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#include <torch/csrc/jit/passes/symbolic_shape_analysis.h>
#include <torch/csrc/jit/passes/symbolic_shape_cache.h>
#include <torch/csrc/lazy/core/cache.h>
// SHAPE CACHINHG CODE
namespace torch {
namespace jit {
namespace {
using CanonicalArg = c10::variant<CanonicalizedSymbolicShape, IValue>;
using CanonicalArgVec = std::vector<CanonicalArg>;
using CanonicalRet = std::vector<CanonicalizedSymbolicShape>;
using ShapeCacheKey = std::tuple<c10::OperatorName, CanonicalArgVec>;
CanonicalArgVec cannonicalizeVec(
const std::vector<SSAInput>& arg_vec,
std::unordered_map<int64_t, int64_t>& ss_map,
bool deep_copy = true) {
CanonicalArgVec canonical_args;
canonical_args.reserve(arg_vec.size());
for (auto& arg : arg_vec) {
if (const IValue* iv = c10::get_if<IValue>(&arg)) {
if (deep_copy) {
canonical_args.push_back(iv->deepcopy());
} else {
canonical_args.push_back(*iv);
}
} else {
auto& ss = c10::get<at::SymbolicShape>(arg);
canonical_args.emplace_back(CanonicalizedSymbolicShape(ss, ss_map));
}
}
return canonical_args;
}
std::vector<CanonicalizedSymbolicShape> cannonicalizeVec(
const std::vector<at::SymbolicShape>& ret_vec,
std::unordered_map<int64_t, int64_t>& ss_map) {
std::vector<CanonicalizedSymbolicShape> canonical_rets;
canonical_rets.reserve(ret_vec.size());
for (auto& ss : ret_vec) {
canonical_rets.emplace_back(CanonicalizedSymbolicShape(ss, ss_map));
}
return canonical_rets;
}
struct ArgumentsHasher {
size_t operator()(const ShapeCacheKey& cacheKey) const {
// TODO: ignore arguments that are not used in shape function (not needed
// initially)
auto& op_name = std::get<0>(cacheKey);
auto& arg_vec = std::get<1>(cacheKey);
size_t hash_val = c10::hash<c10::OperatorName>()(op_name);
hash_val = at::hash_combine(std::hash<size_t>{}(arg_vec.size()), hash_val);
for (const CanonicalArg& arg : arg_vec) {
size_t cur_arg = 0;
if (const IValue* ival = c10::get_if<IValue>(&arg)) {
// IValue doesn't hash List (as Python doesn't), so we will do a custom
// list hash
if (ival->isList()) {
TORCH_INTERNAL_ASSERT(ival->isIntList(), "Unexpected Args in List");
cur_arg = ival->toListRef().size();
for (const IValue& elem_ival : ival->toListRef()) {
cur_arg = at::hash_combine(cur_arg, IValue::hash(elem_ival));
}
} else {
cur_arg = IValue::hash(ival);
}
} else {
cur_arg = c10::get<CanonicalizedSymbolicShape>(arg).hash();
}
hash_val = at::hash_combine(hash_val, cur_arg);
}
return hash_val;
}
};
using ShapeCache = lazy::Cache<
ShapeCacheKey,
std::vector<CanonicalizedSymbolicShape>,
ArgumentsHasher>;
constexpr size_t kShapeCacheSize = 1024;
ShapeCache shapeCache(kShapeCacheSize);
ShapeCacheKey get_cache_key(
const FunctionSchema* schema,
const std::vector<SSAInput>& arg_vec,
std::unordered_map<int64_t, int64_t>& ss_map,
bool deep_copy = true) {
CanonicalArgVec canonical_args = cannonicalizeVec(arg_vec, ss_map, deep_copy);
return std::make_tuple(schema->operator_name(), canonical_args);
}
} // namespace
TORCH_API void cache_shape_function(
const FunctionSchema* schema,
const std::vector<SSAInput>& arg_vec,
const std::vector<at::SymbolicShape>& ret_vec) {
// TODO: compare perf using std::vector<std::tuple<int64_t, int64_t>>
auto ss_map = std::unordered_map<int64_t, int64_t>();
auto cache_key = get_cache_key(schema, arg_vec, ss_map, /* deep_copy */ true);
auto can_ret_vec = std::make_shared<std::vector<CanonicalizedSymbolicShape>>(
cannonicalizeVec(ret_vec, ss_map));
shapeCache.Add(cache_key, can_ret_vec);
}
TORCH_API c10::optional<std::vector<at::SymbolicShape>>
get_cached_shape_function(
const FunctionSchema* schema,
const std::vector<SSAInput>& arg_vec) {
// TODO: compare perf using std::vector<std::tuple<int64_t, int64_t>> for both
// ss_map and inverse_ss_map
auto ss_map = std::unordered_map<int64_t, int64_t>();
auto cache_key =
get_cache_key(schema, arg_vec, ss_map, /* deep_copy */ false);
auto cached_ret_vec = shapeCache.Get(cache_key);
if (cached_ret_vec == nullptr) {
return c10::nullopt;
}
// Decanonicalize the return values
auto inverse_ss_map = std::unordered_map<int64_t, int64_t>();
for (auto& ss_val : ss_map) {
inverse_ss_map[ss_val.second] = ss_val.first;
}
std::vector<at::SymbolicShape> ret_vec;
for (auto& css : *cached_ret_vec) {
ret_vec.emplace_back(css.toSymbolicShape(inverse_ss_map));
}
return ret_vec;
}
// Function only to access the cache, used for testing
TORCH_API void clear_shape_cache() {
shapeCache.Clear();
}
TORCH_API size_t get_shape_cache_size() {
return shapeCache.Numel();
}
void CanonicalizedSymbolicShape::init(
const c10::SymbolicShape& orig_shape,
std::unordered_map<int64_t, int64_t>& ss_map) {
auto sizes = orig_shape.sizes();
if (!sizes) {
values_ = c10::nullopt;
return;
}
values_ = std::vector<int64_t>();
int64_t cur_symbolic_index = -static_cast<int64_t>(ss_map.size()) - 1;
for (auto& cur_shape : *sizes) {
if (cur_shape.is_static()) {
values_->push_back(cur_shape.static_size());
} else {
// Check for aliasing
auto it = ss_map.find(cur_shape.value());
if (it == ss_map.end()) {
values_->push_back(cur_symbolic_index);
ss_map.insert({cur_shape.value(), cur_symbolic_index});
cur_symbolic_index--;
} else {
values_->push_back(it->second);
}
}
}
}
c10::SymbolicShape CanonicalizedSymbolicShape::toSymbolicShape(
std::unordered_map<int64_t, int64_t>& inverse_ss_map) const {
if (!values_.has_value()) {
return c10::SymbolicShape();
}
std::vector<at::ShapeSymbol> sizes;
for (long long cur_val : *values_) {
if (cur_val >= 0) {
sizes.push_back(at::ShapeSymbol::fromStaticSize(cur_val));
continue;
}
auto res = inverse_ss_map.find(cur_val);
if (res != inverse_ss_map.end()) {
sizes.push_back(at::ShapeSymbol::fromStaticSize(res->second));
} else {
auto new_symbol = at::ShapeSymbol::newSymbol();
inverse_ss_map.insert({cur_val, new_symbol.value()});
sizes.push_back(new_symbol);
}
}
return c10::SymbolicShape(std::move(sizes));
}
size_t CanonicalizedSymbolicShape::hash() const {
if (!values_.has_value()) {
return 0x8cc80c80; // random value to prevent hash collisions
}
return c10::hash<std::vector<int64_t>>()(values_.value());
}
bool operator==(
const CanonicalizedSymbolicShape& a,
const CanonicalizedSymbolicShape& b) {
return a.values_ == b.values_;
};
} // namespace jit
} // namespace torch
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