mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-06-13 00:15:35 +08:00
a0965ee198
* models matrix * fix typo and install gpu deps * install llvm deps if needed * fix * testops with cuda * remove pip cache since not work * cuda env * install cuda deps * maybe it will work now * i can't read * all tests in matrix * trim down more * opencl stuff in matrix * opencl pip cache * test split * change cuda test exclusion * test * fix cuda maybe * add models * add more n=auto * third thing * fix bug * cache pip more * change name * update tests * try again cause why not * balance * try again... * try apt cache for cuda * try on gpu: * try cuda again * update packages step * replace libz-dev with zlib1g-dev * only cache cuda * why error * fix gpuocelot bug * apt cache err * apt cache to slow? * opt and image in single runner * add a couple n=autos * remove test matrix * try cuda apt cache again * libz-dev -> zlib1g-dev * remove -s since not supported by xdist * the cache takes too long and doesn't work * combine webgpu and metal tests * combine imagenet to c and cpu tests * torch tests with linters * torch back by itself * small windows clang test with torch tests * fix a goofy windows bug * im dumb * bro * clang with linters * fix pylint error * linter not work on windows * try with clang again * clang and imagenet? * install deps * fix * fix quote * clang by itself (windows too slow) * env vars for imagenet * cache pip for metal and webgpu tests * try torch with metal and webgpu * doesn't work, too long * remove -v * try -n=logical * don't use logical * revert accidental thing * remove some prints unless CI * fix print unless CI * ignore speed tests for slow tests * clang windows in matrix (ubuntu being tested in imagenet->c test) * try manual pip cache * fix windows pip cache path * all manual pip cache * fix pip cache dir for macos * print_ci function in helpers * CI as variable, no print_ci * missed one * cuda tests with docker image * remove setup-python action for cuda * python->python3? * remove -s -v * try fix pip cache * maybe fix * try to fix pip cache * is this the path? * maybe cache pip * try again * create wheels dir * ? * cuda pip deps in dockerfile * disable pip cache for clang * image from ghcr instead of docker hub * why is clang like this * fast deps * try use different caches * remove the fast thing * try with lighter image * remove setup python for cuda * small docker and cuda fast deps * ignore a few more tests * cool docker thing (maybe) * oops * quotes * fix docker command * fix bug * ignore train efficientnet test * remove dockerfile (docker stuff takes too long) * remove docker stuff and normal cuda * oops * ignore the tests for cuda * does this work * ignore test_train on slow backends * add space * llvm ignore same tests as cuda * nvm * ignore lr scheduler tests * get some stats * fix ignore bug * remove extra ' * remove and * ignore test for llvm * change ignored tests and durationon all backends * fix * and -> or * ignore some more cuda tests * finally? * does this fix it * remove durations=0 * add some more tests to llvm * make last pytest more readable * fix * don't train efficientnet on cpu * try w/out pip cache * pip cache seems to be generally better * pytest file markers * try apt fast for cuda * use quick install for apt-fast * apt-fast not worth * apt-get to apt * fix typo * suppress warnings * register markers * disable debug on fuzz tests * change marker names * apt update and apt install in one command * update marker names in test.yml * webgpu pytest marker
1111 lines
56 KiB
Python
1111 lines
56 KiB
Python
import torch
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import time
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import math
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import numpy as np
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import unittest
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import getenv, IMAGE, DEBUG, CI
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from tinygrad.lazy import Device
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import pytest
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pytestmark = pytest.mark.webgpu
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if CI:
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import warnings
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warnings.filterwarnings("ignore", message="Non-empty compiler output encountered")
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FORWARD_ONLY = getenv("FORWARD_ONLY", 0)
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PRINT_TENSORS = getenv("PRINT_TENSORS", 0)
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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, a=-0.5, b=3):
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if tinygrad_fxn is None: tinygrad_fxn = torch_fxn
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ts, tst = prepare_test_op(a, b, shps, vals)
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st = time.monotonic()
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out = torch_fxn(*ts)
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torch_fp = time.monotonic() - st
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st = time.monotonic()
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ret = tinygrad_fxn(*tst).realize()
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tinygrad_fp = time.monotonic() - st
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def compare(s, x,y,atol,rtol):
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if PRINT_TENSORS: print(s, x, y)
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assert x.shape == y.shape, f"shape mismatch: tinygrad={x.shape} | torch={y.shape}"
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try:
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np.testing.assert_allclose(x,y, atol=atol, rtol=rtol)
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except Exception:
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raise Exception(f"{s} failed shape {x.shape}")
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if DEBUG >= 6:
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np.set_printoptions(linewidth=200, suppress=True)
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print(ret.numpy())
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print(out.detach().numpy())
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compare("forward pass", ret.numpy(), out.detach().numpy(), atol=atol, rtol=rtol)
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torch_fbp, tinygrad_fbp = np.nan, np.nan
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if not forward_only and not FORWARD_ONLY:
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st = time.monotonic()
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(out+1).square().mean().backward()
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torch_fbp = time.monotonic() - st
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st = time.monotonic()
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(ret+1).square().mean().backward()
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for tt in tst: tt.grad.realize()
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tinygrad_fbp = time.monotonic() - st
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for i, (t, tt) in enumerate(zip(ts, tst)):
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compare(f"backward pass tensor {i}", tt.grad.numpy(), t.grad.detach().numpy(), atol=grad_atol, rtol=grad_rtol)
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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="")
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def prepare_test_op(a, b, shps, vals):
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torch.manual_seed(0)
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np.random.seed(0)
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if shps is None: ts = [torch.tensor(x, requires_grad=True) for x in vals]
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else: ts = [torch.tensor((np.random.random(size=x) + a) * b, requires_grad=True, dtype=torch.float32) for x in shps]
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tst = [Tensor(x.detach().numpy(), requires_grad=not FORWARD_ONLY) for x in ts]
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return ts, tst
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class TestOps(unittest.TestCase):
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def helper_test_exception(self, shps, torch_fxn, tinygrad_fxn, expected, exact=False, vals=None, a=-0.5, b=3):
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ts, tst = prepare_test_op(a, b, shps, vals)
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with self.assertRaises(expected) as torch_cm:
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torch_fxn(*ts)
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with self.assertRaises(expected) as tinygrad_cm:
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tinygrad_fxn(*tst)
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if exact: self.assertEqual(str(torch_cm.exception), str(tinygrad_cm.exception))
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if not CI: print("\ntesting %40r torch/tinygrad exception: %s / %s" % (shps, torch_cm.exception, tinygrad_cm.exception), end="")
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def test_full_like(self):
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a = Tensor([[1,2,3],[4,5,6]])
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b = torch.tensor([[1,2,3],[4,5,6]])
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helper_test_op([], lambda: torch.full_like(b, 4), lambda: Tensor.full_like(a, 4), forward_only=True)
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def test_full(self):
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helper_test_op([], lambda: torch.full((45,65), 4), lambda: Tensor.full((45,65), 4), forward_only=True)
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def test_zeros(self):
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helper_test_op([], lambda: torch.zeros(45,65), lambda: Tensor.zeros(45,65), forward_only=True)
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helper_test_op([], lambda: torch.zeros([45,65]), lambda: Tensor.zeros([45,65]), forward_only=True)
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helper_test_op([], lambda: torch.zeros([]), lambda: Tensor.zeros([]), forward_only=True)
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def test_zeros_like(self):
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a = Tensor([[1,2,3],[4,5,6]])
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b = torch.tensor([[1,2,3],[4,5,6]])
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helper_test_op([], lambda: torch.zeros_like(b), lambda: Tensor.zeros_like(a), forward_only=True)
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def test_empty_0(self):
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helper_test_op([], lambda: torch.empty(45,65)*0/0, lambda: Tensor.empty(45,65)*0/0, forward_only=True)
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def test_ones(self):
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helper_test_op([], lambda: torch.ones(45,65), lambda: Tensor.ones(45,65), forward_only=True)
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helper_test_op([], lambda: torch.ones([45,65]), lambda: Tensor.ones([45,65]), forward_only=True)
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helper_test_op([], lambda: torch.ones([]), lambda: Tensor.ones([]), forward_only=True)
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def test_ones_like(self):
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a = Tensor([[1,2,3],[4,5,6]])
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b = torch.tensor([[1,2,3],[4,5,6]])
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helper_test_op([], lambda: torch.ones_like(b), lambda: Tensor.ones_like(a), forward_only=True)
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def test_eye(self):
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helper_test_op([], lambda: torch.eye(10), lambda: Tensor.eye(10), forward_only=True)
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helper_test_op([], lambda: torch.eye(1), lambda: Tensor.eye(1), forward_only=True)
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def test_chunk(self):
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tor = torch.arange(13).repeat(8, 1).chunk(6, 1)
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ten = Tensor.arange(13).repeat((8, 1)).chunk(6, 1)
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assert len(tor) == len(ten)
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for i in range(len(tor)):
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helper_test_op([], lambda: tor[i], lambda: ten[i], forward_only=True)
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tor = torch.arange(13).repeat(8, 1).chunk(6, 0)
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ten = Tensor.arange(13).repeat((8, 1)).chunk(6, 0)
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assert len(tor) == len(ten)
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for i in range(len(tor)):
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helper_test_op([], lambda: tor[i], lambda: ten[i], forward_only=True)
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tor = torch.arange(13).repeat(8, 1).chunk(3, -1)
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ten = Tensor.arange(13).repeat((8, 1)).chunk(3, -1)
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assert len(tor) == len(ten)
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for i in range(len(tor)):
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helper_test_op([], lambda: tor[i], lambda: ten[i], forward_only=True)
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tor = torch.arange(13).repeat(8, 3, 3).chunk(3, -2)
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ten = Tensor.arange(13).repeat((8, 3, 3)).chunk(3, -2)
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assert len(tor) == len(ten)
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for i in range(len(tor)):
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helper_test_op([], lambda: tor[i], lambda: ten[i], forward_only=True)
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def test_arange(self):
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helper_test_op([], lambda: torch.arange(10), lambda: Tensor.arange(10), forward_only=True)
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helper_test_op([], lambda: torch.arange(5, 10, 3), lambda: Tensor.arange(5, 10, 3), forward_only=True)
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helper_test_op([], lambda: torch.arange(10, 5, -3), lambda: Tensor.arange(10, 5, -3), forward_only=True)
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helper_test_op([], lambda: torch.arange(11, 5, -3), lambda: Tensor.arange(11, 5, -3), forward_only=True)
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def test_where(self):
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helper_test_op(
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[(100,)],
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lambda x: torch.where(x > 0.5, 4, 2),
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lambda x: (x > 0.5).where(4, 2), forward_only=True)
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for shps in [[(8,),(1,),(1,)], [(10,10),(10,),(10,)], [(100,)]*3, [(10,10)]*3]:
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helper_test_op(
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shps,
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lambda x, a, b: torch.where(x > 0.5, a, b),
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lambda x, a, b: (x > 0.5).where(a, b), forward_only=True)
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def test_where_permute(self):
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helper_test_op(
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[(5, 5)],
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lambda x: torch.where(x > 0.5, 4, 2).permute((1, 0)),
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lambda x: (x > 0.5).where(4, 2).permute((1, 0)), forward_only=True)
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def _test_cmp(self, fxn, reverse=True):
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for shps in [[(3, 4, 5), (3, 4, 5)], [(3, 4, 5), (5,)], [(5,), (3, 4, 5)]]:
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helper_test_op(shps, fxn, fxn, forward_only=True)
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helper_test_op(None, fxn, fxn, forward_only=True, vals=[[0.,1,2], [2.,1,0]])
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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]])
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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]])
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def test_cmp_eq(self): self._test_cmp(lambda x,y: x==y, reverse=False)
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def test_cmp_gt(self): self._test_cmp(lambda x,y: x>y)
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def test_cmp_ge(self): self._test_cmp(lambda x,y: x>=y)
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def test_cmp_lt(self): self._test_cmp(lambda x,y: x<y)
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def test_cmp_le(self): self._test_cmp(lambda x,y: x<=y)
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def test_cmp_eq_backwards(self):
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t1 = torch.ones(4, requires_grad=True)
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t2 = torch.ones(4, requires_grad=True)
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self.assertRaises(RuntimeError, (t1 == t2).sum().backward)
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tt1 = Tensor.ones(4, requires_grad=True)
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tt2 = Tensor.ones(4, requires_grad=True)
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self.assertRaises(RuntimeError, (tt1.eq(tt2)).sum().backward)
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def test_cmp_lt_backwards(self):
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t1 = torch.ones(4, requires_grad=True)
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t2 = torch.ones(4, requires_grad=True)
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self.assertRaises(RuntimeError, (t1 < t2).sum().backward)
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tt1 = Tensor.ones(4, requires_grad=True)
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tt2 = Tensor.ones(4, requires_grad=True)
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self.assertRaises(RuntimeError, (tt1 < tt2).sum().backward)
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def test_floor(self):
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helper_test_op([(45,65)], lambda x: torch.floor(x), lambda x: x.floor(), forward_only=True)
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a, b = Tensor([1.0, 2.1, 0.0, -5.0, -2.5]), torch.tensor([1.0, 2.1, 0.0, -5.0, -2.5])
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helper_test_op([], lambda: torch.floor(b), lambda: Tensor.floor(a), forward_only=True)
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def test_ceil(self):
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helper_test_op([(45,65)], lambda x: torch.ceil(x), lambda x: x.ceil(), forward_only=True)
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a, b = Tensor([1.0, 2.1, 0.0, -5.0, -2.5]), torch.tensor([1.0, 2.1, 0.0, -5.0, -2.5])
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helper_test_op([], lambda: torch.ceil(b), lambda: Tensor.ceil(a), forward_only=True)
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def test_tril(self):
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helper_test_op([(3,3)], lambda x: x.tril(), lambda x: x.tril())
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helper_test_op([(3,3)], lambda x: x.tril(1), lambda x: x.tril(1))
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helper_test_op([(3,3)], lambda x: x.tril(-1), lambda x: x.tril(-1))
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helper_test_op([(5,3,3)], lambda x: x.tril(), lambda x: x.tril())
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helper_test_op([(5,3,3)], lambda x: x.tril(1), lambda x: x.tril(1))
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def test_triu(self):
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helper_test_op([(3,3)], lambda x: x.triu(), lambda x: x.triu())
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helper_test_op([(3,3)], lambda x: x.triu(1), lambda x: x.triu(1))
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helper_test_op([(3,3)], lambda x: x.triu(-1), lambda x: x.triu(-1))
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helper_test_op([(5,3,3)], lambda x: x.triu(), lambda x: x.triu())
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helper_test_op([(5,3,3)], lambda x: x.triu(1), lambda x: x.triu(1))
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def test_maximum(self):
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helper_test_op([(45,65), (45,65)], torch.maximum, Tensor.maximum)
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helper_test_op([(), ()], torch.maximum, Tensor.maximum)
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helper_test_op(None, torch.maximum, Tensor.maximum, vals=[[1., 0., 3., 4.], [1., 2., 3., 0.]])
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def test_minimum(self):
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helper_test_op([(45,65), (45,65)], torch.minimum, Tensor.minimum)
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helper_test_op([(), ()], torch.minimum, Tensor.minimum)
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def test_add(self):
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helper_test_op([(45,68), (45,68)], lambda x,y: x+y, Tensor.add)
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def test_add_number(self):
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helper_test_op([(), ()], lambda x,y: x+y, Tensor.add)
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def test_add3(self):
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helper_test_op([(45,65), (45,65), (45,65)], lambda x,y,z: x+y+z)
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def test_add_simple(self):
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helper_test_op([(256), (256)], lambda x,y: x+y, Tensor.add, forward_only=True)
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def test_broadcasted_add(self):
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helper_test_op([(45,65), (45,1)], lambda x,y: x+y, lambda x,y: x+y)
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helper_test_op([(45,65), ()], lambda x,y: x+y, lambda x,y: x+y)
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def test_broadcasted_add_2(self):
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helper_test_op([(45,65), (65,)], lambda x,y: x+y, lambda x,y: x+y)
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def test_sub(self):
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helper_test_op([(45,65), (45,65)], lambda x,y: x-y, Tensor.sub)
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helper_test_op([(), ()], lambda x,y: x-y, Tensor.sub)
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def test_neg(self):
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helper_test_op([(45,65)], lambda x: -x)
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helper_test_op([()], lambda x: -x)
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def test_mul(self):
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helper_test_op([(64,64), (64,64)], lambda x,y: x*y, Tensor.mul)
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def test_mul_number(self):
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helper_test_op([(), ()], lambda x,y: x*y, Tensor.mul)
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def test_mul_const(self):
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helper_test_op([(45,65)], lambda x: x*2, lambda x: x*2)
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helper_test_op([(45,65)], lambda x: x*-1, lambda x: x*-1)
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helper_test_op([(45,65)], lambda x: 255*x, lambda x: 255*x)
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def test_div(self):
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helper_test_op([(45,65), (45,65)], lambda x,y: x/y, Tensor.div)
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helper_test_op([(), ()], lambda x,y: x/y, Tensor.div)
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def test_div_const(self):
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helper_test_op([(45,65)], lambda x: x/255, lambda x: x/255)
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helper_test_op([(45,65)], lambda x: x/1, lambda x: x/1)
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helper_test_op([(45,65)], lambda x: 1/x, lambda x: 1/x)
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helper_test_op([(45,65)], lambda x: x/2, lambda x: x/2)
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helper_test_op([(45,65)], lambda x: 2/x, lambda x: 2/x)
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helper_test_op([()], lambda x: x/2, lambda x: x/2)
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helper_test_op([()], lambda x: 2/x, lambda x: 2/x)
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@unittest.skipIf(Device.DEFAULT in ["METAL", "WEBGPU"], "WEBGPU does not have support for inf/nan, METAL has issues with -inf")
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def test_mul_const_naninf(self):
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helper_test_op([(45,65)], lambda x: x*float("inf"), lambda x: x*float("inf"))
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helper_test_op([(45,65)], lambda x: x*-float("inf"), lambda x: x*-float("inf"))
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helper_test_op([(45,65)], lambda x: x*float("nan"), lambda x: x*float("nan"))
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@unittest.skipIf(Device.DEFAULT in ["METAL", "WEBGPU"], "WEBGPU does not have support for inf/nan, METAL has issues with -inf")
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def test_div_const_naninf(self):
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helper_test_op([(45,65)], lambda x: x/float("inf"), lambda x: x/float("inf"))
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helper_test_op([(45,65)], lambda x: x/-float("inf"), lambda x: x/-float("inf"))
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helper_test_op([(45,65)], lambda x: x/float("nan"), lambda x: x/float("nan"))
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helper_test_op([(45,65)], lambda x: float("inf")/x, lambda x: float("inf")/x)
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helper_test_op([(45,65)], lambda x: (-float("inf"))/x, lambda x: (-float("inf"))/x)
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helper_test_op([(45,65)], lambda x: float("nan")/x, lambda x: float("nan")/x)
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def test_pow(self):
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# TODO: why is a=0 for these tests?
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helper_test_op([(45,65)], lambda x: x**2, lambda x: Tensor.pow(x,2), a=0)
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helper_test_op([(45,65)], lambda x: x**3, lambda x: Tensor.pow(x,3), a=0)
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helper_test_op([(45,65)], lambda x: x**-2, lambda x: Tensor.pow(x,-2), a=0)
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helper_test_op([(45,65), (45,65)], lambda x,y: x**y, Tensor.pow, a=0)
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helper_test_op([()], lambda x: x**2, lambda x: Tensor.pow(x,2), a=0)
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helper_test_op([()], lambda x: x**-2, lambda x: Tensor.pow(x,-2), a=0)
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# Regression tests for https://github.com/tinygrad/tinygrad/issues/1151
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helper_test_op([(45,65)], lambda x: x**3, lambda x: Tensor.pow(x,3), a=-10)
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helper_test_op([()], lambda x: x**3, lambda x: Tensor.pow(x,3), a=-10)
|
|
def test_pow_const(self):
|
|
helper_test_op([(45,65)], lambda x: x**1.0, lambda x: x**1.0)
|
|
helper_test_op([(45,65)], lambda x: x**-1.0, lambda x: x**-1.0)
|
|
helper_test_op([(45,65)], lambda x: 1.0**x, lambda x: 1.0**x)
|
|
helper_test_op([(45,65)], lambda x: x**2.0, lambda x: x**2.0)
|
|
helper_test_op([(45,65)], lambda x: 2.0**x, lambda x: 2.0**x)
|
|
helper_test_op([()], lambda x: x**2.0, lambda x: x**2.0)
|
|
helper_test_op([()], lambda x: 2.0**x, lambda x: 2.0**x)
|
|
def test_sqrt(self):
|
|
helper_test_op([(45,65)], lambda x: x.sqrt(), Tensor.sqrt, a=0)
|
|
helper_test_op([()], lambda x: x.sqrt(), Tensor.sqrt, a=0)
|
|
def test_rsqrt(self):
|
|
helper_test_op([(45,65)], lambda x: torch.rsqrt(x), Tensor.rsqrt, a=0)
|
|
helper_test_op([()], lambda x: torch.rsqrt(x), Tensor.rsqrt, a=0)
|
|
|
|
def test_sin(self):
|
|
helper_test_op([(45,65)], lambda x: x.sin(), Tensor.sin, a=0)
|
|
def test_cos(self):
|
|
helper_test_op([(45,65)], lambda x: x.cos(), Tensor.cos, a=0)
|
|
def test_tan(self):
|
|
helper_test_op([(45,65)], lambda x: x.tan(), Tensor.tan, a=0)
|
|
|
|
def test_relu(self):
|
|
helper_test_op([(64,64)], lambda x: x.relu(), Tensor.relu)
|
|
helper_test_op([()], lambda x: x.relu(), Tensor.relu)
|
|
def test_relu_exact(self):
|
|
helper_test_op(None, lambda x: x.relu(), Tensor.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)], lambda x: torch.abs(x), Tensor.abs)
|
|
helper_test_op([()], lambda x: torch.abs(x), Tensor.abs)
|
|
def test_log(self):
|
|
helper_test_op([(45,65)], lambda x: torch.log(x), Tensor.log)
|
|
helper_test_op([()], lambda x: torch.log(x), Tensor.log)
|
|
def test_log2(self):
|
|
helper_test_op([(45,65)], lambda x: torch.log2(x), Tensor.log2)
|
|
helper_test_op([()], lambda x: torch.log2(x), Tensor.log2)
|
|
def test_exp(self):
|
|
helper_test_op([(45,65)], lambda x: torch.exp(x), Tensor.exp)
|
|
helper_test_op([()], lambda x: torch.exp(x), Tensor.exp)
|
|
def test_sign(self):
|
|
helper_test_op([(45,65)], lambda x: torch.sign(x), Tensor.sign)
|
|
helper_test_op([()], lambda x: torch.sign(x), Tensor.sign)
|
|
def test_softsign(self):
|
|
helper_test_op([(45,65)], lambda x: torch.nn.functional.softsign(x), Tensor.softsign)
|
|
helper_test_op([()], lambda x: torch.nn.functional.softsign(x), Tensor.softsign)
|
|
def test_sigmoid(self):
|
|
helper_test_op([(45,65)], lambda x: x.sigmoid(), Tensor.sigmoid)
|
|
helper_test_op([(45,65)], lambda x: x.sigmoid(), Tensor.sigmoid, a=100)
|
|
helper_test_op([(45,65)], lambda x: x.sigmoid(), Tensor.sigmoid, a=-100)
|
|
helper_test_op([()], lambda x: x.sigmoid(), Tensor.sigmoid, forward_only=True)
|
|
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)
|
|
helper_test_op([()], lambda x: torch.nn.functional.softplus(x), Tensor.softplus, atol=1e-6, 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, a=100)
|
|
helper_test_op([(45,65)], lambda x: torch.nn.functional.gelu(x, approximate="tanh"), Tensor.gelu, a=-100)
|
|
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, a=100)
|
|
helper_test_op([(45,65)], lambda x: x * torch.sigmoid(1.702 * x), Tensor.quick_gelu, a=-100)
|
|
helper_test_op([()], lambda x: x * torch.sigmoid(1.702 * x), Tensor.quick_gelu)
|
|
def test_elu(self):
|
|
helper_test_op([(45,65)], lambda x: torch.nn.functional.elu(x), 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([()], 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)
|
|
helper_test_op([()], 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)
|
|
helper_test_op([()], 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)
|
|
helper_test_op([()], _mish_pytorch, Tensor.mish, atol=1e-4)
|
|
@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([(32,45,65), (65)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
|
|
helper_test_op([(65), (32,65,45)], lambda x,y: x.matmul(y), Tensor.dot, 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)
|
|
helper_test_op([(32,45,65), (32,65,100)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
|
|
with self.assertRaises(AssertionError):
|
|
a = Tensor(3.14)
|
|
a.matmul(a)
|
|
def test_simple_cumsum(self):
|
|
helper_test_op([(1024)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0), atol=1e-6)
|
|
def test_cumsum(self):
|
|
helper_test_op([(20)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0), atol=1e-6)
|
|
helper_test_op([(20,30)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0), atol=1e-6)
|
|
helper_test_op([(20,30)], lambda x: torch.cumsum(x, dim=1), lambda x: Tensor.cumsum(x, axis=1), atol=1e-6)
|
|
helper_test_op([(20,30,40)], lambda x: torch.cumsum(x, dim=2), lambda x: Tensor.cumsum(x, axis=2), atol=1e-6)
|
|
helper_test_op([(20,30,40)], lambda x: torch.cumsum(x, dim=-1), lambda x: Tensor.cumsum(x, axis=-1), atol=1e-6)
|
|
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_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)
|
|
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(), Tensor.sum, vals=[[1.,1.]])
|
|
def test_sum_full(self):
|
|
helper_test_op([(16384)], lambda x: x.sum(), lambda x: x.sum())
|
|
def test_sum_small_full(self):
|
|
helper_test_op([(45,5)], lambda x: x.sum(), Tensor.sum)
|
|
def test_sum_relu(self):
|
|
helper_test_op([(3,4,5)], lambda x: x.relu().sum().relu(), lambda x: x.relu().sum().relu())
|
|
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))
|
|
helper_test_op([()], lambda x: x.sum(), Tensor.sum)
|
|
def test_min(self):
|
|
helper_test_op([(3,3)], lambda x: x.min(), Tensor.min)
|
|
helper_test_op([(45,3)], lambda x: x.min(), Tensor.min)
|
|
helper_test_op([(45,3)], lambda x: x.min().mul(0.5), lambda x: Tensor.min(x).mul(0.5))
|
|
helper_test_op([()], lambda x: x.min(), Tensor.min)
|
|
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]],
|
|
])
|
|
helper_test_op([(3,4,5,6)], lambda x: x.max(axis=1)[0], lambda x: Tensor.max(x, axis=1))
|
|
helper_test_op([()], lambda x: x.max(), Tensor.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)), lambda x: Tensor.mean(x, axis=(1,2)))
|
|
def test_std(self):
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x), lambda x: Tensor.std(x))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=None, correction=0), lambda x: Tensor.std(x, correction=0))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=None, correction=5), lambda x: Tensor.std(x, correction=5))
|
|
def test_std_axis(self):
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=0), lambda x: Tensor.std(x, axis=0))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=2), lambda x: Tensor.std(x, axis=2))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=[1, 2]), lambda x: Tensor.std(x, axis=[1, 2]))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=None), lambda x: Tensor.std(x, axis=None))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=0, dim=0), lambda x: Tensor.std(x, axis=0, correction=0))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=0, dim=2), lambda x: Tensor.std(x, axis=2, correction=0))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=0, dim=[1, 2]), lambda x: Tensor.std(x, axis=[1, 2], correction=0))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, correction=0, dim=None), lambda x: Tensor.std(x, axis=None, correction=0))
|
|
def test_std_keepdim(self):
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=None, keepdim=True), lambda x: Tensor.std(x, keepdim=True))
|
|
helper_test_op([(45, 65, 85)], lambda x: torch.std(x, dim=0, keepdim=True, correction=0), lambda x: Tensor.std(x, keepdim=True, correction=0, axis=0))
|
|
def test_log_softmax(self):
|
|
helper_test_op([(45,65)], lambda x: torch.nn.LogSoftmax(dim=1)(x), Tensor.log_softmax, atol=1e-7, grad_atol=1e-7)
|
|
helper_test_op([()], lambda x: torch.nn.LogSoftmax(dim=0)(x), 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), 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), 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), lambda x: x.log_softmax(2), 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)
|
|
helper_test_op([(45,65)], lambda x: x.tanh(), Tensor.tanh, atol=1e-6, grad_atol=1e-6, a=-100)
|
|
helper_test_op([()], lambda x: x.tanh(), Tensor.tanh, atol=1e-6, grad_atol=1e-6)
|
|
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), atol=1e-6, grad_atol=1e-6)
|
|
helper_test_op([()], lambda x: torch.nn.functional.hardtanh(x,-val, val), lambda x: x.hardtanh(-val, val), 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)
|
|
helper_test_op([()], 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)
|
|
helper_test_op([()], 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)
|
|
helper_test_op([()], 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)
|
|
helper_test_op([()], 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)
|
|
helper_test_op([()], lambda x: 2-x, lambda x: 2-x)
|
|
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):
|
|
helper_test_op(shapes, torch_op, tinygrad_op, a=-0.5 if tinygrad_op != Tensor.pow else 0.0)
|
|
|
|
def test_broadcast_simple(self):
|
|
helper_test_op([(45,65), (45,1)], lambda x,y: x/y, lambda x,y: x/y)
|
|
helper_test_op([(45,65), ()], lambda x,y: x/y, 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?
|
|
helper_test_op(shapes, torch_op, tinygrad_op, a=-0.5 if tinygrad_op != Tensor.pow else 0.0)
|
|
|
|
def test_slice_in_bounds_1dim(self):
|
|
helper_test_op([(3)], lambda x: x[1:3], lambda x: x[1:3])
|
|
helper_test_op([(3)], lambda x: x[0:2], lambda x: x[0:2])
|
|
helper_test_op([(3)], lambda x: x[-2:2], lambda x: x[-2:2])
|
|
|
|
def test_slice_on_0dim_tensor(self):
|
|
helper_test_op([()], lambda x: x[None], 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[1], lambda x: x[1])
|
|
helper_test_op([(3)], lambda x: x[-2], lambda x: x[-2])
|
|
helper_test_op([(10,10)], lambda x: x[1], lambda x: x[1])
|
|
helper_test_op([(3,3,3)], lambda x: x[1,1,1], lambda x: x[1,1,1])
|
|
|
|
def test_slice_in_bounds_multidim(self):
|
|
helper_test_op([(3,3,3)], lambda x: x[1:2], lambda x: x[1:2])
|
|
helper_test_op([(3,3,3)], lambda x: x[1:2, 2], lambda x: x[1:2, 2])
|
|
helper_test_op([(3,3,3)], lambda x: x[1:2, 1:2], lambda x: x[1:2, 1:2])
|
|
helper_test_op([(3,3,3)], lambda x: x[1:2, 1:2, 0:-1], 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], lambda x: x[None])
|
|
helper_test_op([(3,3,3)], lambda x: x[1:2, None], lambda x: x[1:2, None])
|
|
helper_test_op([(3,3,3)], lambda x: x[1:2, None, 1:2], lambda x: x[1:2, None, 1:2])
|
|
helper_test_op([(3,3,3)], lambda x: x[1:2, 1:2, None, -1], lambda x: x[1:2, 1:2, None, -1])
|
|
|
|
def test_slice_one_endpoint_out_of_bounds(self):
|
|
helper_test_op([(3,3,3)], lambda x: x[0:4], lambda x: x[0:4])
|
|
helper_test_op([(3,3,3)], lambda x: x[-6:4], lambda x: x[-6:4])
|
|
helper_test_op([(3,3,3)], lambda x: x[1:50], lambda x: x[1:50])
|
|
helper_test_op([(3,3,3)], lambda x: x[1:50, 1:2, -1], 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], lambda x: x[::2, ::3, ::4])
|
|
helper_test_op([(7,5,10)], lambda x: x[1:5:2, ::3, ::4], lambda x: x[1:5:2, ::3, ::4])
|
|
helper_test_op([(7,5,10)], lambda x: x[1:5:2, 3, ::4], 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], 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())
|
|
|
|
@unittest.skip("No suppport for tensors with 0s in shape")
|
|
def test_slice_both_endpoints_out_of_bounds(self):
|
|
helper_test_op([(3,3,3)], lambda x: x[5:10], lambda x: x[5:10], forward_only=True)
|
|
helper_test_op([(3,3,3)], lambda x: x[-15:-7], lambda x: x[-15:-7], forward_only=True)
|
|
|
|
@unittest.skip("No suppport for tensors with 0s in shape")
|
|
def test_slice_start_gt_end(self):
|
|
helper_test_op([(3,3,3)], lambda x: x[-2:2], lambda x: x[-2:2], forward_only=True)
|
|
helper_test_op([(3,3,3)], lambda x: x[-2:-5], lambda x: x[-2:-5], forward_only=True)
|
|
|
|
@unittest.skip("No suppport for tensors with 0s in shape")
|
|
def test_slice_empty(self):
|
|
helper_test_op([(10,10)], lambda x: x[1:1], lambda x: x[1:1], forward_only=True)
|
|
|
|
@unittest.skip("No suppport for tensors with 0s in shape")
|
|
def test_slice_zero_in_shape(self):
|
|
helper_test_op([(10,10)], lambda x: x[1:1], lambda x: x[1:1]) # x.shape = (0, 10)
|
|
helper_test_op([(3,3,3)], lambda x: x[-2:-5], lambda x: x[-2:-5]) # x.shape = (0, 3, 3)
|
|
|
|
def test_slice_errors(self):
|
|
a = Tensor.ones(4, 3)
|
|
with self.assertRaises(IndexError):
|
|
a[1, 77, 77, 77] # IndexError: (finds too many indices before the out of bounds)
|
|
a[1, 77] # IndexError: (out of bounds).
|
|
a[0, -77]
|
|
|
|
def test_slice_ellipsis(self):
|
|
helper_test_op([(3,3,3,3)], lambda x: x[..., 0], lambda x: x[..., 0])
|
|
helper_test_op([(3,3,3,3)], lambda x: x[0, ...], lambda x: x[0, ...])
|
|
helper_test_op([(3,3,3,3)], lambda x: x[0, ..., 0], lambda x: x[0, ..., 0])
|
|
helper_test_op([(3,3,3,3)], lambda x: x[0:3, ..., 2:3], lambda x: x[0:3, ..., 2:3])
|
|
helper_test_op([(3,3,3,3)], lambda x: x[None, 0:3, ..., 0, None], 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)))
|
|
|
|
def test_transpose(self):
|
|
helper_test_op([(3,3,3)], lambda x: x.transpose(1,2), lambda x: x.transpose(1,2))
|
|
helper_test_op([(3,3,3)], lambda x: x.transpose(0,2), lambda x: x.transpose(0,2))
|
|
helper_test_op([(1,2,3,4)], lambda x: x.movedim((3,0,2,1),(0,1,2,3)), lambda x: x.permute(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.permute(order=(3,2,1,0)))
|
|
helper_test_op([()], lambda x: x.permute(()), lambda x: x.permute(()))
|
|
|
|
|
|
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)))
|
|
helper_test_op([()], lambda x: torch.reshape(x, []), lambda x: x.reshape([]))
|
|
helper_test_op([(1,)], lambda x: torch.reshape(x, []), lambda x: x.reshape([]))
|
|
helper_test_op([()], lambda x: torch.reshape(x, [1]), lambda x: x.reshape([1]))
|
|
|
|
with self.assertRaises(AssertionError):
|
|
x = Tensor.ones((4,3,6,6))
|
|
x.reshape([])
|
|
|
|
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,)))
|
|
helper_test_op([(4,3,6,6)], lambda x: torch.flip(x, (0,1,3)).flip((0,)), lambda x: x.flip(axis=(0,1,3)).flip(0))
|
|
helper_test_op([(4,3,6,6)], lambda x: torch.flip(x, (3,)), lambda x: x.flip(axis=(-1,)))
|
|
helper_test_op([()], lambda x: torch.flip(x, ()), lambda x: x.flip(axis=()))
|
|
helper_test_op([(1,)], lambda x: torch.flip(x, ()), lambda x: x.flip(axis=()))
|
|
helper_test_op([(4, 3, 6, 6)], lambda x: torch.flip(x, ()), lambda x: x.flip(axis=()))
|
|
|
|
def test_squeeze(self):
|
|
helper_test_op([(1,3,6,6)], lambda x: torch.squeeze(x, 0), lambda x: x.squeeze(dim=0))
|
|
helper_test_op([(4,3,1,6)], lambda x: torch.squeeze(x, 1), lambda x: x.squeeze(dim=1))
|
|
helper_test_op([(4,3,6,6)], lambda x: torch.squeeze(x, 3), lambda x: x.squeeze(dim=3))
|
|
self.helper_test_exception([(4,3,6,6)], lambda x: torch.squeeze(x, 50), lambda x: x.squeeze(dim=50), expected=IndexError, exact=True)
|
|
self.helper_test_exception([(4,3,6,6)], lambda x: torch.squeeze(x, -50), lambda x: x.squeeze(dim=-50), expected=IndexError, exact=True)
|
|
helper_test_op([(4,3,6,1)], lambda x: torch.squeeze(x, -1), lambda x: x.squeeze(dim=-1))
|
|
helper_test_op([(4,3,6,6)], lambda x: torch.squeeze(x), lambda x: x.squeeze())
|
|
helper_test_op([(1,3,6,6)], lambda x: torch.squeeze(x), lambda x: x.squeeze())
|
|
helper_test_op([(2,3,1)], lambda x: torch.squeeze(x), lambda x: x.squeeze())
|
|
helper_test_op([()], lambda x: torch.squeeze(x, -1), lambda x: x.squeeze(dim=-1))
|
|
helper_test_op([()], lambda x: torch.squeeze(x, 0), lambda x: x.squeeze(dim=0))
|
|
self.helper_test_exception([()], lambda x: torch.squeeze(x, 10), lambda x: x.squeeze(dim=10), expected=IndexError, exact=True)
|
|
helper_test_op([()], lambda x: torch.squeeze(x), lambda x: x.squeeze())
|
|
|
|
def test_unsqueeze(self):
|
|
helper_test_op([(4,3,6,6)], lambda x: torch.unsqueeze(x, 0), lambda x: x.unsqueeze(dim=0))
|
|
helper_test_op([(4,3,6,6)], lambda x: torch.unsqueeze(x, 4), lambda x: x.unsqueeze(dim=4))
|
|
helper_test_op([(4,3,6,6)], lambda x: torch.unsqueeze(x, -1), lambda x: x.unsqueeze(dim=-1))
|
|
helper_test_op([(4,3,6,6)], lambda x: torch.unsqueeze(x, -3), lambda x: x.unsqueeze(dim=-3))
|
|
helper_test_op([()], lambda x: torch.unsqueeze(x, 0), lambda x: x.unsqueeze(dim=0))
|
|
|
|
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))
|
|
helper_test_op([()], lambda x: x.flatten(), lambda x: x.flatten())
|
|
helper_test_op([(1,)], lambda x: x.flatten(), lambda x: x.flatten())
|
|
|
|
def test_detach(self):
|
|
helper_test_op([(4,3,6,6)], lambda x: x.detach(), lambda x: x.detach(), forward_only=True)
|
|
helper_test_op([()], 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))
|
|
helper_test_op([()], lambda x: x.expand([]), lambda x: x.expand(shape=[]))
|
|
|
|
@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([(1,4,9,9,9), (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)
|
|
|
|
@unittest.skipIf(Device.DEFAULT == "METAL" and getenv("CI", "") != "", "broken in METAL CI")
|
|
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 or (Device.DEFAULT == "WEBGPU" and getenv("CI","") != "")), "no conv1d on images")
|
|
def test_conv1d(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]:
|
|
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(padding := 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):
|
|
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_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(padding := 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)
|
|
|
|
@unittest.skipIf(Device.DEFAULT == "METAL" and getenv("CI", "") != "", "broken in METAL CI")
|
|
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)
|
|
|
|
@unittest.skipIf(Device.DEFAULT == "METAL" and getenv("CI", "") != "", "broken in METAL CI")
|
|
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))
|
|
|
|
@unittest.skipIf(Device.DEFAULT == "CUDA", "CUDA fails on this")
|
|
def test_maxpool2d_unit_stride(self):
|
|
helper_test_op([(32,2,110,28)],
|
|
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([(32,2,110,28)],
|
|
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([(32,2,110,28)],
|
|
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(-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))
|
|
|
|
with self.assertRaises(AssertionError):
|
|
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):
|
|
x = Tensor.randn(45, 65, 3)
|
|
|
|
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=dim), lambda x, y, z: Tensor.stack([x, y, z], dim=dim))
|
|
|
|
with self.assertRaises(IndexError):
|
|
Tensor.stack([x], 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(45, 65, 3)
|
|
base_repeats = [2, 4, 3]
|
|
|
|
for reps in [[], [4], [2, 1], [3, 2, 2]]:
|
|
repeats = base_repeats + reps
|
|
helper_test_op([(45, 65, 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(AssertionError):
|
|
x.repeat((2, 4))
|
|
|
|
with self.assertRaises(AssertionError):
|
|
x.repeat((2, 0, 4))
|
|
|
|
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))
|
|
|
|
def test_matvec(self):
|
|
helper_test_op([(1,128), (128,128), (128,128)], lambda x,y,z: (x@y).relu()@z, 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.)
|
|
|
|
if __name__ == '__main__':
|
|
np.random.seed(1337)
|
|
unittest.main(verbosity=2)
|