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

Summary:

X-link: facebookresearch/FBGEMM#1495

X-link: pytorch/torchrec#3148

# Context
In our current KVZCH cp loading flow, we will keep hold of weight_id, weight, optimizer tensors throughout the checkpoint loading lifecycle, and at the end when all these tensors are downloaded in hand, we will explicitly call "apply_state_dict" to actually write them by chunk to the backend to ensure id->weight and id->opt are mapped correctly. The problem is when we have large number of weights, we will be short of memory since we need to hold all 3 tensors (double memory issue). To solve this challenge, we are going to save the whole row of (metaheader + weight + opt) as the same "weight" tensor during checkpoint saving, and when downloading the checkpoint, we will be able to extract the id from the header, and directly write the weight+opt part to the backend by id. When loading cp for optimizer, we added a no-op KVTensor, so it won't need to write to backend for optimizer states again.

# This diff only contains backend change
* updated dram backend and mem pool, so it can return the metaheader + weight + optimizer_state together, as well as set them back to backend (use pointers to skip metaheader part when write weight+opt to backend)

Reviewed By: emlin

Differential Revision: D77604158

Author: Jianbo Liu

fbgemm_gpu/src/dram_kv_embedding_cache/dram_kv_embedding_cache.h

@@ -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)
@@ -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));
@@ -609,10 +611,14 @@ class DramKVEmbeddingCache : public kv_db::EmbeddingKVDB {
       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));
+    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(
@@ -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
@@ -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
@@ -826,6 +871,10 @@ 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_;
+  }
+
   void resume_ongoing_eviction() override {
     if (feature_evict_) {
       feature_evict_->resume();
@@ -930,6 +979,192 @@ 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_;
@@ -942,6 +1177,7 @@ 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_;

fbgemm_gpu/src/dram_kv_embedding_cache/dram_kv_embedding_cache_wrapper.h

@@ -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>>(
@@ -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);
@@ -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);

fbgemm_gpu/src/dram_kv_embedding_cache/fixed_block_pool.h

@@ -114,6 +114,22 @@ 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);

fbgemm_gpu/src/ssd_split_embeddings_cache/kv_db_table_batched_embeddings.h

@@ -357,10 +357,10 @@ class EmbeddingKVDB : public std::enable_shared_from_this<EmbeddingKVDB> {
       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));
+    (void)weights;
+    (void)start;
+    (void)length;
+    FBEXCEPTION("Not implemented");
   }
 
   virtual void get_range_from_snapshot(
@@ -402,6 +402,11 @@ 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;
+  }
+
 #ifdef FBGEMM_FBCODE
   folly::coro::Task<void> tensor_stream(
       const at::Tensor& indices,