torch-mlir/lib/Conversion/TorchToTosa/TosaLegalizeCommon.cpp

//===----------------------------------------------------------------------===//
//
// 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
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//

#include "torch-mlir/Conversion/TorchToTosa/TosaLegalizeCommon.h"
#include "torch-mlir/Conversion/TorchToTosa/TosaLegalizeUtils.h"

#include <climits>
#include <cstddef>
#include <cstdint>
#include <iterator>
#include <numeric>

#include "mlir/Dialect/Quant/QuantTypes.h" // from @llvm-project
#include "mlir/Dialect/Tensor/IR/Tensor.h" // from @llvm-project
#include "mlir/Dialect/Tosa/IR/TosaOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"          // from @llvm-project
#include "mlir/IR/Matchers.h"              // from @llvm-project
#include "mlir/IR/PatternMatch.h"          // from @llvm-project
#include "llvm/Support/FormatVariadic.h"

namespace mlir {
namespace tosa {

// Common function for lowering reduce operations to TOSA ops.
template <typename T>
llvm::Optional<Value> convertReduceOpCommon(
    PatternRewriter &rewriter, Operation *op, RankedTensorType output_type,
    Value input_value, ElementsAttr axes_elems, bool keep_dims,
    Type reduce_element_type, bool is_quantized, double input_scale,
    int64_t input_zp, double output_scale, int64_t output_zp) {
  RankedTensorType input_type =
      input_value.getType().dyn_cast<RankedTensorType>();
  if (!input_type)
    return llvm::None;

  ArrayRef<int64_t> input_shape = input_type.getShape();
  ArrayRef<int64_t> output_shape = output_type.getShape();
  auto input_rank = input_shape.size();
  Value val = input_value;

  if (axes_elems.getNumElements() == 0) {
    // No axes means return the original tensor.
    auto identity_op = CreateOpAndInfer<tosa::IdentityOp>(
        rewriter, op->getLoc(), output_type, val);
    val = identity_op.getResult();
  } else {
    // Reduce along each axis
    SmallVector<int64_t> shape_vec(input_shape.begin(), input_shape.end());

    if (is_quantized) {
      val = buildRescaleToInt32(rewriter, op, val, input_scale, input_zp);
    }

    for (int i = 0; i < axes_elems.getNumElements(); i++) {
      int64_t axis_val = axes_elems.getValues<IntegerAttr>()[i].getInt();
      if (axis_val < 0)
        axis_val += input_rank;
      auto axis_attr = rewriter.getI64IntegerAttr(axis_val);

      shape_vec[axis_val] = 1;
      RankedTensorType reduce_type = RankedTensorType::get(
          shape_vec,
          reduce_element_type);

      auto reduce_op = CreateOpAndInfer<T>(rewriter, op->getLoc(), reduce_type,
                                           val, axis_attr);

      val = reduce_op.getResult();
    }

    if (is_quantized) {
      RankedTensorType output_rescale_type =
          RankedTensorType::get(shape_vec, output_type.getElementType());
      val = buildRescale(rewriter, op, output_rescale_type, val, output_scale,
                         0, output_zp, false, true);
    }

    // Optionally squeeze out the reduced axes.
    if (!keep_dims) {
      auto reshape_op = CreateOpAndInfer<tosa::ReshapeOp>(
          rewriter, op->getLoc(), output_type, val,
          rewriter.getI64ArrayAttr(output_shape));
      val = reshape_op.getResult();
    }
  }

  return val;
}

// Lowers ReduceAll to a sequence of TOSA ops.
llvm::Optional<Value>
convertReduceAllOp(PatternRewriter &rewriter, Operation *op,
                   RankedTensorType output_type, Value input_value,
                   ElementsAttr axes_elems, bool keep_dims) {
  RankedTensorType input_type =
      input_value.getType().dyn_cast<RankedTensorType>();
  if (!input_type)
    return llvm::None;

  return convertReduceOpCommon<tosa::ReduceAllOp>(
      rewriter, op, output_type, input_value, axes_elems, keep_dims,
      output_type.getElementType(), false, 1.0f, 0, 1.0f, 0);
}

// Lowers ReduceAny to a sequence of TOSA ops.
llvm::Optional<Value>
convertReduceAnyOp(PatternRewriter &rewriter, Operation *op,
                   RankedTensorType output_type, Value input_value,
                   ElementsAttr axes_elems, bool keep_dims) {
  RankedTensorType input_type =
      input_value.getType().dyn_cast<RankedTensorType>();
  if (!input_type)
    return llvm::None;

  return convertReduceOpCommon<tosa::ReduceAnyOp>(
      rewriter, op, output_type, input_value, axes_elems, keep_dims,
      output_type.getElementType(), false, 1.0f, 0, 1.0f, 0);
}

// Lowers ReduceMin to a sequence of TOSA ops.
llvm::Optional<Value>
convertReduceMinOp(PatternRewriter &rewriter, Operation *op,
                   RankedTensorType output_type, Value input_value,
                   ElementsAttr axes_elems, bool keep_dims) {
  RankedTensorType input_type =
      input_value.getType().dyn_cast<RankedTensorType>();
  if (!input_type)
    return llvm::None;

  return convertReduceOpCommon<tosa::ReduceMinOp>(
      rewriter, op, output_type, input_value, axes_elems, keep_dims,
      output_type.getElementType(), false, 1.0f, 0, 1.0f, 0);
}

// Lowers ReduceMax to a sequence of TOSA ops.
llvm::Optional<Value>
convertReduceMaxOp(PatternRewriter &rewriter, Operation *op,
                   RankedTensorType output_type, Value input_value,
                   ElementsAttr axes_elems, bool keep_dims) {
  RankedTensorType input_type =
      input_value.getType().dyn_cast<RankedTensorType>();
  if (!input_type)
    return llvm::None;

  return convertReduceOpCommon<tosa::ReduceMaxOp>(
      rewriter, op, output_type, input_value, axes_elems, keep_dims,
      output_type.getElementType(), false, 1.0f, 0, 1.0f, 0);
}

// Lowers ReduceProd to a sequence of TOSA ops.
llvm::Optional<Value>
convertReduceProdOp(PatternRewriter &rewriter, Operation *op,
                    RankedTensorType output_type, Value input_value,
                    ElementsAttr axes_elems, bool keep_dims) {
  RankedTensorType input_type =
      input_value.getType().dyn_cast<RankedTensorType>();
  if (!input_type)
    return llvm::None;

  bool input_is_qtype =
      input_type.getElementType().isa<mlir::quant::UniformQuantizedType>();
  bool output_is_qtype =
      output_type.getElementType().isa<mlir::quant::UniformQuantizedType>();

  if (input_is_qtype || output_is_qtype) {
    op->emitOpError("ConvertReduceProdOp: input/output tensor should "
                    "be all floating-point.");
    return llvm::None;
  }

  return convertReduceOpCommon<tosa::ReduceProdOp>(
      rewriter, op, output_type, input_value, axes_elems, keep_dims,
      output_type.getElementType(), false, 1.0f, 0, 1.0f, 0);
}

// Lowers ReduceSum to a sequence of TOSA ops.
llvm::Optional<Value>
convertReduceSumOp(PatternRewriter &rewriter, Operation *op,
                   RankedTensorType output_type, Value input_value,
                   ElementsAttr axes_elems, bool keep_dims) {
  RankedTensorType input_type =
      input_value.getType().dyn_cast<RankedTensorType>();
  if (!input_type)
    return llvm::None;

  bool input_is_qtype =
      input_type.getElementType().isa<mlir::quant::UniformQuantizedType>();
  bool output_is_qtype =
      output_type.getElementType().isa<mlir::quant::UniformQuantizedType>();

  if (input_is_qtype != output_is_qtype) {
    op->emitOpError("ConvertReduceSumOp: input/output tensor should "
                    "be all quantized or all floating-point.");
    return llvm::None;
  }

  double input_scale = 1.0f;
  double output_scale = 1.0f;
  int64_t input_zp = 0;
  int64_t output_zp = 0;
  Type reduce_element_type = input_type.getElementType();

  if (input_is_qtype) {
    auto input_qtype =
        input_type.getElementType().cast<mlir::quant::UniformQuantizedType>();
    auto output_qtype =
        output_type.getElementType().cast<mlir::quant::UniformQuantizedType>();

    int32_t input_shift = 20;

    input_scale =
        static_cast<double>(1 << input_shift) * input_qtype.getScale();
    output_scale =
        1.0 / (output_qtype.getScale() * static_cast<double>(1 << input_shift));

    input_zp = input_qtype.getZeroPoint();
    output_zp = output_qtype.getZeroPoint();
    reduce_element_type = rewriter.getI32Type();
  }

  return convertReduceOpCommon<tosa::ReduceSumOp>(
      rewriter, op, output_type, input_value, axes_elems, keep_dims,
      reduce_element_type, input_is_qtype, input_scale, input_zp, output_scale,
      output_zp);
}

// Lowers ReduceMean to a sequence of TOSA ops.
llvm::Optional<Value>
convertReduceMeanOp(PatternRewriter &rewriter, Operation *op,
                    RankedTensorType output_type, Value input_value,
                    ElementsAttr axes_elems, bool keep_dims) {
  // reduce_mean is lowered as followed:
  // op1 = reduce_sum(input)
  // op2 = mul(op1, 1.0 / num_elements_on_reduced_axis)

  RankedTensorType input_type =
      input_value.getType().dyn_cast<RankedTensorType>();
  if (!input_type)
    return llvm::None;

  bool input_is_qtype =
      input_type.getElementType().isa<mlir::quant::UniformQuantizedType>();
  bool output_is_qtype =
      output_type.getElementType().isa<mlir::quant::UniformQuantizedType>();

  if (input_is_qtype != output_is_qtype) {
    op->emitOpError("ConvertReduceSumOp: input/output tensor should "
                    "be all quantized or all floating-point.");
    return llvm::None;
  }

  // Only supports float type mean() if it's non-quantized
  if (!input_is_qtype && !output_type.getElementType().isa<mlir::FloatType>()) {
    op->emitWarning(
        "Failed convertReduceMean: input unquantized type but output element "
        "not FloatType!");
    return llvm::None;
  }

  int64_t input_rank = input_type.getRank();
  ArrayRef<int64_t> inputShape = input_type.getShape();
  int64_t num_elems_on_reduced_axis = 1;
  for (int i = 0; i < axes_elems.getNumElements(); i++) {
    int64_t axis_val = axes_elems.getValues<IntegerAttr>()[i].getInt();
    if (axis_val < 0)
      axis_val += input_rank;
    if (inputShape[axis_val] < 0)
      op->emitOpError("Failed convertReduceMean: support for dynamic input "
                      "shape not implemented");
    num_elems_on_reduced_axis *= inputShape[axis_val];
  }
  double div_scale = 1.0 / static_cast<double>(num_elems_on_reduced_axis);

  double input_scale = 1.0f;
  double output_scale = 1.0f;
  int64_t input_zp = 0;
  int64_t output_zp = 0;
  Type reduce_element_type = input_type.getElementType();

  if (input_is_qtype) {
    auto input_qtype =
        input_type.getElementType().cast<mlir::quant::UniformQuantizedType>();
    auto output_qtype =
        output_type.getElementType().cast<mlir::quant::UniformQuantizedType>();

    // Combine 'div_scale' as part of output rescale
    output_scale = div_scale * input_qtype.getScale() / output_qtype.getScale();

    input_zp = input_qtype.getZeroPoint();
    output_zp = output_qtype.getZeroPoint();
    reduce_element_type = rewriter.getI32Type();
  }

  auto val = convertReduceOpCommon<tosa::ReduceSumOp>(
      rewriter, op, output_type, input_value, axes_elems, keep_dims,
      reduce_element_type, input_is_qtype, input_scale, input_zp, output_scale,
      output_zp);

  if (!val.has_value())
    return llvm::None;

  if (!input_is_qtype) {
    Value div_const = getTosaConstTensorSingleF32(rewriter, op, div_scale);
    return CreateOpAndInfer<tosa::MulOp>(rewriter, op->getLoc(), output_type,
                                         val.value(), div_const, 0)
        .getResult();
  }

  return val;
}

} // namespace tosa
} // namespace mlir