Add diagnostics code.

egs/librispeech/ASR/transducer_lstm/train.py

@@ -57,8 +57,8 @@ import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
-from encoder import LstmEncoder
 from decoder import Decoder
+from encoder import LstmEncoder
 from joiner import Joiner
 from lhotse.cut import Cut
 from lhotse.dataset.sampling.base import CutSampler
@@ -76,7 +76,15 @@ from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.checkpoint import save_checkpoint_with_global_batch_idx
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    measure_gradient_norms,
+    measure_weight_norms,
+    optim_step_and_measure_param_change,
+    setup_logger,
+    str2bool,
+)
 
 LRSchedulerType = Union[
     torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
@@ -231,6 +239,14 @@ def get_parser():
         help="Accumulate stats on activations, print them and exit.",
     )
 
+    parser.add_argument(
+        "--log-diagnostics",
+        type=str2bool,
+        default=False,
+        help="True to also log parameter norm and "
+        "gradient norm to tensorboard.",
+    )
+
     parser.add_argument(
         "--save-every-n",
         type=int,
@@ -318,7 +334,7 @@ def get_params() -> AttributeDict:
             "batch_idx_train": 0,
             "log_interval": 50,
             "reset_interval": 200,
-            "valid_interval": 3000,  # For the 100h subset, use 800
+            "valid_interval": 3000,  # For the 100h subset, use 1600
             # parameters for encoder
             "feature_dim": 80,
             "subsampling_factor": 4,
@@ -645,6 +661,30 @@ def train_one_epoch(
 
     tot_loss = MetricsTracker()
 
+    def maybe_log_gradients(tag: str):
+        if (
+            params.log_diagnostics
+            and tb_writer is not None
+            and params.batch_idx_train % (params.log_interval * 5) == 0
+        ):
+            tb_writer.add_scalars(
+                tag,
+                measure_gradient_norms(model, norm="l2"),
+                global_step=params.batch_idx_train,
+            )
+
+    def maybe_log_weights(tag: str):
+        if (
+            params.log_diagnostics
+            and tb_writer is not None
+            and params.batch_idx_train % (params.log_interval * 5) == 0
+        ):
+            tb_writer.add_scalars(
+                tag,
+                measure_weight_norms(model, norm="l2"),
+                global_step=params.batch_idx_train,
+            )
+
     cur_batch_idx = params.get("cur_batch_idx", 0)
 
     for batch_idx, batch in enumerate(train_dl):
@@ -669,9 +709,32 @@ def train_one_epoch(
         # NOTE: We use reduction==sum and loss is computed over utterances
         # in the batch and there is no normalization to it so far.
         scaler.scale(loss).backward()
+
+        maybe_log_weights("train/param_norms")
+        maybe_log_gradients("train/grad_norms")
+
+        old_parameters = None
+        if (
+            params.log_diagnostics
+            and tb_writer is not None
+            and params.batch_idx_train % (params.log_interval * 5) == 0
+        ):
+            old_parameters = {
+                n: p.detach().clone() for n, p in model.named_parameters()
+            }
+
         scheduler.step_batch(params.batch_idx_train)
         scaler.step(optimizer)
         scaler.update()
+
+        if old_parameters is not None:
+            deltas = optim_step_and_measure_param_change(model, old_parameters)
+            tb_writer.add_scalars(
+                "train/relative_param_change_per_minibatch",
+                deltas,
+                global_step=params.batch_idx_train,
+            )
+
         optimizer.zero_grad()
 
         if params.print_diagnostics and batch_idx == 5: