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未验证 提交 f94edc3b 编写于 作者: W Wen Sun 提交者： GitHub
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Support both use_calc_stream and sync_op in collective communication API (#46761)

上级 e4eb8d36
展开全部 隐藏空白更改
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Showing with 3014 addition and 144 deletion
paddle/fluid/distributed/collective/ProcessGroup.h
浏览文件 @ f94edc3b
......@@ -125,6 +125,16 @@ class ProcessGroup {
"ProcessGroup%s does not support broadcast", GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> Broadcast(
std::vector<phi::DenseTensor>& /* input tensors */, // NOLINT
std::vector<phi::DenseTensor>& /* output tensors */, // NOLINT
const BroadcastOptions&,
bool) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support broadcast with sync_op flag",
GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> Barrier(
const BarrierOptions& = BarrierOptions()) {
PADDLE_THROW(platform::errors::InvalidArgument(
......@@ -160,14 +170,14 @@ class ProcessGroup {
virtual std::shared_ptr<ProcessGroup::Task> Send_Partial(
phi::DenseTensor&, // NOLINT
int,
int,
int) {
int64_t,
int64_t) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support send_partial", GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> Send_Partial(
phi::DenseTensor&, int, int, int, bool) { // NOLINT
phi::DenseTensor&, int, int64_t, int64_t, bool) { // NOLINT
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support send_partial with sync_op flag",
GetBackendName()));
......@@ -176,14 +186,14 @@ class ProcessGroup {
virtual std::shared_ptr<ProcessGroup::Task> Recv_Partial(
phi::DenseTensor&, // NOLINT
int,
int,
int) {
int64_t,
int64_t) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support recv_partial", GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> Recv_Partial(
phi::DenseTensor&, int, int, int, bool) { // NOLINT
phi::DenseTensor&, int, int64_t, int64_t, bool) { // NOLINT
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support recv_partial with sync_op flag",
GetBackendName()));
......@@ -208,8 +218,8 @@ class ProcessGroup {
virtual std::shared_ptr<ProcessGroup::Task> AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
int offset,
int length) { // NOLINT
int64_t offset,
int64_t length) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support AllGather_Partial", GetBackendName()));
}
......@@ -217,9 +227,9 @@ class ProcessGroup {
virtual std::shared_ptr<ProcessGroup::Task> AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
int offset,
int length,
bool) { // NOLINT
int64_t offset,
int64_t length,
bool) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support AllGather_Partial", GetBackendName()));
}
......@@ -231,6 +241,14 @@ class ProcessGroup {
"ProcessGroup%s does not support AllToAll", GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> AllToAll(
std::vector<phi::DenseTensor>&, // NOLINT
std::vector<phi::DenseTensor>&, // NOLINT
bool) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support alltoall", GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> AllToAll_Single(
std::vector<phi::DenseTensor>&, // NOLINT
std::vector<phi::DenseTensor>&, // NOLINT
......@@ -240,26 +258,66 @@ class ProcessGroup {
"ProcessGroup%s does not support AllToAll_Single", GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> AllToAllSingle(
std::vector<phi::DenseTensor>&, // NOLINT
std::vector<phi::DenseTensor>&, // NOLINT
std::vector<int64_t>&,
std::vector<int64_t>&,
bool) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support alltoall_single", GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> Reduce(
std::vector<phi::DenseTensor>&, // NOLINT
std::vector<phi::DenseTensor>&, // NOLINT
const ReduceOptions& opts) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support Reduce", GetBackendName()));
"ProcessGroup%s does not support reduce", GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> Reduce(
std::vector<phi::DenseTensor>& /* input tensors */, // NOLINT
std::vector<phi::DenseTensor>& /* output tensors */, // NOLINT
const ReduceOptions&,
bool) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support reduce with sync_op flag",
GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> Scatter(
std::vector<phi::DenseTensor>&, // NOLINT
std::vector<phi::DenseTensor>&, // NOLINT
const ScatterOptions&) { // NOLINT
const ScatterOptions&) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support Scatter", GetBackendName()));
"ProcessGroup%s does not support scatter", GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> Scatter(
std::vector<phi::DenseTensor>&, // NOLINT
std::vector<phi::DenseTensor>&, // NOLINT
const ScatterOptions&,
bool) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support scatter with sync_op flag",
GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> ReduceScatter(
std::vector<phi::DenseTensor>&, // NOLINT
std::vector<phi::DenseTensor>&, // NOLINT
const ReduceScatterOptions&,
bool) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support reduce_scatter with sync_op flag",
GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> _ReduceScatterBase(
phi::DenseTensor&, // NOLINT
phi::DenseTensor&, // NOLINT
const ReduceScatterOptions&) { // NOLINT
phi::DenseTensor&, // NOLINT
phi::DenseTensor&, // NOLINT
const ReduceScatterOptions&) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support ReduceScatter", GetBackendName()));
}
......
paddle/fluid/distributed/collective/ProcessGroupCustom.cc
浏览文件 @ f94edc3b
......@@ -267,8 +267,8 @@ void* XcclGetPointerByOffset(void* raw_pointer,
std::shared_ptr<ProcessGroup::Task> ProcessGroupCustom::AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
int offset,
int length) {
int64_t offset,
int64_t length) {
PADDLE_ENFORCE_EQ(
CheckTensorsInCustomPlace(in_tensors, device_type_),
true,
......
paddle/fluid/distributed/collective/ProcessGroupCustom.h
浏览文件 @ f94edc3b
......@@ -80,8 +80,8 @@ class ProcessGroupCustom : public ProcessGroup {
std::shared_ptr<ProcessGroup::Task> AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
int offset,
int length) override;
int64_t offset,
int64_t length) override;
std::shared_ptr<ProcessGroup::Task> AllReduce(
std::vector<phi::DenseTensor>& in_tensors,
......@@ -117,8 +117,8 @@ class ProcessGroupCustom : public ProcessGroup {
std::set<int> used_place_ids_;
private:
void BcastCustomId(std::vector<phi::ccl::CCLRootId>& ccl_ids,
int root, // NOLINT
void BcastCustomId(std::vector<phi::ccl::CCLRootId>& ccl_ids, // NOLINT
int root,
int server_fd);
void BroadcastUniqueCustomID(
......
paddle/fluid/distributed/collective/ProcessGroupNCCL.cc
浏览文件 @ f94edc3b
......@@ -628,6 +628,40 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Broadcast(
CommType::BROADCAST);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Broadcast(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const BroadcastOptions& opts,
bool sync_op,
bool use_calc_stream) {
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors),
true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective(
in_tensors,
out_tensors,
[&](phi::DenseTensor& input,
phi::DenseTensor& output,
ncclComm_t comm,
const gpuStream_t& stream) {
const auto root =
opts.source_rank * in_tensors.size() + opts.source_root;
return platform::dynload::ncclBroadcast(
input.data(),
output.data(),
input.numel(),
platform::ToNCCLDataType(input.type()),
root,
comm,
stream);
},
CommType::BROADCAST,
sync_op,
use_calc_stream);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Barrier(
const BarrierOptions& opts) {
// Only support single card single process
......@@ -782,7 +816,7 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Recv(
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Send_Partial(
phi::DenseTensor& tensors, int dst_rank, int offset, int length) {
phi::DenseTensor& tensors, int dst_rank, int64_t offset, int64_t length) {
// CheckTensorsInDifferentDevices(tensors, static_cast<size_t>(GetSize()));
phi::DenseTensor flatten_tensor;
......@@ -813,8 +847,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Send_Partial(
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Send_Partial(
phi::DenseTensor& tensors,
int dst_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream) {
phi::DenseTensor flatten_tensor;
......@@ -845,7 +879,7 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Send_Partial(
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Recv_Partial(
phi::DenseTensor& tensors, int src_rank, int offset, int length) {
phi::DenseTensor& tensors, int src_rank, int64_t offset, int64_t length) {
// phi::DenseTensor shared_input = tensors.Slice(offset, offset+length);
phi::DenseTensor flatten_tensor;
......@@ -876,8 +910,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Recv_Partial(
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Recv_Partial(
phi::DenseTensor& tensors,
int src_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream) {
phi::DenseTensor flatten_tensor;
......@@ -1009,8 +1043,8 @@ void* GetPointerByOffset(void* raw_pointer,
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
int offset,
int length) {
int64_t offset,
int64_t length) {
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors),
true,
......@@ -1040,8 +1074,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllGather_Partial(
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream) {
PADDLE_ENFORCE_EQ(
......@@ -1114,6 +1148,52 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll(
CommType::ALLTOALL);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
bool sync_op,
bool use_calc_stream) {
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors),
true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(out_tensors),
true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective(
in_tensors,
out_tensors,
[&](phi::DenseTensor& input,
phi::DenseTensor& output,
ncclComm_t comm,
const gpuStream_t& stream) {
size_t offset = 0;
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupStart());
for (auto i = 0; i < size_; i++) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclSend(
GetPointerByOffset(input.data(), offset, input.dtype()),
input.numel() / size_,
platform::ToNCCLDataType(input.dtype()),
i,
comm,
stream));
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv(
GetPointerByOffset(output.data(), offset, input.dtype()),
input.numel() / size_,
platform::ToNCCLDataType(input.dtype()),
i,
comm,
stream));
offset += input.numel() / size_;
}
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupEnd());
},
CommType::ALLTOALL,
sync_op,
use_calc_stream);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll_Single(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
......@@ -1176,6 +1256,72 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll_Single(
CommType::ALLTOALL_SINGLE);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAllSingle(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
std::vector<int64_t>& in_sizes,
std::vector<int64_t>& out_sizes,
bool sync_op,
bool use_calc_stream) {
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors),
true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(out_tensors),
true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective(
in_tensors,
out_tensors,
[&](phi::DenseTensor& input,
phi::DenseTensor& output,
ncclComm_t comm,
const gpuStream_t& stream) {
PADDLE_ENFORCE_EQ(input.dtype() == output.dtype(),
true,
platform::errors::InvalidArgument(
"The dtypes of input and output must be equal."));
std::vector<int64_t> in_dims = phi::vectorize(input.dims());
std::vector<int64_t> out_dims = phi::vectorize(output.dims());
CheckSplitSizes(&in_sizes, in_dims);
CheckSplitSizes(&out_sizes, out_dims);
size_t in_offset = 0, out_offset = 0;
size_t in_length = 0, out_length = 0;
size_t in_row_size = input.numel() / in_dims[0];
size_t out_row_size = output.numel() / out_dims[0];
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupStart());
for (auto i = 0; i < size_; i++) {
in_length = in_sizes[i] * in_row_size;
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclSend(
GetPointerByOffset(input.data(), in_offset, input.dtype()),
in_length,
platform::ToNCCLDataType(input.dtype()),
i,
comm,
stream));
in_offset += in_length;
out_length = out_sizes[i] * out_row_size;
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv(
GetPointerByOffset(output.data(), out_offset, input.dtype()),
out_length,
platform::ToNCCLDataType(input.dtype()),
i,
comm,
stream));
out_offset += out_length;
}
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupEnd());
},
CommType::ALLTOALL_SINGLE,
sync_op,
use_calc_stream);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Reduce(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
......@@ -1204,6 +1350,70 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Reduce(
CommType::REDUCE);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Reduce(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceOptions& opts,
bool sync_op,
bool use_calc_stream) {
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors),
true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective(
in_tensors,
out_tensors,
[&](const phi::DenseTensor& input,
phi::DenseTensor& output,
ncclComm_t comm,
const gpuStream_t& stream) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclReduce(
input.data(),
output.data(),
input.numel(),
platform::ToNCCLDataType(input.dtype()),
ToNCCLRedType(opts.reduce_op),
opts.root_rank,
comm,
stream));
},
CommType::REDUCE,
sync_op,
use_calc_stream);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::ReduceScatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceScatterOptions& opts,
bool sync_op,
bool use_calc_stream) {
return Collective(
in_tensors,
out_tensors,
[&](phi::DenseTensor& input,
phi::DenseTensor& output,
ncclComm_t comm,
const gpuStream_t& stream) {
if (FLAGS_use_stream_safe_cuda_allocator) {
platform::CUDADeviceGuard cuda_guard;
cuda_guard.SetDevice(output.place());
memory::RecordStream(output.Holder(), stream);
}
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclReduceScatter(
input.data(),
output.data(),
output.numel(),
platform::ToNCCLDataType(input.dtype()),
ToNCCLRedType(opts.reduce_op),
comm,
stream));
},
CommType::REDUCE_SCATTER,
sync_op,
use_calc_stream);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Scatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
......@@ -1257,6 +1467,68 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Scatter(
CommType::SCATTER);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Scatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ScatterOptions& opts,
bool sync_op,
bool use_calc_stream) {
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors),
true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(out_tensors),
true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective(
in_tensors,
out_tensors,
[&](phi::DenseTensor& input,
phi::DenseTensor& output,
ncclComm_t comm,
const gpuStream_t& stream) {
PADDLE_ENFORCE_EQ(
output.numel(),
input.numel() / size_,
platform::errors::InvalidArgument(
"Input and output tensors should have the same shape."));
size_t offset = 0;
if (rank_ == opts.root_rank) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupStart());
for (auto i = 0; i < size_; i++) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclSend(
GetPointerByOffset(input.data(), offset, input.dtype()),
input.numel() / size_,
platform::ToNCCLDataType(input.dtype()),
i,
comm,
stream));
offset += input.numel() / size_;
}
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv(
output.data(),
input.numel() / size_,
platform::ToNCCLDataType(input.dtype()),
opts.root_rank,
comm,
stream));
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupEnd());
} else {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv(
output.data(),
input.numel() / size_,
platform::ToNCCLDataType(input.dtype()),
opts.root_rank,
comm,
stream));
}
},
CommType::SCATTER,
sync_op,
use_calc_stream);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::_ReduceScatterBase(
phi::DenseTensor& out_tensor,
phi::DenseTensor& in_tensor,
......
paddle/fluid/distributed/collective/ProcessGroupNCCL.h
浏览文件 @ f94edc3b
......@@ -119,6 +119,13 @@ class ProcessGroupNCCL : public ProcessGroupStream {
std::vector<phi::DenseTensor>& out_tensors,
const BroadcastOptions& = BroadcastOptions()) override;
std::shared_ptr<ProcessGroup::Task> Broadcast(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const BroadcastOptions& opts,
bool sync_op,
bool use_calc_stream) override;
std::shared_ptr<ProcessGroup::Task> Barrier(
const BarrierOptions& = BarrierOptions()) override;
......@@ -142,27 +149,27 @@ class ProcessGroupNCCL : public ProcessGroupStream {
std::shared_ptr<ProcessGroup::Task> Send_Partial(phi::DenseTensor& tensors,
int dst_rank,
int offset,
int length) override;
int64_t offset,
int64_t length) override;
std::shared_ptr<ProcessGroup::Task> Send_Partial(
phi::DenseTensor& tensors,
int dst_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream) override;
std::shared_ptr<ProcessGroup::Task> Recv_Partial(phi::DenseTensor& tensors,
int src_rank,
int offset,
int length) override;
int64_t offset,
int64_t length) override;
std::shared_ptr<ProcessGroup::Task> Recv_Partial(
phi::DenseTensor& tensors,
int src_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream) override;
......@@ -179,20 +186,26 @@ class ProcessGroupNCCL : public ProcessGroupStream {
std::shared_ptr<ProcessGroup::Task> AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
int offset,
int length) override;
int64_t offset,
int64_t length) override;
std::shared_ptr<ProcessGroup::Task> AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream) override;
std::shared_ptr<ProcessGroup::Task> AllToAll(
std::vector<phi::DenseTensor>& in,
std::vector<phi::DenseTensor>& out) override;
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors) override;
std::shared_ptr<ProcessGroup::Task> AllToAll(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
bool sync_op,
bool use_calc_stream) override;
std::shared_ptr<ProcessGroup::Task> AllToAll_Single(
std::vector<phi::DenseTensor>& in,
......@@ -200,15 +213,44 @@ class ProcessGroupNCCL : public ProcessGroupStream {
std::vector<int64_t>& in_sizes,
std::vector<int64_t>& out_sizes) override;
std::shared_ptr<ProcessGroup::Task> AllToAllSingle(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
std::vector<int64_t>& in_sizes,
std::vector<int64_t>& out_sizes,
bool sync_op,
bool use_calc_stream) override;
std::shared_ptr<ProcessGroup::Task> Reduce(
std::vector<phi::DenseTensor>& tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceOptions& opts) override;
std::shared_ptr<ProcessGroup::Task> Reduce(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceOptions& opts,
bool sync_op,
bool use_calc_stream) override;
std::shared_ptr<ProcessGroup::Task> ReduceScatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceScatterOptions& opts,
bool sync_op,
bool use_calc_stream) override;
std::shared_ptr<ProcessGroup::Task> Scatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ScatterOptions&) override;
const ScatterOptions& opts) override;
std::shared_ptr<ProcessGroup::Task> Scatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ScatterOptions& opts,
bool sync_op,
bool use_calc_stream) override;
std::shared_ptr<ProcessGroup::Task> _ReduceScatterBase(
phi::DenseTensor&, // NOLINT
......
paddle/fluid/distributed/collective/ProcessGroupStream.cc
浏览文件 @ f94edc3b
......@@ -70,6 +70,138 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::AllReduce(
"ProcessGroup%s does not support do all_reduce", GetBackendName()));
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::AllToAll(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
bool sync_op) {
return AllToAll(in_tensors,
out_tensors,
sync_op,
/*use_calc_stream*/ false);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::AllToAll(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
bool sync_op,
bool use_calc_stream) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support do alltoall", GetBackendName()));
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::AllToAllSingle(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
std::vector<int64_t>& in_sizes,
std::vector<int64_t>& out_sizes,
bool sync_op) {
return AllToAllSingle(in_tensors,
out_tensors,
in_sizes,
out_sizes,
sync_op,
/*use_calc_stream*/ false);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::AllToAllSingle(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
std::vector<int64_t>& in_sizes,
std::vector<int64_t>& out_sizes,
bool sync_op,
bool use_calc_stream) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support do alltoall_single", GetBackendName()));
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Broadcast(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const BroadcastOptions& opts,
bool sync_op) {
return Broadcast(in_tensors,
out_tensors,
opts,
sync_op,
/*use_calc_stream*/ false);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Broadcast(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const BroadcastOptions& opts,
bool sync_op,
bool use_calc_stream) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support do broadcast", GetBackendName()));
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Reduce(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceOptions& opts,
bool sync_op) {
return Reduce(in_tensors,
out_tensors,
opts,
sync_op,
/*use_calc_stream*/ false);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Reduce(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceOptions& opts,
bool sync_op,
bool use_calc_stream) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support do reduce", GetBackendName()));
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::ReduceScatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceScatterOptions& opts,
bool sync_op) {
return ReduceScatter(in_tensors,
out_tensors,
opts,
sync_op,
/*use_calc_stream*/ false);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::ReduceScatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceScatterOptions& opts,
bool sync_op,
bool use_calc_stream) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support do reduce_scatter", GetBackendName()));
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Scatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ScatterOptions& opts,
bool sync_op) {
return Scatter(in_tensors,
out_tensors,
opts,
sync_op,
/*use_calc_stream*/ false);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Scatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ScatterOptions& opts,
bool sync_op,
bool use_calc_stream) {
PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support do scatter", GetBackendName()));
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Send(
std::vector<phi::DenseTensor>& tensors, int dst_rank, bool sync_op) {
return Send(tensors,
......@@ -90,8 +222,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Send(
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Send_Partial(
phi::DenseTensor& tensors,
int dst_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op) {
return Send_Partial(tensors,
dst_rank,
......@@ -104,8 +236,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Send_Partial(
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Send_Partial(
phi::DenseTensor& tensors,
int dst_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream) {
PADDLE_THROW(platform::errors::InvalidArgument(
......@@ -132,8 +264,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Recv(
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Recv_Partial(
phi::DenseTensor& tensors,
int src_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op) {
return Recv_Partial(tensors,
src_rank,
......@@ -146,8 +278,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Recv_Partial(
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Recv_Partial(
phi::DenseTensor& tensors,
int src_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream) {
PADDLE_THROW(platform::errors::InvalidArgument(
......@@ -157,8 +289,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::Recv_Partial(
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op) {
return AllGather_Partial(in_tensors,
out_tensors,
......@@ -171,8 +303,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::AllGather_Partial(
std::shared_ptr<ProcessGroup::Task> ProcessGroupStream::AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream) {
PADDLE_THROW(platform::errors::InvalidArgument(
......
paddle/fluid/distributed/collective/ProcessGroupStream.h
浏览文件 @ f94edc3b
......@@ -81,6 +81,84 @@ class ProcessGroupStream : public ProcessGroup {
bool sync_op,
bool use_calc_stream);
std::shared_ptr<ProcessGroup::Task> AllToAll(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
bool sync_op) override;
virtual std::shared_ptr<ProcessGroup::Task> AllToAll(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
bool sync_op,
bool use_calc_stream);
std::shared_ptr<ProcessGroup::Task> AllToAllSingle(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
std::vector<int64_t>& in_sizes, // NOLINT
std::vector<int64_t>& out_sizes, // NOLINT
bool sync_op) override;
virtual std::shared_ptr<ProcessGroup::Task> AllToAllSingle(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
std::vector<int64_t>& in_sizes, // NOLINT
std::vector<int64_t>& out_sizes, // NOLINT
bool sync_op,
bool use_calc_stream);
std::shared_ptr<ProcessGroup::Task> Broadcast(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
const BroadcastOptions& opts,
bool sync_op) override;
virtual std::shared_ptr<ProcessGroup::Task> Broadcast(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
const BroadcastOptions& opts,
bool sync_op,
bool use_calc_stream);
std::shared_ptr<ProcessGroup::Task> Reduce(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
const ReduceOptions& opts,
bool sync_op) override;
virtual std::shared_ptr<ProcessGroup::Task> Reduce(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
const ReduceOptions& opts,
bool sync_op,
bool use_calc_stream);
std::shared_ptr<ProcessGroup::Task> ReduceScatter(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
const ReduceScatterOptions& opts,
bool sync_op) override;
virtual std::shared_ptr<ProcessGroup::Task> ReduceScatter(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
const ReduceScatterOptions& opts,
bool sync_op,
bool use_calc_stream);
std::shared_ptr<ProcessGroup::Task> Scatter(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
const ScatterOptions& opts,
bool sync_op) override;
virtual std::shared_ptr<ProcessGroup::Task> Scatter(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
const ScatterOptions& opts,
bool sync_op,
bool use_calc_stream);
std::shared_ptr<ProcessGroup::Task> Send(
std::vector<phi::DenseTensor>& tensors, // NOLINT
int dst_rank,
......@@ -95,15 +173,15 @@ class ProcessGroupStream : public ProcessGroup {
std::shared_ptr<ProcessGroup::Task> Send_Partial(
phi::DenseTensor& tensors, // NOLINT
int dst_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op) override;
virtual std::shared_ptr<ProcessGroup::Task> Send_Partial(
phi::DenseTensor& tensors, // NOLINT
int dst_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream);
......@@ -121,30 +199,30 @@ class ProcessGroupStream : public ProcessGroup {
std::shared_ptr<ProcessGroup::Task> Recv_Partial(
phi::DenseTensor& tensors, // NOLINT
int src_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op) override;
virtual std::shared_ptr<ProcessGroup::Task> Recv_Partial(
phi::DenseTensor& tensors, // NOLINT
int src_rank,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream);
std::shared_ptr<ProcessGroup::Task> AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op) override;
virtual std::shared_ptr<ProcessGroup::Task> AllGather_Partial(
std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
int offset,
int length,
int64_t offset,
int64_t length,
bool sync_op,
bool use_calc_stream);
};
......
paddle/fluid/distributed/collective/Utils.h
浏览文件 @ f94edc3b
......@@ -14,14 +14,26 @@
#pragma once
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/phi/api/include/tensor.h"
#include "paddle/phi/backends/device_guard.h"
#include "paddle/phi/backends/device_manager.h"
#include "paddle/phi/kernels/funcs/concat_and_split_functor.h"
namespace paddle {
namespace distributed {
template <typename DeviceContext, typename T>
struct ConcatDenseTensor {
void operator()(const DeviceContext *context,
const std::vector<phi::DenseTensor> &in,
phi::DenseTensor *out,
int axis = 0) {
phi::funcs::ConcatFunctor<DeviceContext, T> concat_functor;
concat_functor(*context, in, axis, out);
}
};
template <typename DeviceContext, typename T>
struct SplitDenseTensor {
void operator()(const DeviceContext *context,
......@@ -33,17 +45,36 @@ struct SplitDenseTensor {
for (auto *p_tensor : *out) {
shape_refer.emplace_back(p_tensor);
}
operators::math::SplitFunctor<DeviceContext, T> split_functor_;
split_functor_(*context, in, shape_refer, axis, out);
phi::funcs::SplitFunctor<DeviceContext, T> split_functor;
split_functor(*context, in, shape_refer, axis, out);
}
};
#ifdef PADDLE_WITH_CUSTOM_DEVICE
template <typename T>
struct ConcatDenseTensor<platform::CustomDeviceContext, T> {
void operator()(const platform::CustomDeviceContext *context,
const std::vector<phi::DenseTensor> &in,
phi::DenseTensor *out,
int axis = 0) {
auto *out_data = out->data<T>();
auto *device = phi::DeviceManager::GetDeviceWithPlace(context->GetPlace());
size_t offset = 0;
for (const auto &tensor : in) {
const auto *in_data = tensor.data<T>();
auto sz = tensor.numel() * sizeof(T);
device->MemoryCopyD2D(out_data + offset, in_data, sz, nullptr);
offset += sz;
}
}
};
template <typename T>
struct SplitDenseTensor<platform::CustomDeviceContext, T> {
void operator()(const platform::CustomDeviceContext *context,
const phi::DenseTensor &in,
std::vector<phi::DenseTensor *> *out) {
std::vector<phi::DenseTensor *> *out,
int axis = 0) {
auto *in_data = in.data<T>();
auto *device = phi::DeviceManager::GetDeviceWithPlace(context->GetPlace());
size_t offset = 0;
......@@ -57,42 +88,119 @@ struct SplitDenseTensor<platform::CustomDeviceContext, T> {
};
#endif
template <typename DeviceContext>
void ConcatDenseTensorWithType(const DeviceContext *dev_ctx,
const std::vector<phi::DenseTensor> &t_list,
phi::DenseTensor *p_out,
phi::DataType type) {
switch (type) {
case phi::DataType::BOOL:
ConcatDenseTensor<DeviceContext, bool>()(dev_ctx, t_list, p_out);
break;
case phi::DataType::UINT8:
ConcatDenseTensor<DeviceContext, uint8_t>()(dev_ctx, t_list, p_out);
break;
case phi::DataType::INT8:
ConcatDenseTensor<DeviceContext, int8_t>()(dev_ctx, t_list, p_out);
break;
case phi::DataType::INT32:
ConcatDenseTensor<DeviceContext, int32_t>()(dev_ctx, t_list, p_out);
break;
case phi::DataType::INT64:
ConcatDenseTensor<DeviceContext, int64_t>()(dev_ctx, t_list, p_out);
break;
case phi::DataType::FLOAT16:
ConcatDenseTensor<DeviceContext, platform::float16>()(
dev_ctx, t_list, p_out);
break;
case phi::DataType::FLOAT32:
ConcatDenseTensor<DeviceContext, float>()(dev_ctx, t_list, p_out);
break;
case phi::DataType::FLOAT64:
ConcatDenseTensor<DeviceContext, double>()(dev_ctx, t_list, p_out);
break;
default:
PADDLE_THROW(platform::errors::Unimplemented(
"Data type (%s) is not supported when it concats tensors.", type));
}
}
template <typename DeviceContext>
void SplitDenseTensorWithType(const DeviceContext *dev_ctx,
const phi::DenseTensor &p_dense,
const phi::DenseTensor &t_in,
std::vector<phi::DenseTensor *> *p_list,
phi::DataType type) {
switch (type) {
case phi::DataType::BOOL:
SplitDenseTensor<DeviceContext, bool>()(dev_ctx, p_dense, p_list);
SplitDenseTensor<DeviceContext, bool>()(dev_ctx, t_in, p_list);
break;
case phi::DataType::UINT8:
SplitDenseTensor<DeviceContext, uint8_t>()(dev_ctx, p_dense, p_list);
SplitDenseTensor<DeviceContext, uint8_t>()(dev_ctx, t_in, p_list);
break;
case phi::DataType::INT8:
SplitDenseTensor<DeviceContext, int8_t>()(dev_ctx, p_dense, p_list);
SplitDenseTensor<DeviceContext, int8_t>()(dev_ctx, t_in, p_list);
break;
case phi::DataType::INT32:
SplitDenseTensor<DeviceContext, int32_t>()(dev_ctx, p_dense, p_list);
SplitDenseTensor<DeviceContext, int32_t>()(dev_ctx, t_in, p_list);
break;
case phi::DataType::INT64:
SplitDenseTensor<DeviceContext, int64_t>()(dev_ctx, p_dense, p_list);
SplitDenseTensor<DeviceContext, int64_t>()(dev_ctx, t_in, p_list);
break;
case phi::DataType::FLOAT16:
SplitDenseTensor<DeviceContext, platform::float16>()(
dev_ctx, p_dense, p_list);
dev_ctx, t_in, p_list);
break;
case phi::DataType::FLOAT32:
SplitDenseTensor<DeviceContext, float>()(dev_ctx, p_dense, p_list);
SplitDenseTensor<DeviceContext, float>()(dev_ctx, t_in, p_list);
break;
case phi::DataType::FLOAT64:
SplitDenseTensor<DeviceContext, double>()(dev_ctx, p_dense, p_list);
SplitDenseTensor<DeviceContext, double>()(dev_ctx, t_in, p_list);
break;
default:
PADDLE_THROW(platform::errors::Unimplemented(
"Data type (%s) is not supported when it splits tensors for "
"allgather.",
type));
"Data type (%s) is not supported when it splits tensors.", type));
}
}
void ConcatTensor(const phi::DeviceContext *dev_ctx,
const std::vector<phi::DenseTensor> &tensor_list,
const experimental::Tensor *tensor) {
auto *dense_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(tensor->impl()).get();
const auto &place = dev_ctx->GetPlace();
if (platform::is_gpu_place(place)) {
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
ConcatDenseTensorWithType(static_cast<const phi::GPUContext *>(dev_ctx),
tensor_list,
dense_tensor,
tensor->dtype());
#else
PADDLE_THROW(platform::errors::PermissionDenied(
"Paddle can't concat tensor since it's not support GPU, please "
"recompile or reinstall Paddle with GPU support."));
#endif
} else if (platform::is_custom_place(place)) {
#ifdef PADDLE_WITH_CUSTOM_DEVICE
ConcatDenseTensorWithType(
static_cast<const platform::CustomDeviceContext *>(dev_ctx),
tensor_list,
dense_tensor,
tensor->dtype());
#else
PADDLE_THROW(platform::errors::PermissionDenied(
"Paddle can't concat tensor since it's not compiled with "
"CUSTOM_DEVICE, please recompile or reinstall Paddle with "
"CUSTOM_DEVICE support."));
#endif
} else if (platform::is_cpu_place(place)) {
ConcatDenseTensorWithType(static_cast<const phi::CPUContext *>(dev_ctx),
tensor_list,
dense_tensor,
tensor->dtype());
} else {
PADDLE_THROW(platform::errors::Unimplemented(
"Concat tensor not supported on place (%s)", place));
}
}
......@@ -115,8 +223,8 @@ void SplitTensor(const phi::DeviceContext *dev_ctx,
tensor.dtype());
#else
PADDLE_THROW(platform::errors::PermissionDenied(
"Paddle can't split tensor since it's not support NCCL/RCCL, please "
"recompile or reinstall Paddle with NCCL/RCCL support."));
"Paddle can't split tensor since it's not support GPU, please "
"recompile or reinstall Paddle with GPU support."));
#endif
} else if (platform::is_custom_place(place)) {
#ifdef PADDLE_WITH_CUSTOM_DEVICE
......
python/paddle/distributed/communication/stream/__init__.py
浏览文件 @ f94edc3b
......@@ -14,7 +14,16 @@
from .all_gather import all_gather
from .all_reduce import all_reduce
from .send import send
from .alltoall import alltoall
from .alltoall_single import alltoall_single
from .broadcast import broadcast
from .reduce import reduce
from .reduce_scatter import _reduce_scatter_base, reduce_scatter
from .recv import recv
from .scatter import scatter
from .send import send
__all__ = ["all_gather", "all_reduce", "send", "recv"]
__all__ = [
"_reduce_scatter_base", "all_reduce", "alltoall", "alltoall_single",
"broadcast", "reduce", "reduce_scatter", "recv", "scatter", "send"
]
python/paddle/distributed/communication/stream/all_gather.py
浏览文件 @ f94edc3b
......@@ -34,17 +34,18 @@ def _check_tensor_list_shape(tensor_list, shape, nranks=1):
'The tensor_list for all_gather is not correctly-sized.')
def _all_gather_base_in_dygraph(out_tensor, in_tensor, group, sync_op,
use_calc_stream):
def _all_gather_into_tensor_in_dygraph(out_tensor, in_tensor, group, sync_op,
use_calc_stream):
group = collective._get_default_group() if group is None else group
_check_tensor_shape(out_tensor, in_tensor.shape, group.nranks)
if use_calc_stream:
return group.process_group.allgather_base_on_calc_stream(
return group.process_group.allgather_into_tensor_on_calc_stream(
in_tensor, out_tensor)
task = group.process_group.allgather_base(in_tensor, out_tensor, sync_op)
task = group.process_group.allgather_into_tensor(in_tensor, out_tensor,
sync_op)
if sync_op:
task.wait()
......@@ -83,7 +84,7 @@ def all_gather(tensor_or_tensor_list,
tensor_or_tensor_list (Union[Tensor, List[Tensor]]): The output. If it is a tensor, it should be correctly-sized. If it is a list, it
should be empty or contain correctly-sized tensors.
tensor (Tensor): The input tensor on each rank. The result will overwrite this tenor after communication. Support
float16, float32, float64, int32 or int64 as the input data type.
float16, float32, float64, int32, int64, int8, uint8 or bool as the input data type.
group (Group, optional): Communicate in which group. If none is given, use the global group as default.
sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
......@@ -125,8 +126,9 @@ def all_gather(tensor_or_tensor_list,
if framework.in_dygraph_mode():
if paddle.is_tensor(tensor_or_tensor_list):
return _all_gather_base_in_dygraph(tensor_or_tensor_list, tensor,
group, sync_op, use_calc_stream)
return _all_gather_into_tensor_in_dygraph(tensor_or_tensor_list,
tensor, group, sync_op,
use_calc_stream)
else:
return _all_gather_in_dygraph(tensor_or_tensor_list, tensor, group,
sync_op, use_calc_stream)
......
python/paddle/distributed/communication/stream/all_reduce.py
浏览文件 @ f94edc3b
......@@ -70,7 +70,7 @@ def all_reduce(tensor,
Args:
tensor (Tensor): The input tensor on each rank. The result will overwrite this tenor after communication. Support
float16, float32, float64, int32 or int64 as the input data type.
float16, float32, float64, int32, int64, int8, uint8 or bool as the input data type.
op (ReduceOp.SUM|ReduceOp.MAX|ReduceOp.MIN|ReduceOp.PROD, optional): The reduction used. If none is given, use ReduceOp.SUM as default.
group (Group, optional): Communicate in which group. If none is given, use the global group as default.
sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
......
python/paddle/distributed/communication/stream/alltoall.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.fluid.framework as framework
from paddle.distributed import collective
def _check_tensor_shape(tensor, shape, nranks=1):
if tensor.shape != shape:
raise RuntimeError('The tensor for alltoall is not correctly-sized.')
def _check_tensor_list_shape(tensor_list, shape, nranks=1):
if len(tensor_list) != nranks:
raise RuntimeError(
'The tensor_list for alltoall is not correctly-sized.')
for tensor in tensor_list:
if tensor.shape != shape:
raise RuntimeError(
'The tensor_list for alltoall is not correctly-sized.')
def _alltoall_tensor_in_dygraph(out_tensor, in_tensor, group, sync_op,
use_calc_stream):
group = collective._get_default_group() if group is None else group
_check_tensor_shape(out_tensor, in_tensor.shape, group.nranks)
if use_calc_stream:
return group.process_group.alltoall_tensor_on_calc_stream(
in_tensor, out_tensor)
task = group.process_group.alltoall_tensor(in_tensor, out_tensor, sync_op)
if sync_op:
task.wait()
return task
def _alltoall_in_dygraph(out_tensor_list, in_tensor_list, group, sync_op,
use_calc_stream):
group = collective._get_default_group() if group is None else group
if len(in_tensor_list) == 0:
raise RuntimeError("The input tensor_list should not be empty.")
if len(out_tensor_list) == 0:
out_tensor_list += [
paddle.empty_like(tensor) for tensor in in_tensor_list
]
else:
_check_tensor_list_shape(out_tensor_list, in_tensor_list[0].shape,
group.nranks)
if use_calc_stream:
return group.process_group.alltoall_on_calc_stream(
in_tensor_list, out_tensor_list)
task = group.process_group.alltoall(in_tensor_list, out_tensor_list,
sync_op)
if sync_op:
task.wait()
return task
def alltoall(out_tensor_or_tensor_list,
in_tensor_or_tensor_list,
group=None,
sync_op=True,
use_calc_stream=False):
"""
Scatter a tensor (or a tensor list) across devices and gather outputs to another tensor (or a tensor list, respectively).
Args:
out_tensor_or_tensor_list (Union[Tensor, List[Tensor]]): The output. If it is a tensor, it should be correctly-sized.
If it is a list, it should be empty or contain correctly-sized tensors. Its data type should be the same as the input.
in_tensor_or_tensor_list (Union[Tensor, List[Tensor]]): The input to scatter (must be specified on the source rank).
If it is a tensor, it should be correctly-sized. If it is a list, it should contain correctly-sized tensors. Support
float16, float32, float64, int32, int64, int8, uint8 or bool as the input data type.
group (Group, optional): Communicate in which group. If none is given, use the global group as default.
sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
Returns:
Return a task object.
Warning:
This API only supports the dygraph mode now.
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
out_tensor_list = []
if dist.get_rank() == 0:
data1 = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
data2 = paddle.to_tensor([[7, 8, 9], [10, 11, 12]])
else:
data1 = paddle.to_tensor([[13, 14, 15], [16, 17, 18]])
data2 = paddle.to_tensor([[19, 20, 21], [22, 23, 24]])
task = dist.stream.alltoall(out_tensor_list, [data1, data2], sync_op=False)
task.wait()
print(out_tensor_list)
# [[[1, 2, 3], [4, 5, 6]], [[13, 14, 15], [16, 17, 18]]] (2 GPUs, out for rank 0)
# [[[7, 8, 9], [10, 11, 12]], [[19, 20, 21], [22, 23, 24]]] (2 GPUs, out for rank 1)
"""
if group is not None and not group.is_member():
raise RuntimeError(
"The group should not be None and all ranks which invoke this operation should be the member of this group."
)
if not sync_op and use_calc_stream:
raise RuntimeError(
"use_calc_stream can only be true in sync op behavior.")
if out_tensor_or_tensor_list is None:
raise RuntimeError("The output should be specified.")
if in_tensor_or_tensor_list is None:
raise RuntimeError("The input should be specified.")
if framework.in_dygraph_mode():
out_is_tensor = paddle.is_tensor(out_tensor_or_tensor_list)
in_is_tensor = paddle.is_tensor(in_tensor_or_tensor_list)
if out_is_tensor and in_is_tensor:
return _alltoall_tensor_in_dygraph(out_tensor_or_tensor_list,
in_tensor_or_tensor_list, group,
sync_op, use_calc_stream)
elif not out_is_tensor and not in_is_tensor:
return _alltoall_in_dygraph(out_tensor_or_tensor_list,
in_tensor_or_tensor_list, group,
sync_op, use_calc_stream)
else:
raise RuntimeError(
"The output and input should be both tensor or tensor list.")
raise RuntimeError(
"paddle.distributed.stream.alltoall is only supported in dygraph mode now."
)
python/paddle/distributed/communication/stream/alltoall_single.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid.framework as framework
from paddle.distributed import collective
def _alltoall_single_in_dygraph(out_tensor, in_tensor, out_split_sizes,
in_split_sizes, group, sync_op,
use_calc_stream):
group = collective._get_default_group() if group is None else group
if out_split_sizes is None:
out_split_sizes = []
if in_split_sizes is None:
in_split_sizes = []
if use_calc_stream:
return group.process_group.alltoall_single_on_calc_stream(
in_tensor, out_tensor, in_split_sizes, out_split_sizes)
task = group.process_group.alltoall_single(in_tensor, out_tensor,
in_split_sizes, out_split_sizes,
sync_op)
if sync_op:
task.wait()
return task
def alltoall_single(out_tensor,
in_tensor,
out_split_sizes=None,
in_split_sizes=None,
group=None,
sync_op=True,
use_calc_stream=False):
"""
Split and Scatter the splitted input tensor to the out tensor across devices.
Args:
out_tensor(Tensor): The output tensor. Its data type should be the same as the input.
in_tensor (Tensor): The input tensor. Its data type should be float16, float32, float64, int32, int64, int8, uint8 or bool.
out_split_sizes (List[int], optional): Split sizes of out_tensor for dim[0]. If not given, dim[0] of out_tensor must be divisible
by group size and out_tensor will be gathered averagely from all participators. If none is given, use a empty list as default.
in_split_sizes (List[int], optional): Split sizes of in_tensor for dim[0]. If not given, dim[0] of in_tensor must be divisible
by group size and in_tensor will be scattered averagely to all participators. If none is given, use a empty list as default.
group (Group, optional): Communicate in which group. If none is given, use the global group as default.
sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
Returns:
Return a task object.
Warning:
This API only supports the dygraph mode now.
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
local_rank = dist.get_rank()
# case 1
output = paddle.empty([2], dtype="int64")
if local_rank == 0:
data = paddle.to_tensor([0, 1])
else:
data = paddle.to_tensor([2, 3])
task = dist.stream.alltoall_single(output, data, sync_op=False)
task.wait()
out = output.numpy()
# [0, 2] (2 GPUs, out for rank 0)
# [1, 3] (2 GPUs, out for rank 1)
# case 2
size = dist.get_world_size()
output = paddle.empty([(local_rank + 1) * size, size], dtype='float32')
if local_rank == 0:
data = paddle.to_tensor([[0., 0.], [0., 0.], [0., 0.]])
else:
data = paddle.to_tensor([[1., 1.], [1., 1.], [1., 1.]])
out_split_sizes = [local_rank + 1 for i in range(size)]
in_split_sizes = [i + 1 for i in range(size)]
task = dist.stream.alltoall_single(output,
data,
out_split_sizes,
in_split_sizes,
sync_op=False)
task.wait()
out = output.numpy()
# [[0., 0.], [1., 1.]] (2 GPUs, out for rank 0)
# [[0., 0.], [0., 0.], [1., 1.], [1., 1.]] (2 GPUs, out for rank 1)
"""
if group is not None and not group.is_member():
raise RuntimeError(
"The group should not be None and all ranks which invoke this operation should be the member of this group."
)
if not sync_op and use_calc_stream:
raise RuntimeError(
"use_calc_stream can only be true in sync op behavior.")
if framework.in_dygraph_mode():
return _alltoall_single_in_dygraph(out_tensor, in_tensor,
out_split_sizes, in_split_sizes,
group, sync_op, use_calc_stream)
raise RuntimeError(
"paddle.distributed.stream.alltoall_single is only supported in dygraph mode now."
)
python/paddle/distributed/communication/stream/broadcast.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid.framework as framework
from paddle.distributed import collective
def _broadcast_in_dygraph(tensor, src, group, sync_op, use_calc_stream):
group = collective._get_default_group() if group is None else group
if use_calc_stream:
return group.process_group.broadcast_on_calc_stream(tensor, src)
task = group.process_group.broadcast(tensor, src, sync_op)
if sync_op:
task.wait()
return task
def broadcast(tensor, src=0, group=None, sync_op=True, use_calc_stream=False):
"""
Broadcast a tensor to all devices.
Args:
tensor (Tensor): The tensor to broadcast. Support float16, float32, float64, int32, int64, int8, uint8 or bool as its data type.
src (int, optional): Rank of the source device. If none is given, use `0` as default.
group (Group, optional): Communicate in which group. If none is given, use the global group as default.
sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
Returns:
Return a task object.
Warning:
This API only supports the dygraph mode now.
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
local_rank = dist.get_rank()
if local_rank == 0:
data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
else:
data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
task = dist.stream.broadcast(data, src=1, sync_op=False)
task.wait()
out = data.numpy()
# [[1, 2, 3], [1, 2, 3]] (2 GPUs)
"""
if group is not None and not group.is_member():
raise RuntimeError(
"The group should not be None and all ranks which invoke this operation should be the member of this group."
)
if not sync_op and use_calc_stream:
raise RuntimeError(
"use_calc_stream can only be True in sync op behavior.")
if framework.in_dygraph_mode():
return _broadcast_in_dygraph(tensor, src, group, sync_op,
use_calc_stream)
raise RuntimeError(
"paddle.distributed.stream.broadcast is only supported in dygraph mode now."
)
python/paddle/distributed/communication/stream/recv.py
浏览文件 @ f94edc3b
......@@ -64,7 +64,7 @@ def recv(tensor, src=0, group=None, sync_op=True, use_calc_stream=False):
task = dist.stream.recv(data, src=0, sync_op=False)
task.wait()
out = data.numpy()
# [[4, 5, 6], [4, 5, 6]
# [[4, 5, 6], [4, 5, 6]] (2 GPUs)
"""
if group is not None and not group.is_member():
raise RuntimeError(
......
python/paddle/distributed/communication/stream/reduce.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid.framework as framework
from paddle.distributed.communication.group import _get_global_group
from paddle.distributed.communication.reduce import _get_reduce_op, ReduceOp
def _reduce_in_dygraph(tensor, dst, op, group, sync_op, use_calc_stream):
op_type = _get_reduce_op(op, "reduce")
group = _get_global_group() if group is None else group
if use_calc_stream:
return group.process_group.reduce_on_calc_stream(tensor, dst, op_type)
task = group.process_group.reduce(tensor, dst, op_type, sync_op)
if sync_op:
task.wait()
return task
def reduce(tensor,
dst=0,
op=ReduceOp.SUM,
group=None,
sync_op=True,
use_calc_stream=False):
"""
Perform specific reduction (for example, sum, max) on a tensor across devices and send to the destintion device.
Args:
tensor (Tensor): The input tensor on each rank. The result will overwrite this tenor after communication. Support
float16, float32, float64, int32, int64, int8, uint8 or bool as the input data type.
dst (int, optional): Rank of the destination device. If none is given, use `0` as default.
op (ReduceOp.SUM|ReduceOp.MAX|ReduceOp.MIN|ReduceOp.PROD, optional): The reduction used. If none is given, use ReduceOp.SUM as default.
group (Group, optional): Communicate in which group. If none is given, use the global group as default.
sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
Returns:
Return a task object.
Warning:
This API only supports the dygraph mode now.
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
local_rank = dist.get_rank()
if local_rank == 0:
data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
else:
data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
task = dist.stream.reduce(data, dst=0, sync_op=False)
task.wait()
out = data.numpy()
# [[5, 7, 9], [5, 7, 9]] (2 GPUs, out for rank 0)
# [[1, 2, 3], [1, 2, 3]] (2 GPUs, out for rank 1)
"""
if group is not None and not group.is_member():
raise RuntimeError(
"The group should not be None and all ranks which invoke this operation should be the member of this group."
)
if not sync_op and use_calc_stream:
raise RuntimeError(
"use_calc_stream can only be true in sync op behavior.")
if framework.in_dygraph_mode():
return _reduce_in_dygraph(tensor, dst, op, group, sync_op,
use_calc_stream)
raise RuntimeError(
"paddle.distributed.stream.reduce is only supported in dygraph mode now."
)
python/paddle/distributed/communication/stream/reduce_scatter.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.distributed as dist
import paddle.fluid.framework as framework
from paddle.distributed.communication.group import _get_global_group
from paddle.distributed.communication.reduce import _get_reduce_op, ReduceOp
def _check_tensor_shape(tensor, shape, nranks=1):
expect_shape = list(shape)
expect_shape[0] //= nranks
if list(tensor.shape) != expect_shape:
raise RuntimeError(
"The in_tensor for reduce_scatter is not correctly-sized.")
def _check_tensor_list_shape(tensor_list, shape, nranks=1):
if len(tensor_list) != nranks:
raise RuntimeError(
f"The tensor_list for reduce_scatter is not correctly-sized.")
for tensor in tensor_list:
if tensor.shape != shape:
raise RuntimeError(
f"The tensor_list for reduce_scatter is not correctly-sized.")
def _reduce_scatter_tensor_in_dygraph(out_tensor,
in_tensor,
op,
group,
sync_op,
use_calc_stream,
caller="reduce_scatter"):
op_type = _get_reduce_op(op, caller)
group = _get_global_group() if group is None else group
_check_tensor_shape(out_tensor, in_tensor.shape, group.nranks)
if use_calc_stream:
return group.process_group.reduce_scatter_tensor_on_calc_stream(
in_tensor, out_tensor, op_type)
task = group.process_group.reduce_scatter_tensor(in_tensor, out_tensor,
op_type, sync_op)
if sync_op:
task.wait()
return task
def _reduce_scatter_in_dygraph(tensor, tensor_list, op, group, sync_op,
use_calc_stream):
op_type = _get_reduce_op(op, "reduce_scatter")
group = _get_global_group() if group is None else group
_check_tensor_list_shape(tensor_list, tensor.shape, group.nranks)
if use_calc_stream:
return group.process_group.reduce_scatter_on_calc_stream(
tensor_list, tensor, op_type)
task = group.process_group.reduce_scatter(tensor_list, tensor, op_type,
sync_op)
if sync_op:
task.wait()
return task
def reduce_scatter(tensor,
tensor_or_tensor_list,
op=ReduceOp.SUM,
group=None,
sync_op=True,
use_calc_stream=False):
"""
Reduce, then scatter a tensor (or a tensor list) across devices.
Args:
tensor (Tensor): The output tensor on each rank. The result will overwrite this tenor after communication. Support
float16, float32, float64, int32, int64, int8, uint8 or bool as the input data type.
tensor_list (List[Tensor]]): The input to scatter.
If it is a tensor, it should be correctly-sized. If it is a list, it should contain correctly-sized tensors.
op (ReduceOp.SUM|ReduceOp.MAX|ReduceOp.MIN|ReduceOp.PROD, optional): The reduction used. If none is given, use ReduceOp.SUM as default.
group (Group, optional): Communicate in which group. If none is given, use the global group as default.
sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
Returns:
Return a task object.
Warning:
This API only supports the dygraph mode now.
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
if dist.get_rank() == 0:
data1 = paddle.to_tensor([0, 1])
data2 = paddle.to_tensor([2, 3])
else:
data1 = paddle.to_tensor([4, 5])
data2 = paddle.to_tensor([6, 7])
dist.stream.reduce_scatter(data1, [data1, data2])
out = data1.numpy()
# [4, 6] (2 GPUs, out for rank 0)
# [8, 10] (2 GPUs, out for rank 1)
"""
if group is not None and not group.is_member():
raise RuntimeError(
"The group should not be None and all ranks which invoke this operation should be the member of this group."
)
if not sync_op and use_calc_stream:
raise RuntimeError(
"use_calc_stream can only be true in sync op behavior.")
if framework.in_dygraph_mode():
if paddle.is_tensor(tensor_or_tensor_list):
return _reduce_scatter_tensor_in_dygraph(tensor,
tensor_or_tensor_list, op,
group, sync_op,
use_calc_stream)
else:
return _reduce_scatter_in_dygraph(tensor, tensor_or_tensor_list, op,
group, sync_op, use_calc_stream)
raise RuntimeError(
"paddle.distributed.stream.reduce_scatter is only supported in dygraph mode now."
)
def _reduce_scatter_base(out_tensor,
in_tensor,
op=ReduceOp.SUM,
group=None,
sync_op=True,
use_calc_stream=False):
"""
Reduce, then scatter a flattened tensor across devices.
Args:
out_tensor (Tensor): The output tensor on each rank. The result will overwrite this tenor after communication. Support
float16, float32, float64, int32 or int64 as the input data type.
in_tensor (Tensor): The input tensor to reduce and scatter.
op (ReduceOp.SUM|ReduceOp.MAX|ReduceOp.MIN|ReduceOp.PROD, optional): The reduction used. If none is given, use ReduceOp.SUM as default.
group (Group, optional): Communicate in which group. If none is given, use the global group as default.
sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
Returns:
Return a task object.
Warning:
This API will be deprecated in the future, and only supports the dygraph mode now.
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
if dist.get_rank() == 0:
data1 = paddle.to_tensor([7, 8, 9])
data2 = paddle.to_tensor([10, 11, 12])
dist.stream.scatter(data1, src=1)
else:
data1 = paddle.to_tensor([1, 2, 3])
data2 = paddle.to_tensor([4, 5, 6])
dist.stream.scatter(data1, [data1, data2], src=1)
out = data1.numpy()
# [1, 2, 3] (2 GPUs, out for rank 0)
# [4, 5, 6] (2 GPUs, out for rank 1)
"""
if group is not None and not group.is_member():
raise RuntimeError(
"The group should not be None and all ranks which invoke this operation should be the member of this group."
)
if not sync_op and use_calc_stream:
raise RuntimeError(
"use_calc_stream can only be true in sync op behavior.")
if framework.in_dygraph_mode():
return _reduce_scatter_tensor_in_dygraph(out_tensor, in_tensor, op,
group, sync_op,
use_calc_stream,
"_reduce_scatter_base")
raise RuntimeError(
"paddle.distributed.stream._reduce_scatter_base is only supported in dygraph mode now."
)
python/paddle/distributed/communication/stream/scatter.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.distributed as dist
import paddle.fluid.framework as framework
from paddle.distributed import collective
def _check_tensor_shape(tensor, shape, nranks=1):
expect_shape = list(shape)
expect_shape[0] //= nranks
if list(tensor.shape) != expect_shape:
raise RuntimeError("The in_tensor for scatter is not correctly-sized.")
def _check_tensor_list_shape(tensor_list, shape, nranks=1):
if len(tensor_list) != nranks:
raise RuntimeError(
f"The tensor_list for scatter is not correctly-sized.")
for tensor in tensor_list:
if tensor.shape != shape:
raise RuntimeError(
f"The tensor_list for scatter is not correctly-sized.")
def _scatter_tensor_in_dygraph(out_tensor, in_tensor, src, group, sync_op,
use_calc_stream):
group = collective._get_default_group() if group is None else group
src_rank = group.get_group_rank(src)
if src_rank == -1:
raise RuntimeError("Src rank out of group.")
nranks = group.nranks
rank = dist.get_rank()
if rank == src_rank:
_check_tensor_shape(out_tensor, in_tensor.shape, nranks)
if use_calc_stream:
return group.process_group.scatter_tensor_on_calc_stream(
in_tensor, out_tensor, src)
task = group.process_group.scatter_tensor(in_tensor, out_tensor, src,
sync_op)
if sync_op:
task.wait()
return task
def _scatter_in_dygraph(tensor, tensor_list, src, group, sync_op,
use_calc_stream):
group = collective._get_default_group() if group is None else group
src_rank = group.get_group_rank(src)
if src_rank == -1:
raise RuntimeError("Src rank out of group.")
nranks = group.nranks
rank = dist.get_rank()
if rank == src_rank:
if len(tensor_list) == 0:
raise RuntimeError(
"The tensor_list should not be empty on src rank.")
_check_tensor_list_shape(tensor_list, tensor.shape, nranks)
else:
tensor_list = [tensor for _ in range(nranks)]
if use_calc_stream:
return group.process_group.scatter_on_calc_stream(
tensor_list, tensor, src)
task = group.process_group.scatter(tensor_list, tensor, src, sync_op)
if sync_op:
task.wait()
return task
def scatter(tensor,
tensor_or_tensor_list=None,
src=0,
group=None,
sync_op=True,
use_calc_stream=False):
"""
Scatter a tensor (or a tensor list) across devices.
Args:
tensor (Tensor): The output tensor on each rank. The result will overwrite this tenor after communication. Support
float16, float32, float64, int32, int64, int8, uint8 or bool as the input data type.
tensor_or_tensor_list (Union[Tensor, List[Tensor]]): The input to scatter (default is `None`, must be specified on the source rank).
If it is a tensor, it should be correctly-sized. If it is a list, it should contain correctly-sized tensors.
src (int, optional): Rank of the source device. If none is given, use `0` as default.
group (Group, optional): Communicate in which group. If none is given, use the global group as default.
sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
Returns:
Return a task object.
Warning:
This API only supports the dygraph mode now.
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
if dist.get_rank() == 0:
data1 = paddle.to_tensor([7, 8, 9])
data2 = paddle.to_tensor([10, 11, 12])
dist.stream.scatter(data1, src=1)
else:
data1 = paddle.to_tensor([1, 2, 3])
data2 = paddle.to_tensor([4, 5, 6])
dist.stream.scatter(data1, [data1, data2], src=1)
out = data1.numpy()
# [1, 2, 3] (2 GPUs, out for rank 0)
# [4, 5, 6] (2 GPUs, out for rank 1)
"""
if group is not None and not group.is_member():
raise RuntimeError(
"The group should not be None and all ranks which invoke this operation should be the member of this group."
)
if not sync_op and use_calc_stream:
raise RuntimeError(
"use_calc_stream can only be true in sync op behavior.")
if tensor_or_tensor_list is None:
raise RuntimeError("The input should be specified.")
if framework.in_dygraph_mode():
if paddle.is_tensor(tensor_or_tensor_list):
return _scatter_tensor_in_dygraph(tensor, tensor_or_tensor_list,
src, group, sync_op,
use_calc_stream)
else:
return _scatter_in_dygraph(tensor, tensor_or_tensor_list, src,
group, sync_op, use_calc_stream)
raise RuntimeError(
"paddle.distributed.stream.scatter is only supported in dygraph mode now."
)
python/paddle/distributed/communication/stream/send.py
浏览文件 @ f94edc3b
......@@ -64,7 +64,7 @@ def send(tensor, dst=0, group=None, sync_op=True, use_calc_stream=False):
task = dist.stream.recv(data, src=0, sync_op=False)
task.wait()
out = data.numpy()
# [[4, 5, 6], [4, 5, 6]
# [[4, 5, 6], [4, 5, 6]] (2 GPUs)
"""
if group is not None and not group.is_member():
raise RuntimeError(
......
python/paddle/fluid/tests/unittests/collective/CMakeLists.txt
浏览文件 @ f94edc3b
......@@ -282,6 +282,54 @@ if((WITH_GPU OR WITH_ROCM) AND (LINUX))
set_tests_properties(test_communication_stream_allreduce_api
PROPERTIES TIMEOUT "120" LABELS "RUN_TYPE=DIST")
endif()
if((WITH_GPU OR WITH_ROCM) AND (LINUX))
py_test_modules(
test_communication_stream_alltoall_api MODULES
test_communication_stream_alltoall_api ENVS
"PYTHONPATH=..:${PADDLE_BINARY_DIR}/python;http_proxy=;https_proxy=")
set_tests_properties(test_communication_stream_alltoall_api
PROPERTIES TIMEOUT "120" LABELS "RUN_TYPE=DIST")
endif()
if((WITH_GPU OR WITH_ROCM) AND (LINUX))
py_test_modules(
test_communication_stream_alltoall_single_api MODULES
test_communication_stream_alltoall_single_api ENVS
"PYTHONPATH=..:${PADDLE_BINARY_DIR}/python;http_proxy=;https_proxy=")
set_tests_properties(test_communication_stream_alltoall_single_api
PROPERTIES TIMEOUT "120" LABELS "RUN_TYPE=DIST")
endif()
if((WITH_GPU OR WITH_ROCM) AND (LINUX))
py_test_modules(
test_communication_stream_broadcast_api MODULES
test_communication_stream_broadcast_api ENVS
"PYTHONPATH=..:${PADDLE_BINARY_DIR}/python;http_proxy=;https_proxy=")
set_tests_properties(test_communication_stream_broadcast_api
PROPERTIES TIMEOUT "120" LABELS "RUN_TYPE=DIST")
endif()
if((WITH_GPU OR WITH_ROCM) AND (LINUX))
py_test_modules(
test_communication_stream_reduce_api MODULES
test_communication_stream_reduce_api ENVS
"PYTHONPATH=..:${PADDLE_BINARY_DIR}/python;http_proxy=;https_proxy=")
set_tests_properties(test_communication_stream_reduce_api
PROPERTIES TIMEOUT "120" LABELS "RUN_TYPE=DIST")
endif()
if((WITH_GPU OR WITH_ROCM) AND (LINUX))
py_test_modules(
test_communication_stream_reduce_scatter_api MODULES
test_communication_stream_reduce_scatter_api ENVS
"PYTHONPATH=..:${PADDLE_BINARY_DIR}/python;http_proxy=;https_proxy=")
set_tests_properties(test_communication_stream_reduce_scatter_api
PROPERTIES TIMEOUT "120" LABELS "RUN_TYPE=DIST")
endif()
if((WITH_GPU OR WITH_ROCM) AND (LINUX))
py_test_modules(
test_communication_stream_scatter_api MODULES
test_communication_stream_scatter_api ENVS
"PYTHONPATH=..:${PADDLE_BINARY_DIR}/python;http_proxy=;https_proxy=")
set_tests_properties(test_communication_stream_scatter_api
PROPERTIES TIMEOUT "120" LABELS "RUN_TYPE=DIST")
endif()
if((WITH_GPU OR WITH_ROCM) AND (LINUX))
py_test_modules(
test_communication_stream_sendrecv_api MODULES
......
python/paddle/fluid/tests/unittests/collective/communication_stream_alltoall_api_dygraph.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.distributed as dist
import test_communication_api_base as test_base
import test_collective_api_base as test_collective_base
class StreamAllToAllTestCase():
def __init__(self):
self._sync_op = eval(os.getenv("sync_op"))
self._use_calc_stream = eval(os.getenv("use_calc_stream"))
self._backend = os.getenv("backend")
self._shape = eval(os.getenv("shape"))
self._dtype = os.getenv("dtype")
self._seeds = eval(os.getenv("seeds"))
if self._backend not in ["nccl", "gloo"]:
raise NotImplementedError(
"Only support nccl and gloo as the backend for now.")
os.environ["PADDLE_DISTRI_BACKEND"] = self._backend
def run_test_case(self):
dist.init_parallel_env()
test_data_list = []
for seed in self._seeds:
test_data_list.append(
test_collective_base.create_test_data(shape=self._shape,
dtype=self._dtype,
seed=seed))
nranks = len(test_data_list)
data1 = test_data_list[0]
data2 = test_data_list[1]
result1 = np.vstack(
[data1[0:data1.shape[0] // 2, :], data2[0:data2.shape[0] // 2, :]])
result2 = np.vstack(
[data1[data1.shape[0] // 2:, :], data2[data2.shape[0] // 2:, :]])
rank = dist.get_rank()
tensor = paddle.to_tensor(test_data_list[rank])
t1, t2 = paddle.split(tensor, nranks, axis=0)
# case 1: pass an empty tensor list
empty_tensor_list = []
task = dist.stream.alltoall(empty_tensor_list, [t1, t2],
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
result_tensor_list = np.vstack(empty_tensor_list)
if rank == 0:
assert np.allclose(result_tensor_list,
result1,
rtol=1e-05,
atol=1e-05)
else:
assert np.allclose(result_tensor_list,
result2,
rtol=1e-05,
atol=1e-05)
# case 2: pass a pre-sized tensor list
full_tensor_list = [paddle.empty_like(t1) for _ in test_data_list]
task = dist.stream.alltoall(full_tensor_list, [t1, t2],
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
result_tensor_list = np.vstack(full_tensor_list)
if rank == 0:
assert np.allclose(result_tensor_list,
result1,
rtol=1e-05,
atol=1e-05)
else:
assert np.allclose(result_tensor_list,
result2,
rtol=1e-05,
atol=1e-05)
# case 3: pass a pre-sized tensor
out_tensor = paddle.empty_like(tensor)
task = dist.stream.alltoall(out_tensor,
tensor,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
if rank == 0:
assert np.allclose(out_tensor, result1, rtol=1e-05, atol=1e-05)
else:
assert np.allclose(out_tensor, result2, rtol=1e-05, atol=1e-05)
if __name__ == "__main__":
StreamAllToAllTestCase().run_test_case()
python/paddle/fluid/tests/unittests/collective/communication_stream_alltoall_single_api_dygraph.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.distributed as dist
import test_communication_api_base as test_base
import test_collective_api_base as test_collective_base
class StreamAllToAllSingleTestCase():
def __init__(self):
self._sync_op = eval(os.getenv("sync_op"))
self._use_calc_stream = eval(os.getenv("use_calc_stream"))
self._backend = os.getenv("backend")
self._shape = eval(os.getenv("shape"))
self._dtype = os.getenv("dtype")
self._seeds = eval(os.getenv("seeds"))
if self._backend not in ["nccl", "gloo"]:
raise NotImplementedError(
"Only support nccl and gloo as the backend for now.")
os.environ["PADDLE_DISTRI_BACKEND"] = self._backend
def run_test_case(self):
dist.init_parallel_env()
test_data_list = []
for seed in self._seeds:
test_data_list.append(
test_collective_base.create_test_data(shape=self._shape,
dtype=self._dtype,
seed=seed))
nranks = len(test_data_list)
data1 = paddle.to_tensor(test_data_list[0])
data2 = paddle.to_tensor(test_data_list[1])
result1 = np.vstack(
(data1[0:data1.shape[0] // 2, :], data2[0:data2.shape[0] // 2, :]))
result2 = np.vstack(
(data1[data1.shape[0] // 2:, :], data2[data2.shape[0] // 2:, :]))
rank = dist.get_rank()
tensor = paddle.to_tensor(test_data_list[rank])
out_tensor = paddle.empty_like(tensor)
task = dist.stream.alltoall_single(
out_tensor,
tensor,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
if rank == 0:
assert np.allclose(out_tensor, result1, rtol=1e-05, atol=1e-05)
else:
assert np.allclose(out_tensor, result2, rtol=1e-05, atol=1e-05)
if __name__ == "__main__":
StreamAllToAllSingleTestCase().run_test_case()
python/paddle/fluid/tests/unittests/collective/communication_stream_broadcast_api_dygraph.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import paddle
import paddle.distributed as dist
import test_collective_api_base as test_collective_base
class StreamBroadcastTestCase():
def __init__(self):
self._sync_op = eval(os.getenv("sync_op"))
self._use_calc_stream = eval(os.getenv("use_calc_stream"))
self._backend = os.getenv("backend")
self._shape = eval(os.getenv("shape"))
self._dtype = os.getenv("dtype")
self._seeds = eval(os.getenv("seeds"))
if self._backend not in ["nccl", "gloo"]:
raise NotImplementedError(
"Only support nccl and gloo as the backend for now.")
os.environ["PADDLE_DISTRI_BACKEND"] = self._backend
def run_test_case(self):
dist.init_parallel_env()
src_rank = 1
result = test_collective_base.create_test_data(
shape=self._shape, dtype=self._dtype, seed=self._seeds[src_rank])
tensor = paddle.to_tensor(result)
task = dist.stream.broadcast(tensor,
src=src_rank,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
assert np.allclose(tensor, result, rtol=1e-05, atol=1e-05)
if __name__ == "__main__":
StreamBroadcastTestCase().run_test_case()
python/paddle/fluid/tests/unittests/collective/communication_stream_reduce_api_dygraph.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import paddle
import paddle.distributed as dist
import test_collective_api_base as test_collective_base
class StreamReduceTestCase():
def __init__(self):
self._sync_op = eval(os.getenv("sync_op"))
self._use_calc_stream = eval(os.getenv("use_calc_stream"))
self._backend = os.getenv("backend")
self._shape = eval(os.getenv("shape"))
self._dtype = os.getenv("dtype")
self._seeds = eval(os.getenv("seeds"))
if self._backend not in ["nccl", "gloo"]:
raise NotImplementedError(
"Only support nccl and gloo as the backend for now.")
os.environ["PADDLE_DISTRI_BACKEND"] = self._backend
def run_test_case(self):
dist.init_parallel_env()
test_data_list = []
for seed in self._seeds:
test_data_list.append(
test_collective_base.create_test_data(shape=self._shape,
dtype=self._dtype,
seed=seed))
rank = dist.get_rank()
tensor = paddle.to_tensor(test_data_list[rank])
task = dist.stream.reduce(tensor,
dst=1,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
result = sum(test_data_list)
if rank == 1:
assert np.allclose(tensor, result, rtol=1e-05, atol=1e-05)
else:
assert np.allclose(tensor,
test_data_list[rank],
rtol=1e-05,
atol=1e-05)
if __name__ == "__main__":
StreamReduceTestCase().run_test_case()
python/paddle/fluid/tests/unittests/collective/communication_stream_reduce_scatter_api_dygraph.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import paddle
import paddle.distributed as dist
import test_collective_api_base as test_collective_base
class StreamReduceScatterTestCase():
def __init__(self):
self._sync_op = eval(os.getenv("sync_op"))
self._use_calc_stream = eval(os.getenv("use_calc_stream"))
self._backend = os.getenv("backend")
self._shape = eval(os.getenv("shape"))
self._dtype = os.getenv("dtype")
self._seeds = eval(os.getenv("seeds"))
if self._backend not in ["nccl", "gloo"]:
raise NotImplementedError(
"Only support nccl and gloo as the backend for now.")
os.environ["PADDLE_DISTRI_BACKEND"] = self._backend
def run_test_case(self):
dist.init_parallel_env()
test_data_list = []
for seed in self._seeds:
test_data_list.append(
test_collective_base.create_test_data(shape=self._shape,
dtype=self._dtype,
seed=seed))
reduce_result = sum(test_data_list)
result1 = reduce_result[0:reduce_result.shape[0] // 2]
result2 = reduce_result[reduce_result.shape[0] // 2:]
rank = dist.get_rank()
tensor = paddle.to_tensor(test_data_list[rank])
# case 1: pass a pre-sized tensor list
t1, t2 = paddle.split(tensor, 2, axis=0)
result_tensor = paddle.empty_like(t1)
task = dist.stream.reduce_scatter(result_tensor, [t1, t2],
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
if rank == 0:
assert np.allclose(result_tensor, result1, rtol=1e-05, atol=1e-05)
else:
assert np.allclose(result_tensor, result2, rtol=1e-05, atol=1e-05)
# case 2: pass a pre-sized tensor
result_tensor = paddle.empty_like(t1)
task = dist.stream.reduce_scatter(result_tensor,
tensor,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
if rank == 0:
assert np.allclose(result_tensor, result1, rtol=1e-05, atol=1e-05)
else:
assert np.allclose(result_tensor, result2, rtol=1e-05, atol=1e-05)
# case 3: test the legacy API
result_tensor = paddle.empty_like(t1)
task = dist.stream._reduce_scatter_base(
result_tensor,
tensor,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
if rank == 0:
assert np.allclose(result_tensor, result1, rtol=1e-05, atol=1e-05)
else:
assert np.allclose(result_tensor, result2, rtol=1e-05, atol=1e-05)
if __name__ == "__main__":
StreamReduceScatterTestCase().run_test_case()
python/paddle/fluid/tests/unittests/collective/communication_stream_scatter_api_dygraph.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import paddle
import paddle.distributed as dist
import test_collective_api_base as test_collective_base
class StreamScatterTestCase():
def __init__(self):
self._sync_op = eval(os.getenv("sync_op"))
self._use_calc_stream = eval(os.getenv("use_calc_stream"))
self._backend = os.getenv("backend")
self._shape = eval(os.getenv("shape"))
self._dtype = os.getenv("dtype")
self._seeds = eval(os.getenv("seeds"))
if self._backend not in ["nccl", "gloo"]:
raise NotImplementedError(
"Only support nccl and gloo as the backend for now.")
os.environ["PADDLE_DISTRI_BACKEND"] = self._backend
def run_test_case(self):
dist.init_parallel_env()
test_data_list = []
for seed in self._seeds:
test_data_list.append(
test_collective_base.create_test_data(shape=self._shape,
dtype=self._dtype,
seed=seed))
src_rank = 1
src_data = test_data_list[src_rank]
result1 = src_data[0:src_data.shape[0] // 2]
result2 = src_data[src_data.shape[0] // 2:]
rank = dist.get_rank()
# case 1: pass a pre-sized tensor list
tensor = paddle.to_tensor(test_data_list[rank])
t1, t2 = paddle.split(tensor, 2, axis=0)
task = dist.stream.scatter(t1, [t1, t2],
src=src_rank,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
if rank == src_rank:
assert np.allclose(t1, result2, rtol=1e-05, atol=1e-05)
else:
assert np.allclose(t1, result1, rtol=1e-05, atol=1e-05)
# case 2: pass a pre-sized tensor
tensor = paddle.to_tensor(src_data)
t1 = paddle.empty_like(t1)
task = dist.stream.scatter(t1,
tensor,
src=src_rank,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
if rank == src_rank:
assert np.allclose(t1, result2, rtol=1e-05, atol=1e-05)
else:
assert np.allclose(t1, result1, rtol=1e-05, atol=1e-05)
if __name__ == "__main__":
StreamScatterTestCase().run_test_case()
python/paddle/fluid/tests/unittests/collective/communication_stream_sendrecv_api_dygraph.py
浏览文件 @ f94edc3b
......@@ -43,22 +43,25 @@ class StreamSendRecvTestCase():
dtype=self._dtype,
seed=seed))
src_rank = 0
dst_rank = 1
rank = dist.get_rank()
tensor = paddle.to_tensor(test_data_list[rank])
if rank == 0:
task = dist.stream.send(tensor,
dst=1,
dst=dst_rank,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
else:
task = dist.stream.recv(tensor,
src=0,
src=src_rank,
sync_op=self._sync_op,
use_calc_stream=self._use_calc_stream)
if not self._sync_op:
task.wait()
result = test_data_list[0]
result = test_data_list[src_rank]
assert np.allclose(tensor, result, rtol=1e-05, atol=1e-05)
......
python/paddle/fluid/tests/unittests/collective/test_communication_stream_alltoall_api.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle
import itertools
import test_communication_api_base as test_base
class TestCommunicationStreamAllToAllAPI(test_base.CommunicationTestDistBase):
def setUp(self):
super(TestCommunicationStreamAllToAllAPI, self).setUp(num_of_devices=2,
timeout=120)
self._default_envs = {
"backend": "nccl",
"shape": "(100, 200)",
"dtype": "float32",
"seeds": str(self._seeds)
}
self._changeable_envs = {
"sync_op": ["True", "False"],
"use_calc_stream": ["True", "False"]
}
def test_alltoall_stream(self):
envs_list = test_base.gen_product_envs_list(self._default_envs,
self._changeable_envs)
for envs in envs_list:
if eval(envs["use_calc_stream"]) and not eval(envs["sync_op"]):
continue
self.run_test_case("communication_stream_alltoall_api_dygraph.py",
user_defined_envs=envs)
def tearDown(self):
super(TestCommunicationStreamAllToAllAPI, self).tearDown()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/collective/test_communication_stream_alltoall_single_api.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle
import itertools
import test_communication_api_base as test_base
class TestCommunicationStreamAllToAllSingleAPI(
test_base.CommunicationTestDistBase):
def setUp(self):
super(TestCommunicationStreamAllToAllSingleAPI,
self).setUp(num_of_devices=2, timeout=120)
self._default_envs = {
"backend": "nccl",
"shape": "(100, 200)",
"dtype": "float32",
"seeds": str(self._seeds)
}
self._changeable_envs = {
"sync_op": ["True", "False"],
"use_calc_stream": ["True", "False"]
}
def test_alltoall_single_stream(self):
envs_list = test_base.gen_product_envs_list(self._default_envs,
self._changeable_envs)
for envs in envs_list:
if eval(envs["use_calc_stream"]) and not eval(envs["sync_op"]):
continue
self.run_test_case(
"communication_stream_alltoall_single_api_dygraph.py",
user_defined_envs=envs)
def tearDown(self):
super(TestCommunicationStreamAllToAllSingleAPI, self).tearDown()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/collective/test_communication_stream_broadcast_api.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle
import itertools
import test_communication_api_base as test_base
class TestCommunicationStreamBroadcastAPI(test_base.CommunicationTestDistBase):
def setUp(self):
super(TestCommunicationStreamBroadcastAPI, self).setUp(num_of_devices=2,
timeout=120)
self._default_envs = {
"backend": "nccl",
"shape": "(100, 200)",
"dtype": "float32",
"seeds": str(self._seeds)
}
self._changeable_envs = {
"sync_op": ["True", "False"],
"use_calc_stream": ["True", "False"]
}
def test_broadcast_stream(self):
envs_list = test_base.gen_product_envs_list(self._default_envs,
self._changeable_envs)
for envs in envs_list:
if eval(envs["use_calc_stream"]) and not eval(envs["sync_op"]):
continue
self.run_test_case("communication_stream_broadcast_api_dygraph.py",
user_defined_envs=envs)
def tearDown(self):
super(TestCommunicationStreamBroadcastAPI, self).tearDown()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/collective/test_communication_stream_reduce_api.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle
import itertools
import test_communication_api_base as test_base
class TestCommunicationStreamReduceAPI(test_base.CommunicationTestDistBase):
def setUp(self):
super(TestCommunicationStreamReduceAPI, self).setUp(num_of_devices=2,
timeout=120)
self._default_envs = {
"backend": "nccl",
"shape": "(100, 200)",
"dtype": "float32",
"seeds": str(self._seeds)
}
self._changeable_envs = {
"sync_op": ["True", "False"],
"use_calc_stream": ["True", "False"]
}
def test_reduce_stream(self):
envs_list = test_base.gen_product_envs_list(self._default_envs,
self._changeable_envs)
for envs in envs_list:
if eval(envs["use_calc_stream"]) and not eval(envs["sync_op"]):
continue
self.run_test_case("communication_stream_reduce_api_dygraph.py",
user_defined_envs=envs)
def tearDown(self):
super(TestCommunicationStreamReduceAPI, self).tearDown()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/collective/test_communication_stream_reduce_scatter_api.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle
import itertools
import test_communication_api_base as test_base
class TestCommunicationStreamReduceScatterAPI(
test_base.CommunicationTestDistBase):
def setUp(self):
super(TestCommunicationStreamReduceScatterAPI,
self).setUp(num_of_devices=2, timeout=120)
self._default_envs = {
"backend": "nccl",
"shape": "(100, 200)",
"dtype": "float32",
"seeds": str(self._seeds)
}
self._changeable_envs = {
"sync_op": ["True", "False"],
"use_calc_stream": ["True", "False"]
}
def test_reduce_scatter_stream(self):
envs_list = test_base.gen_product_envs_list(self._default_envs,
self._changeable_envs)
for envs in envs_list:
if eval(envs["use_calc_stream"]) and not eval(envs["sync_op"]):
continue
self.run_test_case(
"communication_stream_reduce_scatter_api_dygraph.py",
user_defined_envs=envs)
def tearDown(self):
super(TestCommunicationStreamReduceScatterAPI, self).tearDown()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/collective/test_communication_stream_scatter_api.py 0 → 100644
浏览文件 @ f94edc3b
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle
import itertools
import test_communication_api_base as test_base
class TestCommunicationStreamScatterAPI(test_base.CommunicationTestDistBase):
def setUp(self):
super(TestCommunicationStreamScatterAPI, self).setUp(num_of_devices=2,
timeout=120)
self._default_envs = {
"backend": "nccl",
"shape": "(100, 200)",
"dtype": "float32",
"seeds": str(self._seeds)
}
self._changeable_envs = {
"sync_op": ["True", "False"],
"use_calc_stream": ["True", "False"]
}
def test_reduce_stream(self):
envs_list = test_base.gen_product_envs_list(self._default_envs,
self._changeable_envs)
for envs in envs_list:
if eval(envs["use_calc_stream"]) and not eval(envs["sync_op"]):
continue
self.run_test_case("communication_stream_scatter_api_dygraph.py",
user_defined_envs=envs)
def tearDown(self):
super(TestCommunicationStreamScatterAPI, self).tearDown()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/collective/testslist.csv
浏览文件 @ f94edc3b
......@@ -34,6 +34,12 @@ test_collective_split_row_linear,linux,gpu;rocm,300,DIST,test_runner.py,2,,http_
test_collective_wait,linux,gpu;rocm,300,DIST,test_runner.py,2,,http_proxy=;https_proxy=;PYTHONPATH=..,
test_communication_stream_allgather_api,linux,gpu;rocm,120,DIST,,2,,PYTHONPATH=..;http_proxy=;https_proxy=,
test_communication_stream_allreduce_api,linux,gpu;rocm,120,DIST,,2,,PYTHONPATH=..;http_proxy=;https_proxy=,
test_communication_stream_alltoall_api,linux,gpu;rocm,120,DIST,,2,,PYTHONPATH=..;http_proxy=;https_proxy=,
test_communication_stream_alltoall_single_api,linux,gpu;rocm,120,DIST,,2,,PYTHONPATH=..;http_proxy=;https_proxy=,
test_communication_stream_broadcast_api,linux,gpu;rocm,120,DIST,,2,,PYTHONPATH=..;http_proxy=;https_proxy=,
test_communication_stream_reduce_api,linux,gpu;rocm,120,DIST,,2,,PYTHONPATH=..;http_proxy=;https_proxy=,
test_communication_stream_reduce_scatter_api,linux,gpu;rocm,120,DIST,,2,,PYTHONPATH=..;http_proxy=;https_proxy=,
test_communication_stream_scatter_api,linux,gpu;rocm,120,DIST,,2,,PYTHONPATH=..;http_proxy=;https_proxy=,
test_communication_stream_sendrecv_api,linux,gpu;rocm,120,DIST,,2,,PYTHONPATH=..;http_proxy=;https_proxy=,
test_eager_dist_api,linux,gpu;rocm,120,DIST,test_runner.py,2,,http_proxy=;https_proxy=;PYTHONPATH=..,
test_gen_nccl_id_op,,gpu;rocm;ASCEND;ASCEND_CL,,DIST,../dist_test.sh,2,,http_proxy=;https_proxy=;PYTHONPATH=..,
......
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