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tensorflow/compiler/mlir/quantization/stablehlo/tests/bridge/convert-tf-quant-types.mlir

func.func @cast_op_qint8_int8(%arg0: tensor<1x!tf_type.qint8>) -> tensor<1xi8> {
  // CHECK: "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<1xi8>) -> tensor<1xi8>
  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x!tf_type.qint8>) -> tensor<1xi8>
  func.return %0: tensor<1xi8>
}

// -----

// CHECK-LABEL: func @cast_op_int8_qint8
func.func @cast_op_int8_qint8(%arg0: tensor<1xi8>) -> tensor<1x!tf_type.qint8> {

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/stablehlo/tests/bridge/optimize.mlir

}

// -----

// CHECK-LABEL: func @convolution_add_add_i8
func.func @convolution_add_add_i8(
    %lhs: tensor<?x3x2x1xi8>, %rhs: tensor<2x1x1x1xi8>,
    %zp_offset: tensor<?x2x2x1xi8>, %bias: tensor<1xi8>
  ) -> tensor<?x2x2x1xi8> {
  // CHECK-DAG: %[[conv:.*]] = mhlo.convolution
  // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]]

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/stablehlo/tests/compose-uniform-quantized-type.mlir

    %11 = stablehlo.reshape %2 : (tensor<1x1x1x1xi8>) -> tensor<1xi8>
    %12 = stablehlo.broadcast_in_dim %11, dims = [0] : (tensor<1xi8>) -> tensor<1x3x3x4xi8>
    %13 = stablehlo.convert %12 : (tensor<1x3x3x4xi8>) -> tensor<1x3x3x4xf32>
    %14 = stablehlo.convert %3 : (tensor<3x3x4x4xi8>) -> tensor<3x3x4x4xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/constants.mlir

  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8
  // CHECK: value = dense<[1, 2, 3, 4]> : tensor<4xi8>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 4]> : tensor<4xi8> } : () -> tensor<4xi8>
  func.return %0 : tensor<4xi8>
}

func.func @i16() -> tensor<4xi16> {
  // CHECK-LABEL: @i16

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/constants_offset.mlir

  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8
  // CHECK: value = dense<[1, 2, 3, 4]> : tensor<4xi8>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 4]> : tensor<4xi8> } : () -> tensor<4xi8>
  func.return %0 : tensor<4xi8>
}

func.func @i16() -> tensor<4xi16> {
  // CHECK-LABEL: @i16

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/tfr/tests/canonicalize.mlir

  // %0 = "tf.MaximumFloat"(%arg0, %arg1) : (tensor<*xi8>, tensor<*xi8>) -> tensor<*xi8>
  // %1 = "tf.DequantizeFloat"(%0) : (tensor<*xi8>) -> tensor<*xf32>
// CHECK: %[[MAXIMUMFLOAT_0:.*]] = "tf.MaximumFloat"(%arg0, %arg1) : (tensor<*xi8>, tensor<*xi8>) -> tensor<*xi8>
// CHECK: %[[CAST_0:.*]] = "tfr.cast"(%[[MAXIMUMFLOAT_0]]) : (tensor<*xi8>) -> !tfr.tensor

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-quant.mlir

  // CHECK: %[[CONVERT_1:.*]] = mhlo.bitcast_convert %[[QUANTIZE]] : (tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<2xi8>
  // CHECK: %[[CONVERT_2:.*]] = mhlo.bitcast_convert %[[CONVERT_1]] : (tensor<2xi8>) -> tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>
  // CHECK: %[[DEQUANTIZE:.*]] = mhlo.uniform_dequantize %[[CONVERT_2]] : (tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<2xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/const-fold.mlir

}

// CHECK-LABEL: @cast_ui8_to_i8
func.func @cast_ui8_to_i8() -> tensor<4xi8> {
  %cst = arith.constant dense<[0, 255, 127, 128]> : tensor<4xui8>
  %0 = "tfl.cast"(%cst) : (tensor<4xui8>) -> tensor<4xi8>
  func.return %0 : tensor<4xi8>

// CHECK: %[[CST:.*]] = arith.constant dense<[0, -1, 127, -128]> : tensor<4xi8>
// CHECK:  return %[[CST]]
}

// CHECK-LABEL: @cast_i8_to_i32

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library_tf_drq.mlir

  }

  // For weight-only
  func.func @dequantize_i8(%input : tensor<*xi8>, %scale : tensor<*xf32>, %zp : tensor<*xi32>) -> tensor<*xf32> {
    // Use identity op to avoid the weight being constant-folded.
    %identity = "tf.Identity"(%input) : (tensor<*xi8>) -> tensor<*xi8>
    %input_i32 = "tf.Cast"(%identity) : (tensor<*xi8>) -> tensor<*xi32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library.mlir

    %i8 = "tf.Cast"(%round) : (tensor<*xf32>) -> tensor<*xi8>
    func.return %i8 : tensor<*xi8>
  }

  func.func @dequantize_i8(%input : tensor<*xi8>, %scale : tensor<*xf32>, %zp : tensor<*xi32>) -> tensor<*xf32> {
    // Use identity op to avoid the weight being constant-folded.
    %identity = "tf.Identity"(%input) : (tensor<*xi8>) -> tensor<*xi8>
    %input_i32 = "tf.Cast"(%identity) : (tensor<*xi8>) -> tensor<*xi32>

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