/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include <iterator>
#include <utility>

#include "stablehlo/dialect/StablehloOps.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project
#include "mlir/Dialect/Traits.h"  // from @llvm-project
#include "mlir/IR/Attributes.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project
#include "mlir/IR/Diagnostics.h"  // from @llvm-project
#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/IR/Types.h"  // from @llvm-project
#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassRegistry.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/utils/validators.h"
#include "tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.h"

namespace mlir {
namespace odml {

#define GEN_PASS_DEF_STABLEHLOFUSECONVOLUTIONPASS
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/stablehlo_passes.h.inc"

namespace {

class FuseStablehloMulAndConvolutionPattern
    : public OpRewritePattern<stablehlo::MulOp> {
 public:
  using OpRewritePattern<stablehlo::MulOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(stablehlo::MulOp mul_op,
                                PatternRewriter &rewriter) const override {
    // Variables for capturing values and attributes used while creating ops.
    stablehlo::ConvolutionOp conv_op;
    Operation *bcast_or_const_op;
    shape::ShapeOfOp shape_of_op;
    stablehlo::ConstantOp filter;
    stablehlo::ConstantOp multiplier;
    mlir::ElementsAttr filter_value, mul_value;
    mlir::DenseI64ArrayAttr broadcast_dims;

    // Match and capture values/attributes.
    Value lhs = mul_op.getLhs();
    Value rhs = mul_op.getRhs();
    conv_op = lhs.getDefiningOp<stablehlo::ConvolutionOp>();
    if (conv_op == nullptr) {
      return failure();
    }
    filter = conv_op.getRhs().getDefiningOp<stablehlo::ConstantOp>();
    if (filter == nullptr) {
      return failure();
    }
    // Try to match static broadcast or dynamic broadcast.
    bcast_or_const_op = rhs.getDefiningOp();
    bool is_dynamic_broadcast =
        isa<stablehlo::DynamicBroadcastInDimOp>(bcast_or_const_op);
    multiplier =
        isa<stablehlo::ConstantOp>(bcast_or_const_op)
            ? dyn_cast_or_null<stablehlo::ConstantOp>(bcast_or_const_op)
            : bcast_or_const_op->getOperand(0)
                  .getDefiningOp<stablehlo::ConstantOp>();
    if (multiplier == nullptr) {
      return failure();
    }

    auto result_type = OpTrait::util::getBroadcastedType(filter.getType(),
                                                         multiplier.getType());
    if (!result_type) {
      return rewriter.notifyMatchFailure(mul_op, [&](::mlir::Diagnostic &diag) {
        diag << "entities 'filter, multiplier' failed to satisfy constraint: "
                "non-broadcastable operands";
      });
    }
    filter_value = filter.getValue();
    mul_value = multiplier.getValue();
    // In StableHLO, Conv filter is in HWIO format, Depthwise conv filter is in
    // HW1O format and backprop input conv filter is in HWOI format. Only fuses
    // multiplier if all dimensions other than the out channel dimension are
    // equal to 1.
    if (!TFL::IsDimensionsDegenerateExceptLastOne(
            mul_value.getShapedType().getShape())) {
      return rewriter.notifyMatchFailure(mul_op, [&](::mlir::Diagnostic &diag) {
        diag << "entities 'mul_value' failed to satisfy constraint: "
                "unsupported dimensions";
      });
    }
    if (!is_dynamic_broadcast &&
        !((*conv_op.getODSResults(0).begin()).hasOneUse())) {
      return rewriter.notifyMatchFailure(mul_op, [&](::mlir::Diagnostic &diag) {
        diag << "entities 'conv' failed to satisfy constraint: has one use";
      });
    }
    // For dynamic case, the result of conv should be used by shape_of and mul.
    if (is_dynamic_broadcast) {
      auto conv_uses = (*conv_op.getODSResults(0).begin()).getUses();
      if (std::distance(conv_uses.begin(), conv_uses.end()) != 2 ||
          quant::FindUserOfType<shape::ShapeOfOp>(conv_op) == nullptr ||
          quant::FindUserOfType<stablehlo::MulOp>(conv_op) == nullptr) {
        return rewriter.notifyMatchFailure(mul_op, [&](::mlir::Diagnostic
                                                           &diag) {
          diag << "entities 'conv' failed to satisfy constraint: has two uses "
                  "for dynamic case";
        });
      }
    }

    // Rewrite
    // For dynamic case, we use filter's shape to create a static broadcast.
    broadcast_dims =
        !isa<stablehlo::ConstantOp>(bcast_or_const_op) && !is_dynamic_broadcast
            ? DenseI64ArrayAttr::get(
                  rewriter.getContext(),
                  dyn_cast_or_null<stablehlo::BroadcastInDimOp>(
                      bcast_or_const_op)
                      .getBroadcastDimensions())
            : nullptr;
    if (broadcast_dims == nullptr) {
      const auto filter_rank = filter_value.getShapedType().getRank();
      broadcast_dims =
          DenseI64ArrayAttr::get(rewriter.getContext(), {filter_rank - 1});
    }
    Value broadcast_multiplier = rewriter.create<stablehlo::BroadcastInDimOp>(
        mul_op.getLoc(), filter.getType(), multiplier, broadcast_dims);
    Value new_filter = rewriter.create<stablehlo::MulOp>(
        mul_op.getLoc(), filter.getType(), filter, broadcast_multiplier);
    Value new_conv = rewriter.create<stablehlo::ConvolutionOp>(
        mul_op.getLoc(), conv_op.getType(), conv_op.getLhs(), new_filter,
        conv_op.getWindowStridesAttr(), conv_op.getPaddingAttr(),
        conv_op.getLhsDilationAttr(), conv_op.getRhsDilationAttr(),
        conv_op.getWindowReversalAttr(), conv_op.getDimensionNumbers(),
        conv_op.getFeatureGroupCount(), conv_op.getBatchGroupCount(),
        conv_op.getPrecisionConfigAttr());
    // For static case, replace the convolution op now.
    if (!is_dynamic_broadcast) {
      rewriter.replaceOp(mul_op, {new_conv});
    } else {
      // For dynamic case, create new shape_of op and replace uses.
      shape_of_op = dyn_cast_or_null<stablehlo::DynamicBroadcastInDimOp>(
                        bcast_or_const_op)
                        .getOutputDimensions()
                        .getDefiningOp<shape::ShapeOfOp>();
      // Check if the shape come from the original conv op.
      if (!shape_of_op ||
          shape_of_op.getArg().getDefiningOp<stablehlo::ConvolutionOp>() !=
              conv_op) {
        return failure();
      }
      Value new_shape_of = rewriter.create<shape::ShapeOfOp>(
          mul_op.getLoc(), shape_of_op.getType(), new_conv);
      shape_of_op.replaceAllUsesWith(new_shape_of);
      rewriter.replaceOp(mul_op, {new_conv});
    }

    return success();
  }
};

class StablehloFuseConvolutionPass
    : public impl::StablehloFuseConvolutionPassBase<
          StablehloFuseConvolutionPass> {
 public:
  void runOnOperation() override {
    RewritePatternSet patterns(&getContext());
    patterns.add<FuseStablehloMulAndConvolutionPattern>(&getContext());
    if (failed(applyPatternsGreedily(getOperation(), std::move(patterns)))) {
      return signalPassFailure();
    }
  }
};

}  // namespace

static PassRegistration<StablehloFuseConvolutionPass> pass;

}  // namespace odml
}  // namespace mlir
