Enhance Model Loading By Providing Parallelism, Uses Optional Env Flag

docs/source/en/_toctree.yml

@@ -1107,4 +1107,9 @@
     - local: internal/time_series_utils
       title: Utilities for Time Series
     title: Internal helpers
+  - sections:
+    - local: reference/environment_variables
+      title: Environment Variables
+    title: Reference
   title: API
+

docs/source/en/reference/environment_variables.md

@@ -0,0 +1,58 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Environment Variables
+
+## HF_ENABLE_PARALLEL_LOADING
+
+By default this is disabled. Enables the loading of torch and safetensor based weights to be loaded in parallel. Can decrease the time to load large models significantly, often times producing speed ups around ~50%.
+
+Can be set to a string equal to `"false"` or `"true"`. e.g. `os.environ["HF_ENABLE_PARALLEL_LOADING"] = "true"`.
+
+e.g. `facebook/opt-30b` on an AWS EC2 g4dn.metal instance can be made to load in ~30s with this enabled vs ~55s without it.
+
+Profile before committing to using this environment variable, this will not produce speed ups for smaller models.
+
+```py
+import os
+
+os.environ["HF_ENABLE_PARALLEL_LOADING"] = "true"
+
+from transformers import pipeline
+
+model = pipeline(task="text-generation", model="facebook/opt-30b", device_map="auto")
+```
+
+## HF_PARALLEL_LOADING_WORKERS
+
+Determines how many threads should be used when parallel loading is enabled. Default is `8`. 
+
+If the number of files that are being loaded is less than the number of threads specified, the number that is actually spawned will be equal to the number of files.
+
+e.g. If you specify 8 workers, and there are only 2 files, only 2 workers will be spawned.
+
+Tune as you see fit.
+
+```py
+import os
+
+os.environ["HF_ENABLE_PARALLEL_LOADING"] = "true"
+os.environ["HF_PARALLEL_LOADING_WORKERS"] = "4"
+
+from transformers import pipeline
+
+model = pipeline(task="text-generation", model="facebook/opt-30b", device_map="auto")
+```

src/transformers/modeling_utils.py

@@ -28,6 +28,7 @@
 import warnings
 from collections import defaultdict
 from collections.abc import MutableMapping
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from contextlib import contextmanager
 from dataclasses import dataclass
 from enum import Enum
@@ -836,6 +837,118 @@ def _load_state_dict_into_meta_model(
     return disk_offload_index, cpu_offload_index
 
 
+def load_shard_file(args):
+    (
+        shard_file,
+        state_dict,
+        disk_only_shard_files,
+        is_hqq_or_bnb,
+        is_quantized,
+        device_map,
+        hf_quantizer,
+        key_renaming_mapping,
+        weights_only,
+        model_to_load,
+        expected_keys,
+        reverse_key_renaming_mapping,
+        disk_offload_folder,
+        disk_offload_index,
+        cpu_offload_folder,
+        cpu_offload_index,
+        is_offloaded_safetensors,
+        keep_in_fp32_regex,
+        unexpected_keys,
+        device_mesh,
+    ) = args
+
+    # Skip the load for shards that only contain disk-offloaded weights
+    if shard_file in disk_only_shard_files:
+        return [], disk_offload_index, cpu_offload_index
+
+    map_location = "cpu"
+    if (
+        shard_file.endswith(".safetensors")
+        and not is_hqq_or_bnb
+        and not (is_deepspeed_zero3_enabled() and not is_quantized)
+    ):
+        map_location = "meta"
+    elif (
+        device_map is not None
+        and hf_quantizer is not None
+        and hf_quantizer.quantization_config.quant_method == QuantizationMethod.TORCHAO
+        and (
+            hf_quantizer.quantization_config.quant_type in ["int4_weight_only", "autoquant"]
+            or isinstance(hf_quantizer.quantization_config.quant_type, Int4WeightOnlyConfig)
+        )
+    ):
+        map_location = torch.device([d for d in device_map.values() if d not in ["cpu", "disk"]][0])
+
+    # If shard_file is "", we use the existing state_dict instead of loading it
+    if shard_file != "":
+        state_dict = load_state_dict(
+            shard_file, is_quantized=is_quantized, map_location=map_location, weights_only=weights_only
+        )
+
+    # Fix the key names
+    state_dict = {key_renaming_mapping[k]: v for k, v in state_dict.items() if k in key_renaming_mapping}
+
+    error_msgs = []
+
+    if is_deepspeed_zero3_enabled() and not is_quantized:
+        error_msgs += _load_state_dict_into_zero3_model(model_to_load, state_dict)
+    # Skip it with fsdp on ranks other than 0
+    elif not (is_fsdp_enabled() and not is_local_dist_rank_0() and not is_quantized):
+        disk_offload_index, cpu_offload_index = _load_state_dict_into_meta_model(
+            model_to_load,
+            state_dict,
+            shard_file,
+            expected_keys,
+            reverse_key_renaming_mapping,
+            device_map=device_map,
+            disk_offload_folder=disk_offload_folder,
+            disk_offload_index=disk_offload_index,
+            cpu_offload_folder=cpu_offload_folder,
+            cpu_offload_index=cpu_offload_index,
+            hf_quantizer=hf_quantizer,
+            is_safetensors=is_offloaded_safetensors,
+            keep_in_fp32_regex=keep_in_fp32_regex,
+            unexpected_keys=unexpected_keys,
+            device_mesh=device_mesh,
+        )
+
+    # force memory release to avoid having multiple state dicts in memory as shards are processed
+    del state_dict
+    gc.collect()
+
+    return error_msgs, disk_offload_index, cpu_offload_index
+
+def load_shard_files_with_threadpool(args_list):
+    num_workers = int(os.environ.get("HF_PARALLEL_LOADING_WORKERS", "8"))
+
+    # Do not spawn anymore workers than you need
+    num_workers = min(len(args_list), num_workers)
+
+    logger.info(f"Loading model weights in parallel with {num_workers} workers...")
+
+    error_msgs = []
+
+    with ThreadPoolExecutor(max_workers=num_workers) as executor:
+        with logging.tqdm(total=len(args_list), desc="Loading checkpoint shards") as pbar:
+            futures = [executor.submit(load_shard_file, arg) for arg in args_list]
+            for future in as_completed(futures):
+                result = future.result()
+                (
+                    _error_msgs,
+                    disk_offload_index,
+                    cpu_offload_index,
+                ) = result
+
+                error_msgs += _error_msgs
+
+                pbar.update(1)
+
+    return error_msgs, disk_offload_index, cpu_offload_index
+
 def _add_variant(weights_name: str, variant: Optional[str] = None) -> str:
     if variant is not None:
         path, name = weights_name.rsplit(".", 1)
@@ -2550,9 +2663,9 @@ def tie_encoder_to_decoder_recursively(
             total_decoder_name="",
             total_encoder_name="",
         ):
-            assert isinstance(decoder_pointer, nn.Module) and isinstance(encoder_pointer, nn.Module), (
-                f"{decoder_pointer} and {encoder_pointer} have to be of type nn.Module"
-            )
+            assert isinstance(decoder_pointer, nn.Module) and isinstance(
+                encoder_pointer, nn.Module
+            ), f"{decoder_pointer} and {encoder_pointer} have to be of type nn.Module"
             if hasattr(decoder_pointer, "weight"):
                 assert hasattr(encoder_pointer, "weight")
                 encoder_pointer.weight = decoder_pointer.weight
@@ -2566,9 +2679,9 @@ def tie_encoder_to_decoder_recursively(
             encoder_modules = encoder_pointer._modules
             decoder_modules = decoder_pointer._modules
             if len(decoder_modules) > 0:
-                assert len(encoder_modules) > 0, (
-                    f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
-                )
+                assert (
+                    len(encoder_modules) > 0
+                ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
 
                 all_encoder_weights = {module_name + "/" + sub_name for sub_name in encoder_modules.keys()}
                 encoder_layer_pos = 0
@@ -4894,9 +5007,6 @@ def _load_pretrained_model(
             cpu_offload_folder = tempfile.mkdtemp()
             cpu_offload_index = {}
 
-        # For nice tqdm bars
-        if checkpoint_files is not None and len(checkpoint_files) > 1:
-            checkpoint_files = logging.tqdm(checkpoint_files, desc="Loading checkpoint shards")
         # To be able to iterate, even if we don't use it if the state_dict is already provided
         elif state_dict is not None:
             checkpoint_files = [""]
@@ -4911,64 +5021,45 @@ def _load_pretrained_model(
             expanded_device_map = expand_device_map(device_map, expected_keys)
             caching_allocator_warmup(model_to_load, expanded_device_map, hf_quantizer)
 
-        error_msgs = []
-        # Iterate on all the shards to load the weights
-        for shard_file in checkpoint_files:
-            # Skip the load for shards that only contain disk-offloaded weights
-            if shard_file in disk_only_shard_files:
-                continue
-
-            map_location = "cpu"
-            if (
-                shard_file.endswith(".safetensors")
-                and not is_hqq_or_bnb
-                and not (is_deepspeed_zero3_enabled() and not is_quantized)
-            ):
-                map_location = "meta"
-            elif (
-                device_map is not None
-                and hf_quantizer is not None
-                and hf_quantizer.quantization_config.quant_method == QuantizationMethod.TORCHAO
-                and (
-                    hf_quantizer.quantization_config.quant_type in ["int4_weight_only", "autoquant"]
-                    or isinstance(hf_quantizer.quantization_config.quant_type, Int4WeightOnlyConfig)
-                )
-            ):
-                map_location = torch.device([d for d in device_map.values() if d not in ["cpu", "disk"]][0])
-
-            # If shard_file is "", we use the existing state_dict instead of loading it
-            if shard_file != "":
-                state_dict = load_state_dict(
-                    shard_file, is_quantized=is_quantized, map_location=map_location, weights_only=weights_only
-                )
+        # Prepare and compatabilize arguments for serial and parallel shard loading
+        args_list = [
+            (
+                shard_file,
+                state_dict,
+                disk_only_shard_files,
+                is_hqq_or_bnb,
+                is_quantized,
+                device_map,
+                hf_quantizer,
+                key_renaming_mapping,
+                weights_only,
+                model_to_load,
+                expected_keys,
+                reverse_key_renaming_mapping,
+                disk_offload_folder,
+                disk_offload_index,
+                cpu_offload_folder,
+                cpu_offload_index,
+                is_offloaded_safetensors,
+                keep_in_fp32_regex,
+                unexpected_keys,
+                device_mesh,
+            )
+            for shard_file in checkpoint_files
+        ]
 
-            # Fix the key names
-            state_dict = {key_renaming_mapping[k]: v for k, v in state_dict.items() if k in key_renaming_mapping}
+        error_msgs = []
 
-            if is_deepspeed_zero3_enabled() and not is_quantized:
-                error_msgs += _load_state_dict_into_zero3_model(model_to_load, state_dict)
-            # Skip it with fsdp on ranks other than 0
-            elif not (is_fsdp_enabled() and not is_local_dist_rank_0() and not is_quantized):
-                disk_offload_index, cpu_offload_index = _load_state_dict_into_meta_model(
-                    model_to_load,
-                    state_dict,
-                    shard_file,
-                    expected_keys,
-                    reverse_key_renaming_mapping,
-                    device_map=device_map,
-                    disk_offload_folder=disk_offload_folder,
-                    disk_offload_index=disk_offload_index,
-                    cpu_offload_folder=cpu_offload_folder,
-                    cpu_offload_index=cpu_offload_index,
-                    hf_quantizer=hf_quantizer,
-                    is_safetensors=is_offloaded_safetensors,
-                    keep_in_fp32_regex=keep_in_fp32_regex,
-                    unexpected_keys=unexpected_keys,
-                    device_mesh=device_mesh,
-                )
+        if os.environ.get("HF_ENABLE_PARALLEL_LOADING", "").upper() in ENV_VARS_TRUE_VALUES and not is_deepspeed_zero3_enabled():
+            _error_msgs, disk_offload_index, cpu_offload_index = load_shard_files_with_threadpool(args_list)
+            error_msgs += _error_msgs
+        else:
+            if len(args_list) > 1:
+                args_list = logging.tqdm(args_list, desc="Loading checkpoint shards")
 
-            # force memory release if loading multiple shards, to avoid having 2 state dicts in memory in next loop
-            del state_dict
+            for args in args_list:
+                _error_msgs, disk_offload_index, cpu_offload_index = load_shard_file(args)
+                error_msgs += _error_msgs
 
         # Adjust offloaded weights name and save if needed
         if disk_offload_index is not None and len(disk_offload_index) > 0:
@@ -5513,9 +5604,9 @@ def forward(
         Returns:
             `torch.FloatTensor`: The end logits for SQuAD.
         """
-        assert start_states is not None or start_positions is not None, (
-            "One of start_states, start_positions should be not None"
-        )
+        assert (
+            start_states is not None or start_positions is not None
+        ), "One of start_states, start_positions should be not None"
         if start_positions is not None:
             slen, hsz = hidden_states.shape[-2:]
             start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
@@ -5585,9 +5676,9 @@ def forward(
         """
         # No dependency on end_feature so that we can obtain one single `cls_logits` for each sample.
         hsz = hidden_states.shape[-1]
-        assert start_states is not None or start_positions is not None, (
-            "One of start_states, start_positions should be not None"
-        )
+        assert (
+            start_states is not None or start_positions is not None
+        ), "One of start_states, start_positions should be not None"
         if start_positions is not None:
             start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
             start_states = hidden_states.gather(-2, start_positions).squeeze(-2)  # shape (bsz, hsz)