Don't display Cache Load Factor if compute kernel is not uvm caching (#…

…2529)

Summary:
Pull Request resolved: #2529

This statistic may add confusion unless the `fused_uvm_caching` kernel is being
used, see also: T187360685

Reviewed By: PaulZhang12

Differential Revision: D65231346

fbshipit-source-id: f5410d1183e5682b82a0256ae2eddb38b9f1c767

torchrec/distributed/planner/stats.py

@@ -16,6 +16,7 @@
 
 from torch import nn
 
+from torchrec.distributed.embedding_types import EmbeddingComputeKernel
 from torchrec.distributed.planner.constants import BIGINT_DTYPE, NUM_POOLINGS
 from torchrec.distributed.planner.shard_estimators import _calculate_shard_io_sizes
 from torchrec.distributed.planner.storage_reservations import (
@@ -421,11 +422,14 @@ def log(
                     if hasattr(sharder, "fused_params") and sharder.fused_params
                     else None
                 )
-                cache_load_factor = str(
-                    so.cache_load_factor
-                    if so.cache_load_factor is not None
-                    else sharder_cache_load_factor
-                )
+                cache_load_factor = "None"
+                # Surfacing cache load factor does not make sense if not using uvm caching.
+                if so.compute_kernel == EmbeddingComputeKernel.FUSED_UVM_CACHING.value:
+                    cache_load_factor = str(
+                        so.cache_load_factor
+                        if so.cache_load_factor is not None
+                        else sharder_cache_load_factor
+                    )
                 hash_size = so.tensor.shape[0]
                 param_table.append(
                     [