auto-convert old checkpoints to new format

tdmpc2/common/layers.py

@@ -4,6 +4,7 @@ import torch.nn.functional as F
 from tensordict import from_modules
 from copy import deepcopy
 
+
 class Ensemble(nn.Module):
 	"""
 	Vectorized ensemble of modules.
@@ -161,3 +162,58 @@ def enc(cfg, out={}):
 		else:
 			raise NotImplementedError(f"Encoder for observation type {k} not implemented.")
 	return nn.ModuleDict(out)
+
+
+def api_model_conversion(target_state_dict, source_state_dict):
+	"""
+	Converts a checkpoint from our old API to the new torch.compile compatible API.
+	"""
+	# check whether checkpoint is already in the new format
+	if "_detach_Qs_params.0.weight" in source_state_dict:
+		return source_state_dict
+
+	name_map = ['weight', 'bias', 'ln.weight', 'ln.bias']
+	new_state_dict = dict()
+
+	# rename keys
+	for key, val in list(source_state_dict.items()):
+		if key.startswith('_Qs.'):
+			num = key[len('_Qs.params.'):]
+			new_key = str(int(num) // 4) + "." + name_map[int(num) % 4]
+			new_total_key = "_Qs.params." + new_key
+			del source_state_dict[key]
+			new_state_dict[new_total_key] = val
+			new_total_key = "_detach_Qs_params." + new_key
+			new_state_dict[new_total_key] = val
+		elif key.startswith('_target_Qs.'):
+			num = key[len('_target_Qs.params.'):]
+			new_key = str(int(num) // 4) + "." + name_map[int(num) % 4]
+			new_total_key = "_target_Qs_params." + new_key
+			del source_state_dict[key]
+			new_state_dict[new_total_key] = val
+
+	# add batch_size and device from target_state_dict to new_state_dict
+	for prefix in ('_Qs.', '_detach_Qs_', '_target_Qs_'):
+		for key in ('__batch_size', '__device'):
+			new_key = prefix + 'params.' + key
+			new_state_dict[new_key] = target_state_dict[new_key]
+
+	# check that every key in new_state_dict is in target_state_dict
+	for key in new_state_dict.keys():
+		assert key in target_state_dict, f"key {key} not in target_state_dict"
+	# check that all Qs keys in target_state_dict are in new_state_dict
+	for key in target_state_dict.keys():
+		if 'Qs' in key:
+			assert key in new_state_dict, f"key {key} not in new_state_dict"
+	# check that source_state_dict contains no Qs keys
+	for key in source_state_dict.keys():
+		assert 'Qs' not in key, f"key {key} contains 'Qs'"
+
+	# copy log_std_min and log_std_max from target_state_dict to new_state_dict
+	new_state_dict['log_std_min'] = target_state_dict['log_std_min']
+	new_state_dict['log_std_dif'] = target_state_dict['log_std_dif']
+
+	# copy new_state_dict to source_state_dict
+	source_state_dict.update(new_state_dict)
+
+	return source_state_dict

tdmpc2/tdmpc2.py

@@ -4,6 +4,7 @@ import torch.nn.functional as F
 from common import math
 from common.scale import RunningScale
 from common.world_model import WorldModel
+from common.layers import api_model_conversion
 from tensordict import TensorDict
 
 
@@ -84,39 +85,7 @@ class TDMPC2(torch.nn.Module):
 		"""
 		state_dict = fp if isinstance(fp, dict) else torch.load(fp, map_location=torch.get_default_device())
 		state_dict = state_dict["model"] if "model" in state_dict else state_dict
-		def load_sd_hook(model, local_state_dict, prefix, *args):
+		state_dict = api_model_conversion(self.model.state_dict(), state_dict)
-			name_map = [
-				"weight", "bias", "ln.weight", "ln.bias",
-			]
-			# print("Listing state dict keys (from disk)")
-			# for k in list(local_state_dict.keys()):
-				# print("\t", k)
-
-			sd = model.state_dict()
-			# print("Listing dest state dict keys")
-			# for k in list(sd.keys()):
-				# print("\t", k)
-
-			# print("Maps:")
-			new_sd = dict(sd)
-			for cur_prefix in (prefix, "_target"+prefix[:-1]+"_"):
-				for key, val in list(local_state_dict.items()):
-					if not key.startswith(cur_prefix[:-1]):
-						continue
-					num = key[len(cur_prefix + "params."):]
-					new_key = str(int(num) // 4) + "." + name_map[int(num) % 4]
-					new_total_key = cur_prefix + 'params.' + new_key
-					# print("\t", key, '-->', new_total_key)
-					del local_state_dict[key]
-					new_sd[new_total_key] = val
-					if not cur_prefix.startswith("_target"):
-						new_total_key = "_detach" + cur_prefix[:-1] + "_" + 'params.' + new_key
-						# print("\t", 'DETACH', key, '-->', new_total_key)
-						new_sd[new_total_key] = val
-			local_state_dict.update(new_sd)
-			return local_state_dict
-		load_sd_hook(self.model, state_dict, "_Qs.")
-		assert not set(TensorDict(self.model.state_dict()).keys()).symmetric_difference(set(TensorDict(state_dict).keys()))
 		self.model.load_state_dict(state_dict)
 		return