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Instantiate a big model

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Instantiate a big model

A barrier to accessing very large pretrained models is the amount of memory required. When loading a pretrained PyTorch model, you usually:

  1. Create a model with random weights.
  2. Load your pretrained weights.
  3. Put those pretrained weights in the model.

The first two steps both require a full version of the model in memory and if the model weighs several GBs, you may not have enough memory for two copies of it. This problem is amplified in distributed training environments because each process loads a pretrained model and stores two copies in memory.

The randomly created model is initialized with “empty” tensors, which take space in memory without filling it. The random values are whatever was in this chunk of memory at the time. To improve loading speed, the _fast_init parameter is set to True by default to skip the random initialization for all weights that are correctly loaded.

This guide will show you how Transformers can help you load large pretrained models despite their memory requirements.

Sharded checkpoints

From Transformers v4.18.0, a checkpoint larger than 10GB is automatically sharded by the save_pretrained() method. It is split into several smaller partial checkpoints and creates an index file that maps parameter names to the files they’re stored in.

The maximum shard size is controlled with the max_shard_size parameter, but by default it is 5GB, because it is easier to run on free-tier GPU instances without running out of memory.

For example, let’s shard BioMistral/BioMistral-7B.

>>> with tempfile.TemporaryDirectory() as tmp_dir:
...     model.save_pretrained(tmp_dir, max_shard_size="5GB")
...     print(sorted(os.listdir(tmp_dir)))
['config.json', 'generation_config.json', 'model-00001-of-00006.safetensors', 'model-00002-of-00006.safetensors', 'model-00003-of-00006.safetensors', 'model-00004-of-00006.safetensors', 'model-00005-of-00006.safetensors', 'model-00006-of-00006.safetensors', 'model.safetensors.index.json']

The sharded checkpoint is reloaded with the from_pretrained() method.

>>> with tempfile.TemporaryDirectory() as tmp_dir:
...     model.save_pretrained(tmp_dir, max_shard_size="5GB")
...     new_model = AutoModel.from_pretrained(tmp_dir)

The main advantage of sharded checkpoints for big models is that each shard is loaded after the previous one, which caps the memory usage to only the model size and the largest shard size.

You could also directly load a sharded checkpoint inside a model without the from_pretrained() method (similar to PyTorch’s load_state_dict() method for a full checkpoint). In this case, use the load_sharded_checkpoint() method.

>>> from transformers.modeling_utils import load_sharded_checkpoint

>>> with tempfile.TemporaryDirectory() as tmp_dir:
...     model.save_pretrained(tmp_dir, max_shard_size="5GB")
...     load_sharded_checkpoint(model, tmp_dir)

Shard metadata

The index file determines which keys are in the checkpoint and where the corresponding weights are stored. This file is loaded like any other JSON file and you can get a dictionary from it.

>>> import json

>>> with tempfile.TemporaryDirectory() as tmp_dir:
...     model.save_pretrained(tmp_dir, max_shard_size="5GB")
...     with open(os.path.join(tmp_dir, "model.safetensors.index.json"), "r") as f:
...         index = json.load(f)

>>> print(index.keys())
dict_keys(['metadata', 'weight_map'])

The metadata key provides the total model size.

>>> index["metadata"]
{'total_size': 28966928384}

The weight_map key maps each parameter name (typically state_dict in a PyTorch model) to the shard it’s stored in.

>>> index["weight_map"]
{'lm_head.weight': 'model-00006-of-00006.safetensors',
 'model.embed_tokens.weight': 'model-00001-of-00006.safetensors',
 'model.layers.0.input_layernorm.weight': 'model-00001-of-00006.safetensors',
 'model.layers.0.mlp.down_proj.weight': 'model-00001-of-00006.safetensors',
 ...
}

Accelerate’s Big Model Inference

Make sure you have Accelerate v0.9.0 or later and PyTorch v1.9.0 or later installed.

From Transformers v4.20.0, the from_pretrained() method is supercharged with Accelerate’s Big Model Inference feature to efficiently handle really big models! Big Model Inference creates a model skeleton on PyTorch’s meta device. The randomly initialized parameters are only created when the pretrained weights are loaded. This way, you aren’t keeping two copies of the model in memory at the same time (one for the randomly initialized model and one for the pretrained weights), and the maximum memory consumed is only the full model size.

To enable Big Model Inference in Transformers, set low_cpu_mem_usage=True in the from_pretrained() method.

from transformers import AutoModelForCausalLM

gemma = AutoModelForCausalLM.from_pretrained("google/gemma-7b", low_cpu_mem_usage=True)

Accelerate automatically dispatches the model weights across all available devices, starting with the fastest device (GPU) first and then offloading to the slower devices (CPU and even hard drive). This is enabled by setting device_map="auto" in the from_pretrained() method. When you pass the device_map parameter, low_cpu_mem_usage is automatically set to True so you don’t need to specify it.

from transformers import AutoModelForCausalLM

# these loading methods are equivalent
gemma = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto")
gemma = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", low_cpu_mem_usage=True)

You can also write your own device_map by mapping each layer to a device. It should map all model parameters to a device, but you don’t have to detail where all the submodules of a layer go if the entire layer is on the same device.

device_map = {"model.layers.1": 0, "model.layers.14": 1, "model.layers.31": "cpu", "lm_head": "disk"}

Access hf_device_map attribute to see how Accelerate split the model across devices.

gemma.hf_device_map
{'model.embed_tokens': 0,
 'model.layers.0': 0,
 'model.layers.1': 0,
 'model.layers.2': 0,
 'model.layers.3': 0,
 'model.layers.4': 0,
 'model.layers.5': 0,
 'model.layers.6': 0,
 'model.layers.7': 0,
 'model.layers.8': 0,
 'model.layers.9': 0,
 'model.layers.10': 0,
 'model.layers.11': 0,
 'model.layers.12': 0,
 'model.layers.13': 0,
 'model.layers.14': 'cpu',
 'model.layers.15': 'cpu',
 'model.layers.16': 'cpu',
 'model.layers.17': 'cpu',
 'model.layers.18': 'cpu',
 'model.layers.19': 'cpu',
 'model.layers.20': 'cpu',
 'model.layers.21': 'cpu',
 'model.layers.22': 'cpu',
 'model.layers.23': 'cpu',
 'model.layers.24': 'cpu',
 'model.layers.25': 'cpu',
 'model.layers.26': 'cpu',
 'model.layers.27': 'cpu',
 'model.layers.28': 'cpu',
 'model.layers.29': 'cpu',
 'model.layers.30': 'cpu',
 'model.layers.31': 'cpu',
 'model.norm': 'cpu',
 'lm_head': 'cpu'}

Model data type

PyTorch model weights are normally instantiated as torch.float32 and it can be an issue if you try to load a model as a different data type. For example, you’d need twice as much memory to load the weights in torch.float32 and then again to load them in your desired data type, like torch.float16.

Due to how PyTorch is designed, the torch_dtype parameter only supports floating data types.

To avoid wasting memory like this, explicitly set the torch_dtype parameter to the desired data type or set torch_dtype="auto" to load the weights with the most optimal memory pattern (the data type is automatically derived from the model weights).

specific dtype
auto dtype
from transformers import AutoModelForCausalLM

gemma = AutoModelForCausalLM.from_pretrained("google/gemma-7b", torch_dtype=torch.float16)

You can also set the data type to use for models instantiated from scratch.

import torch
from transformers import AutoConfig, AutoModel

my_config = AutoConfig.from_pretrained("google/gemma-2b", torch_dtype=torch.float16)
model = AutoModel.from_config(my_config)
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