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Merge pull request #485 from maximegmd/patch-4

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fix: finetune model inference needs the dtype fix to work with flash-attn
This commit is contained in:
Aman Gupta Karmani
2023-08-27 16:27:47 -04:00
committed by GitHub
parent 35130711d6 3a011ea1ef
commit f144e98a32
1 changed files with 11 additions and 8 deletions
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19
src/axolotl/utils/models.py
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@@ -355,6 +355,7 @@ def load_model(
if hasattr(module, "weight"): if hasattr(module, "weight"):
module.to(torch.float32) module.to(torch.float32)
needs_fa2_dtype = cfg.adapter is not None
if not cfg.gptq and ( if not cfg.gptq and (
(cfg.adapter == "lora" and load_in_8bit) (cfg.adapter == "lora" and load_in_8bit)
or (cfg.adapter == "qlora" and cfg.load_in_4bit) or (cfg.adapter == "qlora" and cfg.load_in_4bit)
@@ -363,16 +364,18 @@ def load_model(
model = prepare_model_for_kbit_training( model = prepare_model_for_kbit_training(
model, use_gradient_checkpointing=cfg.gradient_checkpointing model, use_gradient_checkpointing=cfg.gradient_checkpointing
) )
needs_fa2_dtype = True
# LlamaRMSNorm layers are in fp32 after kbit_training, so we need to # LlamaRMSNorm layers are in fp32 after kbit_training or full finetune, so we need to
# convert them back to fp16/bf16 for flash-attn compatibility. # convert them back to fp16/bf16 for flash-attn compatibility.
if cfg.flash_attention and cfg.is_llama_derived_model: if needs_fa2_dtype and (cfg.flash_attention and cfg.is_llama_derived_model):
for name, module in model.named_modules(): LOG.info("converting modules to %s for flash attention", cfg.torch_dtype)
if "norm" in name: for name, module in model.named_modules():
if "norm" in name:
module.to(cfg.torch_dtype)
if "lm_head" in name or "embed_tokens" in name:
if hasattr(module, "weight"):
module.to(cfg.torch_dtype) module.to(cfg.torch_dtype)
if "lm_head" in name or "embed_tokens" in name:
if hasattr(module, "weight"):
module.to(cfg.torch_dtype)
model, lora_config = load_adapter(model, cfg, cfg.adapter) model, lora_config = load_adapter(model, cfg, cfg.adapter)