mirror of
https://github.com/zebrajr/pytorch.git
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be7be9fa16
This PR continues to clean clang-tidy warnings in torch/csrc/distributed/c10d, following https://github.com/pytorch/pytorch/pull/124987. Pull Request resolved: https://github.com/pytorch/pytorch/pull/125102 Approved by: https://github.com/ezyang
651 lines
22 KiB
C++
651 lines
22 KiB
C++
#include <ATen/ATen.h>
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#include <ATen/core/op_registration/op_registration.h>
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#include <c10/core/DispatchKey.h>
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#include <torch/csrc/autograd/custom_function.h>
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#include <torch/csrc/autograd/function.h>
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#include <torch/csrc/distributed/c10d/GroupRegistry.hpp>
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#include <torch/csrc/distributed/c10d/ProcessGroup.hpp>
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#include <torch/csrc/distributed/c10d/RankLocal.hpp>
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#include <utility>
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namespace {
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class WorkRegistry {
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public:
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void register_work(
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const at::Tensor& tensor,
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const c10::intrusive_ptr<c10d::Work>& work) {
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auto storage = tensor.storage().getWeakStorageImpl();
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std::unique_lock lock(lock_);
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auto [it, inserted] = registry_.try_emplace(std::move(storage), work);
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TORCH_CHECK(
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inserted || it->second != work,
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"The tensor storage is already associated with another work.");
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}
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c10::intrusive_ptr<c10d::Work> pop_work(const at::Tensor& tensor) {
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const auto storage = tensor.storage().getWeakStorageImpl();
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std::unique_lock lock(lock_);
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auto it = registry_.find(storage);
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if (it == registry_.end()) {
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return nullptr;
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}
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auto work = it->second;
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registry_.erase(it);
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return work;
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}
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~WorkRegistry() {
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// If there are still unwaited work objects, their corresponding process
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// groups should have already been destroyed at this stage. Any attempts to
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// wait for these work objects or to destroy them will only result in
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// confusing errors. Therefore, we simply issue a warning and intentionally
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// allow the unwaited work objects to leak.
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if (!registry_.empty()) {
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TORCH_WARN(
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"At the time of process termination, there are still ",
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registry_.size(),
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" unwaited c10d_functional collective calls. "
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"Please review your program to ensure c10d_functional.wait_tensor() "
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"is invoked on all tensors returned from c10d_functional collective "
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"ops before they are used.");
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}
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for (auto& it : registry_) {
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it.second.release();
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}
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}
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private:
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std::unordered_map<
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c10::weak_intrusive_ptr<c10::StorageImpl>,
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c10::intrusive_ptr<c10d::Work>>
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registry_;
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std::mutex lock_;
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};
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static WorkRegistry process_registry;
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void register_work(
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const at::Tensor& tensor,
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const c10::intrusive_ptr<c10d::Work>& work) {
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if (c10d::get_thread_isolation_mode()) {
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c10d::RankLocal<WorkRegistry>::get().register_work(tensor, work);
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} else {
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process_registry.register_work(tensor, work);
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}
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}
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c10::intrusive_ptr<c10d::Work> pop_work(const at::Tensor& tensor) {
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if (c10d::get_thread_isolation_mode()) {
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return c10d::RankLocal<WorkRegistry>::get().pop_work(tensor);
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} else {
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return process_registry.pop_work(tensor);
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}
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}
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const std::unordered_map<std::string, c10d::ReduceOp> str_to_reduce_op = {
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{"sum", c10d::ReduceOp(c10d::ReduceOp::RedOpType::SUM)},
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{"avg", c10d::ReduceOp(c10d::ReduceOp::RedOpType::AVG)},
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{"product", c10d::ReduceOp(c10d::ReduceOp::RedOpType::PRODUCT)},
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{"min", c10d::ReduceOp(c10d::ReduceOp::RedOpType::MIN)},
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{"max", c10d::ReduceOp(c10d::ReduceOp::RedOpType::MAX)},
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{"band", c10d::ReduceOp(c10d::ReduceOp::RedOpType::BAND)},
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{"bor", c10d::ReduceOp(c10d::ReduceOp::RedOpType::BOR)},
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{"bxor", c10d::ReduceOp(c10d::ReduceOp::RedOpType::BXOR)},
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// TODO: support premul_sum
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// {"premul_sum", c10d::ReduceOp(c10d::ReduceOp::RedOpType::PREMUL_SUM)},
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{"unused", c10d::ReduceOp(c10d::ReduceOp::RedOpType::UNUSED)}};
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c10d::ReduceOp to_reduce_op(const std::string& reduce_op) {
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auto it = str_to_reduce_op.find(reduce_op);
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TORCH_CHECK(
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it != str_to_reduce_op.end(), "Unrecognized reduce_op: ", reduce_op);
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return it->second;
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}
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at::Tensor& all_reduce_(
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at::Tensor& input,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::string reduce_op,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::string group_name) {
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c10d::AllreduceOptions opts;
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opts.reduceOp = to_reduce_op(reduce_op);
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std::vector<at::Tensor> inputs{input};
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auto group = c10d::resolve_process_group(group_name);
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auto work = group->allreduce(inputs, opts);
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c10d::RankLocal<WorkRegistry>::get().register_work(input, work);
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return input;
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}
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at::Tensor all_reduce(
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const at::Tensor& input,
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std::string reduce_op,
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std::string group_name) {
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auto output = input.clone(at::MemoryFormat::Contiguous);
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return all_reduce_(output, std::move(reduce_op), std::move(group_name));
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}
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std::vector<at::Tensor> all_reduce_coalesced_(
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std::vector<at::Tensor> inputs,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::string reduce_op,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::string group_name) {
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c10d::AllreduceCoalescedOptions opts;
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opts.reduceOp = to_reduce_op(reduce_op);
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auto group = c10d::resolve_process_group(group_name);
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auto work = group->allreduce_coalesced(inputs, opts);
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for (const auto& tensor : inputs) {
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c10d::RankLocal<WorkRegistry>::get().register_work(tensor, work);
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}
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return inputs;
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}
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std::vector<at::Tensor> all_reduce_coalesced(
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::vector<at::Tensor> inputs,
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std::string reduce_op,
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std::string group_name) {
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std::vector<at::Tensor> outputs;
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outputs.reserve(inputs.size());
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for (const auto& tensor : inputs) {
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outputs.push_back(tensor.clone(at::MemoryFormat::Contiguous));
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}
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return all_reduce_coalesced_(
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outputs, std::move(reduce_op), std::move(group_name));
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}
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at::Tensor allocate_all_gather_output(
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const at::Tensor& input,
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int64_t group_size) {
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auto output_size = input.sizes().vec();
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output_size[0] *= group_size;
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return at::empty(
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output_size,
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at::TensorOptions().dtype(input.dtype()).device(input.device()));
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}
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std::vector<at::Tensor> all_gather_into_tensor_coalesced(
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std::vector<at::Tensor> inputs,
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int64_t group_size,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::string group_name) {
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std::vector<at::Tensor> outputs;
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outputs.reserve(inputs.size());
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for (const auto& tensor : inputs) {
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outputs.push_back(allocate_all_gather_output(tensor, group_size));
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}
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auto group = c10d::resolve_process_group(group_name);
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auto work = group->allgather_into_tensor_coalesced(outputs, inputs);
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for (const auto& tensor : outputs) {
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c10d::RankLocal<WorkRegistry>::get().register_work(tensor, work);
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}
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return outputs;
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}
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at::Tensor all_gather_into_tensor(
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const at::Tensor& input,
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int64_t group_size,
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std::string group_name) {
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std::vector<at::Tensor> inputs{input};
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return all_gather_into_tensor_coalesced(
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inputs, group_size, std::move(group_name))[0];
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}
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at::Tensor& all_gather_into_tensor_out(
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at::Tensor& input,
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int64_t group_size,
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const std::string& group_name,
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at::Tensor& output) {
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c10d::AllgatherOptions opts;
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auto group = c10d::resolve_process_group(group_name);
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auto work = group->_allgather_base(output, input, opts);
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c10d::RankLocal<WorkRegistry>::get().register_work(output, work);
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return output;
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}
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at::Tensor allocate_reduce_scatter_output(
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const at::Tensor& input,
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const int64_t group_size) {
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auto output_size = input.sizes().vec();
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if (output_size[0] % group_size != 0) {
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LOG(WARNING) << "The first dimension of the reduce_scatter input ("
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<< output_size[0] << ") is not divisible by the group size ("
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<< group_size << ").";
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}
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output_size[0] /= group_size;
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return at::empty(
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output_size,
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at::TensorOptions().dtype(input.dtype()).device(input.device()));
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}
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std::vector<at::Tensor> reduce_scatter_tensor_coalesced(
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std::vector<at::Tensor> inputs,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::string reduce_op,
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int64_t group_size,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::string group_name) {
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c10d::ReduceScatterOptions opts;
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opts.reduceOp = to_reduce_op(reduce_op);
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std::vector<at::Tensor> outputs;
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outputs.reserve(inputs.size());
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for (const auto& tensor : inputs) {
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outputs.push_back(allocate_reduce_scatter_output(tensor, group_size));
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}
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auto group = c10d::resolve_process_group(group_name);
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auto work = group->reduce_scatter_tensor_coalesced(outputs, inputs, opts);
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for (const auto& tensor : outputs) {
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c10d::RankLocal<WorkRegistry>::get().register_work(tensor, work);
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}
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return outputs;
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}
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at::Tensor reduce_scatter_tensor(
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const at::Tensor& input,
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std::string reduce_op,
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int64_t group_size,
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std::string group_name) {
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std::vector<at::Tensor> inputs{input};
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return reduce_scatter_tensor_coalesced(
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inputs, std::move(reduce_op), group_size, std::move(group_name))[0];
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}
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at::Tensor all_to_all_single(
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const at::Tensor& input,
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std::vector<int64_t> output_split_sizes,
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std::vector<int64_t> input_split_sizes,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::string group_name) {
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std::vector<int64_t> output_sizes = input.sizes().vec();
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output_sizes[0] = std::accumulate(
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output_split_sizes.begin(), output_split_sizes.end(), int64_t(0));
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auto output = input.new_empty(output_sizes);
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auto group = c10d::resolve_process_group(group_name);
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auto work = group->alltoall_base(
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output,
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// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
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const_cast<at::Tensor&>(input),
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output_split_sizes,
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input_split_sizes);
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c10d::RankLocal<WorkRegistry>::get().register_work(output, work);
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return output;
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}
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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at::Tensor& broadcast_(at::Tensor& input, int64_t src, std::string group_name) {
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c10d::BroadcastOptions opts;
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opts.rootRank = src;
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std::vector<at::Tensor> inputs{input};
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auto group = c10d::resolve_process_group(group_name);
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auto work = group->broadcast(inputs, opts);
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c10d::RankLocal<WorkRegistry>::get().register_work(input, work);
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return input;
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}
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at::Tensor broadcast(
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const at::Tensor& input,
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int64_t src,
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std::string group_name) {
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auto output = input.clone(at::MemoryFormat::Contiguous);
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return broadcast_(output, src, std::move(group_name));
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}
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at::Tensor wait_tensor(const at::Tensor& tensor) {
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auto work = c10d::RankLocal<WorkRegistry>::get().pop_work(tensor);
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if (work != nullptr) {
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work->wait();
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}
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return tensor;
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}
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} // namespace
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TORCH_LIBRARY(_c10d_functional, m) {
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m.def(
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"all_reduce(Tensor input, str reduce_op, str group_name) -> Tensor",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd, ::all_reduce),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"all_reduce_(Tensor(a!) input, str reduce_op, str group_name) -> Tensor(a!)",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd, ::all_reduce_),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"all_reduce_coalesced(Tensor[] inputs, str reduce_op, str group_name) -> Tensor[]",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd, ::all_reduce_coalesced),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"all_reduce_coalesced_(Tensor[](a!) inputs, str reduce_op, str group_name) -> Tensor[](a!)",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd, ::all_reduce_coalesced_),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"all_gather_into_tensor_out(Tensor input, int group_size, str group_name, *, Tensor(a!) out) -> Tensor(a!)",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd,
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::all_gather_into_tensor_out),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"all_gather_into_tensor(Tensor input, int group_size, str group_name) -> Tensor",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd,
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::all_gather_into_tensor),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"all_gather_into_tensor_coalesced(Tensor[] inputs, int group_size, str group_name) -> Tensor[]",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd,
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::all_gather_into_tensor_coalesced),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"reduce_scatter_tensor(Tensor input, str reduce_op, int group_size, str group_name) -> Tensor",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd, ::reduce_scatter_tensor),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"reduce_scatter_tensor_coalesced(Tensor[] inputs, str reduce_op, int group_size, str group_name) -> Tensor[]",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd,
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::reduce_scatter_tensor_coalesced),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"all_to_all_single("
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"Tensor input, "
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"SymInt[] output_split_sizes, "
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"SymInt[] input_split_sizes, "
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"str group_name) -> Tensor",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd, ::all_to_all_single),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"broadcast(Tensor input, int src, str group_name) -> Tensor",
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torch::dispatch(c10::DispatchKey::CompositeExplicitAutograd, ::broadcast),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"broadcast_(Tensor(a!) input, int src, str group_name) -> Tensor(a!)",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd, ::broadcast_),
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{at::Tag::pt2_compliant_tag});
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m.def(
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"wait_tensor(Tensor tensor) -> Tensor",
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torch::dispatch(
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c10::DispatchKey::CompositeExplicitAutograd, ::wait_tensor),
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{at::Tag::pt2_compliant_tag});
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}
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namespace {
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class AllToAllSingle : public torch::autograd::Function<AllToAllSingle> {
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public:
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static torch::autograd::Variable forward(
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torch::autograd::AutogradContext* ctx,
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const at::Tensor& input,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::vector<int64_t> output_split_sizes,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::vector<int64_t> input_split_sizes,
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// NOLINTNEXTLINE(performance-unnecessary-value-param)
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std::string group_name) {
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// swap sizes for backwards pass
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ctx->saved_data["output_split_sizes"] = input_split_sizes;
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ctx->saved_data["input_split_sizes"] = output_split_sizes;
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ctx->saved_data["group_name"] = group_name;
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return c10::Dispatcher::singleton()
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.findSchemaOrThrow("_c10d_functional::all_to_all_single", "")
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.typed<decltype(all_to_all_single)>()
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.call(input, output_split_sizes, input_split_sizes, group_name);
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}
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static torch::autograd::variable_list backward(
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torch::autograd::AutogradContext* ctx,
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torch::autograd::variable_list grad_out_list) {
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const std::vector<int64_t>& output_split_sizes =
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ctx->saved_data["output_split_sizes"].toIntVector();
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const std::vector<int64_t>& input_split_sizes =
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ctx->saved_data["input_split_sizes"].toIntVector();
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const std::string& group_name = ctx->saved_data["group_name"].toStringRef();
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DCHECK(grad_out_list.size() == 1);
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auto grad_out = grad_out_list[0].contiguous();
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auto out =
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c10::Dispatcher::singleton()
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.findSchemaOrThrow("_c10d_functional::all_to_all_single", "")
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.typed<decltype(all_to_all_single)>()
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.call(grad_out, output_split_sizes, input_split_sizes, group_name);
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// do an explicit wait to avoid cuda stream issues
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// TODO: track active cuda stream in wait
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out = c10::Dispatcher::singleton()
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.findSchemaOrThrow("_c10d_functional::wait_tensor", "")
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.typed<decltype(wait_tensor)>()
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.call(out);
|
|
|
|
return {out, at::Tensor(), at::Tensor(), at::Tensor()};
|
|
}
|
|
};
|
|
|
|
at::Tensor all_to_all_single_autograd(
|
|
const at::Tensor& input,
|
|
const std::vector<int64_t>& output_split_sizes,
|
|
const std::vector<int64_t>& input_split_sizes,
|
|
const std::string& group_name) {
|
|
return AllToAllSingle::apply(
|
|
input, output_split_sizes, input_split_sizes, group_name);
|
|
}
|
|
|
|
class ReduceScatterTensor
|
|
: public torch::autograd::Function<ReduceScatterTensor> {
|
|
public:
|
|
static torch::autograd::Variable forward(
|
|
torch::autograd::AutogradContext* ctx,
|
|
const at::Tensor& input,
|
|
const std::string& reduce_op,
|
|
int64_t group_size,
|
|
const std::string& group_name) {
|
|
TORCH_CHECK(reduce_op == "sum", "Only sum reduce op is supported");
|
|
|
|
ctx->saved_data["group_size"] = group_size;
|
|
ctx->saved_data["group_name"] = group_name;
|
|
|
|
return c10::Dispatcher::singleton()
|
|
.findSchemaOrThrow("_c10d_functional::reduce_scatter_tensor", "")
|
|
.typed<decltype(reduce_scatter_tensor)>()
|
|
.call(input, reduce_op, group_size, group_name);
|
|
}
|
|
|
|
static torch::autograd::variable_list backward(
|
|
torch::autograd::AutogradContext* ctx,
|
|
torch::autograd::variable_list grad_out_list) {
|
|
const int64_t group_size = ctx->saved_data["group_size"].toInt();
|
|
const std::string& group_name = ctx->saved_data["group_name"].toStringRef();
|
|
|
|
DCHECK(grad_out_list.size() == 1);
|
|
auto grad_out = grad_out_list[0];
|
|
|
|
auto out =
|
|
c10::Dispatcher::singleton()
|
|
.findSchemaOrThrow("_c10d_functional::all_gather_into_tensor", "")
|
|
.typed<decltype(all_gather_into_tensor)>()
|
|
.call(grad_out, group_size, group_name);
|
|
|
|
// do an explicit wait to avoid cuda stream issues
|
|
// TODO: track active cuda stream in wait
|
|
out = c10::Dispatcher::singleton()
|
|
.findSchemaOrThrow("_c10d_functional::wait_tensor", "")
|
|
.typed<decltype(wait_tensor)>()
|
|
.call(out);
|
|
|
|
return {
|
|
out,
|
|
at::Tensor(),
|
|
at::Tensor(),
|
|
at::Tensor(),
|
|
};
|
|
}
|
|
};
|
|
|
|
at::Tensor reduce_scatter_tensor_autograd(
|
|
const at::Tensor& input,
|
|
const std::string& reduce_op,
|
|
int64_t group_size,
|
|
const std::string& group_name) {
|
|
return ReduceScatterTensor::apply(input, reduce_op, group_size, group_name);
|
|
}
|
|
|
|
class AllGatherIntoTensor
|
|
: public torch::autograd::Function<AllGatherIntoTensor> {
|
|
public:
|
|
static torch::autograd::Variable forward(
|
|
torch::autograd::AutogradContext* ctx,
|
|
const at::Tensor& input,
|
|
int64_t group_size,
|
|
const std::string& group_name) {
|
|
ctx->saved_data["group_size"] = group_size;
|
|
ctx->saved_data["group_name"] = group_name;
|
|
|
|
return c10::Dispatcher::singleton()
|
|
.findSchemaOrThrow("_c10d_functional::all_gather_into_tensor", "")
|
|
.typed<decltype(all_gather_into_tensor)>()
|
|
.call(input, group_size, group_name);
|
|
}
|
|
|
|
static torch::autograd::variable_list backward(
|
|
torch::autograd::AutogradContext* ctx,
|
|
torch::autograd::variable_list grad_out_list) {
|
|
const int64_t group_size = ctx->saved_data["group_size"].toInt();
|
|
const std::string& group_name = ctx->saved_data["group_name"].toStringRef();
|
|
|
|
DCHECK(grad_out_list.size() == 1);
|
|
auto grad_out = grad_out_list[0];
|
|
|
|
auto out =
|
|
c10::Dispatcher::singleton()
|
|
.findSchemaOrThrow("_c10d_functional::reduce_scatter_tensor", "")
|
|
.typed<decltype(reduce_scatter_tensor)>()
|
|
.call(grad_out, "sum", group_size, group_name);
|
|
|
|
// do an explicit wait to avoid cuda stream issues
|
|
// TODO: track active cuda stream in wait
|
|
out = c10::Dispatcher::singleton()
|
|
.findSchemaOrThrow("_c10d_functional::wait_tensor", "")
|
|
.typed<decltype(wait_tensor)>()
|
|
.call(out);
|
|
|
|
return {
|
|
out,
|
|
at::Tensor(),
|
|
at::Tensor(),
|
|
};
|
|
}
|
|
};
|
|
|
|
at::Tensor all_gather_into_tensor_autograd(
|
|
const at::Tensor& input,
|
|
int64_t group_size,
|
|
const std::string& group_name) {
|
|
return AllGatherIntoTensor::apply(input, group_size, group_name);
|
|
}
|
|
|
|
} // namespace
|
|
|
|
TORCH_LIBRARY(_c10d_functional_autograd, m) {
|
|
m.def(
|
|
"all_to_all_single("
|
|
"Tensor input, "
|
|
"SymInt[] output_split_sizes, "
|
|
"SymInt[] input_split_sizes, "
|
|
"str group_name) -> Tensor",
|
|
torch::dispatch(c10::DispatchKey::Autograd, ::all_to_all_single_autograd),
|
|
{at::Tag::pt2_compliant_tag});
|
|
m.def(
|
|
"reduce_scatter_tensor("
|
|
"Tensor input, "
|
|
"str reduce_op, "
|
|
"int group_size, "
|
|
"str group_name) -> Tensor",
|
|
torch::dispatch(
|
|
c10::DispatchKey::Autograd, ::reduce_scatter_tensor_autograd),
|
|
{at::Tag::pt2_compliant_tag});
|
|
m.def(
|
|
"all_gather_into_tensor("
|
|
"Tensor input, "
|
|
"int group_size, "
|
|
"str group_name) -> Tensor",
|
|
torch::dispatch(
|
|
c10::DispatchKey::Autograd, ::all_gather_into_tensor_autograd),
|
|
{at::Tag::pt2_compliant_tag});
|
|
}
|
|
|
|
namespace {
|
|
// DTensor related comm operations, sharing code with functional collective for
|
|
// now
|
|
at::Tensor shard_dim_alltoall(
|
|
const at::Tensor& input,
|
|
int64_t gather_dim,
|
|
int64_t shard_dim,
|
|
const std::string& group_name) {
|
|
auto group = c10d::resolve_process_group(group_name);
|
|
auto group_size = group->getSize();
|
|
std::vector<int64_t> output_sizes = input.sizes().vec();
|
|
if (output_sizes[shard_dim] % group_size != 0) {
|
|
LOG(WARNING) << "The first dimension of the shard_dim_alltoall input ("
|
|
<< output_sizes[shard_dim]
|
|
<< ") is not divisible by the group size (" << group_size
|
|
<< ").";
|
|
}
|
|
output_sizes[shard_dim] = output_sizes[shard_dim] / group_size;
|
|
std::vector<at::Tensor> inputs;
|
|
inputs.reserve(group_size);
|
|
auto length = output_sizes[shard_dim];
|
|
for (int i = 0; i < group_size; i++) {
|
|
inputs.push_back(input.narrow(shard_dim, i * length, length).contiguous());
|
|
}
|
|
// allocate outputs
|
|
std::vector<at::Tensor> outputs;
|
|
outputs.reserve(group_size);
|
|
for (int i = 0; i < group_size; i++) {
|
|
outputs.push_back(input.new_empty(output_sizes).contiguous());
|
|
}
|
|
auto work = group->alltoall(outputs, inputs);
|
|
|
|
work->wait();
|
|
// TODO: it's very tricky to get the current async behavior work for shard dim
|
|
// alltoall so for now we just keep this comm op to be synchronous. We can
|
|
// revisit later how to support the async case with the Work registry.
|
|
return at::cat(outputs, gather_dim);
|
|
}
|
|
} // namespace
|
|
|
|
// DTensor comm op registry
|
|
TORCH_LIBRARY(_dtensor, m) {
|
|
m.def(
|
|
"shard_dim_alltoall(Tensor input, int gather_dim, int shard_dim, str group_name) -> Tensor",
|
|
torch::dispatch(
|
|
c10::DispatchKey::CompositeExplicitAutograd, ::shard_dim_alltoall),
|
|
{at::Tag::pt2_compliant_tag});
|
|
}
|