468 lines
16 KiB
Python
468 lines
16 KiB
Python
# Owner(s): ["NNC"]
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import torch
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import numpy as np
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import torch._C._te as te
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from torch.testing._internal.common_utils import run_tests
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from torch.testing._internal.jit_utils import JitTestCase
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import unittest
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LLVM_ENABLED = torch._C._llvm_enabled()
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def construct_adder(n: int, dtype=torch.float32):
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A = te.BufHandle("A", [n], dtype)
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B = te.BufHandle("B", [n], dtype)
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def compute(i):
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return A.load([i]) + B.load([i])
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C = te.Compute("C", [n], compute)
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loopnest = te.LoopNest([C])
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loopnest.prepare_for_codegen()
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stmt = te.simplify(loopnest.root_stmt())
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return te.construct_codegen("ir_eval", stmt, [A, B, C])
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class TestTensorExprPyBind(JitTestCase):
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def test_simple_sum(self):
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n = 32
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cg = construct_adder(n)
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tA = torch.randn(n)
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tB = torch.randn(n)
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tC = torch.empty(n)
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cg.call([tA, tB, tC])
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torch.testing.assert_close(tA + tB, tC)
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def test_call_raw(self):
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n = 16
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cg = construct_adder(n, dtype=torch.float64)
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tA = torch.randn(n, dtype=torch.float64)
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tB = torch.randn(n, dtype=torch.float64)
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tC = torch.empty(n, dtype=torch.float64)
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cg.call_raw([tA.data_ptr(), tB.data_ptr(), tC.data_ptr()])
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torch.testing.assert_close(tA + tB, tC)
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def test_external_calls(self):
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dtype = torch.float32
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A = te.BufHandle("A", [1, 4], dtype)
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B = te.BufHandle("B", [4, 1], dtype)
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C = te.BufHandle("C", [1, 1], dtype)
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s = te.ExternalCall(C, "nnc_aten_matmul", [A, B], [])
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loopnest = te.LoopNest(s, [C])
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loopnest.prepare_for_codegen()
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codegen = te.construct_codegen("ir_eval", s, [A, B, C])
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tA = torch.ones(1, 4)
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tB = torch.ones(4, 1)
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tC = torch.empty(1, 1)
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codegen.call([tA, tB, tC])
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torch.testing.assert_close(torch.matmul(tA, tB), tC)
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def test_dynamic_shape(self):
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dN = te.VarHandle(torch.int32)
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A = te.BufHandle([dN], torch.float64)
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B = te.BufHandle([dN], torch.float64)
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def compute(i):
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return A.load(i) - B.load(i)
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C = te.Compute("C", [dN], compute)
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loopnest = te.LoopNest([C])
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loopnest.prepare_for_codegen()
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cg = te.construct_codegen("ir_eval", loopnest.simplify(), [A, B, C, dN])
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def test_with_shape(n):
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tA = torch.randn(n, dtype=torch.double)
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tB = torch.randn(n, dtype=torch.double)
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tC = torch.empty(n, dtype=torch.double)
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cg.call([tA, tB, tC, n])
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torch.testing.assert_close(tA - tB, tC)
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test_with_shape(8)
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test_with_shape(31)
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def test_dynamic_shape_2d(self):
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dN = te.VarHandle(torch.int32)
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dM = te.VarHandle(torch.int32)
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A = te.BufHandle([dN, dM], torch.float64)
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B = te.BufHandle([dN, dM], torch.float64)
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def compute(i, j):
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return A.load([i, j]) - B.load([i, j])
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C = te.Compute("C", [dN, dM], compute)
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loopnest = te.LoopNest([C])
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loopnest.prepare_for_codegen()
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cg = te.construct_codegen("ir_eval", loopnest.simplify(), [A, B, C, dN, dM])
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def test_with_shape(n, m):
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tA = torch.randn(n, m, dtype=torch.double)
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tB = torch.randn(n, m, dtype=torch.double)
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tC = torch.empty(n, m, dtype=torch.double)
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cg.call([tA, tB, tC, n, m])
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torch.testing.assert_close(tA - tB, tC)
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test_with_shape(2, 4)
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test_with_shape(5, 3)
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def test_dtype_error(self):
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te.BufHandle("a", [1], torch.float32) # ok
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self.assertRaises(TypeError, lambda: te.BufHandle("a", [1], "float55"))
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_kernel_with_tensor_inputs(self):
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def f(a, b, c):
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return a + b + c
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device, size = "cpu", (4, 4)
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x = torch.rand(size, device=device)
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y = torch.rand(size, device=device)
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z = torch.rand(size, device=device)
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graph_str = """
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graph(%a.1 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu),
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%b.1 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu),
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%c.1 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu)):
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%6 : int = prim::Constant[value=1]()
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%7 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) = aten::add(%a.1, %b.1, %6)
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%3 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) = aten::add(%7, %c.1, %6)
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return (%3)
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"""
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graph = torch._C.parse_ir(graph_str)
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kernel = te.TensorExprKernel(graph)
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res1 = kernel.run((x, y, z))
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res2 = kernel.fallback((x, y, z))
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correct = f(x, y, z)
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np.testing.assert_allclose(res1.numpy(), correct.numpy(), atol=2e-3)
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np.testing.assert_allclose(res2.numpy(), correct.numpy(), atol=2e-3)
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_kernel_with_scalar_inputs(self):
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def f(a, b, c):
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return a + b + c
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x = torch.tensor(0.1, dtype=torch.float, device="cpu")
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y = torch.tensor(0.6, dtype=torch.float, device="cpu")
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z = torch.tensor(0.7, dtype=torch.float, device="cpu")
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graph_str = """
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graph(%a.1 : Float(requires_grad=0, device=cpu),
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%b.1 : Float(requires_grad=0, device=cpu),
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%c.1 : Float(requires_grad=0, device=cpu)):
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%3 : int = prim::Constant[value=1]()
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%6 : Float(requires_grad=0, device=cpu) = aten::add(%a.1, %b.1, %3)
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%9 : Float(requires_grad=0, device=cpu) = aten::add(%6, %c.1, %3)
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return (%9)
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"""
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graph = torch._C.parse_ir(graph_str)
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kernel = te.TensorExprKernel(graph)
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res1 = kernel.run((x, y, z))
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res2 = kernel.fallback((x, y, z))
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correct = f(x, y, z)
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np.testing.assert_allclose(res1.numpy(), correct.numpy(), atol=2e-3)
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np.testing.assert_allclose(res2.numpy(), correct.numpy(), atol=2e-3)
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_kernel_shape_prop(self):
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device, size = "cpu", (4, 4)
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x = torch.rand(size, device=device)
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y = torch.rand(size, device=device)
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graph_str = """
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graph(%a : Tensor, %b : Tensor):
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%c : Tensor = aten::mul(%a, %b)
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return (%c)
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"""
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graph = torch._C.parse_ir(graph_str)
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exception_thrown = False
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try:
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kernel = te.TensorExprKernel(graph)
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except RuntimeError:
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# Graph doesn't have shape info for inputs => compilation should
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# fail
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exception_thrown = True
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pass
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assert exception_thrown
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# Inject shape info and try compiling again
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example_inputs = [torch.rand(4, 4), torch.rand(4, 4)]
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torch._C._te.annotate_input_shapes(graph, example_inputs)
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torch._C._jit_pass_propagate_shapes_on_graph(graph)
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# Now compilation should pass
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kernel = te.TensorExprKernel(graph)
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res = kernel.run((x, y))
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correct = torch.mul(x, y)
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np.testing.assert_allclose(res.numpy(), correct.numpy(), atol=1e-5)
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_kernel_shape_prop_module(self):
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class TestModule(torch.nn.Module):
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def forward(self, x, y):
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return x * x + y
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graph = torch.jit.script(TestModule()).graph
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# Try compiling the graph as-is. It should fail because it doesn't have
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# shape info.
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exception_thrown = False
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try:
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kernel = te.TensorExprKernel(graph)
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except RuntimeError:
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exception_thrown = True
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pass
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assert exception_thrown
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# Try injecting shape info for graph inputs
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example_inputs = [torch.rand(4, 4), torch.rand(4, 4)]
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exception_thrown = False
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try:
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torch._C._te.annotate_input_shapes(graph, example_inputs)
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except RuntimeError:
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# Graph has a 'self' argument for which we can't set shapes
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exception_thrown = True
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pass
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assert exception_thrown
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# Remove 'self' argument and try annotating shapes one more time
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torch._C._te.remove_unused_self_argument(graph)
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# Inject shape info and try compiling again
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torch._C._te.annotate_input_shapes(graph, example_inputs)
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torch._C._jit_pass_propagate_shapes_on_graph(graph)
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# Now compilation should pass
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kernel = te.TensorExprKernel(graph)
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device, size = "cpu", (4, 4)
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x = torch.rand(size, device=device)
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y = torch.rand(size, device=device)
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res = kernel.run((x, y))
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correct = TestModule().forward(x, y)
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np.testing.assert_allclose(res.numpy(), correct.numpy(), atol=1e-5)
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_kernel_with_t(self):
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def f(a):
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return a.t()
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device, size = "cpu", (3, 4)
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x = torch.rand(size, device=device)
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graph_str = """
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graph(%a.1 : Float(3, 4, strides=[4, 1], requires_grad=0, device=cpu)):
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%3 : Float(4, 3, strides=[4, 1], requires_grad=0, device=cpu) = aten::t(%a.1)
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return (%3)
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"""
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graph = torch._C.parse_ir(graph_str)
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kernel = te.TensorExprKernel(graph)
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res1 = kernel.run((x,))
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res2 = kernel.fallback((x,))
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correct = f(x)
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np.testing.assert_allclose(res1.numpy(), correct.numpy(), atol=2e-3)
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np.testing.assert_allclose(res2.numpy(), correct.numpy(), atol=2e-3)
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_kernel_with_transpose(self):
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def f(a):
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return a.transpose(-1, -2)
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device, size = "cpu", (3, 4)
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x = torch.rand(size, device=device)
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graph_str = """
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graph(%a.1 : Float(3, 4, strides=[4, 1], requires_grad=0, device=cpu)):
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%2 : int = prim::Constant[value=-1]()
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%3 : int = prim::Constant[value=-2]()
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%4 : Float(4, 3, strides=[4, 1], requires_grad=0, device=cpu) = aten::transpose(%a.1, %2, %3)
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return (%4)
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"""
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graph = torch._C.parse_ir(graph_str)
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kernel = te.TensorExprKernel(graph)
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res1 = kernel.run((x,))
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res2 = kernel.fallback((x,))
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correct = f(x)
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np.testing.assert_allclose(res1.numpy(), correct.numpy(), atol=2e-3)
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np.testing.assert_allclose(res2.numpy(), correct.numpy(), atol=2e-3)
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_kernel_with_permute(self):
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def f(a):
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return a.permute([2, 1, 0])
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device, size = "cpu", (3, 4, 5)
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x = torch.rand(size, device=device)
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graph_str = """
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graph(%a.1 : Float(3, 4, 5, strides=[20, 5, 1], requires_grad=0, device=cpu)):
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%1 : int = prim::Constant[value=2]()
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%2 : int = prim::Constant[value=1]()
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%3 : int = prim::Constant[value=0]()
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%4 : int[] = prim::ListConstruct(%1, %2, %3)
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%5 : Float(5, 4, 3, strides=[12, 3, 1], requires_grad=0, device=cpu) = aten::permute(%a.1, %4)
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return (%5)
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"""
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graph = torch._C.parse_ir(graph_str)
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kernel = te.TensorExprKernel(graph)
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res1 = kernel.run((x,))
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res2 = kernel.fallback((x,))
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correct = f(x)
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np.testing.assert_allclose(res1.numpy(), correct.numpy(), atol=2e-3)
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np.testing.assert_allclose(res2.numpy(), correct.numpy(), atol=2e-3)
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_kernel_with_custom_lowering(self):
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def f(a):
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return a.nan_to_num()
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device = "cpu"
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x = torch.ones((2, 2), device=device)
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x[0, 0] = x[1, 1] = torch.nan
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graph_str = """
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graph(%x : Float(2, 2, strides=[2, 1], requires_grad=0, device=cpu)):
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%none : NoneType = prim::Constant()
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%y : Float(2, 2, strides=[2, 1], requires_grad=0, device=cpu) = aten::nan_to_num(%x, %none, %none, %none)
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return (%y)
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"""
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graph = torch._C.parse_ir(graph_str)
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def my_custom_lowering(inputs, out_shape, out_stride, out_type, device):
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def compute(idxs):
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load = inputs[0].as_buf().load(idxs)
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return te.ifThenElse(
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te.ExprHandle.isnan(load), te.ExprHandle.float(0.0), load
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)
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return te.Compute2("custom_nan_to_num", out_shape, compute)
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kernel = te.TensorExprKernel(graph, {"aten::nan_to_num": my_custom_lowering})
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res1 = kernel.run((x,))
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res2 = kernel.fallback((x,))
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correct = f(x)
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np.testing.assert_allclose(res1.numpy(), correct.numpy(), atol=2e-3)
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np.testing.assert_allclose(res2.numpy(), correct.numpy(), atol=2e-3)
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_kernel_with_expand(self):
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def f(a):
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return a.expand((2, 3, 4))
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device = "cpu"
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x = torch.rand((1, 3, 1), device=device)
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graph_str = """
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graph(%a : Float(1, 3, 1, strides=[3, 1, 1], requires_grad=0, device=cpu)):
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%1 : int = prim::Constant[value=2]()
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%2 : int = prim::Constant[value=3]()
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%3 : int = prim::Constant[value=4]()
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%4 : int[] = prim::ListConstruct(%1, %2, %3)
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%5 : bool = prim::Constant[value=0]()
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%6 : Float(2, 3, 4, strides=[12, 4, 0], requires_grad=0, device=cpu) = aten::expand(%a, %4, %5)
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return (%6)
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"""
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graph = torch._C.parse_ir(graph_str)
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kernel = te.TensorExprKernel(graph)
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res1 = kernel.run((x,))
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res2 = kernel.fallback((x,))
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correct = f(x)
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np.testing.assert_allclose(res1.numpy(), correct.numpy(), atol=2e-3)
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np.testing.assert_allclose(res2.numpy(), correct.numpy(), atol=2e-3)
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@unittest.skipIf(not LLVM_ENABLED, "LLVM backend not enabled")
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def test_alloc_in_loop(self):
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a, tmp, b = (
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te.BufHandle(name, [1], torch.float32) for name in ["a", "tmp", "b"]
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)
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body = te.Block([tmp.store([0], a.load([0])), b.store([0], tmp.load([0]))])
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for _ in range(4):
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i = te.VarHandle("i", torch.int32)
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body = te.For.make(i, 0, 100, body)
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nest = te.LoopNest(body, [b])
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nest.prepare_for_codegen()
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f = te.construct_codegen("llvm", nest.simplify(), [a, b])
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ta, tb = (torch.ones(1) for _ in range(2))
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f.call([ta.data_ptr(), tb.data_ptr()])
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class TestExprHandlePyBind(JitTestCase):
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def test_unary_ops(self):
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unary_operators = {
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torch.sin: torch._C._te.sin,
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torch.cos: torch._C._te.cos,
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torch.tan: torch._C._te.tan,
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torch.asin: torch._C._te.asin,
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torch.acos: torch._C._te.acos,
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torch.atan: torch._C._te.atan,
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torch.sinh: torch._C._te.sinh,
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torch.cosh: torch._C._te.cosh,
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torch.tanh: torch._C._te.tanh,
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torch.sigmoid: torch._C._te.sigmoid,
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torch.exp: torch._C._te.exp,
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torch.expm1: torch._C._te.expm1,
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torch.abs: torch._C._te.abs,
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torch.log: torch._C._te.log,
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torch.log2: torch._C._te.log2,
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torch.log10: torch._C._te.log10,
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torch.log1p: torch._C._te.log1p,
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torch.erf: torch._C._te.erf,
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torch.erfc: torch._C._te.erfc,
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torch.sqrt: torch._C._te.sqrt,
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torch.rsqrt: torch._C._te.rsqrt,
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torch.ceil: torch._C._te.ceil,
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torch.floor: torch._C._te.floor,
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torch.round: torch._C._te.round,
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torch.trunc: torch._C._te.trunc,
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torch.lgamma: torch._C._te.lgamma,
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torch.frac: torch._C._te.frac,
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}
|
|
|
|
def construct_te_fn(op, n: int, dtype=torch.float32):
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A = torch._C._te.BufHandle("A", [n], dtype)
|
|
|
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def compute(i):
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return op(A.load([i]))
|
|
|
|
C = te.Compute("C", [n], compute)
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|
|
|
loopnest = te.LoopNest([C])
|
|
loopnest.prepare_for_codegen()
|
|
stmt = te.simplify(loopnest.root_stmt())
|
|
|
|
return te.construct_codegen("ir_eval", stmt, [A, C])
|
|
|
|
n = 10
|
|
a = torch.rand(n)
|
|
for torch_op, te_op in unary_operators.items():
|
|
ref = torch_op(a)
|
|
|
|
te_fn = construct_te_fn(te_op, n, torch.float32)
|
|
res = torch.empty(n)
|
|
te_fn.call([a, res])
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|
assert torch.allclose(ref, res, atol=1e-3, rtol=1e-3)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|