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finish the testing and debugging

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This commit is contained in:
QingruZhang
2023-03-02 01:04:48 +00:00
parent 35cd771c97
commit 7471035885
4 changed files with 58 additions and 62 deletions
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examples/conditional_generation/peft_adalora_seq2seq.py
+16 -9
View File
@@ -26,7 +26,10 @@ batch_size = 8
# creating model
peft_config = AdaLoraConfig(
r=8, lora_alpha=32, lora_dropout=0.1,
init_r=12, target_r=1,
beta1=0.85, beta2=0.85,
tinit=0, tfinal=230, deltaT=1,
lora_alpha=32, lora_dropout=0.1,
task_type=TaskType.SEQ_2_SEQ_LM,
inference_mode=False
)
@@ -98,19 +101,23 @@ model.base_model.peft_config.total_step = len(train_dataloader) * num_epochs
# training and evaluation
model = model.to(device)
global_step = 0
for epoch in range(num_epochs):
model.train()
total_loss = 0
for step, batch in enumerate(tqdm(train_dataloader)):
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
total_loss += loss.detach().float()
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
with torch.autograd.set_detect_anomaly(True):
outputs = model(**batch)
loss = outputs.loss
total_loss += loss.detach().float()
loss.backward()
optimizer.step()
lr_scheduler.step()
model.base_model.update_and_allocate(global_step)
optimizer.zero_grad()
global_step += 1
model.eval()
eval_loss = 0
src/peft/peft_model.py
+2
View File
@@ -180,6 +180,8 @@ class PeftModel(PushToHubMixin, torch.nn.Module):
hook = AlignDevicesHook(io_same_device=True)
if model.peft_config.peft_type == PeftType.LORA:
add_hook_to_module(model.base_model.model, hook)
elif model.peft_config.peft_type == PeftType.ADALORA:
add_hook_to_module(model.base_model.model, hook)
else:
remove_hook_from_submodules(model.prompt_encoder)
add_hook_to_module(model.base_model, hook)
src/peft/tuners/adalora.py
+38 -51
View File
@@ -30,17 +30,15 @@ class AdaLoraConfig(LoraConfig):
This is the configuration class to store the configuration of a [`~peft.AdaLora`].
Args:
r (`int`): Lora attention dimension
target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to.
lora_alpha (`float`): The alpha parameter for Lora scaling.
lora_dropout (`float`): The dropout probability for Lora layers.
merge_weights (`bool`):
Whether to merge the weights of the Lora layers with the base transformer model in `eval` mode.
fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out)
enable_lora ( `List[bool]`): Used with `lora.MergedLinear`.
bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only'
modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable
and saved in the final checkpoint.
target_r (`int`): The target average rank of incremental matrix.
init_r (`int`): The initial rank for each incremental matrix.
tinit (`int`): The steps of initial fine-tuning warmup.
tfinal (`int`): The step of final fine-tuning.
deltaT (`int`): The time internval between two budget allocations.
beta1 (`float`): The hyperparameter of EMA for sensitivity smoothing.
beta2 (`float`): The hyperparameter of EMA for undertainty quantification.
orth_reg_weight (`float`): The coefficient of orthogonal regularization.
total_step (`int`): The total training steps that should be specified before training.
"""
target_r: int = field(default=8, metadata={"help": "Target Lora matrix dimension."})
init_r: int = field(default=12, metadata={"help": "Intial Lora matrix dimension."})
@@ -81,7 +79,8 @@ class AdaLoraModel(LoraModel):
peft_type="ADALORA", task_type="SEQ_2_SEQ_LM", r=8, lora_alpha=32, target_modules=["q", "v"],
lora_dropout=0.01,
)
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> adalora_model = AdaLoraModel(config, model)
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> model = AdaLoraModel(config, model)
**Attributes**:
- **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted.
@@ -94,7 +93,6 @@ class AdaLoraModel(LoraModel):
self.model = model
self._find_and_replace()
mark_only_lora_as_trainable(self.model, self.peft_config.bias)
# self.forward = self.model.forward
self.rankallocator = RankAllocator(config, self.named_parameters())
def _find_and_replace(self):
@@ -173,7 +171,7 @@ class AdaLoraModel(LoraModel):
assert orth_reg_weight > 0
if hasattr(outputs, "loss"):
regu_loss = None
regu_loss = 0
num_param = 0
for n,p in self.model.named_parameters():
if "lora_A" in n or "lora_B" in n:
@@ -181,12 +179,9 @@ class AdaLoraModel(LoraModel):
I = torch.eye(*para_cov.size(), out=torch.empty_like(para_cov))
I.requires_grad = False
num_param += 1
if regu_loss is None:
regu_loss = torch.norm(para_cov-I, p="fro")
else:
regu_loss += torch.norm(para_cov-I, p="fro")
outputs.loss += orth_reg_weight * regu_loss
regu_loss += torch.norm(para_cov-I, p="fro")
regu_loss = regu_loss / num_param
outputs.loss += orth_reg_weight * regu_loss
return outputs
def update_and_allocate(self, global_step):
@@ -195,7 +190,7 @@ class AdaLoraModel(LoraModel):
class SVDLinear(nn.Linear, LoraLayer):
# SVD-based adaptation for a dense layer
# SVD-based adaptation by a dense layer
def __init__(
self,
in_features: int,
@@ -239,8 +234,6 @@ class SVDLinear(nn.Linear, LoraLayer):
nn.init.normal_(self.lora_B, mean=0.0, std=0.02)
def train(self, mode: bool = True):
# def T(w):
# return w.T if self.fan_in_fan_out else w
nn.Linear.train(self, mode)
if self.merge_weights and self.merged:
# Make sure that the weights are not merged
@@ -251,8 +244,6 @@ class SVDLinear(nn.Linear, LoraLayer):
self.merged = False
def eval(self):
# def T(w):
# return w.T if self.fan_in_fan_out else w
nn.Linear.eval(self)
if self.merge_weights and not self.merged:
# Merge the weights and mark it
@@ -263,8 +254,6 @@ class SVDLinear(nn.Linear, LoraLayer):
self.merged = True
def forward(self, x: torch.Tensor):
# def T(w):
# return w.T if self.fan_in_fan_out else w
if self.r > 0 and not self.merged:
result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
if self.r > 0:
@@ -277,9 +266,8 @@ class SVDLinear(nn.Linear, LoraLayer):
if is_bnb_available():
class SVDLinear8bitLt(bnb.nn.Linear8bitLt, LoraLayer):
# Lora implemented in a dense layer
# Low-rank matrix for SVD-based adaptation
def __init__(
self,
in_features,
@@ -315,20 +303,11 @@ if is_bnb_available():
# Freezing the pre-trained weight matrix
self.weight.requires_grad = False
self.ranknum.requires_grad = False
# self.lora_A = nn.Linear(in_features, r, bias=False)
# self.lora_B = nn.Linear(r, out_features, bias=False)
# self.scaling = self.lora_alpha / self.r
# # Freezing the pre-trained weight matrix
# self.weight.requires_grad = False
self.reset_parameters()
def reset_parameters(self):
if hasattr(self, "lora_A"):
# initialize A the same way as the default for nn.Linear and B to zero
# nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
# nn.init.zeros_(self.lora_B.weight)
nn.init.zeros_(self.lora_E)
nn.init.normal_(self.lora_A, mean=0.0, std=0.02)
nn.init.normal_(self.lora_B, mean=0.0, std=0.02)
@@ -347,13 +326,11 @@ if is_bnb_available():
output = (
self.lora_dropout(x) @ (self.lora_A*self.lora_E).T @ self.lora_B.T /(self.ranknum+1e-5)
).to(expected_dtype) * self.scaling
# output = self.lora_B(self.lora_A(self.lora_dropout(x))).to(expected_dtype) * self.scaling
result += output
else:
output = (
self.lora_dropout(x) @ (self.lora_A*self.lora_E).T @ self.lora_B.T /(self.ranknum+1e-5)
) * self.scaling
# output = self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling
result += output
return result
@@ -400,8 +377,8 @@ class RankAllocator(object):
if step <= tinit:
budget = self.init_bgt
mask_ind = False
# Final warmup
elif step > self.total_step - tfinal:
# Final fine-tuning
elif step > total_step - tfinal:
budget = self.target_bgt
mask_ind = True
else:
@@ -415,6 +392,7 @@ class RankAllocator(object):
def update_ipt(self, model):
# Update the sensitivity and uncertainty for every weight
for n,p in model.named_parameters():
if "lora_" in n:
if n not in self.ipt:
@@ -423,8 +401,10 @@ class RankAllocator(object):
self.exp_avg_unc[n] = torch.zeros_like(p)
with torch.no_grad():
self.ipt[n] = (p * p.grad).abs().detach()
# Sensitivity smoothing
self.exp_avg_ipt[n] = self.beta1 * self.exp_avg_ipt[n] + \
(1 - self.beta1)*self.ipt[n]
# Uncertainty quantification
self.exp_avg_unc[n] = self.beta2 * self.exp_avg_unc[n] + \
(1-self.beta2)*(self.ipt[n]-self.exp_avg_ipt[n]).abs()
@@ -443,29 +423,31 @@ class RankAllocator(object):
value_ipt = {}
vector_ipt = {}
triplet_ipt = {}
# Get the importance score for A, E, B
for n,p in model.named_parameters():
if "lora_A" in n:
ipt_score = self._element_score(n)
comb_ipt = torch.mean(ipt_score, dim=1, keepdim=True)
entry_ipt = self._element_score(n)
comb_ipt = torch.mean(entry_ipt, dim=1, keepdim=True)
name_m = n.replace("lora_A", "%s")
if name_m not in vector_ipt:
vector_ipt[name_m] = [comb_ipt]
else:
vector_ipt[name_m].append(comb_ipt)
if "lora_B" in n:
ipt_score = self._element_score(n)
comb_ipt = torch.mean(ipt_score, dim=0, keepdim=False).view(-1, 1)
entry_ipt = self._element_score(n)
comb_ipt = torch.mean(entry_ipt, dim=0, keepdim=False).view(-1, 1)
name_m = n.replace("lora_B", "%s")
if name_m not in vector_ipt:
vector_ipt[name_m] = [comb_ipt]
else:
vector_ipt[name_m].append(comb_ipt)
if "lora_E" in n:
ipt_score = self._element_score(n)
entry_ipt = self._element_score(n)
name_m = n.replace("lora_E", "%s")
value_ipt[name_m] = ipt_score
value_ipt[name_m] = entry_ipt
all_score = []
# Calculate the score for each triplet
for name_m in vector_ipt:
ipt_E = value_ipt[name_m]
ipt_AB = torch.cat(vector_ipt[name_m], dim=1)
@@ -474,11 +456,13 @@ class RankAllocator(object):
triplet_ipt[name_E] = sum_ipt.view(-1, 1)
all_score.append(sum_ipt.view(-1))
# Get the threshold by ranking ipt
mask_threshold = torch.kthvalue(
torch.cat(all_score),
k = self.init_bgt - budget,
)[0].item()
# Mask the unimportant triplets
with torch.no_grad():
for n,p in model.named_parameters():
if "lora_E" in n:
@@ -486,14 +470,17 @@ class RankAllocator(object):
return mask_threshold
def update_and_allocate(self, model, global_step):
if global_step < self.peft_config.total_step - self.tfinal:
# Update the importance score and allocate the budget
if global_step < self.peft_config.total_step - self.peft_config.tfinal:
self.update_ipt(model)
# TODO: Finalize the budget distribution by replacing with new Linear.
budget, mask_ind = self.budget_schedule(global_step)
print("budget:", budget)
if mask_ind:
mask_threshold = self.mask_to_budget(model, budget)
mask_threshold = self.mask_to_budget(model, budget)
print("mask threshold:", mask_threshold)
else:
mask_threshold = None
return budget, mask_threshold
src/peft/utils/save_and_load.py
+2 -2
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@@ -29,7 +29,7 @@ def get_peft_model_state_dict(model, state_dict=None):
"""
if state_dict is None:
state_dict = model.state_dict()
if model.peft_config.peft_type == PeftType.LORA:
if model.peft_config.peft_type in (PeftType.LORA, PeftType.ADALORA):
# to_return = lora_state_dict(model, bias=model.peft_config.bias)
# adapted from `https://github.com/microsoft/LoRA/blob/main/loralib/utils.py`
# to directly with the state dict which is necessary when using DeepSpeed or FSDP
@@ -72,7 +72,7 @@ def set_peft_model_state_dict(model, peft_model_state_dict):
"""
model.load_state_dict(peft_model_state_dict, strict=False)
if model.peft_config.peft_type != PeftType.LORA:
if model.peft_config.peft_type not in (PeftType.LORA, PeftType.ADALORA):
model.prompt_encoder.embedding.load_state_dict(
{"weight": peft_model_state_dict["prompt_embeddings"]}, strict=True
)