chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,709 @@
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""Unittests for tvm.script.parser.tirx"""
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import pytest
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import tvm_ffi
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import tvm.testing
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from tvm import ir, tirx
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from tvm.script.parser import tirx as T
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def test_tir_buffer_proxy():
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buffer_0 = T.Buffer((128, 128), "float32")
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assert (
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isinstance(buffer_0, tirx.Buffer)
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and list(buffer_0.shape) == [128, 128]
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and buffer_0.dtype == ir.PrimType("float32")
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)
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buffer_1 = T.Buffer((64, 64, 64), "int32")
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assert (
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isinstance(buffer_1, tirx.Buffer)
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and list(buffer_1.shape) == [64, 64, 64]
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and buffer_1.dtype == ir.PrimType("int32")
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)
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def test_tir_ptr_proxy():
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ptr_0 = T.handle("int32", "global")
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assert (
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isinstance(ptr_0, tirx.Var)
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and isinstance(ptr_0.ty, ir.PointerType)
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and ptr_0.ty.element_type == ir.PrimType("int32")
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and ptr_0.ty.storage_scope == "global"
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)
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ptr_1 = T.handle("float32", "shared")
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assert (
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isinstance(ptr_1, tirx.Var)
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and isinstance(ptr_1.ty, ir.PointerType)
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and ptr_1.ty.element_type == ir.PrimType("float32")
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and ptr_1.ty.storage_scope == "shared"
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)
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def test_tir_func_name():
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@T.prim_func(s_tir=True)
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def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
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A = T.match_buffer(a, [128, 128])
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B = T.match_buffer(b, [128, 128])
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C = T.match_buffer(c, [128, 128])
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for i, j, k in T.grid(128, 128, 128):
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with T.sblock("update"):
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vi, vj, vk = T.axis.remap("SSR", [i, j, k])
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C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
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assert matmul.__name__ == "matmul"
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assert matmul.attrs["global_symbol"] == "matmul"
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def test_tir_func_private_attrs():
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@T.prim_func(private=True, s_tir=True)
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def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
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T.func_attr({"attr": "value"})
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A = T.match_buffer(a, [128, 128])
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B = T.match_buffer(b, [128, 128])
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C = T.match_buffer(c, [128, 128])
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for i, j, k in T.grid(128, 128, 128):
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with T.sblock("update"):
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vi, vj, vk = T.axis.remap("SSR", [i, j, k])
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C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
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assert "global_symbol" not in matmul.attrs
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def test_tir_func_private_manual_global_symbol_fail():
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with pytest.raises(tvm.error.DiagnosticError):
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@T.prim_func(private=True, s_tir=True)
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def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
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T.func_attr({"global_symbol": "matmul"})
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A = T.match_buffer(a, [128, 128])
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B = T.match_buffer(b, [128, 128])
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C = T.match_buffer(c, [128, 128])
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for i, j, k in T.grid(128, 128, 128):
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with T.sblock("update"):
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vi, vj, vk = T.axis.remap("SSR", [i, j, k])
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C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
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# should not execute
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assert matmul.__name__ == "matmul"
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def test_tir_macro_decorator_signature():
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@T.prim_func(private=True, s_tir=True)
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def evaluate0():
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T.evaluate(0)
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# Ok, no parentheses
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@T.inline
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def func1():
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T.evaluate(0)
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@T.prim_func(private=True, s_tir=True)
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def use1():
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func1()
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tvm.ir.assert_structural_equal(use1, evaluate0)
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# Ok, empty parentheses
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@T.inline()
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def func2():
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T.evaluate(0)
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@T.prim_func(private=True, s_tir=True)
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def use2():
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func2()
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tvm.ir.assert_structural_equal(use1, evaluate0)
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with pytest.raises(ValueError):
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# Wrong: non-keyword argument
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@T.inline(True)
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def func3():
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T.evaluate()
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def test_tir_macro_signature():
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@T.inline
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def assign(i, *args, t1, **kwargs):
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vi, vj, vk = T.axis.remap("SSR", [i, args[0], args[1]])
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kwargs["t3"][vi, vj] = kwargs["t3"][vi, vj] + t1[vi, vk] * kwargs["t2"][vj, vk]
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@T.prim_func(private=True, s_tir=True)
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def matmul_w_macro(a: T.handle, b: T.handle, c: T.handle) -> None:
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A = T.match_buffer(a, [128, 128])
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B = T.match_buffer(b, [128, 128])
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C = T.match_buffer(c, [128, 128])
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for i, j, k in T.grid(128, 128, 128):
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with T.sblock("update"):
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assign(i, j, k, t1=A, t2=B, t3=C)
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@T.prim_func(private=True, s_tir=True)
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def matmul_no_macro(a: T.handle, b: T.handle, c: T.handle) -> None:
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A = T.match_buffer(a, [128, 128])
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B = T.match_buffer(b, [128, 128])
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C = T.match_buffer(c, [128, 128])
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for i, j, k in T.grid(128, 128, 128):
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with T.sblock("update"):
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vi, vj, vk = T.axis.remap("SSR", [i, j, k])
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C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
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tvm.ir.assert_structural_equal(matmul_no_macro, matmul_w_macro)
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def test_tir_macro_hygienic():
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x_value = 128
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@T.inline
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def static_capture(A, B):
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B[()] = A[x_value]
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@T.prim_func(private=True, s_tir=True)
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def use_hygienic(A: T.Buffer((1024,), "int32"), B: T.Buffer((), "int32")) -> None:
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for x_value in T.serial(10):
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static_capture(A, B)
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@T.prim_func(private=True, s_tir=True)
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def expected_hygienic(A: T.Buffer((1024,), "int32"), B: T.Buffer((), "int32")) -> None:
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for x_value in range(10):
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B[()] = A[128]
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tvm.ir.assert_structural_equal(use_hygienic, expected_hygienic)
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def test_tir_inline_late_binding():
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"""Inline defined inside prim_func uses LEGB late binding:
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it sees the current value of variables from its enclosing scope at call time."""
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@T.prim_func(private=True, s_tir=True)
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def use_late_binding(A: T.Buffer((1024,), "int32"), B: T.Buffer((), "int32")) -> None:
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for x_value in T.serial(10):
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@T.inline
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def capture(A, B):
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B[()] = A[x_value]
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capture(A, B)
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@T.prim_func(private=True, s_tir=True)
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def expected(A: T.Buffer((1024,), "int32"), B: T.Buffer((), "int32")) -> None:
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for x_value in range(10):
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B[()] = A[x_value]
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tvm.ir.assert_structural_equal(use_late_binding, expected)
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def test_tir_macro_in_class():
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class Object:
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def __init__(self, x: T.Buffer):
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self.local_x = T.sblock_alloc_buffer(x.shape, x.dtype)
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@T.inline
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def load(self, x: T.Buffer):
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N, M = T.meta_var(self.local_x.shape)
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for i, j in T.grid(N, M):
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with T.sblock("update"):
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vi, vj = T.axis.remap("SS", [i, j])
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self.local_x[vi, vj] = x[vi, vj]
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@T.prim_func(private=True, s_tir=True)
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def func_w_macro(a: T.handle):
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A = T.match_buffer(a, [128, 128])
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o1 = T.meta_var(Object(A))
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o1.load(A)
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o2 = T.meta_var(Object(A))
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o2.load(o1.local_x)
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@T.prim_func(private=True, s_tir=True)
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def func_no_macro(a: T.handle):
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A = T.match_buffer(a, [128, 128])
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local_a = T.sblock_alloc_buffer([128, 128])
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for i, j in T.grid(128, 128):
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with T.sblock("update"):
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vi, vj = T.axis.remap("SS", [i, j])
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local_a[vi, vj] = A[vi, vj]
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local_b = T.sblock_alloc_buffer([128, 128])
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for i, j in T.grid(128, 128):
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with T.sblock("update"):
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vi, vj = T.axis.remap("SS", [i, j])
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local_b[vi, vj] = local_a[vi, vj]
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tvm.ir.assert_structural_equal(func_no_macro, func_w_macro)
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def test_tir_starred_expression():
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dims = (128, 128)
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@T.prim_func(private=True, s_tir=True)
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def starred(a: T.handle) -> None:
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A = T.match_buffer(a, [128, *dims], "int32")
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for i, j, k in T.grid(128, *dims):
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A[i, j, k] = T.int32(1)
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@T.prim_func(private=True, s_tir=True)
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def non_starred(a: T.handle) -> None:
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A = T.match_buffer(a, [128, 128, 128], "int32")
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for i, j, k in T.grid(128, 128, 128):
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A[i, j, k] = T.int32(1)
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tvm.ir.assert_structural_equal(starred, non_starred)
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def test_tir_starred_shape_expression():
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dims = (128, 128)
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@T.prim_func(private=True, s_tir=True)
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def starred(a: T.handle) -> None:
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A = T.match_buffer(a, [128, *dims], "int32")
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for i, j, k in T.grid(*A.shape):
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A[i, j, k] = T.int32(1)
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@T.prim_func(private=True, s_tir=True)
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def non_starred(a: T.handle) -> None:
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A = T.match_buffer(a, [128, 128, 128], "int32")
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for i, j, k in T.grid(128, 128, 128):
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A[i, j, k] = T.int32(1)
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tvm.ir.assert_structural_equal(starred, non_starred)
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def test_tir_dynamic_for_loop():
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dims = (128, 128)
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@T.prim_func(private=True, s_tir=True)
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def starred(a: T.handle) -> None:
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A = T.match_buffer(a, [128, *dims], "int32")
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for iters in T.grid(*A.shape):
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A[iters] = T.int32(1)
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@T.prim_func(private=True, s_tir=True)
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def non_starred(a: T.handle) -> None:
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A = T.match_buffer(a, [128, 128, 128], "int32")
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for i, j, k in T.grid(128, 128, 128):
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A[i, j, k] = T.int32(1)
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tvm.ir.assert_structural_equal(starred, non_starred)
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def test_tir_starred_for_loop():
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dims = (128, 128)
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@T.prim_func(private=True, s_tir=True)
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def starred(a: T.handle, b: T.handle):
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A = T.match_buffer(a, [*dims, 128], "int32")
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B = T.match_buffer(b, dims, "int32")
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for *spatial, reduction in T.grid(*A.shape):
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with T.sblock("reduce"):
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with T.init():
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B[spatial] = T.int32(0)
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B[spatial] = B[spatial] + A[(*spatial, reduction)]
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@T.prim_func(private=True, s_tir=True)
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def non_starred(a: T.handle, b: T.handle):
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A = T.match_buffer(a, [128, 128, 128], "int32")
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B = T.match_buffer(b, [128, 128], "int32")
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for i, j, k in T.grid(128, 128, 128):
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with T.sblock("reduce"):
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with T.init():
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B[i, j] = T.int32(0)
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B[i, j] = B[i, j] + A[i, j, k]
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tvm.ir.assert_structural_equal(starred, non_starred)
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def test_tir_loop_steps():
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N = T.Var("N", "int32")
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@T.prim_func(private=True, s_tir=True)
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def loop_with_steps(
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A: T.Buffer((N,)), B: T.Buffer((N,)), C: T.Buffer((N,)), tid: T.int32, v: T.int32
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):
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for i in T.serial(tid, N, step=2):
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C[i] = A[i] + B[i]
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for i in T.unroll(tid, N, step=3):
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C[i] = A[i] + B[i]
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for i in T.vectorized(tid, N, step=4):
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C[i] = A[i] + B[i]
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for i in T.parallel(tid, N, step=5):
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C[i] = A[i] + B[i]
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for i in T.serial(tid, N, step=v):
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C[i] = A[i] + B[i]
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stmts = loop_with_steps.body.seq
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assert stmts[0].step == 2
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assert stmts[1].step == 3
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assert stmts[2].step == 4
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assert stmts[3].step == 5
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assert stmts[4].step.name == "v"
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def test_tir_empty_tuple_index():
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@T.inline
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def bar(val):
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T.evaluate(val)
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@T.prim_func(private=True, s_tir=True)
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def func_with_empty_tuple(A: T.Buffer((), "int32"), B: T.Buffer((), "int32")):
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bar(val=A[()])
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@T.prim_func(private=True, s_tir=True)
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def expected(A: T.Buffer((), "int32"), B: T.Buffer((), "int32")):
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T.evaluate(A[()])
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tvm.ir.assert_structural_equal(func_with_empty_tuple, expected)
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def test_tir_builtin_expression():
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dims = (128, 128)
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@T.prim_func(private=True, s_tir=True)
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def with_builtin(a: T.handle) -> None:
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A = T.match_buffer(a, [len(dims), *dims], "int32")
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for i, j, k in T.grid(*A.shape):
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A[i, j, k] = T.int32(1 + len(A.shape))
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@T.prim_func(private=True, s_tir=True)
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def evaluated(A: T.Buffer((2, 128, 128), "int32")):
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for i, j, k in T.grid(2, 128, 128):
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A[i, j, k] = 4
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tvm.ir.assert_structural_equal(with_builtin, evaluated)
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def test_thread_binding_dtype():
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@T.prim_func(private=True, s_tir=True)
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def func(A: T.Buffer((128, 128)), B: T.Buffer((128, 128))):
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for i in T.thread_binding(T.int64(128), "threadIdx.x"):
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for j in T.thread_binding(128, "threadIdx.y"):
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B[i, j] = A[i, j]
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loop_i = func.body
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loop_j = loop_i.body
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assert loop_i.loop_var.ty.dtype == "int64"
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assert loop_i.thread_binding.var.ty.dtype == "int64"
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assert loop_j.loop_var.ty.dtype == "int32"
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assert loop_j.thread_binding.var.ty.dtype == "int32"
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||||
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def test_inferred_ty_with_prim_args():
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"""A PrimFunc may have inferred Type"""
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@T.prim_func(s_tir=True)
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def func(M: T.int32, N: T.int32) -> T.int32:
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T.ret(M * N)
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||||
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expected = tvm.relax.FuncType(
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||||
[
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tvm.ir.PrimType("int32"),
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||||
tvm.ir.PrimType("int32"),
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||||
],
|
||||
tvm.ir.PrimType("int32"),
|
||||
purity=True,
|
||||
)
|
||||
tvm.ir.assert_structural_equal(func.ty, expected)
|
||||
|
||||
|
||||
def test_inferred_ty_with_buffer_args():
|
||||
"""PrimFunc buffer arguments are inferred as R.Tensor"""
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func(A: T.Buffer([16, 16], "float32"), B: T.Buffer([256], "int32")) -> T.float32:
|
||||
T.ret(T.float32(42.0))
|
||||
|
||||
expected = tvm.relax.FuncType(
|
||||
[
|
||||
tvm.relax.TensorType([16, 16], "float32"),
|
||||
tvm.relax.TensorType([256], "int32"),
|
||||
],
|
||||
tvm.ir.PrimType("float32"),
|
||||
purity=True,
|
||||
)
|
||||
tvm.ir.assert_structural_equal(func.ty, expected)
|
||||
|
||||
|
||||
def test_inferred_ty_with_internal_allocation():
|
||||
"""A pure function may still write to internal allocations.
|
||||
|
||||
Whether a function writes to internal allocations is not a visible
|
||||
effect, and does not impact the purity of a function.
|
||||
"""
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func(A: T.Buffer([16, 16], "float32")) -> T.float32:
|
||||
Sum = T.decl_buffer([], "float32")
|
||||
Sum[()] = 0.0
|
||||
for i, j in T.grid(16, 16):
|
||||
Sum[()] = Sum[()] + A[i, j]
|
||||
|
||||
T.ret(Sum[()])
|
||||
|
||||
expected = tvm.relax.FuncType(
|
||||
[
|
||||
tvm.relax.TensorType([16, 16], "float32"),
|
||||
],
|
||||
tvm.ir.PrimType("float32"),
|
||||
purity=True,
|
||||
)
|
||||
tvm.ir.assert_structural_equal(func.ty, expected)
|
||||
|
||||
|
||||
def test_inferred_ty_with_output_buffer():
|
||||
"""A pure function may not write to an argument buffer
|
||||
|
||||
If an argument buffer is written to, the function must be impure.
|
||||
"""
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func(A: T.Buffer(16, "float32"), B: T.Buffer(16, "float32")):
|
||||
for i in range(16):
|
||||
B[i] = A[i]
|
||||
|
||||
expected = tvm.relax.FuncType(
|
||||
[
|
||||
tvm.relax.TensorType([16], "float32"),
|
||||
tvm.relax.TensorType([16], "float32"),
|
||||
],
|
||||
tvm.relax.TupleType([]),
|
||||
purity=False,
|
||||
)
|
||||
tvm.ir.assert_structural_equal(func.ty, expected)
|
||||
|
||||
|
||||
def test_inferred_ty_with_dynamic_buffer():
|
||||
"""The inferred Type may contain dynamic shapes"""
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func(a_handle: T.handle, b_handle: T.handle):
|
||||
M = T.int64()
|
||||
N = T.int64()
|
||||
A = T.match_buffer(a_handle, [M, N], "float32")
|
||||
B = T.match_buffer(b_handle, [M * N], "float32")
|
||||
for i, j in T.grid(M, N):
|
||||
B[i * N + j] = A[i, j]
|
||||
|
||||
M = tvm.tirx.Var("M", "int64")
|
||||
N = tvm.tirx.Var("N", "int64")
|
||||
expected = tvm.relax.FuncType(
|
||||
[
|
||||
tvm.relax.TensorType([M, N], "float32"),
|
||||
tvm.relax.TensorType([M * N], "float32"),
|
||||
],
|
||||
tvm.relax.TupleType([]),
|
||||
purity=False,
|
||||
)
|
||||
tvm.ir.assert_structural_equal(func.ty, expected)
|
||||
|
||||
|
||||
def test_reinterpret_nop():
|
||||
"""Test builtin reinterpret op"""
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func(A: T.Buffer((32,), "float32"), B: T.Buffer((32,), "float32")) -> None:
|
||||
T.func_attr({"global_symbol": "main"})
|
||||
for i in T.serial(0, 32):
|
||||
with T.sblock():
|
||||
vi = T.axis.remap("S", [i])
|
||||
B[vi] = T.reinterpret("float32", A[vi])
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def expected(A: T.Buffer((32,), "float32"), B: T.Buffer((32,), "float32")) -> None:
|
||||
T.func_attr({"global_symbol": "main"})
|
||||
for i in T.serial(0, 32):
|
||||
with T.sblock():
|
||||
vi = T.axis.remap("S", [i])
|
||||
B[vi] = A[vi]
|
||||
|
||||
tvm.ir.assert_structural_equal(func, expected)
|
||||
|
||||
|
||||
def test_launch_thread_i64():
|
||||
"""Test launching thread with int64"""
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func() -> None:
|
||||
blockIdx_x = T.launch_thread("blockIdx.x", T.int64(1))
|
||||
if blockIdx_x == T.int64(0):
|
||||
T.evaluate(T.int64(0))
|
||||
else:
|
||||
T.evaluate(T.int64(1))
|
||||
|
||||
assert func.body.node.dom.min.ty.dtype == "int64"
|
||||
assert func.body.node.dom.extent.ty.dtype == "int64"
|
||||
|
||||
|
||||
def test_deterministic_branch():
|
||||
"""Test deterministic branch"""
|
||||
|
||||
def create_func(predicate: bool):
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def func() -> None:
|
||||
if predicate:
|
||||
T.evaluate(0)
|
||||
else:
|
||||
T.evaluate(1)
|
||||
|
||||
return func
|
||||
|
||||
def create_expected(value):
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def expected() -> None:
|
||||
T.evaluate(value)
|
||||
|
||||
return expected
|
||||
|
||||
tvm.ir.assert_structural_equal(create_func(True), create_expected(0))
|
||||
tvm.ir.assert_structural_equal(create_func(False), create_expected(1))
|
||||
|
||||
|
||||
def test_block_annotation_merge():
|
||||
def _to_dict(anno: tvm_ffi.container.Map):
|
||||
result = {}
|
||||
for k, v in anno.items():
|
||||
result[k] = _to_dict(v) if isinstance(v, tvm_ffi.container.Map) else v
|
||||
return result
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func0():
|
||||
with T.sblock():
|
||||
T.sblock_attr({"key1": "block1"})
|
||||
T.sblock_attr({"key2": "block2"})
|
||||
T.evaluate(0)
|
||||
|
||||
assert _to_dict(func0.body.block.annotations) == {"key1": "block1", "key2": "block2"}
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func1():
|
||||
with T.sblock():
|
||||
T.sblock_attr({"key": {"key1": "block1"}})
|
||||
T.sblock_attr({"key": {"key2": "block2"}})
|
||||
T.evaluate(0)
|
||||
|
||||
assert _to_dict(func1.body.block.annotations) == {"key": {"key1": "block1", "key2": "block2"}}
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func2():
|
||||
with T.sblock():
|
||||
T.sblock_attr({"key1": "block1"})
|
||||
T.sblock_attr({"key1": "block1"})
|
||||
T.evaluate(0)
|
||||
|
||||
assert _to_dict(func2.body.block.annotations) == {"key1": "block1"}
|
||||
|
||||
with pytest.raises(RuntimeError):
|
||||
|
||||
@T.prim_func(s_tir=True)
|
||||
def func3():
|
||||
with T.sblock():
|
||||
T.sblock_attr({"key1": "block1"})
|
||||
T.sblock_attr({"key1": "block2"})
|
||||
T.evaluate(0)
|
||||
|
||||
|
||||
def test_alloc_inside_block():
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def func() -> None:
|
||||
with T.sblock():
|
||||
A = T.sblock_alloc_buffer([10], "float32")
|
||||
for i in T.serial(0, 10):
|
||||
B = T.sblock_alloc_buffer([10], "float32")
|
||||
for j in T.serial(0, 10):
|
||||
B[j] = T.float32(j)
|
||||
A[i] += B[j]
|
||||
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def expected() -> None:
|
||||
with T.sblock():
|
||||
A = T.sblock_alloc_buffer([10], "float32")
|
||||
B = T.sblock_alloc_buffer([10], "float32")
|
||||
for i, j in T.grid(10, 10):
|
||||
B[j] = T.float32(j)
|
||||
A[i] += B[j]
|
||||
|
||||
tvm.ir.assert_structural_equal(func, expected)
|
||||
|
||||
|
||||
def test_tir_macro_block_name_suffix():
|
||||
@T.inline
|
||||
def operation(A, idx):
|
||||
with T.sblock("op"):
|
||||
v = T.axis.remap("S", [idx])
|
||||
A[v] = A[v] * T.float32(2)
|
||||
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def func_w_macro(a: T.handle) -> None:
|
||||
A = T.match_buffer(a, [10])
|
||||
for i in T.serial(0, 10):
|
||||
operation(A, i)
|
||||
operation(A, i)
|
||||
operation(A, i)
|
||||
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def expected(a: T.handle) -> None:
|
||||
A = T.match_buffer(a, [10])
|
||||
for i in T.serial(0, 10):
|
||||
with T.sblock("op"):
|
||||
v = T.axis.remap("S", [i])
|
||||
A[v] = A[v] * T.float32(2)
|
||||
with T.sblock("op_1"):
|
||||
v = T.axis.remap("S", [i])
|
||||
A[v] = A[v] * T.float32(2)
|
||||
with T.sblock("op_2"):
|
||||
v = T.axis.remap("S", [i])
|
||||
A[v] = A[v] * T.float32(2)
|
||||
|
||||
tvm.ir.assert_structural_equal(func_w_macro, expected)
|
||||
|
||||
|
||||
def test_ifexp():
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def func(A: T.buffer((128, 128), "float32")):
|
||||
for i, j in T.grid(128, 128):
|
||||
A[i, j] = i if i < j else j
|
||||
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def expected(A: T.buffer((128, 128), "float32")):
|
||||
for i, j in T.grid(128, 128):
|
||||
A[i, j] = T.if_then_else(i < j, i, j)
|
||||
|
||||
tvm.ir.assert_structural_equal(func, expected)
|
||||
|
||||
|
||||
def test_sequence_compare():
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def tir_func(A: T.Buffer((128, 128), "float32")):
|
||||
for i, j in T.grid(128, 128):
|
||||
if 0 < i < 128 and 0 < j < 128:
|
||||
A[i, j] = 1
|
||||
else:
|
||||
A[i, j] = 0
|
||||
|
||||
@T.prim_func(private=True, s_tir=True)
|
||||
def expected(A: T.buffer((128, 128), "float32")):
|
||||
for i, j in T.grid(128, 128):
|
||||
if (0 < i and i < 128) and (0 < j and j < 128):
|
||||
A[i, j] = 1
|
||||
else:
|
||||
A[i, j] = 0
|
||||
|
||||
tvm.ir.assert_structural_equal(tir_func, expected)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
tvm.testing.main()
|
||||
Reference in New Issue
Block a user