1 files changed, 144 insertions, 0 deletions
diff --git a/contrib/llvm-project/llvm/lib/Analysis/TensorSpec.cpp b/contrib/llvm-project/llvm/lib/Analysis/TensorSpec.cpp
new file mode 100644
index 000000000000..f6a5882371a7
--- /dev/null
+++ b/contrib/llvm-project/llvm/lib/Analysis/TensorSpec.cpp
@@ -0,0 +1,144 @@
+//===- TensorSpec.cpp - tensor type abstraction ---------------------------===//
+//
+// 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
+//
+//===----------------------------------------------------------------------===//
+//
+// Implementation file for the abstraction of a tensor type, and JSON loading
+// utils.
+//
+//===----------------------------------------------------------------------===//
+#include "llvm/Config/config.h"
+
+#include "llvm/ADT/Twine.h"
+#include "llvm/Analysis/TensorSpec.h"
+#include "llvm/Support/CommandLine.h"
+#include "llvm/Support/Debug.h"
+#include "llvm/Support/JSON.h"
+#include "llvm/Support/ManagedStatic.h"
+#include "llvm/Support/MemoryBuffer.h"
+#include "llvm/Support/Path.h"
+#include "llvm/Support/raw_ostream.h"
+#include <cassert>
+#include <numeric>
+
+using namespace llvm;
+
+namespace llvm {
+
+#define TFUTILS_GETDATATYPE_IMPL(T, E)                                         \
+  template <> TensorType TensorSpec::getDataType<T>() { return TensorType::E; }
+
+SUPPORTED_TENSOR_TYPES(TFUTILS_GETDATATYPE_IMPL)
+
+#undef TFUTILS_GETDATATYPE_IMPL
+
+TensorSpec::TensorSpec(const std::string &Name, int Port, TensorType Type,
+                       size_t ElementSize, const std::vector<int64_t> &Shape)
+    : Name(Name), Port(Port), Type(Type), Shape(Shape),
+      ElementCount(std::accumulate(Shape.begin(), Shape.end(), 1,
+                                   std::multiplies<int64_t>())),
+      ElementSize(ElementSize) {}
+
+Optional<TensorSpec> getTensorSpecFromJSON(LLVMContext &Ctx,
+                                           const json::Value &Value) {
+  auto EmitError = [&](const llvm::Twine &Message) -> Optional<TensorSpec> {
+    std::string S;
+    llvm::raw_string_ostream OS(S);
+    OS << Value;
+    Ctx.emitError("Unable to parse JSON Value as spec (" + Message + "): " + S);
+    return None;
+  };
+  // FIXME: accept a Path as a parameter, and use it for error reporting.
+  json::Path::Root Root("tensor_spec");
+  json::ObjectMapper Mapper(Value, Root);
+  if (!Mapper)
+    return EmitError("Value is not a dict");
+
+  std::string TensorName;
+  int TensorPort = -1;
+  std::string TensorType;
+  std::vector<int64_t> TensorShape;
+
+  if (!Mapper.map<std::string>("name", TensorName))
+    return EmitError("'name' property not present or not a string");
+  if (!Mapper.map<std::string>("type", TensorType))
+    return EmitError("'type' property not present or not a string");
+  if (!Mapper.map<int>("port", TensorPort))
+    return EmitError("'port' property not present or not an int");
+  if (!Mapper.map<std::vector<int64_t>>("shape", TensorShape))
+    return EmitError("'shape' property not present or not an int array");
+
+#define PARSE_TYPE(T, E)                                                       \
+  if (TensorType == #T)                                                        \
+    return TensorSpec::createSpec<T>(TensorName, TensorShape, TensorPort);
+  SUPPORTED_TENSOR_TYPES(PARSE_TYPE)
+#undef PARSE_TYPE
+  return None;
+}
+
+Optional<std::vector<LoggedFeatureSpec>>
+loadOutputSpecs(LLVMContext &Ctx, StringRef ExpectedDecisionName,
+                StringRef ModelPath, StringRef SpecFileOverride) {
+  SmallVector<char, 128> OutputSpecsPath;
+  StringRef FileName = SpecFileOverride;
+  if (FileName.empty()) {
+    llvm::sys::path::append(OutputSpecsPath, ModelPath, "output_spec.json");
+    FileName = {OutputSpecsPath.data(), OutputSpecsPath.size()};
+  }
+
+  auto BufferOrError = MemoryBuffer::getFileOrSTDIN(FileName);
+  if (!BufferOrError) {
+    Ctx.emitError("Error opening output specs file: " + FileName + " : " +
+                  BufferOrError.getError().message());
+    return None;
+  }
+  auto ParsedJSONValues = json::parse(BufferOrError.get()->getBuffer());
+  if (!ParsedJSONValues) {
+    Ctx.emitError("Could not parse specs file: " + FileName);
+    return None;
+  }
+  auto ValuesArray = ParsedJSONValues->getAsArray();
+  if (!ValuesArray) {
+    Ctx.emitError("Expected an array of {tensor_spec:<TensorSpec>, "
+                  "logging_name:<name>} dictionaries");
+    return None;
+  }
+  std::vector<LoggedFeatureSpec> Ret;
+  for (const auto &Value : *ValuesArray)
+    if (const auto *Obj = Value.getAsObject())
+      if (const auto *SpecPart = Obj->get("tensor_spec"))
+        if (auto TensorSpec = getTensorSpecFromJSON(Ctx, *SpecPart))
+          if (auto LoggingName = Obj->getString("logging_name")) {
+            if (!TensorSpec->isElementType<int64_t>() &&
+                !TensorSpec->isElementType<int32_t>() &&
+                !TensorSpec->isElementType<float>()) {
+              Ctx.emitError(
+                  "Only int64, int32, and float tensors are supported. "
+                  "Found unsupported type for tensor named " +
+                  TensorSpec->name());
+              return None;
+            }
+            Ret.push_back({*TensorSpec, LoggingName->str()});
+          }
+
+  if (ValuesArray->size() != Ret.size()) {
+    Ctx.emitError(
+        "Unable to parse output spec. It should be a json file containing an "
+        "array of dictionaries. Each dictionary must have a 'tensor_spec' key, "
+        "with a json object describing a TensorSpec; and a 'logging_name' key, "
+        "which is a string to use as name when logging this tensor in the "
+        "training log.");
+    return None;
+  }
+  if (Ret.empty() || *Ret[0].LoggingName != ExpectedDecisionName) {
+    Ctx.emitError("The first output spec must describe the decision tensor, "
+                  "and must have the logging_name " +
+                  StringRef(ExpectedDecisionName));
+    return None;
+  }
+  return Ret;
+}
+} // namespace llvm