[Phi] Polish truncated normal kernel and add yaml (#41280)

* polish truncated normal kernel

* add yaml

* add truncated normal kernel and add yaml

* polish unittests and yaml

* import dygraph mehtod

paddle/phi/kernels/cpu/truncated_gaussian_random_kernel.cc

@@ -21,10 +21,141 @@
 #include "paddle/phi/backends/cpu/cpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
 
-#include "paddle/fluid/framework/generator.h"
-
 namespace phi {
 
+// reference: https://gist.github.com/lakshayg/d80172fe5ae3c5d2c2aedb53c250320e
+template <typename T>
+T Erfinv(T x) {
+  if (x < -1 || x > 1) {
+    return std::numeric_limits<T>::quiet_NaN();
+  } else if (x == 1.0) {
+    return std::numeric_limits<T>::infinity();
+  } else if (x == -1.0) {
+    return -std::numeric_limits<T>::infinity();
+  }
+
+  const T LN2 = 6.931471805599453094172321214581e-1;
+
+  const T A0 = 1.1975323115670912564578e0;
+  const T A1 = 4.7072688112383978012285e1;
+  const T A2 = 6.9706266534389598238465e2;
+  const T A3 = 4.8548868893843886794648e3;
+  const T A4 = 1.6235862515167575384252e4;
+  const T A5 = 2.3782041382114385731252e4;
+  const T A6 = 1.1819493347062294404278e4;
+  const T A7 = 8.8709406962545514830200e2;
+
+  const T B0 = 1.0000000000000000000e0;
+  const T B1 = 4.2313330701600911252e1;
+  const T B2 = 6.8718700749205790830e2;
+  const T B3 = 5.3941960214247511077e3;
+  const T B4 = 2.1213794301586595867e4;
+  const T B5 = 3.9307895800092710610e4;
+  const T B6 = 2.8729085735721942674e4;
+  const T B7 = 5.2264952788528545610e3;
+
+  const T C0 = 1.42343711074968357734e0;
+  const T C1 = 4.63033784615654529590e0;
+  const T C2 = 5.76949722146069140550e0;
+  const T C3 = 3.64784832476320460504e0;
+  const T C4 = 1.27045825245236838258e0;
+  const T C5 = 2.41780725177450611770e-1;
+  const T C6 = 2.27238449892691845833e-2;
+  const T C7 = 7.74545014278341407640e-4;
+
+  const T D0 = 1.4142135623730950488016887e0;
+  const T D1 = 2.9036514445419946173133295e0;
+  const T D2 = 2.3707661626024532365971225e0;
+  const T D3 = 9.7547832001787427186894837e-1;
+  const T D4 = 2.0945065210512749128288442e-1;
+  const T D5 = 2.1494160384252876777097297e-2;
+  const T D6 = 7.7441459065157709165577218e-4;
+  const T D7 = 1.4859850019840355905497876e-9;
+
+  const T E0 = 6.65790464350110377720e0;
+  const T E1 = 5.46378491116411436990e0;
+  const T E2 = 1.78482653991729133580e0;
+  const T E3 = 2.96560571828504891230e-1;
+  const T E4 = 2.65321895265761230930e-2;
+  const T E5 = 1.24266094738807843860e-3;
+  const T E6 = 2.71155556874348757815e-5;
+  const T E7 = 2.01033439929228813265e-7;
+
+  const T F0 = 1.414213562373095048801689e0;
+  const T F1 = 8.482908416595164588112026e-1;
+  const T F2 = 1.936480946950659106176712e-1;
+  const T F3 = 2.103693768272068968719679e-2;
+  const T F4 = 1.112800997078859844711555e-3;
+  const T F5 = 2.611088405080593625138020e-5;
+  const T F6 = 2.010321207683943062279931e-7;
+  const T F7 = 2.891024605872965461538222e-15;
+
+  T abs_x = abs(x);
+
+  if (abs_x <= 0.85) {
+    T r = 0.180625 - 0.25 * x * x;
+    T num =
+        (((((((A7 * r + A6) * r + A5) * r + A4) * r + A3) * r + A2) * r + A1) *
+             r +
+         A0);
+    T den =
+        (((((((B7 * r + B6) * r + B5) * r + B4) * r + B3) * r + B2) * r + B1) *
+             r +
+         B0);
+    return x * num / den;
+  }
+
+  T r = sqrt(LN2 - log(1.0 - abs_x));
+
+  T num, den;
+  if (r <= 5.0) {
+    r = r - 1.6;
+    num =
+        (((((((C7 * r + C6) * r + C5) * r + C4) * r + C3) * r + C2) * r + C1) *
+             r +
+         C0);
+    den =
+        (((((((D7 * r + D6) * r + D5) * r + D4) * r + D3) * r + D2) * r + D1) *
+             r +
+         D0);
+  } else {
+    r = r - 5.0;
+    num =
+        (((((((E7 * r + E6) * r + E5) * r + E4) * r + E3) * r + E2) * r + E1) *
+             r +
+         E0);
+    den =
+        (((((((F7 * r + F6) * r + F5) * r + F4) * r + F3) * r + F2) * r + F1) *
+             r +
+         F0);
+  }
+
+  if (x < 0) {
+    return -num / den;
+  } else {
+    return num / den;
+  }
+}
+
+template <typename T>
+struct TruncatedNormal {
+  T mean, std;
+  T a_normal_cdf;
+  T b_normal_cdf;
+  TruncatedNormal(T mean, T std) : mean(mean), std(std) {
+    auto normal_cdf = [](T x) {
+      return (1.0 + std::erf(x / std::sqrt(2.0))) / 2.0;
+    };
+    a_normal_cdf = normal_cdf(-2.0);
+    b_normal_cdf = normal_cdf(2.0);
+  }
+
+  T operator()(T value) const {
+    auto p = a_normal_cdf + (b_normal_cdf - a_normal_cdf) * value;
+    return std::sqrt(2.0) * Erfinv(2 * p - 1) * std + mean;
+  }
+};
+
 template <typename T, typename Context>
 void TruncatedGaussianRandomKernel(const Context& dev_ctx,
                                    const std::vector<int>& shape,
@@ -42,7 +173,13 @@ void TruncatedGaussianRandomKernel(const Context& dev_ctx,
   TruncatedNormal<T> truncated_normal(mean, std);
   int64_t size = tensor->numel();
 
-  auto engine = paddle::framework::GetCPURandomEngine(seed);
+  std::shared_ptr<std::mt19937_64> engine;
+  if (seed) {
+    engine = std::make_shared<std::mt19937_64>();
+    engine->seed(seed);
+  } else {
+    engine = dev_ctx.GetGenerator()->GetCPUEngine();
+  }
   for (int64_t i = 0; i < size; ++i) {
     data[i] = truncated_normal(dist(*engine));
   }

paddle/phi/kernels/gpu/truncated_gaussian_random_kernel.cu

@@ -24,8 +24,6 @@
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/kernel_registry.h"
 
-#include "paddle/fluid/framework/generator.h"
-
 namespace phi {
 
 template <typename T>
@@ -106,8 +104,7 @@ void TruncatedGaussianRandomKernel(const Context& dev_ctx,
   thrust::counting_iterator<int64_t> index_sequence_begin(0);
   int64_t size = tensor->numel();
 
-  int device_id = dev_ctx.GetPlace().GetDeviceId();
-  auto gen_cuda = paddle::framework::GetDefaultCUDAGenerator(device_id);
+  auto gen_cuda = dev_ctx.GetGenerator();
 
   if (gen_cuda->GetIsInitPy() && seed_flag) {
     auto seed_offset = gen_cuda->IncrementOffset(1);

paddle/phi/kernels/truncated_gaussian_random_kernel.h

@@ -14,149 +14,11 @@
 
 #pragma once
 
-#include <limits>
-#include <random>
-
 #include "paddle/phi/common/int_array.h"
 #include "paddle/phi/core/dense_tensor.h"
-#include "paddle/phi/core/device_context.h"
-#include "paddle/phi/infermeta/nullary.h"
 
 namespace phi {
 
-// reference: https://gist.github.com/lakshayg/d80172fe5ae3c5d2c2aedb53c250320e
-template <typename T>
-T Erfinv(T x) {
-  if (x < -1 || x > 1) {
-    return std::numeric_limits<T>::quiet_NaN();
-  } else if (x == 1.0) {
-    return std::numeric_limits<T>::infinity();
-  } else if (x == -1.0) {
-    return -std::numeric_limits<T>::infinity();
-  }
-
-  const T LN2 = 6.931471805599453094172321214581e-1;
-
-  const T A0 = 1.1975323115670912564578e0;
-  const T A1 = 4.7072688112383978012285e1;
-  const T A2 = 6.9706266534389598238465e2;
-  const T A3 = 4.8548868893843886794648e3;
-  const T A4 = 1.6235862515167575384252e4;
-  const T A5 = 2.3782041382114385731252e4;
-  const T A6 = 1.1819493347062294404278e4;
-  const T A7 = 8.8709406962545514830200e2;
-
-  const T B0 = 1.0000000000000000000e0;
-  const T B1 = 4.2313330701600911252e1;
-  const T B2 = 6.8718700749205790830e2;
-  const T B3 = 5.3941960214247511077e3;
-  const T B4 = 2.1213794301586595867e4;
-  const T B5 = 3.9307895800092710610e4;
-  const T B6 = 2.8729085735721942674e4;
-  const T B7 = 5.2264952788528545610e3;
-
-  const T C0 = 1.42343711074968357734e0;
-  const T C1 = 4.63033784615654529590e0;
-  const T C2 = 5.76949722146069140550e0;
-  const T C3 = 3.64784832476320460504e0;
-  const T C4 = 1.27045825245236838258e0;
-  const T C5 = 2.41780725177450611770e-1;
-  const T C6 = 2.27238449892691845833e-2;
-  const T C7 = 7.74545014278341407640e-4;
-
-  const T D0 = 1.4142135623730950488016887e0;
-  const T D1 = 2.9036514445419946173133295e0;
-  const T D2 = 2.3707661626024532365971225e0;
-  const T D3 = 9.7547832001787427186894837e-1;
-  const T D4 = 2.0945065210512749128288442e-1;
-  const T D5 = 2.1494160384252876777097297e-2;
-  const T D6 = 7.7441459065157709165577218e-4;
-  const T D7 = 1.4859850019840355905497876e-9;
-
-  const T E0 = 6.65790464350110377720e0;
-  const T E1 = 5.46378491116411436990e0;
-  const T E2 = 1.78482653991729133580e0;
-  const T E3 = 2.96560571828504891230e-1;
-  const T E4 = 2.65321895265761230930e-2;
-  const T E5 = 1.24266094738807843860e-3;
-  const T E6 = 2.71155556874348757815e-5;
-  const T E7 = 2.01033439929228813265e-7;
-
-  const T F0 = 1.414213562373095048801689e0;
-  const T F1 = 8.482908416595164588112026e-1;
-  const T F2 = 1.936480946950659106176712e-1;
-  const T F3 = 2.103693768272068968719679e-2;
-  const T F4 = 1.112800997078859844711555e-3;
-  const T F5 = 2.611088405080593625138020e-5;
-  const T F6 = 2.010321207683943062279931e-7;
-  const T F7 = 2.891024605872965461538222e-15;
-
-  T abs_x = abs(x);
-
-  if (abs_x <= 0.85) {
-    T r = 0.180625 - 0.25 * x * x;
-    T num =
-        (((((((A7 * r + A6) * r + A5) * r + A4) * r + A3) * r + A2) * r + A1) *
-             r +
-         A0);
-    T den =
-        (((((((B7 * r + B6) * r + B5) * r + B4) * r + B3) * r + B2) * r + B1) *
-             r +
-         B0);
-    return x * num / den;
-  }
-
-  T r = sqrt(LN2 - log(1.0 - abs_x));
-
-  T num, den;
-  if (r <= 5.0) {
-    r = r - 1.6;
-    num =
-        (((((((C7 * r + C6) * r + C5) * r + C4) * r + C3) * r + C2) * r + C1) *
-             r +
-         C0);
-    den =
-        (((((((D7 * r + D6) * r + D5) * r + D4) * r + D3) * r + D2) * r + D1) *
-             r +
-         D0);
-  } else {
-    r = r - 5.0;
-    num =
-        (((((((E7 * r + E6) * r + E5) * r + E4) * r + E3) * r + E2) * r + E1) *
-             r +
-         E0);
-    den =
-        (((((((F7 * r + F6) * r + F5) * r + F4) * r + F3) * r + F2) * r + F1) *
-             r +
-         F0);
-  }
-
-  if (x < 0) {
-    return -num / den;
-  } else {
-    return num / den;
-  }
-}
-
-template <typename T>
-struct TruncatedNormal {
-  T mean, std;
-  T a_normal_cdf;
-  T b_normal_cdf;
-  TruncatedNormal(T mean, T std) : mean(mean), std(std) {
-    auto normal_cdf = [](T x) {
-      return (1.0 + std::erf(x / std::sqrt(2.0))) / 2.0;
-    };
-    a_normal_cdf = normal_cdf(-2.0);
-    b_normal_cdf = normal_cdf(2.0);
-  }
-
-  T operator()(T value) const {
-    auto p = a_normal_cdf + (b_normal_cdf - a_normal_cdf) * value;
-    return std::sqrt(2.0) * Erfinv(2 * p - 1) * std + mean;
-  }
-};
-
 template <typename T, typename Context>
 void TruncatedGaussianRandomKernel(const Context& dev_ctx,
                                    const std::vector<int>& shape,

python/paddle/fluid/initializer.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import math
 from . import framework
 from . import core
-from .framework import _non_static_mode, default_main_program
+from .framework import _non_static_mode, in_dygraph_mode, _in_legacy_dygraph, default_main_program, _current_expected_place
 import numpy as np
 from .core import VarDesc
 from . import unique_name
@@ -417,7 +417,18 @@ class TruncatedNormalInitializer(Initializer):
             out_dtype = var.dtype
             out_var = var
 
-        if framework._non_static_mode():
+        if in_dygraph_mode():
+            out_var = _C_ops.final_state_truncated_gaussian_random(
+                var.shape, self._mean, self._std_dev, self._seed, out_dtype,
+                _current_expected_place())
+            if var.dtype in [VarDesc.VarType.FP16, VarDesc.VarType.BF16]:
+                var_tmp = _C_ops.final_state_cast(out_var, var.dtype)
+                var_tmp._share_underline_tensor_to(var)
+            else:
+                out_var._share_underline_tensor_to(var)
+            return None
+
+        if _in_legacy_dygraph():
             out_var = _C_ops.truncated_gaussian_random(
                 'shape', var.shape, 'dtype', out_dtype, 'mean', self._mean,
                 'std', self._std_dev, 'seed', self._seed)

python/paddle/fluid/tests/unittests/test_truncated_gaussian_random_op.py

@@ -17,10 +17,13 @@ from __future__ import print_function
 import unittest
 import numpy
 
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
+from op_test import OpTest
 from paddle.fluid.op import Operator
 from paddle.fluid.executor import Executor
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TestTrunctedGaussianRandomOp(unittest.TestCase):
@@ -33,15 +36,16 @@ class TestTrunctedGaussianRandomOp(unittest.TestCase):
             "std": 1.,
             "seed": 10,
         }
-
         self.outputs = ["Out"]
 
     def test_cpu(self):
         self.gaussian_random_test(place=fluid.CPUPlace())
+        self.gaussian_random_test_eager(place=fluid.CPUPlace())
 
     def test_gpu(self):
         if core.is_compiled_with_cuda():
             self.gaussian_random_test(place=fluid.CUDAPlace(0))
+            self.gaussian_random_test_eager(place=fluid.CUDAPlace(0))
 
     def gaussian_random_test(self, place):
 
@@ -64,6 +68,17 @@ class TestTrunctedGaussianRandomOp(unittest.TestCase):
         self.assertAlmostEqual(numpy.mean(tensor), .0, delta=0.1)
         self.assertAlmostEqual(numpy.var(tensor), 0.773, delta=0.1)
 
+    # TruncatedNormal.__call__ has no return value, so here call _C_ops api
+    # directly
+    def gaussian_random_test_eager(self, place):
+        with fluid.dygraph.guard(place):
+            with _test_eager_guard():
+                out = paddle._C_ops.final_state_truncated_gaussian_random(
+                    self.attrs["shape"], self.attrs["mean"], self.attrs["std"],
+                    self.attrs["seed"], core.VarDesc.VarType.FP32, place)
+                self.assertAlmostEqual(numpy.mean(out.numpy()), .0, delta=0.1)
+                self.assertAlmostEqual(numpy.var(out.numpy()), 0.773, delta=0.1)
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/utils/code_gen/api.yaml

@@ -1904,6 +1904,19 @@
     func : trunc
   backward : trunc_grad
 
+# python API: paddle.nn.initializer.TruncatedNormal
+- api : truncated_gaussian_random
+  args : (int[] shape, float mean, float std, int seed, DataType dtype=DataType::FLOAT32, Place place={})
+  output : Tensor
+  infer_meta :
+    func : TruncatedGaussianRandomInferMeta
+    param : [shape, mean, std, seed, dtype]
+  kernel :
+    func : truncated_gaussian_random
+    param : [shape, mean, std, seed, dtype]
+    backend : place
+    data_type : dtype
+
 # unfold
 - api : unfold
   args : (Tensor x, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations)