feat(utils/writer.py): support writer add_scalars for writing dict data (#257)

* feat(utils/writer.py): support writer add_scalars interface for writing dict data

* feat(hybrid_zero_optim.py): change grad_norm_groups list to dict

internlm/solver/optimizer/hybrid_zero_optim.py

@@ -497,6 +497,7 @@ class HybridZeroOptimizer(BaseOptimizer):
             grads = [self.padding_grad]
             params = [self.padding_tensor]
 
+        norm = 0
         if self._clip_grad_norm > 0:
             # this norm is before scaling, it will be very large
             norm = compute_norm(
@@ -542,15 +543,15 @@ class HybridZeroOptimizer(BaseOptimizer):
             self._param_store.clear_grads_of_previous_reduced_params()
 
         # compute norm for gradients in the last bucket
-        total_norms = []
+        total_norms = {}
         for group_id in range(self.num_param_groups):
-            total_norms.append(
-                self._compute_norm_with_stage(
-                    group_id=group_id,
-                    last_bucket=True,
-                    last_stage=True,
-                    previous_norm=groups_norms[group_id],
-                )
+            group_name = self.param_groups[group_id]["name"] if "name" in self.param_groups[group_id] else "default"
+            group_name = f"{group_id}_{group_name}"
+            total_norms[group_name] = self._compute_norm_with_stage(
+                group_id=group_id,
+                last_bucket=True,
+                last_stage=True,
+                previous_norm=groups_norms[group_id],
             )
 
         timer("sync_grad").start()
@@ -569,7 +570,7 @@ class HybridZeroOptimizer(BaseOptimizer):
         # found_inf = self._check_overflow()
         # Because you may encounter inf when computing norm
 
-        if -1 in norms:
+        if -1 in norms.values():
             found_inf = True
 
         loss_scale = float(self.loss_scale.item())  # backup
@@ -617,15 +618,17 @@ class HybridZeroOptimizer(BaseOptimizer):
 
         # unscale and clip grads
         # get the global norm
-        global_norm_groups = []
+        global_norm_groups = {}
         if self._clip_grad_norm > 0:
-            for norm in norms:
-                global_norm_groups.append(norm**0.5)
+            for group_name, norm in norms.items():
+                global_norm_groups[group_name] = norm**0.5
 
         # the following operations are performed only on the rank to which parameters are assigned.
         if gpc.config.model.dtype is not torch.float32:
-            if len(single_grad_partition_groups) != 0:
-                self._unscale_and_clip_grads(single_grad_partition_groups, global_norm_groups, loss_scale)
+            if len(single_grad_partition_groups) != 0 and self._clip_grad_norm > 0:
+                self._unscale_and_clip_grads(
+                    single_grad_partition_groups, list(global_norm_groups.values()), loss_scale
+                )
 
         # update the parameters
         timer("step").start()
@@ -652,7 +655,9 @@ class HybridZeroOptimizer(BaseOptimizer):
 
         # update gradients may not be needed here, because the sync_params function is used in initialization,
         # so synchronization is maintained
-        return True, [global_norm / loss_scale for global_norm in global_norm_groups]
+        for group_name, global_norm in global_norm_groups.items():
+            global_norm_groups[group_name] = global_norm / loss_scale
+        return True, global_norm_groups
 
     def broadcast_params(self):
         handles = []

internlm/train/training_internlm.py

@@ -389,23 +389,31 @@ def record_current_batch_training_metrics(
         line = ""
         for key, value in infos.items():
             line += f"{key}={value} "
-            writer.add_scalar(key=key, value=value, step=train_state.step_count)
+            if isinstance(value, dict):
+                writer.add_scalars(key=key, value=value, step=train_state.step_count)
+            else:
+                writer.add_scalar(key=key, value=value, step=train_state.step_count)
 
         if update_panel:
+            # metrics shown with dashboard panels
+            panel_metrics = {
+                "step": batch_count,
+                "lr": lr,
+                "num_consumed_tokens": train_state.num_consumed_tokens,
+                "loss": loss.item(),
+                "flops": tflops,
+                "tgs": tk_per_gpu,
+                "acc": acc_perplex["acc"],
+                "perplexity": acc_perplex["perplexity"],
+                "fwd_bwd_time": fwd_bwd_time,
+            }
+            for norm_key, norm_value in grad_norm.items():
+                panel_metrics[norm_key] = norm_value
+
             logger.info(
-                line,
-                extra={
-                    "step": batch_count,
-                    "lr": lr,
-                    "num_consumed_tokens": train_state.num_consumed_tokens,
-                    "grad_norm": grad_norm,
-                    "loss": loss.item(),
-                    "flops": tflops,
-                    "tgs": tk_per_gpu,
-                    "acc": acc_perplex["acc"],
-                    "perplexity": acc_perplex["perplexity"],
-                    "fwd_bwd_time": fwd_bwd_time,
-                },
+                "{line}",
+                line=line,
+                extra=panel_metrics,
             )
         else:
             logger.info(line)

internlm/utils/writer.py

@@ -134,6 +134,14 @@ class Writer:
         except Exception:
             traceback.print_exc()
 
+    def add_scalars(self, key, value, step):
+        try:
+            assert isinstance(value, dict)
+            if self.enable_tb and self.tb_writer is not None:
+                self.tb_writer.add_scalars(main_tag=key, tag_scalar_dict=value, global_step=step)
+        except Exception:
+            traceback.print_exc()
+
     def add_text(self, key, value, step):
         try:
             if self.enable_tb and self.tb_writer is not None:

train.py

@@ -6,7 +6,6 @@ import time
 import traceback
 from functools import partial
 
-import numpy as np
 import torch
 import torch.distributed as dist
 
@@ -236,7 +235,7 @@ def main(args):
                 train_state.step_count += 1
             else:
                 train_state.inf_nan_skip_batches += 1  # record the amount of updating parameters unsuccessfully.
-                if -1 in grad_norm_groups and gpc.is_rank_for_log():  # -1 encodes a specific failure case
+                if -1 in grad_norm_groups.values() and gpc.is_rank_for_log():  # -1 encodes a specific failure case
                     logger.warning(f"Warning: skip parameter update at step {batch_count}.")
                     send_alert_message(
                         address=gpc.config.alert_address,
@@ -257,7 +256,7 @@ def main(args):
                 trainer=trainer,
                 start_time=start_time,
                 loss=loss,
-                grad_norm=np.array(grad_norm_groups),
+                grad_norm=grad_norm_groups,
                 metric=metric,
                 update_panel=uniscale_logger is not None,
             )