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2442 lines
91 KiB
2442 lines
91 KiB
# this will be the new test_ops for the next level
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# schedule confirms the right things are capable of fusing
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# NOTE: this has overlap with external_test_opt.py
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import unittest, functools
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import numpy as np
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from typing import cast
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from hypothesis import assume, given, settings, strategies as strat
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from tinygrad import nn, dtypes, Device, Tensor, Variable
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from tinygrad.device import is_dtype_supported
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from tinygrad.dtype import DType, ImageDType
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from tinygrad.uop.ops import UOp, Ops, GroupOp, UPat
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from tinygrad.helpers import CI, DEBUG, SPLIT_REDUCEOP, GlobalCounters, Context, getenv, all_same, temp
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from tinygrad.schedule.rangeify import get_rangeify_map, Kernel
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from tinygrad.engine.schedule import create_schedule_with_vars
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from tinygrad.engine.realize import CompiledRunner, run_schedule, lower_schedule
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class KernelCountException(Exception): pass
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def check_schedule(t:Tensor|list[Tensor]|UOp, allowed:int, to_prerealize:list[Tensor]|None=None, filter_sink=True):
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if to_prerealize:
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with Context(DEBUG=0, TRACK_MATCH_STATS=0): Tensor.realize(*to_prerealize)
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if isinstance(t, Tensor): sched = t.schedule()
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elif isinstance(t, list) and isinstance(t[0], Tensor): sched = Tensor.schedule(*t)
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else:
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assert isinstance(t, UOp), f"can't schedule {t}"
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sink = UOp.sink(t) if t.op is not Ops.SINK else t
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becomes_map = get_rangeify_map(sink)
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sched, _ = create_schedule_with_vars(sink.substitute(becomes_map))
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# test lowering all the ScheduleItems to ExecItems
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kernel_cnt = len([si for si,ei in lower_schedule(sched.copy()) if isinstance(ei.prg, CompiledRunner) or not filter_sink])
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if kernel_cnt != allowed:
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print(f"SCHEDULE ISSUE, expecting {allowed} got {len(sched)}")
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if DEBUG >= 3:
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for i,s in enumerate(sched):
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print("kernel", i+1)
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print(s.ast)
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raise KernelCountException(f"{kernel_cnt} != {allowed}")
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return sched
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def _realize_weights(m):
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for p in nn.state.get_parameters(m): p.realize()
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def _test_conv2d(allowed:int, dtype:DType=dtypes.float):
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old_default_float, dtypes.default_float = dtypes.default_float, dtype
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dtypes.default_float = dtype
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Tensor.manual_seed(0)
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BS, CIN = 2, 3
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img = Tensor.randn(BS, CIN, 64, 64, requires_grad=True).realize()
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w = Tensor.uniform(16, CIN, 3, 3, requires_grad=True).realize()
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ret = Tensor.conv2d(img, w).relu().mean().backward()
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dtypes.default_float = old_default_float
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s = Tensor.schedule(ret, img.grad, w.grad)
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run_schedule(s.copy())
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cnt = len([si for si in s if si.ast.op is Ops.SINK])
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assert cnt == allowed, f"expected {allowed} kernels, got {cnt}"
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if getenv("CHECK", 1):
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import torch
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ref_img = torch.tensor(img.numpy(), requires_grad=True)
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ref_w = torch.tensor(w.numpy(), requires_grad=True)
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torch.nn.functional.conv2d(ref_img, ref_w).relu().mean().backward()
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assert ref_img.grad is not None and ref_w.grad is not None and img.grad is not None and w.grad is not None
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np.testing.assert_allclose(img.grad.numpy(), ref_img.grad.detach().numpy(), atol=1e-6 if dtype == dtypes.float else 1e-2)
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np.testing.assert_allclose(w.grad.numpy(), ref_w.grad.detach().numpy(), atol=1e-6 if dtype == dtypes.float else 1e-2)
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class TestSchedule(unittest.TestCase):
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def test_arange_avgpool2d(self, kcount=1):
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x = Tensor.arange(25).reshape(1,1,5,5).cast(dtypes.float32)
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t = x.avg_pool2d(padding=1)
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sched = t.schedule()
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self.assertEqual(len(sched), kcount)
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run_schedule(sched)
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import torch
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torch_out = torch.nn.functional.avg_pool2d(torch.arange(25).reshape(1,1,5,5).float(), kernel_size=(2,2), padding=1).numpy()
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np.testing.assert_allclose(t.numpy(), torch_out)
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def test_arange_avgpool2d_fused_noopt(self):
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with Context(NOOPT=1): self.test_arange_avgpool2d(kcount=1)
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# linearizer error
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@unittest.skip("recursion error no longer raised")
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@unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "needs supports_float4 to fail")
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def test_arange_avgpool2d_fused(self):
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with self.assertRaises(RecursionError):
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with Context(NOOPT=0): self.test_arange_avgpool2d(kcount=1)
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# when we're fusing a reduce, all ReduceOps must have the same N in the dimensions
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# all permutes, reshapes, expands and shrinks push through the reduce
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def test_arange_sum(self):
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a = Tensor.arange(6).reshape(3, 2).sum(axis=1)
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run_schedule(check_schedule(a, 1))
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self.assertListEqual(a.tolist(), [1, 5, 9])
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def test_arange_sum_alt(self):
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a = (Tensor.arange(5).reshape(1,5).expand(6,5)*Tensor(2)).reshape(1,6,5).sum(axis=2)
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run_schedule(check_schedule(a, 1))
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np.testing.assert_equal(a.numpy(), 20)
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def test_permute_arange(self):
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a = Tensor.arange(6).reshape(6, 1, 1).permute(2, 0, 1).sum(axis=1)
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run_schedule(check_schedule(a, 1))
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self.assertListEqual(a.tolist(), [[15]])
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@unittest.skipIf(Device.DEFAULT == "CPU", "devices must mismatch")
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def test_error_on_device_mismatch(self):
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a = Tensor.empty(10)
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b = Tensor.empty(10, device="CPU")
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c = a+b
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with self.assertRaisesRegex(RuntimeError, "all buffers must be on the same device"): check_schedule(c, 1)
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@unittest.skipIf(Device.DEFAULT == "CPU", "devices must mismatch")
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def test_error_on_device_mismatch_alt(self):
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a = Tensor.empty(10)
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b = Tensor.empty((1,), device="CPU").expand(10).contiguous()
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c = a+b
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with self.assertRaisesRegex(RuntimeError, "all buffers must be on the same device"): check_schedule(c, 2)
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@unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
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def test_expand_buffer_before_cast(self):
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a = Tensor.randn(4, 2, 1).realize().permute((1, 0, 2))
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b = a.cast(dtypes.half).expand((2, 4, 4))+2
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run_schedule(check_schedule(b, 1))
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np.testing.assert_allclose(b.numpy(), np.broadcast_to(a.numpy().astype(np.float16), (2, 4, 4))+2)
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def test_indexing_scalars_simple(self):
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X = Tensor.randn(2, 2).realize()
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xt = X[Tensor(1)][Tensor(0)]
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run_schedule(check_schedule(xt, 1))
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np.testing.assert_equal(xt.numpy(), X.numpy()[1][0])
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@unittest.skipIf(CI and Device.DEFAULT == "NV", "crashes on NV CI")
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def test_add_chain_buffers(self):
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N = 31
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with Context(TRACK_MATCH_STATS=0, DEBUG=0):
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bufs = [Tensor(i).reshape((1,)).contiguous().realize() for i in range(N)]
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for X in range(1,N):
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root = bufs[0]
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for i in range(1,N,X):
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root = root + functools.reduce(lambda a,b:a+b, bufs[i:i+X])
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self.assertEqual(root.item(), sum(range(N)))
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@given(strat.sampled_from(range(2,4)), strat.sampled_from(range(2,4)), strat.sampled_from(range(0,4)), strat.sampled_from(range(0,4)))
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@settings(deadline=None)
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def test_indexing_scalars(self, x, y, a, b):
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assume(a<x and b<y)
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X = Tensor.randn(x, y).realize()
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xt = X[Tensor(a)][Tensor(b)]
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run_schedule(check_schedule(xt, 1))
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np.testing.assert_equal(xt.numpy(), X.numpy()[a][b])
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def test_push_pads_elementwise(self):
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x = Tensor.full((4,4), 2.).contiguous().realize()
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y = Tensor.full((4,4), 4.).contiguous().realize()
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z = (x.reciprocal()*y).pad((None, (0,1),)).sum()
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run_schedule(check_schedule(z, 1))
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self.assertEqual(z.item(), 32)
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def test_push_pads_contiguous(self):
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x = Tensor.full((4,1), 2.).contiguous()
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y = Tensor.full((4,4), 4.).contiguous()
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z = (x.reciprocal().expand(4,4)*y).pad((None, (0,1),)).sum()
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run_schedule(check_schedule(z, 1, [x,y]))
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self.assertEqual(z.item(), 32)
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def test_rand(self):
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x = Tensor.rand(32)
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check_schedule(x, 1, [Tensor._device_rng_counters[x.device]])
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def test_rand_recompute_arange(self):
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x = Tensor.rand(32)
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check_schedule(x, 1, [Tensor._device_rng_counters[x.device]])
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def test_empty_is_not_realized(self):
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a = Tensor.empty(10)
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child = a+2
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assert not a.uop.is_realized
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child.realize()
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assert a.uop.is_realized
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# NOTE: because empty does not have an ExecItem if realize is called on a childless empty, it never gets allocated.
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def test_childless_empty_never_allocates(self):
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a = Tensor.empty(10)
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a.realize()
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assert not a.uop.is_realized
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def test_simplify_padded_const(self):
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a = Tensor.empty(1022).cummax(axis=0)
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check_schedule(a, 3)
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def test_basic_binop_fusion(self):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = Tensor.empty(10)
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d = a+b+c
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check_schedule(d, 1)
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def test_basic_binop_fusion_deep(self):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = Tensor.empty(10)
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d = Tensor.empty(10)
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e = a+b+c+d
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check_schedule(e, 1)
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def test_mulacc_fusion(self):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = (a*b).sum()
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check_schedule(c, 1)
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def test_mulacc_relu_fusion(self):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = (a*b).sum().relu()
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check_schedule(c, 1)
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def test_binop_reshape_fusion(self):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = Tensor.empty(5,2)
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d = (a+b).reshape(5,2)+c
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check_schedule(d, 1)
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def test_binop_permute_fusion(self):
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a = Tensor.empty(2,5)
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b = Tensor.empty(2,5)
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c = Tensor.empty(5,2)
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d = (a+b).permute(1,0)+c
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check_schedule(d, 1)
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def test_constants_are_embedded(self):
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a = Tensor.empty(3,3) * 2
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check_schedule(a, 1, filter_sink=False)
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def tests_constants_are_folded(self):
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a = Tensor(2)
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check_schedule(a, 0)
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def test_constants_can_store(self):
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a = Tensor(2).contiguous()
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run_schedule(check_schedule(a, 1))
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np.testing.assert_equal(a.numpy(), 2)
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def test_binop_elu_fusion(self):
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a = Tensor.empty(10)
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b = a.elu()
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check_schedule(b, 1)
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def test_binop_reshape_reduce_fusion(self):
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a = Tensor.empty(100)
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b = Tensor.empty(100)
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c = (a+b).reshape(10, 10).sum(axis=0, keepdim=True)
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check_schedule(c, 1)
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def test_reduce_reshape_binop_fusion(self):
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a = Tensor.empty(10,10)
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b = Tensor.empty(10)
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c = a.sum(axis=0) + b
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check_schedule(c, 1)
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def test_reduce_permute_binop_fusion(self):
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a = Tensor.empty(10,10,10)
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b = Tensor.empty(10,10,1)
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c = a.sum(axis=0, keepdim=True).permute(2,1,0) + b
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check_schedule(c, 1)
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def test_allow_push_permutes(self):
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a = Tensor.randn(10,10,10).realize()
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b = Tensor.randn(10,10,1).realize()
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c = a.sum(axis=0, keepdim=True).permute(2,1,0) + b
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run_schedule(check_schedule(c, 1))
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np.testing.assert_allclose(c.numpy(), np.sum(a.numpy(), axis=0, keepdims=True).transpose(2,1,0)+b.numpy())
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def test_binop_early_reshape_reduce_fusion(self):
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a = Tensor.empty(100)
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b = Tensor.empty(100)
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c = Tensor.empty(10,10)
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d = ((a+b).reshape(10,10) + c).sum(axis=0)
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check_schedule(d, 1)
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def test_diamond_folded(self):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = Tensor.empty(10)
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d = Tensor.empty(10)
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ab = a+b
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e = (ab+c) + (ab+d)
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check_schedule(e, 1)
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def test_cache_binaryop(self):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = a+b
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d = a+b
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check_schedule(d, 0, [c])
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# failing in new lazy
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def test_cache_binaryop_reshaped(self):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = a+b
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d = a.reshape(10,1)+b.reshape(10,1)
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check_schedule(d, 1, [c])
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# failing in new lazy
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def test_cache_binaryop_transpose(self):
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a = Tensor.empty(10,10)
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b = Tensor.empty(10,10)
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c = (a.T*b.T).T #.contiguous()
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d = a*b
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check_schedule(d, 1, [c])
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def test_cache_two_reduceops(self):
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a = Tensor.empty(10)
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b = a.sum()
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c = a.sum()
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bc = b+c
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check_schedule(bc, 1)
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def test_cache_reduce_parent(self):
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x = Tensor.empty(32)
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r0 = x.mean(axis=0, keepdim=True)
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r1 = (x - r0).sum(axis=0).div(2)
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out = r0 + r1
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schedule = check_schedule(out, 2)
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reduceops = [x for si in schedule for x in si.ast.toposort() if x.op in {Ops.REDUCE_AXIS, Ops.REDUCE}]
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assert len(reduceops) == 2
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def test_cache_reduce_multiple_children(self):
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x = Tensor.empty(32)
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y = Tensor.empty(4, 4)
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r0 = x.mean(axis=0, keepdim=True)
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r1 = (x - r0).sum(axis=0).div(2)
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out0 = r0 + y
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out1 = r1 + y
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schedule = check_schedule([out0, out1], 3)
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reduceops = [x for si in schedule for x in si.ast.toposort() if x.op in {Ops.REDUCE_AXIS, Ops.REDUCE}]
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self.assertEqual(len(reduceops), 2) # why is RANGEIFY different?
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def test_div_collapse_buffer(self):
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a = Tensor.full((4,), 4.0).contiguous().realize()
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b = Tensor.full((4,), 2.0).contiguous().realize()
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expr = (a*b)/b
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check_schedule(expr, 0)
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np.testing.assert_allclose(expr.numpy(), np.full((4,), 4.0))
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def test_div_collapse_const(self):
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a = Tensor.full((4,), 4.0).contiguous().realize()
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expr = a/a
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check_schedule(expr, 0)
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np.testing.assert_allclose(expr.numpy(), np.full((4,), 1.0))
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def test_div_collapse(self):
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a = Tensor.full((4,), 1.0).contiguous().realize()
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b = Tensor.full((4,), 2.0).contiguous().realize()
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c = Tensor.full((4,), 3.0).contiguous().realize()
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GlobalCounters.reset()
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expr = (a/b)/c
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expr.realize()
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self.assertEqual(GlobalCounters.kernel_count, 1)
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self.assertLessEqual(GlobalCounters.global_ops, 4*3)
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np.testing.assert_allclose(expr.numpy(), (a.numpy()/b.numpy())/c.numpy())
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def test_dedup_assign(self):
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a = Tensor.ones(4).contiguous().realize()
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b = Tensor.full((4,), 2.).contiguous()
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first = a.assign(b)
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second = a.assign(b)
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check_schedule([first, second], 2) # TODO: 1?
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# NOTE: this is causing "LAZYCACHE=1 incorrectly reuses contiguous const" #4562
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# should contiguous dedup?
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@unittest.skip("we do the exact opposite now")
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def test_dedup_contiguous(self):
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a = Tensor.ones(4).contiguous()
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b = Tensor.ones(4).contiguous()
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sched = check_schedule([a, b], 1)
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run_schedule(sched)
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# a and b share the same underlying device memory
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self.assertIs(a.uop.realized, b.uop.realized)
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def test_clone_doesnt_dedup(self):
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src = Tensor.ones(4).contiguous().realize()
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a = src.clone()
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b = src.clone()
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sched = check_schedule([a, b], 2, filter_sink=False)
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run_schedule(sched)
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# a and b are assigned to the same device Buffer
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self.assertIsNot(a.uop.base.realized, b.uop.base.realized)
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# EMPTY is assigned to a unique device Buffer
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def test_no_dedup_empty(self):
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a = Tensor.empty((4,))
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b = Tensor.empty((4,))
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# NOTE: empty does not have any schedule
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check_schedule([a, b], 0, filter_sink=False)
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self.assertIsNot(a.uop.buffer, b.uop.buffer)
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def test_dedup_outputs(self):
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a = Tensor.full((4, 4), 1.).contiguous().realize()
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b = Tensor.full((4, 4), 1.).contiguous().realize()
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check_schedule([a+b, a+b], 1)
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def test_const_realize(self):
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t = Tensor.ones(2)
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check_schedule(t[0], 0)
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check_schedule(t[1], 0)
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def test_fold_double_unary(self):
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|
y = Tensor.empty(2)
|
|
out = y.sum(keepdim=True).sqrt().neg()
|
|
check_schedule(out, 1)
|
|
|
|
#@unittest.skip("may want to reconsider this")
|
|
def test_fold_batchnorm(self):
|
|
with Tensor.train():
|
|
img = Tensor.empty(1,32,4,4)
|
|
bn = nn.BatchNorm2d(32, track_running_stats=False)
|
|
out = bn(img)
|
|
check_schedule(out, 3)
|
|
|
|
def test_fold_conv_batchnorm_notrain(self):
|
|
with Tensor.train(False):
|
|
img = Tensor.empty(1,3,8,8)
|
|
c1 = nn.Conv2d(3,32,3)
|
|
bn = nn.BatchNorm2d(32, track_running_stats=True)
|
|
out = bn(c1(img)).relu()
|
|
check_schedule(out, 1, [c1.weight, c1.bias])
|
|
|
|
def test_fold_conv_batchnorm_notrain_no_running_stats(self):
|
|
with Tensor.train(False):
|
|
img = Tensor.empty(1,3,8,8)
|
|
c1 = nn.Conv2d(3,32,3)
|
|
bn = nn.BatchNorm2d(32, track_running_stats=False)
|
|
out = bn(c1(img)).relu()
|
|
check_schedule(out, 4, [c1.weight, c1.bias])
|
|
|
|
def test_fold_conv_batchnorm(self):
|
|
with Tensor.train():
|
|
img = Tensor.empty(1,3,8,8)
|
|
c1 = nn.Conv2d(3,32,3)
|
|
bn = nn.BatchNorm2d(32, track_running_stats=False)
|
|
out = bn(c1(img)).relu()
|
|
check_schedule(out, 4, [c1.weight, c1.bias])
|
|
|
|
@unittest.skipUnless(is_dtype_supported(dtypes.ulong), "Needs ulong")
|
|
def test_fold_conv_batchnorm_optim(self):
|
|
# this is too high
|
|
for optim, cnt in [(nn.optim.Adam, 27), (nn.optim.SGD, 7)]:
|
|
with self.subTest(optim=optim.__name__):
|
|
with Tensor.train():
|
|
img = Tensor.ones(1,3,4,4)
|
|
c1 = nn.Conv2d(3,32,3)
|
|
bn = nn.BatchNorm2d(32, track_running_stats=False)
|
|
_realize_weights([c1, bn])
|
|
opt = optim(nn.state.get_parameters([c1, bn]))
|
|
img_bn = bn(c1(img)).elu().sum()
|
|
opt.zero_grad()
|
|
img_bn.backward()
|
|
check_schedule(opt.schedule_step(), cnt)
|
|
|
|
def test_fold_batchnorm_backward(self):
|
|
with Tensor.train():
|
|
x = Tensor.empty((2, 16, 8, 8)).contiguous()
|
|
bn = nn.BatchNorm2d(16)
|
|
bn.weight.requires_grad = bn.bias.requires_grad = x.requires_grad = True
|
|
fw = bn(x).contiguous_backward().relu().contiguous()
|
|
fw.sum().backward()
|
|
# TODO: this is too many
|
|
check_schedule([x.grad, bn.weight.grad, bn.bias.grad, fw], 9)
|
|
|
|
def test_fold_conv_relu(self):
|
|
c1 = nn.Conv2d(3,16,3)
|
|
|
|
# run
|
|
img = Tensor.ones(2,3,64,64)
|
|
out = c1(img).relu()
|
|
check_schedule(out, 1, [c1.weight, c1.bias])
|
|
|
|
def test_fold_conv_relu_alt(self):
|
|
img = Tensor.ones(1,4,8,8)
|
|
c1 = nn.Conv2d(4, 4, kernel_size=3)
|
|
c2 = nn.Conv2d(4, 4, kernel_size=3)
|
|
img_conv = img.sequential([c1, Tensor.relu, c2, Tensor.relu])
|
|
check_schedule(img_conv, 2, [*nn.state.get_parameters(c1), *nn.state.get_parameters(c2), img])
|
|
|
|
def test_fold_conv_relu_nobias(self):
|
|
img = Tensor.ones(1,4,8,8)
|
|
c1 = nn.Conv2d(4, 4, kernel_size=3, bias=False)
|
|
c2 = nn.Conv2d(4, 4, kernel_size=3, bias=False)
|
|
out = img.sequential([c1, Tensor.relu, c2, Tensor.relu])
|
|
check_schedule(out, 2, [c1.weight, c2.weight, img])
|
|
|
|
def test_fold_conv_elu(self):
|
|
c1 = nn.Conv2d(3,16,3)
|
|
|
|
# run
|
|
img = Tensor.rand(2,3,64,64)
|
|
out = c1(img).elu()
|
|
check_schedule(out, 1, [c1.weight, c1.bias, img])
|
|
|
|
def test_fold_conv_elu_alt(self):
|
|
img = Tensor.ones(1,4,8,8).contiguous()
|
|
c1 = nn.Conv2d(4, 4, kernel_size=3)
|
|
c2 = nn.Conv2d(4, 4, kernel_size=3)
|
|
img_conv = img.sequential([c1, Tensor.elu, c2, Tensor.elu])
|
|
check_schedule(img_conv, 2, [*nn.state.get_parameters(c1), *nn.state.get_parameters(c2), img])
|
|
|
|
def test_two_sum(self):
|
|
img = Tensor.empty(64,64)
|
|
x = (img.sum(0) + img.sum(1))
|
|
out = x.relu()
|
|
check_schedule(out, 1)
|
|
|
|
def test_push_permute_through_reshape(self):
|
|
a = Tensor.empty(16,16)
|
|
b = Tensor.empty(16,16)
|
|
c = (a+b).reshape(4,4,4,4).permute(2,3,0,1).contiguous()
|
|
check_schedule(c, 1)
|
|
|
|
#@unittest.skip("failing in old lazy")
|
|
def test_push_permute_through_reshape_alt(self):
|
|
a = Tensor.empty(4,4,4,4)
|
|
b = Tensor.empty(4,4,4,4)
|
|
c = (a+b).reshape(16,16).permute(1,0).contiguous()
|
|
check_schedule(c, 1)
|
|
|
|
def test_no_binop_rerun(self):
|
|
a = Tensor.empty(16)
|
|
b = Tensor.empty(16)
|
|
c = a+b
|
|
d = (a+b).reshape(16,1)
|
|
check_schedule(d, 0, [c])
|
|
|
|
@unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
|
|
def test_multi_permute_should_collapse(self):
|
|
a = Tensor.empty(4,4,4,4)
|
|
b = Tensor.empty(16)
|
|
c = a.sum((0,1)).cast(dtypes.float16).permute(1,0).reshape(4,4,1).permute(1,0,2).reshape(16) + b
|
|
check_schedule(c, 1)
|
|
|
|
def test_fancy_reshape_fusion(self):
|
|
a = Tensor.empty(10)
|
|
b = Tensor.empty(10)
|
|
c = a+b
|
|
d = a.reshape(10,1)+b.reshape(10,1)
|
|
out = c.sum() + d.sum()
|
|
check_schedule(out, 1)
|
|
|
|
def test_children_dont_push(self):
|
|
a = Tensor.empty(10, 10, 1)
|
|
b = Tensor.empty(10, 10, 1)
|
|
d = (a+b).expand(10, 10, 10)
|
|
e = (a+b).permute(2,1,0)
|
|
f = d+e
|
|
check_schedule(f, 1)
|
|
|
|
# failing in new lazy
|
|
@unittest.skip("always fusing elementwise")
|
|
def test_dont_fuse_binops_with_children(self):
|
|
a = Tensor.empty(10)
|
|
b = Tensor.empty(10)
|
|
c = Tensor.empty(10)
|
|
keep_me = a+b
|
|
e = keep_me.sum() # noqa: F841 give keep_me a child (NOTE: BinaryOps won't be a child since it will instant fuse)
|
|
d = keep_me+c
|
|
check_schedule(d, 2)
|
|
check_schedule(keep_me, 0, [d])
|
|
|
|
#@unittest.skip("failing in old lazy")
|
|
def test_permute_breaks_fusion(self):
|
|
a = Tensor.empty(10, 10, 10)
|
|
b = Tensor.empty(10, 10)
|
|
c = (a.sum(axis=2) + b).permute(1,0)
|
|
d = c.permute(1,0)
|
|
check_schedule(d, 1)
|
|
|
|
def test_some_permute_fusion(self):
|
|
a = Tensor.empty(8192, 16)
|
|
b = Tensor.empty(1, 16)
|
|
d = (a.T + b.expand(8192, 16).T)
|
|
c = a + b.expand(8192, 16)
|
|
e = d.T
|
|
check_schedule(c, 1)
|
|
check_schedule(e, 1)
|
|
|
|
def test_shrink_fuse(self):
|
|
a = Tensor.empty(8192, 16)
|
|
b = Tensor.empty(8192, 16)
|
|
c = a * b
|
|
d = Tensor.empty(1, 16)
|
|
e = c[0] * d
|
|
check_schedule(e, 1)
|
|
|
|
def test_expand_fuse(self):
|
|
a = Tensor.empty(1, 16)
|
|
b = Tensor.empty(1, 16)
|
|
c = a * b
|
|
d = Tensor.empty(8192, 16)
|
|
e = c * d
|
|
check_schedule(e, 1)
|
|
|
|
# this is the failing case in openpilot...it's very simple like this
|
|
def test_image_conv_fusion(self):
|
|
w1 = Tensor.empty(16, 16, 1, 1)
|
|
b1 = Tensor.empty(16)
|
|
w2 = Tensor.empty(16, 16, 1, 1)
|
|
b2 = Tensor.empty(16)
|
|
w3 = Tensor.empty(16, 16, 1, 1)
|
|
b3 = Tensor.empty(16)
|
|
|
|
x = Tensor.empty(1, 16, 32, 32)
|
|
x = base = x.image_conv2d(w1, b1)
|
|
x = x.image_conv2d(w2, b2) + base
|
|
x = x.image_conv2d(w3, b3)
|
|
|
|
# NOOP, 3 convs, contiguous
|
|
#check_schedule(x, 5)
|
|
check_schedule(x, 7)
|
|
|
|
def test_image_conv_fusion_minimal(self):
|
|
b1 = Tensor.empty(16)
|
|
b2 = Tensor.empty(16)
|
|
def p(x): return x.permute(1,0).contiguous().reshape(32,16,1).expand(32,16,16).sum(axis=2).permute(1,0)
|
|
|
|
x = Tensor.empty(16, 32)
|
|
x = base = p(x) + b1.reshape(16,1)
|
|
x = p(x)
|
|
x = x + b2.reshape(16,1)
|
|
x = x + base
|
|
del base
|
|
x = p(x)
|
|
check_schedule(x, 4)
|
|
|
|
def test_image_conv_fusion_more_minimal(self):
|
|
b1 = Tensor.empty(16)
|
|
def p(x): return x.permute(1,0).contiguous().reshape(32,16,1).expand(32,16,16).sum(axis=2).permute(1,0)
|
|
|
|
x = Tensor.empty(16, 32)
|
|
x = base = p(x) + b1.reshape(16,1)
|
|
x = p(x)
|
|
del base
|
|
check_schedule(x, 3)
|
|
|
|
def test_resnet_block(self):
|
|
with Tensor.train(False):
|
|
in_planes, planes = 64, 64
|
|
conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
|
|
bn1 = nn.BatchNorm2d(planes)
|
|
conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, stride=1, bias=False)
|
|
bn2 = nn.BatchNorm2d(planes)
|
|
x = Tensor.empty(1, 64, 32, 32)
|
|
out = bn1(conv1(x)).relu()
|
|
out = bn2(conv2(out))
|
|
out = (out + x).relu()
|
|
run_schedule(check_schedule(out, 2, [conv1.weight, conv2.weight]))
|
|
|
|
def test_contiguous_while_contiguous(self):
|
|
x = Tensor.empty(1, 64, 32, 32)
|
|
out = x.contiguous()
|
|
check_schedule(out, 0, filter_sink=False)
|
|
|
|
def test_contiguous_while_not_contiguous(self):
|
|
x = Tensor.empty(1, 64, 32, 32)
|
|
out = x.permute(0,2,3,1).contiguous()
|
|
check_schedule(out, 1, filter_sink=False)
|
|
|
|
def test_fold_with_contiguous(self):
|
|
a = Tensor.randn(16, 16, 16).realize()
|
|
b = Tensor.randn(16, 16).realize()
|
|
c = (a.sum(2).contiguous() + b).contiguous()
|
|
check_schedule(c, 2)
|
|
|
|
def test_kernelize(self):
|
|
a = Tensor.empty(10)
|
|
b = Tensor.empty(10)
|
|
c = (a+b).kernelize()
|
|
d = c+2
|
|
check_schedule(d, 2)
|
|
|
|
def test_kernelize_view(self):
|
|
a = Tensor.empty(4,1)
|
|
b = a*2
|
|
c = b.kernelize()+Tensor.empty(4,4)
|
|
check_schedule(c, 2)
|
|
|
|
def test_kernelize_diamond(self):
|
|
a = Tensor([0]).realize()
|
|
prev_a = (a+1).contiguous()
|
|
a.assign(Tensor([2]))
|
|
a.kernelize(prev_a)
|
|
self.assertEqual((prev_a+a*3).item(), 1+2*3)
|
|
|
|
def test_kernelize_sym(self):
|
|
a = Tensor([1])+Tensor([2])
|
|
a.kernelize()
|
|
b = a/a
|
|
check_schedule(b, 0)
|
|
self.assertEqual(b.item(), 1)
|
|
|
|
# TODO: this requires supporting multiple stores in the AST
|
|
@unittest.expectedFailure
|
|
def test_multioutput_ast(self):
|
|
a = Tensor.zeros(1, dtype=dtypes.int).contiguous().realize().uop
|
|
b = Tensor.zeros(1, dtype=dtypes.int).contiguous().realize().uop
|
|
c = Tensor.arange(4).realize().uop
|
|
kernel = UOp(Ops.KERNEL, src=(a.base, b.base, c.base), arg=Kernel(UOp.sink(c.r(Ops.ADD, (0,))+1, c.r(Ops.ADD, (0,))*2)))
|
|
run_schedule(check_schedule(UOp.sink(a.assign(kernel), b.assign(kernel)), 1))
|
|
self.assertEqual(a.buffer.numpy(), [7])
|
|
self.assertEqual(b.buffer.numpy(), [12])
|
|
|
|
# unlike schedule, kernelize can be called multiple times on a Tensor
|
|
def test_double_kernelize(self):
|
|
a = Tensor.empty(10)
|
|
b = Tensor.empty(10)
|
|
c = (a+b)
|
|
d = c.kernelize()+2
|
|
e = c.kernelize()+d.kernelize()
|
|
check_schedule(e, 3)
|
|
|
|
def test_kernelize_bw(self):
|
|
a = Tensor.full((3,), 2.0, requires_grad=True).contiguous()
|
|
b = Tensor.full((3,), 3.0, requires_grad=True).contiguous()
|
|
x = (a*b).kernelize()
|
|
y = Tensor.eye(3, requires_grad=True)
|
|
z = y.matmul(x).sum()
|
|
z.backward()
|
|
self.assertEqual(z.item(), 18.0)
|
|
self.assertEqual(z.grad.item(), 1.0)
|
|
|
|
def test_kernelize_bw_view(self):
|
|
a = Tensor.full((3,1), 2.0, requires_grad=True).contiguous()
|
|
b = Tensor.full((3,1), 3.0, requires_grad=True).contiguous()
|
|
x = (a*b).kernelize()
|
|
y = Tensor.eye(6, requires_grad=True)
|
|
z = y.matmul(x.expand(3,2).reshape(6)).sum()
|
|
z.backward()
|
|
self.assertEqual(z.item(), 36.0)
|
|
self.assertEqual(z.grad.item(), 1.0)
|
|
|
|
@unittest.skip("no longer supported")
|
|
def test_double_from(self):
|
|
x = Tensor([1,2,3,4])
|
|
out = x.to('python')
|
|
check_schedule(out, 0, filter_sink=False)
|
|
|
|
def _alu_from_tensor(self, t:Tensor):
|
|
s = [s for s in t.schedule() if s.ast.op is Ops.SINK]
|
|
self.assertEqual(len(s), 1)
|
|
return [u.op for u in s[0].ast.toposort() if u.op in GroupOp.ALU]
|
|
|
|
def test_2_pow_is_exp2(self):
|
|
t = 2.0 ** Tensor([1.0, 2.0, 3.0])
|
|
self.assertEqual(self._alu_from_tensor(t), [Ops.EXP2])
|
|
|
|
def test_pow_05_is_sqrt(self):
|
|
t = Tensor([1.0, 2.0, 3.0]) ** 0.5
|
|
self.assertEqual(self._alu_from_tensor(t), [Ops.SQRT])
|
|
|
|
def test_pow_neg_05_is_rsqrt(self):
|
|
t = Tensor([1.0, 2.0, 3.0]) ** -0.5
|
|
self.assertEqual(self._alu_from_tensor(t), [Ops.RECIPROCAL, Ops.SQRT])
|
|
|
|
def test_pow_2_has_1_mul(self):
|
|
t = Tensor([1.0, 2.0, 3.0]) ** Tensor(2.0)
|
|
self.assertEqual(self._alu_from_tensor(t), [Ops.MUL])
|
|
|
|
def test_pow_8_has_3_muls(self):
|
|
t = Tensor([1.0, 2.0, 3.0]) ** 8
|
|
self.assertEqual(self._alu_from_tensor(t), [Ops.MUL, Ops.MUL, Ops.MUL])
|
|
|
|
def test_pow_const_tensor_to_zero(self):
|
|
x = Tensor([1,2,3,4])
|
|
out = x ** Tensor(0.0)
|
|
# NOTE: this is UOp.const(0) + UOp.const(1)
|
|
check_schedule(out, 0)
|
|
|
|
def test_zero_size(self):
|
|
x = Tensor.empty(2, 3, 0)
|
|
out = x + 1
|
|
check_schedule(out, 0, filter_sink=False)
|
|
|
|
def test_zero_size_assign(self):
|
|
f = Tensor.full((2,), 0.).contiguous().realize()
|
|
a = f.shrink_to((0,))
|
|
a.assign(Tensor.ones_like(a))
|
|
check_schedule(a, 0)
|
|
self.assertEqual(a.tolist(), [])
|
|
|
|
def test_zero_size_children(self):
|
|
r = Tensor.ones(1,2).contiguous().realize().sum(axis=(1,), keepdim=True)
|
|
ax = r.reshape(1)*2
|
|
ay = r.reshape(1).shrink(((1,1),))*2
|
|
out = ax+ay.pad(((1, 0),))
|
|
run_schedule(check_schedule(out, 1))
|
|
self.assertEqual(out.item(), 4.)
|
|
|
|
def test_reduce_permute_nofuse(self):
|
|
x = Tensor.empty(32, 32, 32)
|
|
y = Tensor.empty(32, 32)
|
|
out = x.sum(axis=2).T+y
|
|
check_schedule(out, 1)
|
|
|
|
def test_two_elus_sum(self):
|
|
x = Tensor.empty(32, 32)
|
|
y = Tensor.empty(32, 32)
|
|
out = x.sum(1).relu().elu() + y.sum(1).relu().elu()
|
|
check_schedule(out, 1)
|
|
|
|
@unittest.skipUnless(SPLIT_REDUCEOP, "Testing split reducop requires SPLIT_REDUCEOP")
|
|
def test_preserve_multistage_reduce(self):
|
|
big_enough = getenv("REDUCEOP_SPLIT_THRESHOLD", 32768)
|
|
x = Tensor.randn(big_enough).realize()
|
|
out = (x - x.max(keepdim=True)).max()
|
|
run_schedule(check_schedule(out, 4))
|
|
np.testing.assert_allclose(out.numpy(), (x.numpy() - x.numpy().max(keepdims=True)).max())
|
|
|
|
def test_multistage_reduce(self):
|
|
x = Tensor.empty(32, 32, 32)
|
|
out = x.sum(2).relu().sum(1)
|
|
check_schedule(out, 1)
|
|
|
|
def test_multistage_reduce_fork(self):
|
|
x = Tensor.empty(32, 32, 32)
|
|
x = x.sum(2)
|
|
out2 = x + 1
|
|
out = x.relu().sum(1) + out2[0]
|
|
check_schedule(out, 2)
|
|
|
|
@unittest.skip("these two Tensors are the same")
|
|
def test_example_matmul(self):
|
|
x = Tensor.eye(64, requires_grad=True)
|
|
y = Tensor.eye(64, requires_grad=True)
|
|
z = y.matmul(x).sum()
|
|
z.backward()
|
|
out = x.grad.contiguous()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), np.ones((64,64)))
|
|
|
|
def test_example_matmul_contig(self):
|
|
x = Tensor.eye(64, requires_grad=True).contiguous().realize()
|
|
y = Tensor.eye(64, requires_grad=True).contiguous().realize()
|
|
z = y.matmul(x).sum()
|
|
z.backward()
|
|
out = x.grad.contiguous()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), np.ones((64,64)))
|
|
|
|
def test_example_matmul_same(self):
|
|
x = Tensor.eye(64, requires_grad=True)
|
|
z = x.matmul(x).sum()
|
|
z.backward()
|
|
out = x.grad.contiguous()
|
|
run_schedule(check_schedule(out, 1))
|
|
# NOTE: the gradient flows twice
|
|
np.testing.assert_allclose(out.numpy(), 2*np.ones((64,64)))
|
|
|
|
def test_contiguous_add(self):
|
|
x = Tensor.empty(32)
|
|
y = Tensor.empty(32)
|
|
z = Tensor.empty(32)
|
|
out = (x+y).contiguous()+z
|
|
check_schedule(out, 2)
|
|
|
|
def test_double_sum_ref(self):
|
|
x = Tensor.empty(32, 32, 32)
|
|
x = x.sum(2)
|
|
out = x + x[:, 4]
|
|
check_schedule(out, 2)
|
|
|
|
def test_reduce_shrink(self):
|
|
x = Tensor.empty(32, 32)
|
|
y = Tensor.empty(16)
|
|
x = x.sum(1)
|
|
x = x[:16]
|
|
out = x + y
|
|
check_schedule(out, 1)
|
|
|
|
def test_multireduce_shrink(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(32, 32).realize()
|
|
b = Tensor.randn(32, 32).realize()
|
|
c = Tensor.randn(16).realize()
|
|
a_out = a.sum(1)
|
|
a_out = a_out[:16]
|
|
b_out = b.sum(1)
|
|
b_out = b_out[:16]
|
|
out = a_out + b_out + c
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), a.numpy().sum(axis=1)[:16] + b.numpy().sum(axis=1)[:16] + c.numpy(), atol=1e-4, rtol=1e-4)
|
|
|
|
# broken due to const folding and two contiguous are different kernels
|
|
# NOTE: passes after delete_lazy
|
|
def test_const_no_recompute(self):
|
|
x = Tensor(2) + Tensor(2)
|
|
y = Tensor(2) + Tensor(2)
|
|
out = x.contiguous() + y.contiguous()
|
|
check_schedule(out, 2, filter_sink=False)
|
|
|
|
def test_reduce_same_size(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 4).realize()
|
|
out0 = a.sum() + 2
|
|
out1 = a.sum() + 4
|
|
out2 = out0 * out1
|
|
run_schedule(check_schedule([out0, out1, out2], 3)) # TODO: 1?
|
|
np.testing.assert_allclose(out0.numpy(), out0_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-6)
|
|
np.testing.assert_allclose(out1.numpy(), out1_np:=a.numpy().sum()+4, atol=1e-4, rtol=1e-6)
|
|
np.testing.assert_allclose(out2.numpy(), out0_np*out1_np, atol=1e-4, rtol=1e-6)
|
|
|
|
def test_reduce_multiple_paths(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 4).realize()
|
|
out0 = a.sum().exp2()
|
|
# out1 has two paths to a.sum()
|
|
out1 = a.sum() + out0
|
|
run_schedule(check_schedule([out0, out1], 2)) # TODO: 1?
|
|
np.testing.assert_allclose(out0.numpy(), out0_np:=np.exp2(a.numpy().sum()), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+out0_np, atol=1e-4, rtol=1e-6)
|
|
|
|
def test_multireduce_reduce_multiple_paths(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 4).realize()
|
|
out0 = a.sum().exp2()
|
|
out1 = a.sum() + out0
|
|
b = (a + out0 + out1)
|
|
out2 = b.sum().exp2()
|
|
out3 = b.sum() + out2
|
|
# run_schedule(check_schedule([out0, out1, out2, out3], 1))
|
|
run_schedule(check_schedule([out0, out1, out2, out3], 4))
|
|
np.testing.assert_allclose(out0.numpy(), np_out0:=np.exp2(a.numpy().sum()), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out1.numpy(), np_out1:=a.numpy().sum()+np_out0, atol=1e-4, rtol=1e-4)
|
|
np_b = (a.numpy() + np_out0 + np_out1)
|
|
np.testing.assert_allclose(out2.numpy(), np_out2:=np.exp2(np_b.sum()), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out3.numpy(), np_b.sum()+np_out2, atol=1e-4, rtol=1e-4)
|
|
|
|
def test_reduce_ext_reduce_child(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 4).realize()
|
|
b = Tensor.randn(4, 4).realize()
|
|
# b.sum() is not a descendant of the fused nodes
|
|
out0 = a.sum() + b.sum() + 2
|
|
out1 = a.sum() + b.sum() + 4
|
|
# run_schedule(check_schedule([out0, out1], 1))
|
|
run_schedule(check_schedule([out0, out1], 2))
|
|
np.testing.assert_allclose(out0.numpy(), a.numpy().sum()+b.numpy().sum()+2, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+b.numpy().sum()+4, atol=1e-4, rtol=1e-4)
|
|
|
|
def test_reduce_multiple_paths_midreduce(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 4).realize()
|
|
r = a.sum()
|
|
out0 = r.exp2()
|
|
# reduce node in the indirect path from r to out2
|
|
out1 = (a - out0).max()
|
|
out2 = r + out1
|
|
# run_schedule(check_schedule([r, out0, out1, out2], 1))
|
|
run_schedule(check_schedule([r, out0, out1, out2], 4))
|
|
np.testing.assert_allclose(r.numpy(), r_np:=a.numpy().sum(), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out0.numpy(), out0_np:=np.exp2(r_np), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out1.numpy(), out1_np:=(a.numpy() - out0_np).max(), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out2.numpy(), r_np + out1_np, atol=1e-4, rtol=1e-4)
|
|
|
|
def test_reduce_multiple_paths_midreduce_fused(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 4).realize()
|
|
b = Tensor.randn(4, 4).realize()
|
|
out0 = a.sum() + 4
|
|
out1 = b.max() + out0*2
|
|
out2 = a.sum() + out1
|
|
# run_schedule(check_schedule([out0, out1, out2], 1))
|
|
run_schedule(check_schedule([out0, out1, out2], 3))
|
|
np.testing.assert_allclose(out0.numpy(), out0_np:=a.numpy().sum()+4, atol=1e-4, rtol=1e-6)
|
|
np.testing.assert_allclose(out1.numpy(), out1_np:=b.numpy().max() + out0_np*2, atol=1e-4, rtol=1e-6)
|
|
np.testing.assert_allclose(out2.numpy(), a.numpy().sum() + out1_np, atol=1e-4, rtol=1e-6)
|
|
|
|
def test_reduce_multiple_paths_midexpand(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 4).realize()
|
|
b = Tensor.randn(4, 4, 4).realize()
|
|
r = a.sum()
|
|
out0 = r.exp2()
|
|
# e1 is in the indirect path from a.sum() to out1
|
|
e = b + out0
|
|
out1 = r + e[0][0][0]
|
|
# run_schedule(check_schedule([r, out0, out1, e], 3)) # 1 or 2 or 3? should be 1 (one reduce) but the different outputs might make it 3
|
|
run_schedule(check_schedule([r, out0, out1, e], 4))
|
|
np.testing.assert_allclose(r.numpy(), r_np:=a.numpy().sum(), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out0.numpy(), out0_np:=np.exp2(r_np), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(e.numpy(), e_np:=b.numpy() + out0_np, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out1.numpy(), r_np + e_np[0][0][0], atol=1e-4, rtol=1e-4)
|
|
|
|
def test_reduce_expand_child(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn((32, 32, 32)).realize()
|
|
b = Tensor.randn((1, 16)).realize()
|
|
out0 = a.sum() + 2
|
|
out1 = a.sum() + b
|
|
# run_schedule(check_schedule([out0, out1], 2))
|
|
run_schedule(check_schedule([out0, out1], 4))
|
|
np.testing.assert_allclose(out0.numpy(), a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+b.numpy(), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_reduce_shrink_child(self):
|
|
a = Tensor.empty(100, 100)
|
|
b = Tensor.empty(10,)
|
|
c = a.sum() + b[0]
|
|
d = a.sum() + 2
|
|
check_schedule([c, d], 2) # TODO: 1?
|
|
|
|
def test_reduce_multiple_paths_midshrink(self):
|
|
a = Tensor.empty(4, 4)
|
|
r = a.sum(axis=1)
|
|
out0 = r.exp2()
|
|
out1 = out0[0] + out0
|
|
check_schedule([r, out0, out1], 3)
|
|
|
|
def test_reduce_shrink_output(self):
|
|
a = Tensor.empty(4, 4)
|
|
r = a.sum(keepdim=True)
|
|
out0 = r.exp2()
|
|
out1 = out0[0] + Tensor.empty(1, )
|
|
check_schedule([r, out0, out1], 3)
|
|
|
|
def test_std_multireduce_fusion(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
out = x.std(-1)
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), x.numpy().std(axis=-1, ddof=1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_scaled_dot_product_attention_multireduce_fusion(self):
|
|
Tensor.manual_seed(0)
|
|
q = Tensor.randn(32,8,16,8).realize()
|
|
k = Tensor.randn(32,8,16,8).realize()
|
|
v = Tensor.randn(32,8,16,8).realize()
|
|
out = Tensor.scaled_dot_product_attention(q,k,v)
|
|
run_schedule(check_schedule(out, 4))
|
|
if getenv("CHECK", 1):
|
|
import torch
|
|
compare = torch.nn.functional.scaled_dot_product_attention(torch.tensor(q.numpy()),torch.tensor(k.numpy()),torch.tensor(v.numpy()))
|
|
np.testing.assert_allclose(out.numpy(), compare.numpy(), atol=1e-6, rtol=1e-3)
|
|
|
|
out = Tensor.scaled_dot_product_attention(q,k,v)
|
|
run_schedule(check_schedule(out, 4)) # TODO: should be 1?
|
|
if getenv("CHECK", 1):
|
|
import torch
|
|
compare = torch.nn.functional.scaled_dot_product_attention(torch.tensor(q.numpy()),torch.tensor(k.numpy()),torch.tensor(v.numpy()))
|
|
np.testing.assert_allclose(out.numpy(), compare.numpy(), atol=1e-6, rtol=1e-3)
|
|
|
|
def test_ugly_reduceop_pairing(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 32).realize()
|
|
b = Tensor.randn(4, 32).realize()
|
|
c = Tensor.randn(4, 32).realize()
|
|
out = (c * a.sum(-1, keepdim=True)).sum(-1) + (b * a.sum(-1, keepdim=True)).sum(-1) # a.sum has >1 children but should still fuse
|
|
# run_schedule(check_schedule(out, 1))
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), \
|
|
(c.numpy()*a.numpy().sum(axis=-1,keepdims=True)).sum(-1) + (b.numpy()*a.numpy().sum(axis=-1,keepdims=True)).sum(-1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_reduce_expand_reduce_fusion(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 32).realize()
|
|
out = (a+a.sum(-1, keepdim=True)).sum(-1)
|
|
# run_schedule(check_schedule(out, 1))
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), (a.numpy()+a.numpy().sum(axis=-1,keepdims=True)).sum(axis=-1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_reduce_expand_reduce_expand_fusion(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 32).realize()
|
|
out = a+(a+a.sum(-1,keepdim=True)).sum(-1, keepdim=True)
|
|
# run_schedule(check_schedule(out, 2))
|
|
run_schedule(check_schedule(out, 3))
|
|
np.testing.assert_allclose(out.numpy(), \
|
|
a.numpy()+(a.numpy()+a.numpy().sum(axis=-1,keepdims=True)).sum(axis=-1,keepdims=True), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_branching_reduces_and_expands_fusion(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 32).realize()
|
|
out0 = a+a.sum(-1, keepdim=True)
|
|
out1 = out0.sum(-1)
|
|
# run_schedule(check_schedule(out, 2))
|
|
run_schedule(check_schedule([out0, out1], 3))
|
|
np.testing.assert_allclose(out0.numpy(), a.numpy()+a.numpy().sum(axis=-1,keepdims=True), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out1.numpy(), (a.numpy()+a.numpy().sum(axis=-1,keepdims=True)).sum(axis=-1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_multireduce_fusion_simple_sequential(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
y = Tensor.randn(4, 32).realize()
|
|
out = (y + x.sum(axis=-1, keepdim=True)).sum(axis=-1)
|
|
# run_schedule(check_schedule(out, 1))
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), (y.numpy() + x.numpy().sum(axis=-1, keepdims=True)).sum(axis=-1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_multireduce_fusion_simple_parallel(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
y = Tensor.randn(4, 32).realize()
|
|
out = y.sum(axis=-1) + x.sum(axis=-1)
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), y.numpy().sum(axis=-1) + x.numpy().sum(axis=-1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_multireduce_fusion_sequential(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
out = x.std(-1)
|
|
# run_schedule(check_schedule(out, 1))
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), x.numpy().std(axis=-1, ddof=1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_multireduce_fusion_parallel(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
y = Tensor.randn(4, 32).realize()
|
|
out = x.std(-1) + y.std(-1)
|
|
# run_schedule(check_schedule(out, 1))
|
|
run_schedule(check_schedule(out, 3))
|
|
np.testing.assert_allclose(out.numpy(), x.numpy().std(axis=-1, ddof=1) + y.numpy().std(axis=-1, ddof=1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_multireduce_diffops_sequential(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
out = (x - x.max(-1, keepdim=True)).sum(-1)
|
|
# run_schedule(check_schedule(out, 1))
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), (x.numpy() - x.numpy().max(axis=-1, keepdims=True)).sum(axis=-1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_multireduce_fusion_diffops_parallel(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
y = Tensor.randn(4, 32).realize()
|
|
out = x.sum(-1) + y.max(-1)
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), x.numpy().sum(axis=-1) + y.numpy().max(axis=-1), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_multireduce_fusion_sequential_and_parallel(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
y = Tensor.randn(4, 32).realize()
|
|
mu = (x - x.max(axis=-1, keepdim=True)).mean(axis=-1, keepdim=True) + (y - y.max(axis=-1, keepdim=True)).mean(axis=-1, keepdim=True)
|
|
out = [((x - mu).square().sum(-1)/x.shape[-1]).sqrt(), ((y - mu).square().sum(-1)/y.shape[-1]).sqrt()]
|
|
np_mu = (x.numpy() - x.numpy().max(axis=-1, keepdims=True)).mean(axis=-1, keepdims=True) + \
|
|
(y.numpy() - y.numpy().max(axis=-1, keepdims=True)).mean(axis=-1, keepdims=True)
|
|
# run_schedule(check_schedule(out, 1))
|
|
run_schedule(check_schedule(out, 5))
|
|
np.testing.assert_allclose(out[0].numpy(), np.sqrt(np.square(x.numpy() - np_mu).sum(-1)/x.shape[-1]), atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(out[1].numpy(), np.sqrt(np.square(y.numpy() - np_mu).sum(-1)/y.shape[-1]), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_multimatmul_fusion(self):
|
|
Tensor.manual_seed(0)
|
|
a,b = Tensor.randn(4, 64).realize(), Tensor.rand(64,8).realize()
|
|
c,d = Tensor.randn(4, 64).realize(), Tensor.rand(64,8).realize()
|
|
out = a@b + c@d
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), a.numpy()@b.numpy() + c.numpy()@d.numpy(), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_softmax_fusion(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 12, 64, 64).realize()
|
|
out = x.softmax()
|
|
run_schedule(check_schedule(out, 3))
|
|
expected = (x_exp:=np.exp(x.numpy()-x.numpy().max(-1, keepdims=True)))/x_exp.sum(-1, keepdims=True)
|
|
np.testing.assert_allclose(out.numpy(), expected, atol=1e-4, rtol=1e-4)
|
|
|
|
@unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
|
|
def test_softmax_upcast(self):
|
|
# input half, softmax in float
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 12, 64, 64, dtype=dtypes.half).realize()
|
|
out = x.softmax(dtype=dtypes.float)
|
|
sched = out.schedule()
|
|
self.assertEqual(len(sched), 3)
|
|
self.assertEqual(sched[0].bufs[0].dtype, dtypes.float)
|
|
|
|
# input float, softmax in float
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 12, 64, 64, dtype=dtypes.float).realize()
|
|
out = x.softmax(dtype=dtypes.float)
|
|
sched = out.schedule()
|
|
self.assertEqual(len(sched), 3)
|
|
self.assertEqual(sched[0].bufs[0].dtype, dtypes.float)
|
|
|
|
def test_softmax_backward(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 12, 64, 64, requires_grad=True).realize()
|
|
x.softmax().sum().backward()
|
|
run_schedule(check_schedule(x.grad, 4))
|
|
|
|
def test_layernorm_onelayer_fusion(self):
|
|
Tensor.manual_seed(0)
|
|
layer = nn.LayerNorm([10, 10])
|
|
layer.weight = Tensor.randn(10,10).realize()
|
|
layer.bias = Tensor.randn(10,10).realize()
|
|
x = Tensor.randn(20, 5, 10, 10).realize()
|
|
out = layer(x)
|
|
# run_schedule(check_schedule(out, 2))
|
|
run_schedule(check_schedule(out, 3))
|
|
y = (x.numpy() - x.numpy().mean(layer.axis, keepdims=True))
|
|
expected = y / np.sqrt((y*y).mean(layer.axis, keepdims=True) + layer.eps)
|
|
np.testing.assert_allclose(out.numpy(), expected * layer.weight.numpy() + layer.bias.numpy(), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_scaled_dot_product_attention_fusion(self):
|
|
x, y, z, m = (Tensor.empty(32, 8, 16, 16) for _ in range(4))
|
|
out = Tensor.scaled_dot_product_attention(x, y, z, attn_mask=m)
|
|
check_schedule(out, 4)
|
|
|
|
def test_scaled_dot_product_attention_causal_fusion(self):
|
|
x, y, z = (Tensor.empty(32, 8, 16, 16) for _ in range(3))
|
|
out = Tensor.scaled_dot_product_attention(x, y, z, is_causal=True)
|
|
check_schedule(out, 4)
|
|
|
|
def test_adam_step_fusion(self):
|
|
with Tensor.train():
|
|
x = Tensor.empty(4, 64, 32)
|
|
layer = nn.Linear(32, 32*4)
|
|
_realize_weights(layer)
|
|
opt = nn.optim.Adam(nn.state.get_parameters(layer), lr=1e-4)
|
|
layer(x).relu().sum().backward()
|
|
check_schedule(opt.schedule_step(), 19)
|
|
|
|
def test_adam_conv_fuse(self):
|
|
with Tensor.train():
|
|
img = Tensor.empty(2,3,4,4)
|
|
c1 = nn.Conv2d(3,32,3)
|
|
_realize_weights(c1)
|
|
opt = nn.optim.Adam(nn.state.get_parameters(c1), lr=1e-4)
|
|
opt.zero_grad()
|
|
c1(img).relu().sum().backward()
|
|
check_schedule(opt.schedule_step(), 19)
|
|
|
|
def test_adam_2convs_fuse(self):
|
|
with Tensor.train():
|
|
img = Tensor.empty(2,3,4,4)
|
|
c1 = nn.Conv2d(3,16,3,bias=False)
|
|
c2 = nn.Conv2d(16,32,2,bias=False)
|
|
_realize_weights([c1, c2])
|
|
opt = nn.optim.Adam(nn.state.get_parameters([c1, c2]), lr=1e-4)
|
|
opt.zero_grad()
|
|
c2(c1(img).relu()).relu().sum().backward()
|
|
check_schedule(opt.schedule_step(), 21)
|
|
|
|
def test_sgd_conv_fuse(self):
|
|
with Tensor.train():
|
|
img = Tensor.empty(2,3,4,4)
|
|
c1 = nn.Conv2d(3,32,3)
|
|
_realize_weights(c1)
|
|
opt = nn.optim.SGD(nn.state.get_parameters(c1))
|
|
opt.zero_grad()
|
|
c1(img).relu().sum().backward()
|
|
check_schedule(opt.schedule_step(), 5) # TODO: 3?
|
|
|
|
def test_sgd_2convs_fuse(self):
|
|
with Tensor.train():
|
|
img = Tensor.empty(2,3,4,4)
|
|
c1 = nn.Conv2d(3,16,3,bias=False)
|
|
c2 = nn.Conv2d(16,32,2,bias=False)
|
|
_realize_weights([c1, c2])
|
|
opt = nn.optim.SGD(nn.state.get_parameters([c1, c2]))
|
|
opt.zero_grad()
|
|
c2(c1(img).relu()).relu().sum().backward()
|
|
check_schedule(opt.schedule_step(), 7)
|
|
|
|
@unittest.skipUnless(is_dtype_supported(dtypes.ulong), "Needs ulong")
|
|
def test_fold_2convs_sgd_nesterov_momentum_wd(self):
|
|
with Tensor.train():
|
|
img = Tensor.empty(2,3,4,4)
|
|
c1 = nn.Conv2d(3,16,3,bias=False)
|
|
c2 = nn.Conv2d(16,32,2,bias=False)
|
|
_realize_weights([c1, c2])
|
|
opt = nn.optim.SGD(nn.state.get_parameters([c1, c2]), nesterov=True, momentum=0.9, weight_decay=0.1)
|
|
opt.zero_grad()
|
|
c2(c1(img).relu()).relu().sum().backward()
|
|
check_schedule(opt.schedule_step(), 13)
|
|
|
|
def test_sgd_4convs_fuse(self):
|
|
with Tensor.train():
|
|
img = Tensor.empty(2,3,16,16)
|
|
c1 = nn.Conv2d(3,4,3,bias=False)
|
|
c2 = nn.Conv2d(4,8,3,bias=False)
|
|
c3 = nn.Conv2d(8,16,3,bias=False)
|
|
c4 = nn.Conv2d(16,32,3,bias=False)
|
|
_realize_weights([c1, c2, c3, c4])
|
|
opt = nn.optim.SGD(nn.state.get_parameters([c1, c2, c3, c4]))
|
|
opt.zero_grad()
|
|
c4(c3(c2(c1(img).relu()).relu()).relu()).relu().sum().backward()
|
|
check_schedule(opt.schedule_step(), 15)
|
|
|
|
def test_sgd_4convs_fuse_conv_bw(self):
|
|
with Tensor.train():
|
|
img = Tensor.empty(2,3,16,16)
|
|
c1 = nn.Conv2d(3,4,3,bias=False)
|
|
c2 = nn.Conv2d(4,8,3,bias=False)
|
|
c3 = nn.Conv2d(8,16,3,bias=False)
|
|
c4 = nn.Conv2d(16,32,3,bias=False)
|
|
_realize_weights([c1, c2, c3, c4])
|
|
opt = nn.optim.SGD(nn.state.get_parameters([c1, c2, c3, c4]))
|
|
opt.zero_grad()
|
|
c4(c3(c2(c1(img).relu()).relu()).relu()).relu().sum().backward()
|
|
check_schedule(opt.schedule_step(), 15)
|
|
|
|
def test_reduce_simple_chase(self):
|
|
a = Tensor.empty(4, 4, 4)
|
|
r = a.sum(0) + 6
|
|
b = r.sum(0) * 4
|
|
c = r.sum(1) * 2
|
|
check_schedule([b, c], 3)
|
|
|
|
def test_multireduce_simple_chase(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 4, 4).realize()
|
|
r = (a + (a.sum(0, keepdim=True) + 6)).sum(0) * 2
|
|
b = r.sum(0) + 8
|
|
c = r.sum(1) + 12
|
|
np_r = (a.numpy() + (a.numpy().sum(0) + 6)).sum(0) * 2
|
|
# schedule = check_schedule([b,c], 3)
|
|
# self.assertIs(schedule[0].ast[0].src[0].arg, Ops.MUL)
|
|
schedule = check_schedule([b,c], 4)
|
|
run_schedule(schedule)
|
|
np.testing.assert_allclose(b.numpy(), np_r.sum(0) + 8, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(c.numpy(), np_r.sum(1) + 12, atol=1e-4, rtol=1e-4)
|
|
|
|
def test_push_permute_chase(self):
|
|
a = Tensor.empty(4, 4, 4)
|
|
b = Tensor.empty(4, 4)
|
|
r = a.sum(2) + b
|
|
d = r.T * 4
|
|
e = r * d
|
|
check_schedule([d, e], 3)
|
|
|
|
def test_multireduce_push_permute_chase(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(4, 4, 4).realize()
|
|
b = Tensor.randn(4, 4).realize()
|
|
r = a.sum(2) + b
|
|
d = r.T * 4
|
|
e = r * (d + a).sum(2)
|
|
schedule = check_schedule([d, e], 3) # make sure it doesn't fuse
|
|
run_schedule(schedule)
|
|
np.testing.assert_allclose(d.numpy(), (a.numpy().sum(2) + b.numpy()).T * 4, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(e.numpy(), (a.numpy().sum(2) + b.numpy()) * (d.numpy() + a.numpy()).sum(2), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_push_shrink_chase(self):
|
|
a = Tensor.empty(16, 16)
|
|
b = Tensor.empty(4)
|
|
c = Tensor.empty(16, )
|
|
r = a.sum(1) + c
|
|
d = r[:4] * b
|
|
check_schedule(d, 1)
|
|
|
|
def test_multireduce_push_shrink_chase(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(16, 16).realize()
|
|
b = Tensor.randn(4).realize()
|
|
c = Tensor.randn(16, ).realize()
|
|
d = Tensor.randn(16, 16).realize()
|
|
r = a.sum(1) + c
|
|
out = r[:4] * b + d.sum(1)[:4]
|
|
schedule = check_schedule(out, 1)
|
|
run_schedule(schedule)
|
|
np.testing.assert_allclose(out.numpy(), (a.numpy().sum(1) + c.numpy())[:4] * b.numpy() + d.numpy().sum(1)[:4], atol=1e-4, rtol=1e-4)
|
|
|
|
def test_midreduce_nochase(self):
|
|
a = Tensor.empty(16, 16)
|
|
b = (a.sum(0) + a.max(1)) + 2
|
|
check_schedule(b, 1)
|
|
|
|
def test_multireduce_midreduce_nochase(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(16, 16).realize()
|
|
b = (a.sum(0)+a.max(0) + a.max(1)+a.sum(1)) + 2
|
|
schedule = check_schedule(b, 1)
|
|
run_schedule(schedule)
|
|
np.testing.assert_allclose(b.numpy(), a.numpy().sum(0)+a.numpy().max(0) + a.numpy().max(1)+a.numpy().sum(1)+2, atol=1e-4, rtol=1e-4)
|
|
|
|
# pattern in test_transformer
|
|
def test_partial_fuse1(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(16, 16).realize()
|
|
b = Tensor.randn(16, 16).realize()
|
|
c = a.sum() + 2
|
|
d = (a.sum() - b.sum()) * 4
|
|
# run_schedule(check_schedule([c, d], 1))
|
|
run_schedule(check_schedule([c, d], 2))
|
|
np.testing.assert_allclose(c.numpy(), a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(d.numpy(), (a.numpy().sum() - b.numpy().sum()) * 4, atol=1e-4, rtol=1e-4)
|
|
|
|
# pattern in conv
|
|
def test_partial_fuse2(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(16, 16).realize()
|
|
b = Tensor.randn(16, 16).realize()
|
|
c = a.sum() + 2
|
|
d = b.sum() - c
|
|
# run_schedule(check_schedule([c, d], 1))
|
|
run_schedule(check_schedule([c, d], 2))
|
|
np.testing.assert_allclose(c.numpy(), a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(d.numpy(), b.numpy().sum()-(a.numpy().sum()+2), atol=1e-4, rtol=1e-4)
|
|
|
|
# pattern in adam
|
|
def test_partial_fuse3(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(16, 16).realize()
|
|
b = Tensor.randn(16, 16).realize()
|
|
c = a.sum() + 2
|
|
d = a.sum() * 2
|
|
e = c * d
|
|
f = b.sum() - e
|
|
# run_schedule(check_schedule([c, d, e, f], 1))
|
|
run_schedule(check_schedule([c, d, e, f], 4))
|
|
np.testing.assert_allclose(c.numpy(), c_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(d.numpy(), d_np:=a.numpy().sum()*2, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(e.numpy(), e_np:=c_np*d_np, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(f.numpy(), b.numpy().sum() - e_np, atol=1e-4, rtol=1e-4)
|
|
|
|
def test_partial_fuse4(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(16, 16).realize()
|
|
b = Tensor.randn(16, 16).realize()
|
|
c = a.sum() + 2
|
|
d = a.sum() * 2
|
|
e = c * d
|
|
f = (b - d).sum() - e
|
|
# run_schedule(check_schedule([c, d, e, f], 1))
|
|
run_schedule(check_schedule([c, d, e, f], 4))
|
|
np.testing.assert_allclose(c.numpy(), c_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(d.numpy(), d_np:=a.numpy().sum()*2, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(e.numpy(), e_np:=c_np*d_np, atol=1e-4, rtol=1e-4)
|
|
np.testing.assert_allclose(f.numpy(), (b.numpy()-d_np).sum()-e_np, atol=1e-4, rtol=1e-4)
|
|
|
|
def test_pad_reduce_safe(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.rand(3, 4, 5).realize()
|
|
b = Tensor.rand(3, 4, 5).realize()
|
|
out = (a + b).pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum().contiguous()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), np.pad(a.numpy()+b.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=1e-5, rtol=1e-6)
|
|
|
|
def test_multireduce_pad_reduce_safe(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(3, 4, 5).realize()
|
|
b = Tensor.randn(3, 4, 5).realize()
|
|
out = (a.pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum(keepdim=True)+b.pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum()).contiguous()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), np.pad(a.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(keepdims=True) + \
|
|
np.pad(b.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_pad_reduce_unsafe(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.rand(3, 4, 5).realize()
|
|
out = a.log2().pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum().contiguous()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), np.pad(np.log2(a.numpy()), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=1e-5, rtol=1e-6)
|
|
|
|
def test_multireduce_pad_reduce_unsafe(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.randn(3, 4, 5).abs().realize()
|
|
b = Tensor.randn(3, 4, 5).abs().realize()
|
|
out = (a.log2().pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum()+b).abs().log2().pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum().contiguous()
|
|
# run_schedule(check_schedule(out, 1))
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), np.pad(np.log2(np.abs(np.pad(np.log2(a.numpy()), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum() + \
|
|
b.numpy())), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=3e-4, rtol=1e-5)
|
|
|
|
def test_shrink_pad_safe(self):
|
|
a = Tensor.ones((3, )).contiguous().realize()
|
|
b = Tensor.ones((3, )).contiguous().realize()
|
|
out = (a + b).shrink(((0, 1),)).pad(((0, 1),)).contiguous()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_equal(out.numpy(), [2, 0])
|
|
|
|
def test_shrink_pad_unsafe(self):
|
|
a = Tensor.ones((3, )).contiguous().realize()
|
|
out = a.exp2().shrink(((0, 1),)).pad(((0, 1),)).contiguous()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_equal(out.numpy(), [2, 0])
|
|
|
|
def test_base_change_shrink_pad(self):
|
|
a = Tensor.ones(3, 3).contiguous().realize()
|
|
b = a.exp2()
|
|
c = b[:-1, :-1]
|
|
d = c.pad(((0, 1), (0, 1))) * 2
|
|
run_schedule(check_schedule(d, 1))
|
|
np.testing.assert_equal(d.numpy(), np.pad(np.exp2(a.numpy())[:-1, :-1], ((0, 1), (0, 1)))*2)
|
|
|
|
def test_base_change_expand_pad(self):
|
|
a = Tensor.ones(3, 3).contiguous().realize()
|
|
b = a.exp2()
|
|
c = b[:, None, :]
|
|
d = c.pad(((0, 0), (1, 1), (0, 0))) * 2
|
|
run_schedule(check_schedule(d, 1))
|
|
np.testing.assert_equal(d.numpy(), np.pad(np.exp2(a.numpy())[:, None, :], ((0, 0), (1, 1), (0, 0)))*2)
|
|
|
|
def test_fuse_arange_pad_replicate_mode(self):
|
|
x = Tensor.empty(3,3,3,3, requires_grad=True)
|
|
y = x.pad((-1,2,2,-1), mode="replicate")
|
|
dx = y.sum().gradient(x)[0]
|
|
sched = check_schedule(dx, 1)
|
|
run_schedule(sched)
|
|
np.testing.assert_allclose(dx.numpy(), [[[[0.,3.,9.],[0,1.,3.],[0.,0.,0.]]]*3]*3)
|
|
|
|
def test_fuse_arange_avg_pool2d_ceil_mode(self):
|
|
x = Tensor.avg_pool2d(Tensor.empty(1,1,6,6), kernel_size=(3,3), padding=1, stride=3, ceil_mode=True)
|
|
sched = check_schedule(x, 1)
|
|
self.assertEqual(len([x for x in sched[0].ast.backward_slice_with_self if x.op is Ops.REDUCE]), 1)
|
|
|
|
def test_fuse_arange_pad_circular_mode_bw(self):
|
|
x = Tensor.empty(1,1,5,5,5)
|
|
out = x.pad((1,2,3,5,1,2), mode="circular")
|
|
g = out.sum().gradient(x)[0]
|
|
sched = check_schedule(g, 1)
|
|
self.assertEqual(len([x for x in sched[0].ast.backward_slice_with_self if x.op is Ops.REDUCE]), 0)
|
|
|
|
# TODO like openpilot with imagef
|
|
@unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
|
|
def test_base_change_expand_expand(self):
|
|
a = Tensor.ones(4, 4).contiguous().realize()
|
|
b = a.cast(dtypes.half).expand(2, 4, 4)
|
|
c = b.cast(dtypes.int).expand(2, 2, 4, 4)
|
|
run_schedule(check_schedule(c, 1))
|
|
np.testing.assert_equal(c.numpy(), np.ones(((2, 2, 4, 4)), dtype=np.int32))
|
|
|
|
def test_base_change_pad_expand(self):
|
|
a = Tensor.full((4, 4), 1.).contiguous().realize()
|
|
b = Tensor.full((4, 4), 2.).contiguous().realize()
|
|
c = (a + b).pad(((1, 1), (1, 1)))
|
|
d = c.cast(dtypes.int).expand((2, 6, 6)) * 4
|
|
run_schedule(check_schedule(d, 1))
|
|
c_np = np.pad((np.full((4, 4), 2., dtype=np.float32) + np.full((4, 4), 1., dtype=np.float32)), ((1, 1), (1, 1)), constant_values=0.0)
|
|
np.testing.assert_equal(d.numpy(), np.broadcast_to(c_np.astype(np.half), (2, *c_np.shape)) * 4)
|
|
|
|
def test_pad_reduce_unsafe_multiview_st(self):
|
|
P = Tensor.ones(3, 3).contiguous()
|
|
sums = P.sum(axis=1, keepdim=True)
|
|
P /= sums
|
|
p = P[0]
|
|
p = p.pad(((1, 0), ))
|
|
p = p.repeat([2])
|
|
run_schedule(check_schedule(p, 3))
|
|
tiny_ret = p.numpy()
|
|
|
|
P = np.ones((3, 3), dtype=np.float32)
|
|
sums = P.sum(axis=1, keepdims=True)
|
|
P /= sums
|
|
p = P[0]
|
|
p = np.pad(p, (1, 0), 'constant')
|
|
p = np.tile(p, 2)
|
|
np.testing.assert_allclose(tiny_ret, p)
|
|
|
|
def test_bitcast_fuses(self):
|
|
x = Tensor.empty(1, dtype=dtypes.float32)
|
|
a = x.exp2().bitcast(dtypes.int32)
|
|
b = x.bitcast(dtypes.int32)
|
|
check_schedule(a+b, 1) # this should fuse when it makes sense
|
|
|
|
@unittest.skip("disabling subbuffer manually isn't supported anymore")
|
|
def test_bitcast_disable_subbufer(self):
|
|
x = cast(UOp, Tensor.empty(1, dtype=dtypes.float32).realize().uop)
|
|
a = x.alu(Ops.EXP2).cast(dtypes.int32, True, allow_buffer_view=False)
|
|
b = x.cast(dtypes.int32, True, allow_buffer_view=False)
|
|
b = a.alu(Ops.ADD, b)
|
|
check_schedule(b, 1)
|
|
|
|
def test_reduceop_reshape_dont_push(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(10, 20).realize()
|
|
out = x.argmax(1)
|
|
run_schedule(check_schedule(out, 2))
|
|
|
|
def test_conv2d(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4)
|
|
def test_conv2d_fused(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4)
|
|
|
|
def test_resnet_conv2d(self):
|
|
x = Tensor.empty(1, 8, 32, 32)
|
|
w1 = Tensor.empty(8, 8, 3, 3)
|
|
w2 = Tensor.empty(8, 8, 1, 1)
|
|
out = x.conv2d(w1).conv2d(w2)
|
|
check_schedule(out, 2)
|
|
|
|
@unittest.skipUnless(is_dtype_supported(dtypes.half) and is_dtype_supported(dtypes.ulong), "need half and ulong")
|
|
def test_conv2d_half(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4, dtype=dtypes.half)
|
|
@unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
|
|
@unittest.skipIf(Device.DEFAULT == "WEBGPU", "Causes other tests to fail")
|
|
def test_conv2d_fused_half(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4, dtype=dtypes.half)
|
|
|
|
def test_schedule_mem_used(self):
|
|
base = GlobalCounters.mem_used
|
|
Tensor.ones(256).contiguous().realize()
|
|
Tensor.ones(5, 5).contiguous().schedule()
|
|
self.assertEqual(GlobalCounters.mem_used-base, 0)
|
|
|
|
@unittest.skip("TODO: this is consistently creating non reproducible failures")
|
|
def test_schedule_mem_used_with_inputs(self):
|
|
base = GlobalCounters.mem_used
|
|
x = Tensor.ones(256).contiguous().realize()
|
|
(x+Tensor.ones(256).contiguous()).schedule()
|
|
self.assertEqual(GlobalCounters.mem_used-base, 1024)
|
|
|
|
def test_const_schedule(self):
|
|
constv = Tensor.empty(2, 2).uop.const_like(10)
|
|
check_schedule(constv, 0)
|
|
|
|
def test_const_schedule_contig(self):
|
|
constv = Tensor.empty(2, 2).uop.const_like(10).contiguous()
|
|
check_schedule(constv, 1)
|
|
|
|
@unittest.skipIf(Device.DEFAULT != "CL", "image only supported on CL")
|
|
def test_image_matmul(self):
|
|
with Context(IMAGE=2):
|
|
x = Tensor.randn((9, 9)).realize()
|
|
y = Tensor.randn((9, 9)).realize()
|
|
out = x@y
|
|
run_schedule(check_schedule(out, 3))
|
|
np.testing.assert_allclose(out.numpy(), x.numpy()@y.numpy(), atol=1e-4, rtol=1e-4)
|
|
self.assertIsInstance(out.dtype, ImageDType)
|
|
self.assertIsNotNone(out.uop.base.realized)
|
|
self.assertIsInstance(out.uop.base.realized.dtype, ImageDType)
|
|
|
|
def _test_fusion(self, shapes, f, cnt):
|
|
with Context(DEBUG=0, TRACK_MATCH_STATS=0): args = [Tensor.randn(s).realize() for s in shapes]
|
|
run_schedule(check_schedule(compare:=f(*args), cnt))
|
|
if getenv("COMPARE", 1):
|
|
import torch
|
|
good = f(*[torch.tensor(x.numpy()) for x in args])
|
|
np.testing.assert_allclose(compare.numpy(), good.numpy(), atol=1e-4, rtol=1e-4)
|
|
|
|
def test_late_fusion_simple(self):
|
|
self._test_fusion([(4, 4), (4, 1)], lambda a,b:a.sum(1, keepdim=True)+b, 1)
|
|
|
|
def test_late_fusion_post_reshape(self):
|
|
self._test_fusion([(4, 4), (1, 4)], lambda a,b:a.sum(1).reshape(b.shape)+b, 1)
|
|
|
|
def test_late_fusion_post_permute(self):
|
|
self._test_fusion([(4, 6, 4), (4, 4, 1)], lambda a,b:a.sum(1, keepdim=True).permute((2, 0, 1))+b, 1)
|
|
|
|
def test_late_fusion_double_transpose(self):
|
|
self._test_fusion([(32, 16, 1)],
|
|
lambda a:(a.expand(32, 16, 16).sum((2,), keepdim=True).permute((1, 0, 2))+2).permute((1, 0, 2)).contiguous(), 1)
|
|
|
|
def test_late_fusion_post_expand(self):
|
|
self._test_fusion([(32, 32)], lambda a:a-a.sum(1), 2)
|
|
|
|
def test_cast_padded_view(self):
|
|
a = Tensor.arange(4).reshape(1, 4)
|
|
casted_view = a.pad(((0, 1), (0, 0))).cast(dtypes.float)
|
|
casted_view.realize()
|
|
self.assertEqual(casted_view.uop.base.realized.size, 8)
|
|
contig = casted_view.contiguous().realize()
|
|
self.assertEqual(contig.uop.base.realized.size, 8)
|
|
self.assertListEqual(contig.tolist(), [[0.0, 1.0, 2.0, 3.0], [0.0, 0.0, 0.0, 0.0]])
|
|
|
|
# NOTE: we only reorder CAST if it's an EXPAND
|
|
def test_cast_after_shrink(self):
|
|
a = Tensor.arange(4).reshape(1, 4)
|
|
casted_view = a.shrink(((0, 1), (0, 2))).cast(dtypes.float)
|
|
casted_view.realize()
|
|
self.assertEqual(casted_view.uop.base.realized.size, 2)
|
|
realized_view = casted_view.contiguous().realize()
|
|
self.assertEqual(realized_view.uop.base.realized.size, 2)
|
|
self.assertListEqual(realized_view.tolist(), [[0, 1]])
|
|
|
|
def test_cast_const_view(self):
|
|
a = Tensor.ones((4, 4), dtype=dtypes.float32)
|
|
casted_view = a.cast(dtypes.int32)
|
|
run_schedule(check_schedule(casted_view, 0))
|
|
self.assertIsNone(casted_view.uop.base.realized)
|
|
realized_const_view = casted_view.contiguous()
|
|
run_schedule(check_schedule(realized_const_view, 1))
|
|
self.assertListEqual(realized_const_view.tolist(), [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
|
|
|
|
@given(strat.sampled_from(dtypes.all), strat.sampled_from(dtypes.all))
|
|
@unittest.skip("kernel count depends on input")
|
|
def test_cast_padded_const(self, dt1, dt2):
|
|
assume(is_dtype_supported(dt1) and is_dtype_supported(dt2))
|
|
a = Tensor(1, dtype=dt1).reshape(1, 1).pad(((1, 1), None))
|
|
casted_view = a.cast(dt2)
|
|
run_schedule(check_schedule(casted_view, 0))
|
|
realized_const_view = casted_view.contiguous()
|
|
run_schedule(check_schedule(realized_const_view, 1))
|
|
np.testing.assert_equal(realized_const_view.numpy(), [[0], [1], [0]])
|
|
|
|
def test_simple_indexing(self):
|
|
X = Tensor.randn(10, 10).realize()
|
|
idxs = Tensor([0, 2]).realize()
|
|
xt = X[idxs]
|
|
run_schedule(check_schedule(xt, 1))
|
|
np.testing.assert_equal(xt.numpy(), X.numpy()[idxs.numpy()])
|
|
|
|
def test_simple_indexing_alt(self):
|
|
X = Tensor.arange(16).reshape(4, 4)
|
|
xt = X[[1, 2], [-1, 2]]
|
|
run_schedule(check_schedule(xt, 1))
|
|
np.testing.assert_equal(xt.numpy(), (np.arange(16).reshape(4, 4))[[1, 2], [-1, 2]])
|
|
|
|
def test_advanced_indexing(self):
|
|
X = Tensor.arange(10)+1
|
|
xt = X[[0, -1]]
|
|
run_schedule(check_schedule(xt, 1))
|
|
np.testing.assert_equal(xt.numpy(), (np.arange(10)+1)[[0, -1]])
|
|
|
|
def test_advanced_indexing_alt(self):
|
|
X = Tensor.arange(6).reshape(3, 2)+1
|
|
xt = X[[Tensor([2]), Tensor([1])]]
|
|
run_schedule(check_schedule(xt, 1))
|
|
np.testing.assert_equal(xt.numpy(), 6)
|
|
|
|
def test_advanced_simple_indexing_combined(self):
|
|
X = Tensor.arange(16).reshape(4, 4)
|
|
xt = X[1:2, [-1, 2]]
|
|
run_schedule(check_schedule(xt, 1))
|
|
|
|
def test_push_through_reshape(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(10, 20).realize()
|
|
out = x.argmax(1)
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), np.argmax(x.numpy(), 1))
|
|
|
|
def test_arange_push_through_expand(self):
|
|
Tensor.manual_seed(0)
|
|
a = Tensor.arange(4,)
|
|
b = Tensor.randn(4, 4).realize()
|
|
out = (a+b).sum()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), (np.arange(4)+b.numpy()).sum(), atol=1e-5)
|
|
|
|
def test_argmin(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
out = x.argmin(-1)
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_equal(out.numpy(), x.numpy().argmin(axis=-1))
|
|
|
|
def test_argmax(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 32).realize()
|
|
out = x.argmax(-1)
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_equal(out.numpy(), x.numpy().argmax(axis=-1))
|
|
|
|
def test_arange_transposed(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randint(4, 1).realize()
|
|
a = ((Tensor.arange(4,)*x).T).sum()
|
|
run_schedule(check_schedule(a, 1))
|
|
np.testing.assert_equal(a.numpy(), (np.arange(4)*x.numpy()).T.sum())
|
|
|
|
def test_div_padded_arange(self):
|
|
x = Tensor.full((2,2), 16)
|
|
y = x.idiv(Tensor.linspace(2, 8, steps=4, dtype=dtypes.int).reshape(2,2)).pad(((1,1), (1,1)))
|
|
out = y.sum(axis=1)
|
|
run_schedule(check_schedule(out, 1))
|
|
self.assertListEqual(out.tolist(), [0, 12, 4, 0])
|
|
|
|
def test_arange_transposed_descendants(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randint(4, 1).realize()
|
|
a = (Tensor.arange(4,)*x).T
|
|
b = Tensor.randint(4, 4).realize()
|
|
out = (a+b).sum()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_equal(out.numpy(), ((np.arange(4)*x.numpy()).T+b.numpy()).sum())
|
|
|
|
def test_arange_index(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(5, 2).realize()
|
|
a = Tensor.arange(10)
|
|
out = (x + a[2]).sum()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), (x.numpy()+np.arange(10)[2]).sum(), atol=1e-5, rtol=1e-6)
|
|
|
|
def test_arange_index_shrink(self):
|
|
Tensor.manual_seed(0)
|
|
with Context(TRACK_MATCH_STATS=0):
|
|
x = Tensor.randn(11).realize()
|
|
a = Tensor.arange(22)
|
|
out = (x + a[:11]).sum()
|
|
check_schedule(out, 1)
|
|
|
|
def test_arange_index_contiguous(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(5, 2).realize()
|
|
a = Tensor.arange(10).contiguous()
|
|
out = (x + a[2]).sum()
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), (x.numpy()+np.arange(10)[2]).sum(), atol=1e-5, rtol=1e-6)
|
|
|
|
def test_arange_index_child(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(5, 2).realize()
|
|
a = Tensor.arange(10)+1
|
|
out = (x + a[2]).sum()
|
|
run_schedule(check_schedule(out, 1))
|
|
np.testing.assert_allclose(out.numpy(), (x.numpy()+(np.arange(10)+1)[2]).sum(), atol=1e-5, rtol=1e-6)
|
|
|
|
def test_user_contiguous(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(5, 2).realize()
|
|
a = (Tensor.arange(10)+1).contiguous()
|
|
out = (x + a[2]).sum()
|
|
run_schedule(check_schedule(out, 2))
|
|
np.testing.assert_allclose(out.numpy(), (x.numpy()+(np.arange(10)+1)[2]).sum(), atol=1e-5, rtol=1e-6)
|
|
|
|
@unittest.skip("BUFFER_VIEW no longer supported on non-disk devices")
|
|
def test_arange_view_op(self):
|
|
a = Tensor.arange(12).reshape(4, 3).shrink(((1, 2), (1, 3))).contiguous()
|
|
sched = run_schedule(check_schedule(a, 1))
|
|
self.assertIs(sched[1].ast.op, Ops.BUFFER_VIEW)
|
|
np.testing.assert_equal(a.numpy(), [[4, 5]])
|
|
|
|
@unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
|
|
def test_precompute_freqs_cis(self):
|
|
from extra.models.llama import precompute_freqs_cis
|
|
args = {"dim":32 if CI else 128, "end":2048 if CI else 8192, "theta":10000}
|
|
fused = precompute_freqs_cis(**args)
|
|
run_schedule(check_schedule(fused, 1))
|
|
if getenv("CHECK", 1):
|
|
ref = precompute_freqs_cis(**args)
|
|
run_schedule(check_schedule(ref, 1))
|
|
np.testing.assert_equal(fused.numpy(), ref.numpy())
|
|
|
|
def test_fuse_assign_contiguous(self):
|
|
x = Tensor.zeros(4, 4, dtype=dtypes.int).contiguous().realize()
|
|
a = Tensor.arange(8).reshape(4, 2)
|
|
run_schedule(check_schedule(x.shrink((None, (0, 2))).assign(a.contiguous()), 2))
|
|
np.testing.assert_equal(x.numpy(), [[0, 1, 0, 0], [2, 3, 0, 0], [4, 5, 0, 0], [6, 7, 0, 0]])
|
|
|
|
def test_assign_non_contiguous_alt(self): self.test_assign_non_contiguous(alt=True)
|
|
def test_assign_non_contiguous(self, alt=False):
|
|
x = (Tensor.arange(16)-100).reshape(4,4).contiguous().realize()
|
|
xref = x.numpy()
|
|
if alt:
|
|
y = Tensor.randint(2, 4).contiguous().realize()
|
|
a = Tensor.arange(8).reshape(2, 4)+y
|
|
tst = x.shrink(((0, 2), None)).assign(a).realize()
|
|
xref[:2, :] = np.arange(8).reshape(2, 4)+y.numpy()
|
|
else:
|
|
y = Tensor.randint(4, 2).contiguous().realize()
|
|
a = Tensor.arange(8).reshape(4, 2)+y
|
|
tst = x.shrink((None, (0, 2))).assign(a).realize()
|
|
xref[:, :2] = np.arange(8).reshape(4, 2)+y.numpy()
|
|
np.testing.assert_equal(x.numpy(), xref)
|
|
np.testing.assert_equal(tst.numpy(), a.numpy())
|
|
|
|
def test_setitem_sched(self, mop=lambda x:x, expected_kcount=1):
|
|
a = Tensor.arange(16, device="CPU").reshape(4, 4).contiguous().realize()
|
|
a2 = mop(a)
|
|
expected = (a+a2).tolist()
|
|
a.assign(a+a2)
|
|
kcount = len(sched:=a.schedule())
|
|
run_schedule(sched)
|
|
self.assertListEqual(a.tolist(), expected)
|
|
self.assertEqual(kcount, expected_kcount)
|
|
def test_setitem_permuted_sched(self): self.test_setitem_sched(lambda x: x.T, 2)
|
|
def test_setitem_paddded_sched(self): self.test_setitem_sched(lambda x: x.shrink_to(4, 1).pad_to(4, 4), 1)
|
|
|
|
def test_sparse_categorical_crossentropy_simple(self):
|
|
X = Tensor([[0, 2, 3], [1, 2, 3]]).realize()
|
|
Y = Tensor([1, 2]).realize()
|
|
loss = X.sparse_categorical_crossentropy(Y)
|
|
run_schedule(check_schedule(loss, 3))
|
|
np.testing.assert_allclose(loss.item(), 0.878309, atol=1e-5, rtol=1e-6)
|
|
|
|
def test_const_folding_alt(self):
|
|
t = Tensor.full((2,), 1.)
|
|
lt = (t < 0.)
|
|
a = Tensor.empty(2).assign(t*lt.where(-1., 0.))
|
|
b = Tensor.empty(2, dtype=dtypes.bool).assign(lt)
|
|
Tensor.realize(a, b)
|
|
self.assertEqual(a.tolist(), [0., 0.])
|
|
self.assertEqual(b.tolist(), [False, False])
|
|
|
|
@unittest.skipIf(Device.DEFAULT == "WEBGPU", "Validation error on WebGPU")
|
|
def test_mnist_val(self):
|
|
from tinygrad.nn.datasets import mnist
|
|
import torch
|
|
_, Y_train, _, _ = mnist()
|
|
samples = Tensor.randint(BS:=getenv("BS", 512), high=cast(int,Y_train.shape[-1])).realize()
|
|
yt = Tensor.randn(BS, 10).realize()
|
|
with Context(SPLIT_REDUCEOP=0):
|
|
loss = yt.sparse_categorical_crossentropy(Y_train[samples])
|
|
run_schedule(check_schedule(loss, 4))
|
|
loss_fused = loss.numpy()
|
|
loss_ref = torch.nn.CrossEntropyLoss()(torch.tensor(yt.numpy()), torch.tensor(Y_train.numpy())[torch.tensor(samples.numpy())])
|
|
np.testing.assert_allclose(loss_fused, loss_ref.numpy(), atol=1e-6, rtol=1e-6)
|
|
|
|
def test_arange_fuse_grouped_children(self):
|
|
X = Tensor.randn(4, 4).realize()
|
|
r = (X+Tensor.arange(16).reshape(4, 4)).sum()
|
|
out0 = r+2
|
|
out1 = r+3
|
|
run_schedule(check_schedule([out0, out1], 2)) # TODO: 1?
|
|
r_ref = (X.numpy()+np.arange(16).reshape(4, 4)).sum()
|
|
np.testing.assert_allclose(out0.numpy(), r_ref+2, rtol=2e-7)
|
|
np.testing.assert_allclose(out1.numpy(), r_ref+3, rtol=2e-7)
|
|
|
|
def test_recursive_swizzle(self):
|
|
a = Tensor([1,2,3,4]).realize()
|
|
for _ in range(24): a = a + a
|
|
new_uop = a.reshape(4,1).realize().uop
|
|
assert new_uop.base.op is Ops.BUFFER
|
|
|
|
@unittest.skipIf(CI and Device.DEFAULT == "NV", "crashes on NV CI")
|
|
def test_limit_bufs_with_var(self):
|
|
N = 31
|
|
with Context(TRACK_MATCH_STATS=0, DEBUG=0):
|
|
bufs = [Tensor([1]*10).contiguous().realize() for i in range(N)]
|
|
|
|
vi = Variable("i", 0, 9).bind(1)
|
|
vj = Variable("j", 0, 9).bind(2)
|
|
root = bufs[0][vi] + bufs[0][vj]
|
|
for X in range(1,N): root = root + bufs[X][vi] + bufs[X][vj]
|
|
self.assertEqual(root.item(), N * 2)
|
|
|
|
def test_limit_bufs_kernelize(self):
|
|
N = 31
|
|
with Context(TRACK_MATCH_STATS=0, DEBUG=0):
|
|
bufs = [Tensor(i).contiguous().realize() for i in range(N)]
|
|
x = bufs[0]
|
|
for y in bufs[1:]: x = x+y
|
|
x.kernelize()
|
|
kcount = len([s for s in x.uop.toposort() if s.op is Ops.KERNEL])
|
|
z = x+Tensor.empty(1) # z only loads 2 buffers
|
|
sched = z.schedule()
|
|
self.assertEqual(len(sched), kcount+1)
|
|
|
|
class TestSwizzle(unittest.TestCase):
|
|
def test_swizzle_simple(self):
|
|
Tensor.manual_seed(0)
|
|
with Context(DEBUG=0, TRACK_MATCH_STATS=0):
|
|
a = Tensor.randint(32, 32).realize()
|
|
r = (a+a).sum(1).sum(0)
|
|
# double reduce collapses to a single reduce
|
|
run_schedule(check_schedule(r, 1))
|
|
self.assertEqual(r.numpy(), (a.numpy()+a.numpy()).sum(1).sum(0))
|
|
|
|
def test_single_swizzle(self):
|
|
Tensor.manual_seed(0)
|
|
with Context(DEBUG=0, TRACK_MATCH_STATS=0):
|
|
a = Tensor.randint(4, 1).realize()
|
|
b = Tensor.ones((1, 1), dtype=a.dtype).contiguous().realize()
|
|
# ADD(REDUCE(RESHAPE(LOAD)), LOAD) to ADD(REDUCE(RESHAPE(LOAD))), RESHAPE(LOAD)
|
|
r = a.sum(0)+b
|
|
run_schedule(check_schedule(r, 1))
|
|
self.assertEqual(r.numpy(), a.numpy().sum(0)+1)
|
|
|
|
def test_double_swizzle_possible(self):
|
|
Tensor.manual_seed(0)
|
|
with Context(DEBUG=0, TRACK_MATCH_STATS=0):
|
|
a = Tensor.randint(4,).realize()
|
|
b = Tensor.randint(4,).realize()
|
|
# parallel reduce!
|
|
add = a.sum(0)+b.sum(0)
|
|
run_schedule(check_schedule(add, 1))
|
|
self.assertEqual(add.numpy(), a.numpy().sum(0)+b.numpy().sum(0))
|
|
|
|
def test_softmax_one_kernel(self):
|
|
Tensor.manual_seed(0)
|
|
with Context(DEBUG=0, TRACK_MATCH_STATS=0):
|
|
a = Tensor.randn(32, 32).realize()
|
|
t = a.softmax()
|
|
check_schedule(t, 3) # TODO: 1?
|
|
|
|
def test_argmax_one_kernel(self):
|
|
Tensor.manual_seed(0)
|
|
with Context(DEBUG=0, TRACK_MATCH_STATS=0):
|
|
a = Tensor.randn(10, 20).realize()
|
|
t = a.argmax(0)
|
|
check_schedule(t, 2) # TODO: 1?
|
|
|
|
def test_swizzle_reduceop(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4,4).realize()
|
|
y = Tensor.randn(4,4,4).realize()
|
|
out = x.reshape(4,4,1).expand(4,4,4).sum(axis=(1,))+y
|
|
run_schedule(check_schedule(out, 2)) # TODO: 1?
|
|
np.testing.assert_allclose(out.numpy(), np.tile(x.numpy().reshape(4,4,1), (1,1,4)).sum(axis=1)+y.numpy())
|
|
|
|
def test_permute_rewrite(self):
|
|
x = Tensor.randn(4, 4, 16).realize()
|
|
y = Tensor.randn(4, 1, 16).realize()
|
|
z = Tensor.randn(4, 4, 1).realize()
|
|
t = (x*y).sum(axis=(0, 2)).reshape(1, 4, 1).permute(0, 2, 1)+z
|
|
run_schedule(check_schedule(t, 2)) # TODO: 1?
|
|
t_np = (x.numpy()*y.numpy()).sum(axis=(0, 2)).reshape(1, 4, 1).transpose(0, 2, 1)+z.numpy()
|
|
np.testing.assert_allclose(t.numpy(), t_np, atol=1e-6, rtol=1e-3)
|
|
|
|
@unittest.skip("TODO: this swizzle isn't resolvable when there's a mask")
|
|
def test_swizzle_failure_permute(self):
|
|
a = Tensor.empty(45,65).T.reshape(65,1,45).pad((None,None,(0,45))).expand(65,45,90)
|
|
b = Tensor.empty(45,65)
|
|
a_reduce = a.sum(axis=(2,), keepdim=True).sum(axis=(1,))
|
|
b_reduce = b.sum(axis=(0,))
|
|
t = a_reduce+b_reduce
|
|
run_schedule(check_schedule(t, 1))
|
|
|
|
def test_parallel_reduce_possible(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 2, 2).realize()
|
|
y = Tensor.randn(4, 2, 2).realize()
|
|
t = x.sum(axis=1)+y.sum(axis=1)
|
|
run_schedule(check_schedule(t, 1))
|
|
np.testing.assert_allclose(t.numpy(), x.numpy().sum(axis=1)+y.numpy().sum(axis=1), atol=1e-6, rtol=1e-3)
|
|
|
|
# kernels can only have 1 or n in each dim
|
|
def test_dont_parallelize_different_n(self):
|
|
Tensor.manual_seed(0)
|
|
x = Tensor.randn(4, 2, 2).realize()
|
|
y = Tensor.randn(4, 3, 2).realize()
|
|
t = x.sum(axis=1)+y.sum(axis=1)
|
|
run_schedule(check_schedule(t, 1))
|
|
np.testing.assert_allclose(t.numpy(), x.numpy().sum(axis=1)+y.numpy().sum(axis=1), atol=1e-6, rtol=1e-3)
|
|
|
|
def test_unsafe_pad(self):
|
|
x = Tensor.full((2,2), 1.0).contiguous()
|
|
y = x*x.sum((1,)).reciprocal()
|
|
t = y.pad(((0,1),None))
|
|
run_schedule(check_schedule(t, 3))
|
|
np.testing.assert_equal(t.numpy(), [[0.5, 0.5], [0.5, 0.5], [0., 0.]])
|
|
|
|
zero_pm = UPat(Ops.CONST, arg=0)
|
|
class TestView(unittest.TestCase):
|
|
def test_all_masked_out(self):
|
|
# start with non CONST Ops
|
|
a = Tensor.rand(10, 10).realize()
|
|
# all masked out, degrades to const 0
|
|
b = a.pad(((0, 10), None))[10:]
|
|
sched = check_schedule(b.contiguous(), 1)
|
|
run_schedule(sched)
|
|
np.testing.assert_equal(b.numpy(), 0)
|
|
|
|
def test_mask_dim_1(self):
|
|
# mask out dim = 1 works too
|
|
a = Tensor.rand(10, 10).realize()
|
|
b = a.pad((None, (0, 10)))[:, 10:]
|
|
assert b.shape == (10, 10)
|
|
sched = check_schedule(b.contiguous(), 1)
|
|
run_schedule(sched)
|
|
np.testing.assert_equal(b.numpy(), 0)
|
|
|
|
def test_zero_size_alt(self):
|
|
a = Tensor.empty(135, 0, 9)
|
|
b = a.pad(((0, 0), (0, 0), (18, 0)))
|
|
check_schedule(b, 0)
|
|
|
|
def test_partial_mask(self):
|
|
# partial masked out does not degrade into CONST
|
|
a = Tensor.rand(10, 10).realize()
|
|
b = a.pad(((0, 5), None))[5:]
|
|
assert b.shape == (10, 10)
|
|
sched = check_schedule(b.contiguous(), 1)
|
|
run_schedule(sched)
|
|
np.testing.assert_allclose(b.numpy(), np.pad(a.numpy(), ((0, 5), (0, 0)))[5:])
|
|
|
|
# a*VIEW(x), where VIEW(x) = 0
|
|
# x collapses along with its children
|
|
def test_parent_view_collapses(self):
|
|
a = Tensor([1, 2])
|
|
b = Tensor.arange(3).contiguous()
|
|
bv = b.pad(((0, 2),))[-2:]
|
|
# this becomes a late a*0
|
|
late_mul = a*bv
|
|
check_schedule(late_mul, 0)
|
|
# the arange doesn't realize
|
|
self.assertIsNone(b.uop.base.realized)
|
|
# mul doesn't realize
|
|
self.assertIsNone(late_mul.uop.base.realized)
|
|
self.assertEqual(late_mul.tolist(), [0, 0])
|
|
|
|
# SINK has two branches:
|
|
# a*VIEW(x), where VIEW(x) = 0
|
|
# x+2
|
|
# as long as one child realizes, x does not collapse
|
|
def test_parent_multiple_children_no_collapse(self):
|
|
a = Tensor([1, 2])
|
|
b = Tensor.arange(3).contiguous()
|
|
bv = b.pad(((0, 2),))[-2:]
|
|
late_mul = a*bv
|
|
other_child = b+2
|
|
s = check_schedule([late_mul, other_child], 2)
|
|
# the arange becomes a BUFFER
|
|
self.assertIs(b.uop.base.op, Ops.BUFFER)
|
|
# mul still collapses
|
|
self.assertIs(late_mul.uop.base.op, Ops.CONST)
|
|
run_schedule(s)
|
|
self.assertEqual(other_child.tolist(), [2, 3, 4])
|
|
|
|
@unittest.skipIf(Device.DEFAULT == "CPU", "tests copy from another device to cpu")
|
|
class TestCopyFolding(unittest.TestCase):
|
|
def test_const_copy_is_free(self):
|
|
b = Tensor(1).to("CPU")
|
|
check_schedule(b, 0, filter_sink=False)
|
|
assert b.item() == 1
|
|
|
|
def test_one_hot_with_copy(self):
|
|
y = Tensor([1, 2, 3]).to("CPU")
|
|
x = y.one_hot(10)
|
|
check_schedule(x, 3, filter_sink=False)
|
|
|
|
def test_const_copy_multi(self):
|
|
x = Tensor.ones(1, device="CPU").to_(["CPU", "CPU:1"])
|
|
check_schedule(x, 0, filter_sink=False)
|
|
self.assertEqual(x.item(), 1)
|
|
|
|
def test_late_const_copy_folding(self):
|
|
a = Tensor.arange(3).realize()
|
|
zeros = Tensor.zeros(3).realize()
|
|
b = (a*zeros).to("CPU")
|
|
run_schedule(check_schedule(b, 0, filter_sink=False))
|
|
self.assertListEqual(b.tolist(), [0, 0, 0])
|
|
self.assertEqual(b.device, "CPU")
|
|
|
|
def test_alu_after_copy(self):
|
|
a = Tensor.ones((4,)).to("CPU")
|
|
b = Tensor.empty(4, device="CPU")
|
|
add = a+b
|
|
assert all_same([x.device for x in add.uop.src]), f"ALU has different devices! {[x.device for x in add.src]}"
|
|
add.kernelize()
|
|
|
|
def test_alu_before_copy(self):
|
|
buf = Tensor.ones(1).contiguous().realize()
|
|
a = buf+1
|
|
b = a.to("CPU")
|
|
self.assertListEqual(b.tolist(), [2.])
|
|
|
|
def test_copy_to_same_device(self):
|
|
a = Tensor.empty(4).uop
|
|
b = a.copy_to_device(a.device)
|
|
check_schedule(b, 1, filter_sink=False) # TODO: 0?
|
|
|
|
def test_copy_to_same_device_alt(self):
|
|
a = Tensor.empty(4, 4).uop
|
|
b = a.copy_to_device(a.device)
|
|
check_schedule(b, 1, filter_sink=False) # TODO: 0?
|
|
|
|
def test_copy_to_same_device_sched(self):
|
|
a = Tensor.ones(4).contiguous().realize().uop.as_buf()
|
|
t = Tensor(a.copy_to_device(a.device))
|
|
sched = t.schedule()
|
|
assert len([s for s in sched if s.ast.op is Ops.COPY]) == 0
|
|
run_schedule(sched)
|
|
assert t.uop.is_realized, f"didn't realize Tensor {t}"
|
|
self.assertListEqual(t.tolist(), [1.,1.,1.,1.])
|
|
|
|
def test_clone(self):
|
|
a = Tensor.empty(4)
|
|
check_schedule(a.clone(), 1, filter_sink=False)
|
|
|
|
def test_shrink_copy(self):
|
|
a = Tensor.arange(4)
|
|
view = a.shrink(((0, 2),))
|
|
b = view.clone()
|
|
run_schedule(check_schedule(b, 1, filter_sink=False))
|
|
self.assertEqual(b.uop.base.buffer.size, 2)
|
|
self.assertEqual(b.uop.size, 2)
|
|
self.assertListEqual(b.tolist(), [0, 1])
|
|
|
|
def test_expanded_copy(self):
|
|
a = Tensor.arange(2)
|
|
view = a.reshape(2, 1).expand(2, 2)
|
|
b = view.clone()
|
|
run_schedule(check_schedule(b, 1, filter_sink=False))
|
|
self.assertEqual(b.uop.base.buffer.size, 4)
|
|
self.assertEqual(b.uop.size, 4)
|
|
self.assertListEqual(b.tolist(), [[0, 0], [1, 1]])
|
|
|
|
def test_permuted_copy(self):
|
|
a = Tensor.arange(4)
|
|
b = a.reshape(2, 2).permute(1, 0)
|
|
b.realize()
|
|
self.assertListEqual(b.tolist(), [[0, 2], [1, 3]])
|
|
|
|
def test_permute_on_disk(self):
|
|
with open(temp('dt_arange_4_permute'), "wb") as f: f.write(Tensor.arange(4).realize().uop.base.buffer.as_buffer())
|
|
a = Tensor.empty(4, dtype=dtypes.int32, device=f"disk:{temp('dt_arange_4_permute')}")
|
|
b = a.reshape(2, 2).permute(1, 0).to("CPU")
|
|
b.realize()
|
|
self.assertListEqual(b.tolist(), [[0, 2], [1, 3]])
|
|
|
|
def test_permute_on_disk_contiguous(self):
|
|
with open(temp('dt_arange_4_permute_contig'), "wb") as f: f.write(Tensor.arange(4).realize().uop.base.buffer.as_buffer())
|
|
a = Tensor.empty(4, dtype=dtypes.int32, device=f"disk:{temp('dt_arange_4_permute_contig')}")
|
|
b = a.reshape(2, 2).permute(1, 0).contiguous().to("CPU")
|
|
b.realize()
|
|
self.assertListEqual(b.tolist(), [[0, 2], [1, 3]])
|
|
|
|
def test_permute_after_shrink(self):
|
|
a = Tensor.arange(5)
|
|
b = a.shrink(((0, 4),)).reshape(2, 2).permute(1, 0).to("CPU")
|
|
b.realize()
|
|
self.assertListEqual(b.tolist(), [[0, 2], [1, 3]])
|
|
|
|
# NOTE: disk permute must come after COPY
|
|
def test_permute_after_shrink_on_disk(self):
|
|
with open(temp('dt_arange_5_permute'), "wb") as f: f.write(Tensor.arange(5).realize().uop.base.buffer.as_buffer())
|
|
a = Tensor.empty(5, dtype=dtypes.int32, device=f"disk:{temp('dt_arange_5_permute')}")
|
|
b = a.shrink(((0, 4),)).reshape(2, 2).permute(1, 0).to("CPU")
|
|
b.realize()
|
|
self.assertListEqual(b.tolist(), [[0, 2], [1, 3]])
|
|
|
|
class TestBufferUOp(unittest.TestCase):
|
|
# BUFFER has a ShapeTracker of shape=(n,) and stride=(1,)
|
|
def test_buffer_has_buffer(self):
|
|
buf = Tensor.empty(10)
|
|
self.assertIsNotNone(buf.uop.buffer)
|
|
self.assertEqual(buf.uop.shape, (10,))
|
|
# the device Buffer remains unallocated until it's we run the schedule
|
|
self.assertFalse(buf.uop.buffer.is_allocated())
|
|
add = buf+1
|
|
sched = add.schedule()
|
|
self.assertFalse(buf.uop.buffer.is_allocated())
|
|
run_schedule(sched)
|
|
self.assertTrue(buf.uop.buffer.is_allocated())
|
|
|
|
def test_buffer_has_unique_buffer(self):
|
|
buf = Tensor.empty(10)
|
|
buf1 = buf.uop.buffer
|
|
buf2 = buf.uop.buffer
|
|
self.assertIs(buf1, buf2)
|
|
|
|
# we also allow VIEW(BUFFER) to access the underlying device Buffer, as long as it's contiguous
|
|
def test_buffer_view_allowed(self):
|
|
add = Tensor.empty(1, 1)+Tensor.empty(1, 1)
|
|
add.realize()
|
|
self.assertIsNotNone(add.uop.buffer)
|
|
self.assertEqual(add.uop.shape, (1, 1))
|
|
|
|
def test_buffer_view_not_allowed(self):
|
|
permuted_view = Tensor.empty(1, 2, 3).permute(0, 2, 1)
|
|
with self.assertRaisesRegex(AssertionError, "can only be RESHAPE"):
|
|
permuted_view.uop.buffer # cannot access Buffer of a non contiguous VIEW
|
|
|
|
def test_buffer_only_after_realize(self):
|
|
a = Tensor([1])+Tensor([2])
|
|
# accessing realized will return None
|
|
self.assertIsNone(a.uop.realized)
|
|
# accessing Buffer will assert
|
|
with self.assertRaisesRegex(AssertionError, "must be BUFFER"):
|
|
a.uop.buffer # there is no BUFFER on an unrealized ADD
|
|
# Buffer only exists once we realize it
|
|
a.realize()
|
|
self.assertIsNotNone(a.uop.buffer)
|
|
|
|
def test_const_does_not_realize(self):
|
|
a = Tensor(1)+Tensor(2)
|
|
run_schedule(check_schedule(a, 0))
|
|
self.assertIsNone(a.uop.base.realized)
|
|
|
|
def test_var_does_not_realize(self):
|
|
a = Tensor(UOp.variable("a", 0, 10).bind(1))
|
|
run_schedule(check_schedule(a, 0))
|
|
self.assertIsNone(a.uop.base.realized)
|
|
|
|
def test_view_does_not_realize(self):
|
|
a = Tensor.randn(1, 4).expand(4, 4)
|
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a.realize()
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self.assertEqual(a.uop.base.realized.size, 4)
|
|
a2 = a.contiguous().realize()
|
|
self.assertEqual(a2.uop.base.realized.size, 16)
|
|
|
|
class TestContiguous(unittest.TestCase):
|
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def test_contiguous_buffer(self):
|
|
a = Tensor.empty(4)
|
|
b = a.contiguous()
|
|
check_schedule(b, 0)
|
|
|
|
def test_contiguous_buffer_view(self):
|
|
a = Tensor.empty(4)
|
|
b = a.reshape((2, 2)).contiguous()
|
|
check_schedule(b, 0)
|
|
|
|
def test_non_contiguous_buffer_view(self):
|
|
a = Tensor.empty(4, 1)
|
|
b = a.expand((4, 4)).contiguous()
|
|
check_schedule(b, 1)
|
|
|
|
def test_size_change_buffer_view(self):
|
|
a = Tensor.empty(4)
|
|
b = a.reshape((1, 1, 4)).shrink(((0, 1), (0, 1), (0, 3))).contiguous()
|
|
check_schedule(b, 1)
|
|
|
|
def test_double_contiguous_realizes_once(self):
|
|
a = Tensor.empty(4, 1)
|
|
b = a.expand((4, 4)).contiguous().contiguous()
|
|
check_schedule(b, 1)
|
|
|
|
def test_view_does_not_realize(self):
|
|
a = Tensor.empty(4)
|
|
b = a.expand((4, 4))
|
|
check_schedule(b, 0)
|
|
self.assertEqual(b.uop.base.buffer.size, 4)
|
|
|
|
def test_contiguous_view_realizes(self):
|
|
a = Tensor.empty(4)
|
|
b = a.expand((4, 4)).contiguous()
|
|
check_schedule(b, 1)
|
|
self.assertEqual(b.uop.base.buffer.size, 16)
|
|
|
|
class TestUOpBecome(unittest.TestCase):
|
|
# the simplest case, if we create a new BUFFER for this tensor UOp
|
|
def test_new_buffer(self):
|
|
a = Tensor.empty(4, 4)
|
|
b = Tensor.empty(4, 4)
|
|
add = a+b
|
|
check_schedule(add, 1)
|
|
# NOTE: realized base is always a flat buffer
|
|
assert UPat(Ops.BUFFER).match(add.uop.base, {})
|
|
# the Tensor UOp can optionally stack a VIEW on top of the BUFFER, in this case to preserve the (4, 4) shape of the tensor
|
|
assert add.uop is not add.uop.base
|
|
self.assertEqual(add.uop.size, 16)
|
|
self.assertEqual(add.uop.shape, (4, 4))
|
|
|
|
def test_new_buffer_view(self):
|
|
a = Tensor.empty(4, 4)
|
|
b = Tensor.empty(4, 4)
|
|
add = (a+b).reshape(8, 2)
|
|
check_schedule(add, 1)
|
|
assert UPat(Ops.BUFFER).match(add.uop.base, {})
|
|
# the shape is preserverd in the becomes_map.
|
|
self.assertEqual(add.uop.shape, (8, 2))
|
|
assert add.uop is not add.uop.base
|
|
|
|
def test_new_flat_buffer(self):
|
|
a = Tensor.empty(4,)
|
|
b = Tensor.empty(4,)
|
|
add = a+b
|
|
check_schedule(add, 1)
|
|
# BUFFER already has a shape (4,), this tensor just becomes a contiguous BUFFER
|
|
assert UPat(Ops.BUFFER).match(add.uop.base, {})
|
|
|
|
# sometimes we prefer to perform an op before movement ops, in this case we should stack the mops on top of the new buffer
|
|
|
|
def test_reorder_expand(self):
|
|
a = Tensor.empty(4, 1)
|
|
b = a.expand(4, 4).reciprocal()
|
|
check_schedule(b, 1)
|
|
self.assertEqual(b.uop.base.buffer.size, 4)
|
|
self.assertEqual(b.uop.shape, (4, 4))
|
|
|
|
def test_reorder_expand_alt(self):
|
|
x = Tensor.empty(4, 1)
|
|
y = Tensor.empty(4, 1)
|
|
img = Tensor.empty(4, 4)
|
|
z = (img*x) / y
|
|
check_schedule(z, 1)
|
|
|
|
# TODO: rangeify doesn't yet cleanup this kind of re-indexing
|
|
@unittest.expectedFailure
|
|
def test_become_existing_buffer(self):
|
|
a = Tensor.empty(4, 4)
|
|
b = a*1
|
|
assert UPat(Ops.MUL).match(b.uop, {}) # before scheduling it's a mul
|
|
check_schedule(b, 0)
|
|
self.assertIs(a.uop.base.buffer, b.uop.base.buffer)
|
|
|
|
def test_become_buf_with_mops(self):
|
|
a = Tensor.empty(2, 4, 2)
|
|
noop = a.shrink(((1, 2), (0, 4), (0, 2))).reshape(4, 2)*1+0
|
|
# before realizing, this tensor is base
|
|
assert noop.uop is noop.uop.base
|
|
noop.realize()
|
|
# it becomes a realized view after realize
|
|
assert noop.uop is not noop.uop.base
|
|
assert noop.uop.base.op is Ops.BUFFER
|
|
late_add = noop+2
|
|
late_add.realize()
|
|
|
|
def test_become_const_in_base(self):
|
|
a = Tensor.empty(4)
|
|
b = a*0
|
|
assert UPat(Ops.MUL).match(b.uop, {}) # before scheduling it's a mul
|
|
check_schedule(b, 0)
|
|
assert UPat(Ops.CONST, arg=0).match(b.uop.base, {}) # scheduling replaces the tensor uop with a VIEW(BUFFER)
|
|
|
|
def test_become_const_from_const(self):
|
|
const_add = Tensor(1)+Tensor(2)
|
|
assert UPat(Ops.ADD).match(const_add.uop, {})
|
|
check_schedule(const_add, 0)
|
|
assert UPat(Ops.CONST, arg=3).match(const_add.uop.base, {})
|
|
|
|
# tensors can become another realized tensor source
|
|
@unittest.expectedFailure
|
|
def test_become_existing_buf_simple(self):
|
|
a = Tensor.empty(4, 4)
|
|
b = a+0
|
|
check_schedule(b, 0)
|
|
assert b.uop.base.op is Ops.BUFFER
|
|
self.assertIs(a.uop, b.uop)
|
|
|
|
# they can also chain other movement ops on top of the tensor source
|
|
@unittest.expectedFailure
|
|
def test_become_existing_buf_view(self):
|
|
a = Tensor.empty(4, 4)
|
|
b = a.permute((1, 0))+0
|
|
check_schedule(b, 0)
|
|
self.assertEqual(b.uop.st, a.uop.permute((1, 0)).st)
|
|
|
|
@unittest.expectedFailure
|
|
def test_become_existing_buf_view_alt(self):
|
|
a = Tensor.empty(4, 4)
|
|
b = a.permute((1, 0)).reshape((8, 2))+0
|
|
check_schedule(b, 0)
|
|
self.assertEqual(b.uop.st, a.uop.permute((1, 0)).reshape((8, 2)).st)
|
|
|
|
# they can also have other base parents that simplified, in that case we just backtrack to the chained mops
|
|
@unittest.expectedFailure
|
|
def test_become_existing_buf_complex(self):
|
|
a = Tensor.empty(4, 4)
|
|
b = (a.permute((1, 0))+0).reshape((8, 2))+0
|
|
check_schedule(b, 0)
|
|
self.assertEqual(b.uop.st, a.uop.permute((1, 0)).reshape((8, 2)).st)
|
|
assert b.uop.base.op is Ops.BUFFER
|
|
|
|
@unittest.expectedFailure
|
|
def test_become_multiple_choices(self):
|
|
a = Tensor.empty(16)
|
|
b = (a.reshape(1, 1, 4, 1, 4)+0).reshape(1, 1, 4, 4).shrink(((0, 1), (0, 1), (0, 3), (0, 3)))+0
|
|
c = (a.reshape(1, 1, 4, 4)+0).shrink(((0, 1), (0, 1), (0, 3), (0, 3)))+0
|
|
check_schedule([b, c], 0)
|
|
assert all_same([x.uop.base.realized for x in [a,b,c]])
|
|
|
|
def test_setitem_becomes_subbuffer(self):
|
|
a = Tensor.full((4,), 2.).contiguous().realize()
|
|
b = a.shrink(((0, 2),)).assign(Tensor.full((2,), 1.0))
|
|
b.realize()
|
|
assert a.uop.is_realized
|
|
assert a.uop.buffer._base is None
|
|
assert b.uop.op_in_backward_slice_with_self(Ops.SHRINK)
|
|
assert b.uop.base is a.uop.base
|
|
|
|
def test_setitem_offset(self):
|
|
a = Tensor.full((16,), 0.).contiguous().realize()
|
|
b = Tensor.full((16,), 1.).contiguous().realize()
|
|
a_view = a[4:].reshape(3, 4).shrink(((0,2),(0,2))).reshape((4,))
|
|
b.shrink(((0,4),)).assign(a_view).realize()
|
|
self.assertListEqual(b.tolist(), [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main(verbosity=2)
|
|
|