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tensorflow/compiler/mlir/lite/tests/prepare-composite-functions-tf.mlir

//...

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/tensorflow/tests/tensor_list_ops_decomposition.mlir

  // CHECK-NOT: tf.EmptyTensorList
  // CHECK: tf.BroadcastTo
  // CHECK-SAME: tensor<10x16xf32>
  %tl0 = "tf.EmptyTensorList"(%element_shape, %max_size) : (tensor<1xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<16xf32>>>
  // CHECK-NOT: tf.TensorListReserve
  // CHECK: tf.BroadcastTo
  // CHECK-SAME: tensor<10x32xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/get-arithmetic-count.mlir

func.func @testSoftmax(%arg0: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 6400 : i64
  %0 = "tfl.softmax"(%arg0) {beta = 1.000000e+00 : f32} : (tensor<10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>
}

func.func @testTanh(%arg0: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 6400 : i64
  %0 = "tfl.tanh"(%arg0) : (tensor<10x10xf32>) -> tensor<10x10xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/stablehlo/cc/report_test.cc

      return %1 : tensor<1x3xf32>
    }

    func.func private @composite_dot_general_fn(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<1x3xf32> {
      %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
      return %0 : tensor<1x3xf32>
    }
  )mlir";

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github.com/tensorflow/tensorflow

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

    has_token_input_output = false, module = "", platforms = [],
    version = 5 : i64
  } : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
  return %0 : tensor<1x3xf32>
}

// CHECK-LABEL: func.func @qdq_for_dot_general_weight_empty
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x2xf32>) -> tensor<1x3xf32>
// CHECK: %[[CST:.+]] = "tf.Const"() <{value = dense<3.000000e-01> : tensor<2x3xf32>}> : () -> tensor<2x3xf32>

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github.com/tensorflow/tensorflow

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

    %9 = stablehlo.convert %2 : (tensor<2x3xi8>) -> tensor<2x3xf32>
    %10 = stablehlo.dot_general %8, %9, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    %11 = stablehlo.convert %3 : (tensor<1x3xi32>) -> tensor<1x3xf32>
    %12 = stablehlo.subtract %10, %11 : tensor<1x3xf32>  // q1 * q2 - z1 * q2
    %13 = stablehlo.multiply %12, %4 : tensor<1x3xf32>  // s1 * s2

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github.com/tensorflow/tensorflow

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

    return %2 : tensor<1x2xf32>
  }

  func.func private @composite_add_fn(%arg0: tensor<1x2xf32>, %arg1: tensor<1x2xf32>) -> tensor<1x2xf32> attributes {_from_xla_call_module} {
    %0 = stablehlo.add %arg0, %arg1 : tensor<1x2xf32>
    %1 = stablehlo.add %0, %arg1 : tensor<1x2xf32>
    return %1 : tensor<1x2xf32>
  }
}

// -----

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir

// =============================================================================

func.func @sigmoidGrad(%arg0: tensor<?x32xf32>, %arg1: tensor<?x32xf32>) -> tensor<?x32xf32> {
  %0 = "tf.SigmoidGrad"(%arg0, %arg1) : (tensor<?x32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>
  func.return %0 : tensor<?x32xf32>
// CHECK-LABEL: sigmoidGrad
// CHECK-NEXT: [[ONE:%.+]] = arith.constant dense<1.000000e+00> : tensor<f32>

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github.com/tensorflow/tensorflow

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

    "vhlo.return_v1"(%lhs) : (tensor<f32>) -> ()
  }): (tensor<200x100x300xf32>, tensor<10x2xi32>, tensor<10x300xf32>) ->
      tensor<200x100x300xf32>
  func.return %0 : tensor<200x100x300xf32>
}

// CHECK-LABEL: func.func private @scatter(%arg0: tensor<200x100x300xf32>, %arg1: tensor<10x2xi32>, %arg2: tensor<10x300xf32>) -> tensor<200x100x300xf32> {

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/tensorflow/tests/tpu_reorder_replicate_and_partitioned_inputs.mlir

func.func @no_change_to_dag(%arg0: tensor<!tf_type.resource<tensor<10x3xf32>>>, %arg1: tensor<!tf_type.resource<tensor<10x3xf32>>>, %arg2: tensor<!tf_type.resource<tensor<10x3xf32>>>, %arg3: tensor<!tf_type.resource<tensor<10x3xf32>>>) -> (tensor<!tf_type.resource<tensor<10x3xf32>>>, tensor<!tf_type.resource<tensor<10x3xf32>>>, tensor<!tf_type.resource...

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