fix train.py and decode.py

fix

egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py

@@ -66,7 +66,7 @@ from train import DEFAULT_SPEECH_TOKEN
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 
-from icefall.checkpoint import average_checkpoints_with_averaged_model, load_checkpoint
+from icefall.checkpoint import load_checkpoint
 from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
@@ -446,7 +446,7 @@ def save_results(
     test_set_wers = dict()
     for key, results in results_dict.items():
         recog_path = (
-            params.exp_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
         )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
@@ -456,7 +456,7 @@ def save_results(
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
         errs_filename = (
-            params.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
         )
         # we compute CER for aishell dataset.
         results_char = []
@@ -472,7 +472,7 @@ def save_results(
             logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = params.exp_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
+    errs_info = params.res_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
         for key, val in test_set_wers:
@@ -495,9 +495,13 @@ def main():
 
     params = get_params()
     params.update(vars(args))
+
+    params.res_dir = params.exp_dir / f"{params.method}"
+
     params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
     setup_logger(
-        f"{params.exp_dir}/log-{params.method}-beam{params.beam_size}/log-decode-{params.suffix}"
+        params.res_dir
+        / f"log-decode-{params.method}-beam{params.beam_size}-{params.suffix}"
     )
 
     logging.info("Decoding started")
@@ -574,23 +578,20 @@ def main():
     if params.avg > 1:
         start = params.epoch - params.avg + 1
         assert start >= 1, start
-        checkpoint = torch.load(
-            f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
-        )
-        assert "model" not in checkpoint
         # deepspeed converted checkpoint only contains model state_dict
         filenames = [
-            f"{params.exp_dir}/epoch-{epoch}.pt"
+            f"{params.exp_dir}/epoch-{epoch}/pytorch_model.bin"
             for epoch in range(start, params.epoch + 1)
         ]
         avg_checkpoint = average_checkpoints(filenames)
         model.load_state_dict(avg_checkpoint, strict=False)
 
-        filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
+        # filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
-        torch.save(avg_checkpoint, filename)
+        # torch.save(avg_checkpoint, filename)
     else:
         checkpoint = torch.load(
-            f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
+            f"{params.exp_dir}/epoch-{params.epoch}/pytorch_model.bin",
+            map_location="cpu",
         )
         model.load_state_dict(checkpoint, strict=False)
 
@@ -643,8 +644,7 @@ def main():
     logging.info("Done!")
 
 
+if __name__ == "__main__":
     torch.set_num_threads(1)
     torch.set_num_interop_threads(1)
-
-if __name__ == "__main__":
     main()

egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py

@@ -523,7 +523,7 @@ def train_one_epoch(
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
-        if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
+        if batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")
             valid_info = compute_validation_loss(
                 params=params,
@@ -764,7 +764,7 @@ def run(rank, world_size, args):
     if params.sampler_state_dict_path:
         sampler_state_dict = torch.load(params.sampler_state_dict_path)
         sampler_state_dict["max_duration"] = params.max_duration
-    # TODO: load sampler state dict
+
     train_dl = data_module.train_dataloaders(
         train_cuts, sampler_state_dict=sampler_state_dict
     )
@@ -806,15 +806,15 @@ def run(rank, world_size, args):
 
         model.save_checkpoint(
             save_dir=params.exp_dir,
-            tag=f"epoch-{params.cur_epoch}",
+            tag=f"zero-epoch-{params.cur_epoch}",
             client_state={},
             exclude_frozen_parameters=True,
         )
         if rank == 0:
             convert_zero_checkpoint_to_fp32_state_dict(
                 params.exp_dir,
-                f"{params.exp_dir}/epoch-{params.cur_epoch}.pt",
+                f"{params.exp_dir}/epoch-{params.cur_epoch}",
-                tag=f"epoch-{params.cur_epoch}",
+                tag=f"zero-epoch-{params.cur_epoch}",
                 exclude_frozen_parameters=True,
             )
             # save sampler state dict into checkpoint
@@ -824,7 +824,7 @@ def run(rank, world_size, args):
                 f"{params.exp_dir}/epoch-{params.cur_epoch}-sampler.pt",
             )
 
-            os.system(f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}")
+            os.system(f"rm -rf {params.exp_dir}/zero-epoch-{params.cur_epoch}")
 
     logging.info("Done!")