benchmark of fbgemm op - permute_multi_embedding

Summary:
# context
* added both **op-level** and **fn-level** benchmarks for the KT.regroup implementations
* analyze the op-level and fn-level performance in runtime and memory usage
* findings are that:
**a**. In the fn-level performance, the `permute_multi_embedding` (new op) outperforms both the native-pytorch implementation and the `permute_pooled_embs_auto_grad` (current Prod)
**b**. In the op-level performance, the new op is slower than the current prod because the new op integrates 

# performance notes
The good:
1. the algorithm is designed in a way that it doesn't need to know in advance whether the 1-to-N mapping exists in the permutes. 
2. `_all_keys_used_once` is no longer needed
3. no longer need a torch.cat before calling the old operator
4. no need to use `_pin_and_move` for the meta data (arguments), it will be handled inside the operator, it's more friendly to tracing.

The same bad:
1. it requires several HtoD communications (move tensor to device):
a) [resolved] 3 tensors, which are `permutes`, `input_lengths`, and `output_lengths`. Those tensors needs to be on the device so that the cuda kernels has access to it.
b) [resolved] 2 lists of (scalar_t*) pointers, input and output tensor lists.
c) [resolved] Didn't find a good way to let the kernel knows the address of the lists of input/output tensors, because the lists are also need to be on the device. 
2. tensor.contiguous for the backward function, it looks like the grad from the backward are somehow not contiguous.

# benchmark
* op-level results
```
INFO:root:size: 1024 x 57168; permute_multi_embedding: 1.5612200498580933 ms; permute_pooled_embs_auto_grad: 0.9015970826148987 ms
INFO:root:size: 1024 x 134096; permute_multi_embedding: 3.0794131755828857 ms; permute_pooled_embs_auto_grad: 2.114053726196289 ms
INFO:root:size: 1024 x 136752; permute_multi_embedding: 2.6919198036193848 ms; permute_pooled_embs_auto_grad: 2.159184455871582 ms
INFO:root:size: 1024 x 260944; permute_multi_embedding: 4.805435180664063 ms; permute_pooled_embs_auto_grad: 4.098493576049805 ms
INFO:root:size: 1024 x 538432; permute_multi_embedding: 9.359790802001953 ms; permute_pooled_embs_auto_grad: 8.504887580871582 ms
INFO:root:size: 1024 x 536592; permute_multi_embedding: 9.375926017761232 ms; permute_pooled_embs_auto_grad: 8.459586143493652 ms
```
* fn-level results
```
  _regroup_keyed_tenors               | B: 1024     | F: 1020     | device: cuda     | Runtime (P90):   2.8 ms | Memory (P90): 1011.0
  KeyedTensor.regroup                 | B: 1024     | F: 1020     | device: cuda     | Runtime (P90):   5.0 ms | Memory (P90): 1517.0
  KTRegroupAsDict                     | B: 1024     | F: 1020     | device: cuda     | Runtime (P90):   4.9 ms | Memory (P90): 1517.0
  permute_multi_embs                  | B: 1024     | F: 1020     | device: cuda     | Runtime (P90):   2.2 ms | Memory (P90): 1011.0
  _regroup_keyed_tenors_dup           | B: 1024     | F: 1020     | device: cuda     | Runtime (P90):   2.5 ms | Memory (P90): 1011.0
  KeyedTensor.regroup_dup             | B: 1024     | F: 1020     | device: cuda     | Runtime (P90):   2.5 ms | Memory (P90): 1011.0
  KTRegroupAsDict_dup                 | B: 1024     | F: 1020     | device: cuda     | Runtime (P90):   2.5 ms | Memory (P90): 1011.0
  permute_multi_embs_dup              | B: 1024     | F: 1020     | device: cuda     | Runtime (P90):   3.2 ms | Memory (P90): 1011.0
```

# traces
* [files](https://drive.google.com/drive/folders/1_9hOtQUQeFICBVxQtusvpQ_VajduFUmR?usp=sharing)
```
[[email protected] /data/sandcastle/boxes/fbsource (ae677c240)]$ ll *.json
-rw-rw-r-- 1 hhy hhy 8062993 Jun 21 23:26 trace-KeyedTensor.regroup_dup.json
-rw-rw-r-- 1 hhy hhy  949610 Jun 21 23:26 trace-KeyedTensor.regroup.json
-rw-rw-r-- 1 hhy hhy 5140143 Jun 21 23:26 trace-KTRegroupAsDict_dup.json
-rw-rw-r-- 1 hhy hhy  350370 Jun 21 23:26 trace-KTRegroupAsDict.json
-rw-rw-r-- 1 hhy hhy  581033 Jun 21 23:26 trace-permute_multi_embs_dup.json
-rw-rw-r-- 1 hhy hhy  582607 Jun 21 23:26 trace-permute_multi_embs.json
-rw-rw-r-- 1 hhy hhy 8025337 Jun 21 23:26 trace-_regroup_keyed_tenors_dup.json
-rw-rw-r-- 1 hhy hhy 8041586 Jun 21 23:26 trace-_regroup_keyed_tenors.json
```

Differential Revision: D58906839

Author: TroyGarden

torchrec/sparse/jagged_tensor.py

@@ -170,6 +170,21 @@ def _all_keys_used_once(
     return len(key_set) == len(group_set) == len(flat_keys) == len(flat_groups)
 
 
+@torch.fx.wrap
+def permute_multi_embedding(
+    keyed_tensors: List["KeyedTensor"], groups: List[List["str"]]
+) -> List[torch.Tensor]:
+    keys, lengths, values = _desugar_keyed_tensors(keyed_tensors)
+    permutes, in_lengths, out_lengths = _multi_remap_to_groups(keys, lengths, groups)
+    permuted_values = torch.ops.fbgemm.permute_multi_embedding(
+        values,
+        permutes,
+        in_lengths,
+        out_lengths,
+    )
+    return permuted_values
+
+
 @torch.fx.wrap
 def _fbgemm_permute_pooled_embs(
     keyed_tensors: List["KeyedTensor"], groups: List[List["str"]]

torchrec/sparse/tests/jagged_tensor_benchmark.py

@@ -21,6 +21,7 @@
     _regroup_keyed_tensors,
     KeyedJaggedTensor,
     KeyedTensor,
+    permute_multi_embedding,
 )
 from torchrec.sparse.tests.utils import build_groups, build_kts
 
@@ -245,6 +246,17 @@ def main(
                         {"keyed_tensors": kts},
                         profile,
                     )
+                    bench(
+                        "permute_multi_embs" + dup,
+                        labels,
+                        batch_size,
+                        n_dense + n_sparse,
+                        device_type,
+                        run_backward,
+                        permute_multi_embedding,
+                        {"keyed_tensors": kts, "groups": groups},
+                        profile,
+                    )
 
 
 if __name__ == "__main__":