masahi commented on code in PR #14066: URL: https://github.com/apache/tvm/pull/14066#discussion_r1114164315
########## tests/python/relax/test_analysis_suggest_layout_transforms.py: ########## @@ -0,0 +1,827 @@ +# 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. + +import pytest +import tvm.testing + +from tvm import relax, tir +from tvm.script import tir as T + + +def apply_transformations(func, suggested_transfoms, print_transformation=False): + sch = tir.Schedule(func) + for block, per_block_transformations in suggested_transfoms.items(): + blockrv = sch.get_block(block.name_hint) + for obj, index_map in per_block_transformations.items(): + if isinstance(obj, tir.Block): + block_name = obj.name_hint + if print_transformation: + print("Block transformation: ", block_name, " :: ", index_map) + sch.transform_block_layout(block_name, index_map) + else: + assert isinstance(obj, tir.Buffer) + buffer = obj + if print_transformation: + print("Buffer transformation: ", buffer, " :: ", index_map) + sch.transform_layout(blockrv, buffer, index_map) + return sch.mod["main"] + + +def test_nested_blocks(): + @T.prim_func + def nested_block( + arg: T.Buffer((32, 64, 224, 224), "float32"), + relu: T.Buffer((32, 64, 224, 224), "float32"), + ): + for i, j in T.grid(32, 64): + with T.block("outer"): + v_i, v_j = T.axis.remap("SS", [i, j]) + T.reads(arg[v_i, v_j, 0:224, 0:224]) + T.writes(relu[v_i, v_j, 0:224, 0:224]) + for k, l in T.grid(224, 224): + with T.block("inner"): + v_k, v_l = T.axis.remap("SS", [k, l]) + T.reads(arg[v_i, v_j, v_k, v_l]) + T.writes(relu[v_i, v_j, v_k, v_l]) + relu[v_i, v_j, v_k, v_l] = T.max(arg[v_i, v_j, v_k, v_l], T.float32(0)) + + suggested_transforms = relax.analysis.suggest_layout_transforms( + func=nested_block, write_buffer_transforms=[lambda n, c, h, w: (n, h, w, c)] + ) + # no suggestions for nested block. + assert len(suggested_transforms.items()) == 0 + + +def test_mismatch_transformations_and_num_params(): + @T.prim_func + def elemwise( + arg: T.Buffer((32, 64, 224, 224), "float32"), + relu: T.Buffer((32, 64, 224, 224), "float32"), + ): + for i0, i1, i2, i3 in T.grid(32, 64, 224, 224): + with T.block("compute"): + v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3]) + T.reads(arg[v_i0, v_i1, v_i2, v_i3]) + T.writes(relu[v_i0, v_i1, v_i2, v_i3]) + relu[v_i0, v_i1, v_i2, v_i3] = T.max(arg[v_i0, v_i1, v_i2, v_i3], T.float32(0)) + + with pytest.raises(tvm.TVMError, match="Incompatible PrimFunc and write_transformations"): + _ = relax.analysis.suggest_layout_transforms( + func=elemwise, + write_buffer_transforms=[ + lambda n, c, h, w: (n, h, w, c), + lambda n, c, h, w: (n, h, w, c), + lambda n, c, h, w: (n, h, w, c), + ], + ) + + +def test_empty_write_transformations(): + @T.prim_func + def elemwise( + arg: T.Buffer((32, 64, 224, 224), "float32"), + relu: T.Buffer((32, 64, 224, 224), "float32"), + ): + for i0, i1, i2, i3 in T.grid(32, 64, 224, 224): + with T.block("compute"): + v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3]) + T.reads(arg[v_i0, v_i1, v_i2, v_i3]) + T.writes(relu[v_i0, v_i1, v_i2, v_i3]) + relu[v_i0, v_i1, v_i2, v_i3] = T.max(arg[v_i0, v_i1, v_i2, v_i3], T.float32(0)) + + suggested_transforms = relax.analysis.suggest_layout_transforms( + func=elemwise, write_buffer_transforms=[] + ) + assert len(suggested_transforms.items()) == 0 + + +def test_non_bijective_block_transform(): + @T.prim_func + def before( + arg: T.Buffer((32, 64), "float32"), + output: T.Buffer((32, 64), "float32"), + ): + for ax0, ax1 in T.grid(32, 64): + with T.block("compute"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(arg[v_ax0, v_ax1]) + T.writes(output[v_ax0, v_ax1]) + output[v_ax0, v_ax1] = arg[v_ax0, v_ax1] + + suggested_transforms = relax.analysis.suggest_layout_transforms( + func=before, write_buffer_transforms=[lambda n, c: (n, c // 5, c % 5)] + ) + assert len(suggested_transforms.items()) == 0 + + +def test_non_affine_access(): + @T.prim_func + def before( + arg: T.Buffer((32, 64), "float32"), + output: T.Buffer((32 * 64, 10), "float32"), + ): + for ax0, ax1, ax2 in T.grid(32, 64, 10): + with T.block("compute"): + v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) + T.reads(arg[v_ax0, v_ax1]) + T.writes(output[v_ax0 * v_ax1, v_ax2]) + output[v_ax0 * v_ax1, v_ax2] = arg[v_ax0, v_ax1] + + suggested_transforms = relax.analysis.suggest_layout_transforms( + func=before, write_buffer_transforms=[lambda a, b: (b, a)] + ) + assert len(suggested_transforms.items()) == 0 + + +def test_unsupported_write_spatial_layout(): + @T.prim_func + def before( + arg: T.Buffer((4, 4), "float32"), + output: T.Buffer((16), "float32"), + ): + for ax0, ax1 in T.grid(4, 4): + with T.block("flatten"): + v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) + T.reads(arg[v_ax0, v_ax1]) + T.writes(output[v_ax0 * 4 + v_ax1]) + output[v_ax0 * 4 + v_ax1] = arg[v_ax0, v_ax1] + + suggested_transforms = relax.analysis.suggest_layout_transforms( + func=before, write_buffer_transforms=[lambda a: (a // 4, a % 4)] Review Comment: Why is this not supported? -- This is an automated message from the Apache Git Service. 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