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android / platform / external / pytorch / 11ea09effc / . / c10 / cuda / CUDACachingAllocator.cpp
blob: b6368c41c285ed4837be6e444079fb609a177e80 [file] [log] [blame]
#include <c10/cuda/CUDACachingAllocator.h>
#include <c10/cuda/CUDAException.h>
#include <c10/cuda/CUDAFunctions.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/util/UniqueVoidPtr.h>
#include <c10/util/flat_hash_map.h>
#include <c10/util/irange.h>
#include <c10/util/llvmMathExtras.h>
#include <cuda_runtime_api.h>
#include <algorithm>
#include <bitset>
#include <deque>
#include <iterator>
#include <map>
#include <memory>
#include <mutex>
#include <regex>
#include <set>
#include <vector>
namespace c10 {
C10_DEFINE_REGISTRY(FreeCudaMemoryCallbacksRegistry, FreeMemoryCallback);
namespace cuda {
namespace CUDACachingAllocator {
//
// Yet another caching allocator for CUDA device allocations.
//
// - Allocations are associated with a stream. Once freed, blocks can be
// re-allocated on the same stream, but not on any other stream.
// - The allocator attempts to find the smallest cached block that will fit the
// requested size. If the block is larger than the requested size, it may be
// split. If no block is found, the allocator will delegate to cudaMalloc.
// - If the cudaMalloc fails, the allocator will attempt to free one cached
// block of sufficient size that is not split and retry the allocation.
// If this also fails, the allocator will attempt to free all cached blocks
// that are not split and retry the allocation.
// - Large (>1MB) and small allocations are stored in separate pools.
// Small requests are packed into 2MB buffers. Large requests will use the
// smallest available free block or allocate a new block using cudaMalloc.
// - To reduce fragmentation, requests between 1MB and 10MB will allocate and
// split a 20MB block, if no free block of sufficient size is available.
// - To further reduce fragmentation, blocks >= 200MB are not allowed to be
// split. These oversize cached blocks will still satisfy requests within
// 20MB of the oversize cached block size.
//
// With this allocator, allocations and frees should logically be considered
// "usages" of the memory segment associated with streams, just like kernel
// launches. The programmer must insert the proper synchronization if memory
// segments are used from multiple streams.
//
// The library provides a recordStream() function to help insert the correct
// synchronization when allocations are used on multiple streams. This will
// ensure that the block is not reused before each recorded stream completes
// work.
//
/**
* Note [Interaction with CUDA graph capture]
* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
* Graph capture performs a dry run of a region of execution, freezing all CUDA
* work (and virtual addresses used during that work) into a "graph." The graph
* may be "replayed" like a single giant kernel, with greatly reduced CPU
* overhead as well as modestly improved GPU performance.
*
* Because capture bakes in memory addresses, the memory used during capture
* must be available for the graph to use during replay. DeviceCachingAllocator
* assigns and frees memory eagerly and dynamically, so if we're not careful
* about managing graphs' memory, at replay time those memory addresses could be
* use by other tensors.
*
* To guarantee a graph's baked in addresses are safe to reuse in replay,
* DeviceAllocator satisfies allocations from a graph-private memory pool during
* capture, and doesn't begin cudaFreeing those addresses until the graph is
* destroyed.
*
* Within the private pool, allocations are freed and reassigned as usual during
* capture. Memory regions will be used in a consistent order during replay. So
* a private pool doesn't use memory more wastefully than the default pools
* during capture, but it does reserve its high-water mark of used memory away
* from the default pools as long as the capture(s) it served survive
* (regardless whether those captures are idle or replaying).
*
* CUDAGraph's requests for private pools are mediated by
* DeviceAllocator::notifyCaptureBegin, notifyCaptureEnd, and
* notifyCaptureDestroy.
*/
namespace {
using stream_set = ska::flat_hash_set<cuda::CUDAStream>;
constexpr size_t kMinBlockSize =
512; // all sizes are rounded to at least 512 bytes
constexpr size_t kSmallSize = 1048576; // largest "small" allocation is 1 MiB
constexpr size_t kSmallBuffer =
2097152; // "small" allocations are packed in 2 MiB blocks
constexpr size_t kLargeBuffer =
20971520; // "large" allocations may be packed in 20 MiB blocks
constexpr size_t kMinLargeAlloc =
10485760; // allocations between 1 and 10 MiB may use kLargeBuffer
constexpr size_t kRoundLarge = 2097152; // round up large allocations to 2 MiB
using StatTypes = std::array<bool, static_cast<size_t>(StatType::NUM_TYPES)>;
void update_stat(Stat& stat, int64_t amount) {
stat.current += amount;
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
stat.current >= 0,
"Negative tracked stat in CUDA allocator (likely logic error).");
stat.peak = std::max(stat.current, stat.peak);
if (amount > 0) {
stat.allocated += amount;
}
if (amount < 0) {
stat.freed += -amount;
}
}
void reset_accumulated_stat(Stat& stat) {
stat.allocated = 0;
stat.freed = 0;
}
void reset_peak_stat(Stat& stat) {
stat.peak = stat.current;
}
template <typename Func>
void for_each_selected_stat_type(const StatTypes& stat_types, Func f) {
for (const auto stat_type : c10::irange(stat_types.size())) {
if (stat_types[stat_type]) {
f(stat_type);
}
}
}
void update_stat_array(
StatArray& stat_array,
int64_t amount,
const StatTypes& stat_types) {
for_each_selected_stat_type(
stat_types, [&stat_array, amount](size_t stat_type) {
update_stat(stat_array[stat_type], amount);
});
}
struct Block;
struct PrivatePool;
typedef bool (*Comparison)(const Block*, const Block*);
struct BlockPool {
BlockPool(
Comparison comparator,
bool small,
PrivatePool* private_pool = nullptr)
: blocks(comparator), is_small(small), owner_PrivatePool(private_pool) {}
std::set<Block*, Comparison> blocks;
const bool is_small;
PrivatePool* owner_PrivatePool;
};
struct Block {
int device; // gpu
cudaStream_t stream; // allocation stream
stream_set stream_uses; // streams on which the block was used
size_t size; // block size in bytes
BlockPool* pool; // owning memory pool
void* ptr; // memory address
bool allocated; // in-use flag
Block* prev; // prev block if split from a larger allocation
Block* next; // next block if split from a larger allocation
int event_count; // number of outstanding CUDA events
int gc_count; // counter for prioritizing older / less useful blocks for
// garbage collection
Block(
int device,
cudaStream_t stream,
size_t size,
BlockPool* pool,
void* ptr)
: device(device),
stream(stream),
stream_uses(),
size(size),
pool(pool),
ptr(ptr),
allocated(0),
prev(nullptr),
next(nullptr),
event_count(0),
gc_count(0) {}
// constructor for search key
Block(int device, cudaStream_t stream, size_t size)
: device(device),
stream(stream),
stream_uses(),
size(size),
pool(nullptr),
ptr(nullptr),
allocated(0),
prev(nullptr),
next(nullptr),
event_count(0),
gc_count(0) {}
bool is_split() const {
return (prev != nullptr) || (next != nullptr);
}
};
static bool BlockComparator(const Block* a, const Block* b) {
if (a->stream != b->stream) {
return (uintptr_t)a->stream < (uintptr_t)b->stream;
}
if (a->size != b->size) {
return a->size < b->size;
}
return (uintptr_t)a->ptr < (uintptr_t)b->ptr;
}
static std::string format_size(uint64_t size) {
std::ostringstream os;
os.precision(2);
os << std::fixed;
if (size <= 1024) {
os << size << " bytes";
} else if (size <= 1048576) {
os << (size / 1024.0);
os << " KiB";
} else if (size <= 1073741824ULL) {
os << size / 1048576.0;
os << " MiB";
} else {
os << size / 1073741824.0;
os << " GiB";
}
return os.str();
}
struct AllocParams {
AllocParams(
int device,
size_t size,
cudaStream_t stream,
BlockPool* pool,
size_t alloc_size,
DeviceStats& stats)
: search_key(device, stream, size),
pool(pool),
alloc_size(alloc_size),
block(nullptr),
err(cudaSuccess) {}
int device() const {
return search_key.device;
}
cudaStream_t stream() const {
return search_key.stream;
}
size_t size() const {
return search_key.size;
}
Block search_key;
BlockPool* pool;
size_t alloc_size;
Block* block;
StatTypes stat_types = {false};
cudaError_t err;
};
// CUDA graphs helper
struct PrivatePool {
PrivatePool()
: use_count(1),
cudaMalloc_count(0),
large_blocks(BlockComparator, /*is_small=*/false, this),
small_blocks(BlockComparator, /*is_small=*/true, this) {}
PrivatePool(const PrivatePool&) = delete;
PrivatePool(PrivatePool&&) = delete;
PrivatePool& operator=(const PrivatePool&) = delete;
// Number of live graphs using this pool
int use_count;
// Number of unfreed cudaMallocs made for this pool. When use_count and
// cudaMalloc_count drop to zero, we can delete this PrivatePool from
// graph_pools.
int cudaMalloc_count;
// Instead of maintaining private BlockPools here, I could stuff all blocks
// (private or no) into the top-level large_blocks and small_blocks, and
// distinguish private blocks by adding a "pool id" check above the stream
// check in BlockComparator. BlockComparator is performance- critial though,
// I'd rather not add more logic to it.
BlockPool large_blocks;
BlockPool small_blocks;
};
struct MempoolIdHash {
std::size_t operator()(const MempoolId_t& mempool_id) const noexcept {
return mempool_id.first != 0 ? mempool_id.first : mempool_id.second;
}
};
cudaError_t cudaMallocMaybeCapturing(void** p, size_t size) {
#if defined(CUDA_VERSION) && CUDA_VERSION >= 11000
if (at::cuda::currentStreamCaptureStatusMayInitCtx() ==
at::cuda::CaptureStatus::None) {
#endif
return cudaMalloc(p, size);
#if defined(CUDA_VERSION) && CUDA_VERSION >= 11000
} else {
// It's ok to capture cudaMallocs, as long as we never cudaFree those
// addresses before replay.
// Capturing cudaMalloc behaves nicely: it gives the graph new VA,
// but is ignored (won't leakily allocate new memory) in replays.
at::cuda::CUDAStreamCaptureModeGuard g{cudaStreamCaptureModeRelaxed};
return cudaMalloc(p, size);
}
#endif
}
} // namespace
class CachingAllocatorConfig {
public:
static size_t max_split_size() {
return instance().m_max_split_size;
}
static double garbage_collection_threshold() {
return instance().m_garbage_collection_threshold;
}
// This is used to round-up allocation size to nearest power of 2 divisions.
// More description below in function roundup_power2_next_division
// As ane example, if we want 4 divisions between 2's power, this can be done
// using env variable: PYTORCH_CUDA_ALLOC_CONF=roundup_power2_divisions:4
static size_t roundup_power2_divisions() {
return instance().m_roundup_power2_divisions;
}
private:
static CachingAllocatorConfig& instance() {
static CachingAllocatorConfig* s_instance = ([]() {
auto inst = new CachingAllocatorConfig();
inst->parseArgs();
return inst;
})();
return *s_instance;
}
CachingAllocatorConfig()
: m_max_split_size(std::numeric_limits<size_t>::max()),
m_roundup_power2_divisions(0),
m_garbage_collection_threshold(0) {}
size_t m_max_split_size;
size_t m_roundup_power2_divisions;
double m_garbage_collection_threshold;
void parseArgs() {
const char* val = getenv("PYTORCH_CUDA_ALLOC_CONF");
if (val != NULL) {
const std::string config(val);
std::regex exp("[\\s,]+");
std::sregex_token_iterator it(config.begin(), config.end(), exp, -1);
std::sregex_token_iterator end;
std::vector<std::string> options(it, end);
for (auto option : options) {
std::regex exp2("[:]+");
std::sregex_token_iterator it2(option.begin(), option.end(), exp2, -1);
std::sregex_token_iterator end2;
std::vector<std::string> kv(it2, end2);
if (kv.size() >= 2) {
/* Maximum split size in MB. Limited to large size blocks */
if (kv[0].compare("max_split_size_mb") == 0) {
size_t val2 = stoi(kv[1]);
TORCH_CHECK(
val2 > kLargeBuffer / (1024 * 1024),
"CachingAllocator option max_split_size_mb too small, must be > ",
kLargeBuffer / (1024 * 1024),
"");
val2 = std::max(val2, kLargeBuffer / (1024 * 1024));
val2 = std::min(
val2, (std::numeric_limits<size_t>::max() / (1024 * 1024)));
m_max_split_size = val2 * 1024 * 1024;
} else if (kv[0].compare("roundup_power2_divisions") == 0) {
size_t val2 = stoi(kv[1]);
TORCH_CHECK(
llvm::isPowerOf2_64(val2),
"For roundups, the divisons has to be power of 2 ",
"");
m_roundup_power2_divisions = val2;
} else if (kv[0].compare("garbage_collection_threshold") == 0) {
/*
* Perform garbage collection of GPU memory blocks to avoid
* triggering expensive sync-and-reclaim-all operation. Upon setting
* the threshold (e.g., 0.8), the allocator will start reclaiming
* blocks if GPU memory capacity usage exceeds the threshold (i.e.,
* 80% of total memory).
* Values 0.0 and 1.0 are not allowed as they are less meaningful.
*/
double val2 = stod(kv[1]);
TORCH_CHECK(
val2 > 0,
"garbage_collect_threshold too small, set it 0.0~1.0",
"");
TORCH_CHECK(
val2 < 1.0,
"garbage_collect_threshold too big, set it 0.0~1.0",
"");
m_garbage_collection_threshold = val2;
} else {
TORCH_CHECK(false, "Unrecognized CachingAllocator option: ", kv[0]);
}
}
}
}
}
};
class DeviceCachingAllocator {
private:
// lock around all operations
mutable std::recursive_mutex mutex;
// device statistics
DeviceStats stats;
// unallocated cached blocks larger than 1 MB
BlockPool large_blocks;
// unallocated cached blocks 1 MB or smaller
BlockPool small_blocks;
// allocated or in use by a stream. Holds all active allocations,
// whether they came from graph_pools or one of the BlockPools above.
ska::flat_hash_set<Block*> active_blocks;
// captures_underway tracks if a capture might be underway on any stream.
// Most of the time it's zero, in which case malloc can avoid calling
// cudaStreamGetCaptureInfo in the hot path.
int captures_underway = 0;
// See free() for this thing's purpose
std::vector<Block*> needs_events_deferred_until_no_capture;
// outstanding cuda events
ska::flat_hash_map<
cuda::CUDAStream,
std::deque<std::pair<cudaEvent_t, Block*>>>
cuda_events;
// record used memory.
size_t total_allocated_memory = 0;
size_t allowed_memory_maximum = 0;
bool set_fraction = false;
// Members specific to CUDA graphs
// Private pools for CUDA graphs
ska::flat_hash_map<MempoolId_t, std::unique_ptr<PrivatePool>, MempoolIdHash>
graph_pools;
// Pools no longer referenced by any graph. Their BlockPools are eligible for
// free_blocks. Can't be a vector or deque because we might erase entries in
// any order. Could be an std::list, but we don't care much, access and
// insert/erase are rare.
ska::flat_hash_map<MempoolId_t, PrivatePool*, MempoolIdHash>
graph_pools_freeable;
// Maps a capturing stream to its assigned private pool,
// in case we want multiple captures to share the same pool
ska::flat_hash_map<CaptureId_t, MempoolId_t> capture_to_pool_map;
public:
DeviceCachingAllocator()
: large_blocks(BlockComparator, /*is_small=*/false),
small_blocks(BlockComparator, /*is_small=*/true) {
stats.max_split_size = CachingAllocatorConfig::max_split_size();
}
// All public methods (except the above) acquire the allocator mutex.
// Thus, do not call a public method from another public method.
Block* malloc(int device, size_t size, cudaStream_t stream) {
std::unique_lock<std::recursive_mutex> lock(mutex);
if (C10_LIKELY(captures_underway == 0)) {
// Processes end-of-life events for outstanding allocations used on
// multiple streams (checks if their GPU-side uses are complete and
// recycles their memory if so)
//
// Q. Why skip process_events if a capture might be underway?
// A. process_events involves cudaEventQueries, illegal during CUDA graph
// capture.
// Dumb simple solution: defer reclaiming these allocations until after
// capture. Cross-stream memory use is uncommon, so the deferral's
// effect on memory use during capture should be small.
process_events();
}
size = round_size(size);
auto& pool = get_pool(size, stream);
const size_t alloc_size = get_allocation_size(size);
AllocParams params(device, size, stream, &pool, alloc_size, stats);
params.stat_types[static_cast<size_t>(StatType::AGGREGATE)] = true;
params.stat_types[static_cast<size_t>(get_stat_type_for_pool(pool))] = true;
// First, try to get a block from the existing pool.
bool block_found =
// Search pool
get_free_block(params)
// Trigger callbacks and retry search
|| (trigger_free_memory_callbacks(params) && get_free_block(params));
// Can't reuse an existing block; try to get a new one.
if (!block_found) {
// Do garbage collection if the flag is set.
if (C10_UNLIKELY(
CachingAllocatorConfig::garbage_collection_threshold() > 0.0)) {
garbage_collect_cached_blocks();
}
// Attempt allocate
block_found = alloc_block(params, false)
// Free enough available cached blocks to satisfy alloc and retry
// alloc.
|| (release_available_cached_blocks(params) &&
alloc_block(params, false))
// Free all non-split cached blocks and retry alloc.
|| (release_cached_blocks() && alloc_block(params, true));
}
if (!block_found) {
// For any error code other than cudaErrorMemoryAllocation,
// alloc_block should have thrown an exception already.
TORCH_INTERNAL_ASSERT(params.err == cudaErrorMemoryAllocation);
size_t device_free;
size_t device_total;
C10_CUDA_CHECK(cudaMemGetInfo(&device_free, &device_total));
std::string allowed_info;
if (set_fraction) {
allowed_info = format_size(allowed_memory_maximum) + " allowed; ";
}
stats.num_ooms += 1;
// "total capacity": total global memory on GPU
// "allowed": memory is allowed to use, which set by fraction.
// "already allocated": memory allocated by the program using the
// caching allocator
// "free": free memory as reported by the CUDA API
// "cached": memory held by the allocator but not used by the program
//
// The "allocated" amount does not include memory allocated outside
// of the caching allocator, such as memory allocated by other programs
// or memory held by the driver.
//
// The sum of "allocated" + "free" + "cached" may be less than the
// total capacity due to memory held by the driver and usage by other
// programs.
//
// Note that at this point free_cached_blocks has already returned all
// possible "cached" memory to the driver. The only remaining "cached"
// memory is split from a larger block that is partially in-use.
TORCH_CHECK_WITH(
CUDAOutOfMemoryError,
false,
"CUDA out of memory. Tried to allocate ",
format_size(alloc_size),
" (GPU ",
device,
"; ",
format_size(device_total),
" total capacity; ",
format_size(
stats.allocated_bytes[static_cast<size_t>(StatType::AGGREGATE)]
.current),
" already allocated; ",
format_size(device_free),
" free; ",
allowed_info,
format_size(
stats.reserved_bytes[static_cast<size_t>(StatType::AGGREGATE)]
.current),
" reserved in total by PyTorch)",
" If reserved memory is >> allocated memory try setting max_split_size_mb to avoid"
" fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
"");
}
TORCH_INTERNAL_ASSERT(
params.err == cudaSuccess && params.block != nullptr &&
params.block->ptr != nullptr);
Block* block = params.block;
Block* remaining = nullptr;
const bool already_split = block->is_split();
if (should_split(block, size)) {
remaining = block;
block = new Block(device, stream, size, &pool, block->ptr);
block->prev = remaining->prev;
if (block->prev) {
block->prev->next = block;
}
block->next = remaining;
remaining->prev = block;
remaining->ptr = static_cast<char*>(remaining->ptr) + size;
remaining->size -= size;
bool inserted = pool.blocks.insert(remaining).second;
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(inserted);
if (already_split) {
// An already-split inactive block is being shrunk by size bytes.
update_stat_array(
stats.inactive_split_bytes, -block->size, params.stat_types);
} else {
// A new split inactive block is being created from a previously unsplit
// block, size remaining->size bytes.
for_each_selected_stat_type(params.stat_types, [&](size_t stat_type) {
update_stat(stats.inactive_split_bytes[stat_type], remaining->size);
update_stat(stats.inactive_split[stat_type], 1);
});
}
} else if (already_split) {
// An already-split block is becoming active
for_each_selected_stat_type(params.stat_types, [&](size_t stat_type) {
update_stat(stats.inactive_split_bytes[stat_type], -block->size);
update_stat(stats.inactive_split[stat_type], -1);
});
}
block->allocated = true;
bool inserted = active_blocks.insert(block).second;
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(inserted);
for_each_selected_stat_type(params.stat_types, [&](size_t stat_type) {
update_stat(stats.allocation[stat_type], 1);
update_stat(stats.allocated_bytes[stat_type], block->size);
update_stat(stats.active[stat_type], 1);
update_stat(stats.active_bytes[stat_type], block->size);
});
if (block->size >= CachingAllocatorConfig::max_split_size())
update_stat(stats.oversize_allocations, 1);
c10::reportMemoryUsageToProfiler(
block->ptr,
block->size,
stats.allocated_bytes[static_cast<size_t>(StatType::AGGREGATE)].current,
stats.reserved_bytes[static_cast<size_t>(StatType::AGGREGATE)].current,
c10::Device(c10::DeviceType::CUDA, device));
return block;
}
void free(Block* block) {
std::lock_guard<std::recursive_mutex> lock(mutex);
block->allocated = false;
// following logic might modifying underlaying Block, causing the size
// changed. We store ahead for reporting
auto orig_block_ptr = block->ptr;
auto orig_block_size = block->size;
StatTypes stat_types = {false};
stat_types[static_cast<size_t>(StatType::AGGREGATE)] = true;
stat_types[static_cast<size_t>(get_stat_type_for_pool(*(block->pool)))] =
true;
for_each_selected_stat_type(stat_types, [&](size_t stat_type) {
update_stat(stats.allocation[stat_type], -1);
update_stat(stats.allocated_bytes[stat_type], -block->size);
});
if (block->size >= CachingAllocatorConfig::max_split_size())
update_stat(stats.oversize_allocations, -1);
if (!block->stream_uses.empty()) {
if (C10_UNLIKELY(captures_underway)) {
// It's forbidden to cudaEventQuery an event recorded during CUDA graph
// capture. We conservatively defer recording end-of-life events until
// the next call to process_events() (which won't happen until no
// captures are underway)
needs_events_deferred_until_no_capture.push_back(block);
} else {
insert_events(block);
}
} else {
free_block(block);
}
c10::reportMemoryUsageToProfiler(
orig_block_ptr,
-orig_block_size,
stats.allocated_bytes[static_cast<size_t>(StatType::AGGREGATE)].current,
stats.reserved_bytes[static_cast<size_t>(StatType::AGGREGATE)].current,
c10::Device(c10::DeviceType::CUDA, block->device));
}
void* getBaseAllocation(Block* block, size_t* outSize) {
std::lock_guard<std::recursive_mutex> lock(mutex);
while (block->prev) {
block = block->prev;
}
void* basePtr = block->ptr;
if (outSize) {
size_t size = 0;
while (block) {
size += block->size;
block = block->next;
}
*outSize = size;
}
return basePtr;
}
void recordStream(Block* block, cuda::CUDAStream stream) {
std::lock_guard<std::recursive_mutex> lock(mutex);
if (stream.stream() == block->stream) {
// ignore uses on the allocation stream, since those don't require any
// special synchronization
return;
}
block->stream_uses.insert(stream);
}
/** set memory fraction to limit maximum allocated memory **/
void setMemoryFraction(double fraction) {
size_t device_free;
size_t device_total;
C10_CUDA_CHECK(cudaMemGetInfo(&device_free, &device_total));
allowed_memory_maximum = static_cast<size_t>(fraction * device_total);
set_fraction = true;
}
/** returns cached blocks to the system allocator **/
void emptyCache() {
std::lock_guard<std::recursive_mutex> lock(mutex);
release_cached_blocks();
}
/** Retrieves info (total size + largest block) of the memory cache **/
void cacheInfo(size_t* total, size_t* largest) {
std::lock_guard<std::recursive_mutex> lock(mutex);
if (*largest ==
0) { // make an initial guess if a zero *largest is passed in
size_t tmp_bytes;
cudaMemGetInfo(
largest, // Use free memory as an optimistic initial guess of *largest
&tmp_bytes);
}
cache_info_aux(large_blocks, total, largest);
cache_info_aux(small_blocks, total, largest);
for (const auto& gp : graph_pools) {
cache_info_aux(gp.second->large_blocks, total, largest);
cache_info_aux(gp.second->small_blocks, total, largest);
}
}
/** Returns a copy of the memory allocator stats **/
DeviceStats getStats() {
std::lock_guard<std::recursive_mutex> lock(mutex);
return stats;
}
/** Resets the historical accumulation stats for the device **/
void resetAccumulatedStats() {
std::lock_guard<std::recursive_mutex> lock(mutex);
for (const auto statType :
c10::irange(static_cast<size_t>(StatType::NUM_TYPES))) {
reset_accumulated_stat(stats.allocation[statType]);
reset_accumulated_stat(stats.segment[statType]);
reset_accumulated_stat(stats.active[statType]);
reset_accumulated_stat(stats.inactive_split[statType]);
reset_accumulated_stat(stats.allocated_bytes[statType]);
reset_accumulated_stat(stats.reserved_bytes[statType]);
reset_accumulated_stat(stats.active_bytes[statType]);
reset_accumulated_stat(stats.inactive_split_bytes[statType]);
}
stats.num_alloc_retries = 0;
stats.num_ooms = 0;
reset_accumulated_stat(stats.oversize_allocations);
reset_accumulated_stat(stats.oversize_segments);
}
/** Resets the historical peak stats for the device **/
void resetPeakStats() {
std::lock_guard<std::recursive_mutex> lock(mutex);
for (const auto statType :
c10::irange(static_cast<size_t>(StatType::NUM_TYPES))) {
reset_peak_stat(stats.allocation[statType]);
reset_peak_stat(stats.segment[statType]);
reset_peak_stat(stats.active[statType]);
reset_peak_stat(stats.inactive_split[statType]);
reset_peak_stat(stats.allocated_bytes[statType]);
reset_peak_stat(stats.reserved_bytes[statType]);
reset_peak_stat(stats.active_bytes[statType]);
reset_peak_stat(stats.inactive_split_bytes[statType]);
}
reset_peak_stat(stats.oversize_allocations);
reset_peak_stat(stats.oversize_segments);
}
/** Dump a complete snapshot of the memory held by the allocator. Potentially
* VERY expensive. **/
std::vector<SegmentInfo> snapshot() const {
std::lock_guard<std::recursive_mutex> lock(mutex);
std::vector<SegmentInfo> result;
const auto all_blocks = get_all_blocks();
for (const Block* const head_block : all_blocks) {
if (head_block->prev != nullptr) {
continue;
}
result.emplace_back();
SegmentInfo& segment_info = result.back();
segment_info.device = head_block->device;
segment_info.address = reinterpret_cast<int64_t>(head_block->ptr);
segment_info.is_large = (!head_block->pool->is_small);
const Block* block = head_block;
while (block != nullptr) {
segment_info.blocks.emplace_back();
BlockInfo& block_info = segment_info.blocks.back();
block_info.size = block->size;
block_info.allocated = block->allocated;
block_info.active = block->allocated || (block->event_count > 0) ||
!block->stream_uses.empty();
segment_info.total_size += block_info.size;
if (block_info.allocated) {
segment_info.allocated_size += block_info.size;
}
if (block_info.active) {
segment_info.active_size += block_info.size;
}
block = block->next;
}
}
std::sort(
result.begin(),
result.end(),
[](const SegmentInfo& a, const SegmentInfo& b) {
return a.address < b.address;
});
return result;
}
// This function takes the size and number of divisions argument and rounds
// up the size argument for the nearest power-of-2 division.
// For example, if we need to round-up 1200 and number of divisions is 4,
// the size 1200 lies between 1024 and 2048 and if we do 4 divisions between
// them, the values are 1024, 1280, 1536, and 1792. So the function will
// return 1280 as the nearest ceiling of power-2 divison.
static size_t roundup_power2_next_division(size_t size, size_t divisions) {
if (C10_UNLIKELY(size <= 4 || divisions <= 1)) {
return size;
}
if (llvm::isPowerOf2_64(size)) {
return size;
}
// divide the space between these 2's power into equal divisions
// If division is zero, return the power-of-2 ceiling.
size_t power2_floor = llvm::PowerOf2Floor(size);
size_t power2_divison =
power2_floor >> (63 - llvm::countLeadingZeros(divisions));
if (C10_UNLIKELY(power2_divison == 0)) {
return (power2_floor << 1);
}
size_t round_size_floor = size & (~(power2_divison - 1));
return (round_size_floor == size) ? size
: round_size_floor + power2_divison;
}
static size_t round_size(size_t size) {
if (size < kMinBlockSize) {
return kMinBlockSize;
} else {
auto divisions = CachingAllocatorConfig::roundup_power2_divisions();
if (divisions > 0 && size > (kMinBlockSize * divisions)) {
return roundup_power2_next_division(size, divisions);
} else {
return kMinBlockSize * ((size + kMinBlockSize - 1) / kMinBlockSize);
}
}
}
// See Note [Interaction with CUDA graph capture]
// Called by CUDAGraph::capture_begin
void notifyCaptureBegin(CaptureId_t graph_id, MempoolId_t mempool_id) {
std::lock_guard<std::recursive_mutex> lock(mutex);
captures_underway++;
auto it = graph_pools.find(mempool_id);
if (it == graph_pools.end()) {
// mempool_id does not reference an existing pool. Make a new pool for
// this capture.
graph_pools.emplace(mempool_id, std::make_unique<PrivatePool>());
} else {
// mempool_id references an existing pool, which the current capture will
// share. Check this pool is live (at least one other capture already
// references it).
TORCH_INTERNAL_ASSERT(it->second->use_count > 0);
it->second->use_count++;
}
// Maps this graph_id to mempool_id and makes sure this graph_id wasn't
// somehow assigned a mempool_id already. Keeps essential effect (insert)
// out of macro.
bool inserted = capture_to_pool_map.insert({graph_id, mempool_id}).second;
TORCH_INTERNAL_ASSERT(inserted);
}
// Called by CUDAGraph::capture_end
void notifyCaptureEnd(CaptureId_t graph_id) {
std::lock_guard<std::recursive_mutex> lock(mutex);
captures_underway--;
auto it = capture_to_pool_map.find(graph_id);
TORCH_INTERNAL_ASSERT(it != capture_to_pool_map.end());
capture_to_pool_map.erase(it);
}
// Called by CUDAGraph::reset
void notifyCaptureDestroy(MempoolId_t mempool_id) {
std::lock_guard<std::recursive_mutex> lock(mutex);
// The instantiated cudaGraphExec_t has been destroyed. We can't blindly
// delete and cudaFree the mempool its capture used, because
// 1. other graph(s) might share the same pool
// 2. the user might still hold references to output tensors allocated
// during capture.
// To handle 1 and 2, we track the number of graphs using this particular
// mempool. When the count reaches 0, we tell free_cached_blocks it may now
// cudaFree blocks from this graph's pool when it discovers they're unused
// (unsplit).
auto it = graph_pools.find(mempool_id);
TORCH_INTERNAL_ASSERT(it != graph_pools.end());
auto uc = --(it->second->use_count);
TORCH_INTERNAL_ASSERT(uc >= 0);
if (uc == 0) {
// Allows free_cached_blocks to begin cudaFreeing this pool's memory,
// and makes sure this pool wasn't somehow made freeable already.
bool inserted =
graph_pools_freeable.insert({mempool_id, it->second.get()}).second;
TORCH_INTERNAL_ASSERT(inserted);
}
}
private:
// All private methods do not acquire the allocator mutex.
std::vector<const Block*> get_all_blocks() const {
std::vector<const Block*> blocks;
blocks.insert(
blocks.end(), small_blocks.blocks.begin(), small_blocks.blocks.end());
blocks.insert(
blocks.end(), large_blocks.blocks.begin(), large_blocks.blocks.end());
for (const auto& gp : graph_pools) {
blocks.insert(
blocks.end(),
gp.second->small_blocks.blocks.begin(),
gp.second->small_blocks.blocks.end());
blocks.insert(
blocks.end(),
gp.second->large_blocks.blocks.begin(),
gp.second->large_blocks.blocks.end());
}
blocks.insert(blocks.end(), active_blocks.begin(), active_blocks.end());
return blocks;
}
/** moves a block into a pool of cached free blocks */
void free_block(Block* block) {
TORCH_INTERNAL_ASSERT(
!block->allocated && block->event_count == 0 &&
block->stream_uses.empty());
size_t original_block_size = block->size;
auto& pool = *block->pool;
int64_t net_change_inactive_split_blocks = 0;
int64_t net_change_inactive_split_size = 0;
const std::array<Block*, 2> merge_candidates = {block->prev, block->next};
for (Block* merge_candidate : merge_candidates) {
const int64_t subsumed_size =
try_merge_blocks(block, merge_candidate, pool);
if (subsumed_size > 0) {
net_change_inactive_split_blocks -= 1;
net_change_inactive_split_size -= subsumed_size;
}
}
active_blocks.erase(block);
// Makes sure the Block* isn't already present in the pool we're freeing it
// back into.
bool inserted = pool.blocks.insert(block).second;
TORCH_INTERNAL_ASSERT(inserted);
if (block->is_split()) {
net_change_inactive_split_blocks += 1;
net_change_inactive_split_size += block->size;
}
StatTypes stat_types = {false};
stat_types[static_cast<size_t>(StatType::AGGREGATE)] = true;
stat_types[static_cast<size_t>(get_stat_type_for_pool(pool))] = true;
for_each_selected_stat_type(stat_types, [&](size_t stat_type) {
update_stat(
stats.inactive_split[stat_type], net_change_inactive_split_blocks);
update_stat(
stats.inactive_split_bytes[stat_type],
net_change_inactive_split_size);
update_stat(stats.active[stat_type], -1);
update_stat(stats.active_bytes[stat_type], -original_block_size);
});
}
/** combine previously split blocks. returns the size of the subsumed block,
* or 0 on failure. */
size_t try_merge_blocks(Block* dst, Block* src, BlockPool& pool) {
if (!src || src->allocated || src->event_count > 0 ||
!src->stream_uses.empty()) {
return 0;
}
AT_ASSERT(dst->is_split() && src->is_split());
if (dst->prev == src) {
dst->ptr = src->ptr;
dst->prev = src->prev;
if (dst->prev) {
dst->prev->next = dst;
}
} else {
dst->next = src->next;
if (dst->next) {
dst->next->prev = dst;
}
}
const size_t subsumed_size = src->size;
dst->size += subsumed_size;
auto erased = pool.blocks.erase(src);
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(erased == 1);
delete src;
return subsumed_size;
}
BlockPool& get_pool(size_t size, cudaStream_t stream) {
#if defined(CUDA_VERSION) && CUDA_VERSION >= 11000
// captures_underway is a conservative guess that the current stream may be
// capturing. It's only > 0 if some thread has begun and not yet ended a
// capture, so it's usually 0, and we can short-circuit
// cudaStreamCaptureStatus (which does a TLS lookup).
if (C10_UNLIKELY(captures_underway)) {
CaptureId_t id;
cudaStreamCaptureStatus status;
C10_CUDA_CHECK(cudaStreamGetCaptureInfo(stream, &status, &id));
if (status != cudaStreamCaptureStatus::cudaStreamCaptureStatusNone) {
TORCH_INTERNAL_ASSERT(
status !=
cudaStreamCaptureStatus::cudaStreamCaptureStatusInvalidated);
// Retrieves the private pool assigned to this capture.
auto it0 = capture_to_pool_map.find(id);
TORCH_INTERNAL_ASSERT(it0 != capture_to_pool_map.end());
auto it1 = graph_pools.find(it0->second);
TORCH_INTERNAL_ASSERT(it1 != graph_pools.end());
if (size <= kSmallSize) {
return it1->second->small_blocks;
} else {
return it1->second->large_blocks;
}
}
}
#endif
if (size <= kSmallSize) {
return small_blocks;
} else {
return large_blocks;
}
}
StatType get_stat_type_for_pool(const BlockPool& pool) {
return pool.is_small ? StatType::SMALL_POOL : StatType::LARGE_POOL;
}
bool should_split(const Block* block, size_t size) {
size_t remaining = block->size - size;
if (block->pool->is_small) {
return remaining >= kMinBlockSize;
} else {
return (size < CachingAllocatorConfig::max_split_size()) &&
(remaining > kSmallSize);
}
}
static size_t get_allocation_size(size_t size) {
if (size <= kSmallSize) {
return kSmallBuffer;
} else if (size < kMinLargeAlloc) {
return kLargeBuffer;
} else {
return kRoundLarge * ((size + kRoundLarge - 1) / kRoundLarge);
}
}
bool get_free_block(AllocParams& p) {
BlockPool& pool = *p.pool;
if (C10_UNLIKELY(
CachingAllocatorConfig::garbage_collection_threshold() > 0.0)) {
// Track block reuse interval only when garbage collection is enabled.
for (auto& b : pool.blocks) {
++b->gc_count;
}
}
auto it = pool.blocks.lower_bound(&p.search_key);
if (it == pool.blocks.end() || (*it)->stream != p.stream())
return false;
// Do not return an oversized block for a large request
if ((p.size() < CachingAllocatorConfig::max_split_size()) &&
((*it)->size >= CachingAllocatorConfig::max_split_size()))
return false;
// Allow oversized block size to be rounded up but within a limit
if ((p.size() >= CachingAllocatorConfig::max_split_size()) &&
((*it)->size >= p.size() + kLargeBuffer))
return false;
p.block = *it;
(*it)->gc_count = 0; // Denote this block has been used
pool.blocks.erase(it);
return true;
}
bool trigger_free_memory_callbacks(AllocParams& p) {
bool freed_memory = false;
for (const auto& name : FreeCudaMemoryCallbacksRegistry()->Keys()) {
freed_memory |=
FreeCudaMemoryCallbacksRegistry()->Create(name)->Execute();
}
return freed_memory;
}
void garbage_collect_cached_blocks() {
// Free unused cached blocks to reclaim GPU memory.
// Unlike release_cached_blocks(), this does not enforce synchronization and
// therefore should be of less overheads.
size_t gc_threshold = static_cast<size_t>(
CachingAllocatorConfig::garbage_collection_threshold() *
allowed_memory_maximum);
// No need to trigger GC yet
if (total_allocated_memory <= gc_threshold) {
return;
}
const auto target_size = total_allocated_memory - gc_threshold;
size_t gc_reclaimed = 0;
// Calculate the total age of the free-able blocks. We'll use it later to
// get "avg age" threshold.
double total_age = 0.0;
int freeable_block_count = 0;
for (auto& b : large_blocks.blocks) {
if (!b->is_split()) {
total_age += b->gc_count;
++freeable_block_count;
}
}
// No free-able blocks?
if (freeable_block_count == 0) {
return;
}
// Repeat GC until we reach reclaim > target size.
bool block_freed = true;
while (gc_reclaimed < target_size && block_freed == true &&
freeable_block_count > 0) {
// Free blocks exceeding this age threshold first.
double age_threshold = total_age / freeable_block_count;
// Stop iteration if we can no longer free a block.
block_freed = false;
// Free blocks of > avg age. Don't stop upon reaching the target_size,
// we don't want this GC to be triggered frequently.
auto it = large_blocks.blocks.begin();
while (it != large_blocks.blocks.end()) {
Block* block = *it;
++it;
if (!block->is_split() && block->gc_count >= age_threshold) {
block_freed = true;
gc_reclaimed += block->size;
total_age -= block->gc_count; // Decrement the age
freeable_block_count--; // One less block that can be freed
release_block(block);
}
}
}
}
bool alloc_block(AllocParams& p, bool isRetry) {
// Defensively checks for preexisting CUDA error state.
C10_CUDA_CHECK(cudaGetLastError());
size_t size = p.alloc_size;
void* ptr;
if (isRetry) {
stats.num_alloc_retries += 1;
}
if (set_fraction &&
total_allocated_memory + size > allowed_memory_maximum) {
p.err = cudaErrorMemoryAllocation;
return false;
} else {
p.err = cudaMallocMaybeCapturing(&ptr, size);
if (p.err != cudaSuccess) {
if (p.err == cudaErrorMemoryAllocation) {
// If this is the first attempt (!isRetry), we can forgive and clear
// CUDA's
// internal error state.
// If this is the second attempt (isRetry), malloc's TORCH_CHECK_WITH
// will take
// over to throw a helpful exception. The user can choose to catch
// the exception, free some stuff in their script, and attempt their
// allocation again. In this case, we can also forgive and clear
// CUDA's internal error state.
cudaGetLastError();
} else {
// If the error's unrelated to memory allocation, we should throw
// immediately.
C10_CUDA_CHECK(p.err);
}
return false;
}
}
if (p.pool->owner_PrivatePool) {
// The block is for a CUDA graph's PrivatePool.
p.pool->owner_PrivatePool->cudaMalloc_count++;
}
total_allocated_memory += size;
p.block = new Block(p.device(), p.stream(), size, p.pool, (char*)ptr);
for_each_selected_stat_type(p.stat_types, [&](size_t stat_type) {
update_stat(stats.segment[stat_type], 1);
update_stat(stats.reserved_bytes[stat_type], size);
});
if (size >= CachingAllocatorConfig::max_split_size())
update_stat(stats.oversize_segments, 1);
// p.block came from new, not cudaMalloc. It should not be nullptr here.
TORCH_INTERNAL_ASSERT(p.block != nullptr && p.block->ptr != nullptr);
return true;
}
/** Free one or more oversize blocks to the system allocator. But only enough
* **/
/** to satisfy the target size **/
bool release_available_cached_blocks(const AllocParams& p) {
if (CachingAllocatorConfig::max_split_size() ==
std::numeric_limits<size_t>::max())
return false;
BlockPool& pool = *p.pool;
Block key = p.search_key;
key.size = (key.size < CachingAllocatorConfig::max_split_size())
? CachingAllocatorConfig::max_split_size()
: key.size;
auto it = pool.blocks.lower_bound(&key);
if (it == pool.blocks.end() || (*it)->stream != p.stream()) {
// No single block is large enough; free multiple oversize blocks,
// starting with the largest
if (it == pool.blocks.begin())
return false;
size_t totalReleased = 0;
--it; // Back up one item. Now on the largest block for the correct
// stream
while ((totalReleased < key.size) &&
((*it)->size >= CachingAllocatorConfig::max_split_size()) &&
((*it)->stream == p.stream())) {
auto cur = it;
totalReleased += (*it)->size;
if (it != pool.blocks.begin()) {
--it;
release_block(*cur);
} else {
release_block(*cur);
break;
}
}
if (totalReleased < key.size)
return false;
} else {
release_block(*it);
}
return true;
}
bool release_cached_blocks() {
// First ensure that all blocks that can't currently be allocated due to
// outstanding events are returned to the pool.
synchronize_and_free_events();
// Free all non-split cached blocks to system allocator
release_blocks(large_blocks);
release_blocks(small_blocks);
for (auto it = graph_pools_freeable.begin();
it != graph_pools_freeable.end();) {
// See notifyCaptureDestroy for the strategy here.
TORCH_INTERNAL_ASSERT(it->second->use_count == 0);
release_blocks(it->second->small_blocks);
release_blocks(it->second->large_blocks);
if (it->second->cudaMalloc_count == 0) {
auto erase_count = graph_pools.erase(it->first);
TORCH_INTERNAL_ASSERT(erase_count == 1);
it = graph_pools_freeable.erase(it);
} else {
++it;
}
}
return true;
}
void release_block(Block* block) {
C10_CUDA_CHECK(cudaFree((void*)block->ptr));
total_allocated_memory -= block->size;
auto* pool = block->pool;
if (pool->owner_PrivatePool) {
// The cudaFreed block belonged to a CUDA graph's PrivatePool.
TORCH_INTERNAL_ASSERT(pool->owner_PrivatePool->cudaMalloc_count > 0);
pool->owner_PrivatePool->cudaMalloc_count--;
}
StatTypes stat_types = {false};
stat_types[static_cast<size_t>(StatType::AGGREGATE)] = true;
stat_types[static_cast<size_t>(get_stat_type_for_pool(*pool))] = true;
for_each_selected_stat_type(stat_types, [&](size_t stat_type) {
update_stat(stats.segment[stat_type], -1);
update_stat(stats.reserved_bytes[stat_type], -block->size);
});
if (block->size >= CachingAllocatorConfig::max_split_size())
update_stat(stats.oversize_segments, -1);
pool->blocks.erase(block);
delete block;
}
void release_blocks(BlockPool& pool) {
// Frees all non-split blocks
auto it = pool.blocks.begin();
while (it != pool.blocks.end()) {
Block* block = *it;
++it;
if (!block->prev && !block->next) {
release_block(block);
}
}
}
cudaEvent_t create_event_internal() {
cudaEvent_t event;
C10_CUDA_CHECK(cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
return event;
}
void free_event_internal(cudaEvent_t event) {
C10_CUDA_CHECK(cudaEventDestroy(event));
}
void synchronize_and_free_events() {
// Synchronize on outstanding events and then free associated blocks.
// This function syncs, so capture should not be underway. Might as well
// make sure capture-deferred end of life events get processed too.
TORCH_INTERNAL_ASSERT(captures_underway == 0);
insert_events_deferred_until_no_capture();
for (auto& st : cuda_events) {
for (auto& e : st.second) {
cudaEvent_t event = e.first;
Block* block = e.second;
C10_CUDA_CHECK(cudaEventSynchronize(event));
free_event_internal(event);
block->event_count--;
if (block->event_count == 0) {
free_block(block);
}
}
}
cuda_events.clear();
}
void insert_events(Block* block) {
int prev_device;
C10_CUDA_CHECK(cudaGetDevice(&prev_device));
stream_set streams(std::move(block->stream_uses));
AT_ASSERT(block->stream_uses.empty());
for (auto& stream : streams) {
C10_CUDA_CHECK(cudaSetDevice(stream.device_index()));
cudaEvent_t event = create_event_internal();
C10_CUDA_CHECK(cudaEventRecord(event, stream.stream()));
block->event_count++;
cuda_events[stream].emplace_back(event, block);
}
C10_CUDA_CHECK(cudaSetDevice(prev_device));
}
void insert_events_deferred_until_no_capture() {
if (C10_UNLIKELY(needs_events_deferred_until_no_capture.size() > 0)) {
for (auto* block : needs_events_deferred_until_no_capture) {
TORCH_INTERNAL_ASSERT(!block->stream_uses.empty());
insert_events(block);
}
needs_events_deferred_until_no_capture.clear();
}
}
void process_events() {
insert_events_deferred_until_no_capture();
// Process outstanding cudaEvents. Events that are completed are
// removed from the queue, and the 'event_count' for the
// corresponding allocation is decremented. We maintain a separate
// list of events per stream to avoid head-of-line delays if one
// or more streams has long-running operations.
for (auto it = cuda_events.begin(); it != cuda_events.end();) {
while (!it->second.empty()) {
auto& e = it->second.front();
cudaEvent_t event = e.first;
Block* block = e.second;
cudaError_t err = cudaEventQuery(event);
if (err == cudaErrorNotReady) {
// ignore and clear the error if not ready
cudaGetLastError();
break;
} else if (err != cudaSuccess) {
C10_CUDA_CHECK(err);
}
free_event_internal(event);
block->event_count--;
if (block->event_count == 0) {
free_block(block);
}
it->second.pop_front();
}
if (it->second.empty()) {
it = cuda_events.erase(it);
} else {
it++;
}
}
}
// Accumulates sizes of all memory blocks for given device in given pool
void cache_info_aux(const BlockPool& pool, size_t* total, size_t* largest) {
for (const auto& block : pool.blocks) {
const auto blocksize = block->size;
*total += blocksize;
if (blocksize > *largest) {
*largest = blocksize;
}
}
}
};
class THCCachingAllocator {
private:
std::mutex mutex;
// allocated blocks by device pointer
ska::flat_hash_map<void*, Block*> allocated_blocks;
// lock around calls to cudaFree (to prevent deadlocks with NCCL)
mutable std::mutex cuda_free_mutex;
void add_allocated_block(Block* block) {
std::lock_guard<std::mutex> lock(mutex);
allocated_blocks[block->ptr] = block;
}
public:
std::vector<std::unique_ptr<DeviceCachingAllocator>> device_allocator;
std::mutex* getCudaFreeMutex() const {
return &cuda_free_mutex;
}
Block* get_allocated_block(void* ptr, bool remove = false) {
std::lock_guard<std::mutex> lock(mutex);
auto it = allocated_blocks.find(ptr);
if (it == allocated_blocks.end()) {
return nullptr;
}
Block* block = it->second;
if (remove) {
allocated_blocks.erase(it);
}
return block;
}
void init(int device_count) {
const auto size = static_cast<int64_t>(device_allocator.size());
if (size < device_count) {
device_allocator.resize(device_count);
for (const auto i : c10::irange(size, device_count)) {
device_allocator[i] = std::make_unique<DeviceCachingAllocator>();
}
}
}
/** allocates a block which is safe to use from the provided stream */
void malloc(void** devPtr, int device, size_t size, cudaStream_t stream) {
TORCH_INTERNAL_ASSERT(
0 <= device && static_cast<size_t>(device) < device_allocator.size(),
"Allocator not initialized for device ",
device,
": did you call init?");
Block* block = device_allocator[device]->malloc(device, size, stream);
add_allocated_block(block);
*devPtr = (void*)block->ptr;
}
void free(void* ptr) {
if (!ptr) {
return;
}
Block* block = get_allocated_block(ptr, true /* remove */);
if (!block) {
TORCH_CHECK(false, "invalid device pointer: ", ptr);
}
device_allocator[block->device]->free(block);
}
void setMemoryFraction(double fraction, int device) {
TORCH_INTERNAL_ASSERT(
0 <= device && static_cast<size_t>(device) < device_allocator.size(),
"Allocator not initialized for device ",
device,
": did you call init?");
TORCH_INTERNAL_ASSERT(
0 <= fraction && fraction <= 1,
"invalid fraction:",
fraction,
". Please set within (0, 1).");
int activated_device;
cudaGetDevice(&activated_device);
if (activated_device != device) {
cudaSetDevice(device);
}
device_allocator[device]->setMemoryFraction(fraction);
}
void emptyCache() {
for (auto& da : device_allocator)
da->emptyCache();
}
void* getBaseAllocation(void* ptr, size_t* outSize) {
Block* block = get_allocated_block(ptr);
if (!block) {
TORCH_CHECK(false, "invalid device pointer: ", ptr);
}
return device_allocator[block->device]->getBaseAllocation(block, outSize);
}
void recordStream(const DataPtr& ptr, cuda::CUDAStream stream) {
// Empty tensor's storage().data() might be a null ptr. As there is no
// blocks associated with those tensors, it is fine to do nothing here.
if (!ptr.get()) {
return;
}
// If a tensor is not allocated by this instance, simply skip
// This usually happens when CUDA tensors are shared across processes,
// we have implemented reference counting based sharing mechanism to
// guarantee tensors won't be accidentally freed by one process while
// they are still being used in another
if (ptr.get_deleter() != &raw_delete)
return;
Block* block = get_allocated_block(ptr.get());
// block must not be null reaching here
TORCH_INTERNAL_ASSERT(block != nullptr, "No allocated block can be found");
device_allocator[block->device]->recordStream(block, stream);
}
std::vector<SegmentInfo> snapshot() {
std::vector<SegmentInfo> result;
for (auto& da : device_allocator) {
auto snap = da->snapshot();
result.insert(result.end(), snap.begin(), snap.end());
}
return result;
}
};
THCCachingAllocator caching_allocator;
// Returns whether to force all allocations to bypass the caching allocator and
// go straight to cudaMalloc. This setting is useful when debugging GPU memory
// errors, since the caching allocator foils cuda-memcheck.
bool forceUncachedAllocator() {
static bool force_uncached =
getenv("PYTORCH_NO_CUDA_MEMORY_CACHING") != nullptr;
return force_uncached;
}
static void uncached_delete(void* ptr) {
C10_CUDA_CHECK(cudaFree(ptr));
}
// NB: I decided not to fold this into THCCachingAllocator, because the latter
// has a lot more methods and it wasn't altogether clear that they should
// actually be publicly exposed
struct CudaCachingAllocator : public Allocator {
DataPtr allocate(size_t size) const override {
constexpr size_t one_exa_bytes = 1152921504606846976ULL;
TORCH_CHECK_WITH(
CUDAOutOfMemoryError,
size < one_exa_bytes,
"CUDA out of memory. Tried to allocate more than 1EB memory.");
int device;
C10_CUDA_CHECK(cudaGetDevice(&device));
void* r = nullptr;
if (forceUncachedAllocator()) {
// Deliberately don't use cudaMallocMaybeCapturing here, to force an error
// if someone tries to use forceUncachedAllocator while capturing.
C10_CUDA_CHECK(cudaMalloc(&r, size));
return {r, r, &uncached_delete, Device(DeviceType::CUDA, device)};
}
if (size != 0) {
caching_allocator.malloc(
&r, device, size, cuda::getCurrentCUDAStream(device));
}
return {r, r, &raw_delete, Device(DeviceType::CUDA, device)};
}
DeleterFnPtr raw_deleter() const override {
if (forceUncachedAllocator()) {
return &uncached_delete;
} else {
return &raw_delete;
}
}
};
CudaCachingAllocator device_allocator;
Allocator* get(void) {
return &device_allocator;
}
void init(int device_count) {
caching_allocator.init(device_count);
}
void setMemoryFraction(double fraction, int device) {
caching_allocator.setMemoryFraction(fraction, device);
}
void emptyCache(void) {
caching_allocator.emptyCache();
}
void cacheInfo(int dev_id, size_t* cachedAndFree, size_t* largestBlock) {
caching_allocator.device_allocator[dev_id]->cacheInfo(
cachedAndFree, largestBlock);
}
void* getBaseAllocation(void* ptr, size_t* size) {
return caching_allocator.getBaseAllocation(ptr, size);
}
void recordStream(const DataPtr& ptr, cuda::CUDAStream stream) {
caching_allocator.recordStream(ptr, stream);
}
std::mutex* getFreeMutex() {
return caching_allocator.getCudaFreeMutex();
}
static inline void assertValidDevice(int device) {
const auto device_num = caching_allocator.device_allocator.size();
TORCH_CHECK(
0 <= device && device < static_cast<int64_t>(device_num),
"Invalid device argument ",
device,
": did you call init?");
}
DeviceStats getDeviceStats(int device) {
assertValidDevice(device);
return caching_allocator.device_allocator[device]->getStats();
}
void resetAccumulatedStats(int device) {
assertValidDevice(device);
caching_allocator.device_allocator[device]->resetAccumulatedStats();
}
void resetPeakStats(int device) {
assertValidDevice(device);
caching_allocator.device_allocator[device]->resetPeakStats();
}
std::vector<SegmentInfo> snapshot() {
return caching_allocator.snapshot();
}
// CUDAGraph interactions
void notifyCaptureBegin(
int device,
CaptureId_t graph_id,
MempoolId_t mempool_id) {
assertValidDevice(device);
caching_allocator.device_allocator[device]->notifyCaptureBegin(
graph_id, mempool_id);
}
void notifyCaptureEnd(int device, CaptureId_t graph_id) {
assertValidDevice(device);
caching_allocator.device_allocator[device]->notifyCaptureEnd(graph_id);
}
void notifyCaptureDestroy(int device, MempoolId_t mempool_id) {
assertValidDevice(device);
caching_allocator.device_allocator[device]->notifyCaptureDestroy(mempool_id);
}
//
// In CUDA IPC, sender sends a tensor to receiver, getIpcDevPtr
// is called by the receiving process to map the CUDA memory from the sending
// process into its own address space.
//
// CUDA IPC only allows sharing a big memory block associated with a
// cudaIpcMemHandle_t and it can be opened only **once** per context per
// process. There can be multiple types of storage in the same IPC mem block, so
// we must cache the device ptr to construct typed storage as it comes.
//
// ipcMemHandle_to_devptr maps a cudaIpcMemHandle_t to a device pointer in the
// process that can be used to access the memory block in the sender process. It
// only saves a weak_ptr of the device pointer in the map, the shared_ptr will
// be used to reconstruct all storages in this CudaMalloc allocation. And it
// will deleted in cudaIpcCloseMemHandle when its reference count is 0.
//
namespace {
std::mutex IpcMutex;
ska::flat_hash_map<std::string, std::weak_ptr<void>> ipcMemHandle_to_devptr;
} // namespace
std::shared_ptr<void> getIpcDevPtr(std::string handle) {
std::lock_guard<std::mutex> lock(IpcMutex);
auto iter = ipcMemHandle_to_devptr.find(handle);
if (iter != ipcMemHandle_to_devptr.end()) {
auto devptr = iter->second.lock();
if (devptr)
return devptr;
}
// This ipcMemHandle hasn't been opened, or already expired, open it to
// enable IPC access to that mem block.
void* dev = nullptr;
auto ipc_handle = reinterpret_cast<const cudaIpcMemHandle_t*>(handle.c_str());
C10_CUDA_CHECK(
cudaIpcOpenMemHandle(&dev, *ipc_handle, cudaIpcMemLazyEnablePeerAccess));
// devPtr has to be deleted in same device when created.
int curr_device;
C10_CUDA_CHECK(cudaGetDevice(&curr_device));
auto sp = std::shared_ptr<void>(dev, [handle, curr_device](void* ptr) {
cuda::CUDAGuard device_guard(curr_device);
std::lock_guard<std::mutex> deleter_lock(IpcMutex);
C10_CUDA_CHECK(cudaIpcCloseMemHandle(ptr));
ipcMemHandle_to_devptr.erase(handle);
});
std::weak_ptr<void> wp = sp;
// To eliminate an additional search, we can use insert().
// It doesn't overwrite when key already exists(ptr expired).
// But in the deleter for sp we erased the entry,
// this should be safe to do now.
ipcMemHandle_to_devptr.insert(iter, {handle, wp});
return sp;
}
void* raw_alloc(size_t nbytes) {
if (nbytes == 0) {
return nullptr;
}
int device;
C10_CUDA_CHECK(cudaGetDevice(&device));
void* r = nullptr;
caching_allocator.malloc(
&r, device, nbytes, cuda::getCurrentCUDAStream(device));
return r;
}
void* raw_alloc_with_stream(size_t nbytes, cudaStream_t stream) {
if (nbytes == 0) {
return nullptr;
}
int device;
C10_CUDA_CHECK(cudaGetDevice(&device));
void* r = nullptr;
caching_allocator.malloc(&r, device, nbytes, stream);
return r;
}
void raw_delete(void* ptr) {
caching_allocator.free(ptr);
}
} // namespace CUDACachingAllocator
} // namespace cuda
} // namespace c10