impove comment

egs/librispeech/ASR/pruned_transducer_stateless7/optim.py

@@ -64,13 +64,15 @@ class BatchedOptimizer(Optimizer):
         you can do:
         <code>
           with self.batched_params(group["params"]) as batches:
-             for p, state in batches:
+             for p, state, p_names in batches:
                  ...
         </code>
 
         Args:
           group: a parameter group, which is a list of parameters; should be
-                one of self.groups.
+                one of self.param_groups.
+          group_params_names: name for each parameter in group,
+                which is List[str].
         """
         batches = defaultdict(
             list
@@ -79,6 +81,7 @@ class BatchedOptimizer(Optimizer):
             list
         )  # `batches` maps from tuple (dtype_as_str,*shape) to list of str
 
+        assert len(param_group) == len(group_params_names)
         for p, named_p in zip(param_group, group_params_names):
             key = (str(p.dtype), *p.shape)
             batches[key].append(p)
@@ -94,9 +97,9 @@ class BatchedOptimizer(Optimizer):
         stacked_params_dict = dict()
 
         # turn batches into a list, in deterministic order.
-        # pairs will contain pairs of (stacked_param, state), one for each batch
-        # in `batches`.
-        pairs = []
+        # tuples will contain tuples of (stacked_param, state, stacked_params_names),
+        # one for each batch in `batches`.
+        tuples = []
 
         for batch, batch_names in zip(batches, batches_names):
             p = batch[0]
@@ -110,11 +113,11 @@ class BatchedOptimizer(Optimizer):
             )
             p_stacked.grad = grad
             stacked_params_dict[key] = p_stacked
-            pairs.append((p_stacked, state, batch_names))
+            tuples.append((p_stacked, state, batch_names))
 
-        yield pairs  # <-- calling code will do the actual optimization here!
+        yield tuples  # <-- calling code will do the actual optimization here!
 
-        for ((stacked_params, _state, _names), batch) in zip(pairs, batches):
+        for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
             for i, p in enumerate(batch):  # batch is list of Parameter
                 p.copy_(stacked_params[i])
 
@@ -179,6 +182,11 @@ class ScaledAdam(BatchedOptimizer):
         show_dominant_parameters=True,
     ):
 
+        assert parameters_names is not None, (
+            "Please prepare parameters_names,"
+            "which is a List[List[str]]. Each List[str] is for a group"
+            "and each str is for a parameter"
+        )
         defaults = dict(
             lr=lr,
             clipping_scale=clipping_scale,
@@ -193,6 +201,7 @@ class ScaledAdam(BatchedOptimizer):
         )
 
         super(ScaledAdam, self).__init__(params, defaults)
+        assert len(self.param_groups) == len(parameters_names)
         self.parameters_names = parameters_names
         self.show_dominant_parameters = show_dominant_parameters
 
@@ -213,7 +222,6 @@ class ScaledAdam(BatchedOptimizer):
                 loss = closure()
 
         batch = True
-        assert len(self.param_groups) == len(self.parameters_names)
 
         for group, group_params_names in zip(self.param_groups, self.parameters_names):
 
@@ -292,7 +300,7 @@ class ScaledAdam(BatchedOptimizer):
         state["exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format)
 
     def _get_clipping_scale(
-        self, group: dict, pairs: List[Tuple[Tensor, dict, List[str]]]
+        self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]]
     ) -> float:
         """
         Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients
@@ -300,12 +308,16 @@ class ScaledAdam(BatchedOptimizer):
 
         Args:
            group: the parameter group, an item in self.param_groups
-           pairs: a list of pairs of (param, state) where param is a batched set of parameters, with a .grad
-                (1st dim is batch dim) and state is the state-dict where optimization parameters are kept.
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
         """
-        assert len(pairs) >= 1
+        assert len(tuples) >= 1
         clipping_scale = group["clipping_scale"]
-        (first_p, first_state, _) = pairs[0]
+        (first_p, first_state, _) = tuples[0]
         step = first_state["step"]
         if clipping_scale is None or step == 0:
             # no clipping.  return early on step == 0 because the other
@@ -314,7 +326,7 @@ class ScaledAdam(BatchedOptimizer):
         clipping_update_period = group["clipping_update_period"]
 
         tot_sumsq = torch.tensor(0.0, device=first_p.device)
-        for (p, state, param_names) in pairs:
+        for (p, state, param_names) in tuples:
             grad = p.grad
             if grad.is_sparse:
                 raise RuntimeError(
@@ -379,12 +391,27 @@ class ScaledAdam(BatchedOptimizer):
                 )
                 if self.show_dominant_parameters:
                     assert p.shape[0] == len(param_names)
-                    self._show_gradient_dominating_parameter(pairs, tot_sumsq)
+                    self._show_gradient_dominating_parameter(tuples, tot_sumsq)
             return ans
 
-    def _show_gradient_dominating_parameter(self, pairs, tot_sumsq):
+    def _show_gradient_dominating_parameter(
+        self, tuples: List[Tuple[Tensor, dict, List[str]]], tot_sumsq: Tensor
+    ):
+        """
+        Show information of parameter wihch dominanting tot_sumsq.
+
+        Args:
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+            tot_sumsq: sumsq of all parameters. Though it's could be calculated
+                from tuples, we still pass it to save some time.
+        """
         all_sumsq_orig = {}
-        for (p, state, batch_param_names) in pairs:
+        for (p, state, batch_param_names) in tuples:
             # p is a stacked batch parameters.
             batch_grad = p.grad
             if p.numel() == p.shape[0]:  # a batch of scalars

egs/librispeech/ASR/pruned_transducer_stateless7/train.py

@@ -989,9 +989,15 @@ def run(rank, world_size, args):
         model = DDP(model, device_ids=[rank], find_unused_parameters=True)
 
     parameters_names = []
-    parameters_names.append([name_param_pair[0] for name_param_pair in model.named_parameters()])
-    optimizer = ScaledAdam(model.parameters(), lr=params.base_lr,
-            clipping_scale=2.0, parameters_names=parameters_names)
+    parameters_names.append(
+        [name_param_pair[0] for name_param_pair in model.named_parameters()]
+    )
+    optimizer = ScaledAdam(
+        model.parameters(),
+        lr=params.base_lr,
+        clipping_scale=2.0,
+        parameters_names=parameters_names,
+    )
 
     scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)