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Support get/set the whole row of metaheader+weight+optimizer from backend for checkpoint saving/loading #4429

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Support get/set the whole row of metaheader+weight+optimizer from backend for checkpoint saving/loading #4429

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262 changes: 252 additions & 10 deletions fbgemm_gpu/src/dram_kv_embedding_cache/dram_kv_embedding_cache.h
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Original file line number Diff line number Diff line change
Expand Up @@ -105,6 +105,7 @@ class DramKVEmbeddingCache : public kv_db::EmbeddingKVDB {
int64_t num_shards = 8,
int64_t num_threads = 32,
int64_t row_storage_bitwidth = 32,
bool backend_return_whole_row = false,
bool enable_async_update = false,
std::optional<at::Tensor> table_dims = std::nullopt,
std::optional<at::Tensor> hash_size_cumsum = std::nullopt)
Expand All @@ -126,6 +127,7 @@ class DramKVEmbeddingCache : public kv_db::EmbeddingKVDB {
block_alignment_,
/*blocks_per_chunk=*/8192)),
elem_size_(row_storage_bitwidth / 8),
backend_return_whole_row_(backend_return_whole_row),
feature_evict_config_(feature_evict_config) {
executor_ = std::make_unique<folly::CPUThreadPoolExecutor>(std::max<size_t>(
num_threads, facebook::Proc::getCpuInfo().numCpuCores));
Expand Down Expand Up @@ -608,11 +610,15 @@ class DramKVEmbeddingCache : public kv_db::EmbeddingKVDB {
void set_range_to_storage(
const at::Tensor& weights,
const int64_t start,
const int64_t length) {
const auto seq_indices =
at::arange(start, start + length, at::TensorOptions().dtype(at::kLong));
const auto count = at::tensor({length}, at::ScalarType::Long);
folly::coro::blockingWait(set_kv_db_async(seq_indices, weights, count));
const int64_t length) override {
if (backend_return_whole_row_) {
set_kv_with_metaheader_to_storage(weights);
} else {
const auto seq_indices = at::arange(
start, start + length, at::TensorOptions().dtype(at::kLong));
const auto count = at::tensor({length}, at::ScalarType::Long);
folly::coro::blockingWait(set_kv_db_async(seq_indices, weights, count));
}
}

void get_range_from_snapshot(
Expand All @@ -625,10 +631,16 @@ class DramKVEmbeddingCache : public kv_db::EmbeddingKVDB {
CHECK(snapshot_handle == nullptr);
const auto seq_indices =
at::arange(start, start + length, at::TensorOptions().dtype(at::kLong));
const auto count = at::tensor({length}, at::ScalarType::Long);
get_kv_db_async_impl(
seq_indices, weights, count, width_offset, width_length)
.wait();

if (backend_return_whole_row_) {
get_kv_with_metaheader_from_storage(seq_indices, weights);
} else {
const auto count = at::tensor({length}, at::ScalarType::Long);
get_kv_db_async_impl(
seq_indices, weights, count, width_offset, width_length)
.wait();
}

// this is called by checkpoint mostly, and checkpoint should wait until
// eviction finishes so that we could reacha consistent state before/after
// state_dict() calls
Expand All @@ -642,8 +654,41 @@ class DramKVEmbeddingCache : public kv_db::EmbeddingKVDB {
int64_t width_offset = 0,
std::optional<int64_t> width_length = std::nullopt) override {
CHECK(snapshot_handle == nullptr);

if (backend_return_whole_row_) {
get_kv_with_metaheader_from_storage(
ids, weights, width_offset, width_length);
} else {
const auto count = at::tensor({ids.size(0)}, at::ScalarType::Long);
get_kv_db_async_impl(ids, weights, count, width_offset, width_length)
.wait();
}
}

// used for ckpt, get kv with metaheader from storage
void get_kv_with_metaheader_from_storage(
const at::Tensor& ids,
const at::Tensor& weights_with_metaheader,
int64_t width_offset = 0,
std::optional<int64_t> width_length = std::nullopt) {
const auto count = at::tensor({ids.size(0)}, at::ScalarType::Long);
get_kv_db_async_impl(ids, weights, count, width_offset, width_length)
get_kv_db_with_metaheader_async_impl(
ids, weights_with_metaheader, count, width_offset, width_length)
.wait();
}

void set_kv_with_metaheader_to_storage(
const at::Tensor& weights_with_metaheader) {
std::vector<int64_t> keys(weights_with_metaheader.size(0), 0);
for (int64_t i = 0; i < weights_with_metaheader.size(0); ++i) {
keys[i] = FixedBlockPool::get_key(weights_with_metaheader[i].data_ptr());
}
auto indices =
torch::from_blob(keys.data(), {int64_t(keys.size())}, torch::kInt64);
const auto count =
at::tensor({weights_with_metaheader.size(0)}, at::ScalarType::Long);
set_kv_db_with_metaheader_async_impl(
indices, weights_with_metaheader, count)
.wait();
// this is called by checkpoint mostly, and checkpoint should wait until
// eviction finishes so that we could reacha consistent state before/after
Expand Down Expand Up @@ -826,6 +871,16 @@ class DramKVEmbeddingCache : public kv_db::EmbeddingKVDB {

void flush_or_compact(const int64_t timestep) override {}

bool get_backend_return_whole_row() override {
return backend_return_whole_row_;
}

int64_t get_metaheader_width_in_front() override {
return backend_return_whole_row_
? FixedBlockPool::get_metaheader_dim<weight_type>()
: 0;
}

void resume_ongoing_eviction() override {
if (feature_evict_) {
feature_evict_->resume();
Expand Down Expand Up @@ -930,6 +985,192 @@ class DramKVEmbeddingCache : public kv_db::EmbeddingKVDB {
return ret;
}

/// Get embeddings and metaheader from kvstore.
///
/// @param indices The 1D embedding index tensor, should skip on negative
/// value
/// @param weights_with_metaheader The 2D tensor that each row(embeddings) is
/// paired up with relative element in <indices>. This tensor will be
/// filled up with the returned embeddings from KVstore.
/// @param count A single element tensor that contains the number of indices
/// to be processed
///
/// @return None
folly::SemiFuture<std::vector<folly::Unit>>
get_kv_db_with_metaheader_async_impl(
const at::Tensor& indices,
const at::Tensor& weights_with_metaheader,
const at::Tensor& count,
int64_t width_offset = 0,
std::optional<int64_t> width_length = std::nullopt) {
std::vector<folly::Future<folly::Unit>> futures;
auto row_width = weights_with_metaheader.size(1);
auto copy_width = width_length.value_or(row_width);
CHECK_LE(row_width, block_size_);
CHECK_EQ(copy_width, row_width);
auto shardid_to_indexes = shard_input(indices, count);

for (auto iter = shardid_to_indexes.begin();
iter != shardid_to_indexes.end();
iter++) {
const auto shard_id = iter->first;
const auto indexes = iter->second;
auto f =
folly::via(executor_.get())
.thenValue([this,
shard_id,
indexes,
&indices,
&weights_with_metaheader,
width_offset,
row_width](folly::Unit) {
FBGEMM_DISPATCH_INTEGRAL_TYPES(
indices.scalar_type(),
"dram_kvstore_get_with_metaheader",
[this,
shard_id,
indexes,
&indices,
&weights_with_metaheader,
width_offset,
row_width] {
using index_t = scalar_t;
CHECK(indices.is_contiguous());
CHECK(weights_with_metaheader.is_contiguous());
CHECK_EQ(
indices.size(0), weights_with_metaheader.size(0));
auto wlmap = kv_store_.by(shard_id).wlock();
auto indices_data_ptr = indices.data_ptr<index_t>();
auto weights_data_ptr =
weights_with_metaheader.data_ptr<weight_type>();
{
for (auto index_iter = indexes.begin();
index_iter != indexes.end();
index_iter++) {
const auto weights_row_index = *index_iter;
auto weight_idx =
int64_t(indices_data_ptr[weights_row_index]);
const auto cached_iter = wlmap->find(weight_idx);
// Defensive programming
// it shouldn't occur under normal circumstances
if (cached_iter == wlmap->end()) {
std::memset(
&(weights_data_ptr
[weights_row_index * row_width]),
0,
row_width);
continue;
}

// For weight KVT, offset=0 and it will read the whole
// row. For optimizer, offset=dim(metaheader) +
// emb_dim so it will only read the optimizer part
const auto* ptr_offset_from_front =
FixedBlockPool::ptr_offset_from_front<
weight_type>(
cached_iter->second, width_offset);
std::copy(
ptr_offset_from_front,
ptr_offset_from_front + row_width,
&(weights_data_ptr
[weights_row_index * row_width]));
}
}
});
});
futures.push_back(std::move(f));
}
return folly::collect(futures);
}

/// insert embeddings and metaheader into kvstore.
/// current underlying memory management is done through F14FastMap
/// key value pair will be sharded into multiple shards to increase
/// parallelism.
///
/// @param indices The 1D embedding index tensor, should skip on negative
/// value
/// @param weights_with_metaheader The 2D tensor that each row(embeddings with
/// metaheader) is paired up with relative element in <indices>
/// @param count A single element tensor that contains the number of indices
/// to be processed
///
/// @return None
folly::SemiFuture<std::vector<folly::Unit>>
set_kv_db_with_metaheader_async_impl(
const at::Tensor& indices,
const at::Tensor& weights_with_metaheader,
const at::Tensor& count) {
std::vector<folly::Future<folly::Unit>> futures;
auto shardid_to_indexes = shard_input(indices, count);
for (auto iter = shardid_to_indexes.begin();
iter != shardid_to_indexes.end();
iter++) {
const auto shard_id = iter->first;
const auto indexes = iter->second;
auto f =
folly::via(executor_.get())
.thenValue(
[this, shard_id, indexes, &indices, &weights_with_metaheader](
folly::Unit) {
FBGEMM_DISPATCH_INTEGRAL_TYPES(
indices.scalar_type(),
"dram_kv_set_with_metaheader",
[this,
shard_id,
indexes,
&indices,
&weights_with_metaheader] {
using index_t = scalar_t;
CHECK(indices.is_contiguous());
CHECK(weights_with_metaheader.is_contiguous());
CHECK_EQ(
indices.size(0), weights_with_metaheader.size(0));
{
auto wlmap = kv_store_.by(shard_id).wlock();
auto* pool = kv_store_.pool_by(shard_id);
int64_t stride = weights_with_metaheader.size(1);
auto indices_data_ptr = indices.data_ptr<index_t>();
auto weights_data_ptr =
weights_with_metaheader.data_ptr<weight_type>();
for (auto index_iter = indexes.begin();
index_iter != indexes.end();
index_iter++) {
const auto& id_index = *index_iter;
auto id = int64_t(indices_data_ptr[id_index]);
// Defensive programming
// it shouldn't occur under normal circumstances
auto used = FixedBlockPool::get_used(
weights_data_ptr + id_index * stride);
if (!used) {
continue;
}
// use mempool
weight_type* block = nullptr;
// First check if the key already exists
auto it = wlmap->find(id);
if (it != wlmap->end()) {
block = it->second;
} else {
// Key doesn't exist, allocate new block and
// insert.
block =
pool->template allocate_t<weight_type>();
wlmap->insert({id, block});
}
std::copy(
weights_data_ptr + id_index * stride,
weights_data_ptr + (id_index + 1) * stride,
block);
}
}
});
});
futures.push_back(std::move(f));
}
return folly::collect(futures);
}

std::unique_ptr<folly::CPUThreadPoolExecutor> executor_;
// background thread
folly::FunctionScheduler scheduler_;
Expand All @@ -942,6 +1183,7 @@ class DramKVEmbeddingCache : public kv_db::EmbeddingKVDB {
std::atomic_bool is_eviction_ongoing_ = false;
std::vector<std::unique_ptr<ssd::Initializer>> initializers_;
int64_t elem_size_;
bool backend_return_whole_row_;
std::vector<int64_t> sub_table_dims_;
std::vector<int64_t> sub_table_hash_cumsum_;
std::optional<c10::intrusive_ptr<FeatureEvictConfig>> feature_evict_config_;
Expand Down
3 changes: 3 additions & 0 deletions fbgemm_gpu/src/dram_kv_embedding_cache/dram_kv_embedding_cache_wrapper.h
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,7 @@ class DramKVEmbeddingCacheWrapper : public torch::jit::CustomClassHolder {
int64_t row_storage_bitwidth = 32,
const std::optional<at::Tensor>& table_dims = std::nullopt,
const std::optional<at::Tensor>& hash_size_cumsum = std::nullopt,
bool backend_return_whole_row = false,
bool enable_async_update = false) {
if (row_storage_bitwidth == 16) {
impl_ = std::make_shared<kv_mem::DramKVEmbeddingCache<at::Half>>(
Expand All @@ -47,6 +48,7 @@ class DramKVEmbeddingCacheWrapper : public torch::jit::CustomClassHolder {
num_shards,
num_threads,
row_storage_bitwidth,
backend_return_whole_row,
enable_async_update,
table_dims,
hash_size_cumsum);
Expand All @@ -59,6 +61,7 @@ class DramKVEmbeddingCacheWrapper : public torch::jit::CustomClassHolder {
num_shards,
num_threads,
row_storage_bitwidth,
backend_return_whole_row,
enable_async_update,
table_dims,
hash_size_cumsum);
Expand Down
22 changes: 22 additions & 0 deletions fbgemm_gpu/src/dram_kv_embedding_cache/fixed_block_pool.h
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Original file line number Diff line number Diff line change
Expand Up @@ -100,6 +100,12 @@ class FixedBlockPool : public std::pmr::memory_resource {
return std::max(alignof(FixedBlockPool::MetaHeader), alignof(scalar_t));
}

// Get dimension of Metaheader
template <typename scalar_t>
static size_t get_metaheader_dim() {
return sizeof(FixedBlockPool::MetaHeader) / sizeof(scalar_t);
}

// Data pointer retrieval
template <typename scalar_t>
static scalar_t* data_ptr(scalar_t* block) {
Expand All @@ -114,6 +120,22 @@ class FixedBlockPool : public std::pmr::memory_resource {
sizeof(FixedBlockPool::MetaHeader));
}

template <typename scalar_t>
static scalar_t* ptr_offset_from_front(
scalar_t* block,
const int64_t offset) {
return reinterpret_cast<scalar_t*>(
reinterpret_cast<char*>(block) + offset * sizeof(scalar_t));
}

template <typename scalar_t>
static const scalar_t* ptr_offset_from_front(
const scalar_t* block,
const int64_t offset) {
return reinterpret_cast<const scalar_t*>(
reinterpret_cast<const char*>(block) + offset * sizeof(scalar_t));
}

template <typename scalar_t>
static scalar_t get_l2weight(scalar_t* block, size_t dimension) {
scalar_t* data = FixedBlockPool::data_ptr(block);
Expand Down
10 changes: 10 additions & 0 deletions fbgemm_gpu/src/ssd_split_embeddings_cache/kv_db_table_batched_embeddings.h
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Original file line number Diff line number Diff line change
Expand Up @@ -402,6 +402,16 @@ class EmbeddingKVDB : public std::enable_shared_from_this<EmbeddingKVDB> {
return max_D_;
}

virtual bool get_backend_return_whole_row() {
// only DRAM backend can enable this for now
return false;
}

virtual int64_t get_metaheader_width_in_front() {
// will return non-zero if DRAM enables backend_return_whole_row
return 0;
}

#ifdef FBGEMM_FBCODE
folly::coro::Task<void> tensor_stream(
const at::Tensor& indices,
Expand Down
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