refactor the total norm computation in grad clipping in APS (#3243)

Summary:

Refactored the previous code for applying gradient clipping across ddp and fsdp parameter. Added a new funciton _compute_total_norm() that takes in the replicated and sharded params provided in the gradientclippingOpitmizer class and computes the total gradient norm of the given parameter.

Differential Revision: D79128843

Author: Gavin Zhang

torchrec/optim/clipping.py

@@ -59,7 +59,7 @@ def __init__(
         super().__init__(optimizer)
         self._clipping = clipping
         self._max_gradient = max_gradient
-        self._norm_type = norm_type
+        self._norm_type = float(norm_type)
         self._check_meta: bool = True
         self._enable_global_grad_clip = enable_global_grad_clip
         self._step_num = 0
@@ -124,7 +124,7 @@ def step(self, closure: Any = None) -> None:
                 torch.nn.utils.clip_grad_norm_(
                     replicate_params,
                     self._max_gradient,
-                    norm_type=float(self._norm_type),
+                    norm_type=self._norm_type,
                 )
             else:
                 self.clip_grad_norm_()
@@ -135,98 +135,101 @@ def step(self, closure: Any = None) -> None:
         super().step(closure)
         self._step_num += 1
 
-    @torch.no_grad()
     def clip_grad_norm_(self) -> Optional[Union[float, torch.Tensor]]:
         """Clip the gradient norm of all parameters."""
-        max_norm = self._max_gradient
-        norm_type = float(self._norm_type)
+
+        # converts self._norm_type to a float if it's a string. Used in the case where self._norm_type is 'inf'.
         all_grads = []
         total_grad_norm = None
 
-        # Process distributed parameters and gradients
-        for pgs, dist_params in self._sharded_params.items():
-            sharded_grads = [
-                p.grad._local_tensor if isinstance(p.grad, DTensor) else p.grad
-                for p in dist_params
-                if p.grad is not None and p.grad.numel() > 0
-            ]
-            if len(sharded_grads) == 0:
-                continue
-            all_grads.extend(sharded_grads)
-
-            sharded_grad_norm = _batch_cal_norm(
-                sharded_grads,
-                max_norm,
-                norm_type,
-                pgs,
-            )
-            total_grad_norm = (
-                sharded_grad_norm
-                if total_grad_norm is None
-                else (
-                    torch.maximum(total_grad_norm, sharded_grad_norm)
-                    if norm_type == torch.inf
-                    else total_grad_norm + sharded_grad_norm
-                )
-            )
+        sharded_params = self._sharded_params
+        replicate_params = self._replicate_params
 
-        square_sharded_grad_norm = total_grad_norm if total_grad_norm is not None else 0
+        # Process distributed parameters and gradients
+        sharded_grads = {
+            pgs: _get_grads(dist_params) for pgs, dist_params in sharded_params.items()
+        }
+        all_grads.extend(*sharded_grads.values())
 
         # Process replicated parameters and gradients
-        if self._replicate_params:
-            replicated_grads = [
-                p.grad._local_tensor if isinstance(p.grad, DTensor) else p.grad
-                for p in self._replicate_params
-                if p.grad is not None and p.grad.numel() > 0
-            ]
-            all_grads.extend(replicated_grads)
-
-            replicated_grad_norm = _batch_cal_norm(
-                replicated_grads,
-                max_norm,
-                norm_type,
-                None,
-            )
-            total_grad_norm = (
-                replicated_grad_norm
-                if total_grad_norm is None
-                else (
-                    torch.maximum(total_grad_norm, replicated_grad_norm)
-                    if norm_type == torch.inf
-                    else total_grad_norm + replicated_grad_norm
-                )
-            )
-            square_replicated_grad_norm = replicated_grad_norm
-        else:
-            square_replicated_grad_norm = 0
-
-        global log_grad_norm
-        if log_grad_norm:
-            if total_grad_norm is not None and norm_type != torch.inf:
-                # pyre-ignore[58]
-                grad_norm = total_grad_norm ** (1.0 / norm_type)
-            else:
-                grad_norm = total_grad_norm
-
-            rank = dist.get_rank()
-            logger.info(
-                f"Clipping [rank={rank}, step={self._step_num}]: square_sharded_grad_norm = {square_sharded_grad_norm}, square_replicated_grad_norm = {square_replicated_grad_norm}, total_grad_norm = {grad_norm}"
-            )
-
-        # Aggregation
-        if total_grad_norm is None:
-            return
+        replicate_grads = _get_grads(replicate_params)
+        all_grads.extend(replicate_grads)
+
+        total_grad_norm = _compute_total_norm(
+            replicate_grads=replicate_grads,
+            sharded_grads=sharded_grads,
+            norm_type=self._norm_type,
+            max_grad_norm=self._max_gradient,
+        )
 
-        if norm_type != torch.inf:
-            # pyre-ignore [58]: ** is not supported for operand types torch._tensor.Tensor and float.
-            total_grad_norm = total_grad_norm ** (1.0 / norm_type)
         # pyre-ignore [58]: / is not supported for operand types float and Union[float, torch._tensor.Tensor].
-        clip_coef = cast(torch.Tensor, max_norm / (total_grad_norm + 1e-6))
+        clip_coef = cast(torch.Tensor, self._max_gradient / (total_grad_norm + 1e-6))
         clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
         torch._foreach_mul_(all_grads, clip_coef_clamped)
         return total_grad_norm
 
 
+def _get_grads(
+    param_list: List[torch.Tensor],
+) -> List[torch.Tensor]:
+    """Get the gradients of a list of parameters. Converts DTensors to local tensors if needed."""
+    grads = [
+        p.grad._local_tensor if isinstance(p.grad, DTensor) else p.grad
+        for p in param_list
+        if p.grad is not None and p.grad.numel() > 0
+    ]
+    return grads
+
+
+def _compute_total_norm(
+    replicate_grads: List[torch.Tensor],
+    sharded_grads: Dict[Tuple[dist.ProcessGroup], List[torch.Tensor]],
+    norm_type: float = 2.0,  # can be a normal float, or torch.inf
+    max_grad_norm: float = 1.0,
+) -> torch.Tensor:
+    """
+    Given both replicate grads and sharded grads, compute the total norm of the gradients of the full replicate params and the
+    full sharded param (parameters with a process group).
+
+    Args:
+        replicate_grads (List[torch.Tensor]): list of gradients for replicate params
+        sharded_grads (Dict[Tuple[dist.ProcessGroup], List[torch.Tensor]]): dict that maps each process group to a list of gradients for sharded params
+        norm_type (float): type of the used p-norm. Can be torch.inf for infinity norm.
+        max_grad_norm (float): max gradient norm.
+    """
+
+    ## compute the norm |W|^p corresponding to all sharded params W
+    sharded_grad_norm: torch.Tensor = torch.tensor(0.0)
+    combine_norm_operator = torch.maximum if norm_type == torch.inf else torch.add
+
+    # We need to move sharded_grad_norm to the same device as the first shard so that we can do addition (or take max)
+    # this is specifically for the case where sharded_grad_norm is 0, and replicate_grad_norm is not,
+    # because by default torch.tensor(0.0) is on cpu, and replicate_grad_norm is on GPU. For MTIA
+    # specifically, adding a tensor on cpu and a tensor on GPU will result in an error.
+    for pgs, dist_params in sharded_grads.items():
+        current_shard_norm = _batch_cal_norm(dist_params, max_grad_norm, norm_type, pgs)
+        sharded_grad_norm = combine_norm_operator(
+            sharded_grad_norm.to(current_shard_norm.device), current_shard_norm
+        )
+    # compute |W|^p corresponding to all replicate params W
+    # Similar to the case above, we move replicate_grad_norm to the same device as sharded_grad_norm so that we can do addition.
+    replicate_grad_norm: torch.Tensor = (
+        _batch_cal_norm(replicate_grads, max_grad_norm, norm_type)
+        if replicate_grads
+        else torch.tensor(0.0)
+    ).to(sharded_grad_norm.device)
+
+    # In the p-norm case, we are given norms |W_sharded|^p and |W_replicate|^p. To compute the total norm, we need to
+    # sum them and take the p-th root. In the inf-norm case, we are given max(|W_sharded|) and max(|W_replicate|).
+    # To compute the total norm, we need to take max(max(|W_sharded|), max(|W_replicate|).
+    combined_norm = combine_norm_operator(replicate_grad_norm, sharded_grad_norm)
+    total_grad_norm = (
+        combined_norm.pow(1.0 / norm_type) if norm_type != torch.inf else combined_norm
+    )
+
+    return total_grad_norm
+
+
 def _batch_cal_norm(
     grad_list: List[torch.Tensor],
     max_norm: float,
@@ -236,7 +239,6 @@ def _batch_cal_norm(
     """Helper function that calculates the norm of a list of gradients in batches. If process_groups
     are passed in, the norm will be aggregated across all ranks in the process group.
     """
-
     global use_64bit_grad_norm
     if use_64bit_grad_norm:
         grad_norms = torch.linalg.vector_norm(