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tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_drq_per_channel.mlir

// CHECK-DAG: %[[CONST:.*]] = arith.constant dense<0.000000e+00> : tensor<2x1024xf32>
// CHECK: %0 = "quantfork.qcast"(%[[CONST]]) : (tensor<2x1024xf32>) -> tensor<2x1024x!quant.uniform<i8<-127:127>:f32, 3.9370078740157481E-9>>
// CHECK: %1 = "quantfork.dcast"(%0) : (tensor<2x1024x!quant.uniform<i8<-127:127>:f32, 3.9370078740157481E-9>>) -> tensor<2x1024xf32>

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github.com/tensorflow/tensorflow

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

    %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>>
    %3 = "quantfork.dcast"(%2) : (tensor<1x2x2x3x!quant.uniform<i8:f32, 8.000000e-03>>) -> tensor<*xf32>

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github.com/tensorflow/tensorflow

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

  %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x3x3x!quant.uniform<i8:f32, 1.000000e+0:-100>>, tensor<2x4x3x3x!quant.uniform<i8:f32:0,...

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/ops.mlir

@testLstmIntermediates(%arg0: tensor<1x528x!quant.uniform<i8:f32, 0.037248000502586365:-19>>, %arg1: tensor<2048x528x!quant.uniform<i8<-127:127>:f32, 0.059801999479532242>>, %arg2: tensor<2048x528x!quant.uniform<i8<-127:127>:f32, 0.031925998628139496>>, %arg3: tensor<2048x528x!quant.uniform<i8<-127:127>:f32, 0.056272000074386597>>, %arg4: tensor<2048x528x!quant.uniform<i8<-127:127>:f32, 0.063763998448848724>>, %arg5: tensor<2048x640x!quant.uniform<i8<-127:127>:f32, 0.013358999975025654>>, %arg6: tenso...

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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_PER_TENSOR:.*]] = "quantfork.qcast"(%[[WEIGHT_CONST]]) {{.*}} (tensor<2x3x3x2xf32>) -> tensor<2x3x3x2x!quant.uniform<i8:f32, {{.*}}>>
// CHECK: %[[DQ_WEIGHT:.*]] = "quantfork.dcast"(%[[Q_WEIGHT_PER_TENSOR]])

// CHECK: %[[Q_ACTIVATION:.*]] = "quantfork.qcast"(%[[ARG_0]])
// CHECK-SAME: -> tensor<1x3x2x3x!quant.uniform<i8:f32, 0.018920717052384919:-128>>
// CHECK: %[[DQ_ACTIVATION:.*]] = "quantfork.dcast"(%[[Q_ACTIVATION]])

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/prepare-quantize-dynamic-range.mlir

// CustomOp-DAG: %[[q_b:.*]] = "tfl.quantize"(%[[b]]) <{qtype = tensor<2048x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>}> : (tensor<2048x1x1x1xf32>) -> tensor<2048x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/quantize-numeric-verify.mlir

func.func @CheckNumericVerifyMultipleUsers(%arg0: tensor<1x5x5x3xf32>) -> tensor<1x5x5x3xf32> {
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x5x5x3x!quant.uniform<i8:f32, 0.1>>, volatile} : (tensor<1x5x5x3xf32>) -> tensor<1x5x5x3x!quant.uniform<i8:f32, 0.1>>
  %1 = "tfl.dequantize"(%0) : (tensor<1x5x5x3x!quant.uniform<i8:f32, 0.1>>) -> tensor<1x5x5x3xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/stablehlo/transforms/compose_uniform_quantized_type_pass.cc

// %13 = stablehlo.convert %11  // i8->i32 cast for q1.
// %14 = stablehlo.convert %3  // [Optional] i8->i32 cast for z1.
// %15 = stablehlo.broadcast_in_dim %14  // Operand = %4 if no `convert` above.
// %16 = stablehlo.subtract %13, %15  // q1 - z1
// %17 = stablehlo.convert %12  // i8->i32 cast for q2.
// %18 = stablehlo.convert %6  // [Optional] i8->i32 cast for z2.

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/optimize-after-quantization.mlir

  %w = arith.constant dense<2.0> : tensor<3x3x3x3xf32>
  %q = "tfl.quantize"(%w) {qtype = tensor<3x3x3x3x!quant.uniform<i8:f32, 0.1:1>>} : (tensor<3x3x3x3xf32>) -> tensor<3x3x3x3x!quant.uniform<i8:f32, 0.1:1>>
  %dq = "tfl.dequantize"(%q) : (tensor<3x3x3x3x!quant.uniform<i8:f32, 0.1:1>>) -> tensor<3x3x3x3xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/common/uniform_quantized_types_test.cc

  constexpr absl::string_view kFullyQuantizedAdd = R"mlir(
    func.func @fully_quantized_add(%arg0: tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>>) -> tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>> {
      %0 = stablehlo.add %arg0, %arg0 : tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>>
      return %0 : tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>>
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(kFullyQuantizedAdd);

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