Set cache_precision = weights_precision in TBE if it is not explicitl…

…y set

Summary:
X-link: facebookresearch/FBGEMM#461

For historical reasons, we have decoupled cache_precision and weights_precision. This was to allow for lower precision embedding, while the hot ones in the cache are using higher precision. But it is not used.

This decoupling has been source of unintentional difference in cache_precision and weights_precision for typical EMO usecases where we do want cache_precision == weights_precision. We had enforced this in torchrec (see [this](https://www.internalfb.com/code/fbsource/[3868325cdafd]/fbcode/torchrec/distributed/batched_embedding_kernel.py?lines=962-963%2C1446-1447)), but as new stacks are enabled, this could be overwritten. 

Here we enforce cache_precision = weights_precision if cache precision is not explicitly set.

Differential Revision: D65865527

fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_training.py

@@ -580,7 +580,7 @@ def __init__(  # noqa C901
         cache_load_factor: float = 0.2,
         cache_sets: int = 0,
         cache_reserved_memory: float = 0.0,
-        cache_precision: SparseType = SparseType.FP32,
+        cache_precision: Optional[SparseType] = None,
         weights_precision: SparseType = SparseType.FP32,
         output_dtype: SparseType = SparseType.FP32,
         enforce_hbm: bool = False,
@@ -619,6 +619,7 @@ def __init__(  # noqa C901
         uvm_host_mapped: bool = False,
     ) -> None:
         super(SplitTableBatchedEmbeddingBagsCodegen, self).__init__()
+
         self.uuid = str(uuid.uuid4())
         self.logging_table_name: str = self.get_table_name_for_logging(table_names)
         self.pooling_mode = pooling_mode
@@ -627,6 +628,9 @@ def __init__(  # noqa C901
             os.environ.get("FBGEMM_TBE_BOUNDS_CHECK_MODE", bounds_check_mode.value)
         )
         self.weights_precision = weights_precision
+        cache_precision = (
+            weights_precision if cache_precision is None else cache_precision
+        )
         self.output_dtype: int = output_dtype.as_int()
         assert (
             not prefetch_pipeline or cache_algorithm == CacheAlgorithm.LRU
@@ -1175,20 +1179,13 @@ def __init__(  # noqa C901
                 ),
             )
 
-        if cache_precision == SparseType.FP32:
-            cache_embedding_dtype = torch.float32
-        elif cache_precision == SparseType.FP16:
-            cache_embedding_dtype = torch.float16
-        else:
-            raise AssertionError(f"cache_precision {cache_precision} not supported!")
-
         self._apply_cache_state(
             cache_state,
             cache_algorithm,
             cache_load_factor,
             cache_sets,
             cache_reserved_memory,
-            dtype=cache_embedding_dtype,
+            cache_precision,
         )
 
         self.log(f"Contents: {table_names}")
@@ -2643,7 +2640,7 @@ def _apply_cache_state(
         cache_load_factor: float,
         cache_sets: int,
         cache_reserved_memory: float,
-        dtype: torch.dtype,
+        cache_precision: SparseType,
     ) -> None:
         self.cache_algorithm = cache_algorithm
         self.timestep = 1
@@ -2663,6 +2660,17 @@ def _apply_cache_state(
 
         self._init_uvm_cache_stats()
 
+        if cache_precision == SparseType.FP32:
+            dtype = torch.float32
+        elif cache_precision == SparseType.FP16:
+            dtype = torch.float16
+        else:
+            dtype = torch.float32  # not relevant, but setting it to keep linter happy
+            if not self.use_cpu > 0:
+                raise AssertionError(
+                    f"cache_precision {cache_precision} not supported!"
+                )
+
         # NOTE: no cache for CPU mode!
         if cache_state.total_cache_hash_size == 0 or self.use_cpu:
             self.register_buffer(
@@ -2740,7 +2748,8 @@ def _apply_cache_state(
             f"{cache_algorithm}, {cache_sets} sets, "
             f"load_factor: {cache_load_factor : .3f}, "
             f"cache_size: {cache_size / 1024.0 / 1024.0 / 1024.0 : .2f}GB, "
-            f"cache_precision: {dtype}"
+            f"cache_precision: {dtype}, "
+            f"weights_precision: {self.weights_precision}"
         )
 
         self.total_cache_hash_size = cache_state.total_cache_hash_size