From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/train_utils/average_nbest_models.py | 183 +++++++++++++++++++--------------------------
1 files changed, 77 insertions(+), 106 deletions(-)
diff --git a/funasr/train_utils/average_nbest_models.py b/funasr/train_utils/average_nbest_models.py
index 96e1384..67f1e55 100644
--- a/funasr/train_utils/average_nbest_models.py
+++ b/funasr/train_utils/average_nbest_models.py
@@ -9,117 +9,88 @@
import torch
from typing import Collection
+import os
+import torch
+import re
+from collections import OrderedDict
+from functools import cmp_to_key
-from funasr.train.reporter import Reporter
+
+def _get_checkpoint_paths(output_dir: str, last_n: int = 5, use_deepspeed=False, **kwargs):
+ """
+ Get the paths of the last 'last_n' checkpoints by parsing filenames
+ in the output directory.
+ """
+ try:
+ if not use_deepspeed:
+ checkpoint = torch.load(os.path.join(output_dir, "model.pt"), map_location="cpu")
+ else:
+ checkpoint = torch.load(
+ os.path.join(output_dir, "model.pt", "mp_rank_00_model_states.pt"),
+ map_location="cpu",
+ )
+ avg_keep_nbest_models_type = checkpoint["avg_keep_nbest_models_type"]
+ val_step_or_eoch = checkpoint[f"val_{avg_keep_nbest_models_type}_step_or_eoch"]
+ sorted_items = sorted(val_step_or_eoch.items(), key=lambda x: x[1], reverse=True)
+ sorted_items = (
+ sorted_items[:last_n] if avg_keep_nbest_models_type == "acc" else sorted_items[-last_n:]
+ )
+ checkpoint_paths = []
+ for key, value in sorted_items[:last_n]:
+ if not use_deepspeed:
+ ckpt = os.path.join(output_dir, key)
+ else:
+ ckpt = os.path.join(output_dir, key, "mp_rank_00_model_states.pt")
+ checkpoint_paths.append(ckpt)
+
+ except:
+ print(f"{checkpoint} does not exist, avg the lastet checkpoint.")
+ # List all files in the output directory
+ files = os.listdir(output_dir)
+ # Filter out checkpoint files and extract epoch numbers
+ checkpoint_files = [f for f in files if f.startswith("model.pt.e")]
+ # Sort files by epoch number in descending order
+ checkpoint_files.sort(key=lambda x: int(re.search(r"(\d+)", x).group()), reverse=True)
+ # Get the last 'last_n' checkpoint paths
+ checkpoint_paths = [os.path.join(output_dir, f) for f in checkpoint_files[:last_n]]
+ return checkpoint_paths
@torch.no_grad()
-def average_nbest_models(
- output_dir: Path,
- reporter: Reporter,
- best_model_criterion: Sequence[Sequence[str]],
- nbest: Union[Collection[int], int],
- suffix: Optional[str] = None,
- oss_bucket=None,
- pai_output_dir=None,
-) -> None:
- """Generate averaged model from n-best models
-
- Args:
- output_dir: The directory contains the model file for each epoch
- reporter: Reporter instance
- best_model_criterion: Give criterions to decide the best model.
- e.g. [("valid", "loss", "min"), ("train", "acc", "max")]
- nbest: Number of best model files to be averaged
- suffix: A suffix added to the averaged model file name
+def average_checkpoints(output_dir: str, last_n: int = 5, **kwargs):
"""
- if isinstance(nbest, int):
- nbests = [nbest]
- else:
- nbests = list(nbest)
- if len(nbests) == 0:
- warnings.warn("At least 1 nbest values are required")
- nbests = [1]
- if suffix is not None:
- suffix = suffix + "."
- else:
- suffix = ""
+ Average the last 'last_n' checkpoints' model state_dicts.
+ If a tensor is of type torch.int, perform sum instead of average.
+ """
+ checkpoint_paths = _get_checkpoint_paths(output_dir, last_n, **kwargs)
+ print(f"average_checkpoints: {checkpoint_paths}")
+ state_dicts = []
- # 1. Get nbests: List[Tuple[str, str, List[Tuple[epoch, value]]]]
- nbest_epochs = [
- (ph, k, reporter.sort_epochs_and_values(ph, k, m)[: max(nbests)])
- for ph, k, m in best_model_criterion
- if reporter.has(ph, k)
- ]
+ # Load state_dicts from checkpoints
+ for path in checkpoint_paths:
+ if os.path.isfile(path):
+ state_dicts.append(torch.load(path, map_location="cpu")["state_dict"])
+ else:
+ print(f"Checkpoint file {path} not found.")
- _loaded = {}
- for ph, cr, epoch_and_values in nbest_epochs:
- _nbests = [i for i in nbests if i <= len(epoch_and_values)]
- if len(_nbests) == 0:
- _nbests = [1]
+ # Check if we have any state_dicts to average
+ if len(state_dicts) < 1:
+ print("No checkpoints found for averaging.")
+ return
- for n in _nbests:
- if n == 0:
- continue
- elif n == 1:
- # The averaged model is same as the best model
- e, _ = epoch_and_values[0]
- op = output_dir / f"{e}epoch.pb"
- sym_op = output_dir / f"{ph}.{cr}.ave_1best.{suffix}pb"
- if sym_op.is_symlink() or sym_op.exists():
- sym_op.unlink()
- sym_op.symlink_to(op.name)
- else:
- op = output_dir / f"{ph}.{cr}.ave_{n}best.{suffix}pb"
- logging.info(
- f"Averaging {n}best models: " f'criterion="{ph}.{cr}": {op}'
- )
-
- avg = None
- # 2.a. Averaging model
- for e, _ in epoch_and_values[:n]:
- if e not in _loaded:
- if oss_bucket is None:
- _loaded[e] = torch.load(
- output_dir / f"{e}epoch.pb",
- map_location="cpu",
- )
- else:
- buffer = BytesIO(
- oss_bucket.get_object(os.path.join(pai_output_dir, f"{e}epoch.pb")).read())
- _loaded[e] = torch.load(buffer)
- states = _loaded[e]
-
- if avg is None:
- avg = states
- else:
- # Accumulated
- for k in avg:
- avg[k] = avg[k] + states[k]
- for k in avg:
- if str(avg[k].dtype).startswith("torch.int"):
- # For int type, not averaged, but only accumulated.
- # e.g. BatchNorm.num_batches_tracked
- # (If there are any cases that requires averaging
- # or the other reducing method, e.g. max/min, for integer type,
- # please report.)
- pass
- else:
- avg[k] = avg[k] / n
-
- # 2.b. Save the ave model and create a symlink
- if oss_bucket is None:
- torch.save(avg, op)
- else:
- buffer = BytesIO()
- torch.save(avg, buffer)
- oss_bucket.put_object(os.path.join(pai_output_dir, f"{ph}.{cr}.ave_{n}best.{suffix}pb"),
- buffer.getvalue())
-
- # 3. *.*.ave.pb is a symlink to the max ave model
- if oss_bucket is None:
- op = output_dir / f"{ph}.{cr}.ave_{max(_nbests)}best.{suffix}pb"
- sym_op = output_dir / f"{ph}.{cr}.ave.{suffix}pb"
- if sym_op.is_symlink() or sym_op.exists():
- sym_op.unlink()
- sym_op.symlink_to(op.name)
+ # Average or sum weights
+ avg_state_dict = OrderedDict()
+ for key in state_dicts[0].keys():
+ tensors = [state_dict[key].cpu() for state_dict in state_dicts]
+ # Check the type of the tensor
+ if str(tensors[0].dtype).startswith("torch.int"):
+ # Perform sum for integer tensors
+ summed_tensor = sum(tensors)
+ avg_state_dict[key] = summed_tensor
+ else:
+ # Perform average for other types of tensors
+ stacked_tensors = torch.stack(tensors)
+ avg_state_dict[key] = torch.mean(stacked_tensors, dim=0)
+ checkpoint_outpath = os.path.join(output_dir, f"model.pt.avg{last_n}")
+ torch.save({"state_dict": avg_state_dict}, checkpoint_outpath)
+ return checkpoint_outpath
--
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