Lunderberg commented on a change in pull request #9727: URL: https://github.com/apache/tvm/pull/9727#discussion_r770123610
########## File path: src/tir/ir/index_map.cc ########## @@ -0,0 +1,153 @@ +/* + * 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. + */ + +/*! + * \file index_map.cc + */ + +#include "tvm/tir/index_map.h" + +#include <tvm/arith/analyzer.h> +#include <tvm/arith/iter_affine_map.h> +#include <tvm/tir/op.h> + +#include <sstream> + +namespace tvm { +namespace tir { + +IndexMap::IndexMap(Array<Var> initial_indices, Array<PrimExpr> final_indices) { + auto n = make_object<IndexMapNode>(); + n->initial_indices = std::move(initial_indices); + n->final_indices = std::move(final_indices); + data_ = std::move(n); +} + +IndexMap IndexMap::Inverse(Array<Range> initial_ranges) const { + // Dummy variables to represent the inverse's inputs. + Array<Var> output_vars; + for (size_t i = 0; i < (*this)->final_indices.size(); i++) { + PrimExpr index = (*this)->final_indices[i]; + // TODO(Lunderberg): Better names for these variables. A variable + // that is passed through unmodified (`index` is an element of + // `initial_indices`) should use that input index's name. A pair + // of output indices variables split from a single input index + // should be named (X.outer,X.inner). + std::stringstream ss; + ss << "axis" << i; + Var var_index(ss.str(), index.dtype()); + output_vars.push_back(var_index); + } + + // Dummy ranges for the extent of each input. + Map<Var, Range> input_iters; + ICHECK_EQ((*this)->initial_indices.size(), initial_ranges.size()); + for (size_t i = 0; i < initial_ranges.size(); i++) { + input_iters.Set((*this)->initial_indices[i], initial_ranges[i]); + } + + // Unpack the output indices into linear combinations of the initial + // indices. + arith::Analyzer analyzer; + auto iter_map = DetectIterMap((*this)->final_indices, input_iters, 1, true, &analyzer); + CHECK(iter_map.size()) << "Index transformation was not bijective."; + + // Determine expressions for the input variables, in terms of the + // output variables. + Map<Var, PrimExpr> inverse_exprs_map = + InverseAffineIterMap(iter_map, Array<PrimExpr>(output_vars.begin(), output_vars.end())); + + // Unpack the map to an array, maintaining the same parameter order. + Array<PrimExpr> inverse_exprs; + for (const auto& index : (*this)->initial_indices) { + inverse_exprs.push_back(inverse_exprs_map.at(index)); + } + + return IndexMap(output_vars, inverse_exprs); +} + +Array<PrimExpr> IndexMapNode::MapIndices(const Array<PrimExpr>& indices) const { + ICHECK_EQ(indices.size(), initial_indices.size()); + + arith::Analyzer analyzer; + + for (size_t i = 0; i < initial_indices.size(); i++) { + analyzer.Bind(initial_indices[i], indices[i]); + } + + Array<PrimExpr> output; + for (const auto& output_dim : final_indices) { + output.push_back(analyzer.Simplify(output_dim)); + } + + return output; +} + +Array<Range> IndexMapNode::MapRanges(const Array<Range>& ranges) const { + ICHECK_EQ(ranges.size(), initial_indices.size()); + + Map<Var, Range> input_iters; + for (size_t i = 0; i < initial_indices.size(); i++) { + input_iters.Set(initial_indices[i], ranges[i]); + } + + arith::Analyzer analyzer; + auto iter_sums = DetectIterMap(final_indices, input_iters, 1, true, &analyzer); + + Array<Range> output; + for (const auto& iter_sum : iter_sums) { + PrimExpr min = iter_sum->base; + PrimExpr extent = 0; + for (const auto& term : iter_sum->args) { + extent += term->extent * term->scale; + } + output.push_back(Range::FromMinExtent(min, extent)); + } + + return output; +} + +Array<PrimExpr> IndexMapNode::MapShape(const Array<PrimExpr>& shape) const { + ICHECK_EQ(shape.size(), initial_indices.size()); + + Array<Range> ranges; + for (auto& dim : shape) { + ranges.push_back(Range(0, dim)); + } + Array<Range> mapped = MapRanges(std::move(ranges)); + + Array<PrimExpr> output; + for (auto& range : mapped) { + ICHECK(is_zero(range->min)); + output.push_back(range->extent); + } + Review comment: At present all mappings are surjective, but I'd like to add in the future. The use case would be to have implied padding be present in the physical layout, with elements that don't have a representation in the logical layout. For example, if a 1-d tensor has size 50 and warp size 32, calling `.transform_layout(lambda i: [i//warp_size, i%warp_size])` could map it to the [2,32] shape, even though that increases the total number of elements. That said, I haven't fully thought through the implications of that, and some of the use cases would require a specific value for the padding (e.g. padding a tensor out with 0 prior to running conv2d with pad_value=0). -- 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]
