda x: x.unsqueeze(4))
helper_test_op([(4,3,6,6)], lambda x: x.unsqueeze(-1))
helper_test_op([(4,3,6,6)], lambda x: x.unsqueeze(-3))
helper_test_op([()], lambda x: x.unsqueeze(0))
def test_flatten(self):
for axis in range(3):
helper_test_op([(4,3,6,6)], lambda x: x.flatten(start_dim=axis))
for axis in range(3):
helper_test_op([(4,3,6,6)], lambda x: x.flatten(end_dim=axis))
helper_test_op([(4,3,6,6)], lambda x: x.flatten(start_dim=1, end_dim=3))
helper_test_op([()], lambda x: x.flatten())
helper_test_op([(1,)], lambda x: x.flatten())
def test_unflatten(self):
helper_test_op([(4,3,6,6)], lambda x: x.unflatten(0, (2, 2)))
helper_test_op([(4,3,6,6)], lambda x: x.unflatten(3, (3, 2)))
helper_test_op([(4,3,6,6)], lambda x: x.unflatten(-1, (3, 2, 1)))
def test_detach(self):
helper_test_op([(4,3,6,6)], lambda x: x.detach(), forward_only=True)
helper_test_op([()], lambda x: x.detach(), forward_only=True)
def test_expand(self):
helper_test_op([(4,3,1,6)], lambda x: x.expand((4,3,2,6)))
helper_test_op([(1,1,1,1)], lambda x: x.expand((4,3,2,6)))
helper_test_op([(4,3,1,6)], lambda x: x.expand((6,1,4,3,2,6)))
helper_test_op([(4,3,1,6)], lambda x: x.expand((0,1,4,3,2,6)))
helper_test_op([(4,3,1,6)], lambda x: x.expand((4,3,0,6)))
helper_test_op([()], lambda x: x.expand((4,3,2,6)))
helper_test_op([()], lambda x: x.expand([]))
with self.assertRaises((ValueError, RuntimeError)): Tensor.ones(4,3,1,6).expand(4,1,1,6)
with self.assertRaises((ValueError, RuntimeError)): Tensor.ones(4,3,1,6).expand(4,6,1,6)
with self.assertRaises((ValueError, RuntimeError)): Tensor.ones(4,3,1,6).expand(3,1,6)
with self.assertRaises((ValueError, RuntimeError)): Tensor.ones(4,3,2,6).expand(4,3,0,6)
@unittest.skip("very slow")
def test_sd_big_conv(self):
# internal shape (1, 1, 512, 62, 62, 512, 3, 3) overflows a int
helper_test_op([(1,256,64,64), (512,256,3,3)],
lambda x,w: torch.nn.functional.conv2d(x, w),
lambda x,w: x.conv2d(w), atol=1e-2)
@unittest.skip("slow")
def test_large_bs_conv(self):
# large batch size can cause OpenCL image to exceed max image height on macOS
# (or cause the conv kernel to overflow short sampling coords)
helper_test_op([(4096,3,3,3), (1,3,3,3)],
lambda x,w: torch.nn.functional.conv2d(x, w),
lambda x,w: x.conv2d(w), atol=1e-4, rtol=1e-2)
@unittest.skip("slow")
def test_large_ic_conv(self):
# large input channel count can cause OpenCL image to exceed max image width on macOS
helper_test_op([(1,2048,3,3), (1,2048,3,3)],
lambda x,w: torch.nn.functional.conv2d(x, w),
lambda x,w: x.conv2d(w), atol=1e-4)
def test_biased_conv2d(self):
C = 8
helper_test_op([(1,C,5,5), (C,C,1,1), (C,)],
lambda x,w,b: torch.nn.functional.conv2d(torch.nn.functional.conv2d(x,w,b).relu(),w,b),
lambda x,w,b: Tensor.conv2d(x,w,b).relu().conv2d(w,b), atol=1e-4)
def test_simple_conv2d(self):
helper_test_op([(1,4,9,9), (4,4,3,3)],
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)
@unittest.skipIf(IMAGE>0, "no conv3d on images")
def test_simple_conv3d(self):
helper_test_op([(1,4,9,9,9), (4,4,3,3,3)],
lambda x,w: torch.nn.functional.conv3d(x,w).relu(),
lambda x,w: Tensor.conv2d(x,w).relu(), atol=1e-4, grad_rtol=1e-5)
@unittest.skipIf(IMAGE>0, "no conv3d on images")
def test_padded_conv3d(self):
helper_test_op([(1,4,5,5,5), (4,4,3,3,3)],
lambda x,w: torch.nn.functional.conv3d(x,w,padding=1).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=[1,1,1,1,1,1]).relu(), atol=1e-4, grad_rtol=1e-5)
def test_simple_conv2d_m4(self):
helper_test_op([(1,16,18,18), (16,16,3,3)],
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_simple_conv2d_1x1(self):
helper_test_op([(1,4,9,9), (4,4,1,1)],
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_simple_conv2d_1x1_m4(self):
helper_test_op([(1,16,32,32), (16,16,1,1)],
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_nested_conv2d(self):
helper_test_op([(1,32,9,9), (32,32,3,3), (32,32,3,3)],
lambda x,w1,w2: torch.nn.functional.conv2d(torch.nn.functional.conv2d(x,w1).relu(), w2).relu(),
lambda x,w1,w2: x.conv2d(w1).relu().conv2d(w2).relu(), atol=1e-4, grad_rtol=1e-5)
# expect reduce nodes == 3
def test_simple_conv2d_nhwc(self):
# weights (from tf): filter_height x filter_width x in_channels x out_channels
helper_test_op([(2,9,9,10), (3,3,10,20)],
lambda x,w: torch.nn.functional.conv2d(x.permute(0,3,1,2),w.permute(3,2,0,1)).relu(),
lambda x,w: Tensor.conv2d(x.permute(0,3,1,2),w.permute(3,2,0,1)).relu(), atol=1e-4, grad_rtol=1e-5)
def test_simple_conv2d_batched(self):
helper_test_op([(2,4,9,9), (4,4,3,3)],
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)
# conv transpose
def test_simple_conv_transpose2d(self):
helper_test_op([(2,4,9,9), (4,4,3,3)],
lambda x,w: torch.nn.functional.conv_transpose2d(x,w).relu(),
lambda x,w: Tensor.conv_transpose2d(x,w).relu(), atol=1e-4, grad_rtol=1e-5)
def test_bias_conv_transpose2d(self):
helper_test_op([(2,4,9,9), (4,4,3,3), (4,)],
lambda x,w,b: torch.nn.functional.conv_transpose2d(x,w,b).relu(),
lambda x,w,b: Tensor.conv_transpose2d(x,w,b).relu(), atol=1e-4, grad_rtol=1e-5)
def test_grouped_conv_transpose2d(self):
helper_test_op([(2,4,9,9), (4,4,3,3)],
lambda x,w: torch.nn.functional.conv_transpose2d(x,w,groups=2).relu(),
lambda x,w: Tensor.conv_transpose2d(x,w,groups=2).relu(), atol=1e-4, grad_rtol=1e-5)
def test_padded_conv_transpose2d(self):
for padding in [(1,2), (2,1), 2, 1, 0]:
helper_test_op([(2,4,9,9), (4,4,3,3)],
lambda x,w: torch.nn.functional.conv_transpose2d(x,w,padding=padding).relu(),
lambda x,w: Tensor.conv_transpose2d(x,w,padding=padding).relu(), atol=1e-4, grad_rtol=1e-5)
def test_dilated_conv_transpose2d(self):
for dilation in [(1,2), (2,1), 2, 1]:
helper_test_op([(2,4,9,9), (4,4,3,3)],
lambda x,w: torch.nn.functional.conv_transpose2d(x,w,dilation=dilation).relu(),
lambda x,w: Tensor.conv_transpose2d(x,w,dilation=dilation).relu(), atol=1e-4, grad_rtol=1e-5)
def test_strided_conv_transpose2d(self):
for stride in [(2,1), (1,2), 1]:
helper_test_op([(2,4,4,5), (4,4,3,3)],
lambda x,w: torch.nn.functional.conv_transpose2d(x,w, stride=stride).relu(),
lambda x,w: Tensor.conv_transpose2d(x,w,stride=stride).relu(), atol=1e-4, grad_rtol=1e-5)
def test_output_padded_conv_transpose2d(self):
for output_padding, stride in [((1,1), (2,3)), ((2,1), (3,2))]:
helper_test_op([(2,4,6,5), (4,4,3,3),(4,)],
lambda x,w,b: torch.nn.functional.conv_transpose2d(x,w,b,output_padding=output_padding,stride=stride).relu(),
lambda x,w,b: Tensor.conv_transpose2d(x,w,b,output_padding=output_padding,stride=stride).relu(), atol=1e-4, grad_rtol=1e-5)
@unittest.skipIf(IMAGE>0, "no conv3d on images")
def test_simple_conv_transpose3d(self):
helper_test_op([(2,4,9,9,9), (4,4,3,3,3)],
lambda x,w: torch.nn.functional.conv_transpose3d(x,w).relu(),
lambda x,w: Tensor.conv_transpose2d(x,w).relu(), atol=1e-4, grad_rtol=1e-5)
@unittest.skipIf((IMAGE>0), "no conv1d on images")
def test_conv1d(self):
for bs in [1,8]:
for cin in [1,3]:
for H in [1,2,5]:
for groups in [1,3] if cin == 3 and H == 5 else [1]:
with self.subTest(batch_size=bs, channels=cin, groups=groups, height=H):
helper_test_op([(bs,cin,11), (6,cin//groups,H)],
lambda x,w: torch.nn.functional.conv1d(x,w,groups=groups).relu(),
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5)
@unittest.skipIf(IMAGE>0, "no conv1d on images")
def test_simple_padding_conv1d(self):
bs = 6
cin = 2
groups = 1
H = 5
p = (1,1)
helper_test_op([(bs,cin,11), (6,cin//groups,H)],
lambda x,w: torch.nn.functional.conv1d(torch.nn.functional.pad(x, p),w).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=p).relu(), atol=1e-4)
@unittest.skipIf(IMAGE>0, "no conv1d on images")
def test_strided_conv1d_simple(self):
bs, H = 2, 3
helper_test_op([(bs,1,5), (1,1,H)],
lambda x,w: torch.nn.functional.conv1d(x,w,stride=2).relu(),
lambda x,w: Tensor.conv2d(x,w,stride=2).relu(), atol=1e-4)
@unittest.skipIf(IMAGE>0, "no conv1d on images")
def test_asymmetric_padding_conv1d(self):
for p in [(0,1), (2,1), (2,0)]:
with self.subTest(p):
for n in [3,4]:
for k in [2]:
helper_test_op([(1,1,n), (1,1,k)],
lambda x,w: torch.nn.functional.conv1d(torch.nn.functional.pad(x, p),w).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=p).relu(), atol=1e-4)
helper_test_op([(1,1,n), (1,1,k)],
lambda x,w: torch.nn.functional.conv1d(torch.nn.functional.pad(x, p),w).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=p).relu(), atol=1e-4)
def _test_conv2d(self, bs=1, cin=1):
for H in [1,2,3]:
for W in [1,2,3,5]:
for groups in [1,3] if cin == 3 and H == 3 and W == 3 else [1]:
with self.subTest(batch_size=bs, channels=cin, groups=groups, height=H, width=W):
helper_test_op([(bs,cin,11,7), (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_conv2d(self): self._test_conv2d(bs=1, cin=3)
def test_conv2d_bs_4_cin_3(self): self._test_conv2d(bs=4, cin=3)
def test_conv2d_bs_1_cin_1(self): self._test_conv2d(bs=1, cin=1)
def test_conv2d_bs_4_cin_1(self): self._test_conv2d(bs=4, cin=1)
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_depthwise_conv2d(self):
bs = 1
groups = 32
rcout = 1
cin = 1
helper_test_op([(bs,groups*cin,32,32), (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_simple(self):
bs,H,W = 2,3,1
helper_test_op([(bs,1,5,1), (1,1,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,stride=2).relu(),
lambda x,w: Tensor.conv2d(x,w,stride=2).relu(), atol=1e-4)
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_negative_padding_conv2d(self):
n,k = 10, 3
helper_test_op([(1,1,n,n), (1,1,k,k)],
lambda x,w: torch.nn.functional.conv2d(x[:, :, 1:-1, 1:-1],w).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=-1).relu(), atol=1e-4)
helper_test_op([(1,1,n,n), (1,1,k,k)],
lambda x,w: torch.nn.functional.conv2d(x[:, :, 1:, 1:],w).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=(-1,0,-1,0)).relu(), atol=1e-4)
def test_simple_padding_conv2d(self):
p = (1,1,1,1)
helper_test_op(None,
lambda x,w: torch.nn.functional.conv2d(torch.nn.functional.pad(x, p),w).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=p).relu(), atol=1e-4, vals=[[[[[2.,3.]]]], [[[[1.]]]]])
def test_asymmetric_padding_conv2d(self):
for p in [(0,1,0,1), (2,1,2,1), (2,0,2,1)]:
with self.subTest(p):
for n in [3,4]:
for k in [2]:
helper_test_op([(1,1,n,n), (1,1,k,k)],
lambda x,w: torch.nn.functional.conv2d(torch.nn.functional.pad(x, p),w).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=p).relu(), atol=1e-4)
helper_test_op([(1,1,n,n), (1,1,k,k)],
lambda x,w: torch.nn.functional.conv2d(torch.nn.functional.pad(x, p),w).relu(),
lambda x,w: Tensor.conv2d(x,w,padding=p).relu(), atol=1e-4)
def test_padded_conv2d_p21(self):
bs,cin,H,W,padding = 4, 3, 3, 3, (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_padded_conv2d_p22(self):
bs,cin,H,W,padding = 4, 3, 3, 3, (2,2)
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)
def test_padded_conv2d_1x1(self):
bs,cin,H,W,padding = 4, 3, 1, 1, 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)
def test_padded_conv2d_bs1(self):
bs,cin,H,W,padding = 1, 3, 3, 3, 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_padding_add(self):
helper_test_op([(64,64), (60,60)],
lambda x,w: x+torch.nn.functional.pad(w, (2,2,2,2)),
lambda x,w: x+w.pad2d((2,2,2,2)))
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_simple(self):
ksz = (2,2)
helper_test_op([(1,1,2,3)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz),
lambda x: Tensor.max_pool2d(x, kernel_size=ksz))
def test_maxpool2d(self):
for ksz in [(2,2), (3,3), 2, 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),
lambda x: Tensor.max_pool2d(x, kernel_size=ksz))
def test_maxpool2d_bigger_stride(self):
for stride in [(2,3), (3,2), 2, 3]:
with self.subTest(stride=stride):
helper_test_op([(32,2,110,28)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(2,2), stride=stride),
lambda x: Tensor.max_pool2d(x, kernel_size=(2,2), stride=stride))
def test_maxpool2d_bigger_stride_dilation(self):
for stride, dilation in zip([(2,3), (3,2), 2, 3, 4], [(3,2), (2,3), 2, 3, 6]):
with self.subTest(stride=stride):
helper_test_op([(32,2,110,28)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(2,2), stride=stride, dilation=dilation),
lambda x: Tensor.max_pool2d(x, kernel_size=(2,2), stride=stride, dilation=dilation))
@unittest.skipIf(Device.DEFAULT == "CUDA", "CUDA fails on this")
def test_maxpool2d_unit_stride(self):
helper_test_op([(8, 2, 17, 14)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(5,5), stride=1),
lambda x: Tensor.max_pool2d(x, kernel_size=(5,5), stride=1))
def test_maxpool2d_smaller_stride(self):
for stride in [(2,3), (3,2), 2, 3]:
with self.subTest(stride=stride):
helper_test_op([(8, 2, 17, 14)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(5,5), stride=stride),
lambda x: Tensor.max_pool2d(x, kernel_size=(5,5), stride=stride))
def test_maxpool2d_dilation(self):
for dilation in [(2, 3), (3, 2), 2, 3]:
helper_test_op([(8, 2, 17, 14)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(5,5), dilation=dilation),
lambda x: Tensor.max_pool2d(x, kernel_size=(5,5), dilation=dilation))
def test_avgpool2d(self):
shape = (32,2,111,28)
for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]:
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_global_avgpool2d(self):
helper_test_op([(32,2,111,28)],
lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=(111,28)),
lambda x: Tensor.avg_pool2d(x, kernel_size=(111,28)), rtol=1e-5)
def test_cat(self):
for dim in range(-2, 3):
helper_test_op([(45,65,9), (45,65,9), (45,65,9)], lambda x,y,z: torch.cat((x,y,z), dim), lambda x,y,z: x.cat(y, z, dim=dim))
# zero in non-cat axis
helper_test_op([(45,0,9), (45,0,9), (45,0,9)], lambda x,y,z: torch.cat((x,y,z), 0), lambda x,y,z: x.cat(y, z, dim=0))
# zero in cat axis
helper_test_op([(45,0,9), (45,1,9), (45,2,9)], lambda x,y,z: torch.cat((x,y,z), 1), lambda x,y,z: x.cat(y, z, dim=1))
helper_test_op([(45,0,9), (45,0,9), (45,0,9)], lambda x,y,z: torch.cat((x,y,z), 1), lambda x,y,z: x.cat(y, z, dim=1))
with self.assertRaises(IndexError):
a = Tensor(3.14)
a.cat(a)
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_stack(self):
for dim in range(-1, 3):
helper_test_op([(45,65,3), (45,65,3), (45,65,3)], lambda x, y, z: torch.stack((x, y, z), dim), lambda x, y, z: Tensor.stack([x, y, z], dim))
with self.assertRaises(IndexError):
Tensor.stack([Tensor.randn(45, 65, 3)], dim=77)
a = Tensor(3.14)
np.testing.assert_allclose(Tensor.stack([a, a]).numpy(), Tensor([3.14, 3.14]).numpy())
def test_repeat(self):
x = Tensor.randn(4, 6, 3)
base_repeats = [2, 4, 3]
for reps in [[], [4], [2, 1], [3, 2, 2]]:
repeats = base_repeats + reps
helper_test_op([(4, 6, 3)], lambda x: x.repeat(*repeats), lambda x: x.repeat(repeats))
helper_test_op([()], lambda x: x.repeat(*repeats), lambda x: x.repeat(repeats))
with self.assertRaises(ValueError):
x.repeat((2, 4))
np.testing.assert_allclose(x.repeat((2, 0, 4)).numpy(), Tensor.zeros(8, 0, 12).numpy())
def test_simple_repeat(self):
repeats = [3, 3, 4]
helper_test_op([(3, 3)], lambda x: x.repeat(*repeats), lambda x: x.repeat(repeats))
def test_clip(self):
helper_test_op([(45,65)], lambda x: x.clip(-2.3, 1.2))
helper_test_op([(45,65)], lambda x: x.clip(0, 0))
helper_test_op([(45,65)], lambda x: x.clip(10, 100))
helper_test_op([(45,65)], lambda x: x.clip(0, 0.1))
helper_test_op([(45,65)], lambda x: x.clip(-0.3, -0.2))
helper_test_op([(45,65)], lambda x: x.clip(3, 0))
def test_matvecmat(self):
helper_test_op([(1,128), (128,128), (128,128)], lambda x,y,z: (x@y).relu()@z, atol=1e-4)
def test_matvec(self):
helper_test_op([(1,128), (128,128)], lambda x,y: (x@y).relu(), atol=1e-4)
# this was the failure in llama early realizing freqs_cis
def test_double_slice(self):
helper_test_op([(4,4)], lambda x: x[:, 1:2][1:2])
helper_test_op([(4,4)], lambda x: x[1:3][1:2])
helper_test_op([(4,4)], lambda x: x[:, 1:2][0:1])
helper_test_op([(4,4)], lambda x: x[:, 1:2][:, 0:1])
@unittest.skip("this test is broken #862")
def test_max_inf(self):
n = Tensor([1, float("nan")]).max().numpy()
assert math.isnan(n.item()), f"{n.item()} is not nan"
def test_inf_where(self):
x = Tensor.full((3, 3), float("inf"))
n = (x < 0).where(x, 1).numpy()
assert np.all(n == 1.)
def _get_index_randoms(self):
# indices cannot have gradient
a = torch.randint(low=-1, high=1, size=(2,1,1,1,1,1), dtype=torch.int64, requires_grad=False)
b = torch.randint(high=1, size=(1,3,1,1,1,1), dtype=torch.int64, requires_grad=False)
c = torch.randint(low=-5, high=5, size=(1,1,4,1,1,1), dtype=torch.int64, requires_grad=False)
d = torch.randint(high=4, size=(2,1,1,5,1,1), dtype=torch.int64, requires_grad=False)
e = torch.randint(high=1, size=(1,1,1,1,6,1), dtype=torch.int64, requires_grad=False)
i, j, k, o, p = [Tensor(tor.detach().numpy().astype(np.int32), requires_grad=False) for tor in [a,b,c,d,e]]
return a,b,c,d,e,i,j,k,o,p
def test_slice_fancy_indexing_no_dim_collapse(self):
a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
# no dim collapse from int or dim injection from None
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,d,e], lambda x: x[i,j,k,o,p])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[:,b,c,d,:], lambda x: x[:,j,k,o,:])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,...], lambda x: x[i,j,...])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,...,e], lambda x: x[i,...,p])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[...,c,:,e], lambda x: x[...,k,:,p])
def test_slice_fancy_indexing_dim_collapse_int(self):
a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
# dim collapse from int
helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,b,c,d,e], lambda x: x[1,j,k,o,p])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,3,d,e], lambda x: x[i,j,3,o,p])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,b,2,d,2], lambda x: x[1,j,2,o,2])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,2,2,2,e], lambda x: x[i,2,2,2,p])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,:,3:11:2,d,0:2], lambda x: x[1,:,3:11:2,o,0:2])
def test_slice_fancy_indexing_dim_inject_none(self):
a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
# dim injection from None
helper_test_op([(2,5,6,5,3,4)], lambda x: x[None,b,c,d,e], lambda x: x[None,j,k,o,p])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,d,None], lambda x: x[i,j,k,o,None])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,None,d,e], lambda x: x[i,j,None,o,p])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[None,b,c,d,None], lambda x: x[None,j,k,o,None])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,:,None,d,e], lambda x: x[i,:,None,o,p])
def test_slice_fancy_indexing_dim_inject_and_collapse(self):
a,b,c,d,e,i,j,k,o,p = self._get_index_randoms() # noqa
# dim injection and collapse
helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,b,None,d,1], lambda x: x[1,j,None,o,1])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[None,b,2,d,None], lambda x: x[None,j,2,o,None])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[...,1,d,None], lambda x: x[...,1,o,None])
def test_slice_fancy_indexing_with_tensors(self):
# indexing using idx with different dim
helper_test_op([(2,3)], lambda x: x[torch.tensor([[0,0,0],[0,0,0]]), torch.tensor(1)],
lambda x: x[Tensor([[0,0,0],[0,0,0]]), Tensor(1)])
helper_test_op([(2,3)], lambda x: x[torch.tensor([1]), torch.tensor([[0,0,0],[0,0,0]])],
lambda x: x[Tensor([1]), Tensor([[0,0,0],[0,0,0]])])
helper_test_op([(2,3)], lambda x: x[torch.tensor([[0,0,0],[0,0,0]]), torch.tensor([2,1,1])],
lambda x: x[Tensor([[0,0,0],[0,0,0]]), Tensor([2,1,1])])
helper_test_op([(2,3)], lambda x: x[torch.tensor([[0,1,-1],[-1,-2,0]]), torch.tensor([2,1,-1])],
lambda x: x[Tensor([[0,1,-1],[-1,-2,0]]), Tensor([2,1,-1])])
def test_slice_fancy_indexing_list_indices(self):
a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[0]]], lambda x: x[[[0]]])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[0],b,c,d,:], lambda x: x[[0],j,k,o,:])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[[0]]],b,c,d,[[1]]], lambda x: x[[[[0]]],j,k,o,[[1]]])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[1,0],b,c,d,:], lambda x: x[[1,0],j,k,o,:])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[1,2,3],...], lambda x: x[i,j,k,[1,2,3],...])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[[1],[2],[3]],...], lambda x: x[i,j,k,[[1],[2],[3]],...])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,[2,1,0],c,[2,1,0],e], lambda x: x[i,[2,1,0],k,[2,1,0],p])
def test_slice_fancy_indexing_tuple_indices(self):
a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
helper_test_op([(2,5,6,5,3,4)], lambda x: x[(((0,),),)], lambda x: x[(((0,),),)])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[(0,),b,c,d,:], lambda x: x[(0,),j,k,o,:])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[(1,0),b,c,d,:], lambda x: x[(1,0),j,k,o,:])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,(1,2,3),...], lambda x: x[i,j,k,(1,2,3),...])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,((2,),(1,),(0,)),c,(2,1,0)], lambda x: x[i,((2,),(1,),(0,)),k,(2,1,0)])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,(2,1,0),None,c,(2,1,0),e], lambda x: x[1,(2,1,0),None,k,(2,1,0),p])
def test_slice_fancy_indexing_list_with_tensors(self):
a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a]], lambda x: x[[i]])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,1]], lambda x: x[[i,1]])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,[1,1]]], lambda x: x[[i,[1,1]]])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,(1,1)]], lambda x: x[[i,(1,1)]])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,b,c,d,e]], lambda x: x[[i,j,k,o,p]])
def test_slice_fancy_indexing_errors(self):
a = Tensor.ones(10,11,12)
# tensors used as indices must be int tensors
with self.assertRaises(IndexError): a[Tensor(1.1)]
with self.assertRaises(IndexError): a[Tensor([True, True])]
# shape mismatch, cannot broadcast. either exception is okay
with self.assertRaises((IndexError, ValueError)): a[Tensor.randint(3,1,1,1), Tensor.randint(1,4,1,1), Tensor.randint(2,4,4,1)]
with self.assertRaises((IndexError, ValueError)): a[Tensor.randint(3,1,1,1), Tensor.randint(1,4,1,1,1)]
def test_gather(self):
# indices cannot have gradient
# indices cannot be negative (torch gather)
b = torch.randint(3, size=[3,4,5], dtype=torch.int64, requires_grad=False)
a = Tensor(b.detach().numpy().astype(np.int32), dtype=dtypes.int32, requires_grad=False)
helper_test_op([(4,5,6)], lambda x: x.gather(index=b, dim=0), lambda x: x.gather(idx=a, dim=0))
helper_test_op([(4,5,6)], lambda x: x.gather(index=b, dim=1), lambda x: x.gather(idx=a, dim=1))
helper_test_op([(4,5,6)], lambda x: x.gather(index=b, dim=2), lambda x: x.gather(idx=a, dim=2))
helper_test_op([(3,4,5)], lambda x: x.gather(index=b, dim=0), lambda x: x.gather(idx=a, dim=0))
self.helper_test_exception([(4,5,6)], lambda x: x.gather(index=torch.tensor([1], dtype=torch.int64), dim=0),
lambda x: x.gather(idx=Tensor([1], dtype=dtypes.int32), dim=0), expected=(RuntimeError, AssertionError))
self.helper_test_exception([(2,1,1)], lambda x: x.gather(index=b, dim=0),
lambda x: x.gather(idx=a, dim=0), expected=(RuntimeError, AssertionError))
def test_scaled_product_attention(self):
helper_test_op([(32,8,16,64), (32,8,16,64), (32,8,16,64)], torch.nn.functional.scaled_dot_product_attention, Tensor.scaled_dot_product_attention)
helper_test_op([(32,8,16,64), (32,8,16,64), (32,8,16,64), (32,8,16,16)],
lambda x,y,z,m: torch.nn.functional.scaled_dot_product_attention(x,y,z,attn_mask=m),
lambda x,y,z,m: Tensor.scaled_dot_product_attention(x,y,z,attn_mask=m))
def test_scaled_product_attention_causal(self):
helper_test_op([(32,8,16,64), (32,8,16,64), (32,8,16,64)],
lambda x,y,z: torch.nn.functional.scaled_dot_product_attention(x,y,z,is_causal=True),
lambda x,y,z: Tensor.scaled_dot_product_attention(x,y,z,is_causal=True))
def test_binary_crossentropy(self):
helper_test_op([(32,10), (32,10)], lambda x,y: torch.nn.functional.binary_cross_entropy(x.sigmoid(),torch.clip(y,0,1)),
lambda x,y: x.sigmoid().binary_crossentropy(y.clip(0,1)))
helper_test_op([(32,10), (32,10)], lambda x,y: torch.nn.functional.binary_cross_entropy_with_logits(x,torch.clip(y,0,1)),
lambda x,y: x.binary_crossentropy_logits(y.clip(0,1)))
helper_test_op([(32,10), (32,10)], lambda x,y: torch.nn.functional.binary_cross_entropy_with_logits(x,torch.clip(y,0,1)),
lambda x,y: x.sigmoid().binary_crossentropy(y.clip(0,1)))
helper_test_op([(32,10), (32,10)], lambda x,y: torch.nn.functional.binary_cross_entropy(x.sigmoid(),torch.clip(y,0,1)),
lambda x,y: x.binary_crossentropy_logits(y.clip(0,1)))
def test_one_hot(self):
data = [1, 2, 4]
helper_test_op([], lambda: torch.nn.functional.one_hot(torch.tensor(data), 6).type(torch.int32),
lambda: Tensor(data).one_hot(6), forward_only=True)
data = [[[1, 2, 3], [0, 3, 5]], [[1, 2, 3], [0, 3, 5]]]
helper_test_op([], lambda: torch.nn.functional.one_hot(torch.tensor(data), 8).type(torch.int32),
lambda: Tensor(data).one_hot(8), forward_only=True)
if __name__ == '__main__':
np.random.seed(1337)
unittest.main(verbosity=2)
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