MasterJH5574 commented on a change in pull request #8354: URL: https://github.com/apache/tvm/pull/8354#discussion_r680087655
########## File path: tests/python/unittest/test_tir_specialize.py ########## @@ -0,0 +1,199 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=missing-function-docstring, missing-module-docstring + +import tvm +from tvm import tir +from tvm.script import ty + + [email protected] +def matmul(a: ty.handle, b: ty.handle, c: ty.handle, n: ty.int32) -> None: + m = tir.var("int32") + A = tir.match_buffer(a, [m, n]) + B = tir.match_buffer(b, [m, n]) + C = tir.match_buffer(c, [m, m]) + + with tir.block([m, m, tir.reduce_axis(0, n)], "update") as [vi, vj, vk]: + with tir.init(): + C[vi, vj] = 0.0 + C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk] + + [email protected] +def matmul_128(a: ty.handle, b: ty.handle, c: ty.handle) -> None: + A = tir.match_buffer(a, [128, 128]) + B = tir.match_buffer(b, [128, 128]) + C = tir.match_buffer(c, [128, 128]) + + with tir.block([128, 128, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]: + with tir.init(): + C[vi, vj] = 0.0 + C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk] + + [email protected] +def matmul_m_128(a: ty.handle, b: ty.handle, c: ty.handle) -> None: + m = tir.var("int32") + A = tir.match_buffer(a, [m, 128]) + B = tir.match_buffer(b, [m, 128]) + C = tir.match_buffer(c, [m, m]) + + with tir.block([m, m, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]: + with tir.init(): + C[vi, vj] = 0.0 + C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk] + + [email protected] +def matmul_m_8x(a: ty.handle, b: ty.handle, c: ty.handle) -> None: + x = tir.var("int32") + m = tir.var("int32") + A = tir.match_buffer(a, [m, x * 8]) + B = tir.match_buffer(b, [m, x * 8]) + C = tir.match_buffer(c, [m, m]) + + with tir.block([m, m, tir.reduce_axis(0, x * 8)], "update") as [vi, vj, vk]: + with tir.init(): + C[vi, vj] = 0.0 + C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk] + + [email protected] +def element_wise(a: ty.handle, c: ty.handle) -> None: + m = tir.var("int32") + n = tir.var("int32") + A = tir.match_buffer(a, (m, n), "float32") + C = tir.match_buffer(c, (m, n), "float32") + + B = tir.alloc_buffer((m, n), "float32") + + with tir.block([m, n], "B") as [vi, vj]: + B[vi, vj] = A[vi, vj] * 2.0 + + with tir.block([m, n], "C") as [vi, vj]: + C[vi, vj] = B[vi, vj] + 1.0 + + [email protected] +def element_wise_128_64(a: ty.handle, c: ty.handle) -> None: + A = tir.match_buffer(a, (128, 64), "float32") + C = tir.match_buffer(c, (128, 64), "float32") + B = tir.alloc_buffer((128, 64), "float32") + + with tir.block([128, 64], "B") as [vi, vj]: + B[vi, vj] = A[vi, vj] * 2.0 + + with tir.block([128, 64], "C") as [vi, vj]: + C[vi, vj] = B[vi, vj] + 1.0 + + [email protected] +def element_wise_128_n(a: ty.handle, c: ty.handle) -> None: + n = tir.var("int32") + A = tir.match_buffer(a, (128, n), "float32") + C = tir.match_buffer(c, (128, n), "float32") + B = tir.alloc_buffer((128, n), "float32") + + with tir.block([128, n], "B") as [vi, vj]: + B[vi, vj] = A[vi, vj] * 2.0 + + with tir.block([128, n], "C") as [vi, vj]: + C[vi, vj] = B[vi, vj] + 1.0 + + [email protected] +def mem_copy( + a: ty.handle, b: ty.handle, m: ty.int32, n: ty.int32, p: ty.int32, q: ty.int32 +) -> None: + A = tir.match_buffer(a, (m, n), "float32", strides=[p, 1], elem_offset=q) + B = tir.match_buffer(b, (m, n), "float32", strides=[p, 1], elem_offset=q) + + with tir.block([m, n], "") as [vi, vj]: + B[vi, vj] = A[vi, vj] + + [email protected] +def mem_copy_16_16_8_4(a: ty.handle, b: ty.handle) -> None: + A = tir.match_buffer(a, (16, 16), "float32", strides=[8, 1], elem_offset=4) + B = tir.match_buffer(b, (16, 16), "float32", strides=[8, 1], elem_offset=4) + + with tir.block([16, 16], "") as [vi, vj]: + B[vi, vj] = A[vi, vj] + + [email protected] +def mem_copy_m_n_p_n(a: ty.handle, b: ty.handle, m: ty.int32, n: ty.int32, p: ty.int32) -> None: + A = tir.match_buffer(a, (m, n), "float32", strides=[p, 1], elem_offset=n) + B = tir.match_buffer(b, (m, n), "float32", strides=[p, 1], elem_offset=n) + + with tir.block([m, n], "") as [vi, vj]: + B[vi, vj] = A[vi, vj] + + +def test_specialize_nothing(): + func = matmul.specialize({}) + assert func.same_as(matmul) # Pointer the same + + +def test_specialize_matmul(): + a, _, _, n = matmul.params + # fully specialized + func = matmul.specialize({a: tir.decl_buffer((128, 128))}) + tvm.ir.assert_structural_equal(func, matmul_128) + # partially specialized + func = matmul.specialize({n: 128}) + tvm.ir.assert_structural_equal(func, matmul_m_128) + # symbolic specialized + func = matmul.specialize({n: tir.Var("x", "int32") * 8}) + tvm.ir.assert_structural_equal(func, matmul_m_8x) + + +def test_specialize_elemwise(): + a, c = element_wise.params + C = element_wise.buffer_map[c] + # fully specialized + func = element_wise.specialize({a: tir.decl_buffer((128, 64))}) + tvm.ir.assert_structural_equal(func, element_wise_128_64) + # partially specialized + func = element_wise.specialize({c: tir.decl_buffer((128, C.shape[1]))}) + tvm.ir.assert_structural_equal(func, element_wise_128_n) + + +def test_specialize_mem_copy(): + a, _, m, n, p, q = mem_copy.params + # fully specialized + func = mem_copy.specialize({a: tir.decl_buffer((16, 16), strides=[8, 1], elem_offset=4)}) + tvm.ir.assert_structural_equal(func, mem_copy_16_16_8_4) + func = mem_copy.specialize({n: 16, m: 16, p: 8, q: 4}) + tvm.ir.assert_structural_equal(func, mem_copy_16_16_8_4) + # partially specialized + func = mem_copy.specialize({q: n}) + tvm.ir.assert_structural_equal(func, mem_copy_m_n_p_n) + + +def test_specialize_recursive_load(): + # TODO(Siyuan): add recursive Load testcase, e.g. A[C[i]] + pass Review comment: I saw this TODO item today. Is it intended to leave the TODO here? Or it was because we forgot to write the unittest? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
