chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,428 @@
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// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <cmath>
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#include <cstdint>
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#include <cstdio>
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#include <cstdlib>
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#include <utility>
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#include <vector>
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/sparse_coo_tensor.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/funcs/math_function_impl.h"
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#include "paddle/phi/kernels/funcs/transpose_function.cuh"
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/** Reserved value for indicating "empty". */
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#define EMPTY_CELL (0)
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/** CUDA naive thread block size. */
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#define BLOCK_SIZE (256)
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__inline__ __device__ int8_t atomicCAS(int8_t* address,
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int8_t compare,
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int8_t val) {
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auto address_value = reinterpret_cast<uintptr_t>(address);
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int32_t* base_address = reinterpret_cast<int32_t*>(
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reinterpret_cast<char*>(address) - (address_value & 3));
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int32_t int_val = static_cast<int32_t>(val) << ((address_value & 3) * 8);
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int32_t int_comp = static_cast<int32_t>(compare) << ((address_value & 3) * 8);
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return static_cast<int8_t>(atomicCAS(base_address, int_comp, int_val));
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}
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// TODO(ShigureNyako): can we do this more efficiently?
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__inline__ __device__ int16_t atomicCAS(int16_t* address,
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int16_t compare,
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int16_t val) {
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auto address_value = reinterpret_cast<uintptr_t>(address);
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int32_t* base_address = reinterpret_cast<int32_t*>(
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reinterpret_cast<char*>(address) - (address_value & 2));
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int32_t int_val = static_cast<int32_t>(val) << ((address_value & 2) * 8);
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int32_t int_comp = static_cast<int32_t>(compare) << ((address_value & 2) * 8);
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return static_cast<int16_t>(atomicCAS(base_address, int_comp, int_val));
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}
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__inline__ __device__ int64_t atomicCAS(int64_t* address,
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int64_t compare,
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int64_t val) {
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using AtomicCAS64Type = unsigned long long; // NOLINT(runtime/int)
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return static_cast<int64_t>(
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atomicCAS(reinterpret_cast<AtomicCAS64Type*>(address),
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static_cast<AtomicCAS64Type>(compare),
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static_cast<AtomicCAS64Type>(val)));
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}
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namespace phi {
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namespace sparse {
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template <typename dtype = int>
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__device__ uint64_t hash_func_64b(dtype* data, int n = 4) {
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uint64_t hash = UINT64_C(14695981039346656037);
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for (int j = 0; j < n; j++) {
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hash ^= static_cast<unsigned int>(data[j]);
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hash *= UINT64_C(1099511628211);
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}
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// hash = (hash >> 60) ^ (hash & 0xFFFFFFFFFFFFFFF);
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return hash;
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}
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template <typename key_type>
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__device__ int hash(key_type key, int _capacity) {
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return static_cast<uint64_t>(key) % _capacity;
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}
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template <typename key_type, typename val_type>
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class GPUHashTable {
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private:
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// public:
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bool free_pointers;
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const int _capacity;
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const int _divisor;
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const int _width;
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key_type* table_keys;
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val_type* table_vals;
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void insert_many_coords(const GPUContext& dev_ctx,
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const int* coords,
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const int n);
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void lookup_many_coords(const GPUContext& dev_ctx,
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const int* coords,
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val_type* results,
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const int* kernel_sizes,
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const int* tensor_strides,
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const int n,
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const int kernel_volume);
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public:
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GPUHashTable(DenseTensor* table_keys,
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DenseTensor* table_vals,
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const int divisor,
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const int width)
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: _capacity(table_keys->dims()[0]),
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free_pointers(false),
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table_keys(table_keys->data<key_type>()),
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table_vals(table_vals->data<val_type>()),
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_divisor(divisor),
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_width(width) {}
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~GPUHashTable() {
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if (free_pointers) {
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cudaFree(table_keys);
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cudaFree(table_vals);
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}
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}
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void insert_coords(const GPUContext& dev_ctx, const DenseTensor& coords);
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void lookup_coords(const GPUContext& dev_ctx,
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const DenseTensor& coords,
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const int* kernel_sizes,
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const int* tensor_strides,
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int kernel_volume,
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DenseTensor* results);
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int get_divisor() { return _divisor; }
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int get_capacity() { return _capacity; }
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};
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using hashtable = GPUHashTable<int64_t, int>;
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using hashtable32 = GPUHashTable<int, int>;
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template <typename key_type = int64_t, typename val_type = int>
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__global__ void insert_coords_kernel(key_type* table_keys,
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val_type* table_vals,
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const int* coords,
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int n,
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int _capacity,
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int _width) {
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < n) {
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key_type key = (key_type)(hash_func_64b(coords + idx * _width, _width));
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int value = idx + 1;
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int slot = hash(key, _capacity);
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while (true) {
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key_type prev = atomicCAS(&table_keys[slot], EMPTY_CELL, key);
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if (prev == EMPTY_CELL || prev == key) {
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table_vals[slot] = value;
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return;
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}
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slot = (slot + 1) % _capacity;
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}
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}
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}
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template <typename key_type = int64_t, typename val_type = int, bool odd>
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__global__ void lookup_coords_kernel(key_type* table_keys,
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val_type* table_vals,
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const int* coords,
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val_type* vals,
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const int* kernel_sizes,
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const int* strides,
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int n,
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int _capacity,
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int kernel_volume,
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int _width) {
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int tidx = blockIdx.x * blockDim.x + threadIdx.x;
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int idx = tidx / kernel_volume;
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if (idx >= n) return;
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int _kernel_idx = tidx % kernel_volume;
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int kernel_idx = _kernel_idx;
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const int* in_coords = coords + _width * idx;
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int coords_out[4];
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// coords_out[2] = in_coords[2];
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// coords_out[3] = in_coords[3];
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coords_out[0] = in_coords[0];
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if constexpr (odd) {
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#pragma unroll
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for (int i = 0; i <= _width - 2; i++) {
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int cur_offset = _kernel_idx % kernel_sizes[i];
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cur_offset -= (kernel_sizes[i] - 1) / 2;
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coords_out[i + 1] = in_coords[i + 1] * strides[i] + cur_offset;
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_kernel_idx /= kernel_sizes[i];
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}
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} else {
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#pragma unroll
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for (int i = _width - 2; i >= 0; i--) {
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int cur_offset = _kernel_idx % kernel_sizes[i];
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cur_offset -= (kernel_sizes[i] - 1) / 2;
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coords_out[i + 1] = in_coords[i + 1] * strides[i] + cur_offset;
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_kernel_idx /= kernel_sizes[i];
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}
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}
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if (idx < n) {
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key_type key = (key_type)(hash_func_64b(coords_out, _width));
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int slot = hash(key, _capacity);
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while (true) {
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key_type cur_key = table_keys[slot];
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if (key == cur_key) {
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vals[idx * kernel_volume + kernel_idx] =
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table_vals[slot] -
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1; // need to subtract 1 to avoid extra operations in python
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}
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if (table_keys[slot] == EMPTY_CELL) {
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return;
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}
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slot = (slot + 1) % _capacity;
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}
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}
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}
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__global__ void set_kernel_sizes_and_strides_tensor(int* kernel_size_tensor,
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int* strides_tensor,
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int kernel_size0,
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int kernel_size1,
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int kernel_size2,
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int stride0,
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int stride1,
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int stride2) {
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < 6) {
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switch (idx) {
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case 0:
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kernel_size_tensor[idx] = kernel_size0;
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break;
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case 1:
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kernel_size_tensor[idx] = kernel_size1;
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break;
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case 2:
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kernel_size_tensor[idx] = kernel_size2;
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break;
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case 3:
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strides_tensor[idx - 3] = stride0;
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break;
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case 4:
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strides_tensor[idx - 3] = stride1;
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break;
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case 5:
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strides_tensor[idx - 3] = stride2;
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break;
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default:
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break;
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}
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}
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}
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template <typename key_type, typename val_type>
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void GPUHashTable<key_type, val_type>::insert_many_coords(
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const GPUContext& dev_ctx, const int* coords, const int n) {
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insert_coords_kernel<key_type, val_type>
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<<<(n + BLOCK_SIZE - 1) / BLOCK_SIZE, BLOCK_SIZE, 0, dev_ctx.stream()>>>(
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table_keys, table_vals, coords, n, _capacity, _width);
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}
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template <typename key_type, typename val_type>
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void GPUHashTable<key_type, val_type>::insert_coords(
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const GPUContext& dev_ctx, const DenseTensor& coords) {
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insert_many_coords(dev_ctx, coords.data<int>(), coords.dims()[0]);
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}
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template <typename key_type, typename val_type>
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void GPUHashTable<key_type, val_type>::lookup_many_coords(
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const GPUContext& dev_ctx,
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const int* coords,
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val_type* results,
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const int* kernel_sizes,
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const int* strides,
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const int n,
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const int kernel_volume) {
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if (kernel_volume % 2)
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lookup_coords_kernel<key_type, val_type, true>
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<<<(n * kernel_volume + BLOCK_SIZE - 1) / BLOCK_SIZE,
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BLOCK_SIZE,
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0,
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dev_ctx.stream()>>>(table_keys,
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table_vals,
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coords,
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results,
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kernel_sizes,
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strides,
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n,
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_capacity,
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kernel_volume,
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_width);
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else
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lookup_coords_kernel<key_type, val_type, false>
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<<<(n * kernel_volume + BLOCK_SIZE - 1) / BLOCK_SIZE,
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BLOCK_SIZE,
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0,
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dev_ctx.stream()>>>(table_keys,
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table_vals,
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coords,
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results,
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kernel_sizes,
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strides,
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n,
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_capacity,
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kernel_volume,
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_width);
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}
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template <typename key_type, typename val_type>
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void GPUHashTable<key_type, val_type>::lookup_coords(const GPUContext& dev_ctx,
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const DenseTensor& coords,
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const int* kernel_sizes,
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const int* strides,
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const int kernel_volume,
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DenseTensor* results) {
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int32_t* results_data = results->data<int32_t>();
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lookup_many_coords(dev_ctx,
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coords.data<int>(),
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results_data,
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kernel_sizes,
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strides,
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coords.dims()[0],
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kernel_volume);
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}
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template <typename IntT>
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void build_sparse_conv_kmap(const GPUContext& dev_ctx,
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const SparseCooTensor& x,
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const std::string& key,
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const std::vector<int>& kernel_sizes,
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const std::vector<int>& strides,
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const int kernel_volume,
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const bool is2D,
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SparseCooTensor* out) {
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int nnz = x.nnz();
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const KmapCache* in_kmap_cache_ptr = x.GetKmapCache(key);
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out->ClearKmaps();
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KmapCache* out_kmap_cache_ptr = nullptr;
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bool to_insert = false;
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if (in_kmap_cache_ptr == nullptr) {
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KmapCache kmap_cache;
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out_kmap_cache_ptr = out->SetKmapCache(key, kmap_cache);
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if (out_kmap_cache_ptr->hashmap_keys == nullptr) {
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DenseTensor* tmp_hashmap_keys = new DenseTensor();
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tmp_hashmap_keys->Resize({2 * x.nnz()});
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dev_ctx.template Alloc<IntT>(tmp_hashmap_keys);
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funcs::SetConstant<GPUContext, IntT> set_zero;
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set_zero(dev_ctx, tmp_hashmap_keys, static_cast<IntT>(0));
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out_kmap_cache_ptr->hashmap_keys = tmp_hashmap_keys;
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to_insert = true;
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}
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if (out_kmap_cache_ptr->hashmap_values == nullptr) {
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DenseTensor* tmp_hashmap_values = new DenseTensor();
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tmp_hashmap_values->Resize({2 * x.nnz()});
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dev_ctx.template Alloc<int32_t>(tmp_hashmap_values);
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funcs::SetConstant<GPUContext, int32_t> set_zero;
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set_zero(dev_ctx, tmp_hashmap_values, static_cast<int32_t>(0));
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out_kmap_cache_ptr->hashmap_values = tmp_hashmap_values;
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}
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if (out_kmap_cache_ptr->coords == nullptr) {
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DenseTensor* tmp_indices = new DenseTensor();
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tmp_indices->Resize({x.indices().dims()[1], x.indices().dims()[0]});
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dev_ctx.template Alloc<int32_t>(tmp_indices);
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// transpose indices
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std::vector<int> perm = {1, 0};
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funcs::TransposeGPUKernelDriver<int32_t>(
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dev_ctx, x.indices(), perm, tmp_indices);
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out_kmap_cache_ptr->coords = tmp_indices;
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}
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const int divisor = 128;
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const int width = is2D ? 3 : 4;
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auto hashmap =
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GPUHashTable<IntT, int32_t>(out_kmap_cache_ptr->hashmap_keys,
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out_kmap_cache_ptr->hashmap_values,
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divisor,
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width);
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if (to_insert) {
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hashmap.insert_coords(dev_ctx, *(out_kmap_cache_ptr->coords));
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}
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DenseTensor* tmp_out_in_map = new DenseTensor();
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tmp_out_in_map->Resize(
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{(x.nnz() + divisor - 1) / divisor * divisor, kernel_volume});
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dev_ctx.template Alloc<int32_t>(tmp_out_in_map);
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out_kmap_cache_ptr->out_in_map = tmp_out_in_map;
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funcs::SetConstant<GPUContext, int32_t> set_neg_one;
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set_neg_one(
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dev_ctx, out_kmap_cache_ptr->out_in_map, static_cast<int32_t>(-1));
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// need to put kernel_sizes and strides to GPU
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auto kernel_sizes_tensor = phi::Empty<int32_t>(dev_ctx, {3});
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auto strides_tensor = phi::Empty<int32_t>(dev_ctx, {3});
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set_kernel_sizes_and_strides_tensor<<<1, 32, 0, dev_ctx.stream()>>>(
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kernel_sizes_tensor.data<int32_t>(),
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strides_tensor.data<int32_t>(),
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kernel_sizes[0],
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kernel_sizes[1],
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kernel_sizes[2],
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strides[0],
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strides[1],
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strides[2]);
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hashmap.lookup_coords(dev_ctx,
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*(out_kmap_cache_ptr->coords),
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kernel_sizes_tensor.data<int32_t>(),
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strides_tensor.data<int32_t>(),
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kernel_volume,
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out_kmap_cache_ptr->out_in_map);
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} else {
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// out tensor takes the kmaps from x
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out->SetKmaps(x.GetKmaps());
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// force clear the kmaps of x
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const_cast<SparseCooTensor&>(x).ClearKmaps();
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}
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const KmapCache* new_out_kmap_cache_ptr = out->GetKmapCache(key);
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assert(new_out_kmap_cache_ptr != nullptr);
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assert(new_out_kmap_cache_ptr->hashmap_keys != nullptr);
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assert(new_out_kmap_cache_ptr->hashmap_values != nullptr);
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assert(new_out_kmap_cache_ptr->coords != nullptr);
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assert(new_out_kmap_cache_ptr->out_in_map != nullptr);
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return;
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}
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} // namespace sparse
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} // namespace phi
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Reference in New Issue
Block a user