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import logging
import os
import torch
from peft.tuners.lora import LoraLayer
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
def lora_kbit_setting(model, training_args):
for name, module in model.named_modules():
if isinstance(module, LoraLayer):
if training_args.bf16:
module = module.to(torch.bfloat16)
if 'norm' in name:
module = module.to(torch.float32)
if 'lm_head' in name or 'embed_tokens' in name:
if hasattr(module, 'weight'):
if training_args.bf16 and module.weight.dtype == torch.float32:
module = module.to(torch.bfloat16)
def maybe_zero_3(param, ignore_status=False, name=None):
if hasattr(param, "ds_id"):
if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
if not ignore_status:
logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}")
with zero.GatheredParameters([param]):
param = param.data.detach().cpu().clone()
else:
param = param.detach().cpu().clone()
return param
# Borrowed from peft.utils.get_peft_model_state_dict
def get_peft_state_maybe_zero_3(named_params, bias):
if bias == "none":
to_return = {k: t for k, t in named_params if "lora_" in k}
elif bias == "all":
to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
elif bias == "lora_only":
to_return = {}
maybe_lora_bias = {}
lora_bias_names = set()
for k, t in named_params:
if "lora_" in k:
to_return[k] = t
bias_name = k.split("lora_")[0] + "bias"
lora_bias_names.add(bias_name)
elif "bias" in k:
maybe_lora_bias[k] = t
for k, t in maybe_lora_bias:
if bias_name in lora_bias_names:
to_return[bias_name] = t
else:
raise NotImplementedError
to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()}
return to_return
def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True):
to_return = {k: t for k, t in named_params if "lora_" not in k}
if require_grad_only:
to_return = {k: t for k, t in to_return.items() if t.requires_grad}
to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
return to_return
def get_state_maybe_zero_3(named_params, keys_to_match=[''], require_grad_only=True):
to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
if require_grad_only:
to_return = {k: t for k, t in to_return.items() if t.requires_grad}
to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
return to_return
def find_all_linear_names(model, skip_keywords=['connector', 'vision_tower']):
cls = torch.nn.Linear
lora_module_names = set()
skip_keywords = skip_keywords
for name, module in model.named_modules():
if any(skip_keyword in name for skip_keyword in skip_keywords) or 'lm_head' in name or 'output_layer' in name or 'head' in name:
continue
if isinstance(module, cls):
names = name.split('.')
#lora_module_names.add(names[0] if len(names) == 1 else names[-1])
lora_module_names.add(name)
# if 'lm_head' in lora_module_names:
# lora_module_names.remove('lm_head')
return list(lora_module_names)
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