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Add API usage logging for several other RPC APIs. (#67722)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67722

ghstack-source-id: 142259452

Test Plan: waitforbuildbot

Reviewed By: jaceyca, fduwjj

Differential Revision: D32118872

fbshipit-source-id: 041ab5601221b1846c56ce4bb63364bec9ad28b0
This commit is contained in:
Pritam Damania
2021-11-03 14:00:23 -07:00
committed by Facebook GitHub Bot
parent 5fd93fb5f8
commit 05e17e7ff6
5 changed files with 7 additions and 1 deletions
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1
torch/csrc/distributed/autograd/autograd.cpp
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@ -12,6 +12,7 @@ void backward(
int64_t context_id,
const variable_list& roots,
bool retain_graph) {
C10_LOG_API_USAGE_ONCE("torch.distributed.autograd.backward");
RECORD_FUNCTION(
kDistAutogradBackwardProfilingKey, std::vector<c10::IValue>());
try {

2
torch/csrc/distributed/rpc/py_rref.cpp
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@ -121,6 +121,7 @@ TypePtr tryInferTypeWithTypeHint(
PyRRef::PyRRef(c10::intrusive_ptr<RRef> rref)
: rref_(std::move(rref)), profilingFuture_(c10::nullopt) {
TORCH_CHECK(rref_, "PyRRef must not wrap nullptr");
C10_LOG_API_USAGE_ONCE("torch.distributed.rref");
}
PyRRef::PyRRef(const py::object& value, const py::object& type_hint)
@ -181,6 +182,7 @@ std::string PyRRef::ownerName() const {
}
py::object PyRRef::toHere(const float timeoutSeconds) const {
C10_LOG_API_USAGE_ONCE("torch.distributed.rref.to_here");
if (rref_->isOwner()) {
return localValue();
} else {

1
torch/distributed/nn/api/remote_module.py
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@ -208,6 +208,7 @@ class _RemoteModule(nn.Module):
>>> rpc.shutdown()
"""
super().__init__()
torch._C._log_api_usage_once("torch.distributed.nn.api.remote_module")
enable_moving_cpu_tensors_to_cuda = self._prepare_init(remote_device)

2
torch/distributed/optim/optimizer.py
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@ -1,6 +1,7 @@
from typing import List, Optional
import logging
import torch
import torch.distributed.rpc as rpc
import torch.jit as jit
import torch.nn as nn
@ -186,6 +187,7 @@ class DistributedOptimizer:
"""
def __init__(self, optimizer_class, params_rref, *args, **kwargs):
torch._C._log_api_usage_once("torch.distributed.optim.DistributedOptimizer")
per_worker_params_rref = defaultdict(list)
for param in params_rref:
per_worker_params_rref[param.owner()].append(param)

2
torch/distributed/rpc/__init__.py
View File

@ -110,7 +110,7 @@ if is_available():
:ref:`rpc-backends` for more information and find which options
are available.
"""
torch._C._log_api_usage_once("torch.distributed.init_rpc")
if backend is not None and not isinstance(
backend, backend_registry.BackendType
):