256x32x32x2xf32 - Code Search

tensorflow/compiler/mlir/lite/tests/optimize.mlir

    padding = "SAME",
    stride_h = 1 : i32,
    stride_w = 1 : i32
  } : (tensor<256x32x32x3xf32>, tensor<2x3x3x3xf32>, tensor<2xf32>) -> tensor<256x32x32x2xf32>
  %1 = "tfl.add"(%0, %cst) {fused_activation_function = "NONE"} : (tensor<256x32x32x2xf32>, tensor<1x1x1x2xf32>) -> tensor<256x32x32x2xf32>
  func.return %1 : tensor<256x32x32x2xf32>

  // CHECK-DAG: %cst = arith.constant dense<[2.000000e+00, 4.000000e+00]> : tensor<2xf32>

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/experimental/tac/tests/get-op-cost.mlir

func.func @func_0_CPU(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  // CHECK: tac.cost = 7.864320e+05
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<256x32x32x3xf32>, tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32>
  func.return %0 : tensor<256x32x32x3xf32>
}

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github.com/tensorflow/tensorflow

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

func.func @conv(tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>, tensor<256x3x32x32xf32>) -> (tensor<256x8x7x16xf32>, tensor<256x16x32x32xf32>, tensor<256x8x6x16xf32>, tensor<256x32x32x16xf32>, tensor<256x32x32x16xf32>) {
^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>, %arg2: tensor<256x3x32x32xf32>) :
   // OK

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/import_json.json

// CHECK: return %[[RES0]] : tensor<256x32x32x16xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "CONV_2D"
    }
  ],
  "subgraphs": [
    {
      "tensors": [

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/optional_input.json

// CHECK: return %[[RES0]] : tensor<256x32x32x16xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "CONV_2D"
    }
  ],
  "subgraphs": [
    {
      "tensors": [

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github.com/tensorflow/tensorflow

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

// RUN: tf-opt -split-input-file -verify-diagnostics -tfl-get-arithmetic-count %s | FileCheck %s

func.func @testConv2D(tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x32x32x16xf32> {
^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>):
  // CHECK: _arithmetic_count = 230686720 : i64

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/experimental/tac/tests/compute-cost.mlir

// CHECK: tac.cost = 7.864320e+05
func.func @func_0_CPU(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<256x32x32x3xf32>, tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32>
  func.return %0 : tensor<256x32x32x3xf32>
}

// CHECK: tac.cost = 157286.4

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir

func.func @testConv(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>) -> tensor<256x30x30x16xf32> {
  // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"

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github.com/tensorflow/tensorflow

tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir

// -----

// CHECK-LABEL: func @testValidConv2D
func.func @testValidConv2D(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x32x32x16xf32> {
  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x32x32x16xf32>
  func.return %0 : tensor<256x32x32x16xf32>
}

// -----

// CHECK-LABEL: func @testValidDynamicConv2D

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github.com/tensorflow/tensorflow

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

// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK: return %0 : tensor<8xf32>
}

// CHECK-LABEL: fakeQuantWithConv2D
func.func @fakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) {
^bb0(%arg: tensor<256x32x32x3xf32>) :
  %in = arith.constant dense<0.0> : tensor<3x3x3x16xf32>
  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<15.0> : tensor<f32>

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