/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/jit/tensorexpr
NameSizeModeActions
operators/-0755rm
analysis.h58880644editdlrm
block_codegen.h42110644editdlrm
bounds_inference.h22300644editdlrm
bounds_overlap.h33290644editdlrm
codegen.h64020644editdlrm
cpp_codegen.h22780644editdlrm
cpp_intrinsics.h7190644editdlrm
cuda_codegen.h77820644editdlrm
cuda_random.h26420644editdlrm
dim_arg.h8840644editdlrm
eval.h96390644editdlrm
exceptions.h32530644editdlrm
expr.h115880644editdlrm
external_functions.h12740644editdlrm
external_functions_registry.h23430644editdlrm
fwd_decls.h28060644editdlrm
graph_opt.h25530644editdlrm
half_support.h50380644editdlrm
hash_provider.h79300644editdlrm
intrinsic_symbols.h4200644editdlrm
ir.h226220644editdlrm
ir_cloner.h20690644editdlrm
ir_mutator.h20100644editdlrm
ir_printer.h36930644editdlrm
ir_simplifier.h150900644editdlrm
ir_verifier.h12400644editdlrm
ir_visitor.h18250644editdlrm
kernel.h92100644editdlrm
llvm_codegen.h31800644editdlrm
llvm_jit.h19650644editdlrm
loopnest.h215990644editdlrm
mem_dependency_checker.h130030644editdlrm
reduction.h67420644editdlrm
registerizer.h124980644editdlrm
stmt.h211380644editdlrm
tensor.h76400644editdlrm
tensorexpr_init.h2680644editdlrm
types.h38800644editdlrm
unique_name_manager.h9400644editdlrm
var_substitutor.h17530644editdlrm
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/jit/tensorexpr/hash_provider.h (7930B)
#pragma once #include #include #include #include namespace torch { namespace jit { namespace tensorexpr { struct TORCH_API SimplifierHashType { SimplifierHashType() = default; explicit SimplifierHashType(size_t s) : _h(s) {} bool operator==(const SimplifierHashType& other) const; bool operator!=(const SimplifierHashType& other) const; bool operator<(const SimplifierHashType& other) const; bool operator==(const size_t other) const; bool operator!=(const size_t other) const; size_t _h{0}; }; } // namespace tensorexpr } // namespace jit } // namespace torch namespace std { template <> struct hash { size_t operator()(const torch::jit::tensorexpr::SimplifierHashType& k) const { return k._h; } }; } // namespace std namespace torch { namespace jit { namespace tensorexpr { #define CACHE_GUARD() \ if (cachedHash(v)) { \ return; \ } class Term; class Polynomial; /* Expression hasher providing comparable values representing sub-exprs. * Uses memoization to avoid excessive recursion. */ class TORCH_API HashProvider : public IRVisitor { public: template SimplifierHashType hash(T e) { // NOLINTNEXTLINE(clang-analyzer-core.CallAndMessage) e->accept(this); return hashOf(e); } bool cachedHash(ExprPtr e) { return exprToHash_.find(e) != exprToHash_.end(); } bool cachedHash(StmtPtr s) { return stmtToHash_.find(s) != stmtToHash_.end(); } void clearCache() { exprToHash_.clear(); stmtToHash_.clear(); } void visit(AddPtr v) override; void visit(SubPtr v) override; void visit(MulPtr v) override; void visit(DivPtr v) override; void visit(ModPtr v) override; void visit(RoundOffPtr v) override; void visit(MaxPtr v) override; void visit(MinPtr v) override; void visit(AndPtr v) override; void visit(OrPtr v) override; void visit(XorPtr v) override; void visit(LshiftPtr v) override; void visit(RshiftPtr v) override; void visit(CompareSelectPtr v) override; // NOLINTNEXTLINE #define IMM_VISIT(Type, Name) \ void visit(Name##ImmPtr v) override { \ CACHE_GUARD(); \ putHash(v, hash_combine(#Name, v->value())); \ } AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_VISIT); #undef IMM_VISIT void visit(CastPtr v) override; void visit(VarPtr v) override; void visit(RampPtr v) override; void visit(LoadPtr v) override; void visit(StorePtr v) override; void visit(BlockPtr v) override; void visit(ForPtr v) override; void visit(BroadcastPtr v) override; void visit(IfThenElsePtr v) override; void visit(IntrinsicsPtr v) override; void visit(AllocatePtr v) override; void visit(FreePtr v) override; void visit(CondPtr v) override; void visit(TermPtr v) override; void visit(PolynomialPtr v) override; void visit(MaxTermPtr v) override; void visit(MinTermPtr v) override; template SimplifierHashType hash_combine(const Types&... args) { SimplifierHashType seed; _hash_combine(seed, args...); return seed; } private: SimplifierHashType hashOf(ExprPtr e) { auto it = exprToHash_.find(e); if (it != exprToHash_.end()) { return it->second; } // As a failsafe fall back to IRPrinter. std::stringstream ss; IRPrinter printer(ss); e->accept(&printer); SimplifierHashType hash = SimplifierHashType(te_hash(ss.str())); putHash(e, hash); return hash; } SimplifierHashType hashOf(StmtPtr s) { auto it = stmtToHash_.find(s); if (it != stmtToHash_.end()) { return it->second; } // As a failsafe fall back to IRPrinter. std::stringstream ss; IRPrinter printer(ss); s->accept(&printer); SimplifierHashType hash = SimplifierHashType(te_hash(ss.str())); putHash(s, hash); return hash; } // Hash funcs for various types, numbers are random. template void _hash_combine(SimplifierHashType& seed, const T& val) { seed._h ^= te_hash(val) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4); } void _hash_combine(SimplifierHashType& seed, const char* val) { seed._h ^= te_hash(val) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4); } // at:::Half doesn't have a prime_number_hash, so cast to short. void _hash_combine(SimplifierHashType& seed, const at::Half& val) { seed._h ^= te_hash((uint16_t)val) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4); } void _hash_combine(SimplifierHashType& seed, const Dtype& val) { seed._h ^= te_hash(val.ToCppString()) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4); } void _hash_combine(SimplifierHashType& seed, ExprPtr e) { _hash_combine(seed, hash(e)); } template void _hash_combine( SimplifierHashType& seed, const T& val, const Types&... args) { _hash_combine(seed, val); _hash_combine(seed, args...); } void putHash(ExprPtr e, SimplifierHashType h) { auto res = exprToHash_.emplace(e, h); if (res.second == false) { // This is always a logic bug since we should check the cache first. throw std::runtime_error("hash collision"); } } void putHash(StmtPtr s, SimplifierHashType h) { auto res = stmtToHash_.emplace(s, h); if (res.second == false) { // This is always a logic bug since we should check the cache first. throw std::runtime_error("hash collision"); } } std::unordered_map exprToHash_; std::unordered_map stmtToHash_; UniqueNameManager name_manager_; size_t te_hash(SimplifierHashType val) { return val._h; } size_t te_hash(int64_t val) { // put the thing down. size_t h = val ^ 0x647AA4D20C0B; // bit flip it. size_t h2 = ~h; // and reverse byte order. size_t h3 = 0; for (unsigned int i = 0; i < 64; i += 8) { h3 |= ((h2 >> i) & 0xFF) << (64 - i - 8); } return h3; } size_t te_hash(int32_t val) { int64_t v2 = val; return te_hash(v2); } size_t te_hash(uint32_t val) { int64_t v2 = val; return te_hash(v2); } size_t te_hash(uint64_t val) { int64_t v2 = val; return te_hash(v2); } size_t te_hash(int16_t val) { int64_t v2 = val; return te_hash(v2); } size_t te_hash(std::string val) { size_t hash{0}; int64_t intval{0}; int64_t s = val.size() - 1; while (s >= 0) { for (unsigned int i = 0; i < 8; ++i) { if (s < 0) break; // NOLINTNEXTLINE(bugprone-signed-char-misuse) int64_t c = val.data()[s]; intval |= (c << (i * 8)); s--; } hash ^= te_hash(intval); intval = 0; } return hash; } size_t te_hash(double d) { // memcpy as type punning. Should be optimized out. // NOLINTNEXTLINE(cppcoreguidelines-init-variables) int64_t n; std::memcpy(&n, &d, sizeof d); return te_hash(n); } size_t te_hash(float d) { // memcpy as type punning. Should be optimized out. // NOLINTNEXTLINE(cppcoreguidelines-init-variables) int32_t n; std::memcpy(&n, &d, sizeof d); return te_hash(n); } size_t te_hash(at::Half d) { // memcpy as type punning. Should be optimized out. // NOLINTNEXTLINE(cppcoreguidelines-init-variables) int16_t n; std::memcpy(&n, &d, sizeof d); return te_hash(n); } size_t te_hash(at::BFloat16 d) { // memcpy as type punning. Should be optimized out. // NOLINTNEXTLINE(cppcoreguidelines-init-variables) int16_t n; std::memcpy(&n, &d, sizeof d); return te_hash(n); } }; } // namespace tensorexpr } // namespace jit } // namespace torch