[TorchRec] fix stride_per_key_per_rank in stagger scenario in D74366343 #3112
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This pull request was exported from Phabricator. Differential Revision: D76903646 |
Summary: Pull Request resolved: meta-pytorch#2587 # context * Previously for a KJT, only the following fields and `_keys` are stored in the pytree flatten specs. All other arguments/parameters would be derived accordingly. ``` _fields = [ "_values", "_weights", "_lengths", "_offsets", ] ``` * Particularly, the `stride` (int) of a KJT, which represents the `batch_size`, is computed by `_maybe_compute_stride_kjt`: ``` def _maybe_compute_stride_kjt( keys: List[str], stride: Optional[int], lengths: Optional[torch.Tensor], offsets: Optional[torch.Tensor], stride_per_key_per_rank: Optional[List[List[int]]], ) -> int: if stride is None: if len(keys) == 0: stride = 0 elif stride_per_key_per_rank is not None and len(stride_per_key_per_rank) > 0: stride = max([sum(s) for s in stride_per_key_per_rank]) elif offsets is not None and offsets.numel() > 0: stride = (offsets.numel() - 1) // len(keys) elif lengths is not None: stride = lengths.numel() // len(keys) else: stride = 0 return stride ``` * The previously stored pytree flatten specs are enough if the `batch_size` is static, however, this no longer holds true in a variable batch size scenario, where the `stride_per_key_per_rank` is not `None`. * An example is that with `dedup_ebc`, where the actual batch_size is variable (depending on the dedup data), but the output of the ebc should always be the **true** `stride` (static). * During ir_export, the output shape will be calculated from `kjt.stride()` function, which would be incorrect if the pytree specs only contains the `keys`. * This diff adds the `stride` into the KJT pytree flatten/unflatten functions so that a fakified KJT would have the correct stride value. Differential Revision: D66400821 Reviewed By: PaulZhang12
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…torch#3111) Summary: Pull Request resolved: meta-pytorch#3111 # context * original diff D74366343 broke cogwheel test and was reverted * the error stack P1844048578 is shown below: ``` File "/dev/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/dev/torch/nn/modules/module.py", line 1784, in _call_impl return forward_call(*args, **kwargs) File "/dev/torchrec/distributed/train_pipeline/runtime_forwards.py", line 84, in __call__ data = request.wait() File "/dev/torchrec/distributed/types.py", line 334, in wait ret: W = self._wait_impl() File "/dev/torchrec/distributed/embedding_sharding.py", line 655, in _wait_impl kjts.append(w.wait()) File "/dev/torchrec/distributed/types.py", line 334, in wait ret: W = self._wait_impl() File "/dev/torchrec/distributed/dist_data.py", line 426, in _wait_impl return type(self._input).dist_init( File "/dev/torchrec/sparse/jagged_tensor.py", line 2993, in dist_init return kjt.sync() File "/dev/torchrec/sparse/jagged_tensor.py", line 2067, in sync self.length_per_key() File "/dev/torchrec/sparse/jagged_tensor.py", line 2281, in length_per_key _length_per_key = _maybe_compute_length_per_key( File "/dev/torchrec/sparse/jagged_tensor.py", line 1192, in _maybe_compute_length_per_key _length_per_key_from_stride_per_key(lengths, stride_per_key) File "/dev/torchrec/sparse/jagged_tensor.py", line 1144, in _length_per_key_from_stride_per_key if _use_segment_sum_csr(stride_per_key): File "/dev/torchrec/sparse/jagged_tensor.py", line 1131, in _use_segment_sum_csr elements_per_segment = sum(stride_per_key) / len(stride_per_key) ZeroDivisionError: division by zero ``` * the complaint is `stride_per_key` is an empty list, which comes from the following function call: ``` stride_per_key = _maybe_compute_stride_per_key( self._stride_per_key, self._stride_per_key_per_rank, self.stride(), self._keys, ) ``` * the only place this `stride_per_key` could be empty is when the `stride_per_key_per_rank.dim() != 2` ``` def _maybe_compute_stride_per_key( stride_per_key: Optional[List[int]], stride_per_key_per_rank: Optional[torch.IntTensor], stride: Optional[int], keys: List[str], ) -> Optional[List[int]]: if stride_per_key is not None: return stride_per_key elif stride_per_key_per_rank is not None: if stride_per_key_per_rank.dim() != 2: # after permute the kjt could be empty return [] rt: List[int] = stride_per_key_per_rank.sum(dim=1).tolist() if not torch.jit.is_scripting() and is_torchdynamo_compiling(): pt2_checks_all_is_size(rt) return rt elif stride is not None: return [stride] * len(keys) else: return None ``` # the main change from D74366343 is that the `stride_per_key_per_rank` in `dist_init`: * baseline ``` if stagger > 1: stride_per_key_per_rank_stagger: List[List[int]] = [] local_world_size = num_workers // stagger for i in range(len(keys)): stride_per_rank_stagger: List[int] = [] for j in range(local_world_size): stride_per_rank_stagger.extend( stride_per_key_per_rank[i][j::local_world_size] ) stride_per_key_per_rank_stagger.append(stride_per_rank_stagger) stride_per_key_per_rank = stride_per_key_per_rank_stagger ``` * D76875546 (correct, this diff) ``` if stagger > 1: indices = torch.arange(num_workers).view(stagger, -1).T.reshape(-1) stride_per_key_per_rank = stride_per_key_per_rank[:, indices] ``` * D74366343 (incorrect, reverted) ``` if stagger > 1: local_world_size = num_workers // stagger indices = [ list(range(i, num_workers, local_world_size)) for i in range(local_world_size) ] stride_per_key_per_rank = stride_per_key_per_rank[:, indices] ``` Differential Revision: D76903646
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This pull request was exported from Phabricator. Differential Revision: D76903646 |
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Summary:
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stride_per_keyis an empty list, which comes from the following function call:stride_per_keycould be empty is when thestride_per_key_per_rank.dim() != 2the main change from D74366343 is that the
stride_per_key_per_rankindist_init:Rollback Plan:
Differential Revision: D76875546