[phi] Migrate truncated_gaussian_random XPU kernel to PHI (#45529)

* migrate truncated_gaussian_random kernel to phi, test=kunlun

* reuse CPU kernel, test=kunlun

* debug kernel, test=kunlun

* migrate truncated_gaussian_random kernel to phi, test=kunlun

* split truncated_normal, test=kunlun

* try fix error from CI, test=kunlun

paddle/fluid/operators/truncated_gaussian_random_op_xpu.cc

-/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#ifdef PADDLE_WITH_XPU
-
-#include <limits>
-#include <random>
-
-#include "paddle/fluid/framework/generator.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/truncated_gaussian_random_op.h"
-
-namespace paddle {
-namespace operators {
-
-template <typename DeviceContext, typename T>
-class XPUTruncatedGaussianRandomKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    float mean = context.Attr<float>("mean");
-    float std = context.Attr<float>("std");
-    auto* tensor = context.Output<framework::Tensor>("Out");
-    T* data = tensor->mutable_data<T>(context.GetPlace());
-
-    std::uniform_real_distribution<T> dist(std::numeric_limits<float>::min(),
-                                           1.0);
-    TruncatedNormal<T> truncated_normal(mean, std);
-    int64_t size = tensor->numel();
-
-    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
-    // TODO(pangyoki): implement GetXPURandomEngine to set different seeds on
-    // corresponding XPU device.
-    auto engine = framework::GetCPURandomEngine(seed);
-
-    std::unique_ptr<T[]> data_cpu(new T[size]);
-
-    for (int64_t i = 0; i < size; ++i) {
-      data_cpu[i] = truncated_normal(dist(*engine));
-    }
-
-    memory::Copy(context.GetPlace(),
-                 data,
-                 platform::CPUPlace(),
-                 reinterpret_cast<void*>(data_cpu.get()),
-                 size * sizeof(T));
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OP_XPU_KERNEL(
-    truncated_gaussian_random,
-    ops::XPUTruncatedGaussianRandomKernel<paddle::platform::XPUDeviceContext,
-                                          float>);
-
-#endif  // PADDLE_WITH_XPU

paddle/phi/kernels/cpu/truncated_gaussian_random_kernel.cc

@@ -20,142 +20,10 @@
 
 #include "paddle/phi/backends/cpu/cpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/truncated_normal.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,

paddle/phi/kernels/funcs/truncated_normal.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+// 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;
+  }
+};

paddle/phi/kernels/xpu/truncated_gaussian_random_kernel.cc

+/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/phi/kernels/truncated_gaussian_random_kernel.h"
+
+#include <limits>
+#include <random>
+
+#include "paddle/fluid/memory/memcpy.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/truncated_normal.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void TruncatedGaussianRandomKernel(const Context& dev_ctx,
+                                   const std::vector<int>& shape,
+                                   float mean,
+                                   float std,
+                                   int seed,
+                                   DataType dtype,
+                                   DenseTensor* out) {
+  T* data = dev_ctx.template Alloc<T>(out);
+
+  std::uniform_real_distribution<T> dist(std::numeric_limits<float>::min(),
+                                         1.0);
+  TruncatedNormal<T> truncated_normal(mean, std);
+  int64_t size = out->numel();
+  std::unique_ptr<T[]> data_cpu(new T[size]);
+
+  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_cpu[i] = truncated_normal(dist(*engine));
+  }
+
+  paddle::memory::Copy(dev_ctx.GetPlace(),
+                       data,
+                       phi::CPUPlace(),
+                       reinterpret_cast<void*>(data_cpu.get()),
+                       size * sizeof(T));
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(truncated_gaussian_random,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::TruncatedGaussianRandomKernel,
+                   float) {}