From 100db44fc52324949f51aecd6d95e504621b0348 Mon Sep 17 00:00:00 2001 From: Guoxia Wang Date: Wed, 18 Aug 2021 10:28:32 +0800 Subject: [PATCH] support class center sample of PartialFC (#34106) * support class center sample of PartialFC --- .../fluid/operators/class_center_sample_op.cc | 147 ++++++ .../fluid/operators/class_center_sample_op.cu | 486 ++++++++++++++++++ .../fluid/operators/class_center_sample_op.h | 114 ++++ .../fluid/tests/unittests/CMakeLists.txt | 6 +- .../unittests/parallel_class_center_sample.py | 110 ++++ .../unittests/test_class_center_sample_op.py | 222 ++++++++ .../test_parallel_class_center_sample.py | 29 ++ .../white_list/no_check_set_white_list.py | 1 + python/paddle/nn/functional/__init__.py | 4 +- python/paddle/nn/functional/common.py | 153 ++++++ tools/static_mode_white_list.py | 1 + 11 files changed, 1271 insertions(+), 2 deletions(-) create mode 100644 paddle/fluid/operators/class_center_sample_op.cc create mode 100644 paddle/fluid/operators/class_center_sample_op.cu create mode 100644 paddle/fluid/operators/class_center_sample_op.h create mode 100644 python/paddle/fluid/tests/unittests/parallel_class_center_sample.py create mode 100644 python/paddle/fluid/tests/unittests/test_class_center_sample_op.py create mode 100644 python/paddle/fluid/tests/unittests/test_parallel_class_center_sample.py diff --git a/paddle/fluid/operators/class_center_sample_op.cc b/paddle/fluid/operators/class_center_sample_op.cc new file mode 100644 index 00000000000..6a1df7ec62c --- /dev/null +++ b/paddle/fluid/operators/class_center_sample_op.cc @@ -0,0 +1,147 @@ +// Copyright (c) 2021 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/operators/class_center_sample_op.h" + +namespace paddle { +namespace operators { + +class ClassCenterSampleOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + void InferShape(framework::InferShapeContext* ctx) const override { + OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", + "ClassCenterSample"); + OP_INOUT_CHECK(ctx->HasOutput("RemappedLabel"), "Output", "RemappedLabel", + "ClassCenterSample"); + OP_INOUT_CHECK(ctx->HasOutput("SampledLocalClassCenter"), "Output", + "SampledLocalClassCenter", "ClassCenterSample"); + + auto x_dims = ctx->GetInputDim("Label"); + PADDLE_ENFORCE_EQ(x_dims.size(), 1, + platform::errors::InvalidArgument( + "Rank of Input(Label) should be equal to 1, " + "but the value given is %d.", + x_dims.size())); + + ctx->SetOutputDim("RemappedLabel", x_dims); + auto num_samples = ctx->Attrs().Get("num_samples"); + ctx->SetOutputDim("SampledLocalClassCenter", + framework::make_ddim({num_samples})); + } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return framework::OpKernelType( + OperatorWithKernel::IndicateVarDataType(ctx, "Label"), + ctx.device_context()); + } +}; + +class ClassCenterSampleOpMaker : public framework::OpProtoAndCheckerMaker { + public: + void Make() override { + AddInput( + "Label", + "(Tensor) The input of ClassCenterSample op. Each value " + "of Label is an integer label."); + AddOutput("RemappedLabel", + "(Tensor) Output tensor with same shape as Label. " + "Each label is remap using sampled class."); + AddOutput("SampledLocalClassCenter", + "(Tensor) The sampled class center for local rank," + "value in [0, num_classes)."); + AddAttr( + "num_classes", + "A positive integer to specify the number of classes at local rank. " + "Note that num_classes of each GPU can be different."); + AddAttr( + "num_samples", + "A positive integer to specify the number of class center to sample."); + AddAttr("ring_id", "(int default 0) nccl communication ring id.") + .SetDefault(0); + AddAttr("nranks", "(int default 1) The total number of GPUs.") + .SetDefault(1); + AddAttr("rank", "(int default 0) The rank id in nranks.") + .SetDefault(0); + AddAttr("fix_seed", + "A flag indicating whether to use a fixed seed to generate " + "random negative class center. NOTE: DO NOT set this flag to" + "true in training. Setting this flag to true is only useful " + "in unittest or for debug") + .SetDefault(false); + AddAttr("seed", + "Random seed used to generate random negative class center. " + "[default 0].") + .SetDefault(0); + AddComment(R"DOC( + Class center sample method is proposed from the paper PartialFC that only sample a subset of the class centers. + The process of sampling subset class centers is straightforward: 1) First select the positive class centers; + 2) Randomly sample negative class centers. Specifically, given a Label tensor, shape [batch_size], select all + the positive class centers and randomly sample negative class centers, then remap the input label tensor using + the sampled class centers. Note that if the number of the positive class centers is greater than the input + num_samples, it keeps all the positive class centers and the shape of SampledLocalClassCenter will be + [num_positive_class_centers]. The op supports CPU, single GPU and multi GPU. + + For more information, Partial FC: Training 10 Million Identities on a Single Machine + arxiv: https://arxiv.org/abs/2010.05222 + + Examples: + For CPU or only one GPU + Given: + Label: [11, 5 , 1 , 3 , 12, 2 , 15, 19, 18, 19] + num_classes = 20 + num_samples = 6 + Then: + RemappedLabel: [4, 3, 0, 2, 5, 1, 6, 8, 7, 8] + SampledLocalClassCenter: [1 , 2 , 3 , 5 , 11, 12, 15, 18, 19] + + For multi GPU + Given: + rank0: + Label: [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ] + num_classes = 10 + num_samples = 6 + ring_id = 0 + nranks = 2 + rank = 0 + rank1: + Label: [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ] + num_classes = 10 + num_samples = 6 + ring_id = 0 + nranks = 2 + rank = 1 + Then: + rank0: + RemappedLabel: [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ] + SampledLocalClassCenter: [0, 2, 4, 8, 9, 3] + rank1: + RemappedLabel: [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ] + SampledLocalClassCenter: [0, 1, 2, 3, 5, 7, 8] +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +namespace plat = paddle::platform; +REGISTER_OP_WITHOUT_GRADIENT(class_center_sample, ops::ClassCenterSampleOp, + ops::ClassCenterSampleOpMaker); +REGISTER_OP_CPU_KERNEL(class_center_sample, + ops::ClassCenterSampleCPUKernel, + ops::ClassCenterSampleCPUKernel); diff --git a/paddle/fluid/operators/class_center_sample_op.cu b/paddle/fluid/operators/class_center_sample_op.cu new file mode 100644 index 00000000000..cfcfd04e6fc --- /dev/null +++ b/paddle/fluid/operators/class_center_sample_op.cu @@ -0,0 +1,486 @@ +// Copyright (c) 2021 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_HIP +#include +#include +#include +typedef hiprandState curandState; +namespace cub = hipcub; +#else +#include +#include +#include +#endif + +#include +#include +#include "paddle/fluid/operators/class_center_sample_op.h" + +#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) +#include "paddle/fluid/platform/collective_helper.h" +#include "paddle/fluid/platform/nccl_helper.h" +#endif + +namespace paddle { +namespace operators { +#define CUDA_KERNEL_LOOP(i, n) \ + for (int32_t i = blockIdx.x * blockDim.x + threadIdx.x, \ + step = blockDim.x * gridDim.x; \ + i < (n); i += step) + +using Tensor = framework::Tensor; + +static constexpr int kNumCUDAThreads = 512; +static constexpr int kNumMaxinumNumBlocks = 4096; + +inline int32_t NumBlocks(const int32_t n) { + return std::min((n + kNumCUDAThreads - 1) / kNumCUDAThreads, + kNumMaxinumNumBlocks); +} + +template +__global__ void RandomSampleClassCenter(const int64_t n, int64_t seed, + int64_t increment, + const int64_t max_val, T* buffer) { + const int id = blockIdx.x * blockDim.x + threadIdx.x; + curandState localState; + size_t local_seed = + (static_cast(seed) + 0x9E3779B9U + + (static_cast(id) << 6U) + (static_cast(id) >> 2U)); +#ifdef PADDLE_WITH_HIP + hiprand_init(local_seed, id, increment, &localState); + CUDA_KERNEL_LOOP(i, n) { + buffer[i] = static_cast(hiprand(&localState) % max_val); + } +#else + curand_init(local_seed, id, increment, &localState); + CUDA_KERNEL_LOOP(i, n) { + buffer[i] = static_cast(curand(&localState) % max_val); + } +#endif +} + +template +__global__ void Range(const int64_t n, T* out) { + CUDA_KERNEL_LOOP(i, n) { out[i] = static_cast(i); } +} + +template +__global__ void MarkPositiveClassCenter(const int64_t n, const int64_t rank, + const T* class_interval_ptr, + const int num_classes, const T* labels, + T* out) { + CUDA_KERNEL_LOOP(i, n) { + T label = labels[i] - class_interval_ptr[rank]; + if (label >= 0 && label < num_classes) { + out[label] = label - num_classes; + } + } +} + +template +__device__ void FindIntervalIndex(const T* class_interval_ptr, + const int64_t nranks, const T value, + int64_t* find_index) { + int64_t start = 0; + int64_t end = nranks; + int64_t mid = ((end - start) >> 1) + start + 1; + while (start < end) { + if (class_interval_ptr[mid] == value) break; + if (class_interval_ptr[mid] > value) + end = mid - 1; + else + start = mid; + mid = ((end - start) >> 1) + start + 1; + } + *find_index = min(mid, end); +} + +template +__global__ void GetClassCenterBound(const int64_t n, const int64_t nranks, + const T* class_interval_ptr, + const T* key_ptr, const T* value_ptr, + T* bound_index, T* bound_value) { + CUDA_KERNEL_LOOP(i, n) { + if (i != 0) { + int64_t cur_index, pre_index; + FindIntervalIndex(class_interval_ptr, nranks, key_ptr[i], &cur_index); + FindIntervalIndex(class_interval_ptr, nranks, key_ptr[i - 1], &pre_index); + if (cur_index > pre_index) { + assert(cur_index < nranks); +#pragma unroll + for (int32_t j = pre_index + 1; j <= cur_index; ++j) { + bound_index[j] = static_cast(i); + bound_value[j] = value_ptr[i]; + } + } + } + } + CUDA_KERNEL_LOOP(i, nranks + 1) { + int64_t first_index, last_index; + FindIntervalIndex(class_interval_ptr, nranks, key_ptr[0], &first_index); + FindIntervalIndex(class_interval_ptr, nranks, key_ptr[n - 1], &last_index); + if (i <= first_index) { + bound_index[i] = 0; + bound_value[i] = value_ptr[0]; + } else if (i > last_index) { + bound_index[i] = n; + bound_value[i] = value_ptr[n - 1] + 1; + } + } +} + +template +__global__ void GetRemappedLabel(const int64_t n, const int64_t nranks, + const T* sampled_class_interval_ptr, + const T* bound_index, const T* bound_value, + const T* label_map_key, T* label_map_value, + T* mapped_label) { + CUDA_KERNEL_LOOP(i, n) { +#pragma unroll + for (int64_t j = 0; j < nranks; j++) { + if (i >= bound_index[j] && i < bound_index[j + 1]) { + label_map_value[i] = + label_map_value[i] - bound_value[j] + sampled_class_interval_ptr[j]; + } + } + mapped_label[label_map_key[i]] = label_map_value[i]; + } +} + +// aligned vector generates vectorized load/store on CUDA +template +struct alignas(sizeof(T) * Size) AlignedVector { + T val[Size]; +}; + +template +inline int VectorizedSize(const T* pointer) { + uint64_t address = reinterpret_cast(pointer); + constexpr int vec4 = std::alignment_of>::value; // NOLINT + if (address % vec4 == 0) { + return 4; + } + return 1; +} + +#undef CUDA_KERNEL_LOOP + +template +class NotEqualToPreviousAdjacentIterator { + public: + using self_type = NotEqualToPreviousAdjacentIterator; + using value_type = T; + using difference_type = std::ptrdiff_t; + using pointer = T*; + using reference = T; + using iterator_category = std::input_iterator_tag; + + public: + __host__ __device__ __forceinline__ + NotEqualToPreviousAdjacentIterator(const T* arr, int64_t offset) + : arr_(arr), offset_(offset) {} + + __host__ __device__ __forceinline__ reference operator*() const { + return offset_ == 0 ? 0 : (arr_[offset_] == arr_[offset_ - 1] ? 0 : 1); + } + + template + __host__ __device__ __forceinline__ self_type operator+(Distance n) const { + self_type ret(arr_, offset_ + n); + return ret; + } + + template + __host__ __device__ __forceinline__ reference operator[](Distance n) const { + return *(*this + n); + } + + private: + const T* arr_; + int64_t offset_; +}; + +template +struct ActualNumSampledFunctor { + __host__ __device__ __forceinline__ T operator()(const T& a, + const T& b) const { + return max(num_samples, (b - a)); + } + T num_samples; + explicit ActualNumSampledFunctor(const T num) : num_samples(num) {} +}; + +template +class MemoryBuffer { + public: + MemoryBuffer(const int num_buffer_ele, const int num_temp_ele, + const int nranks, const platform::Place& place) { + offset1 = 0; + offset2 = offset1 + num_buffer_ele; + offset3 = offset2 + num_buffer_ele; + offset4 = offset3 + num_buffer_ele; + offset5 = offset4 + num_buffer_ele; + offset6 = offset5 + (nranks + 1); + offset7 = offset6 + (nranks + 1); + offset8 = offset7 + (nranks + 1); + offset9 = offset8 + num_temp_ele; + + buffer_ptr = buffer.mutable_data( + {4 * num_buffer_ele + 3 * (nranks + 1) + num_temp_ele}, place); + } + + T* cub_sort_keys_ptr() { return buffer_ptr + offset1; } + T* cub_sort_keys_out_ptr() { return buffer_ptr + offset2; } + T* cub_sort_values_ptr() { return buffer_ptr + offset3; } + T* cub_sort_values_out_ptr() { return buffer_ptr + offset4; } + T* bound_index_ptr() { return buffer_ptr + offset5; } + T* bound_value_ptr() { return buffer_ptr + offset6; } + T* class_interval_ptr() { return buffer_ptr + offset7; } + void* cub_temp_storage_ptr() { + return reinterpret_cast(buffer_ptr + offset8); + } + + private: + Tensor buffer; + T* buffer_ptr; + int offset1; + int offset2; + int offset3; + int offset4; + int offset5; + int offset6; + int offset7; + int offset8; + int offset9; +}; + +template +class ClassCenterSampleCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* label = ctx.Input("Label"); + auto* remapped_label = ctx.Output("RemappedLabel"); + auto* sampled_local_class_center = + ctx.Output("SampledLocalClassCenter"); + int num_classes = ctx.Attr("num_classes"); + int num_samples = ctx.Attr("num_samples"); + + int rid = ctx.Attr("ring_id"); + int nranks = ctx.Attr("nranks"); + int rank = ctx.Attr("rank"); + + int seed = ctx.Attr("seed"); + bool fix_seed = ctx.Attr("fix_seed"); + PADDLE_ENFORCE_GT(num_classes, 0, + platform::errors::InvalidArgument( + "The value 'num_classes' for Op(class_center_sample) " + "must be greater than 0, " + "but the value given is %d.", + num_classes)); + + PADDLE_ENFORCE_GT(num_samples, 0, + platform::errors::InvalidArgument( + "The value 'num_samples' for Op(class_center_sample) " + "must be greater than 0, " + "but the value given is %d.", + num_samples)); + + PADDLE_ENFORCE_LE(num_samples, num_classes, + platform::errors::InvalidArgument( + "The value 'num_samples' for Op(class_center_sample) " + "must be less than or equal to %d, " + "but the value given is %d.", + num_classes, num_samples)); + + auto& dev_ctx = ctx.template device_context(); + auto place = BOOST_GET_CONST(platform::CUDAPlace, dev_ctx.GetPlace()); + + int batch_size = label->numel(); + // Algorithm: + // We first randomly generate a value in [0, num_classes) on each position + // in a array(shape[num_classes]). Then, we mark the element as negative + // value in the array according input label. Now, we can sort the array + // by ascending to ensure that the positive class center always in the + // front of the sorted array. So, we can get the sampled class center + // index by sorted keys. Finally, we can get the rempped label by remap + // the input label according sampled class center. + + // step 1: Calculate num classes per device using nccl all reduce + std::vector shard_dim_vec(nranks + 1, 0); + shard_dim_vec[rank + 1] = num_classes; + Tensor num_classes_per_device; + framework::TensorFromVector(shard_dim_vec, ctx.cuda_device_context(), + &num_classes_per_device); + T* num_classes_per_device_ptr = num_classes_per_device.data(); + +#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) + if (nranks > 1) { + const auto& comm = + platform::NCCLCommContext::Instance().Get(rid, ctx.GetPlace()); + // use global calculate stream + const auto calcu_stream = + static_cast( + platform::DeviceContextPool::Instance().Get(ctx.GetPlace())) + ->stream(); + PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::ncclAllReduce( + num_classes_per_device_ptr, num_classes_per_device_ptr, + num_classes_per_device.numel(), + platform::ToNCCLDataType(num_classes_per_device.type()), ncclSum, + comm->comm(), calcu_stream)); + } +#endif + + // step 2: Determine temporary device storage requirements + int num_buffer_ele = std::max(batch_size, num_classes); + size_t cub_sort_temp_store_size = 0; + PADDLE_ENFORCE_CUDA_SUCCESS((cub::DeviceRadixSort::SortPairs( + nullptr, cub_sort_temp_store_size, nullptr, nullptr, nullptr, nullptr, + num_buffer_ele, 0, sizeof(T) * 8, ctx.cuda_device_context().stream()))); + + size_t cub_sum_temp_store_size = 0; + NotEqualToPreviousAdjacentIterator unique_counting_iter_temp(nullptr, 0); + PADDLE_ENFORCE_CUDA_SUCCESS( + (cub::DeviceScan::InclusiveSum, + T*>( + nullptr, cub_sum_temp_store_size, unique_counting_iter_temp, + nullptr, batch_size, ctx.cuda_device_context().stream()))); + + size_t cub_scan_temp_store_size = 0; + ActualNumSampledFunctor actual_num_sampled_op_temp(num_samples); + PADDLE_ENFORCE_CUDA_SUCCESS((cub::DeviceScan::InclusiveScan( + nullptr, cub_scan_temp_store_size, num_classes_per_device_ptr, + num_classes_per_device_ptr, actual_num_sampled_op_temp, nranks + 1, + ctx.cuda_device_context().stream()))); + + size_t cub_temp_storage_bytes = + std::max(std::max(cub_sort_temp_store_size, cub_scan_temp_store_size), + cub_sum_temp_store_size); + int num_temp_ele = cub_temp_storage_bytes / sizeof(T) + 1; + + // step 3: Alloc buffer memory so that we can reuse allocated memory + MemoryBuffer memory_buffer = + MemoryBuffer(num_buffer_ele, num_temp_ele, nranks, ctx.GetPlace()); + + T* cub_sort_keys_ptr = memory_buffer.cub_sort_keys_ptr(); + T* cub_sort_keys_out_ptr = memory_buffer.cub_sort_keys_out_ptr(); + T* cub_sort_values_ptr = memory_buffer.cub_sort_values_ptr(); + T* cub_sort_values_out_ptr = memory_buffer.cub_sort_values_out_ptr(); + T* bound_index_ptr = memory_buffer.bound_index_ptr(); + T* bound_value_ptr = memory_buffer.bound_value_ptr(); + T* class_interval_ptr = memory_buffer.class_interval_ptr(); + void* cub_temp_storage_ptr = memory_buffer.cub_temp_storage_ptr(); + + // step 4: Calculate class interval among nranks + PADDLE_ENFORCE_CUDA_SUCCESS((cub::DeviceScan::InclusiveSum( + cub_temp_storage_ptr, cub_temp_storage_bytes, + num_classes_per_device_ptr, class_interval_ptr, nranks + 1, + ctx.cuda_device_context().stream()))); + + // step 5: random sample negative class center + int vec_size = VectorizedSize(cub_sort_keys_ptr); + int increment = ((num_classes - 1) / + (NumBlocks(num_classes) * kNumCUDAThreads * vec_size) + + 1) * + vec_size; + if (!fix_seed) { + std::random_device rnd; + seed = rnd(); + } + RandomSampleClassCenter<<>>( + num_classes, seed + rank, increment, num_classes, cub_sort_keys_ptr); + + // step 6: mark positive class center as negative value + // fill the sort values to index 0, 1, ..., batch_size-1 + MarkPositiveClassCenter<<>>( + batch_size, rank, class_interval_ptr, num_classes, label->data(), + cub_sort_keys_ptr); + Range<<>>(num_buffer_ele, + cub_sort_values_ptr); + + // step 7: sort class center by ascending, so that positive class center + // always be sampled. + PADDLE_ENFORCE_CUDA_SUCCESS((cub::DeviceRadixSort::SortPairs( + cub_temp_storage_ptr, cub_temp_storage_bytes, cub_sort_keys_ptr, + cub_sort_keys_out_ptr, cub_sort_values_ptr, cub_sort_values_out_ptr, + num_classes, 0, sizeof(T) * 8, ctx.cuda_device_context().stream()))); + + // step 8: sort input label ascending + PADDLE_ENFORCE_CUDA_SUCCESS((cub::DeviceRadixSort::SortPairs( + cub_temp_storage_ptr, cub_temp_storage_bytes, label->data(), + cub_sort_keys_out_ptr, cub_sort_values_ptr, cub_sort_keys_ptr, + batch_size, 0, sizeof(T) * 8, ctx.cuda_device_context().stream()))); + + // step 9: Calculate new index using InclusiveSum on ascending sorted input + // label + NotEqualToPreviousAdjacentIterator unique_counting_iter( + cub_sort_keys_out_ptr, 0); + PADDLE_ENFORCE_CUDA_SUCCESS((cub::DeviceScan::InclusiveSum< + NotEqualToPreviousAdjacentIterator, T*>( + cub_temp_storage_ptr, cub_temp_storage_bytes, unique_counting_iter, + cub_sort_values_ptr, batch_size, ctx.cuda_device_context().stream()))); + + // step 10: Calculate new class center bound among ranks + GetClassCenterBound<<>>( + batch_size, nranks, class_interval_ptr, cub_sort_keys_out_ptr, + cub_sort_values_ptr, bound_index_ptr, bound_value_ptr); + + // step 11: Calculate actual number of sampled class per device. + // Since maybe num_positive_class_center > num_samples, + // we need to ensure all positive class center per device are sampled. + ActualNumSampledFunctor actual_num_sampled_op(num_samples); + PADDLE_ENFORCE_CUDA_SUCCESS((cub::DeviceScan::InclusiveScan( + cub_temp_storage_ptr, cub_temp_storage_bytes, bound_value_ptr, + num_classes_per_device_ptr, actual_num_sampled_op, nranks + 1, + ctx.cuda_device_context().stream()))); + + // step 12: Calculate actual sampled class interval among nranks + PADDLE_ENFORCE_CUDA_SUCCESS((cub::DeviceScan::InclusiveSum( + cub_temp_storage_ptr, cub_temp_storage_bytes, + num_classes_per_device_ptr, class_interval_ptr, nranks + 1, + ctx.cuda_device_context().stream()))); + + // step 13: Get remapped label for output + GetRemappedLabel<<>>( + batch_size, nranks, class_interval_ptr, bound_index_ptr, + bound_value_ptr, cub_sort_keys_ptr, cub_sort_values_ptr, + remapped_label->mutable_data(ctx.GetPlace())); + + // step 14: Get sampled class center for output + framework::TensorCopySync(num_classes_per_device, platform::CPUPlace(), + &num_classes_per_device); + T actual_num_samples = num_classes_per_device.data()[rank + 1]; + T* sampled_local_class_center_ptr = + sampled_local_class_center->mutable_data({actual_num_samples}, + ctx.GetPlace()); + memory::Copy(place, sampled_local_class_center_ptr, place, + cub_sort_values_out_ptr, actual_num_samples * sizeof(T), + nullptr); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_CUDA_KERNEL( + class_center_sample, + ops::ClassCenterSampleCUDAKernel, + ops::ClassCenterSampleCUDAKernel); diff --git a/paddle/fluid/operators/class_center_sample_op.h b/paddle/fluid/operators/class_center_sample_op.h new file mode 100644 index 00000000000..24ce9ace3bf --- /dev/null +++ b/paddle/fluid/operators/class_center_sample_op.h @@ -0,0 +1,114 @@ +// Copyright (c) 2021 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 +#include +#include +#include +#include "paddle/fluid/framework/generator.h" +#include "paddle/fluid/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; +template +class ClassCenterSampleCPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* label = ctx.Input("Label"); + auto* remapped_label = ctx.Output("RemappedLabel"); + auto* sampled_local_class_center = + ctx.Output("SampledLocalClassCenter"); + int num_classes = ctx.Attr("num_classes"); + int num_samples = ctx.Attr("num_samples"); + + int seed = ctx.Attr("seed"); + bool fix_seed = ctx.Attr("fix_seed"); + PADDLE_ENFORCE_GT(num_classes, 0, + platform::errors::InvalidArgument( + "The value 'num_classes' for Op(class_center_sample) " + "must be greater than 0, " + "but the value given is %d.", + num_classes)); + + PADDLE_ENFORCE_GT(num_samples, 0, + platform::errors::InvalidArgument( + "The value 'num_samples' for Op(class_center_sample) " + "must be greater than 0, " + "but the value given is %d.", + num_samples)); + + PADDLE_ENFORCE_LE(num_samples, num_classes, + platform::errors::InvalidArgument( + "The value 'num_samples' for Op(class_center_sample) " + "must be less than or equal to %d, " + "but the value given is %d.", + num_classes, num_samples)); + + int64_t numel = label->numel(); + auto* label_ptr = label->data(); + + // get unique positive class center by ascending + std::set> unique_label; + for (int64_t i = 0; i < numel; ++i) { + unique_label.insert(label_ptr[i]); + } + + // constrcut a lookup table and get sampled_local_class_center + std::vector actual_sampled; + std::map new_class_dict; + T idx = 0; + for (auto& t : unique_label) { + new_class_dict[t] = idx; + actual_sampled.push_back(t); + idx++; + } + + if (!fix_seed) { + std::random_device rnd; + seed = rnd(); + } + std::uniform_int_distribution dist(0, num_classes - 1); + auto engine = framework::GetCPURandomEngine(seed); + // sample negative class center randomly + while (unique_label.size() < static_cast(num_samples)) { + T neg = dist(*engine); + if (unique_label.find(neg) == unique_label.end()) { + unique_label.insert(neg); + // unorder for negative class center + actual_sampled.push_back(neg); + } + } + + int actual_num_samples = unique_label.size(); + T* sampled_local_class_center_ptr = + sampled_local_class_center->mutable_data({actual_num_samples}, + ctx.GetPlace()); + idx = 0; + for (auto& t : actual_sampled) { + sampled_local_class_center_ptr[idx] = t; + idx++; + } + + // remap the input label to sampled class + auto* remmaped_label_ptr = remapped_label->mutable_data(ctx.GetPlace()); + for (int64_t i = 0; i < numel; ++i) { + remmaped_label_ptr[i] = new_class_dict[label_ptr[i]]; + } + } +}; + +} // namespace operators +} // namespace paddle diff --git a/python/paddle/fluid/tests/unittests/CMakeLists.txt b/python/paddle/fluid/tests/unittests/CMakeLists.txt index 9d8b5fb699e..5b9a37cfb62 100644 --- a/python/paddle/fluid/tests/unittests/CMakeLists.txt +++ b/python/paddle/fluid/tests/unittests/CMakeLists.txt @@ -1,4 +1,5 @@ -file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py") +file(GLOB TEST_OPS RELATIVE +"${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py") string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}") set(GC_ENVS FLAGS_eager_delete_tensor_gb=0.0 FLAGS_fast_eager_deletion_mode=1 FLAGS_memory_fraction_of_eager_deletion=1.0) set(dist_ENVS http_proxy="" https_proxy="") @@ -28,6 +29,7 @@ list(APPEND DIST_TEST_OPS test_parallel_dygraph_pipeline_parallel) list(APPEND DIST_TEST_OPS test_parallel_dygraph_tensor_parallel) list(APPEND DIST_TEST_OPS test_parallel_dygraph_sharding_parallel) list(APPEND DIST_TEST_OPS test_parallel_dygraph_mp_layers) +list(APPEND DIST_TEST_OPS test_parallel_class_center_sample) list(APPEND DIST_TEST_OPS test_parallel_margin_cross_entropy) set(MIXED_DIST_TEST_OPS ${DIST_TEST_OPS}) #remove distribute unittests. @@ -196,6 +198,7 @@ if ((NOT WITH_GPU) AND (NOT WITH_ROCM)) LIST(REMOVE_ITEM TEST_OPS test_mixed_precision) LIST(REMOVE_ITEM TEST_OPS test_fleet_base_single) LIST(REMOVE_ITEM TEST_OPS test_dygraph_recompute) + list(REMOVE_ITEM TEST_OPS test_parallel_class_center_sample) LIST(REMOVE_ITEM TEST_OPS test_parallel_margin_cross_entropy) elseif(WITH_GPU) if (${CUDNN_VERSION} VERSION_LESS 7100) @@ -908,6 +911,7 @@ if(WITH_DISTRIBUTE AND WITH_GPU AND WITH_NCCL) set_tests_properties(test_parallel_dygraph_tensor_parallel PROPERTIES TIMEOUT 200) set_tests_properties(test_parallel_dygraph_sharding_parallel PROPERTIES TIMEOUT 120) set_tests_properties(test_parallel_dygraph_mp_layers PROPERTIES TIMEOUT 120) + set_tests_properties(test_parallel_class_center_sample PROPERTIES TIMEOUT 120) set_tests_properties(test_parallel_margin_cross_entropy PROPERTIES TIMEOUT 120) if(${NCCL_VERSION} VERSION_GREATER_EQUAL 2212) set_tests_properties(test_parallel_dygraph_sparse_embedding PROPERTIES TIMEOUT 120) diff --git a/python/paddle/fluid/tests/unittests/parallel_class_center_sample.py b/python/paddle/fluid/tests/unittests/parallel_class_center_sample.py new file mode 100644 index 00000000000..e1126138eac --- /dev/null +++ b/python/paddle/fluid/tests/unittests/parallel_class_center_sample.py @@ -0,0 +1,110 @@ +# Copyright (c) 2021 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. + +from __future__ import division +from __future__ import print_function + +import unittest + +import paddle +import numpy as np +import random +import paddle.distributed as dist +import paddle.fluid as fluid +import paddle.distributed.fleet as fleet +from paddle import framework + + +def set_random_seed(seed): + """Set random seed for reproducability.""" + random.seed(seed) + np.random.seed(seed) + paddle.seed(seed) + fleet.meta_parallel.model_parallel_random_seed(seed) + + +def class_center_sample_numpy(label, classes_list, num_samples): + unique_label = np.unique(label) + nranks = len(classes_list) + class_interval = np.cumsum(np.insert(classes_list, 0, 0)) + pos_class_center_per_device = [] + unique_label_per_device = [] + + for i in range(nranks): + index = np.logical_and(unique_label >= class_interval[i], + unique_label < class_interval[i + 1]) + pos_class_center_per_device.append(unique_label[index] - class_interval[ + i]) + unique_label_per_device.append(unique_label[index]) + + num_samples_per_device = [] + for pos_class_center in pos_class_center_per_device: + num_samples_per_device.append(max(len(pos_class_center), num_samples)) + sampled_class_interval = np.cumsum(np.insert(num_samples_per_device, 0, 0)) + + remapped_dict = {} + for i in range(nranks): + for idx, v in enumerate(unique_label_per_device[i], + sampled_class_interval[i]): + remapped_dict[v] = idx + + remapped_label = [] + for l in label: + remapped_label.append(remapped_dict[l]) + + return remapped_label, pos_class_center_per_device + + +class TestParallelClassCenterSampleOp(unittest.TestCase): + def setUp(self): + strategy = fleet.DistributedStrategy() + fleet.init(is_collective=True, strategy=strategy) + + def test_class_center_sample(self): + + rank_id = dist.get_rank() + nranks = dist.get_world_size() + + seed = 1025 + set_random_seed(seed) + paddle.seed(rank_id * 10) + random.seed(seed) + np.random.seed(seed) + + batch_size = 20 + num_samples = 6 + + for dtype in ('int32', 'int64'): + for _ in range(5): + classes_list = np.random.randint(10, 15, (nranks, )) + num_class = np.sum(classes_list) + + np_label = np.random.randint( + 0, num_class, (batch_size, ), dtype=dtype) + label = paddle.to_tensor(np_label, dtype=dtype) + np_remapped_label, np_sampled_class_center_per_device = class_center_sample_numpy( + np_label, classes_list, num_samples) + remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample( + label, classes_list[rank_id], num_samples) + np.testing.assert_allclose(remapped_label.numpy(), + np_remapped_label) + np_sampled_class_index = np_sampled_class_center_per_device[ + rank_id] + np.testing.assert_allclose( + sampled_class_index.numpy()[:len(np_sampled_class_index)], + np_sampled_class_index) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_class_center_sample_op.py b/python/paddle/fluid/tests/unittests/test_class_center_sample_op.py new file mode 100644 index 00000000000..752ca307dd8 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_class_center_sample_op.py @@ -0,0 +1,222 @@ +# Copyright (c) 2021 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. + +from __future__ import print_function + +import unittest +import numpy as np +import math +import random +import paddle +import paddle.fluid.core as core +from op_test import OpTest +from paddle.fluid import Program, program_guard + + +def class_center_sample_numpy(label, classes_list, num_samples): + unique_label = np.unique(label) + nranks = len(classes_list) + class_interval = np.cumsum(np.insert(classes_list, 0, 0)) + pos_class_center_per_device = [] + unique_label_per_device = [] + + for i in range(nranks): + index = np.logical_and(unique_label >= class_interval[i], + unique_label < class_interval[i + 1]) + pos_class_center_per_device.append(unique_label[index] - class_interval[ + i]) + unique_label_per_device.append(unique_label[index]) + + num_samples_per_device = [] + for pos_class_center in pos_class_center_per_device: + num_samples_per_device.append(max(len(pos_class_center), num_samples)) + sampled_class_interval = np.cumsum(np.insert(num_samples_per_device, 0, 0)) + + remapped_dict = {} + for i in range(nranks): + for idx, v in enumerate(unique_label_per_device[i], + sampled_class_interval[i]): + remapped_dict[v] = idx + + remapped_label = [] + for l in label: + remapped_label.append(remapped_dict[l]) + + return np.array(remapped_label), np.array(pos_class_center_per_device) + + +class TestClassCenterSampleOp(OpTest): + def initParams(self): + self.op_type = "class_center_sample" + self.batch_size = 20 + self.num_samples = 6 + self.num_classes = 10 + self.seed = 2021 + + def init_dtype(self): + self.dtype = np.int64 + + def init_fix_seed(self): + self.fix_seed = True + + def setUp(self): + self.initParams() + self.init_dtype() + self.init_fix_seed() + label = np.random.randint( + 0, self.num_classes, (self.batch_size, ), dtype=self.dtype) + + remapped_label, sampled_class_center = class_center_sample_numpy( + label, [self.num_classes], self.num_samples) + + self.inputs = {'Label': label} + self.outputs = { + 'RemappedLabel': remapped_label.astype(self.dtype), + 'SampledLocalClassCenter': sampled_class_center.astype(self.dtype) + } + + self.attrs = { + 'num_classes': self.num_classes, + 'num_samples': self.num_samples, + 'seed': self.seed, + 'fix_seed': self.fix_seed, + } + + def test_check_output(self): + self.check_output(no_check_set=['SampledLocalClassCenter']) + + +class TestClassCenterSampleOpINT32(TestClassCenterSampleOp): + def init_dtype(self): + self.dtype = np.int32 + + +class TestClassCenterSampleOpFixSeed(TestClassCenterSampleOp): + def init_fix_seed(self): + self.fix_seed = True + + +class TestClassCenterSampleV2(unittest.TestCase): + def setUp(self): + self.initParams() + np.random.seed(self.seed) + paddle.framework.random._manual_program_seed(2021) + self.places = [paddle.fluid.CPUPlace()] + if core.is_compiled_with_cuda(): + self.places.append(paddle.fluid.CUDAPlace(0)) + + def initParams(self): + self.batch_size = 10 + self.num_samples = 6 + self.num_classes = 20 + self.seed = 0 + self.init_dtype() + + def init_dtype(self): + self.dtype = np.int64 + + def test_static(self): + for place in self.places: + self.check_static_result(place=place) + + def check_static_result(self, place): + with program_guard(Program(), Program()): + label_np = np.random.randint( + 0, self.num_classes, (self.batch_size, ), dtype=self.dtype) + + label = paddle.static.data( + name='label', shape=[self.batch_size], dtype=self.dtype) + remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample( + label, self.num_classes, self.num_samples, seed=self.seed) + + remapped_label_np, sampled_class_center_np = class_center_sample_numpy( + label_np, [self.num_classes], self.num_samples) + exe = paddle.fluid.Executor(place) + [remapped_label_res, sampled_class_index_res] = exe.run( + paddle.fluid.default_main_program(), + feed={'label': label_np}, + fetch_list=[remapped_label, sampled_class_index]) + np.testing.assert_allclose(remapped_label_res, remapped_label_np) + np.testing.assert_allclose( + sampled_class_index_res[:len(sampled_class_center_np[0])], + sampled_class_center_np[0]) + + def test_dynamic(self): + for place in self.places: + self.check_dynamic_result(place=place) + + def check_dynamic_result(self, place): + with paddle.fluid.dygraph.guard(place): + label_np = np.random.randint( + 0, self.num_classes, (self.batch_size, ), dtype=self.dtype) + label = paddle.to_tensor(label_np, dtype=self.dtype) + + remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample( + label, self.num_classes, self.num_samples, seed=self.seed) + + remapped_label_np, sampled_class_center_np = class_center_sample_numpy( + label_np, [self.num_classes], self.num_samples) + + remapped_label_res = remapped_label.numpy() + sampled_class_index_res = sampled_class_index.numpy() + np.testing.assert_allclose(remapped_label_res, remapped_label_np) + np.testing.assert_allclose( + sampled_class_index_res[:len(sampled_class_center_np[0])], + sampled_class_center_np[0]) + + +class TestClassCenterSampleV2INT32(TestClassCenterSampleV2): + def init_dtype(self): + self.dtype = np.int32 + + +class TestClassCenterSampleAPIError(unittest.TestCase): + def setUp(self): + self.initParams() + np.random.seed(self.seed) + self.places = [paddle.fluid.CPUPlace()] + if core.is_compiled_with_cuda(): + self.places.append(paddle.fluid.CUDAPlace(0)) + + def initParams(self): + self.batch_size = 20 + self.num_samples = 15 + self.num_classes = 10 + self.seed = 2021 + self.init_dtype() + + def init_dtype(self): + self.dtype = np.int64 + + def test_dynamic_errors(self): + def test_num_samples(): + for place in self.places: + with paddle.fluid.dygraph.guard(place): + label_np = np.random.randint( + 0, + self.num_classes, (self.batch_size, ), + dtype=self.dtype) + label = paddle.to_tensor(label_np) + + remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample( + label, + self.num_classes, + self.num_samples, + seed=self.seed) + + self.assertRaises(ValueError, test_num_samples) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_parallel_class_center_sample.py b/python/paddle/fluid/tests/unittests/test_parallel_class_center_sample.py new file mode 100644 index 00000000000..19fc617ea25 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_parallel_class_center_sample.py @@ -0,0 +1,29 @@ +# Copyright (c) 2021 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. + +from __future__ import print_function + +import unittest +import paddle.fluid as fluid + +from test_parallel_dygraph_dataparallel import TestMultipleGpus + + +class TestParallelClassCenterSample(TestMultipleGpus): + def test_parallel_class_center_sample(self): + self.run_mnist_2gpu('parallel_class_center_sample.py') + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/white_list/no_check_set_white_list.py b/python/paddle/fluid/tests/unittests/white_list/no_check_set_white_list.py index 32ac4f412a8..2492caff2f9 100644 --- a/python/paddle/fluid/tests/unittests/white_list/no_check_set_white_list.py +++ b/python/paddle/fluid/tests/unittests/white_list/no_check_set_white_list.py @@ -31,4 +31,5 @@ no_check_set_white_list = [ 'rnn', 'fusion_lstm', 'softmax_with_cross_entropy', + 'class_center_sample', ] diff --git a/python/paddle/nn/functional/__init__.py b/python/paddle/nn/functional/__init__.py index 04e0b7c140d..e10f0f1686d 100644 --- a/python/paddle/nn/functional/__init__.py +++ b/python/paddle/nn/functional/__init__.py @@ -55,6 +55,7 @@ from .common import unfold # noqa: F401 from .common import interpolate # noqa: F401 from .common import upsample # noqa: F401 from .common import bilinear # noqa: F401 +from .common import class_center_sample # noqa: F401 from .conv import conv1d # noqa: F401 from .conv import conv1d_transpose # noqa: F401 from .common import linear # noqa: F401 @@ -200,5 +201,6 @@ __all__ = [ #noqa 'temporal_shift', 'batch_norm', 'layer_norm', - 'instance_norm' + 'instance_norm', + 'class_center_sample', ] diff --git a/python/paddle/nn/functional/common.py b/python/paddle/nn/functional/common.py index 4bc137222d2..aee8ea2a3cc 100644 --- a/python/paddle/nn/functional/common.py +++ b/python/paddle/nn/functional/common.py @@ -1564,3 +1564,156 @@ def label_smooth(label, prior_dist=None, epsilon=0.1, name=None): outputs={"Out": smooth_label}, attrs={"epsilon": float(epsilon)}) return smooth_label + + +def class_center_sample(label, num_classes, num_samples, group=None, seed=None): + """ + Class center sample method is proposed from the paper PartialFC that only sample a subset of the class centers. + The process of sampling subset class centers is straightforward: + + 1. First select the positive class centers; + 2. Then randomly sample negative class centers. + + Specifically, given a label tensor, shape [batch_size], select all the positive class centers and randomly + sample negative class centers, then remap the input label tensor using the sampled class centers. + + For more information, Partial FC: Training 10 Million Identities on a Single Machine + arxiv: https://arxiv.org/abs/2010.05222 + + .. hint:: + If the number of the positive class centers is greater than the input num_samples, it keeps all the positive + class centers and the shape of sampled_class_center will be [num_positive_class_centers]. + + The API supports CPU, single GPU and multi GPU. + + Args: + label (Tensor): 1-D tensor with shape [N], each label in [0, num_classes) + num_classes (int): A positive integer to specify the number of classes at local rank. + Note that num_classes of each GPU can be different. + num_samples (int): A positive integer to specify the number of class center to sample. + group (Group, optional): The abstract representation of group. + See paddle.distributed.collective.Group. Default is ``None``. + seed (int, optional): Random seed. Default is ``None``. + + Returns: + Tuple of two ``Tensor`` : (remapped_label, sampled_class_center), remapped label using sampled class center, + sampled class center from [0, num_classes). + + Examples: + + .. code-block:: python + + # CPU or single GPU + import paddle + num_classes = 20 + batch_size = 10 + num_samples = 6 + label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64') + remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes, num_samples) + + print(label) + print(remapped_label) + print(sampled_class_index) + + # the output is + #Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True, + # [11, 5 , 1 , 3 , 12, 2 , 15, 19, 18, 19]) + #Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True, + # [4, 3, 0, 2, 5, 1, 6, 8, 7, 8]) + #Tensor(shape=[9], dtype=int64, place=CPUPlace, stop_gradient=True, + # [1 , 2 , 3 , 5 , 11, 12, 15, 18, 19]) + + .. code-block:: python + + # required: distributed + # Multi GPU, test_class_center_sample.py + import paddle + import paddle.distributed as dist + strategy = dist.fleet.DistributedStrategy() + dist.fleet.init(is_collective=True, strategy=strategy) + batch_size = 10 + num_samples = 6 + rank_id = dist.get_rank() + # num_classes of each GPU can be different, e.g num_classes_list = [10, 8] + num_classes_list = [10, 10] + num_classes = paddle.sum(paddle.to_tensor(num_classes_list)) + label = paddle.randint(low=0, high=num_classes.item(), shape=[batch_size], dtype='int64') + label_list = [] + dist.all_gather(label_list, label) + label = paddle.concat(label_list, axis=0) + remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes_list[rank_id], num_samples) + + print(label) + print(remapped_label) + print(sampled_class_index) + + #python -m paddle.distributed.launch --gpus=0,1 test_class_center_sample.py + # rank 0 output: + #Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True, + # [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ]) + #Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True, + # [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ]) + #Tensor(shape=[6], dtype=int64, place=CUDAPlace(0), stop_gradient=True, + # [0, 2, 4, 8, 9, 3]) + + # rank 1 output: + #Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True, + # [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ]) + #Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True, + # [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ]) + #Tensor(shape=[7], dtype=int64, place=CUDAPlace(1), stop_gradient=True, + # [0, 1, 2, 3, 5, 7, 8]) + """ + if group is not None and not group.is_member(): + return + + ring_id = 0 if group is None else group.id + rank = 0 + nranks = 1 + if core.is_compiled_with_dist(): + parallel_env = paddle.distributed.ParallelEnv() + global_rank = parallel_env.rank + rank = global_rank if group is None else group.get_group_rank( + global_rank) + nranks = parallel_env.world_size if group is None else group.nranks + + if num_samples > num_classes: + raise ValueError( + 'Expected num_samples less than or equal to {}, got num_samples {}'. + format(num_classes, num_samples)) + + if (seed is None or seed == 0) and default_main_program().random_seed != 0: + seed = default_main_program().random_seed + + if in_dygraph_mode(): + remapped_label, sampled_class_center = core.ops.class_center_sample( + label, 'num_classes', num_classes, 'num_samples', num_samples, + 'ring_id', ring_id, 'nranks', nranks, 'rank', rank, 'fix_seed', + seed is not None, 'seed', seed if seed is not None else 0) + return remapped_label, sampled_class_center + + check_variable_and_dtype(label, 'label', ['int64', 'int32'], + 'class_center_sample') + op_type = 'class_center_sample' + helper = LayerHelper(op_type, **locals()) + remapped_label = helper.create_variable_for_type_inference( + dtype=label.dtype) + sampled_class_center = helper.create_variable_for_type_inference( + dtype=label.dtype) + helper.append_op( + type=op_type, + inputs={'Label': label}, + outputs={ + 'RemappedLabel': remapped_label, + 'SampledLocalClassCenter': sampled_class_center + }, + attrs={ + 'num_classes': num_classes, + 'num_samples': num_samples, + 'ring_id': ring_id, + 'nranks': nranks, + 'rank': rank, + 'fix_seed': seed is not None, + 'seed': seed if seed is not None else 0 + }) + return remapped_label, sampled_class_center diff --git a/tools/static_mode_white_list.py b/tools/static_mode_white_list.py index d2f95c235b0..4255e1f4e44 100644 --- a/tools/static_mode_white_list.py +++ b/tools/static_mode_white_list.py @@ -719,5 +719,6 @@ STATIC_MODE_TESTING_LIST = [ 'test_sgd_op_bf16', 'test_marker_op', 'test_c_embedding_op', + 'test_class_center_sample_op', 'test_margin_cross_entropy_op', ] -- GitLab