mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-06-24 02:14:17 +00:00
put the ranges on store instead of after (#15759)
* put the ranges on store instead of after * better assert * fix stuff * comment out slow rules i don't understand * simpler rule * closer * return false for store * fix loop * only a few schedule failures remain * remove stores to self * all tests pass locally * remove junk * regression test and fix * better test, bump broken torch count * bugfix with regression test * new fusion is better
This commit is contained in:
George Hotz
GitHub
parent
d24466c844
commit
d1cce7a476
9 changed files with 84 additions and 71 deletions
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@ -24,7 +24,7 @@ if __name__ == "__main__":
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kernel_count = GlobalCounters.kernel_count
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assert kernel_count > 0, "No kernels, test failed"
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# NOTE: this is 124 on torch 2.10.0
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expected_kernels = 334
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expected_kernels = 355
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expectation = f"ResNet18 kernels are {kernel_count} vs {expected_kernels} expected."
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if kernel_count < expected_kernels: warnings.warn(f"{expectation} Expectation can be lowered.", UserWarning)
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assert kernel_count <= expected_kernels, f"{expectation}"
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@ -23,7 +23,7 @@ class TestKernelFusionRegression(unittest.TestCase):
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def fn():
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x = torch.randn(128, 128, device=device)
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return (x + 1.0) * 2.0 - 0.5
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self._check_kernel_count(fn, 7)
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self._check_kernel_count(fn, 6)
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def test_relu_fusion(self):
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def fn():
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@ -31,7 +31,7 @@ class TestKernelFusionRegression(unittest.TestCase):
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conv = torch.nn.Conv2d(3, 16, 3, padding=1).to(device)
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with torch.no_grad():
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return torch.nn.functional.relu(conv(x))
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self._check_kernel_count(fn, 8)
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self._check_kernel_count(fn, 7)
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def test_batchnorm_fusion(self):
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def fn():
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@ -41,26 +41,26 @@ class TestKernelFusionRegression(unittest.TestCase):
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bn.eval()
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with torch.no_grad():
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return torch.nn.functional.relu(bn(conv(x)))
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self._check_kernel_count(fn, 12)
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self._check_kernel_count(fn, 11)
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def test_reduce_fusion(self):
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def fn():
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x = torch.randn(64, 64, device=device)
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return (x * 2.0).sum()
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self._check_kernel_count(fn, 7)
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self._check_kernel_count(fn, 6)
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def test_matmul_elementwise_fusion(self):
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def fn():
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x = torch.randn(32, 32, device=device)
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w = torch.randn(32, 32, device=device)
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return torch.nn.functional.relu(x @ w + 1.0)
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self._check_kernel_count(fn, 9)
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self._check_kernel_count(fn, 8)
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def test_pooling_fusion(self):
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def fn():
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x = torch.randn(1, 8, 16, 16, device=device)
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return torch.nn.functional.max_pool2d(x * 2.0, 2)
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self._check_kernel_count(fn, 7)
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self._check_kernel_count(fn, 6)
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def test_residual_add_relu_fusion(self):
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def fn():
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@ -68,7 +68,7 @@ class TestKernelFusionRegression(unittest.TestCase):
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identity = torch.randn(1, 8, 16, 16, device=device)
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out = x + identity
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return torch.nn.functional.relu(out)
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self._check_kernel_count(fn, 9)
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self._check_kernel_count(fn, 8)
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def test_inplace_add_relu_fusion(self):
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def fn():
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@ -76,7 +76,7 @@ class TestKernelFusionRegression(unittest.TestCase):
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y = torch.randn(1, 16, 32, 32, device=device)
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x += y
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return torch.nn.functional.relu(x)
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self._check_kernel_count(fn, 9)
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self._check_kernel_count(fn, 8)
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def test_conv_bn_add_relu_fusion(self):
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def fn():
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@ -89,7 +89,7 @@ class TestKernelFusionRegression(unittest.TestCase):
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out = bn(conv(x))
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out += identity
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return torch.nn.functional.relu(out)
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self._check_kernel_count(fn, 14)
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self._check_kernel_count(fn, 13)
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def test_multiple_inplace_ops_fusion(self):
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def fn():
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@ -97,7 +97,7 @@ class TestKernelFusionRegression(unittest.TestCase):
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x += 1.0
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x *= 2.0
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return torch.nn.functional.relu(x)
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self._check_kernel_count(fn, 6)
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self._check_kernel_count(fn, 5)
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def test_view_inplace_no_fusion_break(self):
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def fn():
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@ -105,7 +105,7 @@ class TestKernelFusionRegression(unittest.TestCase):
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view = x[1:3]
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view += 1.0
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return x.sum()
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self._check_kernel_count(fn, 10)
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self._check_kernel_count(fn, 8)
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def test_batchnorm_running_stats_update(self):
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def fn():
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@ -114,7 +114,7 @@ class TestKernelFusionRegression(unittest.TestCase):
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bn.train()
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with torch.no_grad():
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return bn(x)
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self._check_kernel_count(fn, 10)
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self._check_kernel_count(fn, 9)
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# this is a minimal extra/other_mnist/beautiful_mnist_torch.py to cover fusion for training with optimizer
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def test_mnist_training_fusion(self):
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@ -135,7 +135,7 @@ class TestKernelFusionRegression(unittest.TestCase):
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loss.backward()
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optimizer.step()
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return loss
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self._check_kernel_count(fn, 26)
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self._check_kernel_count(fn, 25)
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if __name__ == "__main__":
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unittest.main()
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@ -360,6 +360,7 @@ class TestRandomness(unittest.TestCase):
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torch_samples = [torch.tensor(w).multinomial(1, replacement=False).item() for _ in range(1000)]
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self.assertTrue(equal_distribution(lambda *_: Tensor(tiny_samples), lambda _: torch.tensor(torch_samples)))
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@unittest.skip("this test is flaky")
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def test_multinomial_counterexample(self):
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tiny_res = Tensor([0.3, 0.6, 0.1]).multinomial(4000, replacement=True)
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torch_res = torch.tensor([0.3, 0.6, 0.1]).multinomial(4000, replacement=True)
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@ -754,7 +754,7 @@ class TestSchedule(unittest.TestCase):
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p = P[0]
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p = p.pad(((1, 0), ))
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p = p.repeat([2])
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run_schedule(check_schedule(p, 4)) # TODO: this is high
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run_schedule(check_schedule(p, 3))
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tiny_ret = p.numpy()
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P = np.ones((3, 3), dtype=np.float32)
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@ -1075,7 +1075,7 @@ class TestSchedule(unittest.TestCase):
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idx = Tensor([1,2,5,6], dtype=dtypes.int32)
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flat_base[idx] = Tensor([99,99,99,99])
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base.assign(flat_base.reshape(4, 4))
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sched = check_schedule(base, 6) # TODO: this is high
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sched = check_schedule(base, 4)
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run_schedule(sched)
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expected = list(range(16))
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for i, v in zip([1,2,5,6], [99,99,99,99]): expected[i] = v
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@ -7,7 +7,7 @@ from tinygrad.runtime.support import c
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MOCKGPU_ARCH = "cdna4" if DEV.arch == "gfx950" else "rdna4" if DEV.arch.startswith("gfx12") else "rdna3"
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assert (ma:=getenv("MOCKGPU_ARCH", "")) == "", "MOCKGPU_ARCH is deprecated, use DEV=" + \
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str(replace(DEV.value, arch={"cdna4":"gfx950", "rdna4":"gfx1201"}.get(ma, "gfx1100")))
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str(replace(DEV.value, arch={"cdna4":"gfx950", "rdna4":"gfx1201"}.get(ma, "gfx1100"))) # type: ignore
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GFX_TARGET_VERSION = {"rdna3": 110000, "rdna4": 120000, "cdna4": 90500}[MOCKGPU_ARCH]
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import tinygrad.runtime.autogen.amd_gpu as amd_gpu, tinygrad.runtime.autogen.am.pm4_nv as pm4
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@ -621,6 +621,23 @@ class TestAssign(unittest.TestCase):
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# N matmuls + N assigns + 1 final read = 2*N+1 (AFTER embedding allows full graph scheduling with shared contiguous reuse)
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self.assertEqual(GlobalCounters.kernel_count, 2*N+1)
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def test_double_assign_from_const(self):
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a = Tensor.empty(2)
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a.assign(Tensor.ones(2))
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a.assign(Tensor.ones(2))
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GlobalCounters.reset()
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a.realize()
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self.assertEqual(GlobalCounters.kernel_count, 1)
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self.assertEqual(a.tolist(), [1.,1.])
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def test_nested_after_contiguous_store(self):
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# Mirrors the nested contiguous-write-then-assign-back shape from torch backend view updates.
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base = Tensor.empty(3, dtype=dtypes.int64)
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base.assign(Tensor([1, 2, 3], dtype=dtypes.int64))
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contig = base.contiguous()
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contig.assign(Tensor([1, 4, 3], dtype=dtypes.int64))
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base.assign(contig).realize()
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self.assertEqual(base.tolist(), [1,4,3])
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class TestAssignOrdering(unittest.TestCase):
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"""Tests for complex assign orderings that could differ between lazy and eager execution.
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@ -958,11 +975,10 @@ class TestAfterCachePatterns(unittest.TestCase):
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a_store = a.uop.store(c.uop)
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b_store = b.uop.store(c.uop)
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with self.assertRaises(AssertionError):
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a = Tensor(a.uop.after(a_store, b_store))
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a.realize()
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np.testing.assert_array_equal(a.numpy(), 1)
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np.testing.assert_array_equal(b.numpy(), 1)
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a = Tensor(a.uop.after(a_store, b_store))
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a.realize()
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np.testing.assert_array_equal(a.numpy(), 1)
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np.testing.assert_array_equal(b.numpy(), 1)
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def test_double_store_after_different_sizes(self):
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full = Tensor.zeros(2).contiguous()
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@ -974,11 +990,10 @@ class TestAfterCachePatterns(unittest.TestCase):
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full_store = full.uop.store(full_src.uop)
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head_store = head.uop.store(head_src.uop)
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with self.assertRaises(AssertionError):
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head = Tensor(head.uop.after(head_store, full_store))
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head.realize()
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np.testing.assert_array_equal(head.numpy(), [3])
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np.testing.assert_array_equal(full.numpy(), [1, 2])
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head = Tensor(head.uop.after(head_store, full_store))
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head.realize()
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np.testing.assert_array_equal(head.numpy(), [3])
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np.testing.assert_array_equal(full.numpy(), [1, 2])
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if __name__ == "__main__":
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unittest.main()
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@ -27,13 +27,11 @@ def realize_store_after_src(ctx:dict[UOp, None], dest:UOp, src:UOp):
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pm_generate_realize_map = PatternMatcher([
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# always realize
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(UPat({Ops.COPY, Ops.CONTIGUOUS}, name="tr"), realize),
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# realize AFTER of STORE+AFTER
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(UPat(Ops.AFTER, src=(UPat(), UPat(Ops.STORE)), allow_any_len=True, name="tr"), realize),
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(UPat({Ops.COPY, Ops.CONTIGUOUS, Ops.STORE}, name="tr"), realize),
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# realize srcs of these
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(UPat((Ops.COPY, Ops.MSELECT, Ops.MSTACK), name="rb"), realize_srcs),
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# sometimes realize/unrealize src of store+after
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(UPat(Ops.AFTER, src=(UPat(), UPat(Ops.STORE, src=(UPat.var("dest"), UPat.var("src"))))), realize_store_after_src),
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# sometimes we need to realize the src of STORE if there's a self-access
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(UPat(Ops.STORE, src=(UPat.var("dest"), UPat.var("src"))), realize_store_after_src),
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])
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@dataclass(frozen=True)
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@ -60,8 +58,7 @@ def create_bufferize_and_index_based_on_ranges(ctx:IndexingContext, x:UOp):
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new_srcs = []
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for s in x.src:
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new_src = s
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if s.op in {Ops.PARAM, Ops.BUFFER_VIEW, Ops.MSTACK, Ops.MSELECT} or \
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(s.op is Ops.AFTER and not any(c.op in {Ops.STORE, Ops.END} for c in s.src[1:])):
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if s.op in {Ops.PARAM, Ops.BUFFER_VIEW, Ops.MSTACK, Ops.MSELECT, Ops.AFTER}:
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if x in ctx.range_map: new_src = new_src.index(*ctx.range_map[x][0])
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elif s in ctx.realize_map:
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realized_ranges = ctx.realize_map[s]
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@ -166,8 +163,8 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
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# no ranges on kernels, they are internal
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if x.op in {Ops.CALL, Ops.FUNCTION, Ops.LINEAR}: continue
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# only STORE+AFTER has range
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if x.op is Ops.AFTER and all(s.op is not Ops.STORE for s in x.src[1:]): continue
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# AFTER doesn't have range
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if x.op is Ops.AFTER: continue
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# treat MSTACK/MSELECT like SINK
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if x.op in {Ops.MSTACK, Ops.MSELECT}: continue
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@ -180,15 +177,21 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
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# 2. from the single consumer if this op only has one consumer
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# 3. potentially new if this op has 2+ consumers
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shape = x._shape
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if x.op is Ops.STORE:
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# TODO: TestTensorVariable.test_symbolic_var_sum_alt_name fails with this, fix canonicalize on variables.
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#assert x.src[0].shape == x.src[1].shape, f"STORE must have matching input shapes, {x.src[0].shape} != {x.src[1].shape}"
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shape = x.src[0].shape
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consumer_rngs = [rctx.range_map[c][0] for c in consumer_map[x] if c in rctx.range_map]
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if x in rctx.realize_map:
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# if this is in the realize_map, we create new ranges (at the output)
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out_rngs = tuple(rctx.new_range(s) for s in x.shape)
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out_rngs = tuple(rctx.new_range(s) for s in shape)
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# all ranges are ended now
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ending_ranges[x] = []
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# mark all ranges as ended
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assert rctx.realize_map[x] is None
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rctx.realize_map[x] = list(range(len(x.shape)))
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rctx.realize_map[x] = list(range(len(shape)))
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elif len(consumer_rngs) == 0:
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# if no consumers have ranges and this isn't realized, this doesn't have ranges either.
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continue
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@ -214,7 +217,7 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
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minimum_valid = UOp.const(dtypes.bool, False).usum(valids)
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_out_rngs.append(graph_rewrite(minimum_valid.where(local_rngs[0], UOp.invalid()), symbolic, name="minimum_valid"))
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else:
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_out_rngs.append(rctx.new_range(x.shape[i]))
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_out_rngs.append(rctx.new_range(shape[i]))
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_realize_axis.append(i)
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out_rngs = tuple(_out_rngs)
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@ -231,7 +234,7 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
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ending_ranges[x] = []
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if len(_realize_axis):
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rctx.realize_map[x] = _realize_axis
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out_rngs = tuple([(rctx.new_range(x.shape[i]) if i in _realize_axis else r) for i,r in enumerate(out_rngs)])
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out_rngs = tuple([(rctx.new_range(shape[i]) if i in _realize_axis else r) for i,r in enumerate(out_rngs)])
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# TODO: some ops don't have shape, enable this after the `.st` property is removed
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#assert len(out_rngs) == len(x.shape), \
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@ -71,8 +71,7 @@ pm_mops = PatternMatcher([
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if r.src[0]._shape is not None and len(idx.src[1:]) == len(r.shape) else None),
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# move movement ops and INDEX after AFTER (but not when AFTER has a raw STORE with shaped children — from replace_contig_with_store_after)
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(UPat(GroupOp.Movement|{Ops.INDEX}, name="r").after(name="a", allow_any_len=True),
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lambda r,a: UOp(r.op, r.dtype, (a.replace(src=(r.src[0],)+a.src[1:]),)+r.src[1:], r.arg)
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if a.src[0]._shape is not None and not any(s.op is Ops.STORE and s.src[0]._shape is not None for s in a.src[1:]) else None),
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lambda r,a: UOp(r.op, r.dtype, (a.replace(src=(r.src[0],)+a.src[1:]),)+r.src[1:], r.arg)),
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(UPat(GroupOp.Movement, name="r").end(name="a", allow_any_len=True), lambda r,a: a.replace(src=(r.src[0],)+a.src[1:])),
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# lower SHAPED_WMMA to WMMA with CONTRACT/UNROLL
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(UPat(Ops.SHAPED_WMMA, name="x"), lower_shaped_wmma),
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@ -81,21 +80,14 @@ pm_mops = PatternMatcher([
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# *****************
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# 0. do some cleanup rewrites, mostly copied from the old stuff
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def fix_store_after_hazard(after:UOp, target:UOp, src:UOp):
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def fix_store_hazard(target:UOp, src:UOp):
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# PERMUTE and FLIP reorder indices, SHRINK can have overlapping regions when dest is also shrunk
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unsafe = {Ops.PERMUTE, Ops.FLIP} | ({Ops.SHRINK} if target.op_in_backward_slice_with_self(Ops.SHRINK) else set())
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base = target.base
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reaches_base: dict[UOp, bool] = {}
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for s in src.toposort(gate=lambda s: s.op is not Ops.CONTIGUOUS):
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reaches_base[s] = s is base or any(reaches_base.get(c) for c in s.src)
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if reaches_base[s] and s.op in unsafe: return after.replace(src=(after.src[0], target.store(src.contiguous())))
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def normalize_store_after_target_chain(after:UOp, target:UOp, src:UOp):
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root_target = target
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while root_target.op is Ops.AFTER: root_target = root_target.src[0]
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# when RHS depends on the previous assign result, break with contiguous
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if target in src.toposort(): src = src.contiguous()
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return after.replace(src=(root_target, root_target.store(src)))
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if reaches_base[s] and s.op in unsafe: return target.store(src.contiguous())
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def split_reduceop(reduce:UOp, x:UOp):
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||||
if prod(reduce.shape) == 0: return None
|
||||
|
|
@ -173,6 +165,9 @@ earliest_rewrites = mop_cleanup+PatternMatcher([
|
|||
lambda x,copy: x.replace(src=(copy.replace(src=(x.src[0],)+copy.src[1:]),)+x.src[1:]) \
|
||||
if isinstance(x.device, str) and x.device.startswith("DISK") else None),
|
||||
|
||||
# SINK only ever references the base
|
||||
(UPat(Ops.SINK, name="x"), lambda x: x.replace(src=tuple(y.base for y in x.src))),
|
||||
|
||||
# ** copy rules **
|
||||
|
||||
# COPY and source size need to match
|
||||
|
|
@ -182,22 +177,17 @@ earliest_rewrites = mop_cleanup+PatternMatcher([
|
|||
# copy only to different device
|
||||
(UPat(Ops.COPY, src=(UPat.var("x"), UPat()), name="copy"), lambda x,copy: x.f(Ops.NOOP) if x.device == copy.device else None),
|
||||
|
||||
# ** assign rules (STORE+AFTER) **
|
||||
# ** store rules **
|
||||
|
||||
# move bitcast from store+after target to source
|
||||
(UPat(Ops.AFTER, src=(UPat(Ops.BITCAST, src=(UPat(name="target"),)), UPat(Ops.STORE, src=(UPat(Ops.BITCAST), UPat(name="src"))))),
|
||||
lambda target, src: target.after(target.store(src.bitcast(target.dtype)))),
|
||||
# fix store hazard (dest is in used in src) by adding contiguous: TestAssign.test_post_flipped_assignment
|
||||
(UPat(Ops.STORE, src=(UPat(name="target"), UPat(name="src"))), fix_store_hazard),
|
||||
|
||||
# wrap STORE in inner AFTER when target is a view — gives the STORE its own ranges from the view shape
|
||||
(UPat(Ops.AFTER, src=(UPat(name="buf"), UPat(Ops.STORE, src=(UPat(name="target"), UPat()))), name="after"),
|
||||
lambda after, buf, target: after.replace(src=(buf, target.after(after.src[1]))) if target.shape != buf.shape else None),
|
||||
# remove two STOREs that store the same thing to the same place: TestSchedule.test_dedup_assign
|
||||
(UPat.var("buf").after(UPat.var("buf").store(UPat.var("src")), name="a1").after(UPat.var("a1").store(UPat.var("src"))), lambda buf,src,a1:a1),
|
||||
|
||||
# make source contiguous if it has hazardous movement ops on the dest buffer
|
||||
(UPat(Ops.AFTER, src=(UPat(), UPat(Ops.STORE, src=(UPat(name="target"), UPat(name="src")))), name="after"), fix_store_after_hazard),
|
||||
|
||||
# normalize target chain: walk through AFTERs to root, insert contiguous if needed
|
||||
(UPat(Ops.AFTER, src=(UPat(Ops.AFTER, name="target"), UPat(Ops.STORE, src=(UPat(), UPat(name="src")))), name="after"),
|
||||
lambda after, target, src: normalize_store_after_target_chain(after, target, src)),
|
||||
# move bitcast from store dest to source: TestAssign.test_assign_bitcast
|
||||
(UPat(Ops.STORE, src=(UPat(Ops.BITCAST, src=(UPat(name="target"),)), UPat(name="src"))),
|
||||
lambda target, src: target.store(src.bitcast(target.dtype))),
|
||||
|
||||
# ** size 0 **
|
||||
|
||||
|
|
@ -257,6 +247,9 @@ def remove_bufferize(src:UOp, buf:UOp, idx:UOp):
|
|||
if (x.op is Ops.BUFFERIZE and x.arg.addrspace == AddrSpace.GLOBAL) or x.op is Ops.MSTACK:
|
||||
accessed_buffers.append(x)
|
||||
return False
|
||||
if x.op is Ops.STORE:
|
||||
# don't look inside stores, this doesn't count toward buffer accesses
|
||||
return False
|
||||
if x.op is Ops.PARAM:
|
||||
accessed_buffers.append(x)
|
||||
if x.op is Ops.INDEX:
|
||||
|
|
@ -326,6 +319,10 @@ pm_const_buffer_folding = pm_mops+PatternMatcher([
|
|||
pm_remove_bufferize = PatternMatcher([
|
||||
# remove reindexing with cost function
|
||||
(UPat.var("src").f(Ops.BUFFERIZE, allow_any_len=True, name="buf").f(Ops.INDEX, allow_any_len=True, name="idx"), remove_bufferize),
|
||||
# STORE to self is NOOP
|
||||
(UPat.var("x").store(UPat.var("x")), lambda x: UOp(Ops.NOOP)),
|
||||
# END on NOOP is NOOP
|
||||
(UPat(Ops.END, src=(UPat(Ops.NOOP, name="x"),), allow_any_len=True), lambda x: x),
|
||||
])
|
||||
|
||||
def late_buffer_view(t:UOp, b:UOp):
|
||||
|
|
@ -391,9 +388,6 @@ def bufferize_to_store(ctx:itertools.count, x:UOp, idx:UOp, allow_locals=True):
|
|||
if (after:=x.src[0]).op is Ops.AFTER:
|
||||
buf = after.src[0].buf_uop.base
|
||||
if not (stores := [s for s in after.src[1:] if s.op is Ops.STORE and s.src[0].op is Ops.INDEX]): return buf
|
||||
# the ranges are created on the AFTER, and the stores might be different sizes
|
||||
# so we block all multi store AFTERs
|
||||
assert len(stores) <= 1, "rangeify doesn't support multiple stores on one after"
|
||||
# BUFFERIZE(INDEX(...)); store through the underlying global index instead.
|
||||
ended_stores = []
|
||||
for store in stores:
|
||||
|
|
@ -482,8 +476,8 @@ def handle_after(ctx:LocalAddBufferContext, after:UOp):
|
|||
buf = after.buf_uop
|
||||
# HACK to put the buffer in the MAP instead of MSTACK/MSELECT
|
||||
if buf.op in {Ops.MSTACK, Ops.MSELECT}: buf = buf.src[0]
|
||||
assert buf not in ctx.map
|
||||
ctx.map[buf] = after
|
||||
# NOTE: this is bottom up, so we only add it once
|
||||
if buf not in ctx.map: ctx.map[buf] = after
|
||||
return buf
|
||||
|
||||
def renumber_range(ctx:LocalAddBufferContext, r:UOp):
|
||||
|
|
|
|||
|
|
@ -285,8 +285,8 @@ symbolic = symbolic_simple+commutative+PatternMatcher([
|
|||
((UPat.var("x", dtypes.weakint) + UPat.cvar("c")).cast(dtypes.sints, name="cast"), lambda x,c,cast:x.cast(cast.dtype)+c.cast(cast.dtype)),
|
||||
# only RANGE/IF/STORE/KERNEL have side effects
|
||||
(UPat(Ops.AFTER, name="x"), lambda x: x.replace(src=(x.src[0],)+
|
||||
tuple(flatten([(y,) if y.op in {Ops.RANGE, Ops.STORE, Ops.CALL, Ops.FUNCTION, Ops.BARRIER, Ops.END, Ops.UNROLL, Ops.LINEAR, Ops.BUFFERIZE}
|
||||
else y.src for y in x.src[1:]])))),
|
||||
tuple(dedup(flatten([(y,) if y.op in {Ops.RANGE, Ops.STORE, Ops.CALL, Ops.FUNCTION, Ops.BARRIER, Ops.END, Ops.UNROLL, Ops.LINEAR, Ops.BUFFERIZE}
|
||||
else y.src for y in x.src[1:]]))))),
|
||||
# after with 1 src is just src[0]
|
||||
(UPat(Ops.AFTER, src=(UPat.var("s"),)), lambda s: s),
|
||||
# VECTORIZE/CONST
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue