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Subject [GitHub] [incubator-tvm] MarisaKirisame commented on a change in pull request #5039: [Relay] GradientCell Relay Pass
Date Mon, 23 Mar 2020 16:46:32 GMT
MarisaKirisame commented on a change in pull request #5039: [Relay] GradientCell Relay Pass
URL: https://github.com/apache/incubator-tvm/pull/5039#discussion_r396597422
 
 

 ##########
 File path: src/relay/transforms/gradient_cell.cc
 ##########
 @@ -0,0 +1,320 @@
+/*
+ * 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 gradient_cell.cc
+ *
+ * \brief Convert all tensors to a Gradient Cell
+ * 
+ * This pass delays or removes memory allocation by converting tensors into 
+ * GradCell, an algebraic data type defined in gradient.rly
+ * 
+ * This will delay or decrease memory usage. All calls to
+ * ones, ones_like, zeros, zeros_like will call the One or Zero constructor
+ * of GradCell, which will not instantiate in memory until needed. All other cases result
+ * in using the Raw constructor which means the tensor is instantiated in memory.
+ * 
+ * It also overloads + and * operation which can increase performance when doing
+ * operations involving tensors with values of only 0 or 1.
+ * 
+ * Note: this pass can only be used with functions where the input/output types are
+ * a combination of TupleTypes and TensorTypes
+ * 
+ * This pass optimizes 6 ops:
+ * - add
+ * - multiply
+ * - ones
+ * - ones_like
+ * - zeros
+ * - zeros_like
+ * 
+ * This pass makes use of three visitor. The most important one visits the entire function,
+ * one is used for wrap inputs and one to unwrap outputs.
+ * 
+ * For example:
+ * fn: TensorType[(10,10), float32] -> TensorType[(10,10), float32]
+ * 
+ * After this pass
+ * fn: GradCell[TensorType[(10,10), float32]] -> GradCell[TensorType[(10,10), float32]]
+ * 
+ * Thus, it is necessary to wrap this outer function so that the input/output types remain
the same
+ */
+
+#include <tvm/relay/analysis.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/ir/type_functor.h>
+#include <tvm/relay/transform.h>
+#include "let_list.h"
+
+namespace tvm {
+namespace relay {
+
+/*!
+* \brief Get constructor of GradCell TypeDef with name_hint
+*
+* module must have TypeDefinition of GradCell (defined in gradient.rly)
+*/
+Constructor getGradCellConstructor(IRModule module, std::string name_hint) {
+  TypeData gradCell = module->LookupTypeDef("GradCell");
 
 Review comment:
   can you refactor this function into module?

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