reserve a method during torchscript (#3152)

Summary:
Pull Request resolved: #3152

X-link: facebookresearch/FBGEMM#247

This method is useful to help recompute buffers for torchscripted model in pyhon.

Reviewed By: seanx92

Differential Revision: D63000116

fbshipit-source-id: 43ad420b22ac2e06b59e2189dada1a0b30befcad

fbgemm_gpu/fbgemm_gpu/split_embedding_configs.py

@@ -68,6 +68,19 @@ def get(self, name: str) -> int:
         return self.config[name]
 
 
+def sparse_type_to_int(sparse_type: "SparseType") -> int:
+    return {
+        SparseType.FP32.value: 0,
+        SparseType.FP16.value: 1,
+        SparseType.INT8.value: 2,
+        SparseType.INT4.value: 3,
+        SparseType.INT2.value: 4,
+        SparseType.BF16.value: 5,
+        SparseType.FP8.value: 6,
+        SparseType.MX4.value: 7,
+    }[sparse_type.value]
+
+
 @enum.unique
 class SparseType(enum.Enum):
     FP32 = "fp32"
@@ -104,16 +117,7 @@ def from_int(ty: int) -> "SparseType":
             raise ValueError(f"Unsupported sparse type: {ty}")
 
     def as_int(self) -> int:
-        return {
-            SparseType.FP32.value: 0,
-            SparseType.FP16.value: 1,
-            SparseType.INT8.value: 2,
-            SparseType.INT4.value: 3,
-            SparseType.INT2.value: 4,
-            SparseType.BF16.value: 5,
-            SparseType.FP8.value: 6,
-            SparseType.MX4.value: 7,
-        }[self.value]
+        return sparse_type_to_int(self)
 
     @staticmethod
     def from_dtype(dtype: torch.dtype, is_mx: bool = False) -> "SparseType":

fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_inference.py

@@ -17,7 +17,7 @@
 import torch  # usort:skip
 from torch import nn, Tensor  # usort:skip
 
-from fbgemm_gpu.split_embedding_configs import SparseType
+from fbgemm_gpu.split_embedding_configs import sparse_type_to_int, SparseType
 from fbgemm_gpu.split_table_batched_embeddings_ops_common import (
     BoundsCheckMode,
     CacheAlgorithm,
@@ -943,6 +943,7 @@ def reset_embedding_spec_location(
             for spec in self.embedding_specs
         ]
 
+    @torch.jit.export
     def recompute_module_buffers(self) -> None:
         """
         Compute module buffers that're on meta device and are not materialized in reset_weights_placements_and_offsets().
@@ -955,7 +956,7 @@ def recompute_module_buffers(self) -> None:
         ):
             return
 
-        weights_tys_int = [e[3].as_int() for e in self.embedding_specs]
+        weights_tys_int = [sparse_type_to_int(e[3]) for e in self.embedding_specs]
         self.weights_tys = torch.tensor(
             [weights_tys_int[t] for t in self.feature_table_map],
             device=self.current_device,
@@ -968,8 +969,9 @@ def recompute_module_buffers(self) -> None:
             dtype=torch.int64,
         )
         dims = [e[2] for e in self.embedding_specs]
-        D_offsets_list = [dims[t] for t in self.feature_table_map]
-        D_offsets_list = [0] + list(accumulate(D_offsets_list))
+        D_offsets_list = [0]
+        for t in self.feature_table_map:
+            D_offsets_list.append(dims[t] + D_offsets_list[-1])
         self.D_offsets = torch.tensor(
             D_offsets_list, device=self.current_device, dtype=torch.int32
         )
@@ -999,6 +1001,9 @@ def recompute_module_buffers(self) -> None:
         self.table_wise_cache_miss = torch.empty_like(
             self.table_wise_cache_miss, device=self.current_device
         )
+        self.weights_uvm = torch.empty_like(
+            self.weights_uvm, device=self.current_device
+        )
 
     def _apply_split(
         self,