AndrewZhaoLuo commented on a change in pull request #8069: URL: https://github.com/apache/tvm/pull/8069#discussion_r648609224
########## File path: src/relay/transforms/fp32_to_fp16.cc ########## @@ -0,0 +1,337 @@ +/* + * 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 fp32_to_fp16.cc + * \brief Rewrite a graph into an fp16 form. + */ +#include "fp32_to_fp16.h" + +#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)); + } +}; + +// 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>; + +// A function which maps CallNodes to their initial conversion color +using ColorFunc = std::function<FP16ConversionCategory(const CallNode*)>; + +// A function which maps green CallNodes to wanted accumulation and output dtypes +using OutputDtypeFunc = std::function<FP16OpDType(const CallNode*)>; + +class AmpGraphCreator : public ExprMutator { + private: + CachedCastNodes cast_nodes_cache; + const ColorFunc colorer; + const OutputDtypeFunc output_dtype_func; + + 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 new_attrs = Attrs(call->attrs); + if (new_attrs.get() != nullptr) { + // TODO(AndrewZhaoLuo): Figure out a better way to do this + // modify output_dtype attributes (accumulation dtypes for ops) + if (auto attrs = new_attrs.as<Conv1DAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv1DTransposeAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv2DAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv2DTransposeAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv2DWinogradAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv2DWinogradNNPACKWeightTransformAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<DeformableConv2DAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv3DAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv3DTransposeAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv3DWinogradAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<DenseAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<BatchMatmulAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } + + // modify dtype attributes (creating new tensors of type dtype) + if (auto attrs = new_attrs.as<InitOpAttrs>()) { + ModifyAttrsDType(attrs, accumulation_dtype); + } + } + + return new_attrs; + } + + template <typename T> + void 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. + */ + T* mutable_attrs = const_cast<T*>(attrs); + + DataType cur_type = (mutable_attrs->out_dtype); + if (cur_type.is_float() || cur_type.is_void()) mutable_attrs->out_dtype = accumulation_dtype; + } + + template <typename T> + void 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. + */ + T* mutable_attrs = const_cast<T*>(attrs); + DataType cur_type = (mutable_attrs->dtype); + + if (cur_type.is_float() || cur_type.is_void()) mutable_attrs->dtype = accumulation_dtype; + } + + 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 IsFP16Type(const Type& t, bool ignore_non_float = false) const { + /* Returns whether t is a type with only fp16 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 == DataType::Float(16); + } else if (const TupleTypeNode* tuple_type = t.as<TupleTypeNode>()) { + for (Type t : tuple_type->fields) { + if (!IsFP16Type(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; + } + + const ExprNode* expr_node = expr.as<ExprNode>(); + if (!expr_node) { + LOG(FATAL) << "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 = expr_dtype == wanted_dtype ? expr : 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); + } else { + LOG(FATAL) << "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: + explicit AmpGraphCreator(ColorFunc colorer, OutputDtypeFunc output_dtype_func) + : ExprMutator(), colorer(colorer), output_dtype_func(output_dtype_func) {} + + Expr VisitExpr_(const CallNode* call_node) { + FP16ConversionCategory initial_color = colorer(call_node); + auto new_op = this->Mutate(call_node->op); + + // Mutate arguments to FP16 form first if possible and keep track of whether all floating point + // tensors are in FP16 form already. This is useful for propagating color. + Array<Expr> new_args; + Array<Type> new_arg_types; + bool all_args_fp16_compatible = true; + for (Expr arg : call_node->args) { + Expr new_arg = this->Mutate(arg); + Type new_arg_type = GetType(new_arg); + new_args.push_back(new_arg); + new_arg_types.push_back(new_arg_type); + + if (all_args_fp16_compatible) { + // We can cast Vars and Constants to the right types so don't care about the types. + bool is_fp16_compatible = IsFP16Type(new_arg_type, true) || arg->IsInstance<VarNode>() || + arg->IsInstance<ConstantNode>(); + all_args_fp16_compatible &= is_fp16_compatible; + } + } + + // Determine the final color. + FP16ConversionCategory final_color; + if (initial_color == GRAY) { + final_color = all_args_fp16_compatible ? GREEN : RED; Review comment: An example with concat. We have two branches whose outputs are fed into concat. The first branch has a RED operation and returns an FP32 tensor. The second branch returns an FP16 tensor. Now that I say this, it might be better to be a bit smarter about GRAY ops when we have heterogeneous floating point types coming in. E.g. let's say we had a concat with 10 fp16 args and 1 fp32 arg. It would be wasteful to default convert everything to fp32 and set the color as RED in this case. I will change this so the number of fp16/fp32 args are taken into account. If there is a majority of fp16 or a tie we color GREEN else we color RED. Thoughts? ########## File path: src/relay/transforms/fp32_to_fp16.cc ########## @@ -0,0 +1,337 @@ +/* + * 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 fp32_to_fp16.cc + * \brief Rewrite a graph into an fp16 form. + */ +#include "fp32_to_fp16.h" + +#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)); + } +}; + +// 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>; + +// A function which maps CallNodes to their initial conversion color +using ColorFunc = std::function<FP16ConversionCategory(const CallNode*)>; + +// A function which maps green CallNodes to wanted accumulation and output dtypes +using OutputDtypeFunc = std::function<FP16OpDType(const CallNode*)>; + +class AmpGraphCreator : public ExprMutator { + private: + CachedCastNodes cast_nodes_cache; + const ColorFunc colorer; + const OutputDtypeFunc output_dtype_func; + + 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 new_attrs = Attrs(call->attrs); + if (new_attrs.get() != nullptr) { + // TODO(AndrewZhaoLuo): Figure out a better way to do this + // modify output_dtype attributes (accumulation dtypes for ops) + if (auto attrs = new_attrs.as<Conv1DAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv1DTransposeAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv2DAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv2DTransposeAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv2DWinogradAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv2DWinogradNNPACKWeightTransformAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<DeformableConv2DAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv3DAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv3DTransposeAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<Conv3DWinogradAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<DenseAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } else if (auto attrs = new_attrs.as<BatchMatmulAttrs>()) { + ModifyAttrsOutputDType(attrs, accumulation_dtype); + } + + // modify dtype attributes (creating new tensors of type dtype) + if (auto attrs = new_attrs.as<InitOpAttrs>()) { + ModifyAttrsDType(attrs, accumulation_dtype); + } + } + + return new_attrs; + } + + template <typename T> + void 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. + */ + T* mutable_attrs = const_cast<T*>(attrs); + + DataType cur_type = (mutable_attrs->out_dtype); + if (cur_type.is_float() || cur_type.is_void()) mutable_attrs->out_dtype = accumulation_dtype; + } + + template <typename T> + void 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. + */ + T* mutable_attrs = const_cast<T*>(attrs); + DataType cur_type = (mutable_attrs->dtype); + + if (cur_type.is_float() || cur_type.is_void()) mutable_attrs->dtype = accumulation_dtype; + } + + 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 IsFP16Type(const Type& t, bool ignore_non_float = false) const { + /* Returns whether t is a type with only fp16 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 == DataType::Float(16); + } else if (const TupleTypeNode* tuple_type = t.as<TupleTypeNode>()) { + for (Type t : tuple_type->fields) { + if (!IsFP16Type(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; + } + + const ExprNode* expr_node = expr.as<ExprNode>(); + if (!expr_node) { + LOG(FATAL) << "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 = expr_dtype == wanted_dtype ? expr : 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); + } else { + LOG(FATAL) << "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: + explicit AmpGraphCreator(ColorFunc colorer, OutputDtypeFunc output_dtype_func) + : ExprMutator(), colorer(colorer), output_dtype_func(output_dtype_func) {} + + Expr VisitExpr_(const CallNode* call_node) { + FP16ConversionCategory initial_color = colorer(call_node); + auto new_op = this->Mutate(call_node->op); + + // Mutate arguments to FP16 form first if possible and keep track of whether all floating point + // tensors are in FP16 form already. This is useful for propagating color. + Array<Expr> new_args; + Array<Type> new_arg_types; + bool all_args_fp16_compatible = true; + for (Expr arg : call_node->args) { + Expr new_arg = this->Mutate(arg); + Type new_arg_type = GetType(new_arg); + new_args.push_back(new_arg); + new_arg_types.push_back(new_arg_type); + + if (all_args_fp16_compatible) { + // We can cast Vars and Constants to the right types so don't care about the types. + bool is_fp16_compatible = IsFP16Type(new_arg_type, true) || arg->IsInstance<VarNode>() || + arg->IsInstance<ConstantNode>(); + all_args_fp16_compatible &= is_fp16_compatible; + } + } + + // Determine the final color. + FP16ConversionCategory final_color; + if (initial_color == GRAY) { + final_color = all_args_fp16_compatible ? GREEN : RED; + } else { + final_color = initial_color; + } + + // Create the new arguments to the call. + DataType wanted_arg_dtypes = final_color == GREEN ? DataType::Float(16) : DataType::Float(32); + auto call_args_and_types = CastAllArgs(new_args, new_arg_types, wanted_arg_dtypes); + + Array<Expr> call_args = call_args_and_types.first; + Array<Type> call_arg_types; + + if (call_node->op.as<FunctionNode>()) { + // Function Nodes don't store type info in the Call, it should be a [] + call_arg_types = call_node->type_args; + } else { + call_arg_types = call_args_and_types.second; + } + + // Finally create the new attributes. + if (final_color == GREEN) { + FP16OpDType output_dtypes = output_dtype_func(call_node); + + Attrs new_attrs = GetNewAttrs(call_node, output_dtypes.accumulation_dtype); + Expr output = Call(new_op, call_args, new_attrs, call_arg_types, call_node->span); + if (output_dtypes.accumulation_dtype != output_dtypes.output_dtype) { + output = CastArg(output, GetType(output), output_dtypes.output_dtype); + } + return output; + } else { + return Call(new_op, call_args, call_node->attrs, call_arg_types, call_node->span); + } Review comment: Done -- This is an automated message from the Apache Git Service. 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