LLM: Support load_low_bit loading models in shards format (#8612)

* shards_model

---------

Co-authored-by: leonardozcm <leonaordo1997zcm@gmail.com>

python/llm/src/bigdl/llm/transformers/model.py

@@ -14,9 +14,14 @@
 # limitations under the License.
 #
 
+import gc
 import transformers
 from transformers.configuration_utils import PretrainedConfig
-from .utils import extract_local_archive_file, load_state_dict, load
+from .utils import extract_local_archive_file, \
+    load_state_dict, \
+    load, \
+    get_local_shard_files, \
+    fix_key
 from bigdl.llm.ggml.quantize import ggml_tensor_qtype
 from bigdl.llm.utils.common import invalidInputError
 
@@ -147,12 +152,51 @@ class _BaseAutoModelClass:
         # and the tensor shape of int4 weights without quantization.
         model = ggml_convert_quant(model, qtype, convert_shape_only=True)
         # Load the quantized model at last.
-        archive_file = extract_local_archive_file(pretrained_model_name_or_path,
-                                                  subfolder,
-                                                  variant)
-        state_dict = load_state_dict(archive_file)
-        load(model, state_dict)
-        del state_dict
+        resolved_archive_file, is_sharded = extract_local_archive_file(
+            pretrained_model_name_or_path,
+            subfolder,
+            variant)
+        if is_sharded:
+            resolved_archive_file, sharded_metadata = \
+                get_local_shard_files(pretrained_model_name_or_path,
+                                      resolved_archive_file,
+                                      subfolder=subfolder)
+            start_prefix = ""
+            prefix = model.base_model_prefix
+            loaded_keys = [fix_key(key) for key in sharded_metadata["all_checkpoint_keys"]]
+            if len(prefix) > 0:
+                has_prefix_module = any(s.startswith(prefix) for s in loaded_keys)
+            else:
+                has_prefix_module = False
+
+            model_cls = type(model)
+            if len(model_cls.base_model_prefix) > 0 and \
+                not hasattr(model, model_cls.base_model_prefix) and \
+                    has_prefix_module:
+                start_prefix = model_cls.base_model_prefix + "."
+            from transformers.modeling_utils import _load_state_dict_into_model
+            error_msgs = []
+            for shard_file in resolved_archive_file:
+                state_dict = load_state_dict(shard_file)
+                error_msgs += _load_state_dict_into_model(model, state_dict, start_prefix)
+                # force memory release
+                del state_dict
+                gc.collect()
+
+            if len(error_msgs) > 0:
+                error_msg = "\n\t".join(error_msgs)
+                if "size mismatch" in error_msg:
+                    error_msg += (
+                        "\n\tYou may consider adding `ignore_mismatched_sizes=True`"
+                        " in the model `from_pretrained` method."
+                    )
+                invalidInputError(False, "Error(s) in loading state_dict"
+                                         f"for {model.__class__.__name__}:\n\t{error_msg}")
+
+        else:
+            state_dict = load_state_dict(resolved_archive_file)
+            load(model, state_dict)
+            del state_dict
 
         return model
 

python/llm/src/bigdl/llm/transformers/utils.py

@@ -48,6 +48,7 @@ from torch import nn
 
 
 WEIGHTS_NAME = "pytorch_model.bin"
+WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json"
 
 
 def extract_local_archive_file(pretrained_model_name_or_path, subfolder, variant):
@@ -59,7 +60,18 @@ def extract_local_archive_file(pretrained_model_name_or_path, subfolder, variant
         archive_file = os.path.join(
             pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_NAME, variant)
         )
-        return archive_file
+        return archive_file, False
+    elif os.path.isfile(
+        os.path.join(pretrained_model_name_or_path,
+                     subfolder,
+                     _add_variant(WEIGHTS_INDEX_NAME, variant))
+    ):
+        # Load from a sharded PyTorch checkpoint
+        archive_file = os.path.join(
+            pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant)
+        )
+        is_sharded = True
+        return archive_file, is_sharded
     else:
         invalidInputError(False,
                           f"Error no file named {_add_variant(WEIGHTS_NAME, variant)}"
@@ -89,3 +101,31 @@ def load(module: nn.Module, state_dict, prefix=""):
     for name, child in module._modules.items():
         if child is not None:
             load(child, state_dict, prefix + name + ".")
+
+
+def get_local_shard_files(pretrained_model_name_or_path, index_filename, subfolder=""):
+    import json
+
+    invalidInputError(os.path.isfile(index_filename),
+                      "Can't find a checkpoint index"
+                      f" ({index_filename}) in {pretrained_model_name_or_path}.")
+
+    with open(index_filename, "r") as f:
+        index = json.loads(f.read())
+
+    shard_filenames = sorted(set(index["weight_map"].values()))
+    sharded_metadata = index["metadata"]
+    sharded_metadata["all_checkpoint_keys"] = list(index["weight_map"].keys())
+    sharded_metadata["weight_map"] = index["weight_map"].copy()
+
+    shard_filenames = [os.path.join(pretrained_model_name_or_path, subfolder, f)
+                       for f in shard_filenames]
+    return shard_filenames, sharded_metadata
+
+
+def fix_key(key):
+    if "beta" in key:
+        return key.replace("beta", "bias")
+    if "gamma" in key:
+        return key.replace("gamma", "weight")
+    return key