Gaussian Error Linear Units (GELUs)

Yongqiang Cheng2024-08-24 23:37

Gaussian Error Linear Units {GELUs}

  • [1. Gaussian Error Linear Units (GELUs)](#1. Gaussian Error Linear Units (GELUs))
  • [2. PyTorch GELU](#2. PyTorch GELU)
  • [3. TensorFlow GELU](#3. TensorFlow GELU)
    • [3.1. `tf.nn.gelu`](#3.1. tf.nn.gelu)
    • [3.2. `tf.keras.activations.gelu`](#3.2. tf.keras.activations.gelu)
    • [3.3. `tf.keras.ops.gelu`](#3.3. tf.keras.ops.gelu)
  • [4. Compute a polynomial approximation of the error function](#4. Compute a polynomial approximation of the error function)
    • [4.1. `tensorflow/compiler/xla/client/lib/math.cc`](#4.1. tensorflow/compiler/xla/client/lib/math.cc)
    • [4.2. `tensorflow/compiler/mlir/lite/tests/optimize.mlir`](#4.2. tensorflow/compiler/mlir/lite/tests/optimize.mlir)
  • [5. Compute a rational approximation of the error function](#5. Compute a rational approximation of the error function)
    • [5.1. `third_party/xla/xla/client/lib/math.cc`](#5.1. third_party/xla/xla/client/lib/math.cc)
    • [5.2. `tensorflow/compiler/mlir/lite/tests/optimize.mlir`](#5.2. tensorflow/compiler/mlir/lite/tests/optimize.mlir)
  • [6. TensorFlow Lite GELU](#6. TensorFlow Lite GELU)
    • [6.1. `tensorflow/lite/kernels/activations.cc`](#6.1. tensorflow/lite/kernels/activations.cc)
    • [6.2. `tensorflow/lite/kernels/internal/reference/gelu.h`](#6.2. tensorflow/lite/kernels/internal/reference/gelu.h)
    • [6.3. `tensorflow/lite/delegates/gpu/common/tasks/elementwise.cc`](#6.3. tensorflow/lite/delegates/gpu/common/tasks/elementwise.cc)
  • References

1. Gaussian Error Linear Units (GELUs)

https://arxiv.org/abs/1606.08415

The Gaussian Error Linear Unit (GELU) is a high-performing neural network activation function.

2. PyTorch GELU

https://pytorch.org/docs/stable/generated/torch.nn.GELU.html

classtorch.nn.GELU(approximate='none')

Applies the Gaussian Error Linear Units function.

GELU ( x ) = x ∗ Φ ( x ) \text{GELU}(x) = x * \Phi(x) GELU(x)=x∗Φ(x)

where Φ ( x ) \Phi(x) Φ(x) is the Cumulative Distribution Function for Gaussian Distribution.

When the approximate argument is tanh, Gelu is estimated with:

GELU ( x ) = 0.5 ∗ x ∗ ( 1 + Tanh ( 2 / π ∗ ( x + 0.044715 ∗ x 3 ) ) ) \text{GELU}(x) = 0.5 * x * (1 + \text{Tanh}(\sqrt{2 / \pi} * (x + 0.044715 * x^3))) GELU(x)=0.5∗x∗(1+Tanh(2/π ∗(x+0.044715∗x3)))

Args:

approximate (str, optional) : the gelu approximation algorithm to use: 'none' | 'tanh'. Default: 'none'

Shape:

  • Input: (*), * means any number of dimensions.
  • Output: (*), same shape as the input.

Examples:

m = nn.GELU()
input = torch.randn(2)
output = m(input)

3. TensorFlow GELU

3.1. tf.nn.gelu

https://www.tensorflow.org/api_docs/python/tf/nn/gelu

3.2. tf.keras.activations.gelu

https://www.tensorflow.org/api_docs/python/tf/keras/activations/gelu

3.3. tf.keras.ops.gelu

https://www.tensorflow.org/api_docs/python/tf/keras/ops/gelu

4. Compute a polynomial approximation of the error function

4.1. tensorflow/compiler/xla/client/lib/math.cc

v2.12.1 - tensorflow/compiler/xla/client/lib/math.cc
https://github.com/tensorflow/tensorflow/blob/v2.12.1/tensorflow/compiler/xla/client/lib/math.cc

// Compute a polynomial approximation of the error function.
// This is the same approximation used by Eigen.
static XlaOp ErfImpl32(XlaOp x) {
  static const std::array<float, 7> kAlpha{
      -2.72614225801306e-10f, 2.77068142495902e-08f,  -2.10102402082508e-06f,
      -5.69250639462346e-05f, -7.34990630326855e-04f, -2.95459980854025e-03f,
      -1.60960333262415e-02f,
  };

  static const std::array<float, 5> kBeta{
      -1.45660718464996e-05f, -2.13374055278905e-04f, -1.68282697438203e-03f,
      -7.37332916720468e-03f, -1.42647390514189e-02f,
  };

  x = Clamp(ScalarLike(x, -4.f), x, ScalarLike(x, 4.f));
  auto x2 = x * x;
  return x * EvaluatePolynomial<float>(x2, kAlpha) /
         EvaluatePolynomial<float>(x2, kBeta);
}

// Evaluate the polynomial given `x` and coefficients in decreasing order.
template <typename FP>
XlaOp EvaluatePolynomial(XlaOp x, absl::Span<const FP> coefficients) {
  static_assert(std::is_floating_point<FP>::value,
                "Template-argument 'FP' must be a floating-point type");
  XlaOp poly = ScalarLike(x, 0.0);
  for (FP c : coefficients) {
    poly = poly * x + ScalarLike(x, c);
  }
  return poly;
}

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

v2.12.1 - tensorflow/compiler/mlir/lite/tests/optimize.mlir
https://github.com/tensorflow/tensorflow/blob/v2.12.1/tensorflow/compiler/mlir/lite/tests/optimize.mlir

func.func @gelu(%arg0: tensor<3xf32>) -> tensor<3xf32> {
  %cst = arith.constant dense<0.707106769> : tensor<f32>
  %cst_0 = arith.constant dense<5.000000e-01> : tensor<f32>
  %cst_1 = arith.constant dense<1.000000e+00> : tensor<f32>
  %0 = "tfl.mul"(%arg0, %cst_0) {fused_activation_function = "NONE"} : (tensor<3xf32>, tensor<f32>) -> tensor<3xf32>
  %1 = "tfl.mul"(%arg0, %cst) {fused_activation_function = "NONE"} : (tensor<3xf32>, tensor<f32>) -> tensor<3xf32>
  %2 = "tf.Erf"(%1) : (tensor<3xf32>) -> tensor<3xf32>
  %3 = "tfl.add"(%2, %cst_1) {fused_activation_function = "NONE"} : (tensor<3xf32>, tensor<f32>) -> tensor<3xf32>
  %4 = "tfl.mul"(%0, %3) {fused_activation_function = "NONE"} : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
  func.return %4 : tensor<3xf32>

// CHECK-LABEL:gelu
// CHECK: "tfl.gelu"(%arg0) {approximate = false} : (tensor<3xf32>) -> tensor<3xf32>
}

5. Compute a rational approximation of the error function

5.1. third_party/xla/xla/client/lib/math.cc

v2.17.0 - third_party/xla/xla/client/lib/math.cc
https://github.com/tensorflow/tensorflow/blob/v2.17.0/third_party/xla/xla/client/lib/math.cc

// Compute a rational approximation of the error function.
static XlaOp ErfImpl32(XlaOp x) {
  static const std::array<float, 5> kAlpha{
      0.00022905065861350646f, 0.0034082910107109506f, 0.050955695062380861f,
      0.18520832239976145f, 1.128379143519084f};

  static const std::array<float, 7> kBeta{-1.1791602954361697e-7,
                                          0.000023547966471313185f,
                                          0.0010179625278914885f,
                                          0.014070470171167667f,
                                          0.11098505178285362f,
                                          0.49746925110067538f,
                                          1.0f};

  // We clamp x to be within [-c;c] where c = erfinv(1-2^-23), outside of
  // which x should be +/-1.
  constexpr float kErfInvOneMinusHalfULP = 3.7439211627767994f;
  x = Clamp(ScalarLike(x, -kErfInvOneMinusHalfULP), x,
            ScalarLike(x, kErfInvOneMinusHalfULP));
  auto x2 = x * x;
  return (x * EvaluatePolynomial<float>(x2, kAlpha)) /
         EvaluatePolynomial<float>(x2, kBeta);
}

// Evaluate the polynomial given `x` and coefficients in decreasing order.
template <typename FP>
XlaOp EvaluatePolynomial(XlaOp x, absl::Span<const FP> coefficients) {
  static_assert(std::is_floating_point<FP>::value,
                "Template-argument 'FP' must be a floating-point type");
  if (coefficients.empty()) {
    return ScalarLike(x, FP(0.0));
  }
  XlaOp poly = ScalarLike(x, coefficients[0]);
  for (int i = 1; i < coefficients.size(); ++i) {
    FP c = coefficients[i];
    poly = poly * x + ScalarLike(x, c);
  }
  return poly;
}

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

v2.17.0 - tensorflow/compiler/mlir/lite/tests/optimize.mlir
https://github.com/tensorflow/tensorflow/blob/v2.17.0/tensorflow/compiler/mlir/lite/tests/optimize.mlir

func.func @gelu(%arg0: tensor<3xf32>) -> tensor<3xf32> {
  %cst = arith.constant dense<0.707106769> : tensor<f32>
  %cst_0 = arith.constant dense<5.000000e-01> : tensor<f32>
  %cst_1 = arith.constant dense<1.000000e+00> : tensor<f32>
  %0 = "tfl.mul"(%arg0, %cst_0) {fused_activation_function = "NONE"} : (tensor<3xf32>, tensor<f32>) -> tensor<3xf32>
  %1 = "tfl.mul"(%arg0, %cst) {fused_activation_function = "NONE"} : (tensor<3xf32>, tensor<f32>) -> tensor<3xf32>
  %2 = "tf.Erf"(%1) : (tensor<3xf32>) -> tensor<3xf32>
  %3 = "tfl.add"(%2, %cst_1) {fused_activation_function = "NONE"} : (tensor<3xf32>, tensor<f32>) -> tensor<3xf32>
  %4 = "tfl.mul"(%0, %3) {fused_activation_function = "NONE"} : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
  func.return %4 : tensor<3xf32>

// CHECK-LABEL:gelu
// CHECK: "tfl.gelu"(%arg0) <{approximate = false}> : (tensor<3xf32>) -> tensor<3xf32>
}

6. TensorFlow Lite GELU

6.1. tensorflow/lite/kernels/activations.cc

https://github.com/tensorflow/tensorflow/blob/v2.17.0/tensorflow/lite/kernels/activations.cc

TfLiteStatus GeluPrepare(TfLiteContext* context, TfLiteNode* node);
TfLiteStatus GeluEval(TfLiteContext* context, TfLiteNode* node);

6.2. tensorflow/lite/kernels/internal/reference/gelu.h

https://github.com/tensorflow/tensorflow/blob/v2.17.0/tensorflow/lite/kernels/internal/reference/gelu.h

...
namespace gelu_internal {

constexpr float kSqrt2dPi = M_2_SQRTPI * M_SQRT1_2;  // sqrt( 2 / pi )

}  // namespace gelu_internal

// Plain implementations for GELU. Used for populating lookup table.
inline float GeluTransform(float in) {
  // Note: 0.5 * x * ( 1 + erf( x / sqrt( 2 ) ) ) is commonly used, but cause
  // catastropic cancellation for large negative inputs. Rewriting the
  // expression via erfc avoids the numerical stability issues.
  return 0.5f * in * std::erfc(in * static_cast<float>(-M_SQRT1_2));
}

inline float GeluTransformApproximate(float in) {
  // 0.5 * x * ( 1 + tanh( sqrt( 2 / pi ) * ( x + 0.044715 * x^3 ) ) )
  return 0.5f * in *
         (1.f + std::tanh(gelu_internal::kSqrt2dPi *
                          // Note: Avoid std::pow for integer exponents
                          // as it leads to much slower performance.
                          (in + 0.044715f * in * in * in)));
}
...

6.3. tensorflow/lite/delegates/gpu/common/tasks/elementwise.cc

https://github.com/tensorflow/tensorflow/blob/v2.17.0/tensorflow/lite/delegates/gpu/common/tasks/elementwise.cc

...
    case OperationType::GELU:
      // OpenCL has erfc and so it can use the more accurate gelu calculation
      // as compared to the OpenGL and Vulkan implementations.
      // gelu(x) = 0.5 * x * erfc(x * -sqrt(0.5))
      result =
          "$0 = INIT_FLT4(0.5f) * $1 * erfc($1 * "
          "INIT_FLT4(-0.70710678118654752440f));";
      break;
...

References

[1] Yongqiang Cheng, https://yongqiang.blog.csdn.net/

相关推荐
Sakuya__8 个月前
3D Gaussian Splatting复现
python·nerf·gaussian·splatting
ScienceLi112510 个月前
4D Gaussian Splatting：用于实时的动态场景渲染
3d视觉·gaussian·splatting·视图渲染
知识在于积累10 个月前
高斯积分-Gaussian Quadrature
gaussian·高斯积分·quadrature
ScienceLi112510 个月前
3D Gaussian Splatting：用于实时的辐射场渲染
3d视觉·gaussian·splatting·视图渲染
热门推荐
01RAG 实践- Ollama+RagFlow 部署本地知识库02组基轨迹建模 GBTM的介绍与实现（Stata 或 R）032024年高教社杯数学建模国赛C题超详细解题思路分析04CCF-CSP认证考试 202406-3 文本分词 100分题解05苍穹外卖面试总结06【2024数模国赛赛题思路公开】国赛B题思路丨附可运行代码丨无偿自提07【立创EDA-PCB设计基础】6.布线铺铜实战及细节详解08Coze扣子平台完整体验和实践（附国内和国际版对比）09【2024高教社杯全国大学生数学建模竞赛】B题 生产过程中的决策问题——解题思路 代码 论文102024 高教社杯 数学建模国赛 （A题）深度剖析|“板凳龙” 闹元宵|数学建模完整代码+建模过程全解全析