Minor improvements in jagged tensor identification (#919)

Summary:
Pull Request resolved: #919

ATT. Concretely:

1. Tagging jagged tensor nodes in the fx graph now proceeds even if the nodes have a rank different from 2. This is to acomodate for the cases when jagged tensors are unsqueezed / reshaped at the path from the input (where they should be tagged for downstream shape inference) to the fbgemm op (where they are detected).

2. Inputs with the `shape[0]` being equal to one of the JT `shape[0]`, but not having an offsets tag attached are now ignored instead of failing the whole jagged tensor map inference.

3. Instead of falling back to the jagged batch-dim based JT shape inference, we now either fully rely on the inferred jagged tensor map or not at all if we fail to infer one.

4. Add `jagged_index_select` to the list of anchor ops for recognizing and tagging the jagged tensors and offsets in the fx graph.

Reviewed By: qxy11

Differential Revision: D48825713

fbshipit-source-id: 32851a0180dd47bfbc6669216cdf79817af64670

fx2ait/fx2ait/tensor_spec.py

@@ -345,9 +345,12 @@ def _try_getting_jagged_tensor_map(
         for i, inp in enumerate(inputs):
             if inp.shape[0] in jagged_tensor_batch_dims:
                 offsets_name = fx_inputs[i].meta.get("offsets_name", None)
-                if offsets_name is None or len(offsets_name) > 1:
+                if offsets_name is None:
+                    # not a jagged tensor
+                    continue
+                if len(offsets_name) > 1:
                     # offsets name attached to the jagged tensor's
-                    # fx.Node is either unavailable or ambiguous
+                    # fx.Node is either ambiguous: failing here
                     return None
                 offsets_name = list(offsets_name)[0]
                 if offsets_name not in seen_offsets_names:
@@ -387,7 +390,7 @@ def from_input_list_with_batch_size_jagged_tensor(
             jagged_tensor_batch_dims=jagged_tensor_batch_dims,
             fx_inputs=fx_inputs,
         )
-        if jagged_tensor_map is not None:
+        if jagged_tensor_map:
             logger.info("Successfully detected a jagged_tensor_map:")
             for input_id, jagged_tensor_id in jagged_tensor_map.items():
                 logger.info(f"{input_id=}, {jagged_tensor_id=}")
@@ -407,7 +410,7 @@ def from_input_list_with_batch_size_jagged_tensor(
             batch_dim_lower_bound: int = 0
             batch_dim_upper_bound: int = 0
             batch_dim_name: str = ""
-            if jagged_tensor_map is not None and ind in jagged_tensor_map:
+            if jagged_tensor_map and ind in jagged_tensor_map:
                 batch_dim_lower_bound = 0  # when all sequences are empty
                 # if the maximum sequence length for this jagged tensor was not
                 # inferred from the offsets, we use the globally configured
@@ -417,7 +420,7 @@ def from_input_list_with_batch_size_jagged_tensor(
                 )
                 batch_dim_upper_bound = max_batch_size * max_seq_len
                 batch_dim_name = f"batch_size_jagged_tensor_id_{jagged_tensor_map[ind]}"
-            elif batch_dim in jagged_tensor_batch_dims:
+            elif not jagged_tensor_map and batch_dim in jagged_tensor_batch_dims:
                 batch_dim_lower_bound = 0
                 max_seq_len = max_seq_lens_from_offsets.get(
                     batch_dim, max_sequence_length