[embedding] isolate cache_op from forward (#1645)

Co-authored-by: ric <mkkt_bkkt@mail.ustc.edu.cn>

colossalai/nn/parallel/layers/cache_embedding/freq_aware_embedding.py

@@ -64,6 +64,7 @@ class FreqAwareEmbeddingBag(BaseEmbeddingBag):
         cuda_row_num = int(num_embeddings * cache_ratio)
         # configure weight & cache
         self._preprocess(_weight, cuda_row_num, ids_freq_mapping, warmup_ratio, buffer_size, pin_weight)
+        self.cache_op = True
 
     def _weight_alloc(self, dtype, device):
         weight = torch.empty(self.num_embeddings, self.embedding_dim, dtype=dtype, device=device)
@@ -97,8 +98,8 @@ class FreqAwareEmbeddingBag(BaseEmbeddingBag):
                                                evict_strategy=self.evict_strategy)
         self.cache_weight_mgr.reorder(ids_freq_mapping, warmup_ratio)
 
-    def forward(self, input, offsets=None, per_sample_weights=None, shape_hook=None, cache_op=True):
-        if cache_op:
+    def forward(self, input, offsets=None, per_sample_weights=None, shape_hook=None):
+        if self.cache_op:
             with torch.no_grad():
                 input = self.cache_weight_mgr.prepare_ids(input)
 
@@ -119,6 +120,9 @@ class FreqAwareEmbeddingBag(BaseEmbeddingBag):
     def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
         yield self.cache_weight_mgr.cuda_cached_weight
 
+    def set_cache_op(self, cache_op: bool = True):
+        self.cache_op = cache_op
+
 
 ############################# Perf Log ###################################
 

colossalai/nn/parallel/layers/cache_embedding/parallel_freq_aware_embedding.py

@@ -60,6 +60,7 @@ class ParallelFreqAwareEmbeddingBag(FreqAwareEmbeddingBag):
               self).__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq,
                              sparse, _weight, mode, include_last_offset, dtype, device, cache_ratio, ids_freq_mapping,
                              warmup_ratio, buffer_size, pin_weight, evict_strategy)
+        self.cache_op = True
 
     def _weight_alloc(self, dtype, device):
         weight = torch.empty(self.num_embeddings, self.embedding_dim_per_partition, device=device, dtype=dtype)
@@ -72,15 +73,16 @@ class ParallelFreqAwareEmbeddingBag(FreqAwareEmbeddingBag):
                                           compute_attr=ComputePattern.TP1D)
         return ColoTensor.from_torch_tensor(weight, spec=colo_tensor_spec)
 
-    def forward(self,
+    def forward(
+        self,
         indices,
         offsets=None,
         per_sample_weights=None,
         shape_hook=None,
         scatter_dim=0,
         gather_dim=-1,
-                cache_op: bool = True):
-        if cache_op:
+    ):
+        if self.cache_op:
             with torch.no_grad():
                 indices = self.cache_weight_mgr.prepare_ids(indices)
         output_shard = F.embedding_bag(indices.cuda(), self.cache_weight_mgr.cuda_cached_weight, offsets, self.max_norm,
@@ -94,6 +96,9 @@ class ParallelFreqAwareEmbeddingBag(FreqAwareEmbeddingBag):
                                       gather_dim=gather_dim)
         return output_full
 
+    def set_cache_op(self, cache_op: bool = True):
+        self.cache_op = cache_op
+
     @classmethod
     def from_pretrained(
         cls,

colossalai/nn/parallel/layers/cache_embedding/parallel_freq_aware_embedding_tablewise.py

@@ -81,13 +81,16 @@ class ParallelFreqAwareEmbeddingBagTablewise(FreqAwareEmbeddingBag):
         for rank in self.rank_of_tables:
             self.embedding_dim_per_rank[rank] += embedding_dim
 
-    def forward(self,
+        self.cache_op = True
+
+    def forward(
+        self,
         indices: torch.Tensor,
         offsets: torch.Tensor = None,
         per_sample_weights=None,
         shape_hook=None,
         already_split_along_rank=True,
-                cache_op=True):
+    ):
         if not already_split_along_rank:
             # not recommanded. it takes time.
             batch_size = (offsets.shape[0]) // self.global_tables_num
@@ -97,7 +100,7 @@ class ParallelFreqAwareEmbeddingBagTablewise(FreqAwareEmbeddingBag):
             # recommanded.
             batch_size = (offsets.shape[0]) // len(self.assigned_table_list)
             local_indices, local_offsets, local_per_sample_weights = indices, offsets, per_sample_weights
-        if cache_op:
+        if self.cache_op:
             with torch.no_grad():
                 indices = self.cache_weight_mgr.prepare_ids(local_indices)
         local_output = F.embedding_bag(indices.cuda(), self.cache_weight_mgr.cuda_cached_weight, local_offsets,
@@ -185,6 +188,9 @@ class ParallelFreqAwareEmbeddingBagTablewise(FreqAwareEmbeddingBag):
             local_per_sample_weights = torch.cat(local_per_sample_weights_list, 0)
         return local_indices, local_offsets, local_per_sample_weights
 
+    def set_cache_op(self, cache_op: bool = True):
+        self.cache_op = cache_op
+
     def print_comm_stats_(self):
         self.cache_weight_mgr.print_comm_stats()