comaniac commented on a change in pull request #8069: URL: https://github.com/apache/tvm/pull/8069#discussion_r652216408
########## File path: src/relay/transforms/to_mixed_precision.cc ########## @@ -0,0 +1,420 @@ +/* + * 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 to_mixed_precision.cc + * \brief Automatic mixed floating point precision for relay graphs. i.e. turn a graph into fp16. + * + */ + +#include <tvm/ir/attrs.h> +#include <tvm/relay/expr_functor.h> +#include <tvm/relay/transform.h> +#include <tvm/runtime/object.h> + +#include <utility> + +#include "pattern_utils.h" + +namespace tvm { +namespace relay { + +// A callable which hashes std::pair +struct pair_hash { + template <class T1, class T2> + std::size_t operator()(const std::pair<T1, T2>& pair) const { + auto h1 = std::hash<T1>()(pair.first); + auto h2 = std::hash<T2>()(pair.second); + + // Use boost's combine_hash strategy + return h1 ^ (h1 + 0x9e3779b9 + (h2 << 6) + (h2 >> 2)); + } +}; + +// MIXED_PRECISION_ALWAYS ops should always be done in lower precision due to the speed and memory +// savings. MIXED_PRECISION_FOLLOW ops can be done in lower precision but don't have speedups to +// justify a cast. MIXED_PRECISION_NEVER colored ops should not be done in lower precision due to +// numerical reasons. +enum MixedTypeConversionCategory : int { + MIXED_PRECISION_ALWAYS = 0, + MIXED_PRECISION_FOLLOW = 1, + MIXED_PRECISION_NEVER = 2 +}; + +// A map of a parent node and a wanted dtype to existing nodes casted to the wanted dtype +using CachedCastNodes = std::unordered_map<std::pair<const ExprNode*, DataType>, Expr, pair_hash>; + +// Return array is of type : [MixedTypeConversionCategory (int), String, String] +// The fields are : [ConversionCategory, accumulation_datatype, output_datatype] +// Call is a call node, DataType is the mixed precision type +using FTVMMixedPrecisionConversionType = runtime::TypedPackedFunc<Array<ObjectRef>( + const Call& call_node, const std::string& target_dtype_str)>; + +class MixedPrecisionPass : public MixedModeMutator { + private: + CachedCastNodes cast_nodes_cache_; + + /*! \brief The target datatype we want to convert to e.g. FP16 */ + const DataType mixed_precision_type; Review comment: Please check through all changes you've made. ```suggestion const DataType mixed_precision_type_; ``` ########## File path: src/relay/transforms/to_mixed_precision.cc ########## @@ -0,0 +1,420 @@ +/* + * 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 to_mixed_precision.cc + * \brief Automatic mixed floating point precision for relay graphs. i.e. turn a graph into fp16. + * + */ + +#include <tvm/ir/attrs.h> +#include <tvm/relay/expr_functor.h> +#include <tvm/relay/transform.h> +#include <tvm/runtime/object.h> + +#include <utility> + +#include "pattern_utils.h" + +namespace tvm { +namespace relay { + +// A callable which hashes std::pair +struct pair_hash { + template <class T1, class T2> + std::size_t operator()(const std::pair<T1, T2>& pair) const { + auto h1 = std::hash<T1>()(pair.first); + auto h2 = std::hash<T2>()(pair.second); + + // Use boost's combine_hash strategy + return h1 ^ (h1 + 0x9e3779b9 + (h2 << 6) + (h2 >> 2)); + } +}; + +// MIXED_PRECISION_ALWAYS ops should always be done in lower precision due to the speed and memory +// savings. MIXED_PRECISION_FOLLOW ops can be done in lower precision but don't have speedups to +// justify a cast. MIXED_PRECISION_NEVER colored ops should not be done in lower precision due to +// numerical reasons. +enum MixedTypeConversionCategory : int { + MIXED_PRECISION_ALWAYS = 0, + MIXED_PRECISION_FOLLOW = 1, + MIXED_PRECISION_NEVER = 2 +}; + +// A map of a parent node and a wanted dtype to existing nodes casted to the wanted dtype +using CachedCastNodes = std::unordered_map<std::pair<const ExprNode*, DataType>, Expr, pair_hash>; + +// Return array is of type : [MixedTypeConversionCategory (int), String, String] +// The fields are : [ConversionCategory, accumulation_datatype, output_datatype] +// Call is a call node, DataType is the mixed precision type +using FTVMMixedPrecisionConversionType = runtime::TypedPackedFunc<Array<ObjectRef>( + const Call& call_node, const std::string& target_dtype_str)>; + +class MixedPrecisionPass : public MixedModeMutator { + private: + CachedCastNodes cast_nodes_cache_; Review comment: docstring? ########## File path: src/relay/transforms/to_mixed_precision.cc ########## @@ -0,0 +1,420 @@ +/* + * 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 to_mixed_precision.cc + * \brief Automatic mixed floating point precision for relay graphs. i.e. turn a graph into fp16. + * + */ + +#include <tvm/ir/attrs.h> +#include <tvm/relay/expr_functor.h> +#include <tvm/relay/transform.h> +#include <tvm/runtime/object.h> + +#include <utility> + +#include "pattern_utils.h" + +namespace tvm { +namespace relay { + +// A callable which hashes std::pair +struct pair_hash { + template <class T1, class T2> + std::size_t operator()(const std::pair<T1, T2>& pair) const { + auto h1 = std::hash<T1>()(pair.first); + auto h2 = std::hash<T2>()(pair.second); + + // Use boost's combine_hash strategy + return h1 ^ (h1 + 0x9e3779b9 + (h2 << 6) + (h2 >> 2)); + } +}; + +// MIXED_PRECISION_ALWAYS ops should always be done in lower precision due to the speed and memory +// savings. MIXED_PRECISION_FOLLOW ops can be done in lower precision but don't have speedups to +// justify a cast. MIXED_PRECISION_NEVER colored ops should not be done in lower precision due to +// numerical reasons. +enum MixedTypeConversionCategory : int { + MIXED_PRECISION_ALWAYS = 0, + MIXED_PRECISION_FOLLOW = 1, + MIXED_PRECISION_NEVER = 2 +}; + +// A map of a parent node and a wanted dtype to existing nodes casted to the wanted dtype +using CachedCastNodes = std::unordered_map<std::pair<const ExprNode*, DataType>, Expr, pair_hash>; + +// Return array is of type : [MixedTypeConversionCategory (int), String, String] +// The fields are : [ConversionCategory, accumulation_datatype, output_datatype] +// Call is a call node, DataType is the mixed precision type +using FTVMMixedPrecisionConversionType = runtime::TypedPackedFunc<Array<ObjectRef>( + const Call& call_node, const std::string& target_dtype_str)>; + +class MixedPrecisionPass : public MixedModeMutator { + private: + CachedCastNodes cast_nodes_cache_; + + /*! \brief The target datatype we want to convert to e.g. FP16 */ + const DataType mixed_precision_type; + + // Map of Ops with no associated FTVMMixedPrecisionConversionType to the times they were + // encountered. Used for emitting warnings on missing ops in the pass. + std::unordered_map<std::string, int> missing_ops; + + Attrs GetNewAttrs(const CallNode* call, const DataType& accumulation_dtype) const { + /* If the accumulation dtype is in the attributes make a copy and mutate the field. */ + Attrs cur_attrs = call->attrs; + if (cur_attrs.get() != nullptr) { + // TODO(AndrewZhaoLuo): Figure out a better way to do this + // modify output_dtype attributes (accumulation dtypes for ops) + if (auto attrs = cur_attrs.as<Conv1DAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<Conv1DTransposeAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<Conv2DAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<Conv2DTransposeAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<Conv2DWinogradAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<Conv2DWinogradNNPACKWeightTransformAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<DeformableConv2DAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<Conv3DAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<Conv3DTransposeAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<Conv3DWinogradAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<DenseAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = cur_attrs.as<BatchMatmulAttrs>()) { + return ModifyAttrsOutputDType(attrs, accumulation_dtype); + } + + // modify dtype attributes (creating new tensors of type dtype) + if (auto attrs = cur_attrs.as<InitOpAttrs>()) { + return ModifyAttrsDType(attrs, accumulation_dtype); + } + } + + return cur_attrs; + } + + template <typename T> + Attrs ModifyAttrsOutputDType(const T* attrs, const DataType& accumulation_dtype) const { + /* + Helper template to modify relevant attributes with out_dtype type. + These represent accumulation dtypes for some operations e.g. + conv2d might take in fp16 and give a fp32 result. + Attrs is const because we get it as a const. + */ + DataType cur_type = (attrs->out_dtype); + ObjectPtr<T> new_attrs = make_object<T>(*attrs); + if (cur_type.is_float() || cur_type.is_void()) new_attrs->out_dtype = accumulation_dtype; + return Attrs(new_attrs); + } + + template <typename T> + Attrs ModifyAttrsDType(const T* attrs, const DataType& accumulation_dtype) const { + /* + Helper template to modify relevant attributes with dtype type. + This determines the output dtype for some ops. For example + zeros creates a tensor of zeros of the specified dtype. + Attrs is const because we get it as a const. + */ + DataType cur_type = (attrs->dtype); + ObjectPtr<T> new_attrs = make_object<T>(*attrs); + if (cur_type.is_float() || cur_type.is_void()) new_attrs->dtype = accumulation_dtype; + return Attrs(new_attrs); + } + + Type GetType(const Expr& expr) const { + auto mod = IRModule::FromExpr(expr); + mod = transform::InferType()(mod); + + if (expr.as<FunctionNode>()) { + return mod->Lookup("main")->checked_type(); + } else { + return mod->Lookup("main").as<FunctionNode>()->body->checked_type(); + } + } + + bool IsMixedPrecisionType(const Type& t, bool ignore_non_float = false) const { + /* Returns whether t is a type with only target mixed precision type elements. + If ignore_non_float, then ignore non-floating types. + */ + if (const TensorTypeNode* tensor_type = t.as<TensorTypeNode>()) { + return (!ignore_non_float || (tensor_type->dtype).is_float()) && + tensor_type->dtype == mixed_precision_type; + } else if (const TupleTypeNode* tuple_type = t.as<TupleTypeNode>()) { + for (Type t : tuple_type->fields) { + if (!IsMixedPrecisionType(t, ignore_non_float)) return false; + } + return true; + } else { + LOG(FATAL) << "Unsupported type " << t << " we don't know how to handle"; + return false; + } + } + + Expr CachedCast(const Expr& expr, const DataType& expr_dtype, const DataType& wanted_dtype) { + /* Cast tensor to the wanted datatype, returning a cached version if it's already been done. */ + + // If this is not a floating point type, do not cast. E.g. it might be an integer + if (!expr_dtype.is_float()) { + return expr; + } + + if (expr_dtype == wanted_dtype) { + return expr; + } + + const ExprNode* expr_node = expr.as<ExprNode>(); + CHECK(expr_node) << "Non-expression node found in cast: " << expr; + + // Use cached result if possible. + auto search = cast_nodes_cache_.find({expr_node, wanted_dtype}); + if (search != cast_nodes_cache_.end()) { + return search->second; + } + + Expr result = Cast(expr, wanted_dtype); + cast_nodes_cache_[{expr_node, wanted_dtype}] = result; + + // Reverse the cache result, e.g. if we want to reverse the cast simply point to original node + const ExprNode* new_expr_node = result.as<ExprNode>(); + cast_nodes_cache_[{new_expr_node, expr_dtype}] = expr; + return result; + } + + Expr CastArg(const Expr& expr, const Type& expr_type, const DataType& wanted_dtype) { + /* Helper for casting arguments to call_nodes handling all relevant cases. */ + if (const TensorTypeNode* tensor_type = expr_type.as<TensorTypeNode>()) { + return CachedCast(expr, tensor_type->dtype, wanted_dtype); + } else if (const TupleTypeNode* tuple_type = expr_type.as<TupleTypeNode>()) { + Array<Expr> new_expr; + bool all_same = true; + for (size_t i = 0; i < (tuple_type->fields).size(); i++) { + Expr tuple_element = GetField(expr, i); + Type tuple_element_dtype = (tuple_type->fields)[i]; + Expr casted_element = CastArg(tuple_element, tuple_element_dtype, wanted_dtype); + new_expr.push_back(casted_element); + all_same &= casted_element.same_as(tuple_element); + } + return all_same ? expr : Tuple(new_expr); + } + CHECK(0) << "Unsupported type " << expr_type << " we don't know how to cast for arguments!"; + return expr; + } + + std::pair<Array<Expr>, Array<Type>> CastAllArgs(const Array<Expr>& cur_args, + const Array<Type>& cur_arg_types, + const DataType& wanted_dtype) { + Array<Expr> new_args; + Array<Type> new_arg_types; + for (size_t i = 0; i < cur_args.size(); i++) { + Expr cur_arg = cur_args[i]; + Type cur_arg_type = cur_arg_types[i]; + Expr new_arg = CastArg(cur_arg, cur_arg_type, wanted_dtype); + Type new_arg_type = GetType(new_arg); + new_args.push_back(new_arg); + new_arg_types.push_back(new_arg_type); + } + return {new_args, new_arg_types}; + } + + public: + using MixedModeMutator::VisitExpr_; + + explicit MixedPrecisionPass(DataType mixed_precision_type = DataType::Float(16)) + : MixedModeMutator(), mixed_precision_type(mixed_precision_type) { + if (!mixed_precision_type.is_float() && !mixed_precision_type.is_bfloat16()) { + LOG(FATAL) << "Only support IEEE floating point mixed precision types and bfloat16, but got " + << mixed_precision_type; + } + } + + Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final { + const CallNode* post_call_node = post.as<CallNode>(); + CHECK(post_call_node) << "Expected a CallNode, but got " << post; + + Expr cur_op = post_call_node->op; + + // Get info on the operation being called: + // conversion category (int), accumulation dtype (str), output dtype (str) + MixedTypeConversionCategory initial_category; + DataType accumulation_dtype, output_dtype; + if (cur_op.as<FunctionNode>()) { + // Avoid messing with functions to avoid changing signature + initial_category = MIXED_PRECISION_NEVER; + accumulation_dtype = DataType::Float(32); + output_dtype = DataType::Float(32); + } else if (cur_op.as<OpNode>()) { + static auto attr_map = + Op::GetAttrMap<FTVMMixedPrecisionConversionType>("FTVMMixedPrecisionConversionType"); + Op op = Downcast<Op>(cur_op); + if (attr_map.count(op)) { + // Calculate the conversion category and dtypes from registered attribute. + FTVMMixedPrecisionConversionType func = attr_map[op]; + Array<ObjectRef> op_descriptor = + func(GetRef<Call>(pre_call_node), DLDataType2String(mixed_precision_type)); + ICHECK(op_descriptor.size() == 3) + << "got the wrong number of returned arguments (expected 3 got " << op_descriptor.size() + << ") from FTVMMixedPrecisionConversionType for " << AsText(op, false); + + int64_t op_conversion_type = Downcast<Integer>(op_descriptor[0])->value; + initial_category = static_cast<MixedTypeConversionCategory>(op_conversion_type); + accumulation_dtype = DataType(String2DLDataType(Downcast<String>(op_descriptor[1]))); + output_dtype = DataType(String2DLDataType(Downcast<String>(op_descriptor[2]))); + } else { + missing_ops[op->name] += 1; + + // If not registered, by default assume is a generic FOLLOW operation. + initial_category = MIXED_PRECISION_FOLLOW; + accumulation_dtype = mixed_precision_type; + output_dtype = mixed_precision_type; + } + } else { + LOG(FATAL) << "Unsupported op type in CallNode: " << pre_call_node->op; + } + + // First check if all the new mutated args are in lower precision form + Array<Type> cur_arg_types; + bool all_args_mixed_type_compatible = true; + for (Expr arg : post_call_node->args) { + Type cur_arg_type = GetType(arg); + cur_arg_types.push_back(cur_arg_type); + + if (initial_category == MIXED_PRECISION_FOLLOW && all_args_mixed_type_compatible) { + // We can cast Vars and Constants to the right types so don't care about the types. + bool is_mixed_type_compatible = IsMixedPrecisionType(cur_arg_type, true) || + arg->IsInstance<VarNode>() || + arg->IsInstance<ConstantNode>(); + all_args_mixed_type_compatible &= is_mixed_type_compatible; + } + } + + // Determine the final category we want for conversion + MixedTypeConversionCategory final_category = initial_category; + if (initial_category == MIXED_PRECISION_FOLLOW) { + final_category = + all_args_mixed_type_compatible ? MIXED_PRECISION_ALWAYS : MIXED_PRECISION_NEVER; + } + + // Create the new arguments to the call. + DataType wanted_arg_dtypes = + final_category == MIXED_PRECISION_ALWAYS ? mixed_precision_type : DataType::Float(32); + auto call_args_and_types = CastAllArgs(post_call_node->args, cur_arg_types, wanted_arg_dtypes); + Array<Expr> new_args = call_args_and_types.first; + Array<Type> new_arg_types; + + if (pre_call_node->op.as<FunctionNode>()) { + // Function Nodes don't store type info in the Call, it should be a [] + new_arg_types = pre_call_node->type_args; + } else { + new_arg_types = call_args_and_types.second; + } + + // Finally create the new attributes. + if (final_category == MIXED_PRECISION_ALWAYS) { + Attrs new_attrs = GetNewAttrs(pre_call_node, accumulation_dtype); + Expr output = Call(cur_op, new_args, new_attrs, new_arg_types, pre_call_node->span); + if (accumulation_dtype != output_dtype) { + output = CastArg(output, GetType(output), output_dtype); + } + return output; + } + + return Call(cur_op, new_args, pre_call_node->attrs, new_arg_types, pre_call_node->span); + } + + Expr VisitExpr_(const FunctionNode* func) final { + // Erase the ret_type annotation and let the normal pass recalculate + const_cast<FunctionNode*>(func)->ret_type = Type(nullptr); + return ExprMutator::VisitExpr_(func); + } + + Expr VisitExpr_(const LetNode* op) final { + // First convert as much of the bound computation to lower precision as possible + Expr value = this->Mutate(op->value); + + // Then rewrite the var type and associated expression + Var var = Downcast<Var>(this->Mutate(op->var)); + VarNode* mutable_var = const_cast<VarNode*>((op->var).as<VarNode>()); + mutable_var->type_annotation = GetType(value); + mutable_var->checked_type_ = mutable_var->type_annotation; + + // Mutate body last as it may depend on previous results + Expr body = this->Mutate(op->body); + return Let(var, value, body, op->span); + } + + // To access map of ops not registered for error reporting + friend Expr ToMixedPrecision(const Expr& expr, const DataType& mixed_precision_type, + int missing_op_mode); +}; + +Expr ToMixedPrecision(const Expr& expr, const DataType& mixed_precision_type, int missing_op_mode) { + /* + missing_op_mode: + + 0: Does not allow any missing ops. Will throw errors and terminate the pass when encountering any. + 1: Allow missing ops but throw warnings. + 2: Allow missing ops and silently ignore them. + */ + ICHECK(missing_op_mode >= 0 && missing_op_mode <= 2) + << " missing_op_mode must be either 0, 1, or 2 got " << missing_op_mode; + + MixedPrecisionPass converter = MixedPrecisionPass(mixed_precision_type); + auto result = converter.Mutate(expr); + + for (auto it = converter.missing_ops.begin(); + missing_op_mode != 2 && it != converter.missing_ops.end(); it++) { + std::string op_name = it->first; + int appear_count = it->second; + + LOG(WARNING) << "Op \"" << op_name << "\" not registered " + << "FTVMMixedPrecisionConversionType appears " << appear_count << " in graph."; + } + + if (converter.missing_ops.size() != 0 && missing_op_mode == 0) { + CHECK(0) << "Missing ops were found, please fix!"; Review comment: I think this is not supposed to be fixed by end-users. We should direct them to the discuss forum to post what is missing, or provide the tutorial link of AMP pass in the future to help them fix it. cc @areusch ```suggestion CHECK(0) << "Missing ops were found"; ``` ########## File path: src/relay/transforms/to_mixed_precision.cc ########## @@ -0,0 +1,420 @@ +/* + * 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 to_mixed_precision.cc + * \brief Automatic mixed floating point precision for relay graphs. i.e. turn a graph into fp16. + * + */ + +#include <tvm/ir/attrs.h> +#include <tvm/relay/expr_functor.h> +#include <tvm/relay/transform.h> +#include <tvm/runtime/object.h> + +#include <utility> + +#include "pattern_utils.h" + +namespace tvm { +namespace relay { + +// A callable which hashes std::pair +struct pair_hash { + template <class T1, class T2> + std::size_t operator()(const std::pair<T1, T2>& pair) const { + auto h1 = std::hash<T1>()(pair.first); + auto h2 = std::hash<T2>()(pair.second); + + // Use boost's combine_hash strategy + return h1 ^ (h1 + 0x9e3779b9 + (h2 << 6) + (h2 >> 2)); + } +}; + +// MIXED_PRECISION_ALWAYS ops should always be done in lower precision due to the speed and memory +// savings. MIXED_PRECISION_FOLLOW ops can be done in lower precision but don't have speedups to +// justify a cast. MIXED_PRECISION_NEVER colored ops should not be done in lower precision due to +// numerical reasons. +enum MixedTypeConversionCategory : int { + MIXED_PRECISION_ALWAYS = 0, + MIXED_PRECISION_FOLLOW = 1, + MIXED_PRECISION_NEVER = 2 +}; + +// A map of a parent node and a wanted dtype to existing nodes casted to the wanted dtype +using CachedCastNodes = std::unordered_map<std::pair<const ExprNode*, DataType>, Expr, pair_hash>; + +// Return array is of type : [MixedTypeConversionCategory (int), String, String] +// The fields are : [ConversionCategory, accumulation_datatype, output_datatype] +// Call is a call node, DataType is the mixed precision type +using FTVMMixedPrecisionConversionType = runtime::TypedPackedFunc<Array<ObjectRef>( + const Call& call_node, const std::string& target_dtype_str)>; + +class MixedPrecisionPass : public MixedModeMutator { + private: + CachedCastNodes cast_nodes_cache_; + + /*! \brief The target datatype we want to convert to e.g. FP16 */ + const DataType mixed_precision_type; + + // Map of Ops with no associated FTVMMixedPrecisionConversionType to the times they were + // encountered. Used for emitting warnings on missing ops in the pass. + std::unordered_map<std::string, int> missing_ops; Review comment: ```suggestion /*! \brief Map of Ops with no associated FTVMMixedPrecisionConversionType to the times they were * encountered. Used for emitting warnings on missing ops in the pass. */ std::unordered_map<std::string, int> missing_ops_; ``` -- 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. For queries about this service, please contact Infrastructure at: [email protected]
