chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/randperm_kernel.cc

@@ -0,0 +1,160 @@
+// 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.
+
+#include "paddle/phi/kernels/randperm_kernel.h"
+
+#include <array>
+#include <cstdint>
+#include <limits>
+
+#include "paddle/common/flags.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+COMMON_DECLARE_bool(use_accuracy_compatible_kernel);
+
+namespace phi {
+
+// ---------------------------------------------------------------------------
+// This is NOT the same as std::mt19937 or std::mt19937_64.
+// Using this engine ensures bit-for-bit identical output with torch.randperm.
+// ---------------------------------------------------------------------------
+
+constexpr int MERSENNE_STATE_N = 624;
+constexpr int MERSENNE_STATE_M = 397;
+constexpr uint32_t MATRIX_A = 0x9908b0df;
+constexpr uint32_t UMASK = 0x80000000;
+constexpr uint32_t LMASK = 0x7fffffff;
+
+class TorchMT19937Engine {
+ public:
+  inline explicit TorchMT19937Engine(uint64_t seed = 5489) {
+    init_with_uint32(seed);
+  }
+
+  inline uint32_t operator()() {
+    if (--(left_) == 0) {
+      next_state();
+    }
+    uint32_t y = *(state_.data() + next_++);
+    y ^= (y >> 11);
+    y ^= (y << 7) & 0x9d2c5680;
+    y ^= (y << 15) & 0xefc60000;
+    y ^= (y >> 18);
+    return y;
+  }
+
+  inline uint64_t random64() {
+    uint32_t r1 = (*this)();
+    uint32_t r2 = (*this)();
+    return (static_cast<uint64_t>(r1) << 32) | static_cast<uint64_t>(r2);
+  }
+
+ private:
+  std::array<uint32_t, MERSENNE_STATE_N> state_;
+  int left_ = 1;
+  uint32_t next_ = 0;
+
+  inline void init_with_uint32(uint64_t seed) {
+    state_[0] = seed & 0xffffffff;
+    for (int j = 1; j < MERSENNE_STATE_N; j++) {
+      state_[j] = (1812433253 * (state_[j - 1] ^ (state_[j - 1] >> 30)) + j);
+    }
+    left_ = 1;
+    next_ = 0;
+  }
+
+  inline uint32_t mix_bits(uint32_t u, uint32_t v) {
+    return (u & UMASK) | (v & LMASK);
+  }
+
+  inline uint32_t twist(uint32_t u, uint32_t v) {
+    return (mix_bits(u, v) >> 1) ^ (v & 1 ? MATRIX_A : 0);
+  }
+
+  inline void next_state() {
+    uint32_t* p = state_.data();
+    left_ = MERSENNE_STATE_N;
+    next_ = 0;
+
+    for (int j = MERSENNE_STATE_N - MERSENNE_STATE_M + 1; --j; p++) {
+      *p = p[MERSENNE_STATE_M] ^ twist(p[0], p[1]);
+    }
+    for (int j = MERSENNE_STATE_M; --j; p++) {
+      *p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], p[1]);
+    }
+    *p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], state_[0]);
+  }
+};
+
+template <typename T, typename Context>
+void RandpermKernel(const Context& dev_ctx,
+                    int n,
+                    DataType dtype UNUSED,
+                    DenseTensor* out) {
+  T* out_data = dev_ctx.template Alloc<T>(out);
+
+  if (FLAGS_use_accuracy_compatible_kernel) {
+    // MT19937 engine with that seed so the random sequence is identical.
+    uint64_t seed = dev_ctx.GetGenerator()->GetCurrentSeed();
+    TorchMT19937Engine engine(seed);
+
+    if (n < static_cast<int>(std::numeric_limits<uint32_t>::max() / 20)) {
+      // For small n: classic Fisher-Yates shuffle using 32-bit random values
+      for (int i = 0; i < n; ++i) {
+        out_data[i] = static_cast<T>(i);
+      }
+      for (int i = 0; i < n - 1; i++) {
+        int64_t z = engine() % (n - i);
+        T save = out_data[i];
+        out_data[i] = out_data[z + i];
+        out_data[z + i] = save;
+      }
+    } else {
+      // For large n: inside-out Fisher-Yates using 64-bit random values
+      for (int i = 0; i < n; i++) {
+        int64_t z = static_cast<int64_t>(engine.random64() % (i + 1));
+        out_data[i] = out_data[z];
+        out_data[z] = static_cast<T>(i);
+      }
+    }
+
+    // Advance the generator state so that successive randperm calls within the
+    // same run produce different results
+    dev_ctx.GetGenerator()->SetCurrentSeed(engine());
+  } else {
+    int seed = 0;
+    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 (int i = 0; i < n; ++i) {
+      out_data[i] = static_cast<T>(i);
+    }
+    std::shuffle(out_data, out_data + n, *engine);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(randperm,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::RandpermKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}