[Unified Checkpoint] Fix fp32 dtype for using newest paddle(PaddlePad…

…dle#9360)
DesmonDay · Nov 4, 2024 · e281a32 · e281a32
1 parent 62ecb24
commit e281a32
Show file tree

Hide file tree

Showing 6 changed files with 9 additions and 42 deletions.
diff --git a/paddlenlp/trainer/unified_checkpoint/check_completion.py b/paddlenlp/trainer/unified_checkpoint/check_completion.py
@@ -30,11 +30,6 @@
 from paddlenlp.utils.log import logger
 from paddlenlp.utils.nested import flatten_list
 
-try:
-    from paddle.base import core
-except:
-    core = None
-
 from .utils import (
     get_expected_state_dict,
     is_sharding_split_param_mode,
@@ -200,7 +195,7 @@ def check_dynamic_load(args, weight_map, existed_files, is_master_weights=False,
                 if args.use_expert_parallel and dp_rank > 0 and not getattr(state_dict[key], "no_sync", False):
                     continue
 
-                if is_master_weights and state_dict[key].dtype == core.VarDesc.VarType.FP32:
+                if is_master_weights and state_dict[key].dtype == paddle.float32:
                     continue
 
                 if not is_master_weights:

diff --git a/paddlenlp/trainer/unified_checkpoint/load_dynamic.py b/paddlenlp/trainer/unified_checkpoint/load_dynamic.py
@@ -22,11 +22,6 @@
 import paddle.distributed as dist
 from paddle.distributed import fleet
 
-try:
-    from paddle.base import core
-except:
-    core = None
-
 from paddlenlp.peft import LoRAModel, PrefixModelForCausalLM
 from paddlenlp.transformers.model_utils import _load_state_dict_into_model
 from paddlenlp.transformers.utils import device_guard, is_safetensors_available
@@ -474,7 +469,7 @@ def check_optimizer_param(parameter):
         key_name = key.split("/")
         static_name = struct2static_name_mappings[key_name[0]]
         if has_master_weights:
-            if model_state_dict[key_name[0]].dtype != core.VarDesc.VarType.FP32:
+            if model_state_dict[key_name[0]].dtype != paddle.float32:
                 key_name = "_".join([static_name, FP32_MASTER, key_name[1]])
             else:
                 key_name = "_".join([static_name, key_name[1]])

diff --git a/paddlenlp/trainer/unified_checkpoint/load_local.py b/paddlenlp/trainer/unified_checkpoint/load_local.py
@@ -16,13 +16,9 @@
 import gc
 import os
 
+import paddle
 from tqdm.auto import tqdm
 
-try:
-    from paddle.base import core
-except:
-    core = None
-
 from paddlenlp.peft import LoRAModel, PrefixModelForCausalLM
 from paddlenlp.transformers.model_utils import (
     _load_state_dict_into_model,
@@ -252,7 +248,7 @@ def load_resolved_archive_file(resolved_archive_file, sharded_metadata, expected
         key_name = key.split("/")
         static_name = struct2static_name_mappings[key_name[0]]
         if has_master_weights:
-            if model_state_dict[key_name[0]].dtype != core.VarDesc.VarType.FP32:
+            if model_state_dict[key_name[0]].dtype != paddle.float32:
                 key_name = "_".join([static_name, FP32_MASTER, key_name[1]])
             else:
                 key_name = "_".join([static_name, key_name[1]])

diff --git a/paddlenlp/trainer/unified_checkpoint/load_save_single_card.py b/paddlenlp/trainer/unified_checkpoint/load_save_single_card.py
@@ -19,11 +19,6 @@
 
 import paddle
 
-try:
-    from paddle.base import core
-except:
-    core = None
-
 from paddlenlp.peft import LoRAModel, PrefixModelForCausalLM
 from paddlenlp.transformers.model_utils import (
     _load_state_dict_into_model,
@@ -120,7 +115,7 @@ def save_single_card_optimizer(model, optimizer, output_dir):
     fp32_weight = {}
     for k, v in state_dict.items():
         static2struct_name_mappings[v.name] = k
-        if master_weights is not None and v.dtype == core.VarDesc.VarType.FP32:
+        if master_weights is not None and v.dtype == paddle.float32:
             fp32_weight[k] = v
 
     # rename optimizer param
@@ -226,7 +221,7 @@ def load_single_card_optimizer(model, optimizer, resume_from_checkpoint: str):
         key_name = key.split("/")
         static_name = struct2static_name_mappings[key_name[0]]
         if has_master_weights:
-            if model_state_dict[key_name[0]].dtype != core.VarDesc.VarType.FP32:
+            if model_state_dict[key_name[0]].dtype != paddle.float32:
                 key_name = "_".join([static_name, FP32_MASTER, key_name[1]])
             else:
                 key_name = "_".join([static_name, key_name[1]])

diff --git a/paddlenlp/trainer/unified_checkpoint/unified_checkpoint.py b/paddlenlp/trainer/unified_checkpoint/unified_checkpoint.py
@@ -19,11 +19,6 @@
 import paddle
 from paddle.distributed import fleet
 
-try:
-    from paddle.base import core
-except:
-    core = None
-
 from paddlenlp.peft import LoRAModel, PrefixModelForCausalLM
 from paddlenlp.trainer.argparser import strtobool
 from paddlenlp.trainer.utils.helper import distributed_isfile
@@ -281,7 +276,7 @@ def load_non_merge_optimizer(self, model, optimizer, resume_from_checkpoint):
             key_name = key.split("/")
             static_name = struct2static_name_mappings[key_name[0]]
             if has_master_weights:
-                if model_state_dict[key_name[0]].dtype != core.VarDesc.VarType.FP32:
+                if model_state_dict[key_name[0]].dtype != paddle.float32:
                     key_name = "_".join([static_name, FP32_MASTER, key_name[1]])
                 else:
                     key_name = "_".join([static_name, key_name[1]])
@@ -529,7 +524,7 @@ def unified_optimizer_into_shards(
     fp32_weight = {}
     for k, v in state_dict.items():
         static2struct_name_mappings[v.name] = k
-        if master_weights is not None and v.dtype == core.VarDesc.VarType.FP32:
+        if master_weights is not None and v.dtype == paddle.float32:
             if args.dataset_rank > 0:  # deal with different dataset rank.
                 continue
             fp32_weight[k] = v

diff --git a/paddlenlp/trainer/unified_checkpoint/utils.py b/paddlenlp/trainer/unified_checkpoint/utils.py
@@ -21,11 +21,6 @@
 import paddle.distributed as dist
 from paddle.distributed import fleet
 
-try:
-    from paddle.base import core
-except:
-    core = None
-
 from paddlenlp.peft import LoRAModel, PrefixModelForCausalLM
 from paddlenlp.trainer.trainer_utils import ExplicitEnum, ShardingOption
 from paddlenlp.trainer.utils.helper import distributed_isfile
@@ -231,11 +226,7 @@ def get_expected_keys(args, sharded_metadata, model, optimizer, is_master_weight
     expected_keys = []
     for key in list(sharded_metadata["all_optimizer_keys"]):
         key_name = key.split("/")[0]
-        if (
-            is_master_weights
-            and key_name in model_state_dict
-            and model_state_dict[key_name].dtype == core.VarDesc.VarType.FP32
-        ):
+        if is_master_weights and key_name in model_state_dict and model_state_dict[key_name].dtype == paddle.float32:
             continue
 
         if args.use_expert_parallel and args.data_parallel_rank > 0: