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tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize.mlir

// CHECK: %[[maxpool:.*]] = "tf.MaxPool"
// CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[maxpool]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[reshape:.*]] = "tf.Reshape"(%[[dq1]]
// CHECK: %[[q2:.*]] = "quantfork.qcast"(%[[reshape]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_ptq.mlir

// CHECK: %[[q0:.*]] = "quantfork.qcast"(%arg0)
// CHECK: %[[dq0:.*]] = "quantfork.dcast"(%[[q0]])
// CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
// CHECK: %[[maxpool:.*]] = "tf.MaxPool"(%[[dq0]])
// CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[maxpool]])
// CHECK-SAME: quant.uniform<i8:f32, 0.010039215461880554:-1>
// CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]])

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/tensorflow/tests/convert_fake_quant_to_qdq.mlir

}

// CHECK: %[[q1:.*]] = "quantfork.qcast"(%arg0)
// CHECK-SAME: tensor<3x!quant.uniform<i8:f32, 0.0076314610593459188:-3>>
// CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]])
// CHECK: %[[q2:.*]] = "quantfork.qcast"(%arg1)
// CHECK-SAME: tensor<4x3x!quant.uniform<i8<-127:127>:f32:1, {0.003937007874015748,0.0039370079913477263:-25,0.003937007874015748:51}>>
// CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_ptq_per_channel.mlir

// CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]])

// CHECK: %[[q2:.*]] = "quantfork.qcast"(%arg0)
// CHECK-SAME: tensor<1x3x4x3x!quant.uniform<i8:f32, 0.58754816990272674:-128>>
// CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])

// CHECK: %[[call:.*]] = "tf.PartitionedCall"(%[[dq2]], %[[dq1]], %[[dq0]])
// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu6_fn_10
// CHECK: %[[q3:.*]] = "quantfork.qcast"(%[[call]]) {volatile}

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize_per_channel.mlir

    // CHECK: %[[q_weight:.*]] = "quantfork.qcast"
    // CHECK-SAME: -> tensor<2x2x!quant.uniform<i8<-127:127>:f32:1, {0.049663885371891529,0.060200210631363035}>>
    // CHECK: %[[dq_weight:.*]] = "quantfork.dcast"(%[[q_weight]])
    %cst = "tf.Const"() {device = "", value = dense<[[-6.30731344, 5.4962182], [1.80364347, -7.64542675]]> : tensor<2x2xf32>} : () -> tensor<2x2xf32>
    // CHECK: %[[q_act:.*]] = "quantfork.qcast"(%arg0)

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant_4bit.mlir

// CHECK:  %[[fq:.*]] = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg0, %cst, %cst_0)
// CHECK:  %[[q:.*]] = "quantfork.qcast"(%[[fq]]) : (tensor<8x3xf32>) -> tensor<8x3x!quant.uniform<i4:f32:1, {1.000000e+00:-8,1.000000e+00:-7,1.000000e+00:-8}>>
// CHECK:  %[[dq:.*]] = "quantfork.dcast"(%[[q]])
// CHECK:  return %[[dq]]
}

// CHECK-LABEL: fakeQuantForActivation

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant.mlir

// CHECK:  %[[fq:.*]] = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg0, %cst, %cst_0)
// CHECK:  %[[q:.*]] = "quantfork.qcast"(%[[fq]]) : (tensor<8x3xf32>) -> tensor<8x3x!quant.uniform<i8:f32:1, {1.000000e+00:-128,1.000000e+00:-127,1.000000e+00:-128}>>
// CHECK:  %[[dq:.*]] = "quantfork.dcast"(%[[q]])
// CHECK:  return %[[dq]]
}

// CHECK-LABEL: fakeQuantForActivation

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_xla.mlir

    %0 = "quantfork.qcast"(%cst) : (tensor<2x2x3x2xf32>) -> tensor<2x2x3x2x!quant.uniform<i8<-127:127>:f32:3, {4.000000e-03,5.000000e-03}>>
    %1 = "quantfork.dcast"(%0) : (tensor<2x2x3x2x!quant.uniform<i8<-127:127>:f32:3, {4.000000e-03,5.000000e-03}>>) -> tensor<*xf32>
    %2 = "quantfork.qcast"(%arg0) : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3x!quant.uniform<i8:f32, 8.000000e-03>>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/insert_weight_param.mlir

// CHECK: %[[CST:.+]] = "tf.Const"() <{value = dense<3.000000e-01> : tensor<2x3x3x2xf32>}> : () -> tensor<2x3x3x2xf32>
// CHECK: %[[Q:.+]] = "quantfork.qcast"(%[[CST]]) : (tensor<2x3x3x2xf32>) -> tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>
// CHECK: %[[DQ:.+]] = "quantfork.dcast"(%[[Q]]) : (tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>) -> tensor<2x3x3x2xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/tensorflow/utils/fake_quant_utils.h

  }
};

// Inserts a "quant.qcast" and "quant.dcast" op pair (QDQs) in place of the
// tf.FakeQyantWithMinMax{Vars|VarsPerChannel|Args}Op
// before the op being constant folded. Since the constant
// folding logic will use a "arith.constant" op to replace the
// "tf.FakeQuantWithMinMaxVarsOp", the "quant.qcast" op is used to preserve
// the quantization parameters as a TypeAttr and "quant.dcast" op used to

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