3x2xf16 - Code Search

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

  func.return %0: tensor<3x2xf32>
}

func.func @sparse_f16() -> tensor<3x2xf16> {
  // CHECK-LABEL: @sparse_f16

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github.com/tensorflow/tensorflow

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

  func.return %0: tensor<3x2xf32>
}

func.func @sparse_f16() -> tensor<3x2xf16> {
  // CHECK-LABEL: @sparse_f16

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github.com/tensorflow/tensorflow

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

// CHECK-LABEL: func @floordiv_f16_broadcast
func.func @floordiv_f16_broadcast(%arg0: tensor<2x3xf16>, %arg1: tensor<3xf16>) -> tensor<2x3xf16> {
  // CHECK-NEXT:  chlo.broadcast_divide
  // CHECK-NEXT:  mhlo.floor
  // CHECK-NEXT:  return
  %0 = "tf.FloorDiv"(%arg0, %arg1) : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>
  func.return %0: tensor<2x3xf16>
}

// -----

// CHECK-LABEL: func @floordiv_dynamic

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range-float16.mlir

  // CHECK: %[[DQ_2:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_3:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_4:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_5:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/legalize-tf-no-runtime-verification.mlir

func.func @broadcast_to_bf16(%arg0: tensor<3xbf16>, %arg1: tensor<2xi64>) -> tensor<3x3xbf16> {
  %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xbf16>, tensor<2xi64>) -> tensor<3x3xbf16>
  func.return %0: tensor<3x3xbf16>

// CHECK-LABEL: broadcast_to_bf16
// CHECK:  [[CST:%.*]] = arith.constant dense<1.000000e+00> : tensor<3x3xbf16>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/convert_func_to_bfloat16.mlir

// CHECK-LABEL: @add_f64(%arg0: tensor<3x3xbf16>, %arg1: tensor<3x3xbf16>) -> tensor<3x3xbf16>
func.func @add_f64(%arg0: tensor<3x3xf64>, %arg1: tensor<3x3xf64>) -> tensor<3x3xf64> {
  // CHECK-NOT: f64
  // CHECK: stablehlo.add
  %0 = stablehlo.add %arg0, %arg1: (tensor<3x3xf64>, tensor<3x3xf64>) -> tensor<3x3xf64>
  return %0 : tensor<3x3xf64>
}

// -----

// CHECK-LABEL: @constant_f32() -> tensor<2x2xbf16>

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github.com/tensorflow/tensorflow

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

func.func @bitcastI32ToI16(%arg0: tensor<8xi32>) -> tensor<8x2xi16> {
  %0 = "tf.Bitcast"(%arg0) : (tensor<8xi32>) -> tensor<8x2xi16>
  func.return %0 : tensor<8x2xi16>

// CHECK-LABEL: bitcastI32ToI16
// CHECK: %[[RES0:.*]] = "tfl.bitcast"(%arg0) : (tensor<8xi32>) -> tensor<8x2xi16>
// CHECK:  return %[[RES0]] : tensor<8x2xi16>
}

func.func @bitcastI16ToUI32(%arg0: tensor<8x2xi16>) -> tensor<8xui32> {

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github.com/tensorflow/tensorflow

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

  %dq = "quantfork.dcast"(%q) : (tensor<3x2x!quant.uniform<i8:f32, 0.013075299590241675:-64>>) -> tensor<3x2xf32>
  func.return %dq : tensor<3x2xf32>
}

// -----

// CHECK-LABEL: @remove_volatile_qdq_with_requantization
// CHECK-SAME: %[[ARG0:.*]]: tensor<3x2xf32>
func.func @remove_volatile_qdq_with_requantization(%arg0: tensor<3x2xf32>) -> tensor<3x2xf32> {
  // CHECK: %[[Q1:.*]] = stablehlo.uniform_quantize %[[ARG0]]

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/split-merged-operands.mlir

  // CHECK-DAG:  %[[CST_1:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf16>}> : () -> tensor<4x4xf16>
  // CHECK-DAG:  %[[DQ_0:.*]] = "tfl.dequantize"(%[[CST_0]]) : (tensor<4x4xf16>) -> tensor<4x4xf32>
  // CHECK-DAG:  %[[DQ_1:.*]] = "tfl.dequantize"(%[[CST_1]]) : (tensor<4x4xf16>) -> tensor<4x4xf32>

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github.com/tensorflow/tensorflow

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

// -----

// CHECK-LABEL: transpose_simple_2d
func.func @transpose_simple_2d() -> tensor<3x2xf32> {
  %0 = stablehlo.constant dense<[[0.000000e+0, 1.000000e+0, 2.000000e+0], [3.000000e+0, 4.000000e+0, 5.000000e+0]]> : tensor<2x3xf32>
  %1 = stablehlo.transpose %0, dims = [1, 0] : (tensor<2x3xf32>) -> tensor<3x2xf32>
  return %1 : tensor<3x2xf32>
}

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