mirror of
https://github.com/pytorch/pytorch.git
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b38f153d91
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/57426 Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D28293191 Pulled By: Chillee fbshipit-source-id: b8fc44299acf2569c11e87e1991a2b724434b15d
310 lines
11 KiB
Python
310 lines
11 KiB
Python
import torch
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import numpy as np
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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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class kernel_arena_scope(object):
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def __enter__(self):
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self.scope = torch._C._te.KernelScope()
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def __exit__(self, typ, val, traceback):
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self.scope = None
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class TestTensorExprPyBind(JitTestCase):
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def test_simple_sum(self):
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with kernel_arena_scope():
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dtype = torch._C._te.Dtype.Float
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N = 32
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dN = torch._C._te.ExprHandle.int(N)
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A = torch._C._te.Placeholder('A', dtype, [dN])
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B = torch._C._te.Placeholder('B', dtype, [dN])
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def compute(i):
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return A.load([i]) + B.load([i])
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C = torch._C._te.Compute('C', [torch._C._te.DimArg(dN, 'i')], compute)
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loopnest = torch._C._te.LoopNest([C])
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loopnest.prepare_for_codegen()
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stmt = torch._C._te.simplify(loopnest.root_stmt())
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cg = torch._C._te.construct_codegen('ir_eval', stmt, [torch._C._te.BufferArg(x) for x in [A, B, C]])
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tA = torch.rand(N) * 5
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tB = torch.rand(N) * 6
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tC = torch.empty(N)
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cg.call([tA, tB, tC])
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torch.testing.assert_allclose(tA + tB, tC)
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def test_external_calls(self):
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with kernel_arena_scope():
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dtype = torch._C._te.Dtype.Float
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ZERO = torch._C._te.ExprHandle.int(0)
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ONE = torch._C._te.ExprHandle.int(1)
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FOUR = torch._C._te.ExprHandle.int(4)
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A = torch._C._te.BufHandle('A', [ONE, FOUR], dtype)
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B = torch._C._te.BufHandle('B', [FOUR, ONE], dtype)
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C = torch._C._te.BufHandle('C', [ONE, ONE], dtype)
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s = torch._C._te.ExternalCall(C, "nnc_aten_matmul", [A, B], [])
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loopnest = torch._C._te.LoopNest(s, [C])
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loopnest.prepare_for_codegen()
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codegen = torch._C._te.construct_codegen('ir_eval', s, [torch._C._te.BufferArg(x) for x in [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_allclose(torch.matmul(tA, tB), tC)
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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 = torch._C._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 = torch._C._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 : Float(4, 4, strides=[4, 1], requires_grad=0, device=cpu) = 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 = torch._C._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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# TODO: once we have shape propagation as well we should erase type
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# info for %c from the input IR and run shape propagation here - it
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# should be able to reconstruct that info
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# Now compilation should pass
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kernel = torch._C._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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@unittest.skip("Does not work until shape propagation is implemented")
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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 = torch._C._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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graph = 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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# TODO: once we have shape propagation as well we should erase type
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# info for %c from the input IR and run shape propagation here - it
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# should be able to reconstruct that info
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# Now compilation should pass
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kernel = torch._C._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 = torch._C._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 = torch._C._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 = torch._C._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_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 = torch._C._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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if __name__ == '__main__':
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run_tests()
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