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//===- ReleaseModeModelRunner.h - Fast, precompiled model runner ---------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements a model runner wrapping an AOT compiled ML model.
// Only inference is supported.
//
//===----------------------------------------------------------------------===//
#ifndef LLVM_ANALYSIS_RELEASEMODEMODELRUNNER_H
#define LLVM_ANALYSIS_RELEASEMODEMODELRUNNER_H
#include "llvm/ADT/StringExtras.h"
#include "llvm/Analysis/MLModelRunner.h"
#include "llvm/Analysis/TensorSpec.h"
#include "llvm/Support/ErrorHandling.h"
#include "llvm/Support/MD5.h"
#include <memory>
namespace llvm {
/// ReleaseModeModelRunner - production mode implementation of the
/// MLModelRunner. It uses an AOT-compiled SavedModel for efficient execution.
struct EmbeddedModelRunnerOptions {
/// Feed and Fetch feature prefixes - i.e. a feature named "foo" will be
/// looked up as {FeedPrefix}_foo; and the output named "bar" will be looked
/// up as {FetchPrefix}_bar
StringRef FeedPrefix = "feed_";
StringRef FetchPrefix = "fetch_";
/// ModelSelector is the name (recognized by the AOT-ed model) of a sub-model
/// to use. "" is allowed if the model doesn't support sub-models.
StringRef ModelSelector = "";
EmbeddedModelRunnerOptions &setFeedPrefix(StringRef Value) {
FeedPrefix = Value;
return *this;
}
EmbeddedModelRunnerOptions &setFetchPrefix(StringRef Value) {
FetchPrefix = Value;
return *this;
}
EmbeddedModelRunnerOptions &setModelSelector(StringRef Value) {
ModelSelector = Value;
return *this;
}
};
template <class TGen>
class ReleaseModeModelRunner final : public MLModelRunner {
public:
/// FeatureNames' type should be an indexed collection of std::string, like
/// std::array or std::vector, that has a size() method.
template <class FType>
ReleaseModeModelRunner(LLVMContext &Ctx, const FType &InputSpec,
StringRef DecisionName,
const EmbeddedModelRunnerOptions &Options = {})
: MLModelRunner(Ctx, MLModelRunner::Kind::Release, InputSpec.size() + 1),
CompiledModel(std::make_unique<TGen>()) {
assert(CompiledModel && "The CompiledModel should be valid");
// Set up the model_selector past all the InputSpecs in all cases.
// - if the model doesn't have such a feature, but the user requested it,
// we report error. Same if the model supports it but the user didn't
// specify it
// - finally, we compute the MD5 hash of the user input and set the value
// of the model selector to {high, low}
bool InputIsPresent = true;
populateTensor(InputSpec.size(),
TensorSpec::createSpec<uint64_t>("model_selector", {2}),
Options.FeedPrefix, InputIsPresent);
// If we hit the "report an error" cases outlined above, continue with the
// set up in case there's some custom diagnostics handler installed and it
// doesn't promptly exit.
if (Options.ModelSelector.empty() && InputIsPresent)
Ctx.emitError(
"A model selector was not specified but the underlying model "
"requires selecting one because it exposes a model_selector input");
uint64_t High = 0;
uint64_t Low = 0;
if (!Options.ModelSelector.empty()) {
if (!InputIsPresent)
Ctx.emitError("A model selector was specified but the underlying model "
"does not expose a model_selector input");
const auto Hash = MD5::hash(arrayRefFromStringRef(Options.ModelSelector));
High = Hash.high();
Low = Hash.low();
}
getTensor<uint64_t>(InputSpec.size())[0] = High;
getTensor<uint64_t>(InputSpec.size())[1] = Low;
// At this point, the model selector is set up. If the user didn't provide
// one, but the model has a model_selector, it'll be set to (0, 0) which
// the composite model should treat as error as part of its implementation
// (but that should only matter if there is a custom handler that doesn't
// exit on error)
for (size_t I = 0; I < InputSpec.size(); ++I)
populateTensor(I, InputSpec[I], Options.FeedPrefix, InputIsPresent);
ResultIndex = CompiledModel->LookupResultIndex(Options.FetchPrefix.str() +
DecisionName.str());
assert(ResultIndex >= 0 && "Cannot find DecisionName in inlining model");
}
virtual ~ReleaseModeModelRunner() = default;
static bool classof(const MLModelRunner *R) {
return R->getKind() == MLModelRunner::Kind::Release;
}
private:
// fetch the model-provided buffer for the given Spec, or let MLModelRunner
// create a scratch buffer. Indicate back to the caller if the model had that
// input in the first place.
void populateTensor(size_t Pos, const TensorSpec &Spec, StringRef Prefix,
bool &InputIsPresent) {
const int Index =
CompiledModel->LookupArgIndex((Prefix + Spec.name()).str());
void *Buffer = nullptr;
InputIsPresent = Index >= 0;
if (InputIsPresent)
Buffer = CompiledModel->arg_data(Index);
setUpBufferForTensor(Pos, Spec, Buffer);
}
void *evaluateUntyped() override {
CompiledModel->Run();
return CompiledModel->result_data(ResultIndex);
}
int32_t ResultIndex = -1;
std::unique_ptr<TGen> CompiledModel;
};
/// A mock class satisfying the interface expected by ReleaseModeModelRunner for
/// its `TGen` parameter. Useful to avoid conditional compilation complexity, as
/// a compile-time replacement for a real AOT-ed model.
class NoopSavedModelImpl final {
#define NOOP_MODEL_ERRMSG \
"The mock AOT-ed saved model is a compile-time stub and should not be " \
"called."
public:
NoopSavedModelImpl() = default;
int LookupArgIndex(const std::string &) { llvm_unreachable(NOOP_MODEL_ERRMSG); }
int LookupResultIndex(const std::string &) { llvm_unreachable(NOOP_MODEL_ERRMSG); }
void Run() { llvm_unreachable(NOOP_MODEL_ERRMSG); }
void *result_data(int) { llvm_unreachable(NOOP_MODEL_ERRMSG); }
void *arg_data(int) { llvm_unreachable(NOOP_MODEL_ERRMSG); }
#undef NOOP_MODEL_ERRMSG
};
template <class T> bool isEmbeddedModelEvaluatorValid() { return true; }
template <> inline bool isEmbeddedModelEvaluatorValid<NoopSavedModelImpl>() {
return false;
}
} // namespace llvm
#endif // LLVM_ANALYSIS_RELEASEMODEMODELRUNNER_H
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