added option to do backward AG over smaller set of gpus instead of full DDP

Author: Naman Goyal

fairscale/nn/data_parallel/fully_sharded_data_parallel.py

@@ -332,6 +332,7 @@ def __init__(
         offload_config: Optional[OffloadConfig] = None,
         state_dict_on_rank_0_only: bool = False,
         gradient_predivide_factor: Optional[float] = None,
+        zero2_process_group: Optional[ProcessGroup] = None,
     ):
         try:
             import torch._C
@@ -380,6 +381,9 @@ def __init__(
                     "parameter uses all the available ranks for the optimal performance."
                 )
         self.reshard_after_forward = self._orig_reshard_after_forward = reshard_after_forward
+
+        self.zero2_process_group = zero2_process_group
+
         self.disable_reshard_on_root = disable_reshard_on_root
         self.mixed_precision = mixed_precision
         self.fp32_reduce_scatter = fp32_reduce_scatter
@@ -518,6 +522,9 @@ def __init__(
                 if isinstance(m, FullyShardedDataParallel):
                     m._free_ssd_offload()
 
+        if self.zero2_process_group is not None:
+            assert not self.move_params_to_cpu
+
     def _get_gradient_predivide_factor(self, world_size: int) -> float:
         factor: int = 1
         while world_size % factor == 0 and world_size / factor > factor:
@@ -1419,7 +1426,10 @@ def forward(self, *args: Any, **kwargs: Any) -> torch.Tensor:
         outputs = self.module(*args, **kwargs)
 
         if self.reshard_after_forward:
-            self._free_full_params()
+            if self.zero2_process_group is not None:
+                self._zero2_shard_to_smaller_group()
+            else:
+                self._free_full_params()
             if self.mixed_precision or self.move_params_to_cpu:
                 self._free_fp16_param_shard()
 
@@ -1499,7 +1509,10 @@ def _pre_backward_hook(*unused: Any) -> None:
             # idempotent.  So in case they are called unnecessarily, they don't incur much
             # overhead.
             if self.reshard_after_forward:
-                self._rebuild_full_params()
+                if self.zero2_process_group is not None:
+                    self._zero2_rebuild_full_params()
+                else:
+                    self._rebuild_full_params()
                 if (
                     self.reshard_after_forward
                     and self._fsdp_forward_ordering is not None
@@ -2006,6 +2019,126 @@ def update_p_data(custom_output_tensor: Optional[torch.Tensor] = None) -> None:
             torch.cuda.current_stream().wait_stream(self._streams["all_gather"])
         return output_tensors
 
+
+    @torch.no_grad()
+    def _zero2_rebuild_full_params(self, force_full_precision: bool = False, wait_for_all_gather = True) -> Optional[List[Tuple[torch.Tensor, bool]]]:
+        """
+        Gather all shards of params.
+
+        Note, this is idempotent if full params are already gathered. Callers
+        assume the idempotency. So please keep it that way.
+
+        Args:
+            force_full_precision (bool, Optional): by default params will be gathered
+                in ``compute_dtype`` (e.g., FP16), unless *force_full_precision* is
+                ``True``, in which case they will be gathered in full precision
+                (e.g., FP32), possibly in fresh storage. The parameter that's being
+                rebuilt will end up in full precision as well.
+
+        Returns:
+            A list of tuples, where the first element is the full-sized param
+            and the second element is a bool indicating if it's safe for the
+            caller to free the full-sized param. This will be ``None`` if
+            ``force_full_precision=False`` and the full params are already gathered.
+        """
+        output_tensors: List[Tuple[torch.Tensor, bool]] = []
+
+        def update_p_data(custom_output_tensor: Optional[torch.Tensor] = None) -> None:
+            """
+            Helper function to update p.data pointer.
+
+            Args:
+                custom_output_tensor (torch.Tensor, Optional): if not None, this
+                tensor contains the data we just gathered.
+            """
+            if custom_output_tensor is not None:
+                assert p._is_sharded
+                p.data = custom_output_tensor
+                output_tensors.append((p.data, True))
+            elif not p._is_sharded:
+                if (self.mixed_precision or self.move_params_to_cpu) and not force_full_precision:
+                    assert p._fp16_shard is not None
+                    p.data = p._fp16_shard
+                    output_tensors.append((p.data, True))
+                else:
+                    # Here p.data == p._fp32_shard, so it's not safe to free.
+                    output_tensors.append((p.data, False))
+            else:
+                p.data = p._full_param_padded
+                output_tensors.append((p.data, True))
+            # Trim any padding and reshape to match original size.
+            p.data = p.data[: p._orig_size.numel()].view(p._orig_size)
+
+        if self._has_shared_params:
+            # self.has_full_params flag can be out of sync if a shared param is
+            # sharded by another FSDP instance. An example is that in eval case
+            # with reshard_after_forward=False but the sharing instance has
+            # reshard_after_forward=True. Then, on the second forward, the
+            # other instance can shard the shared param and but this instance
+            # can mistakenly think the full param is already gathered from the
+            # has_full_params flag.
+            #
+            # Therefore, we update the flag accordingly here.
+            self.has_full_params = not any(p._full_param_padded.storage().size() == 0 for p in self.params)
+
+        # Early exit if we already have full params and don't need full precision.
+        if self.has_full_params and not force_full_precision:
+            if wait_for_all_gather:
+                torch.cuda.current_stream().wait_stream(self._streams["all_gather"])
+            for p in self.params:
+                update_p_data()
+            return output_tensors
+
+        self.has_full_params = True
+
+        with torch.cuda.stream(self._streams["all_gather"]):
+
+            for p in self.params:
+                if not p._is_sharded:  # e.g., when world_size == 1
+                    update_p_data()
+                else:
+                    # Skip if already built. Only shared param can be rebuilt multiple times.
+                    # A corner case is p._orig_size = (1,), which means the shape equality is
+                    # not a perfect check. But we assume we don't share a param with shape (1,).
+                    if p.data.shape == p._orig_size and hasattr(p, "_is_shared") and p._is_shared:
+                        continue
+                    # If self.move_params_to_cpu and force_full_precision, we need to cast
+                    # the FP32 CPU param to CUDA for the all-gather.
+                    p_data = p.data.to(p._full_param_padded.device, non_blocking=True)
+
+                    p_size = p._full_param_padded.size()
+                    assert p_size.numel() % self.world_size == 0
+                    if self.mixed_precision and force_full_precision:
+                        # Allocate fresh tensor in full precision since we are in
+                        # mixed precision and full precision rebuild is asked.
+                        output_tensor = p_data.new_zeros(p_size)
+                    else:
+                        if p._full_param_padded.storage().size() != p_size.numel():
+                            # Allocate based on full size from all shards.
+                            alloc_storage_(p._full_param_padded, size=p_size)
+                        output_tensor = p._full_param_padded
+
+                    # Fill output_tensor with (p.data for each shard in self.world_size)
+                    if hasattr(dist, "_all_gather_base") and enable_nccl_base_collectives:
+                        # New version of PyTorch has all_gather_base, which is faster than chunk and then all_gather.
+                        dist._all_gather_base(output_tensor, p._zero2_fp16_shard , group=self.zero2_process_group)
+                    else:
+                        chunks = list(output_tensor.chunk(self.world_size))
+                        dist.all_gather(chunks, p._zero2_fp16_shard, group=self.zero2_process_group)
+
+                    # Set p.data = output_tensor (with padding trimmed)
+                    update_p_data(output_tensor)
+
+                    if (self.mixed_precision or self.move_params_to_cpu) and not force_full_precision:
+                        self._free_zero2_param_shard([p])
+
+                    if self.move_params_to_cpu and (self.params[0].dtype == self.compute_dtype):
+                        self._free_zero2_param_shard([p])
+        if wait_for_all_gather:
+            torch.cuda.current_stream().wait_stream(self._streams["all_gather"])
+        return output_tensors
+
+
     @torch.no_grad()
     def _use_full_params(self) -> None:
         """
@@ -2074,6 +2207,38 @@ def _free_full_params(self, params: Optional[List[Parameter]] = None) -> None:
             free_storage_(p._full_param_padded)
             torch.cuda.current_stream().synchronize()
 
+
+    def _zero2_shard_to_smaller_group(self, params: Optional[List[Parameter]] = None):
+        if params is None:
+            params = self.params
+        self.has_full_params = False
+        current_stream = torch.cuda.current_stream()
+        for p in params:
+            if not p._is_sharded:  # e.g., world_size == 1
+                if self.mixed_precision or self.move_params_to_cpu:
+                    self._free_fp16_param_shard([p])
+                continue
+            # Cases for when zero2 world size > 1 but less than zero3 size
+            zero2_world_size = dist.get_world_size(self.zero2_process_group)
+            zero2_rank = dist.get_rank(self.zero2_process_group)
+            chunks = p._full_param_padded.chunk(zero2_world_size)
+
+            p._zero2_fp16_shard = torch.empty_like(chunks[zero2_rank])
+            p._zero2_fp16_shard.copy_(chunks[zero2_rank])
+
+            # Don't let PyTorch reuse this memory until all work in the current
+            # stream is complete.
+            p._full_param_padded.record_stream(current_stream)
+            # There may be external references to the Tensor Storage that we
+            # can't modify, such as references that are created by
+            # ctx.save_for_backward in the forward pass. Thus when we
+            # unshard parameters, we should reuse the original Tensor
+            # Storage object and unshard it in-place. For now, just resize
+            # the Storage to 0 to save memory.
+            free_storage_(p._full_param_padded)
+            torch.cuda.current_stream().synchronize()
+
+
     def local_metadata_dict(self) -> Dict[str, Any]:
         """
         Get the information needed to reconstruct the model from shards offline.
@@ -2238,6 +2403,19 @@ def _free_fp16_param_shard(self, params: Optional[List[Parameter]] = None) -> No
                 p._fp16_shard.record_stream(current_stream)
                 free_storage_(p._fp16_shard)
 
+    @torch.no_grad()
+    def _free_zero2_param_shard(self, params: Optional[List[Parameter]] = None) -> None:
+        """Free storage for FP16 shards for a list of params."""
+        if params is None:
+            params = self.params
+        current_stream = torch.cuda.current_stream()
+        for p in params:
+            if p._zero2_fp16_shard is not None:
+                # _fp16_shard is allocated in "fp32_to_fp16" stream, so we can't
+                # free it until the work in the current stream completes.
+                p._zero2_fp16_shard.record_stream(current_stream)
+                free_storage_(p._zero2_fp16_shard)
+
     def assert_state(self, state: Union[TrainingState, List[TrainingState]]) -> None:
         """Assert we are in the given state."""
         # Since assert can be turned off and this error checking