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#include <torch/csrc/autograd/input_buffer.h>
#include <ATen/CachedTensorUtils.h>
#include <ATen/LegacyBatchedTensorImpl.h>
#include <ATen/SparseCsrTensorUtils.h>
#include <ATen/TensorOperators.h>
#include <ATen/TensorSubclassLikeUtils.h>
#include <ATen/core/grad_mode.h>
#include <ATen/native/SparseTensorUtils.h>
#include <c10/core/DeviceGuard.h>
#include <c10/core/Event.h>
#include <c10/core/StreamGuard.h>
#include <optional>
#include <cstddef>
#include <utility>
#include <vector>
namespace torch::autograd {
namespace {
// look what you made me do >.<
// Divergent paths for per-Impl stream recording that leak implementation
// details of the impls should not be needed here.
// See https://github.com/pytorch/pytorch/issues/60306
// TODO: clean this up when https://github.com/pytorch/pytorch/issues/60306 is
// improved
void record_stream_any_impl(Variable& var, c10::Stream& stream) {
// NOLINTNEXTLINE(bugprone-unchecked-optional-access)
const auto guard = c10::impl::VirtualGuardImpl(device_of(var).value().type());
if (C10_UNLIKELY(at::isBatchedTensor(var))) {
auto* impl = at::maybeGetBatchedImpl(var);
if (impl) {
guard.recordDataPtrOnStream(impl->value().storage().data_ptr(), stream);
} else {
TORCH_INTERNAL_ASSERT(false, "Expected batched tensor");
}
} else {
switch (var.layout()) {
case c10::kSparseCsr:
case c10::kSparseCsc:
case c10::kSparseBsr:
case c10::kSparseBsc: {
auto* impl = at::sparse_csr::get_sparse_csr_impl(var);
guard.recordDataPtrOnStream(
impl->values().storage().data_ptr(), stream);
guard.recordDataPtrOnStream(
impl->compressed_indices().storage().data_ptr(), stream);
guard.recordDataPtrOnStream(
impl->plain_indices().storage().data_ptr(), stream);
break;
}
case c10::kSparse: {
auto* impl = at::sparse::get_sparse_impl(var);
guard.recordDataPtrOnStream(
impl->values().storage().data_ptr(), stream);
guard.recordDataPtrOnStream(
impl->indices().storage().data_ptr(), stream);
break;
}
case c10::kStrided:
guard.recordDataPtrOnStream(var.storage().data_ptr(), stream);
break;
default:
TORCH_INTERNAL_ASSERT(
false, "Unknown layout in record_stream_any_impl");
}
}
}
bool can_accumulate_inplace(const Variable& v) {
return (
// `v` is a "vanilla" Tensor
!(at::isTensorSubclassLike(v) || v._is_zerotensor() || v.is_nested()) &&
// with a favorable memory layout
v.is_non_overlapping_and_dense() &&
// and we hold the last reference
at::caching::adjusted_use_count(v) == 1 && v.has_storage() &&
v.storage().use_count() == 1);
}
} // anonymous namespace
static void accumulate(
std::vector<Variable>& buffer,
const size_t pos,
Variable&& var) {
TORCH_INTERNAL_ASSERT(pos < buffer.size());
auto& old_var = buffer[pos];
// If we hold the last reference to `old_var` AND its storage we will try to
// repurpose it to store the output. (Or, if `old_var` is sparse then `var`
// becomes the candidate output Tensor.) We only do this if:
// 1) GradMode is disabled since Autograd has special handling for inplace
// mutation which we don't want to trigger.
//
// 2) We hold the last reference.
// (Both `.use_count` and `.storage().use_count()` are one)
//
// 3) The candidate tensor is a contiguous, non-overlapping, dense, and
// otherwise stock standard Tensor.
//
// 4) The candidate is mutable. Currently only ZeroTensors are immutable.
//
// 5) The other Tensor is not a Tensor subclass (except sparse), since
// it's hard to predict the semantics of arbitrary subclass behavior.
// NOLINTNEXTLINE(bugprone-branch-clone)
if (at::GradMode::is_enabled()) {
buffer[pos] = old_var + var;
} else if (
// ATen doesn't route sparse additions correctly...
old_var.is_sparse() || old_var.is_sparse_csr()) {
if (can_accumulate_inplace(var)) {
buffer[pos] = var.add_(old_var);
} else {
buffer[pos] = var + old_var;
}
} else if (
can_accumulate_inplace(old_var) && !at::isTensorSubclassLike(var)) {
buffer[pos] = old_var.add_(var);
} else {
buffer[pos] = old_var + var;
}
}
void InputBuffer::add(
size_t pos,
Variable&& var,
const std::optional<c10::Stream>& opt_producer_stream,
const std::optional<c10::Stream>& opt_consumer_stream) {
TORCH_INTERNAL_ASSERT(pos < buffer.size());
if (!var.defined()) {
return;
}
// Switches to accumulate device
// The device (and stream) chosen for accumulation is:
// (1) var is not a CUDA/privateuse1 variable. Accumulation happens on var's
// device. (2) var is a CUDA/privateuse1 variable and it, the consumer, and
// the producer share the same device:
// (2a) Uses the consumer's stream as the accumulation stream
// (2b) Syncs the accumulation stream with the producer's stream (if
// different) (2c) Accumulates.
// (3) var is a CUDA/privateuse1 variable and it shares a device with the
// consumer but not the producer:
// (3a) Uses the consumer's stream as the accumulation stream
// (3b) Syncs the accumulation stream with the consumer device's default
// stream (3c) Accumulates.
// (4) var is a CUDA/privateuse1 variable and it shares a device with the
// producer but not the consumer:
// (4a) Uses the producer device's default stream as the accumulation
// stream (4b) Syncs the accumulation stream with the producer's
// stream (4c) Accumulates.
// (5) var is a CUDA/privateuse1 variable and it does not share a device with
// the consumer or producer.
// Accumulation happens on the var device's default stream.
auto const device = device_of(var);
TORCH_INTERNAL_ASSERT(device.has_value());
std::optional<c10::Stream> opt_accumulate_stream = std::nullopt;
const auto device_type = device->type();
if (device->is_cuda() || device->is_privateuseone()) {
const auto on_producer =
opt_producer_stream && device == opt_producer_stream->device();
const auto on_consumer =
opt_consumer_stream && device == opt_consumer_stream->device();
if (on_producer && on_consumer) {
// (2a)
opt_accumulate_stream = opt_consumer_stream;
if (opt_accumulate_stream != opt_producer_stream) {
// (2b)
auto event = c10::Event{device_type};
event.record(*opt_producer_stream);
opt_accumulate_stream->wait(event);
record_stream_any_impl(var, *opt_accumulate_stream);
}
} else {
std::optional<c10::Stream> opt_sync_stream = std::nullopt;
const auto guard = c10::impl::VirtualGuardImpl{device_type};
if (on_consumer && !on_producer) {
// (3a)
opt_accumulate_stream = opt_consumer_stream;
opt_sync_stream = guard.getDefaultStream(opt_consumer_stream->device());
} else if (on_producer && !on_consumer) {
// (4a)
opt_accumulate_stream =
guard.getDefaultStream(opt_producer_stream->device());
opt_sync_stream = opt_producer_stream;
} else {
// (5)
opt_accumulate_stream = guard.getDefaultStream(*device);
}
if (opt_sync_stream && (opt_accumulate_stream != opt_sync_stream)) {
// (3b), (4b)
c10::OptionalDeviceGuard device_guard{opt_sync_stream->device()};
auto event = c10::Event{device_type};
event.record(*opt_sync_stream);
opt_accumulate_stream->wait(event);
const auto guard = c10::impl::VirtualGuardImpl(device_type);
record_stream_any_impl(var, *opt_accumulate_stream);
}
}
}
auto& old_var = buffer[pos];
if (!old_var.defined()) {
buffer[pos] = std::move(var);
} else {
if (opt_accumulate_stream) {
c10::OptionalStreamGuard stream_guard{opt_accumulate_stream};
accumulate(buffer, pos, std::move(var));
} else {
// (1) non-CUDA/privateuse1 variable
// Accumulation happens on variable's device
c10::OptionalDeviceGuard device_guard{device};
accumulate(buffer, pos, std::move(var));
}
}
}
auto InputBuffer::variables(InputBuffer&& g) -> std::vector<Variable> {
std::vector<Variable> result = std::move(g.buffer);
return result;
}
} // namespace torch::autograd
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