Backward Compat with Torchmetrics

composer/core/state.py

@@ -18,6 +18,7 @@
 from torch.optim import Optimizer
 from torch.utils.data import DataLoader, Dataset
 from torchmetrics import Metric
+from torchmetrics.metric import jit_distributed_available
 
 from composer.core.data_spec import DataSpec
 from composer.core.event import Event
@@ -149,12 +150,26 @@ def _ensure_backwards_compatible_checkpointing(state_dict: Dict[str, Any]):
     # v0.4.1 removed the leading underscores for the keys in the state_dict
     # It also renamed _is_model_ddp_wrapped to is_model_ddp
     state = {}
-    for k, v in state_dict.items():
-        if k == '_is_model_ddp_wrapped':
-            k = 'is_model_ddp'
-        if k.startswith('_'):
-            k = k[1:]
-        state[k] = v
+    for attribute_name, serialized_value in state_dict.items():
+        if attribute_name == '_is_model_ddp_wrapped':
+            attribute_name = 'is_model_ddp'
+        if attribute_name.startswith('_'):
+            attribute_name = attribute_name[1:]
+        # Torchmetrics adds a new attribute as of 0.11 which must be added to deserialized metrics
+        if attribute_name == 'train_metrics':
+            for metric_name in serialized_value.keys():
+                metric = serialized_value[metric_name]
+                if not hasattr(metric, 'distributed_available_fn'):
+                    metric.distributed_available_fn = jit_distributed_available
+                    serialized_value[metric_name] = metric
+        elif attribute_name == 'eval_metrics':
+            for evaluator_name, eval_metrics in serialized_value.items():
+                for metric_name in eval_metrics.keys():
+                    metric = eval_metrics[metric_name]
+                    if not hasattr(metric, 'distributed_available_fn'):
+                        metric.distributed_available_fn = jit_distributed_available
+                        serialized_value[evaluator_name][metric_name] = metric
+        state[attribute_name] = serialized_value
     return state
 
 
@@ -1049,14 +1064,14 @@ def load_state_dict(
             elif attribute_name == 'train_metrics':
                 state_field_value = getattr(self, attribute_name)
                 for metric_name, metric in serialized_value.items():
-                    state_field_value[metric_name] = metric
                     metric._device = self.device._device
+                    state_field_value[metric_name] = metric
             elif attribute_name == 'eval_metrics':
                 state_field_value = getattr(self, attribute_name)
                 for eval_key, eval_metrics in serialized_value.items():
                     for metric_name, metric in eval_metrics.items():
-                        state_field_value[eval_key][metric_name] = metric
                         metric._device = self.device._device
+                        state_field_value[eval_key][metric_name] = metric
             elif attribute_name in _STATE_DICT_SERIALIZED_ATTRIBUTES:
                 state_field_value = getattr(self, attribute_name)
                 for target in ensure_tuple(state_field_value):

composer/models/huggingface.py

@@ -19,7 +19,7 @@
 
 from composer.metrics import InContextLearningMetric
 from composer.models.base import ComposerModel
-from composer.utils import MissingConditionalImportError, get_file, import_object
+from composer.utils import MissingConditionalImportError, get_file, import_object, safe_torch_load
 
 if TYPE_CHECKING:
     import transformers
@@ -226,7 +226,7 @@ def hf_from_composer_checkpoint(
         get_file(checkpoint_path, str(local_checkpoint_save_location))
 
         # load the state dict in
-        loaded_state_dict = torch.load(local_checkpoint_save_location, map_location='cpu')
+        loaded_state_dict = safe_torch_load(local_checkpoint_save_location)
 
         hf_state = loaded_state_dict['state']['integrations']['huggingface']
         hf_model_state = hf_state['model']
@@ -512,7 +512,7 @@ def write_huggingface_pretrained_from_composer_checkpoint(
     # download the checkpoint file
     get_file(str(checkpoint_path), str(local_checkpoint_save_location))
 
-    composer_state_dict = torch.load(local_checkpoint_save_location, map_location='cpu')
+    composer_state_dict = safe_torch_load(local_checkpoint_save_location)
 
     config = get_hf_config_from_composer_state_dict(composer_state_dict)
     config.save_pretrained(output_folder)

composer/utils/__init__.py

@@ -6,7 +6,7 @@
 from composer.utils.auto_log_hparams import (convert_flat_dict_to_nested_dict, convert_nested_dict_to_flat_dict,
                                              extract_hparams)
 from composer.utils.batch_helpers import batch_get, batch_set
-from composer.utils.checkpoint import PartialFilePath, load_checkpoint, save_checkpoint
+from composer.utils.checkpoint import PartialFilePath, load_checkpoint, safe_torch_load, save_checkpoint
 from composer.utils.collect_env import (configure_excepthook, disable_env_report, enable_env_report,
                                         get_composer_env_dict, print_env)
 from composer.utils.device import get_device, is_tpu_installed
@@ -48,6 +48,7 @@
     'StringEnum',
     'load_checkpoint',
     'save_checkpoint',
+    'safe_torch_load',
     'ensure_folder_is_empty',
     'ensure_folder_has_no_conflicting_files',
     'export_for_inference',

composer/utils/checkpoint.py

@@ -14,6 +14,7 @@
 import tempfile
 import textwrap
 import warnings
+from pathlib import Path
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -399,6 +400,25 @@ def filter_func(state_dict: Dict) -> None:
     return filter_func
 
 
+def safe_torch_load(composer_states_filepath: Union[Path, str], map_location: str = 'cpu'):
+    """Load a torch checkpoint, catching errors due to backwards compatibility issues.
+
+    Args:
+        composer_states_filepath: The path to the checkpoint file.
+        map_location: The location to load the checkpoint to.
+    """
+    try:
+        state_dict = torch.load(composer_states_filepath, map_location=map_location)
+        return state_dict
+    except TypeError as e:
+        if 'Accuracy.__new__() missing 1 required positional argument' in str(e):
+            raise Exception('As of v0.10.0, torchmetrics introduces a new required argument to Accuracy which '
+                            'breaks backwards compatibility. Unfortunately, this means that older checkpoints '
+                            'cannot be loaded with the metrics. In order to successfully load this model, please '
+                            'pass `load_ignore_keys = ["state/train_metrics/*", "state/eval_metrics/*"]`.') from e
+        raise e
+
+
 def _restore_checkpoint(
     state: State,
     logger: Logger,
@@ -413,7 +433,7 @@ def _restore_checkpoint(
 ) -> Optional[List[Dict[str, Any]]]:
     """Restore a checkpoint into ``state`` and returns the rng state dicts (if ``load_weights_only`` is False)."""
     # Now, all ranks load the checkpoint that local rank zero downloaded
-    state_dict = torch.load(composer_states_filepath, map_location='cpu')
+    state_dict = safe_torch_load(composer_states_filepath)
     if ignore_keys:
         # Filter provided list of key paths
         if not callable(ignore_keys):

composer/utils/inference.py

@@ -19,7 +19,7 @@
 import torch.nn as nn
 
 from composer.utils import dist
-from composer.utils.checkpoint import download_checkpoint
+from composer.utils.checkpoint import download_checkpoint, safe_torch_load
 from composer.utils.device import get_device
 from composer.utils.iter_helpers import ensure_tuple
 from composer.utils.misc import is_model_ddp, is_model_deepspeed, model_eval_mode
@@ -172,7 +172,7 @@ def export_for_inference(
                                                                  node_checkpoint_folder=tempdir,
                                                                  object_store=load_object_store,
                                                                  progress_bar=True)
-            state_dict = torch.load(composer_states_filepath, map_location='cpu')
+            state_dict = safe_torch_load(composer_states_filepath)
             missing_keys, unexpected_keys = model.load_state_dict(state_dict['state']['model'], strict=load_strict)
             if len(missing_keys) > 0:
                 log.warning(f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}")