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Merge branch 'main' of https://github.com/unslothai/unsloth

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This commit is contained in:
Daniel Han 2024-10-27 15:09:42 -07:00
commit a9afc2f291
1 changed files with 10 additions and 4 deletions
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14
unsloth/models/llama.py
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@ -1958,8 +1958,9 @@ class FastLlamaModel:
if "embed_tokens" in new_target_modules:
print("Unsloth: Training embed_tokens in mixed precision to save VRAM")
dtype = model.model.model.embed_tokens.modules_to_save.default.weight.dtype
model.model.model.embed_tokens.modules_to_save.default\
.to(device = "cuda:0", non_blocking = True)
.to(device = "cuda:0", dtype=(dtype if (dtype != torch.float16) else torch.float32), non_blocking = True)
model.model.model.embed_tokens.modules_to_save.default.requires_grad_(True)
# [TODO] Move old embed_tokens to CPU - should be disk!
@ -1971,8 +1972,9 @@ class FastLlamaModel:
if "lm_head" in new_target_modules:
print("Unsloth: Training lm_head in mixed precision to save VRAM")
dtype = model.model.model.lm_head.modules_to_save.default.weight.dtype
model.model.lm_head.modules_to_save.default\
.to(device = "cuda:0", non_blocking = True)
.to(device = "cuda:0", dtype=(dtype if (dtype != torch.float16) else torch.float32), non_blocking = True)
model.model.lm_head.modules_to_save.default.requires_grad_(True)
# [TODO] Move old lm_head to CPU - should be disk!
@ -2209,16 +2211,20 @@ class FastLlamaModel:
if train_embed_tokens:
print("Unsloth: Training embed_tokens in mixed precision to save VRAM")
assert(hasattr(model.model.model.embed_tokens, "modules_to_save"))
dtype = model.model.model.embed_tokens.modules_to_save.default.weight.dtype
model.model.model.embed_tokens.modules_to_save.default\
.to(device = "cuda:0", non_blocking = True)
.to(device = "cuda:0", dtype=(dtype if (dtype != torch.float16) else torch.float32), non_blocking = True)
model.model.model.embed_tokens.modules_to_save.default.requires_grad_(True)
pass
if train_lm_head:
print("Unsloth: Training lm_head in mixed precision to save VRAM")
assert(hasattr(model.model.lm_head, "modules_to_save"))
dtype = model.model.lm_head.modules_to_save.default.weight.dtype
model.model.lm_head.modules_to_save.default\
.to(device = "cuda:0", non_blocking = True)
.to(device = "cuda:0", dtype=(dtype if (dtype != torch.float16) else torch.float32), non_blocking = True)
model.model.lm_head.modules_to_save.default.requires_grad_(True)
pass