partial_concat_op.cu 8.5 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
/* 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/fluid/platform/device_context.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/operators/partial_concat_op.h"
#include "paddle/fluid/platform/float16.h"

namespace plat = paddle::platform;

namespace paddle {
namespace operators {

#define CEIL_DIV(x, y) (((x) + (y)-1) / (y))

using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;

template <class T>
__global__ void ConcatPartialCUDAKernel(T **in, T *out, int64_t all_length,
                                        int64_t in_batch_len,
                                        int64_t start_index,
                                        int64_t out_batch_len,
                                        int64_t part_length) {
  int id = blockIdx.x * blockDim.x + threadIdx.x;
  while (id < all_length) {
    int64_t bs_id = id / out_batch_len;
    int64_t bs_index = id % out_batch_len;
    int64_t var_id = bs_index / part_length;
    int64_t part_index = bs_index % part_length;
    int64_t in_id = start_index + part_index;
    const T *tmp = in[var_id];
    out[id] = tmp[bs_id * in_batch_len + in_id];
    id += blockDim.x * gridDim.x;
  }
}

template <class T>
__global__ void ConcatPartialGradCUDAKernel(
    T **in, const T *out, int64_t all_length, int64_t in_batch_len,
    int64_t start_index, int64_t out_batch_len, int64_t part_length) {
  int id = blockIdx.x * blockDim.x + threadIdx.x;
  while (id < all_length) {
    int64_t bs_id = id / out_batch_len;
    int64_t bs_index = id % out_batch_len;
    int64_t var_id = bs_index / part_length;
    int64_t part_index = bs_index % part_length;
    int64_t in_id = start_index + part_index;
    T *tmp = in[var_id];
    tmp[bs_id * in_batch_len + in_id] = out[id];
    id += blockDim.x * gridDim.x;
  }
}

template <typename T>
class PartialConcatOpCUDAKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    auto in_vars = ctx.MultiInput<Tensor>("X");
    Tensor *out = ctx.Output<Tensor>("Out");
    PADDLE_ENFORCE_EQ(in_vars[0] != nullptr, true,
                      platform::errors::InvalidArgument(
                          "The input of partial concat should not be null."));

    auto input_dim = in_vars[0]->dims();
    PADDLE_ENFORCE_EQ(input_dim.size(), 2,
                      platform::errors::InvalidArgument(
                          "Only supports 2-D array with batch size in the 1st "
                          "dimension and data in the 2nd."));
    auto in_size = input_dim[1];
    // may be negative
    auto start_index = ctx.Attr<int>("start_index");
    start_index = ComputeStartIndex(start_index, in_size);

    auto partial_len = ctx.Attr<int>("length");
    if (partial_len < 0) {
      partial_len = in_size - start_index;
    }

    int in_num = in_vars.size();
    int batch_size = input_dim[0];
    int out_batch_len = partial_len * in_num;
    int all_length = batch_size * out_batch_len;

    constexpr size_t theory_sm_threads = 1024;
    auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
    auto stream = dev_ctx.stream();
    auto max_threads = dev_ctx.GetMaxPhysicalThreadCount();
    auto sm_count = max_threads / theory_sm_threads;
    size_t tile_size = 0;
    int grids;
    int blocks;
    auto ComputeKernelParameter = [&](size_t length) {
      if (length >= max_threads)
        tile_size = 1024;
      else if (length < max_threads && length > sm_count * 128)
        tile_size = 512;
      else if (length <= sm_count * 128)
        tile_size = 256;
      grids = CEIL_DIV(length, tile_size);
      blocks = tile_size;
    };

    auto place = ctx.GetPlace();
    T *out_data = out->mutable_data<T>(place);

    std::vector<const T *> in_data;
    for (int i = 0; i < in_num; ++i)
      in_data.emplace_back(in_vars[i]->data<T>());

    auto tmp_in_array = memory::Alloc(dev_ctx, in_data.size() * sizeof(T *));
121
    memory::Copy(dev_ctx.GetPlace(), tmp_in_array->ptr(), platform::CPUPlace(),
122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189
                 reinterpret_cast<void *>(in_data.data()),
                 in_data.size() * sizeof(T *), dev_ctx.stream());

    T **in_array_data = reinterpret_cast<T **>(tmp_in_array->ptr());
    ComputeKernelParameter(all_length);
    ConcatPartialCUDAKernel<T><<<grids, blocks, 0, stream>>>(
        in_array_data, out->data<T>(), all_length, in_size, start_index,
        out_batch_len, partial_len);
  }
};

template <typename T>
class PartialConcatGradOpCUDAKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    auto *out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
    auto ins = ctx.MultiInput<LoDTensor>("X");
    auto outs = ctx.MultiOutput<LoDTensor>(framework::GradVarName("X"));

    PADDLE_ENFORCE_EQ(ins[0] != nullptr, true,
                      platform::errors::InvalidArgument(
                          "The input of partial concat should not be null."));
    // all parameters
    auto batch_size = ins[0]->dims()[0];
    auto in_size = ins[0]->dims()[1];
    // may be negative
    auto start_index = ctx.Attr<int>("start_index");
    start_index = ComputeStartIndex(start_index, in_size);
    auto partial_len = ctx.Attr<int>("length");
    if (partial_len < 0) partial_len = in_size - start_index;

    auto in_num = ins.size();
    auto grad_batch_len = partial_len * in_num;
    auto all_length = grad_batch_len * batch_size;
    // initialize
    auto &place = *ctx.template device_context<platform::CUDADeviceContext>()
                       .eigen_device();
    for (size_t i = 0; i < outs.size(); ++i) {
      outs[i]->mutable_data<T>(ctx.GetPlace());
      auto dxt = framework::EigenVector<T>::Flatten(*outs[i]);
      dxt.device(place) = dxt.constant(static_cast<T>(0));
    }

    constexpr size_t theory_sm_threads = 1024;
    auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
    auto stream = dev_ctx.stream();
    auto max_threads = dev_ctx.GetMaxPhysicalThreadCount();
    auto sm_count = max_threads / theory_sm_threads;
    size_t tile_size = 0;
    int grids;
    int blocks;
    auto ComputeKernelParameter = [&](size_t length) {
      if (length >= max_threads)
        tile_size = 1024;
      else if (length < max_threads && length > sm_count * 128)
        tile_size = 512;
      else if (length <= sm_count * 128)
        tile_size = 256;
      grids = CEIL_DIV(length, tile_size);
      blocks = tile_size;
    };

    std::vector<const T *> out_data;
    for (size_t i = 0; i < in_num; ++i) {
      out_data.emplace_back(outs[i]->data<T>());
    }
    auto tmp_out_array = memory::Alloc(dev_ctx, out_data.size() * sizeof(T *));

190
    memory::Copy(dev_ctx.GetPlace(), tmp_out_array->ptr(), platform::CPUPlace(),
191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217
                 reinterpret_cast<void *>(out_data.data()),
                 out_data.size() * sizeof(T *), dev_ctx.stream());

    T **out_grad_data = reinterpret_cast<T **>(tmp_out_array->ptr());
    ComputeKernelParameter(all_length);
    ConcatPartialGradCUDAKernel<T><<<grids, blocks, 0, stream>>>(
        out_grad_data, out_grad->data<T>(), all_length, in_size, start_index,
        grad_batch_len, partial_len);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(partial_concat, ops::PartialConcatOpCUDAKernel<float>,
                        ops::PartialConcatOpCUDAKernel<double>,
                        ops::PartialConcatOpCUDAKernel<int>,
                        ops::PartialConcatOpCUDAKernel<int64_t>,
                        ops::PartialConcatOpCUDAKernel<plat::float16>);

REGISTER_OP_CUDA_KERNEL(partial_concat_grad,
                        ops::PartialConcatGradOpCUDAKernel<float>,
                        ops::PartialConcatGradOpCUDAKernel<double>,
                        ops::PartialConcatGradOpCUDAKernel<int>,
                        ops::PartialConcatGradOpCUDAKernel<int64_t>,
                        ops::PartialConcatGradOpCUDAKernel<plat::float16>);