| /* |
| * 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 combine_parallel_conv2d.cc |
| * \brief Combine parallel 2d convolutions into a single convolution. |
| * |
| * This pass replaces convolutions that share the same input node and the same |
| * arguments (except that the number of output channels can be different) with a |
| * single convolution. The weight of the new 2d convolution is the concatenation |
| * of the original weights. Elemwise and broadcast ops following conv2d are also |
| * combined if possible. |
| * |
| * This prevents launching multiple kernels in networks with multiple |
| * convolution branches, such as Inception block. |
| */ |
| |
| #include <tvm/relay/analysis.h> |
| #include <tvm/relay/attrs/nn.h> |
| #include <tvm/relay/attrs/transform.h> |
| #include <tvm/relay/expr_functor.h> |
| #include <tvm/relay/op_attr_types.h> |
| #include <tvm/relay/transform.h> |
| |
| #include <unordered_map> |
| #include <unordered_set> |
| |
| #include "./combine_parallel_op.h" |
| #include "./expr_subst.h" |
| #include "pattern_util.h" |
| |
| namespace tvm { |
| namespace relay { |
| |
| class ParallelConv2DCombiner : public ParallelOpCombiner { |
| public: |
| explicit ParallelConv2DCombiner(uint64_t min_num_branches) |
| : ParallelOpCombiner("nn.conv2d", min_num_branches) {} |
| |
| protected: |
| bool IsSupportedOp(const CallNode* n) { return n->attrs.as<Conv2DAttrs>()->groups == 1; } |
| |
| bool CanOpsBeCombined(const CallNode* a, const CallNode* b) { |
| StructuralEqual eq; |
| const Layout kOIHW("OIHW"); |
| const auto* attrs_a = a->attrs.as<Conv2DAttrs>(); |
| const auto* attrs_b = b->attrs.as<Conv2DAttrs>(); |
| CHECK(attrs_a); |
| CHECK(attrs_b); |
| const auto* tweight_a = a->args[1]->type_as<TensorTypeNode>(); |
| const auto* tweight_b = b->args[1]->type_as<TensorTypeNode>(); |
| const auto shape_a = |
| tir::BijectiveLayout(Layout(attrs_a->kernel_layout), kOIHW).ForwardShape(tweight_a->shape); |
| const auto shape_b = |
| tir::BijectiveLayout(Layout(attrs_b->kernel_layout), kOIHW).ForwardShape(tweight_b->shape); |
| |
| return eq(attrs_a->strides, attrs_b->strides) && eq(attrs_a->padding, attrs_b->padding) && |
| eq(attrs_a->dilation, attrs_b->dilation) && eq(attrs_a->groups, attrs_b->groups) && |
| eq(attrs_a->data_layout, attrs_b->data_layout) && |
| eq(attrs_a->kernel_layout, attrs_b->kernel_layout) && |
| eq(attrs_a->out_dtype, attrs_b->out_dtype) && |
| eq(attrs_a->out_layout, attrs_b->out_layout) && eq(shape_a[2], shape_b[2]) && |
| eq(shape_a[3], shape_b[3]); |
| } |
| |
| Call MakeCombinedOp(const Group& branches) { |
| const Op& conv2d = Op::Get("nn.conv2d"); |
| Expr data = branches[0][0]->args[0]; |
| Expr new_weight; |
| IndexExpr new_channels; |
| std::tie(new_weight, new_channels) = TransformWeight(branches); |
| |
| const CallNode* group_root = branches[0][0]; |
| const auto* attrs = group_root->attrs.as<Conv2DAttrs>(); |
| CHECK(attrs); |
| const auto new_attrs = make_object<Conv2DAttrs>(); |
| new_attrs->strides = attrs->strides; |
| new_attrs->padding = attrs->padding; |
| new_attrs->dilation = attrs->dilation; |
| new_attrs->groups = attrs->groups; |
| new_attrs->kernel_size = attrs->kernel_size; |
| new_attrs->data_layout = attrs->data_layout; |
| new_attrs->kernel_layout = attrs->kernel_layout; |
| new_attrs->out_layout = attrs->out_layout; |
| new_attrs->out_dtype = attrs->out_dtype; |
| new_attrs->channels = new_channels; |
| |
| const std::string& layout = |
| new_attrs->out_layout == "" ? new_attrs->data_layout : new_attrs->out_layout; |
| channel_pos_ = layout.find('C'); |
| CHECK_NE(channel_pos_, std::string::npos); |
| |
| return Call(conv2d, {data, new_weight}, Attrs{new_attrs}, {}); |
| } |
| |
| bool IsArgCompatible(const CallNode* a, const CallNode* b, size_t index) { |
| StructuralEqual eq; |
| auto ta = a->args[index]->type_as<TensorTypeNode>(); |
| auto tb = b->args[index]->type_as<TensorTypeNode>(); |
| auto toutput_a = a->type_as<TensorTypeNode>(); |
| auto toutput_b = b->type_as<TensorTypeNode>(); |
| |
| if (!eq(ta->dtype, tb->dtype) || ta->shape.size() != tb->shape.size()) return false; |
| |
| // Position of the 'C' dimension in the argument |
| size_t arg_channel_pos = channel_pos_ - toutput_a->shape.size() + ta->shape.size(); |
| |
| // Channel super-dimension shoule be present and not broadcasted |
| if ((arg_channel_pos > channel_pos_) || // size_t overflow |
| !eq(ta->shape[arg_channel_pos], toutput_a->shape[channel_pos_]) || |
| !eq(tb->shape[arg_channel_pos], toutput_b->shape[channel_pos_])) |
| return false; |
| |
| for (size_t i = 0; i < ta->shape.size(); i++) { |
| if (i == arg_channel_pos) continue; |
| if (!eq(ta->shape[i], tb->shape[i])) return false; |
| } |
| return true; |
| } |
| |
| Call MakeCombinedCallFromFollowingOps(const Expr& data, const Group& branches, size_t depth, |
| size_t parent_index) { |
| Array<Expr> new_args; |
| const CallNode* call = branches[0][depth]; |
| size_t ndim = call->type_as<TensorTypeNode>()->shape.size(); |
| |
| for (size_t i = 0; i < call->args.size(); i++) { |
| if (i == parent_index) { |
| new_args.push_back(data); |
| continue; |
| } |
| |
| size_t arg_ndim = call->args[i]->type_as<TensorTypeNode>()->shape.size(); |
| size_t arg_channel_pos = channel_pos_ - ndim + arg_ndim; |
| Array<Expr> tuple; |
| for (const auto& branch : branches) { |
| tuple.push_back(branch[depth]->args[i]); |
| } |
| |
| auto concat = MakeConcatenate(Tuple(tuple), arg_channel_pos); |
| new_args.push_back(std::move(concat)); |
| } |
| |
| return Call(call->op, new_args, call->attrs, {}); |
| } |
| |
| void UpdateGroupOutput(const Expr& data, const Group& branches, size_t depth, |
| ExprSubstMap* subst_map) { |
| int64_t index = 0; |
| |
| for (const auto& branch : branches) { |
| const CallNode* conv2d = branch[0]; |
| int64_t channels = GetConv2DSuperChannelsDim(conv2d); |
| Array<Integer> begin; |
| Array<Integer> end; |
| for (size_t i = 0; i < channel_pos_; i++) { |
| begin.push_back(0); |
| end.push_back(-1); |
| } |
| begin.push_back(index); |
| index += channels; |
| end.push_back(channels); |
| Array<Integer> strides(begin.size(), 1); |
| auto slice = MakeStridedSlice(data, begin, end, strides, "size"); |
| subst_map->insert({GetRef<Expr>(branch[depth]), slice}); |
| } |
| } |
| |
| private: |
| /* \brief index of channel dimension */ |
| size_t channel_pos_; |
| |
| std::tuple<Expr, IndexExpr> TransformWeight(const Group& branches) { |
| int64_t num_filters = 0; // number of filters of the transformed weight |
| Array<Expr> weights; |
| for (const auto& branch : branches) { |
| auto conv2d = branch[0]; |
| weights.push_back(conv2d->args[1]); |
| auto channels = GetConv2DSuperChannelsDim(conv2d); |
| num_filters += channels; |
| } |
| auto index = branches[0][0]->attrs.as<Conv2DAttrs>()->kernel_layout.find('O'); |
| CHECK_NE(index, std::string::npos); |
| return std::make_tuple(MakeConcatenate(Tuple(weights), index), |
| tir::make_const(DataType::Int(32), num_filters)); |
| } |
| }; |
| |
| /*! \brief Combine parallel conv2d if number of branches >= min_num_branches */ |
| Expr CombineParallelConv2D(const Expr& expr, uint64_t min_num_branches) { |
| return ParallelConv2DCombiner(min_num_branches).Combine(expr); |
| } |
| |
| namespace transform { |
| |
| Pass CombineParallelConv2D(uint64_t min_num_branches) { |
| runtime::TypedPackedFunc<Function(Function, IRModule, PassContext)> pass_func = |
| [=](Function f, IRModule m, PassContext pc) { |
| return Downcast<Function>(CombineParallelConv2D(f, min_num_branches)); |
| }; |
| return CreateFunctionPass(pass_func, 4, "CombineParallelConv2d", {"InferType"}); |
| } |
| |
| TVM_REGISTER_GLOBAL("relay._transform.CombineParallelConv2D").set_body_typed(CombineParallelConv2D); |
| |
| } // namespace transform |
| |
| } // namespace relay |
| } // namespace tvm |