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https://github.com/ml-explore/mlx.git
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Adds send/recv ops in distributed (#1366)
This commit is contained in:
parent
1d94ac3f90
commit
cdb59faea6
@ -17,3 +17,6 @@ made available.
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init
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all_sum
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all_gather
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send
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recv
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recv_like
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@ -10,7 +10,7 @@
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namespace mlx::core::distributed {
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void signal_and_wait(const array& in, const array& out, const Stream s) {
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void signal_and_wait(const array& in, const array& out, const Stream& s) {
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auto& d = metal::device(s.device);
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d.end_encoding(s.index);
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auto command_buffer = d.get_command_buffer(s.index);
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@ -81,4 +81,62 @@ void AllGather::eval_gpu(
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signal_and_wait(in, out, stream());
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}
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void Send::eval_gpu(
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const std::vector<array>& inputs,
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std::vector<array>& outputs) {
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assert(inputs.size() == 1);
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assert(outputs.size() == 1);
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auto& in = inputs[0];
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auto& out = outputs[0];
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// Schedule an async send on the comm stream
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auto task = [in = in, out = out, group = group(), dst = dst_]() mutable {
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if (in.event().valid()) {
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in.event().wait();
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}
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distributed::detail::send(group, in, dst);
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out.event().signal();
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};
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scheduler::enqueue(detail::communication_stream(), std::move(task));
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// Encode a signal event for the input but not a wait since we don't need to
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// wait on the output.
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auto& s = stream();
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auto& d = metal::device(s.device);
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d.end_encoding(s.index);
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auto command_buffer = d.get_command_buffer(s.index);
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if (in.event().valid()) {
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command_buffer->encodeSignalEvent(
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static_cast<MTL::Event*>(in.event().raw_event().get()),
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in.event().value());
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}
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}
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void Recv::eval_gpu(
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const std::vector<array>& inputs,
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std::vector<array>& outputs) {
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assert(inputs.size() == 0);
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assert(outputs.size() == 1);
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auto& out = outputs[0];
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out.set_data(allocator::malloc_or_wait(out.nbytes()));
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// Schedule an async recv on the comm stream
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auto task = [out = out, group = group(), src = src_]() mutable {
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distributed::detail::recv(group, out, src);
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out.event().signal();
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};
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scheduler::enqueue(detail::communication_stream(), std::move(task));
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// Encode a wait event as there is no input for the recv to encode a signal.
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auto& s = stream();
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auto& d = metal::device(s.device);
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auto command_buffer = d.get_command_buffer(s.index);
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command_buffer->encodeWait(
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static_cast<MTL::Event*>(out.event().raw_event().get()),
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out.event().value());
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}
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} // namespace mlx::core::distributed
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@ -126,6 +126,8 @@ NO_GPU_MULTI(CustomKernel)
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namespace distributed {
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NO_GPU_MULTI(AllReduce)
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NO_GPU_MULTI(AllGather)
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NO_GPU_MULTI(Send)
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NO_GPU_MULTI(Recv)
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} // namespace distributed
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} // namespace mlx::core
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@ -50,17 +50,4 @@ struct Group {
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*/
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Group init(bool strict = false);
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namespace detail {
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/* Return the communication stream. */
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Stream communication_stream();
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/* Perform an all reduce sum operation */
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void all_sum(Group group, const array& input, array& output);
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/* Perform an all reduce sum operation */
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void all_gather(Group group, const array& input, array& output);
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} // namespace detail
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} // namespace mlx::core::distributed
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@ -12,7 +12,13 @@ Stream communication_stream();
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/* Perform an all reduce sum operation */
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void all_sum(Group group, const array& input, array& output);
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/* Perform an all reduce sum operation */
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/* Perform an all gather operation */
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void all_gather(Group group, const array& input, array& output);
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/** Send an array to the dst rank */
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void send(Group group, const array& input, int dst);
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/** Recv an array from the src rank */
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void recv(Group group, array& out, int src);
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} // namespace mlx::core::distributed::detail
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@ -48,6 +48,8 @@ struct MPIWrapper {
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LOAD_SYMBOL(MPI_Comm_free, comm_free);
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LOAD_SYMBOL(MPI_Allreduce, all_reduce);
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LOAD_SYMBOL(MPI_Allgather, all_gather);
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LOAD_SYMBOL(MPI_Send, send);
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LOAD_SYMBOL(MPI_Recv, recv);
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// Objects
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LOAD_SYMBOL(ompi_mpi_comm_world, comm_world_);
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@ -142,6 +144,8 @@ struct MPIWrapper {
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MPI_Comm);
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int (*comm_split)(MPI_Comm, int, int, MPI_Comm*);
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int (*comm_free)(MPI_Comm*);
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int (*send)(const void*, int, MPI_Datatype, int, int, MPI_Comm);
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int (*recv)(void*, int, MPI_Datatype, int, int, MPI_Comm, MPI_Status*);
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// Objects
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MPI_Comm comm_world_;
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@ -285,6 +289,29 @@ void all_gather(Group group, const array& input_, array& output) {
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to_comm(group));
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}
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void send(Group group, const array& input_, int dst) {
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array input = ensure_row_contiguous(input_);
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mpi().send(
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input.data<void>(),
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input.size(),
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mpi().datatype(input),
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dst,
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0,
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to_comm(group));
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}
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void recv(Group group, array& out, int src) {
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MPI_Status status;
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mpi().recv(
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out.data<void>(),
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out.size(),
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mpi().datatype(out),
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src,
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MPI_ANY_TAG,
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to_comm(group),
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&status);
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}
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} // namespace detail
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} // namespace mlx::core::distributed
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@ -34,6 +34,8 @@ Stream communication_stream() {
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void all_sum(Group group, const array& input, array& output) {}
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void all_gather(Group group, const array& input, array& output) {}
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void send(Group group, const array& input, int dst) {}
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void recv(Group group, array& out, int src) {}
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} // namespace detail
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@ -1,5 +1,7 @@
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// Copyright © 2024 Apple Inc.
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#include <sstream>
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#include "mlx/distributed/ops.h"
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#include "mlx/distributed/primitives.h"
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@ -57,4 +59,59 @@ array all_gather(
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{x});
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}
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array send(
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const array& x,
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int dst,
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std::optional<Group> group_ /* = std::nullopt */,
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StreamOrDevice s /* = {} */) {
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auto group = to_group(group_);
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if (group.size() == 1) {
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throw std::invalid_argument("Cannot send to a singleton group");
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}
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if (dst < 0 || dst >= group.size()) {
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std::ostringstream msg;
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msg << "Invalid destination=" << dst << " for a group of size "
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<< group.size();
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throw std::invalid_argument(msg.str());
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}
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return array(
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{0}, int32, std::make_shared<Send>(to_stream(s), group, dst), {x});
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}
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array recv(
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std::vector<int> shape,
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Dtype dtype,
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int src,
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std::optional<Group> group_ /* = std::nullopt */,
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StreamOrDevice s /* = {} */) {
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auto group = to_group(group_);
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if (group.size() == 1) {
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throw std::invalid_argument("Cannot recv from a singleton group");
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}
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if (src < 0 || src >= group.size()) {
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std::ostringstream msg;
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msg << "Invalid source=" << src << " for a group of size " << group.size();
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throw std::invalid_argument(msg.str());
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}
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return array(
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std::move(shape),
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std::move(dtype),
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std::make_shared<Recv>(to_stream(s), group, src),
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std::vector<array>{});
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}
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array recv_like(
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const array& x,
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int src,
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std::optional<Group> group_ /* = std::nullopt */,
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StreamOrDevice s /* = {} */) {
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return recv(x.shape(), x.dtype(), src, group_, s);
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}
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} // namespace mlx::core::distributed
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@ -13,9 +13,29 @@ array all_sum(
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const array& x,
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std::optional<Group> group = std::nullopt,
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StreamOrDevice s = {});
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array all_gather(
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const array& x,
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std::optional<Group> group = std::nullopt,
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StreamOrDevice S = {});
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array send(
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const array& x,
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int dst,
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std::optional<Group> group = std::nullopt,
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StreamOrDevice s = {});
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array recv(
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std::vector<int> shape,
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Dtype dtype,
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int src,
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std::optional<Group> group = std::nullopt,
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StreamOrDevice s = {});
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array recv_like(
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const array& x,
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int src,
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std::optional<Group> group = std::nullopt,
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StreamOrDevice s = {});
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} // namespace mlx::core::distributed
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@ -35,7 +35,7 @@ std::pair<std::vector<array>, std::vector<int>> AllReduce::vmap(
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const std::vector<int>& axes) {
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switch (reduce_type_) {
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case Sum:
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return {{all_sum(inputs[0], group())}, axes};
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return {{all_sum(inputs[0], group(), stream())}, axes};
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default:
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throw std::runtime_error("Only all reduce sum is supported for now");
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}
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@ -47,7 +47,7 @@ std::vector<array> AllReduce::jvp(
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const std::vector<int>& argnums) {
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switch (reduce_type_) {
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case Sum:
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return {all_sum(tangents[0], group())};
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return {all_sum(tangents[0], group(), stream())};
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default:
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throw std::runtime_error("Only all reduce sum is supported for now");
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}
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@ -75,14 +75,14 @@ void AllGather::eval_cpu(
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std::pair<std::vector<array>, std::vector<int>> AllGather::vmap(
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const std::vector<array>& inputs,
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const std::vector<int>& axes) {
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return {{all_gather(inputs[0], group())}, axes};
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return {{all_gather(inputs[0], group(), stream())}, axes};
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}
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std::vector<array> AllGather::jvp(
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const std::vector<array>& primals,
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const std::vector<array>& tangents,
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const std::vector<int>& argnums) {
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return {all_gather(tangents[0], group())};
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return {all_gather(tangents[0], group(), stream())};
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}
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std::vector<array> AllGather::vjp(
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@ -98,4 +98,29 @@ std::vector<array> AllGather::vjp(
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return {slice(cotangents[0], starts, stops)};
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}
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void Send::eval_cpu(
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const std::vector<array>& inputs,
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std::vector<array>& outputs) {
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assert(inputs.size() == 1);
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assert(outputs.size() == 1);
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distributed::detail::send(group(), inputs[0], dst_);
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}
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std::pair<std::vector<array>, std::vector<int>> Send::vmap(
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const std::vector<array>& inputs,
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const std::vector<int>& axes) {
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return {{send(inputs[0], dst_, group(), stream())}, axes};
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}
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void Recv::eval_cpu(
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const std::vector<array>& inputs,
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std::vector<array>& outputs) {
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assert(inputs.size() == 0);
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assert(outputs.size() == 1);
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outputs[0].set_data(allocator::malloc_or_wait(outputs[0].nbytes()));
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distributed::detail::recv(group(), outputs[0], src_);
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}
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} // namespace mlx::core::distributed
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@ -97,4 +97,39 @@ class AllGather : public DistPrimitive {
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DEFINE_PRINT(AllGather);
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};
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class Send : public DistPrimitive {
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public:
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Send(Stream stream, Group group, int dst)
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: DistPrimitive(stream, group), dst_(dst) {}
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void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
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override;
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void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
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override;
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std::pair<std::vector<array>, std::vector<int>> vmap(
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const std::vector<array>& inputs,
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const std::vector<int>& axes) override;
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DEFINE_PRINT(Send);
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private:
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int dst_;
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};
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class Recv : public DistPrimitive {
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public:
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Recv(Stream stream, Group group, int src)
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: DistPrimitive(stream, group), src_(src) {}
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void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
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override;
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void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
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override;
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DEFINE_PRINT(Recv);
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private:
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int src_;
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};
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} // namespace mlx::core::distributed
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@ -4,6 +4,7 @@
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#include <nanobind/stl/optional.h>
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#include <nanobind/stl/shared_ptr.h>
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#include <nanobind/stl/variant.h>
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#include <nanobind/stl/vector.h>
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#include "mlx/distributed/distributed.h"
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#include "mlx/distributed/ops.h"
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@ -121,4 +122,90 @@ void init_distributed(nb::module_& parent_module) {
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Returns:
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array: The concatenation of all ``x`` arrays.
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)pbdoc");
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m.def(
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"send",
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&distributed::send,
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"x"_a,
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"dst"_a,
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nb::kw_only(),
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"group"_a = nb::none(),
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"stream"_a = nb::none(),
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nb::sig(
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"def send(x: array, dst: int, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array"),
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R"pbdoc(
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Send an array from the current process to the process that has rank
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``dst`` in the group.
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Args:
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x (array): Input array.
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dst (int): Rank of the destination process in the group.
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group (Group): The group of processes that will participate in the
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sned. If set to ``None`` the global group is used. Default:
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``None``.
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stream (Stream, optional): Stream or device. Defaults to ``None``
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in which case the default stream of the default device is used.
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Returns:
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array: An empty array which when evaluated the send is performed.
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)pbdoc");
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m.def(
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"recv",
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&distributed::recv,
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"shape"_a,
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"dtype"_a,
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"src"_a,
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nb::kw_only(),
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"group"_a = nb::none(),
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"stream"_a = nb::none(),
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nb::sig(
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"def recv(shape: Sequence[int], dtype: Dtype, src: int, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array"),
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R"pbdoc(
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Recv an array with shape ``shape`` and dtype ``dtype`` from process
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with rank ``src``.
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Args:
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shape (Tuple[int]): The shape of the array we are receiving.
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dtype (Dtype): The data type of the array we are receiving.
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src (int): Rank of the source process in the group.
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group (Group): The group of processes that will participate in the
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recv. If set to ``None`` the global group is used. Default:
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``None``.
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stream (Stream, optional): Stream or device. Defaults to ``None``
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in which case the default stream of the default device is used.
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Returns:
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array: The array that was received from ``src``.
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)pbdoc");
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m.def(
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"recv_like",
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&distributed::recv_like,
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"x"_a,
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"src"_a,
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nb::kw_only(),
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"group"_a = nb::none(),
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"stream"_a = nb::none(),
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nb::sig(
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"def recv_like(x: array, src: int, *, group: Optional[Group] = None, stream: Union[None, Stream, Device] = None) -> array"),
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R"pbdoc(
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Recv an array with shape and type like ``x`` from process with rank
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``src``.
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It is equivalent to calling ``mx.distributed.recv(x.shape, x.dtype, src)``.
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Args:
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x (array): An array defining the shape and dtype of the array we are
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receiving.
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src (int): Rank of the source process in the group.
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group (Group): The group of processes that will participate in the
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recv. If set to ``None`` the global group is used. Default:
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``None``.
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stream (Stream, optional): Stream or device. Defaults to ``None``
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in which case the default stream of the default device is used.
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Returns:
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array: The array that was received from ``src``.
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)pbdoc");
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}
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@ -93,6 +93,23 @@ class TestDistributed(mlx_tests.MLXTestCase):
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self.assertTrue(mx.all(z == z_target))
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def test_send_recv(self):
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world = mx.distributed.init()
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pairs = world.split(world.rank() // 2)
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neighbor = (pairs.rank() + 1) % 2
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send = pairs.rank() == 0
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x = mx.ones(10)
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for i in range(10):
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if send:
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mx.eval(mx.distributed.send(2 * x, neighbor, group=pairs))
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else:
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x = mx.distributed.recv_like(x, neighbor, group=pairs)
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mx.eval(x)
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send = not send
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self.assertTrue(mx.all(x == (1024 if pairs.rank() == 0 else 512)))
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||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
Loading…
Reference in New Issue
Block a user