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未验证 提交 1e56ca8a 编写于 作者: L lilong12 提交者： GitHub
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Use densetensor instead of Tensor for ProcessGroup (#41403)

上级 1cdd88f6
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paddle/fluid/distributed/collective/Common.cc
浏览文件 @ 1e56ca8a
...@@ -17,11 +17,11 @@ ...@@ -17,11 +17,11 @@
namespace paddle { namespace paddle {
namespace distributed { namespace distributed {
std::vector<Place> GetPlaceList(const std::vector<Tensor>& tensors) { std::vector<Place> GetPlaceList(const std::vector<phi::DenseTensor>& tensors) {
std::vector<Place> places; std::vector<Place> places;
places.reserve(tensors.size()); places.reserve(tensors.size());
for (auto& tensor : tensors) { for (auto& tensor : tensors) {
places.push_back(tensor.inner_place()); places.push_back(tensor.place());
} }
return places; return places;
} }
...@@ -40,16 +40,12 @@ std::string GetKeyFromPlaces(const std::vector<Place>& places) { ...@@ -40,16 +40,12 @@ std::string GetKeyFromPlaces(const std::vector<Place>& places) {
return placeList; return placeList;
} }
static bool CheckTensorsInPlace(const std::vector<Tensor>& tensors, bool CheckTensorsInCudaPlace(const std::vector<phi::DenseTensor>& tensors) {
phi::AllocationType type) { return std::all_of(tensors.cbegin(), tensors.cend(),
return std::all_of(tensors.cbegin(), tensors.cend(), [&](const Tensor& t) { [&](const phi::DenseTensor& t) {
return t.place().GetType() == type; return platform::is_gpu_place(t.place());
}); });
} }
bool CheckTensorsInCudaPlace(const std::vector<Tensor>& tensors) {
return CheckTensorsInPlace(tensors, phi::AllocationType::GPU);
}
} // namespace distributed } // namespace distributed
} // namespace paddle } // namespace paddle
paddle/fluid/distributed/collective/Common.h
浏览文件 @ 1e56ca8a
...@@ -16,18 +16,18 @@ ...@@ -16,18 +16,18 @@
#include "paddle/fluid/platform/place.h" #include "paddle/fluid/platform/place.h"
#include "paddle/phi/api/include/api.h" #include "paddle/phi/api/include/api.h"
#include "paddle/phi/common/place.h"
#include "paddle/phi/core/dense_tensor.h"
namespace paddle { namespace paddle {
namespace distributed { namespace distributed {
using Tensor = paddle::experimental::Tensor;
using Place = paddle::platform::Place; using Place = paddle::platform::Place;
// Get the list of devices from list of tensors // Get the list of devices from list of tensors
std::vector<Place> GetPlaceList(const std::vector<Tensor>& tensors); std::vector<Place> GetPlaceList(const std::vector<phi::DenseTensor>& tensors);
// Get the deviceList String from the list of devices // Get the deviceList String from the list of devices
std::string GetKeyFromPlaces(const std::vector<Place>& places); std::string GetKeyFromPlaces(const std::vector<Place>& places);
bool CheckTensorsInCudaPlace(const std::vector<Tensor>& tensors); bool CheckTensorsInCudaPlace(const std::vector<phi::DenseTensor>& tensors);
} // namespace distributed } // namespace distributed
} // namespace paddle } // namespace paddle
paddle/fluid/distributed/collective/ProcessGroup.cc
浏览文件 @ 1e56ca8a
...@@ -17,7 +17,8 @@ ...@@ -17,7 +17,8 @@
namespace paddle { namespace paddle {
namespace distributed { namespace distributed {
ProcessGroup::Task::Task(int rank, const std::vector<Tensor>& inputTensors, ProcessGroup::Task::Task(int rank,
const std::vector<phi::DenseTensor>& inputTensors,
CommType comm_type) CommType comm_type)
: rank_(rank), comm_type_(comm_type) {} : rank_(rank), comm_type_(comm_type) {}
......
paddle/fluid/distributed/collective/ProcessGroup.h
浏览文件 @ 1e56ca8a
...@@ -54,7 +54,7 @@ class ProcessGroup { ...@@ -54,7 +54,7 @@ class ProcessGroup {
public: public:
class Task { class Task {
public: public:
Task(int rank, const std::vector<Tensor>& inputTensors, Task(int rank, const std::vector<phi::DenseTensor>& inputTensors,
CommType opType = CommType::UNKNOWN); CommType opType = CommType::UNKNOWN);
virtual ~Task(); virtual ~Task();
...@@ -79,25 +79,21 @@ class ProcessGroup { ...@@ -79,25 +79,21 @@ class ProcessGroup {
virtual const std::string GetBackendName() const = 0; virtual const std::string GetBackendName() const = 0;
virtual std::shared_ptr<ProcessGroup::Task> AllReduce( virtual std::shared_ptr<ProcessGroup::Task> AllReduce(
std::vector<Tensor>& /* tensors */, std::vector<phi::DenseTensor>& /* input tensors */, // NOLINT
std::vector<phi::DenseTensor>& /* output tensors */, // NOLINT
const AllreduceOptions& = AllreduceOptions()) { const AllreduceOptions& = AllreduceOptions()) {
PADDLE_THROW(platform::errors::InvalidArgument( PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support allreduce", GetBackendName())); "ProcessGroup%s does not support allreduce", GetBackendName()));
} }
virtual std::shared_ptr<ProcessGroup::Task> Broadcast( virtual std::shared_ptr<ProcessGroup::Task> Broadcast(
std::vector<Tensor>& /* tensors */, std::vector<phi::DenseTensor>& /* input tensors */, // NOLINT
std::vector<phi::DenseTensor>& /* output tensors */, // NOLINT
const BroadcastOptions& = BroadcastOptions()) { const BroadcastOptions& = BroadcastOptions()) {
PADDLE_THROW(platform::errors::InvalidArgument( PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support broadcast", GetBackendName())); "ProcessGroup%s does not support broadcast", GetBackendName()));
} }
virtual void Broadcast(const phi::DenseTensor* in, phi::DenseTensor* out) {
PADDLE_THROW(platform::errors::Fatal(
"ProcessGroup%s does not support broadcast for static mode runtime",
GetBackendName()));
}
virtual std::shared_ptr<ProcessGroup::Task> Barrier( virtual std::shared_ptr<ProcessGroup::Task> Barrier(
const BarrierOptions& = BarrierOptions()) { const BarrierOptions& = BarrierOptions()) {
PADDLE_THROW(platform::errors::InvalidArgument( PADDLE_THROW(platform::errors::InvalidArgument(
...@@ -105,41 +101,42 @@ class ProcessGroup { ...@@ -105,41 +101,42 @@ class ProcessGroup {
} }
virtual std::shared_ptr<ProcessGroup::Task> Send( virtual std::shared_ptr<ProcessGroup::Task> Send(
std::vector<Tensor>& tensors /* tensors */, int dst_rank) { // NOLINT std::vector<phi::DenseTensor>&, int) { // NOLINT
PADDLE_THROW(platform::errors::InvalidArgument( PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support send", GetBackendName())); "ProcessGroup%s does not support send", GetBackendName()));
} }
virtual std::shared_ptr<ProcessGroup::Task> Recv( virtual std::shared_ptr<ProcessGroup::Task> Recv(
std::vector<Tensor>& tensors /* tensors */, int src_rank) { // NOLINT std::vector<phi::DenseTensor>& tensors, int) { // NOLINT
PADDLE_THROW(platform::errors::InvalidArgument( PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support receive", GetBackendName())); "ProcessGroup%s does not support receive", GetBackendName()));
} }
virtual std::shared_ptr<ProcessGroup::Task> AllGather( virtual std::shared_ptr<ProcessGroup::Task> AllGather(
std::vector<Tensor>& in_tensors /* tensors */, // NOLINT std::vector<phi::DenseTensor>&, // NOLINT
std::vector<Tensor>& out_tensors /* tensors */) { // NOLINT std::vector<phi::DenseTensor>&) { // NOLINT
PADDLE_THROW(platform::errors::InvalidArgument( PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support AllGather", GetBackendName())); "ProcessGroup%s does not support AllGather", GetBackendName()));
} }
virtual std::shared_ptr<ProcessGroup::Task> AllToAll( virtual std::shared_ptr<ProcessGroup::Task> AllToAll(
std::vector<Tensor>& in /* tensors */, // NOLINT std::vector<phi::DenseTensor>&, // NOLINT
std::vector<Tensor>& out /* tensors */) { // NOLINT std::vector<phi::DenseTensor>&) { // NOLINT
PADDLE_THROW(platform::errors::InvalidArgument( PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support AllToAll", GetBackendName())); "ProcessGroup%s does not support AllToAll", GetBackendName()));
} }
virtual std::shared_ptr<ProcessGroup::Task> Reduce( virtual std::shared_ptr<ProcessGroup::Task> Reduce(
std::vector<Tensor>& tensors /* tensors */, // NOLINT std::vector<phi::DenseTensor>&, // NOLINT
const ReduceOptions& opts) { // NOLINT std::vector<phi::DenseTensor>&, // NOLINT
const ReduceOptions& opts) {
PADDLE_THROW(platform::errors::InvalidArgument( 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> Scatter( virtual std::shared_ptr<ProcessGroup::Task> Scatter(
std::vector<Tensor>& in_tensors /* tensors */, // NOLINT std::vector<phi::DenseTensor>&, // NOLINT
std::vector<Tensor>& out_tensors /* tensors */, // NOLINT std::vector<phi::DenseTensor>&, // NOLINT
const ScatterOptions&) { // NOLINT const ScatterOptions&) { // NOLINT
PADDLE_THROW(platform::errors::InvalidArgument( PADDLE_THROW(platform::errors::InvalidArgument(
"ProcessGroup%s does not support Scatter", GetBackendName())); "ProcessGroup%s does not support Scatter", GetBackendName()));
......
paddle/fluid/distributed/collective/ProcessGroupGloo.cc
浏览文件 @ 1e56ca8a
...@@ -27,6 +27,7 @@ ...@@ -27,6 +27,7 @@
#include <gloo/broadcast.h> #include <gloo/broadcast.h>
#include <gloo/reduce.h> #include <gloo/reduce.h>
#include <gloo/scatter.h> #include <gloo/scatter.h>
#include "paddle/fluid/distributed/collective/Common.h"
#include "paddle/fluid/distributed/collective/ProcessGroupGloo.h" #include "paddle/fluid/distributed/collective/ProcessGroupGloo.h"
#include "paddle/fluid/framework/fleet/gloo_wrapper.h" #include "paddle/fluid/framework/fleet/gloo_wrapper.h"
#include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/enforce.h"
...@@ -105,107 +106,104 @@ reduce_func get_function(const ReduceOp& r) { ...@@ -105,107 +106,104 @@ reduce_func get_function(const ReduceOp& r) {
exit(-1); exit(-1);
} }
bool CheckTensorsInCPUPlace(const std::vector<Tensor>& tensors) {
return std::all_of(tensors.cbegin(), tensors.cend(), [&](const Tensor& t) {
return t.place() == PlaceType::kCPU;
});
}
template <typename T> template <typename T>
T* get_data(const Tensor& tensor) { T* get_data(phi::DenseTensor& tensor) { // NOLINT
auto raw_tensor = std::dynamic_pointer_cast<phi::DenseTensor>(tensor.impl()); return reinterpret_cast<T*>(tensor.data());
return static_cast<T*>(raw_tensor->data());
} }
template <typename T> template <typename T>
std::vector<T*> get_multi_data(const std::vector<Tensor>& tensors) { std::vector<T*> get_multi_data(
std::vector<T*> ret(tensors.size()); std::vector<phi::DenseTensor>& tensors) { // NOLINT
std::vector<T*> ret;
ret.reserve(tensors.size());
for (size_t i = 0; i < tensors.size(); i++) { for (size_t i = 0; i < tensors.size(); i++) {
ret[i] = get_data<T>(tensors[i]); ret.push_back(get_data<T>(tensors[i]));
} }
return ret; return ret;
} }
template <typename T, typename P> template <typename T, typename P>
void set_output(P& opts, const Tensor& tensor) { // NOLINT void set_output(P& opts, phi::DenseTensor& tensor) { // NOLINT
opts.setOutput(get_data<T>(tensor), tensor.numel()); opts.setOutput(get_data<T>(tensor), tensor.numel());
} }
template <typename T, typename P> template <typename T, typename P>
void set_input(P& opts, const Tensor& tensor) { // NOLINT void set_input(P& opts, phi::DenseTensor& tensor) { // NOLINT
opts.setInput(get_data<T>(tensor), tensor.numel()); opts.setInput(get_data<T>(tensor), tensor.numel());
} }
template <typename T, typename P> template <typename T, typename P>
void set_outputs(P& opts, const std::vector<Tensor>& tensors) { // NOLINT void set_outputs(P& opts, // NOLINT
std::vector<phi::DenseTensor>& tensors) { // NOLINT
opts.setOutputs(get_multi_data<T>(tensors), tensors[0].numel()); opts.setOutputs(get_multi_data<T>(tensors), tensors[0].numel());
} }
template <typename T, typename P> template <typename T, typename P>
void set_inputs(P& opts, const std::vector<Tensor>& tensors) { // NOLINT void set_inputs(P& opts, // NOLINT
std::vector<phi::DenseTensor>& tensors) { // NOLINT
opts.setInputs(get_multi_data<T>(tensors), tensors[0].numel()); opts.setInputs(get_multi_data<T>(tensors), tensors[0].numel());
} }
template <typename T, typename P> template <typename T, typename P>
void set_inputs_for_scatter(P& opts, // NOLINT void set_inputs_for_scatter(P& opts, // NOLINT
const std::vector<Tensor>& tensors, // NOLINT phi::DenseTensor& tensor, // NOLINT
int nranks) { int nranks) {
std::vector<T*> ret(nranks); std::vector<T*> ret;
auto raw_tensor = ret.reserve(nranks);
std::dynamic_pointer_cast<phi::DenseTensor>(tensors[0].impl()); T* raw_pointer = reinterpret_cast<T*>(tensor.data());
T* raw_pointer = reinterpret_cast<T*>(raw_tensor->data());
size_t offset = 0; size_t offset = 0;
for (int i = 0; i < nranks; i++) { for (int i = 0; i < nranks; i++) {
ret[i] = raw_pointer + offset; ret.push_back(raw_pointer + offset);
offset += tensors[0].numel() / nranks; offset += tensor.numel() / nranks;
} }
opts.setInputs(ret, tensors[0].numel() / nranks); opts.setInputs(ret, tensor.numel() / nranks);
} }
ProcessGroupGloo::GlooTask::GlooTask(int rank, ProcessGroupGloo::GlooTask::GlooTask(
const std::vector<Tensor>& inputs, int rank, const std::vector<phi::DenseTensor>& inputs, CommType comm_type)
CommType comm_type) : ProcessGroup::Task(rank, inputs, comm_type) {}
: ProcessGroup::Task(rank, inputs, comm_type) {
PADDLE_ENFORCE_EQ(CheckTensorsInCPUPlace(inputs), true,
platform::errors::Fatal(
"Only CPU place is supported for ProcessGroupGloo."));
}
ProcessGroupGloo::ProcessGroupGloo( ProcessGroupGloo::ProcessGroupGloo(
const std::shared_ptr<paddle::distributed::Store>& store, int rank, const std::shared_ptr<distributed::Store>& store, int rank, int world_size,
int world_size, int gid, const std::shared_ptr<GlooOptions> options) int gid, const std::shared_ptr<GlooOptions> options)
: ProcessGroup(rank, world_size, gid), : ProcessGroup(rank, world_size, gid),
_tag(0), _tag(0),
_store(new GlooStore(store)) { _store(new GlooStore(store)) {
_context = std::make_shared<gloo::rendezvous::Context>(rank, world_size); _context = std::make_shared<gloo::rendezvous::Context>(rank, world_size);
auto prefix_store = auto prefix_store =
::gloo::rendezvous::PrefixStore(std::to_string(0), *_store); ::gloo::rendezvous::PrefixStore(std::to_string(gid), *_store);
_context->connectFullMesh(prefix_store, options->device); _context->connectFullMesh(prefix_store, options->device);
} }
class BroadcastGlooTask : public ProcessGroupGloo::GlooTask { class BroadcastGlooTask : public ProcessGroupGloo::GlooTask {
public: public:
BroadcastGlooTask(const std::shared_ptr<gloo::Context>& context, BroadcastGlooTask(const std::shared_ptr<gloo::Context>& context,
const std::vector<Tensor>& inputs, int rank, int root, std::vector<phi::DenseTensor>& inputs, // NOLINT
uint32_t tag) std::vector<phi::DenseTensor>& outputs, // NOLINT
int rank, int root, uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::BROADCAST), : ProcessGroupGloo::GlooTask(rank, inputs, CommType::BROADCAST),
_context(context), _context(context),
_root(root), _root(root),
_inputs(inputs), _inputs(inputs),
_outputs(outputs),
_tag(tag) {} _tag(tag) {}
void Run() override { _do_broadcast(_inputs[0]); } void Run() override { _do_broadcast(_inputs[0], _outputs[0]); }
private: private:
std::shared_ptr<gloo::Context> _context; std::shared_ptr<gloo::Context> _context;
const int _root; const int _root;
std::vector<Tensor> _inputs{}; std::vector<phi::DenseTensor> _inputs{};
std::vector<phi::DenseTensor> _outputs{};
const uint32_t _tag; const uint32_t _tag;
void _do_broadcast(const Tensor& tensor) { void _do_broadcast(phi::DenseTensor& in, phi::DenseTensor& out) { // NOLINT
gloo::BroadcastOptions opts(_context); gloo::BroadcastOptions opts(_context);
const auto& dtype = tensor.type(); const auto& dtype = in.dtype();
GENERATE_FUNC(dtype, set_output, opts, tensor); if (rank_ == _root) {
GENERATE_FUNC(dtype, set_input, opts, in);
}
GENERATE_FUNC(dtype, set_output, opts, out);
opts.setRoot(_root); opts.setRoot(_root);
opts.setTag(_tag); opts.setTag(_tag);
gloo::broadcast(opts); gloo::broadcast(opts);
...@@ -213,12 +211,14 @@ class BroadcastGlooTask : public ProcessGroupGloo::GlooTask { ...@@ -213,12 +211,14 @@ class BroadcastGlooTask : public ProcessGroupGloo::GlooTask {
}; };
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Broadcast( std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Broadcast(
std::vector<Tensor>& inputs, const BroadcastOptions& opts) { std::vector<phi::DenseTensor>& inputs,
std::vector<phi::DenseTensor>& outputs, const BroadcastOptions& opts) {
auto root = opts.source_rank; auto root = opts.source_rank;
std::unique_ptr<BroadcastGlooTask> task; std::unique_ptr<BroadcastGlooTask> task;
auto tag = next_tag(); auto tag = next_tag();
auto context = get_context(); auto context = get_context();
task = std::make_unique<BroadcastGlooTask>(context, inputs, rank_, root, tag); task = std::make_unique<BroadcastGlooTask>(context, inputs, outputs, rank_,
root, tag);
task->Run(); task->Run();
return task; return task;
} }
...@@ -226,19 +226,22 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Broadcast( ...@@ -226,19 +226,22 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Broadcast(
class AllreduceGlooTask : public ProcessGroupGloo::GlooTask { class AllreduceGlooTask : public ProcessGroupGloo::GlooTask {
public: public:
AllreduceGlooTask(int rank, const std::shared_ptr<gloo::Context>& context, AllreduceGlooTask(int rank, const std::shared_ptr<gloo::Context>& context,
std::vector<Tensor>& inputs, ReduceOp reduce_op, // NOLINT std::vector<phi::DenseTensor>& inputs, // NOLINT
uint32_t tag) std::vector<phi::DenseTensor>& outputs, // NOLINT
ReduceOp reduce_op, uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::ALLREDUCE), : ProcessGroupGloo::GlooTask(rank, inputs, CommType::ALLREDUCE),
_context(context), _context(context),
_inputs(inputs), _inputs(inputs),
_outputs(outputs),
_reduce_op(reduce_op), _reduce_op(reduce_op),
_tag(tag) {} _tag(tag) {}
void Run() override { _do_allreduce(_inputs); } void Run() override { _do_allreduce(_inputs, _outputs); }
private: private:
std::shared_ptr<gloo::Context> _context; std::shared_ptr<gloo::Context> _context;
std::vector<Tensor> _inputs; std::vector<phi::DenseTensor> _inputs;
std::vector<phi::DenseTensor> _outputs;
const ReduceOp _reduce_op; const ReduceOp _reduce_op;
uint32_t _tag; uint32_t _tag;
...@@ -255,11 +258,12 @@ class AllreduceGlooTask : public ProcessGroupGloo::GlooTask { ...@@ -255,11 +258,12 @@ class AllreduceGlooTask : public ProcessGroupGloo::GlooTask {
fn = get_function<T>(op); fn = get_function<T>(op);
} }
void _do_allreduce(std::vector<Tensor>& tensors) { // NOLINT void _do_allreduce(std::vector<phi::DenseTensor>& ins, // NOLINT
const auto& dtype = tensors[0].type(); std::vector<phi::DenseTensor>& outs) { // NOLINT
const auto& dtype = ins[0].dtype();
gloo::AllreduceOptions opts(_context); gloo::AllreduceOptions opts(_context);
GENERATE_FUNC(dtype, set_inputs, opts, tensors); GENERATE_FUNC(dtype, set_inputs, opts, ins);
GENERATE_FUNC(dtype, set_outputs, opts, tensors); GENERATE_FUNC(dtype, set_outputs, opts, outs);
opts.setReduceFunction(_get_function(dtype, _reduce_op)); opts.setReduceFunction(_get_function(dtype, _reduce_op));
opts.setTag(_tag); opts.setTag(_tag);
gloo::allreduce(opts); gloo::allreduce(opts);
...@@ -267,11 +271,12 @@ class AllreduceGlooTask : public ProcessGroupGloo::GlooTask { ...@@ -267,11 +271,12 @@ class AllreduceGlooTask : public ProcessGroupGloo::GlooTask {
}; };
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllReduce( std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllReduce(
std::vector<Tensor>& inputs, const AllreduceOptions& opts) { std::vector<phi::DenseTensor>& inputs,
std::vector<phi::DenseTensor>& outputs, const AllreduceOptions& opts) {
auto tag = next_tag(); auto tag = next_tag();
std::shared_ptr<GlooTask> task; std::shared_ptr<GlooTask> task;
auto context = get_context(); auto context = get_context();
task = std::make_shared<AllreduceGlooTask>(rank_, context, inputs, task = std::make_shared<AllreduceGlooTask>(rank_, context, inputs, outputs,
opts.reduce_op, tag); opts.reduce_op, tag);
task->Run(); task->Run();
return task; return task;
...@@ -280,7 +285,7 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllReduce( ...@@ -280,7 +285,7 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllReduce(
class BarrierGlooTask : public ProcessGroupGloo::GlooTask { class BarrierGlooTask : public ProcessGroupGloo::GlooTask {
public: public:
BarrierGlooTask(int rank, const std::shared_ptr<gloo::Context>& context) BarrierGlooTask(int rank, const std::shared_ptr<gloo::Context>& context)
: ProcessGroupGloo::GlooTask(rank, std::vector<Tensor>{}, : ProcessGroupGloo::GlooTask(rank, std::vector<phi::DenseTensor>{},
CommType::BARRIER), CommType::BARRIER),
_context(context) {} _context(context) {}
...@@ -307,8 +312,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Barrier( ...@@ -307,8 +312,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Barrier(
class AllgatherGlooTask : public ProcessGroupGloo::GlooTask { class AllgatherGlooTask : public ProcessGroupGloo::GlooTask {
public: public:
AllgatherGlooTask(int rank, const std::shared_ptr<gloo::Context>& context, AllgatherGlooTask(int rank, const std::shared_ptr<gloo::Context>& context,
std::vector<Tensor>& inputs, // NOLINT std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<Tensor>& outputs, // NOLINT std::vector<phi::DenseTensor>& outputs, // NOLINT
uint32_t tag) uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::ALLGATHER), : ProcessGroupGloo::GlooTask(rank, inputs, CommType::ALLGATHER),
_context(context), _context(context),
...@@ -320,13 +325,13 @@ class AllgatherGlooTask : public ProcessGroupGloo::GlooTask { ...@@ -320,13 +325,13 @@ class AllgatherGlooTask : public ProcessGroupGloo::GlooTask {
private: private:
std::shared_ptr<gloo::Context> _context; std::shared_ptr<gloo::Context> _context;
std::vector<Tensor> _inputs; std::vector<phi::DenseTensor> _inputs;
std::vector<Tensor> _outputs; std::vector<phi::DenseTensor> _outputs;
uint32_t _tag; uint32_t _tag;
void _do_allgather(std::vector<Tensor>& in, // NOLINT void _do_allgather(std::vector<phi::DenseTensor>& in, // NOLINT
std::vector<Tensor>& out) { // NOLINT std::vector<phi::DenseTensor>& out) { // NOLINT
const auto& dtype = in[0].type(); const auto& dtype = in[0].dtype();
gloo::AllgatherOptions opts(_context); gloo::AllgatherOptions opts(_context);
GENERATE_FUNC(dtype, set_input, opts, in[0]); GENERATE_FUNC(dtype, set_input, opts, in[0]);
GENERATE_FUNC(dtype, set_output, opts, out[0]); GENERATE_FUNC(dtype, set_output, opts, out[0]);
...@@ -336,7 +341,8 @@ class AllgatherGlooTask : public ProcessGroupGloo::GlooTask { ...@@ -336,7 +341,8 @@ class AllgatherGlooTask : public ProcessGroupGloo::GlooTask {
}; };
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllGather( std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllGather(
std::vector<Tensor>& in_tensors, std::vector<Tensor>& out_tensors) { std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors) {
std::shared_ptr<AllgatherGlooTask> task; std::shared_ptr<AllgatherGlooTask> task;
auto tag = next_tag(); auto tag = next_tag();
auto context = get_context(); auto context = get_context();
...@@ -349,20 +355,23 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllGather( ...@@ -349,20 +355,23 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllGather(
class ReduceGlooTask : public ProcessGroupGloo::GlooTask { class ReduceGlooTask : public ProcessGroupGloo::GlooTask {
public: public:
ReduceGlooTask(int rank, const std::shared_ptr<gloo::Context>& context, ReduceGlooTask(int rank, const std::shared_ptr<gloo::Context>& context,
std::vector<Tensor>& in, ReduceOp reduce_op, // NOLINT std::vector<phi::DenseTensor>& inputs, // NOLINT
int dst, uint32_t tag) std::vector<phi::DenseTensor>& outputs, // NOLINT
: ProcessGroupGloo::GlooTask(rank, in, CommType::REDUCE), ReduceOp reduce_op, int dst, uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::REDUCE),
_context(context), _context(context),
_inputs(in), _inputs(inputs),
_outputs(outputs),
_reduce_op(reduce_op), _reduce_op(reduce_op),
_dst(dst), _dst(dst),
_tag(tag) {} _tag(tag) {}
void Run() override { _do_reduce(_inputs, _dst); } void Run() override { _do_reduce(_inputs, _outputs, _dst); }
private: private:
std::shared_ptr<gloo::Context> _context; std::shared_ptr<gloo::Context> _context;
std::vector<Tensor> _inputs; std::vector<phi::DenseTensor> _inputs;
std::vector<phi::DenseTensor> _outputs;
const ReduceOp _reduce_op; const ReduceOp _reduce_op;
int _dst; int _dst;
uint32_t _tag; uint32_t _tag;
...@@ -380,11 +389,13 @@ class ReduceGlooTask : public ProcessGroupGloo::GlooTask { ...@@ -380,11 +389,13 @@ class ReduceGlooTask : public ProcessGroupGloo::GlooTask {
fn = get_function<T>(op); fn = get_function<T>(op);
} }
void _do_reduce(std::vector<Tensor>& tensors, int dst) { // NOLINT void _do_reduce(std::vector<phi::DenseTensor>& inputs, // NOLINT
const auto& dtype = tensors[0].type(); std::vector<phi::DenseTensor>& outputs, // NOLINT
int dst) {
const auto& dtype = inputs[0].dtype();
gloo::ReduceOptions opts(_context); gloo::ReduceOptions opts(_context);
GENERATE_FUNC(dtype, set_input, opts, tensors[0]); GENERATE_FUNC(dtype, set_input, opts, inputs[0]);
GENERATE_FUNC(dtype, set_output, opts, tensors[0]); GENERATE_FUNC(dtype, set_output, opts, outputs[0]);
opts.setReduceFunction(_get_function(dtype, _reduce_op)); opts.setReduceFunction(_get_function(dtype, _reduce_op));
opts.setTag(_tag); opts.setTag(_tag);
opts.setRoot(dst); opts.setRoot(dst);
...@@ -393,11 +404,12 @@ class ReduceGlooTask : public ProcessGroupGloo::GlooTask { ...@@ -393,11 +404,12 @@ class ReduceGlooTask : public ProcessGroupGloo::GlooTask {
}; };
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Reduce( std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Reduce(
std::vector<Tensor>& tensors, const ReduceOptions& opts) { std::vector<phi::DenseTensor>& inputs,
std::vector<phi::DenseTensor>& outputs, const ReduceOptions& opts) {
std::shared_ptr<ReduceGlooTask> task; std::shared_ptr<ReduceGlooTask> task;
auto tag = next_tag(); auto tag = next_tag();
auto context = get_context(); auto context = get_context();
task = std::make_shared<ReduceGlooTask>(rank_, context, tensors, task = std::make_shared<ReduceGlooTask>(rank_, context, inputs, outputs,
opts.reduce_op, opts.root_rank, tag); opts.reduce_op, opts.root_rank, tag);
task->Run(); task->Run();
return task; return task;
...@@ -406,8 +418,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Reduce( ...@@ -406,8 +418,8 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Reduce(
class ScatterGlooTask : public ProcessGroupGloo::GlooTask { class ScatterGlooTask : public ProcessGroupGloo::GlooTask {
public: public:
ScatterGlooTask(int rank, const std::shared_ptr<gloo::Context>& context, ScatterGlooTask(int rank, const std::shared_ptr<gloo::Context>& context,
std::vector<Tensor>& inputs, // NOLINT std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<Tensor>& outputs, // NOLINT std::vector<phi::DenseTensor>& outputs, // NOLINT
int src, int size, uint32_t tag) int src, int size, uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::SCATTER), : ProcessGroupGloo::GlooTask(rank, inputs, CommType::SCATTER),
_context(context), _context(context),
...@@ -421,18 +433,19 @@ class ScatterGlooTask : public ProcessGroupGloo::GlooTask { ...@@ -421,18 +433,19 @@ class ScatterGlooTask : public ProcessGroupGloo::GlooTask {
private: private:
std::shared_ptr<gloo::Context> _context; std::shared_ptr<gloo::Context> _context;
std::vector<Tensor> _inputs; std::vector<phi::DenseTensor> _inputs;
std::vector<Tensor> _outputs; std::vector<phi::DenseTensor> _outputs;
int _src; int _src;
int _size; int _size;
uint32_t _tag; uint32_t _tag;
void _do_scatter(std::vector<Tensor>& in, std::vector<Tensor>& out, // NOLINT void _do_scatter(std::vector<phi::DenseTensor>& in, // NOLINT
std::vector<phi::DenseTensor>& out, // NOLINT
int src) { int src) {
const auto& dtype = in[0].type(); const auto& dtype = in[0].dtype();
gloo::ScatterOptions opts(_context); gloo::ScatterOptions opts(_context);
if (rank_ == src) { if (rank_ == src) {
GENERATE_FUNC(dtype, set_inputs_for_scatter, opts, in, _size); GENERATE_FUNC(dtype, set_inputs_for_scatter, opts, in[0], _size);
} }
GENERATE_FUNC(dtype, set_output, opts, out[0]); GENERATE_FUNC(dtype, set_output, opts, out[0]);
opts.setRoot(src); opts.setRoot(src);
...@@ -442,8 +455,8 @@ class ScatterGlooTask : public ProcessGroupGloo::GlooTask { ...@@ -442,8 +455,8 @@ class ScatterGlooTask : public ProcessGroupGloo::GlooTask {
}; };
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Scatter( std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Scatter(
std::vector<Tensor>& in_tensors, std::vector<Tensor>& out_tensors, std::vector<phi::DenseTensor>& in_tensors,
const ScatterOptions& opts) { std::vector<phi::DenseTensor>& out_tensors, const ScatterOptions& opts) {
std::shared_ptr<ScatterGlooTask> task; std::shared_ptr<ScatterGlooTask> task;
auto tag = next_tag(); auto tag = next_tag();
auto context = get_context(); auto context = get_context();
......
paddle/fluid/distributed/collective/ProcessGroupGloo.h
浏览文件 @ 1e56ca8a
...@@ -36,7 +36,8 @@ class ProcessGroupGloo : public ProcessGroup { ...@@ -36,7 +36,8 @@ class ProcessGroupGloo : public ProcessGroup {
class GlooTask : public ProcessGroup::Task, class GlooTask : public ProcessGroup::Task,
public std::enable_shared_from_this<GlooTask> { public std::enable_shared_from_this<GlooTask> {
public: public:
explicit GlooTask(int rank, const std::vector<Tensor>& input_tensors, explicit GlooTask(int rank,
const std::vector<phi::DenseTensor>& input_tensors,
CommType comm_type); CommType comm_type);
~GlooTask() = default; ~GlooTask() = default;
...@@ -106,25 +107,30 @@ class ProcessGroupGloo : public ProcessGroup { ...@@ -106,25 +107,30 @@ class ProcessGroupGloo : public ProcessGroup {
~ProcessGroupGloo() = default; ~ProcessGroupGloo() = default;
std::shared_ptr<ProcessGroup::Task> Broadcast( std::shared_ptr<ProcessGroup::Task> Broadcast(
std::vector<Tensor>& inputs, std::vector<phi::DenseTensor>& inputs,
std::vector<phi::DenseTensor>& outputs,
const BroadcastOptions& = BroadcastOptions()) override; const BroadcastOptions& = BroadcastOptions()) override;
std::shared_ptr<ProcessGroup::Task> AllReduce( std::shared_ptr<ProcessGroup::Task> AllReduce(
std::vector<Tensor>& inputs, std::vector<phi::DenseTensor>& inputs,
std::vector<phi::DenseTensor>& outputs,
const AllreduceOptions& opts = AllreduceOptions()) override; const AllreduceOptions& opts = AllreduceOptions()) override;
std::shared_ptr<ProcessGroup::Task> Barrier( std::shared_ptr<ProcessGroup::Task> Barrier(
const BarrierOptions& = BarrierOptions()) override; const BarrierOptions& = BarrierOptions()) override;
std::shared_ptr<ProcessGroup::Task> AllGather( std::shared_ptr<ProcessGroup::Task> AllGather(
std::vector<Tensor>& in_tensors, std::vector<phi::DenseTensor>& in_tensors,
std::vector<Tensor>& out_tensors) override; std::vector<phi::DenseTensor>& out_tensors) override;
std::shared_ptr<ProcessGroup::Task> Reduce( std::shared_ptr<ProcessGroup::Task> Reduce(
std::vector<Tensor>& tensors, const ReduceOptions& opts) override; std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceOptions& opts) override;
std::shared_ptr<ProcessGroup::Task> Scatter(std::vector<Tensor>& in_tensors, std::shared_ptr<ProcessGroup::Task> Scatter(
std::vector<Tensor>& out_tensors, std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ScatterOptions&) override; const ScatterOptions&) override;
std::shared_ptr<::gloo::Context> get_context() { return _context; } std::shared_ptr<::gloo::Context> get_context() { return _context; }
......
paddle/fluid/distributed/collective/ProcessGroupHCCL.cc
浏览文件 @ 1e56ca8a
...@@ -44,14 +44,14 @@ void SyncDefaultStream( ...@@ -44,14 +44,14 @@ void SyncDefaultStream(
std::shared_ptr<ProcessGroupHCCL::HCCLTask> ProcessGroupHCCL::CreateTask( std::shared_ptr<ProcessGroupHCCL::HCCLTask> ProcessGroupHCCL::CreateTask(
std::vector<Place> places, int rank, CommType comm_type, std::vector<Place> places, int rank, CommType comm_type,
const std::vector<Tensor>& inputs) { const std::vector<phi::DenseTensor>& inputs) {
return std::make_shared<ProcessGroupHCCL::HCCLTask>(places, rank, comm_type, return std::make_shared<ProcessGroupHCCL::HCCLTask>(places, rank, comm_type,
inputs); inputs);
} }
ProcessGroupHCCL::HCCLTask::HCCLTask(const std::vector<Place>& places, int rank, ProcessGroupHCCL::HCCLTask::HCCLTask(
CommType CommType, const std::vector<Place>& places, int rank, CommType CommType,
const std::vector<Tensor>& inputs) const std::vector<phi::DenseTensor>& inputs)
: Task(rank, inputs, CommType), places_(places) { : Task(rank, inputs, CommType), places_(places) {
control_events_.resize(places.size()); control_events_.resize(places.size());
hcclComms_.resize(places.size()); hcclComms_.resize(places.size());
...@@ -60,8 +60,8 @@ ProcessGroupHCCL::HCCLTask::HCCLTask(const std::vector<Place>& places, int rank, ...@@ -60,8 +60,8 @@ ProcessGroupHCCL::HCCLTask::HCCLTask(const std::vector<Place>& places, int rank,
ProcessGroupHCCL::HCCLTask::~HCCLTask() {} ProcessGroupHCCL::HCCLTask::~HCCLTask() {}
void ProcessGroupHCCL::HCCLTask::SetOutputs( void ProcessGroupHCCL::HCCLTask::SetOutputs(
std::vector<Tensor>& outputs) { // NOLINT std::vector<phi::DenseTensor>& outputs) { // NOLINT
outputs_ = std::make_shared<std::vector<Tensor>>(outputs); outputs_ = std::make_shared<std::vector<phi::DenseTensor>>(outputs);
} }
void ProcessGroupHCCL::HCCLTask::SynchronizeStreams() { void ProcessGroupHCCL::HCCLTask::SynchronizeStreams() {
...@@ -166,8 +166,8 @@ void ProcessGroupHCCL::CreateHCCLManagerCache( ...@@ -166,8 +166,8 @@ void ProcessGroupHCCL::CreateHCCLManagerCache(
template <typename Fn> template <typename Fn>
std::shared_ptr<ProcessGroup::Task> ProcessGroupHCCL::Collective( std::shared_ptr<ProcessGroup::Task> ProcessGroupHCCL::Collective(
std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, Fn fn, std::vector<phi::DenseTensor>& inputs,
CommType op_type) { std::vector<phi::DenseTensor>& outputs, Fn fn, CommType op_type) {
const auto places = GetPlaceList(inputs); const auto places = GetPlaceList(inputs);
const auto key = GetKeyFromPlaces(places); const auto key = GetKeyFromPlaces(places);
...@@ -208,91 +208,44 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupHCCL::Collective( ...@@ -208,91 +208,44 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupHCCL::Collective(
return task; return task;
} }
template <typename Fn>
std::shared_ptr<ProcessGroup::Task> ProcessGroupHCCL::PointToPoint(
std::vector<Tensor>& tensors, Fn fn, int dst_rank, CommType op_type) {
const auto places = GetPlaceList(tensors);
const auto key = GetKeyFromPlaces(places);
{
std::lock_guard<std::mutex> lock(mutex_);
if (places_to_hcclcomm_.find(key) == places_to_hcclcomm_.end()) {
CreateHCCLManagerCache(key, places);
}
}
auto& hccl_comms = places_to_hcclcomm_[key];
SyncDefaultStream(places, places_to_events_[key], places_to_ctx_[key]);
auto task = CreateTask(places, rank_, op_type, tensors);
// construct uninitialize guard for device
// if (FLAGS_use_stream_safe_npu_allocator) {
// for (size_t i = 0; i < tensors.size(); ++i) {
// platform::NPUDeviceGuard guard(places[i].GetDeviceId());
// auto dense_tensor =
// std::dynamic_pointer_cast<phi::DenseTensor>(tensors[i].impl());
// memory::RecordStream(dense_tensor->Holder(),
// places_to_ctx_[key][i]->stream());
// }
// }
for (size_t i = 0; i < tensors.size(); ++i) {
platform::NPUDeviceGuard guard(places[i].GetDeviceId());
const auto& hccl_stream = places_to_ctx_[key][i]->stream();
fn(tensors[i], hccl_comms[i]->GetHcclComm(), hccl_stream, dst_rank);
}
for (size_t i = 0; i < tensors.size(); ++i) {
platform::NPUDeviceGuard guard(places[i].GetDeviceId());
task->control_events_[i].Record(*places_to_ctx_[key][i]);
}
return task;
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupHCCL::AllReduce( std::shared_ptr<ProcessGroup::Task> ProcessGroupHCCL::AllReduce(
std::vector<Tensor>& tensors, const AllreduceOptions& opts) { std::vector<phi::DenseTensor>& in_tensors, // NOLINT
// PADDLE_ENFORCE_EQ( std::vector<phi::DenseTensor>& out_tensors, // NOLINT
// CheckTensorsInNPUPlace(tensors), true, const AllreduceOptions& opts) {
// platform::errors::InvalidArgument("All inputs should be in return Collective(in_tensors, out_tensors,
// NPUPlace.")); [&](phi::DenseTensor& input, phi::DenseTensor& output,
return Collective( HcclComm comm, const aclrtStream& stream) {
tensors, tensors,
[&](const Tensor& input, Tensor& output, HcclComm comm,
const aclrtStream& stream) {
auto input_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(input.impl());
auto output_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(output.impl());
return platform::dynload::HcclAllReduce( return platform::dynload::HcclAllReduce(
input_tensor->data(), output_tensor->data(), input_tensor->numel(), input.data(), output.data(), input.numel(),
platform::ToHCCLDataType(input.type()), platform::ToHCCLDataType(input.dtype()),
ToHCCLRedType(opts.reduce_op), comm, stream); ToHCCLRedType(opts.reduce_op), comm, stream);
}, },
CommType::ALLREDUCE); CommType::ALLREDUCE);
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupHCCL::Broadcast( std::shared_ptr<ProcessGroup::Task> ProcessGroupHCCL::Broadcast(
std::vector<Tensor>& tensors, const BroadcastOptions& opts) { std::vector<phi::DenseTensor>& in_tensors, // NOLINT
std::vector<phi::DenseTensor>& out_tensors, // NOLINT
const BroadcastOptions& opts) {
// PADDLE_ENFORCE_EQ( // PADDLE_ENFORCE_EQ(
// CheckTensorsInNPUPlace(tensors), true, // CheckTensorsInNPUPlace(tensors), true,
// platform::errors::InvalidArgument("All inputs should be in // platform::errors::InvalidArgument("All inputs should be in
// CudaPlace.")); // CudaPlace."));
return Collective( return Collective(
tensors, tensors, in_tensors, out_tensors,
[&](Tensor& input, Tensor& output, HcclComm comm, [&](phi::DenseTensor& input, phi::DenseTensor& output, HcclComm comm,
const aclrtStream& stream) { const aclrtStream& stream) {
const auto root = opts.source_rank * tensors.size() + opts.source_root; int root = opts.source_rank * in_tensors.size() + opts.source_root;
auto input_tensor = if (rank_ == root) {
std::dynamic_pointer_cast<phi::DenseTensor>(input.impl());
auto output_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(output.impl());
return platform::dynload::HcclBroadcast( return platform::dynload::HcclBroadcast(
input_tensor->data(), input_tensor->numel(), input.data(), input.numel(),
platform::ToHCCLDataType(input.type()), root, comm, stream); platform::ToHCCLDataType(input.dtype()), root, comm, stream);
} else {
return platform::dynload::HcclBroadcast(
output.data(), output.numel(),
platform::ToHCCLDataType(output.dtype()), root, comm, stream);
}
}, },
CommType::BROADCAST); CommType::BROADCAST);
} }
......
paddle/fluid/distributed/collective/ProcessGroupHCCL.h
浏览文件 @ 1e56ca8a
...@@ -46,7 +46,7 @@ class ProcessGroupHCCL : public ProcessGroup { ...@@ -46,7 +46,7 @@ class ProcessGroupHCCL : public ProcessGroup {
public std::enable_shared_from_this<HCCLTask> { public std::enable_shared_from_this<HCCLTask> {
public: public:
HCCLTask(const std::vector<Place>& places, int rank, CommType CommType, HCCLTask(const std::vector<Place>& places, int rank, CommType CommType,
const std::vector<Tensor>& inputs); const std::vector<phi::DenseTensor>& inputs);
bool IsCompleted(); bool IsCompleted();
...@@ -56,7 +56,7 @@ class ProcessGroupHCCL : public ProcessGroup { ...@@ -56,7 +56,7 @@ class ProcessGroupHCCL : public ProcessGroup {
void Synchronize(); void Synchronize();
void SetOutputs(std::vector<Tensor>& outputs); // NOLINT void SetOutputs(std::vector<phi::DenseTensor>& outputs); // NOLINT
virtual ~HCCLTask(); virtual ~HCCLTask();
...@@ -65,7 +65,7 @@ class ProcessGroupHCCL : public ProcessGroup { ...@@ -65,7 +65,7 @@ class ProcessGroupHCCL : public ProcessGroup {
protected: protected:
std::vector<Place> places_; std::vector<Place> places_;
std::vector<std::shared_ptr<HCCLCommManager>> hcclComms_; std::vector<std::shared_ptr<HCCLCommManager>> hcclComms_;
std::shared_ptr<std::vector<Tensor>> outputs_; std::shared_ptr<std::vector<phi::DenseTensor>> outputs_;
private: private:
}; };
...@@ -78,17 +78,19 @@ class ProcessGroupHCCL : public ProcessGroup { ...@@ -78,17 +78,19 @@ class ProcessGroupHCCL : public ProcessGroup {
} }
std::shared_ptr<ProcessGroup::Task> AllReduce( std::shared_ptr<ProcessGroup::Task> AllReduce(
std::vector<Tensor>& tensors, std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const AllreduceOptions& = AllreduceOptions()) override; const AllreduceOptions& = AllreduceOptions()) override;
std::shared_ptr<ProcessGroup::Task> Broadcast( std::shared_ptr<ProcessGroup::Task> Broadcast(
std::vector<Tensor>& tensors, std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const BroadcastOptions& = BroadcastOptions()) override; const BroadcastOptions& = BroadcastOptions()) override;
protected: protected:
virtual std::shared_ptr<ProcessGroupHCCL::HCCLTask> CreateTask( virtual std::shared_ptr<ProcessGroupHCCL::HCCLTask> CreateTask(
std::vector<Place> places, int rank, CommType opType, std::vector<Place> places, int rank, CommType opType,
const std::vector<Tensor>& inputs); const std::vector<phi::DenseTensor>& inputs);
std::shared_ptr<Store> store_; std::shared_ptr<Store> store_;
std::shared_ptr<HCCLCommManager> hccl_comm_; std::shared_ptr<HCCLCommManager> hccl_comm_;
...@@ -113,15 +115,10 @@ class ProcessGroupHCCL : public ProcessGroup { ...@@ -113,15 +115,10 @@ class ProcessGroupHCCL : public ProcessGroup {
template <typename Fn> template <typename Fn>
std::shared_ptr<ProcessGroup::Task> Collective( std::shared_ptr<ProcessGroup::Task> Collective(
std::vector<Tensor>& inputs, // NOLINT std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<Tensor>& outputs, // NOLINT std::vector<phi::DenseTensor>& outputs, // NOLINT
Fn fn, CommType op_type); Fn fn, CommType op_type);
template <typename Fn>
std::shared_ptr<ProcessGroup::Task> PointToPoint(
std::vector<Tensor>& tensors, // NOLINT
Fn fn, int dst_rank, CommType op_type);
void CreateHCCLManagerCache(const std::string& places_key, void CreateHCCLManagerCache(const std::string& places_key,
const std::vector<Place>& places); const std::vector<Place>& places);
}; };
......
paddle/fluid/distributed/collective/ProcessGroupHeter.cc
浏览文件 @ 1e56ca8a
...@@ -26,13 +26,13 @@ namespace distributed { ...@@ -26,13 +26,13 @@ namespace distributed {
using Place = paddle::platform::Place; using Place = paddle::platform::Place;
std::shared_ptr<ProcessGroupHeter::HeterTask> ProcessGroupHeter::CreateTask( std::shared_ptr<ProcessGroupHeter::HeterTask> ProcessGroupHeter::CreateTask(
int rank, CommType comm_type, const std::vector<Tensor>& inputs) { int rank, CommType comm_type, const std::vector<phi::DenseTensor>& inputs) {
return std::make_shared<ProcessGroupHeter::HeterTask>(rank, comm_type, return std::make_shared<ProcessGroupHeter::HeterTask>(rank, comm_type,
inputs); inputs);
} }
ProcessGroupHeter::HeterTask::HeterTask(int rank, CommType CommType, ProcessGroupHeter::HeterTask::HeterTask(
const std::vector<Tensor>& inputs) int rank, CommType CommType, const std::vector<phi::DenseTensor>& inputs)
: Task(rank, inputs, CommType) {} : Task(rank, inputs, CommType) {}
ProcessGroupHeter::HeterTask::~HeterTask() {} ProcessGroupHeter::HeterTask::~HeterTask() {}
...@@ -86,247 +86,176 @@ static void _do_add(T* dst, T* src, size_t size) { ...@@ -86,247 +86,176 @@ static void _do_add(T* dst, T* src, size_t size) {
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupHeter::AllReduce( std::shared_ptr<ProcessGroup::Task> ProcessGroupHeter::AllReduce(
std::vector<Tensor>& tensors, const AllreduceOptions& opts) { std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors, const AllreduceOptions& opts) {
#if defined(PADDLE_WITH_NCCL) #if defined(PADDLE_WITH_NCCL)
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(tensors), true, CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(out_tensors), true,
platform::errors::InvalidArgument("All outputs should be in CudaPlace."));
#endif #endif
// Step1: do allreduce in inner cluster // Step1: do allreduce in inner cluster
auto task = inner_pg_->AllReduce(tensors, opts); auto task = inner_pg_->AllReduce(in_tensors, in_tensors, opts);
task->Wait(); task->Wait();
// Step2: copy tensors to CPU // Step2: copy tensors to CPU
if (local_rank_ == 0) { if (local_rank_ == 0) {
std::vector<Tensor> cpu_tensors; std::vector<phi::DenseTensor> cpu_tensors;
cpu_tensors.reserve(tensors.size()); cpu_tensors.reserve(in_tensors.size());
for (size_t i = 0; i < tensors.size(); i++) { for (size_t i = 0; i < in_tensors.size(); i++) {
auto dense_gpu_tensor = auto gpu_tensor = in_tensors[i];
std::dynamic_pointer_cast<phi::DenseTensor>(tensors[i].impl()); auto cpu_tensor = cpu_tensors[i];
phi::DenseTensorMeta meta = phi::DenseTensorMeta( cpu_tensor.Resize(gpu_tensor.dims());
dense_gpu_tensor->dtype(), dense_gpu_tensor->dims()); framework::TensorCopySync(gpu_tensor, platform::CPUPlace(), &cpu_tensor);
std::shared_ptr<phi::DenseTensor> dense_cpu_tensor =
std::make_shared<phi::DenseTensor>(
std::make_unique<paddle::experimental::DefaultAllocator>(
paddle::platform::CPUPlace())
.get(),
meta);
dense_cpu_tensor->ResizeAndAllocate(dense_gpu_tensor->dims());
cpu_tensors[i] = paddle::experimental::Tensor(dense_cpu_tensor);
framework::TensorCopySync(*dense_gpu_tensor, platform::CPUPlace(),
dense_cpu_tensor.get());
} }
// Step3: do inter cluster allreduce // Step3: do inter cluster allreduce
if (with_switch_) { if (with_switch_) {
if (local_rank_ == 0) { if (local_rank_ == 0) {
HeterClient* client_ = HeterClient* client_ =
HeterClient::GetInstance({switch_endpoint_}, {}, 0).get(); HeterClient::GetInstance({switch_endpoint_}, {}, 0).get();
auto dense_cpu_tensor = auto dense_cpu_tensor = cpu_tensors[0];
std::dynamic_pointer_cast<phi::DenseTensor>(cpu_tensors[0].impl());
std::vector<int> send_size; std::vector<int> send_size;
send_size.push_back(dense_cpu_tensor->numel()); send_size.push_back(dense_cpu_tensor.numel());
int ret = client_->Send( int ret = client_->Send(
gid_, {dense_cpu_tensor->name()}, send_size, gid_, {dense_cpu_tensor.name()}, send_size, dense_cpu_tensor.data(),
dense_cpu_tensor->data(), dense_cpu_tensor.numel() *
dense_cpu_tensor->numel() * framework::DataTypeSize(dense_cpu_tensor.dtype()));
framework::DataTypeSize(dense_cpu_tensor->dtype()));
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet( PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"Send to the switch module error.")); "Send to the switch module error."));
phi::DenseTensorMeta meta = phi::DenseTensorMeta( phi::DenseTensorMeta meta = phi::DenseTensorMeta(
dense_cpu_tensor->dtype(), dense_cpu_tensor->dims()); dense_cpu_tensor.dtype(), dense_cpu_tensor.dims());
std::shared_ptr<phi::DenseTensor> dense_cpu_tensor2 = std::shared_ptr<phi::DenseTensor> dense_cpu_tensor2 =
std::make_shared<phi::DenseTensor>( std::make_shared<phi::DenseTensor>(
std::make_unique<paddle::experimental::DefaultAllocator>( std::make_unique<paddle::experimental::DefaultAllocator>(
paddle::platform::CPUPlace()) paddle::platform::CPUPlace())
.get(), .get(),
meta); meta);
dense_cpu_tensor2->ResizeAndAllocate(dense_cpu_tensor->dims()); dense_cpu_tensor2->ResizeAndAllocate(dense_cpu_tensor.dims());
Tensor cpu_tensor_temp =
paddle::experimental::Tensor(dense_cpu_tensor2);
ret = client_->Recv( ret = client_->Recv(
gid_, {dense_cpu_tensor->name()}, dense_cpu_tensor2->data(), gid_, {dense_cpu_tensor.name()}, dense_cpu_tensor2->data(),
dense_cpu_tensor2->numel() * dense_cpu_tensor2->numel() *
framework::DataTypeSize(dense_cpu_tensor2->dtype())); framework::DataTypeSize(dense_cpu_tensor2->dtype()));
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet( PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"Recv from the switch module error.")); "Recv from the switch module error."));
switch (dense_cpu_tensor->dtype()) { switch (dense_cpu_tensor.dtype()) {
case DataType::FLOAT32: case DataType::FLOAT32:
_do_add<float>(reinterpret_cast<float*>(dense_cpu_tensor->data()), _do_add<float>(reinterpret_cast<float*>(dense_cpu_tensor.data()),
reinterpret_cast<float*>(dense_cpu_tensor2->data()), reinterpret_cast<float*>(dense_cpu_tensor2->data()),
dense_cpu_tensor->numel()); dense_cpu_tensor.numel());
break; break;
case DataType::FLOAT64: case DataType::FLOAT64:
_do_add<double>( _do_add<double>(
reinterpret_cast<double*>(dense_cpu_tensor->data()), reinterpret_cast<double*>(dense_cpu_tensor.data()),
reinterpret_cast<double*>(dense_cpu_tensor2->data()), reinterpret_cast<double*>(dense_cpu_tensor2->data()),
dense_cpu_tensor->numel()); dense_cpu_tensor.numel());
break; break;
case DataType::INT32: case DataType::INT32:
_do_add<int>(reinterpret_cast<int*>(dense_cpu_tensor->data()), _do_add<int>(reinterpret_cast<int*>(dense_cpu_tensor.data()),
reinterpret_cast<int*>(dense_cpu_tensor2->data()), reinterpret_cast<int*>(dense_cpu_tensor2->data()),
dense_cpu_tensor->numel()); dense_cpu_tensor.numel());
break; break;
default: default:
PADDLE_THROW(platform::errors::PreconditionNotMet( PADDLE_THROW(platform::errors::PreconditionNotMet(
"Unsupported data type (%s) to do add.", "Unsupported data type (%s) to do add.",
framework::DataType2String(dense_cpu_tensor->dtype()))); framework::DataType2String(dense_cpu_tensor.dtype())));
} }
} }
} else { } else {
auto gloo_task = inter_pg_->AllReduce(cpu_tensors, opts); auto gloo_task = inter_pg_->AllReduce(cpu_tensors, cpu_tensors, opts);
gloo_task->Wait(); gloo_task->Wait();
} }
// Step4: copy cpu tensors to gpu // Step4: copy cpu tensors to gpu
// copy cpu tensors to gpu // copy cpu tensors to gpu
for (size_t i = 0; i < tensors.size(); i++) { for (size_t i = 0; i < in_tensors.size(); i++) {
auto dense_gpu_tensor = auto gpu_tensor = out_tensors[i];
std::dynamic_pointer_cast<phi::DenseTensor>(tensors[i].impl()); auto cpu_tensor = cpu_tensors[i];
auto dense_cpu_tensor = framework::TensorCopySync(cpu_tensor, cpu_tensor.place(), &gpu_tensor);
std::dynamic_pointer_cast<phi::DenseTensor>(cpu_tensors[i].impl());
framework::TensorCopySync(*dense_cpu_tensor, dense_cpu_tensor->place(),
dense_gpu_tensor.get());
} }
} }
// Step5: broadcast among inner cluster // Step5: broadcast among inner cluster
auto b_opts = BroadcastOptions(); auto b_opts = BroadcastOptions();
b_opts.source_root = 0; b_opts.source_rank = 0;
auto broadcast_task = inner_pg_->Broadcast(tensors, b_opts); auto broadcast_task = inner_pg_->Broadcast(out_tensors, out_tensors, b_opts);
broadcast_task->Wait(); broadcast_task->Wait();
return CreateTask(rank_, CommType::ALLREDUCE, tensors); return CreateTask(rank_, CommType::ALLREDUCE, in_tensors);
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupHeter::Broadcast( std::shared_ptr<ProcessGroup::Task> ProcessGroupHeter::Broadcast(
std::vector<Tensor>& tensors, const BroadcastOptions& opts) { std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors, const BroadcastOptions& opts) {
#if defined(PADDLE_WITH_NCCL) #if defined(PADDLE_WITH_NCCL)
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(tensors), true, CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(out_tensors), true,
platform::errors::InvalidArgument("All outputs should be in CudaPlace."));
#endif #endif
// Step1: do broadcast in inner cluster // Step1: do broadcast in inner cluster
auto b_opts = BroadcastOptions(); auto b_opts = BroadcastOptions();
b_opts.source_root = 0; b_opts.source_rank = 0;
inner_pg_->Broadcast(tensors, b_opts); inner_pg_->Broadcast(in_tensors, out_tensors, b_opts);
if (local_rank_ == 0) { if (local_rank_ == 0) {
std::vector<Tensor> cpu_tensors; std::vector<phi::DenseTensor> cpu_tensors;
cpu_tensors.reserve(tensors.size()); cpu_tensors.reserve(in_tensors.size());
for (size_t i = 0; i < tensors.size(); i++) { for (size_t i = 0; i < in_tensors.size(); i++) {
auto dense_gpu_tensor = auto gpu_tensor = in_tensors[i];
std::dynamic_pointer_cast<phi::DenseTensor>(tensors[i].impl()); auto cpu_tensor = cpu_tensors[i];
phi::DenseTensorMeta meta = phi::DenseTensorMeta( cpu_tensor.Resize(gpu_tensor.dims());
dense_gpu_tensor->dtype(), dense_gpu_tensor->dims()); framework::TensorCopySync(gpu_tensor, platform::CPUPlace(), &cpu_tensor);
std::shared_ptr<phi::DenseTensor> dense_cpu_tensor =
std::make_shared<phi::DenseTensor>(
std::make_unique<paddle::experimental::DefaultAllocator>(
paddle::platform::CPUPlace())
.get(),
meta);
dense_cpu_tensor->ResizeAndAllocate(dense_gpu_tensor->dims());
cpu_tensors[i] = paddle::experimental::Tensor(dense_cpu_tensor);
framework::TensorCopySync(*dense_gpu_tensor, platform::CPUPlace(),
dense_cpu_tensor.get());
} }
if (with_switch_) { if (with_switch_) {
if (local_rank_ == 0) { if (local_rank_ == 0) {
HeterClient* client_ = HeterClient* client_ =
HeterClient::GetInstance({switch_endpoint_}, {}, 0).get(); HeterClient::GetInstance({switch_endpoint_}, {}, 0).get();
auto dense_cpu_tensor = auto dense_cpu_tensor = cpu_tensors[0];
std::dynamic_pointer_cast<phi::DenseTensor>(cpu_tensors[0].impl());
if (gloo_rank_ == 0) { if (gloo_rank_ == 0) {
std::vector<int> send_size; std::vector<int> send_size;
send_size.push_back(dense_cpu_tensor->numel()); send_size.push_back(dense_cpu_tensor.numel());
int ret = client_->Send( int ret = client_->Send(
gid_, {dense_cpu_tensor->name()}, send_size, gid_, {dense_cpu_tensor.name()}, send_size,
dense_cpu_tensor->data(), dense_cpu_tensor.data(),
dense_cpu_tensor->numel() * dense_cpu_tensor.numel() *
framework::DataTypeSize(dense_cpu_tensor->dtype())); framework::DataTypeSize(dense_cpu_tensor.dtype()));
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet( PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"Send to the switch module error.")); "Send to the switch module error."));
} else { } else {
int ret = client_->Recv( int ret = client_->Recv(
gid_, {dense_cpu_tensor->name()}, dense_cpu_tensor->data(), gid_, {dense_cpu_tensor.name()}, dense_cpu_tensor.data(),
dense_cpu_tensor->numel() * dense_cpu_tensor.numel() *
framework::DataTypeSize(dense_cpu_tensor->dtype())); framework::DataTypeSize(dense_cpu_tensor.dtype()));
PADDLE_ENFORCE_EQ(ret, 0, PADDLE_ENFORCE_EQ(ret, 0,
platform::errors::PreconditionNotMet( platform::errors::PreconditionNotMet(
"Receive from the switch module error.")); "Receive from the switch module error."));
ret = client_->Recv( ret = client_->Recv(
gid_, {dense_cpu_tensor->name()}, dense_cpu_tensor->data(), gid_, {dense_cpu_tensor.name()}, dense_cpu_tensor.data(),
dense_cpu_tensor->numel() * dense_cpu_tensor.numel() *
framework::DataTypeSize(dense_cpu_tensor->dtype())); framework::DataTypeSize(dense_cpu_tensor.dtype()));
PADDLE_ENFORCE_EQ(ret, 0, PADDLE_ENFORCE_EQ(ret, 0,
platform::errors::PreconditionNotMet( platform::errors::PreconditionNotMet(
"Receive from the switch module error.")); "Receive from the switch module error."));
} }
} }
} else { } else {
auto gloo_task = inter_pg_->Broadcast(cpu_tensors, opts); auto gloo_task = inter_pg_->Broadcast(cpu_tensors, cpu_tensors, opts);
gloo_task->Wait(); gloo_task->Wait();
} }
for (size_t i = 0; i < tensors.size(); i++) { for (size_t i = 0; i < in_tensors.size(); i++) {
auto dense_gpu_tensor = auto gpu_tensor = out_tensors[i];
std::dynamic_pointer_cast<phi::DenseTensor>(tensors[i].impl()); auto cpu_tensor = cpu_tensors[i];
auto dense_cpu_tensor = framework::TensorCopySync(cpu_tensor, gpu_tensor.place(), &gpu_tensor);
std::dynamic_pointer_cast<phi::DenseTensor>(cpu_tensors[i].impl());
framework::TensorCopySync(*dense_cpu_tensor, dense_cpu_tensor->place(),
dense_gpu_tensor.get());
} }
} }
auto broadcast_task = inner_pg_->Broadcast(tensors, b_opts); auto broadcast_task = inner_pg_->Broadcast(out_tensors, out_tensors, b_opts);
broadcast_task->Wait(); broadcast_task->Wait();
return CreateTask(rank_, CommType::BROADCAST, tensors); return CreateTask(rank_, CommType::BROADCAST, in_tensors);
}
void ProcessGroupHeter::Broadcast(const phi::DenseTensor* in,
phi::DenseTensor* out) {
// Step1: do broadcast in inner cluster
inner_pg_->Broadcast(in, out);
if (local_rank_ == 0) {
phi::DenseTensorMeta meta = phi::DenseTensorMeta(in->dtype(), in->dims());
std::shared_ptr<phi::DenseTensor> dense_cpu_tensor =
std::make_shared<phi::DenseTensor>(
std::make_unique<paddle::experimental::DefaultAllocator>(
paddle::platform::CPUPlace())
.get(),
meta);
dense_cpu_tensor->ResizeAndAllocate(in->dims());
Tensor cpu_tensor = paddle::experimental::Tensor(dense_cpu_tensor);
framework::TensorCopySync(*in, platform::CPUPlace(),
dense_cpu_tensor.get());
if (with_switch_) {
if (local_rank_ == 0) {
HeterClient* client_ =
HeterClient::GetInstance({switch_endpoint_}, {}, 0).get();
if (gloo_rank_ == 0) {
std::vector<int> send_size;
send_size.push_back(in->numel());
int ret = client_->Send(
gid_, {in->name()}, send_size, dense_cpu_tensor->data(),
in->numel() * framework::DataTypeSize(in->dtype()));
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"Send to the switch module error."));
} else {
int ret =
client_->Recv(gid_, {in->name()}, dense_cpu_tensor->data(),
in->numel() * framework::DataTypeSize(in->dtype()));
PADDLE_ENFORCE_EQ(ret, 0,
platform::errors::PreconditionNotMet(
"Receive from the switch module error."));
}
}
} else {
std::vector<Tensor> cpu_tensors = {cpu_tensor};
auto gloo_task = inter_pg_->Broadcast(cpu_tensors);
gloo_task->Wait();
}
framework::TensorCopySync(*dense_cpu_tensor, out->place(), out);
}
inner_pg_->Broadcast(out, out);
} }
} // namespace distributed } // namespace distributed
......
paddle/fluid/distributed/collective/ProcessGroupHeter.h
浏览文件 @ 1e56ca8a
...@@ -66,7 +66,8 @@ class ProcessGroupHeter : public ProcessGroup { ...@@ -66,7 +66,8 @@ class ProcessGroupHeter : public ProcessGroup {
class HeterTask : public ProcessGroup::Task, class HeterTask : public ProcessGroup::Task,
public std::enable_shared_from_this<HeterTask> { public std::enable_shared_from_this<HeterTask> {
public: public:
HeterTask(int rank, CommType CommType, const std::vector<Tensor>& inputs); HeterTask(int rank, CommType CommType,
const std::vector<phi::DenseTensor>&);
bool IsCompleted(); bool IsCompleted();
...@@ -89,18 +90,16 @@ class ProcessGroupHeter : public ProcessGroup { ...@@ -89,18 +90,16 @@ class ProcessGroupHeter : public ProcessGroup {
} }
std::shared_ptr<ProcessGroup::Task> AllReduce( std::shared_ptr<ProcessGroup::Task> AllReduce(
std::vector<Tensor>& tensors, std::vector<phi::DenseTensor>&, std::vector<phi::DenseTensor>&,
const AllreduceOptions& = AllreduceOptions()) override; const AllreduceOptions& = AllreduceOptions()) override;
std::shared_ptr<ProcessGroup::Task> Broadcast( std::shared_ptr<ProcessGroup::Task> Broadcast(
std::vector<Tensor>& tensors, std::vector<phi::DenseTensor>&, std::vector<phi::DenseTensor>&,
const BroadcastOptions& = BroadcastOptions()) override; const BroadcastOptions& = BroadcastOptions()) override;
void Broadcast(const phi::DenseTensor* in, phi::DenseTensor* out) override;
protected: protected:
virtual std::shared_ptr<ProcessGroupHeter::HeterTask> CreateTask( virtual std::shared_ptr<ProcessGroupHeter::HeterTask> CreateTask(
int rank, CommType opType, const std::vector<Tensor>& inputs); int rank, CommType opType, const std::vector<phi::DenseTensor>& inputs);
private: private:
std::shared_ptr<Store> store_; std::shared_ptr<Store> store_;
......
paddle/fluid/distributed/collective/ProcessGroupNCCL.cc
浏览文件 @ 1e56ca8a
...@@ -41,14 +41,14 @@ void SyncDefaultStream( ...@@ -41,14 +41,14 @@ void SyncDefaultStream(
std::shared_ptr<ProcessGroupNCCL::NCCLTask> ProcessGroupNCCL::CreateTask( std::shared_ptr<ProcessGroupNCCL::NCCLTask> ProcessGroupNCCL::CreateTask(
std::vector<Place> places, int rank, CommType comm_type, std::vector<Place> places, int rank, CommType comm_type,
const std::vector<Tensor>& inputs) { const std::vector<phi::DenseTensor>& inputs) {
return std::make_shared<ProcessGroupNCCL::NCCLTask>(places, rank, comm_type, return std::make_shared<ProcessGroupNCCL::NCCLTask>(places, rank, comm_type,
inputs); inputs);
} }
ProcessGroupNCCL::NCCLTask::NCCLTask(const std::vector<Place>& places, int rank, ProcessGroupNCCL::NCCLTask::NCCLTask(
CommType CommType, const std::vector<Place>& places, int rank, CommType CommType,
const std::vector<Tensor>& inputs) const std::vector<phi::DenseTensor>& inputs)
: Task(rank, inputs, CommType), places_(places) { : Task(rank, inputs, CommType), places_(places) {
control_events_.resize(places.size()); control_events_.resize(places.size());
ncclComms_.resize(places.size()); ncclComms_.resize(places.size());
...@@ -57,8 +57,8 @@ ProcessGroupNCCL::NCCLTask::NCCLTask(const std::vector<Place>& places, int rank, ...@@ -57,8 +57,8 @@ ProcessGroupNCCL::NCCLTask::NCCLTask(const std::vector<Place>& places, int rank,
ProcessGroupNCCL::NCCLTask::~NCCLTask() {} ProcessGroupNCCL::NCCLTask::~NCCLTask() {}
void ProcessGroupNCCL::NCCLTask::SetOutputs( void ProcessGroupNCCL::NCCLTask::SetOutputs(
std::vector<Tensor>& outputs) { // NOLINT std::vector<phi::DenseTensor>& outputs) { // NOLINT
outputs_ = std::make_shared<std::vector<Tensor>>(outputs); outputs_ = std::make_shared<std::vector<phi::DenseTensor>>(outputs);
} }
void ProcessGroupNCCL::NCCLTask::SynchronizeStreams() { void ProcessGroupNCCL::NCCLTask::SynchronizeStreams() {
...@@ -180,8 +180,8 @@ void ProcessGroupNCCL::CreateNCCLManagerCache( ...@@ -180,8 +180,8 @@ void ProcessGroupNCCL::CreateNCCLManagerCache(
template <typename Fn> template <typename Fn>
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Collective( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Collective(
std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, Fn fn, std::vector<phi::DenseTensor>& inputs,
CommType op_type) { std::vector<phi::DenseTensor>& outputs, Fn fn, CommType op_type) {
const auto places = GetPlaceList(inputs); const auto places = GetPlaceList(inputs);
const auto key = GetKeyFromPlaces(places); const auto key = GetKeyFromPlaces(places);
...@@ -205,9 +205,7 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Collective( ...@@ -205,9 +205,7 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Collective(
if (FLAGS_use_stream_safe_cuda_allocator) { if (FLAGS_use_stream_safe_cuda_allocator) {
for (size_t i = 0; i < inputs.size(); ++i) { for (size_t i = 0; i < inputs.size(); ++i) {
cuda_guard.SetDevice(places[i]); cuda_guard.SetDevice(places[i]);
auto dense_tensor = memory::RecordStream(inputs[i].Holder(),
std::dynamic_pointer_cast<phi::DenseTensor>(inputs[i].impl());
memory::RecordStream(dense_tensor->Holder(),
places_to_ctx_[key][i]->stream()); places_to_ctx_[key][i]->stream());
} }
} }
...@@ -267,7 +265,8 @@ void ProcessGroupNCCL::Collective(const phi::DenseTensor* in, ...@@ -267,7 +265,8 @@ void ProcessGroupNCCL::Collective(const phi::DenseTensor* in,
template <typename Fn> template <typename Fn>
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::PointToPoint( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::PointToPoint(
std::vector<Tensor>& tensors, Fn fn, int dst_rank, CommType op_type) { std::vector<phi::DenseTensor>& tensors, Fn fn, int dst_rank,
CommType op_type) {
const auto places = GetPlaceList(tensors); const auto places = GetPlaceList(tensors);
const auto key = GetKeyFromPlaces(places); const auto key = GetKeyFromPlaces(places);
...@@ -290,9 +289,7 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::PointToPoint( ...@@ -290,9 +289,7 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::PointToPoint(
if (FLAGS_use_stream_safe_cuda_allocator) { if (FLAGS_use_stream_safe_cuda_allocator) {
for (size_t i = 0; i < tensors.size(); ++i) { for (size_t i = 0; i < tensors.size(); ++i) {
cuda_guard.SetDevice(places[i]); cuda_guard.SetDevice(places[i]);
auto dense_tensor = memory::RecordStream(tensors[i].Holder(),
std::dynamic_pointer_cast<phi::DenseTensor>(tensors[i].impl());
memory::RecordStream(dense_tensor->Holder(),
places_to_ctx_[key][i]->stream()); places_to_ctx_[key][i]->stream());
} }
} }
...@@ -314,20 +311,16 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::PointToPoint( ...@@ -314,20 +311,16 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::PointToPoint(
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllReduce( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllReduce(
std::vector<Tensor>& tensors, const AllreduceOptions& opts) { std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors, const AllreduceOptions& opts) {
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(tensors), true, CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective( return Collective(in_tensors, out_tensors,
tensors, tensors, [&](const phi::DenseTensor& input, phi::DenseTensor& output,
[&](const Tensor& input, Tensor& output, ncclComm_t comm, ncclComm_t comm, const gpuStream_t& stream) {
const gpuStream_t& stream) {
auto input_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(input.impl());
auto output_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(output.impl());
return platform::dynload::ncclAllReduce( return platform::dynload::ncclAllReduce(
input_tensor->data(), output_tensor->data(), input_tensor->numel(), input.data(), output.data(), input.numel(),
platform::ToNCCLDataType(input.type()), platform::ToNCCLDataType(input.type()),
ToNCCLRedType(opts.reduce_op), comm, stream); ToNCCLRedType(opts.reduce_op), comm, stream);
}, },
...@@ -335,23 +328,21 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllReduce( ...@@ -335,23 +328,21 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllReduce(
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Broadcast( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Broadcast(
std::vector<Tensor>& tensors, const BroadcastOptions& opts) { std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors, const BroadcastOptions& opts) {
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(tensors), true, CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective( return Collective(in_tensors, out_tensors,
tensors, tensors, [&](phi::DenseTensor& input, phi::DenseTensor& output,
[&](Tensor& input, Tensor& output, ncclComm_t comm, ncclComm_t comm, const gpuStream_t& stream) {
const gpuStream_t& stream) { const auto root = opts.source_rank * in_tensors.size() +
const auto root = opts.source_rank * tensors.size() + opts.source_root; opts.source_root;
auto input_tensor = return platform::dynload::ncclBroadcast(
std::dynamic_pointer_cast<phi::DenseTensor>(input.impl()); input.data(), output.data(), input.numel(),
auto output_tensor = platform::ToNCCLDataType(input.type()), root, comm,
std::dynamic_pointer_cast<phi::DenseTensor>(output.impl()); stream);
return platform::dynload::ncclBcast(
input_tensor->data(), input_tensor->numel(),
platform::ToNCCLDataType(input.type()), root, comm, stream);
}, },
CommType::BROADCAST); CommType::BROADCAST);
} }
...@@ -374,23 +365,24 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Barrier( ...@@ -374,23 +365,24 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Barrier(
places.emplace_back(place_id); places.emplace_back(place_id);
} }
std::vector<Tensor> barrierTensors; std::vector<phi::DenseTensor> barrierTensors;
barrierTensors.reserve(places.size()); barrierTensors.reserve(places.size());
platform::CUDADeviceGuard gpuGuard; platform::CUDADeviceGuard gpuGuard;
for (auto& place : places) { for (auto& place : places) {
gpuGuard.SetDeviceIndex(place.GetDeviceId()); gpuGuard.SetDeviceIndex(place.GetDeviceId());
auto dt = full({1}, 0, phi::DataType::FLOAT32, phi::GPUPlace()); auto dt = full({1}, 0, phi::DataType::FLOAT32, phi::GPUPlace());
barrierTensors.push_back(dt); barrierTensors.push_back(
*std::dynamic_pointer_cast<phi::DenseTensor>(dt.impl()));
} }
auto task = ProcessGroupNCCL::AllReduce(barrierTensors); auto task = ProcessGroupNCCL::AllReduce(barrierTensors, barrierTensors);
auto nccl_task = dynamic_cast<ProcessGroupNCCL::NCCLTask*>(task.get()); auto nccl_task = dynamic_cast<ProcessGroupNCCL::NCCLTask*>(task.get());
nccl_task->barrierTensors_ = std::move(barrierTensors); nccl_task->barrierTensors_ = std::move(barrierTensors);
return task; return task;
} }
void CheckTensorsInDifferentDevices(const std::vector<Tensor>& tensors, void CheckTensorsInDifferentDevices(
const size_t num_devices) { const std::vector<phi::DenseTensor>& tensors, const size_t num_devices) {
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
tensors.size() == 0, false, tensors.size() == 0, false,
platform::errors::InvalidArgument("Tensor list must be nonempty.")); platform::errors::InvalidArgument("Tensor list must be nonempty."));
...@@ -402,11 +394,11 @@ void CheckTensorsInDifferentDevices(const std::vector<Tensor>& tensors, ...@@ -402,11 +394,11 @@ void CheckTensorsInDifferentDevices(const std::vector<Tensor>& tensors,
std::set<Place> used_devices; std::set<Place> used_devices;
for (const auto& t : tensors) { for (const auto& t : tensors) {
PADDLE_ENFORCE_EQ(t.is_gpu() && t.is_dense_tensor(), true, PADDLE_ENFORCE_EQ(platform::is_gpu_place(t.place()), true,
platform::errors::InvalidArgument( platform::errors::InvalidArgument(
"Tensors must be CUDA and dense tensor.")); "Tensors must be CUDA and dense tensor."));
const auto inserted = used_devices.insert(t.inner_place()).second; const auto inserted = used_devices.insert(t.place()).second;
PADDLE_ENFORCE_EQ(inserted, true, PADDLE_ENFORCE_EQ(inserted, true,
platform::errors::InvalidArgument( platform::errors::InvalidArgument(
"Tensors must be on distinct GPU devices.")); "Tensors must be on distinct GPU devices."));
...@@ -414,60 +406,53 @@ void CheckTensorsInDifferentDevices(const std::vector<Tensor>& tensors, ...@@ -414,60 +406,53 @@ void CheckTensorsInDifferentDevices(const std::vector<Tensor>& tensors,
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Send( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Send(
std::vector<Tensor>& tensors, int dst_rank) { std::vector<phi::DenseTensor>& tensors, int dst_rank) {
CheckTensorsInDifferentDevices(tensors, static_cast<size_t>(GetSize())); CheckTensorsInDifferentDevices(tensors, static_cast<size_t>(GetSize()));
auto task = PointToPoint( auto task = PointToPoint(tensors,
tensors, [&](phi::DenseTensor& input, ncclComm_t comm,
[&](Tensor& input, ncclComm_t comm, const gpuStream_t& stream, const gpuStream_t& stream, int dst_rank) {
int dst_rank) {
auto input_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(input.impl());
return platform::dynload::ncclSend( return platform::dynload::ncclSend(
input_tensor->data(), input_tensor->numel(), input.data(), input.numel(),
platform::ToNCCLDataType(input.type()), dst_rank, comm, stream); platform::ToNCCLDataType(input.dtype()),
dst_rank, comm, stream);
}, },
dst_rank, CommType::SEND); dst_rank, CommType::SEND);
return task; return task;
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Recv( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Recv(
std::vector<Tensor>& tensors, int src_rank) { std::vector<phi::DenseTensor>& tensors, int src_rank) {
CheckTensorsInDifferentDevices(tensors, static_cast<size_t>(GetSize())); CheckTensorsInDifferentDevices(tensors, static_cast<size_t>(GetSize()));
auto task = PointToPoint( auto task = PointToPoint(tensors,
tensors, [&](phi::DenseTensor& output, ncclComm_t comm,
[&](Tensor& output, ncclComm_t comm, const gpuStream_t& stream, const gpuStream_t& stream, int src_rank) {
int src_rank) {
auto output_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(output.impl());
return platform::dynload::ncclRecv( return platform::dynload::ncclRecv(
output_tensor->data(), output_tensor->numel(), output.data(), output.numel(),
platform::ToNCCLDataType(output.type()), src_rank, comm, stream); platform::ToNCCLDataType(output.dtype()),
src_rank, comm, stream);
}, },
src_rank, CommType::RECV); src_rank, CommType::RECV);
return task; return task;
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllGather( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllGather(
std::vector<Tensor>& in_tensors, std::vector<Tensor>& out_tensors) { std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors) {
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors), true, CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(out_tensors), true, CheckTensorsInCudaPlace(out_tensors), true,
platform::errors::InvalidArgument("All outputs should be in CudaPlace.")); platform::errors::InvalidArgument("All outputs should be in CudaPlace."));
return Collective( return Collective(in_tensors, out_tensors,
in_tensors, out_tensors, [&](const phi::DenseTensor& input, phi::DenseTensor& output,
[&](const Tensor& input, Tensor& output, ncclComm_t comm, ncclComm_t comm, const gpuStream_t& stream) {
const gpuStream_t& stream) {
auto input_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(input.impl());
auto output_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(output.impl());
return platform::dynload::ncclAllGather( return platform::dynload::ncclAllGather(
input_tensor->data(), output_tensor->data(), input_tensor->numel(), input.data(), output.data(), input.numel(),
platform::ToNCCLDataType(input.type()), comm, stream); platform::ToNCCLDataType(input.dtype()), comm,
stream);
}, },
CommType::ALLGATHER); CommType::ALLGATHER);
} }
...@@ -493,10 +478,12 @@ void* GetPointerByOffset(void* raw_pointer, size_t offset, ...@@ -493,10 +478,12 @@ void* GetPointerByOffset(void* raw_pointer, size_t offset,
PADDLE_THROW(platform::errors::Unimplemented( PADDLE_THROW(platform::errors::Unimplemented(
"This datatype in nccl is not supported.")); "This datatype in nccl is not supported."));
} }
return nullptr;
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll(
std::vector<Tensor>& in_tensors, std::vector<Tensor>& out_tensors) { std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors) {
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors), true, CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
...@@ -505,24 +492,20 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll( ...@@ -505,24 +492,20 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll(
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective( return Collective(
in_tensors, out_tensors, in_tensors, out_tensors,
[&](const Tensor& input, Tensor& output, ncclComm_t comm, [&](phi::DenseTensor& input, phi::DenseTensor& output, ncclComm_t comm,
const gpuStream_t& stream) { const gpuStream_t& stream) {
auto input_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(input.impl());
auto output_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(output.impl());
size_t offset = 0; size_t offset = 0;
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupStart()); PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupStart());
for (auto i = 0; i < size_; i++) { for (auto i = 0; i < size_; i++) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclSend( PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclSend(
GetPointerByOffset(input_tensor->data(), offset, input.type()), GetPointerByOffset(input.data(), offset, input.dtype()),
input_tensor->numel() / size_, input.numel() / size_, platform::ToNCCLDataType(input.dtype()), i,
platform::ToNCCLDataType(input.type()), i, comm, stream)); comm, stream));
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv( PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv(
GetPointerByOffset(output_tensor->data(), offset, input.type()), GetPointerByOffset(output.data(), offset, input.dtype()),
input_tensor->numel() / size_, input.numel() / size_, platform::ToNCCLDataType(input.dtype()), i,
platform::ToNCCLDataType(input.type()), i, comm, stream)); comm, stream));
offset += input_tensor->numel() / size_; offset += input.numel() / size_;
} }
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupEnd()); PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupEnd());
}, },
...@@ -530,29 +513,26 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll( ...@@ -530,29 +513,26 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::AllToAll(
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Reduce( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Reduce(
std::vector<Tensor>& tensors, const ReduceOptions& opts) { std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors, const ReduceOptions& opts) {
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(tensors), true, CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective( return Collective(
tensors, tensors, in_tensors, out_tensors,
[&](const Tensor& input, Tensor& output, ncclComm_t comm, [&](const phi::DenseTensor& input, phi::DenseTensor& output,
const gpuStream_t& stream) { ncclComm_t comm, const gpuStream_t& stream) {
auto input_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(input.impl());
auto output_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(output.impl());
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclReduce( PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclReduce(
input_tensor->data(), output_tensor->data(), input.numel(), input.data(), output.data(), input.numel(),
platform::ToNCCLDataType(input.type()), platform::ToNCCLDataType(input.dtype()),
ToNCCLRedType(opts.reduce_op), opts.root_rank, comm, stream)); ToNCCLRedType(opts.reduce_op), opts.root_rank, comm, stream));
}, },
CommType::REDUCE); CommType::REDUCE);
} }
std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Scatter( std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Scatter(
std::vector<Tensor>& in_tensors, std::vector<Tensor>& out_tensors, std::vector<phi::DenseTensor>& in_tensors,
const ScatterOptions& opts) { std::vector<phi::DenseTensor>& out_tensors, const ScatterOptions& opts) {
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors), true, CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
...@@ -561,31 +541,27 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Scatter( ...@@ -561,31 +541,27 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupNCCL::Scatter(
platform::errors::InvalidArgument("All inputs should be in CudaPlace.")); platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
return Collective( return Collective(
in_tensors, out_tensors, in_tensors, out_tensors,
[&](const Tensor& input, Tensor& output, ncclComm_t comm, [&](phi::DenseTensor& input, phi::DenseTensor& output, ncclComm_t comm,
const gpuStream_t& stream) { const gpuStream_t& stream) {
auto input_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(input.impl());
auto output_tensor =
std::dynamic_pointer_cast<phi::DenseTensor>(output.impl());
size_t offset = 0; size_t offset = 0;
if (rank_ == opts.root_rank) { if (rank_ == opts.root_rank) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupStart()); PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupStart());
for (auto i = 0; i < size_; i++) { for (auto i = 0; i < size_; i++) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclSend( PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclSend(
GetPointerByOffset(input_tensor->data(), offset, input.type()), GetPointerByOffset(input.data(), offset, input.dtype()),
input_tensor->numel() / size_, input.numel() / size_, platform::ToNCCLDataType(input.dtype()),
platform::ToNCCLDataType(input.type()), i, comm, stream)); i, comm, stream));
offset += input_tensor->numel() / size_; offset += input.numel() / size_;
} }
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv( PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv(
output_tensor->data(), input_tensor->numel() / size_, output.data(), input.numel() / size_,
platform::ToNCCLDataType(input.type()), opts.root_rank, comm, platform::ToNCCLDataType(input.dtype()), opts.root_rank, comm,
stream)); stream));
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupEnd()); PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclGroupEnd());
} else { } else {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv( PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclRecv(
output_tensor->data(), input_tensor->numel() / size_, output.data(), input.numel() / size_,
platform::ToNCCLDataType(input.type()), opts.root_rank, comm, platform::ToNCCLDataType(input.dtype()), opts.root_rank, comm,
stream)); stream));
} }
}, },
......
paddle/fluid/distributed/collective/ProcessGroupNCCL.h
浏览文件 @ 1e56ca8a
...@@ -51,7 +51,7 @@ class ProcessGroupNCCL : public ProcessGroup { ...@@ -51,7 +51,7 @@ class ProcessGroupNCCL : public ProcessGroup {
public std::enable_shared_from_this<NCCLTask> { public std::enable_shared_from_this<NCCLTask> {
public: public:
NCCLTask(const std::vector<Place>& places, int rank, CommType CommType, NCCLTask(const std::vector<Place>& places, int rank, CommType CommType,
const std::vector<Tensor>& inputs); const std::vector<phi::DenseTensor>& inputs);
bool IsCompleted(); bool IsCompleted();
...@@ -61,17 +61,17 @@ class ProcessGroupNCCL : public ProcessGroup { ...@@ -61,17 +61,17 @@ class ProcessGroupNCCL : public ProcessGroup {
void Synchronize(); void Synchronize();
void SetOutputs(std::vector<Tensor>& outputs); // NOLINT void SetOutputs(std::vector<phi::DenseTensor>& outputs); // NOLINT
virtual ~NCCLTask(); virtual ~NCCLTask();
std::vector<EventManager> control_events_; std::vector<EventManager> control_events_;
std::vector<Tensor> barrierTensors_; std::vector<phi::DenseTensor> barrierTensors_;
protected: protected:
std::vector<Place> places_; std::vector<Place> places_;
std::vector<std::shared_ptr<NCCLCommManager>> ncclComms_; std::vector<std::shared_ptr<NCCLCommManager>> ncclComms_;
std::shared_ptr<std::vector<Tensor>> outputs_; std::shared_ptr<std::vector<phi::DenseTensor>> outputs_;
private: private:
}; };
...@@ -84,40 +84,46 @@ class ProcessGroupNCCL : public ProcessGroup { ...@@ -84,40 +84,46 @@ class ProcessGroupNCCL : public ProcessGroup {
} }
std::shared_ptr<ProcessGroup::Task> AllReduce( std::shared_ptr<ProcessGroup::Task> AllReduce(
std::vector<Tensor>& tensors, std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const AllreduceOptions& = AllreduceOptions()) override; const AllreduceOptions& = AllreduceOptions()) override;
std::shared_ptr<ProcessGroup::Task> Broadcast( std::shared_ptr<ProcessGroup::Task> Broadcast(
std::vector<Tensor>& tensors, std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const BroadcastOptions& = BroadcastOptions()) override; const BroadcastOptions& = BroadcastOptions()) override;
std::shared_ptr<ProcessGroup::Task> Barrier( std::shared_ptr<ProcessGroup::Task> Barrier(
const BarrierOptions& = BarrierOptions()) override; const BarrierOptions& = BarrierOptions()) override;
std::shared_ptr<ProcessGroup::Task> Send(std::vector<Tensor>& tensors, std::shared_ptr<ProcessGroup::Task> Send(
int dst_rank) override; std::vector<phi::DenseTensor>& tensors, int dst_rank) override;
std::shared_ptr<ProcessGroup::Task> Recv(std::vector<Tensor>& tensors, std::shared_ptr<ProcessGroup::Task> Recv(
int src_rank) override; std::vector<phi::DenseTensor>& tensors, int src_rank) override;
std::shared_ptr<ProcessGroup::Task> AllGather( std::shared_ptr<ProcessGroup::Task> AllGather(
std::vector<Tensor>& in_tensors, std::vector<phi::DenseTensor>& in_tensors,
std::vector<Tensor>& out_tensors) override; std::vector<phi::DenseTensor>& out_tensors) override;
std::shared_ptr<ProcessGroup::Task> AllToAll( std::shared_ptr<ProcessGroup::Task> AllToAll(
std::vector<Tensor>& in, std::vector<Tensor>& out) override; std::vector<phi::DenseTensor>& in,
std::vector<phi::DenseTensor>& out) override;
std::shared_ptr<ProcessGroup::Task> Reduce( std::shared_ptr<ProcessGroup::Task> Reduce(
std::vector<Tensor>& tensors, const ReduceOptions& opts) override; std::vector<phi::DenseTensor>& tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ReduceOptions& opts) override;
std::shared_ptr<ProcessGroup::Task> Scatter(std::vector<Tensor>& in_tensors, std::shared_ptr<ProcessGroup::Task> Scatter(
std::vector<Tensor>& out_tensors, std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors,
const ScatterOptions&) override; const ScatterOptions&) override;
protected: protected:
virtual std::shared_ptr<ProcessGroupNCCL::NCCLTask> CreateTask( virtual std::shared_ptr<ProcessGroupNCCL::NCCLTask> CreateTask(
std::vector<Place> places, int rank, CommType opType, std::vector<Place> places, int rank, CommType opType,
const std::vector<Tensor>& inputs); const std::vector<phi::DenseTensor>& inputs);
protected: protected:
std::shared_ptr<Store> store_; std::shared_ptr<Store> store_;
...@@ -142,8 +148,8 @@ class ProcessGroupNCCL : public ProcessGroup { ...@@ -142,8 +148,8 @@ class ProcessGroupNCCL : public ProcessGroup {
template <typename Fn> template <typename Fn>
std::shared_ptr<ProcessGroup::Task> Collective( std::shared_ptr<ProcessGroup::Task> Collective(
std::vector<Tensor>& inputs, // NOLINT std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<Tensor>& outputs, // NOLINT std::vector<phi::DenseTensor>& outputs, // NOLINT
Fn fn, CommType op_type); Fn fn, CommType op_type);
template <typename Fn> template <typename Fn>
...@@ -152,7 +158,7 @@ class ProcessGroupNCCL : public ProcessGroup { ...@@ -152,7 +158,7 @@ class ProcessGroupNCCL : public ProcessGroup {
template <typename Fn> template <typename Fn>
std::shared_ptr<ProcessGroup::Task> PointToPoint( std::shared_ptr<ProcessGroup::Task> PointToPoint(
std::vector<Tensor>& tensors, // NOLINT std::vector<phi::DenseTensor>& tensors, // NOLINT
Fn fn, int dst_rank, CommType op_type); Fn fn, int dst_rank, CommType op_type);
void CreateNCCLManagerCache(const std::string& places_key, void CreateNCCLManagerCache(const std::string& places_key,
......
paddle/fluid/distributed/collective/reducer.cc
浏览文件 @ 1e56ca8a
...@@ -734,7 +734,11 @@ void EagerReducer::ProcessUnusedDenseVars() { ...@@ -734,7 +734,11 @@ void EagerReducer::ProcessUnusedDenseVars() {
distributed::AllreduceOptions opts; distributed::AllreduceOptions opts;
opts.reduce_op = ReduceOp::SUM; opts.reduce_op = ReduceOp::SUM;
std::vector<Tensor> reduce_tensors = {global_used_vars_}; std::vector<Tensor> reduce_tensors = {global_used_vars_};
process_group_->AllReduce(reduce_tensors, opts)->Synchronize(); std::vector<phi::DenseTensor> in_out;
for (auto &t : reduce_tensors) {
in_out.push_back(*std::dynamic_pointer_cast<phi::DenseTensor>(t.impl()));
}
process_group_->AllReduce(in_out, in_out, opts)->Synchronize();
framework::TensorToVector<int>(*global_used_tensor, *dev_ctx, framework::TensorToVector<int>(*global_used_tensor, *dev_ctx,
&local_used_vars_); &local_used_vars_);
...@@ -820,7 +824,11 @@ void EagerReducer::FusedAllReduceSchedule(EagerGroup *group, ...@@ -820,7 +824,11 @@ void EagerReducer::FusedAllReduceSchedule(EagerGroup *group,
// all_reduce // all_reduce
std::vector<Tensor> reduce_tensors = {group->dense_contents_}; std::vector<Tensor> reduce_tensors = {group->dense_contents_};
group->task = process_group_->AllReduce(reduce_tensors, opts); std::vector<phi::DenseTensor> in_out;
for (auto &t : reduce_tensors) {
in_out.push_back(*std::dynamic_pointer_cast<phi::DenseTensor>(t.impl()));
}
group->task = process_group_->AllReduce(in_out, in_out, opts);
// split in FinalizeBackward() // split in FinalizeBackward()
} }
...@@ -871,7 +879,11 @@ void EagerReducer::AllReduceSparse(EagerGroup *group, ...@@ -871,7 +879,11 @@ void EagerReducer::AllReduceSparse(EagerGroup *group,
distributed::AllreduceOptions opts; distributed::AllreduceOptions opts;
opts.reduce_op = ReduceOp::SUM; opts.reduce_op = ReduceOp::SUM;
std::vector<Tensor> reduce_tensors = {rows_num_tensor}; std::vector<Tensor> reduce_tensors = {rows_num_tensor};
process_group_->AllReduce(reduce_tensors, opts)->Synchronize(); std::vector<phi::DenseTensor> in_out;
for (auto &t : reduce_tensors) {
in_out.push_back(*std::dynamic_pointer_cast<phi::DenseTensor>(t.impl()));
}
process_group_->AllReduce(in_out, in_out, opts)->Synchronize();
framework::TensorToVector<int64_t>(*rows_num_dense_tensor, *dev_ctx, framework::TensorToVector<int64_t>(*rows_num_dense_tensor, *dev_ctx,
&rows_num_vector); &rows_num_vector);
...@@ -908,8 +920,15 @@ void EagerReducer::AllReduceSparse(EagerGroup *group, ...@@ -908,8 +920,15 @@ void EagerReducer::AllReduceSparse(EagerGroup *group,
std::vector<Tensor> src_rows_tensors = {src_rows_tensor}; std::vector<Tensor> src_rows_tensors = {src_rows_tensor};
std::vector<Tensor> dst_rows_tensors = {dst_rows_tensor}; std::vector<Tensor> dst_rows_tensors = {dst_rows_tensor};
process_group_->AllGather(src_rows_tensors, dst_rows_tensors) std::vector<phi::DenseTensor> in;
->Synchronize(); std::vector<phi::DenseTensor> out;
for (auto &t : src_rows_tensors) {
in.push_back(*std::dynamic_pointer_cast<phi::DenseTensor>(t.impl()));
}
for (auto &t : dst_rows_tensors) {
out.push_back(*std::dynamic_pointer_cast<phi::DenseTensor>(t.impl()));
}
process_group_->AllGather(in, out)->Synchronize();
framework::Vector<int64_t> dst_rows_vector(rows_num, 0); framework::Vector<int64_t> dst_rows_vector(rows_num, 0);
auto *dst_rows_dense_tensor = auto *dst_rows_dense_tensor =
...@@ -934,8 +953,17 @@ void EagerReducer::AllReduceSparse(EagerGroup *group, ...@@ -934,8 +953,17 @@ void EagerReducer::AllReduceSparse(EagerGroup *group,
std::vector<Tensor> src_value_tensors = {src_value_tensor}; std::vector<Tensor> src_value_tensors = {src_value_tensor};
std::vector<Tensor> dst_value_tensors = {dst_value_tensor}; std::vector<Tensor> dst_value_tensors = {dst_value_tensor};
process_group_->AllGather(src_value_tensors, dst_value_tensors) std::vector<phi::DenseTensor> src_dense;
->Synchronize(); std::vector<phi::DenseTensor> dst_dense;
for (auto &t : src_value_tensors) {
src_dense.push_back(
*std::dynamic_pointer_cast<phi::DenseTensor>(t.impl()));
}
for (auto &t : dst_value_tensors) {
dst_dense.push_back(
*std::dynamic_pointer_cast<phi::DenseTensor>(t.impl()));
}
process_group_->AllGather(src_dense, dst_dense)->Synchronize();
src->set_rows(dst_rows_vector); src->set_rows(dst_rows_vector);
*(src->mutable_value()) = *(src->mutable_value()) =
......
paddle/fluid/operators/collective/c_allgather_op.cu.cc
浏览文件 @ 1e56ca8a
...@@ -18,7 +18,9 @@ limitations under the License. */ ...@@ -18,7 +18,9 @@ limitations under the License. */
#include "paddle/fluid/platform/collective_helper.h" #include "paddle/fluid/platform/collective_helper.h"
#include "paddle/fluid/platform/device/gpu/nccl_helper.h" #include "paddle/fluid/platform/device/gpu/nccl_helper.h"
#endif #endif
#include "paddle/fluid/distributed/collective/ProcessGroup.h"
#include "paddle/fluid/framework/convert_utils.h" #include "paddle/fluid/framework/convert_utils.h"
#include "paddle/phi/api/include/tensor.h"
namespace paddle { namespace paddle {
namespace operators { namespace operators {
...@@ -35,6 +37,18 @@ class CAllGatherOpCUDAKernel : public framework::OpKernel<T> { ...@@ -35,6 +37,18 @@ class CAllGatherOpCUDAKernel : public framework::OpKernel<T> {
int nranks = ctx.Attr<int>("nranks"); int nranks = ctx.Attr<int>("nranks");
int rid = ctx.Attr<int>("ring_id"); int rid = ctx.Attr<int>("ring_id");
auto map = distributed::ProcessGroupMapFromGid::getInstance();
if (map->has(rid)) {
// Use ProcessGroup
distributed::ProcessGroup* pg = map->get(rid);
std::vector<phi::DenseTensor> in_tensor;
std::vector<phi::DenseTensor> out_tensor;
in_tensor.push_back(*in);
out_tensor.push_back(*out);
auto task = pg->AllGather(in_tensor, out_tensor);
task->Wait();
return;
}
auto place = ctx.GetPlace(); auto place = ctx.GetPlace();
auto comm = platform::NCCLCommContext::Instance().Get(rid, place); auto comm = platform::NCCLCommContext::Instance().Get(rid, place);
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
......
paddle/fluid/operators/collective/c_broadcast_op.cu.cc
浏览文件 @ 1e56ca8a
...@@ -41,7 +41,12 @@ class CBroadcastOpCUDAKernel : public framework::OpKernel<T> { ...@@ -41,7 +41,12 @@ class CBroadcastOpCUDAKernel : public framework::OpKernel<T> {
if (map->has(rid)) { if (map->has(rid)) {
// Use ProcessGroup // Use ProcessGroup
distributed::ProcessGroup* pg = map->get(rid); distributed::ProcessGroup* pg = map->get(rid);
pg->Broadcast(x, out); std::vector<phi::DenseTensor> in_tensor;
std::vector<phi::DenseTensor> out_tensor;
in_tensor.push_back(*x);
out_tensor.push_back(*out);
auto task = pg->Broadcast(in_tensor, out_tensor);
task->Wait();
return; return;
} }
......
paddle/fluid/pybind/distributed_py.cc
浏览文件 @ 1e56ca8a
...@@ -115,8 +115,10 @@ void BindDistributed(py::module *m) { ...@@ -115,8 +115,10 @@ void BindDistributed(py::module *m) {
auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
distributed::AllreduceOptions opts; distributed::AllreduceOptions opts;
opts.reduce_op = op; opts.reduce_op = op;
std::vector<Tensor> tensors = {tensor}; auto dense =
return self.AllReduce(tensors, opts); std::dynamic_pointer_cast<phi::DenseTensor>(tensor.impl());
std::vector<phi::DenseTensor> tensors = {*dense};
return self.AllReduce(tensors, tensors, opts);
}, },
py::arg("tensor"), py::arg("op") = distributed::ReduceOp::SUM, py::arg("tensor"), py::arg("op") = distributed::ReduceOp::SUM,
py::call_guard<py::gil_scoped_release>()) py::call_guard<py::gil_scoped_release>())
...@@ -127,8 +129,10 @@ void BindDistributed(py::module *m) { ...@@ -127,8 +129,10 @@ void BindDistributed(py::module *m) {
auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
distributed::BroadcastOptions opts; distributed::BroadcastOptions opts;
opts.source_rank = source_rank; opts.source_rank = source_rank;
std::vector<Tensor> tensors = {tensor}; auto dense =
return self.Broadcast(tensors, opts); std::dynamic_pointer_cast<phi::DenseTensor>(tensor.impl());
std::vector<phi::DenseTensor> tensors = {*dense};
return self.Broadcast(tensors, tensors, opts);
}, },
py::arg("tensor"), py::arg("source_rank"), py::arg("tensor"), py::arg("source_rank"),
py::call_guard<py::gil_scoped_release>()) py::call_guard<py::gil_scoped_release>())
...@@ -146,7 +150,9 @@ void BindDistributed(py::module *m) { ...@@ -146,7 +150,9 @@ void BindDistributed(py::module *m) {
[](distributed::ProcessGroup &self, py::handle py_tensor, [](distributed::ProcessGroup &self, py::handle py_tensor,
int dst) { int dst) {
auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
std::vector<Tensor> tensors = {tensor}; auto dense =
std::dynamic_pointer_cast<phi::DenseTensor>(tensor.impl());
std::vector<phi::DenseTensor> tensors = {*dense};
return self.Send(tensors, dst); return self.Send(tensors, dst);
}, },
py::arg("tensor"), py::arg("dst"), py::arg("tensor"), py::arg("dst"),
...@@ -156,7 +162,9 @@ void BindDistributed(py::module *m) { ...@@ -156,7 +162,9 @@ void BindDistributed(py::module *m) {
[](distributed::ProcessGroup &self, py::handle py_tensor, [](distributed::ProcessGroup &self, py::handle py_tensor,
int src) { int src) {
auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0);
std::vector<Tensor> tensors = {tensor}; auto dense =
std::dynamic_pointer_cast<phi::DenseTensor>(tensor.impl());
std::vector<phi::DenseTensor> tensors = {*dense};
return self.Recv(tensors, src); return self.Recv(tensors, src);
}, },
py::arg("tensor"), py::arg("src"), py::arg("tensor"), py::arg("src"),
...@@ -167,8 +175,12 @@ void BindDistributed(py::module *m) { ...@@ -167,8 +175,12 @@ void BindDistributed(py::module *m) {
py::handle py_out_tensor) { py::handle py_out_tensor) {
auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0);
auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0);
std::vector<Tensor> in_tensors = {in_tensor}; auto in_dense = std::dynamic_pointer_cast<phi::DenseTensor>(
std::vector<Tensor> out_tensors = {out_tensor}; in_tensor.impl());
auto out_dense = std::dynamic_pointer_cast<phi::DenseTensor>(
out_tensor.impl());
std::vector<phi::DenseTensor> in_tensors = {*in_dense};
std::vector<phi::DenseTensor> out_tensors = {*out_dense};
return self.AllGather(in_tensors, out_tensors); return self.AllGather(in_tensors, out_tensors);
}, },
py::arg("in"), py::arg("out"), py::arg("in"), py::arg("out"),
...@@ -179,8 +191,12 @@ void BindDistributed(py::module *m) { ...@@ -179,8 +191,12 @@ void BindDistributed(py::module *m) {
py::handle py_out_tensor) { py::handle py_out_tensor) {
auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0);
auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0);
std::vector<Tensor> in_tensors = {in_tensor}; auto in_dense = std::dynamic_pointer_cast<phi::DenseTensor>(
std::vector<Tensor> out_tensors = {out_tensor}; in_tensor.impl());
auto out_dense = std::dynamic_pointer_cast<phi::DenseTensor>(
out_tensor.impl());
std::vector<phi::DenseTensor> in_tensors = {*in_dense};
std::vector<phi::DenseTensor> out_tensors = {*out_dense};
return self.AllToAll(in_tensors, out_tensors); return self.AllToAll(in_tensors, out_tensors);
}, },
py::arg("in"), py::arg("out"), py::arg("in"), py::arg("out"),
...@@ -193,8 +209,10 @@ void BindDistributed(py::module *m) { ...@@ -193,8 +209,10 @@ void BindDistributed(py::module *m) {
distributed::ReduceOptions opts; distributed::ReduceOptions opts;
opts.reduce_op = op; opts.reduce_op = op;
opts.root_rank = dst; opts.root_rank = dst;
std::vector<Tensor> tensors = {in_tensor}; auto dense = std::dynamic_pointer_cast<phi::DenseTensor>(
return self.Reduce(tensors, opts); in_tensor.impl());
std::vector<phi::DenseTensor> tensors = {*dense};
return self.Reduce(tensors, tensors, opts);
}, },
py::arg("tensor"), py::arg("dst"), py::arg("tensor"), py::arg("dst"),
py::arg("op") = distributed::ReduceOp::SUM, py::arg("op") = distributed::ReduceOp::SUM,
...@@ -207,8 +225,12 @@ void BindDistributed(py::module *m) { ...@@ -207,8 +225,12 @@ void BindDistributed(py::module *m) {
auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0);
distributed::ScatterOptions opts; distributed::ScatterOptions opts;
opts.root_rank = src; opts.root_rank = src;
std::vector<Tensor> in_tensors = {in_tensor}; auto in_dense = std::dynamic_pointer_cast<phi::DenseTensor>(
std::vector<Tensor> out_tensors = {out_tensor}; in_tensor.impl());
auto out_dense = std::dynamic_pointer_cast<phi::DenseTensor>(
out_tensor.impl());
std::vector<phi::DenseTensor> in_tensors = {*in_dense};
std::vector<phi::DenseTensor> out_tensors = {*out_dense};
return self.Scatter(in_tensors, out_tensors, opts); return self.Scatter(in_tensors, out_tensors, opts);
}, },
py::arg("in"), py::arg("out"), py::arg("src"), py::arg("in"), py::arg("out"), py::arg("src"),
......
python/paddle/fluid/tests/unittests/init_process_group.py
浏览文件 @ 1e56ca8a
...@@ -46,6 +46,11 @@ class TestProcessGroupFp32(unittest.TestCase): ...@@ -46,6 +46,11 @@ class TestProcessGroupFp32(unittest.TestCase):
group = paddle.distributed.collective.Group(-1, 2, 0, [-1, -2]) group = paddle.distributed.collective.Group(-1, 2, 0, [-1, -2])
ret = paddle.distributed.barrier(group) ret = paddle.distributed.barrier(group)
assert ret == None assert ret == None
paddle.enable_static()
in_tensor = paddle.empty((1, 2))
in_tensor2 = paddle.empty((1, 2))
paddle.distributed.broadcast(in_tensor, src=0)
paddle.distributed.all_gather([in_tensor, in_tensor2], in_tensor)
print("test ok\n") print("test ok\n")
......
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