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Support device_map=sequential & max_memory config parameters #903

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merged 3 commits into from
Dec 4, 2023
Merged

Support device_map=sequential & max_memory config parameters #903

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f041301
Support device_map sequential (and others). Support max_memory in cfg.
brthor Nov 28, 2023
2d7137f
Update documentation in README accordingly.
brthor Nov 28, 2023
e8972f6
Update README.md
winglian Dec 4, 2023
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5 changes: 5 additions & 0 deletions README.md
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Expand Up @@ -612,6 +612,11 @@ eval_sample_packing:
sample_packing_eff_est:
total_num_tokens:

# Passed through to transformers when loading the model when launched without accelerate
device_map:
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# Defines the max memory usage per gpu on the system. Passed through to transformers when loading the model.
max_memory:

# If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
adapter: lora
# If you already have a lora model trained that you want to load, put that here.
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2 changes: 1 addition & 1 deletion src/axolotl/utils/config.py
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Expand Up @@ -27,7 +27,7 @@ def get_device():

cfg.device = get_device()
if cfg.world_size == 1:
cfg.device_map = "auto"
cfg.device_map = cfg.device_map or "auto"
else:
if cfg.device.startswith("cuda"):
cfg.device_map = {"": torch.cuda.current_device()}
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1 change: 1 addition & 0 deletions src/axolotl/utils/models.py
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Expand Up @@ -216,6 +216,7 @@ def load_model(
model_kwargs = {}

model_kwargs["device_map"] = cfg.device_map
model_kwargs["max_memory"] = cfg.max_memory
model_kwargs["torch_dtype"] = cfg.torch_dtype

if cfg.model_revision:
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