Resources and Error Resolvers

[siddhantprateek]

To resolve serverless-dynamodb-local dependency: https://github.com/99x/serverless-dynamodb-local/issues/294

[Anindyadeep]

Can I build the nest for my bird like this ...

// Package cluster provides local access to cluster-level metadata
/*
 * Copyright (c) 2018-2023, NVIDIA CORPORATION. All rights reserved.
 */
package cluster

import (
    "context"
    "io"

    "github.com/NVIDIA/aistore/api/apc"
    "github.com/NVIDIA/aistore/cluster/meta"
    "github.com/NVIDIA/aistore/cmn"
    "github.com/NVIDIA/aistore/cmn/cos"
)

type (
    GetReaderResult struct {
        R        io.ReadCloser
        Err      error
        ExpCksum *cos.Cksum
        Size     int64
        ErrCode  int
    }

    BackendProvider interface {
        Provider() string
        MaxPageSize() uint
        CreateBucket(bck *meta.Bck) (errCode int, err error)
        ListObjects(bck *meta.Bck, msg *apc.LsoMsg, lst *cmn.LsoResult) (errCode int, err error)
        ListBuckets(qbck cmn.QueryBcks) (bcks cmn.Bcks, errCode int, err error)
        PutObj(r io.ReadCloser, lom *LOM) (errCode int, err error)
        DeleteObj(lom *LOM) (errCode int, err error)

        // with context
        HeadBucket(ctx context.Context, bck *meta.Bck) (bckProps cos.StrKVs, errCode int, err error)
        HeadObj(ctx context.Context, lom *LOM) (objAttrs *cmn.ObjAttrs, errCode int, err error)
        GetObj(ctx context.Context, lom *LOM, owt cmn.OWT) (errCode int, err error)
        GetObjReader(ctx context.Context, lom *LOM) GetReaderResult
    }
)

Or may be something like this

#ifndef TENSORFLOW_COMPILER_MLIR_XLA_HLO_FUNCTION_IMPORTER_H_
#define TENSORFLOW_COMPILER_MLIR_XLA_HLO_FUNCTION_IMPORTER_H_

#include <unordered_map>

#include "absl/types/optional.h"
#include "mlir/IR/Attributes.h"  // TF:llvm-project
#include "mlir/IR/Builders.h"  // TF:llvm-project
#include "mlir/IR/Function.h"  // TF:llvm-project
#include "mlir/IR/MLIRContext.h"  // TF:llvm-project
#include "mlir/IR/Module.h"  // TF:llvm-project
#include "mlir/IR/StandardTypes.h"  // TF:llvm-project
#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"
#include "tensorflow/compiler/mlir/xla/ir/hlo_ops.h"
#include "tensorflow/compiler/xla/status.h"
#include "tensorflow/compiler/xla/statusor.h"
#include "tensorflow/compiler/xla/xla_data.pb.h"
#include "tensorflow/core/platform/types.h"

namespace xla {

class HloModule;
class HloComputation;
class HloInstruction;
class Shape;

// Helper class for importing HloComputations.
class HloFunctionImporter {
 public:
  static StatusOr<mlir::FuncOp> ImportFunction(
      mlir::ModuleOp module, mlir::Builder* builder,
      std::unordered_map<xla::HloComputation*, mlir::FuncOp>* function_map,
      xla::HloComputation* computation);

 private:
  HloFunctionImporter(
      mlir::ModuleOp module, mlir::Builder* builder,
      std::unordered_map<xla::HloComputation*, mlir::FuncOp>* function_map)
      : context_(module.getContext()),
        module_(module),
        builder_(builder),
        function_map_(function_map) {}

  StatusOr<mlir::FuncOp> ImportFunction(xla::HloComputation* computation);

  // Imports the given computation in the specified region.
  tensorflow::Status ImportComputation(HloComputation* computation,
                                       mlir::Region* region);

  // Imports instructions from the given computation in the specified block.
  // Assumes that the block already has correct arguments populated.
  tensorflow::Status ImportInstructions(HloComputation* computation,
                                        mlir::Block* block);

  // Imports an instruction.
  StatusOr<mlir::Operation*> ImportInstruction(xla::HloInstruction* instruction,
                                               mlir::OpBuilder* func_builder);

  // Gets the MLIR operand values from an HLO Instruction.
  StatusOr<llvm::SmallVector<mlir::Value, 4>> GetOperands(
      xla::HloInstruction* instruction);

  // Converts xla Tensor type to the corresponding MLIR type.
  StatusOr<mlir::RankedTensorType> ConvertTensorType(const xla::Shape& shape);

  // Returns the output type of an HloInstruction.
  StatusOr<mlir::Type> GetReturnType(xla::HloInstruction* instruction);

  // Takes a list of HloInstructions and generates the list of types used for
  // input, bypassing tuples to subsets.
  Status GetMlirTypes(const std::vector<xla::HloInstruction*>& instructions,
                      llvm::SmallVectorImpl<mlir::Type>* types);

  // Returns the Mlir Value for the corresponding HloInstruction.
  StatusOr<mlir::Value> GetMlirValue(xla::HloInstruction* instruction);

  // Converts an XLA PrecisionConfig to the corresponding MLIR attribute.
  mlir::NamedAttribute ConvertPrecisionConfig(xla::HloInstruction* instruction);

  // Converts an XLA ComparisonDirection to the corresponding MLIR attribute.
  mlir::NamedAttribute ConvertComparisonDirection(
      xla::HloInstruction* instruction);

  // Converts the dimensions of an HLO instruction into an MLIR attribute.
  mlir::DenseIntElementsAttr ConvertDimensions(
      llvm::ArrayRef<tensorflow::int64> op_dimensions);

  // Converts Array ref to an DenseIntElementsAttr.
  mlir::DenseIntElementsAttr Convert(llvm::ArrayRef<int64_t> op_dimensions);

  // Converts Array ref to padding attribute. Input is a flattened list of
  // padding low and padding high for each of the spatial dimensions.
  mlir::NamedAttribute ConvertPadding(llvm::ArrayRef<int64_t> padding);

  // Converts the dot dimensions to attribute.
  mlir::NamedAttribute ConvertDotDimensionNumbers(
      const DotDimensionNumbers& dnums);

  // Converts the conv dimensions to attributes.
  mlir::NamedAttribute ConvertConvDimensionNumbers(
      const xla::ConvolutionDimensionNumbers& dnums);

  // Converts the gather dimensions to attributes.
  mlir::NamedAttribute ConvertGatherDimensionNumbers(
      const xla::GatherDimensionNumbers& dnums);

  // Converts the scatter dimensions to attributes.
  mlir::NamedAttribute ConvertScatterDimensionNumbers(
      const xla::ScatterDimensionNumbers& dnums);

  // Converts replica groups to attribute
  mlir::NamedAttribute ConvertReplicaGroups(
      const std::vector<ReplicaGroup>& replica_groups);

  // Converts channel id to attribute
  mlir::NamedAttribute ConvertChannelHandle(
      absl::optional<tensorflow::int64> channel_id);

  // Converts channel handle to attribute
  mlir::NamedAttribute ConvertChannelHandle(const xla::ChannelHandle& channel);

  // Converts XLA instruction source target pairs to MLIR attribute.
  mlir::NamedAttribute ConvertSourceTargetPairs(
      const std::vector<std::pair<tensorflow::int64, tensorflow::int64>>&
          source_target_pairs);

  mlir::MLIRContext* context_;
  mlir::ModuleOp module_;
  mlir::Builder* builder_;

  // Mapping from HloComputation to the created MLIR function.
  std::unordered_map<xla::HloComputation*, mlir::FuncOp>* function_map_;

  // Mapping from HloInstructions to the associative MLIR values.
  std::unordered_map<xla::HloInstruction*, mlir::Value> instruction_value_map_;
};

}  // namespace xla

#endif  // TENSORFLOW_COMPILER_MLIR_XLA_HLO_FUNCTION_IMPORTER_H_

lul

[siddhantprateek]

@Anindyadeep Interesting feature request, we can have separate issue and discussion on this.

[Anindyadeep]

No No that does not make sense, I need it here rn

[siddhantprateek]

@Anindyadeep you can go ahead and open a new issue on this. Feel free to guide yourself and I have added some resources that may help.

Resources

How to create Github Issue

[Anindyadeep]

Okay I am making a PR