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Update llama.py

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Daniel Han 2025-01-06 22:06:00 -08:00 • committed by GitHub
commit 706b4ed717
1 changed files with 46 additions and 23 deletions
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69
unsloth/models/llama.py
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@ -1967,29 +1967,41 @@ class FastLlamaModel:
if "embed_tokens" in new_target_modules:
print("Unsloth: Training embed_tokens in mixed precision to save VRAM")
dtype = model.base_model.model.embed_tokens.modules_to_save.default.weight.dtype
model.base_model.model.embed_tokens.modules_to_save.default\
.to(device = "cuda:0", dtype=(dtype if (dtype != torch.float16) else torch.float32), non_blocking = True)
model.base_model.model.embed_tokens.modules_to_save.default.requires_grad_(True)
new_dtype = model.get_input_embeddings().modules_to_save.default.weight.dtype
if new_dtype == torch.float16:
# See https://github.com/unslothai/unsloth/pull/1200
# Tesla T4 must use float32 and not float16
new_dtype = torch.float32
pass
model.get_input_embeddings().modules_to_save.default\
.to(device = "cuda:0", dtype = new_dtype, non_blocking = True)
model.get_input_embeddings().modules_to_save.default.requires_grad_(True)
# [TODO] Move old embed_tokens to CPU - should be disk!
model.base_model.model.embed_tokens.original_module\
model.get_input_embeddings().original_module\
.to(device = "cpu", non_blocking = True)
model.base_model.model.embed_tokens.original_module.requires_grad_(False)
model.get_input_embeddings().original_module.requires_grad_(False)
pass
if "lm_head" in new_target_modules:
print("Unsloth: Training lm_head in mixed precision to save VRAM")
dtype = model.base_model.model.lm_head.modules_to_save.default.weight.dtype
model.base_model.lm_head.modules_to_save.default\
.to(device = "cuda:0", dtype=(dtype if (dtype != torch.float16) else torch.float32), non_blocking = True)
model.base_model.lm_head.modules_to_save.default.requires_grad_(True)
new_dtype = model.get_output_embeddings().modules_to_save.default.weight.dtype
if new_dtype == torch.float16:
# See https://github.com/unslothai/unsloth/pull/1200
# Tesla T4 must use float32 and not float16
new_dtype = torch.float32
pass
model.get_output_embeddings().modules_to_save.default\
.to(device = "cuda:0", dtype = new_dtype, non_blocking = True)
model.get_output_embeddings().modules_to_save.default.requires_grad_(True)
# [TODO] Move old lm_head to CPU - should be disk!
model.base_model.lm_head.original_module\
model.get_output_embeddings().original_module\
.to(device = "cpu", non_blocking = True)
model.base_model.lm_head.original_module.requires_grad_(False)
model.get_output_embeddings().original_module.requires_grad_(False)
pass
return model
@ -2216,25 +2228,36 @@ class FastLlamaModel:
model = FastLlamaModel.patch_peft_model(model, use_gradient_checkpointing)
# Now patch lm_head and embed_tokens
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"))
assert(hasattr(model.get_input_embeddings(), "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", dtype=(dtype if (dtype != torch.float16) else torch.float32), non_blocking = True)
model.model.model.embed_tokens.modules_to_save.default.requires_grad_(True)
new_dtype = model.get_input_embeddings().modules_to_save.default.weight.dtype
if new_dtype == torch.float16:
# See https://github.com/unslothai/unsloth/pull/1200
# Tesla T4 must use float32 and not float16
new_dtype = torch.float32
pass
model.get_input_embeddings().modules_to_save.default\
.to(device = "cuda:0", dtype = new_dtype, non_blocking = True)
model.get_input_embeddings().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"))
assert(hasattr(model.get_output_embeddings(), "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", dtype=(dtype if (dtype != torch.float16) else torch.float32), non_blocking = True)
model.model.lm_head.modules_to_save.default.requires_grad_(True)
new_dtype = model.get_output_embeddings().modules_to_save.default.weight.dtype
if new_dtype == torch.float16:
# See https://github.com/unslothai/unsloth/pull/1200
# Tesla T4 must use float32 and not float16
new_dtype = torch.float32
pass
model.get_output_embeddings().modules_to_save.default\
.to(device = "cuda:0", dtype = new_dtype, non_blocking = True)
model.get_output_embeddings().modules_to_save.default.requires_grad_(True)
pass
# Patch tokenizer to pad to the right