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import os
import torch
import re
from collections import deque
from onmt.utils.logging import logger
from onmt.inputters.inputter import vocabs_to_dict
from onmt.modules.lora import lora_state_dict
def build_model_saver(model_opt, opt, model, vocabs, optim, device_id):
# _check_save_model_path
save_model_path = os.path.abspath(opt.save_model)
os.makedirs(os.path.dirname(save_model_path), exist_ok=True)
model_saver = ModelSaver(
opt.save_model,
model,
model_opt,
vocabs,
optim,
opt.keep_checkpoint,
opt.save_format,
device_id,
)
return model_saver
def load_checkpoint(ckpt_path):
"""Load checkpoint from `ckpt_path` if any else return `None`."""
checkpoint = None
if ckpt_path:
logger.info("Loading checkpoint from %s" % ckpt_path)
checkpoint = torch.load(ckpt_path, map_location=torch.device("cpu"))
if "model" in checkpoint.keys():
# This preserves backward-compat for models using customed layernorm
def fix_key(s):
s = re.sub(
r"(.*)\.layer_norm((_\d+)?)\.b_2", r"\1.layer_norm\2.bias", s
)
s = re.sub(
r"(.*)\.layer_norm((_\d+)?)\.a_2", r"\1.layer_norm\2.weight", s
)
return s
checkpoint["model"] = {
fix_key(k): v for k, v in checkpoint["model"].items()
}
# Force add_ffnbias to True if bias found in model w_1 keys
for key in checkpoint["model"].keys():
if "w_1.bias" in key:
checkpoint["opt"].add_ffnbias = True
if not hasattr(checkpoint["opt"], "num_kv"):
checkpoint["opt"].num_kv = 0
if not hasattr(checkpoint["opt"], "add_ffnbias"):
checkpoint["opt"].add_ffnbias = False
if not hasattr(checkpoint["opt"], "parallel_residual"):
checkpoint["opt"].parallel_residual = False
if not hasattr(checkpoint["opt"], "shared_layer_norm"):
checkpoint["opt"].shared_layer_norm = False
if not hasattr(checkpoint["opt"], "use_ckpting"):
checkpoint["opt"].use_ckpting = []
if not hasattr(checkpoint["opt"], "relative_positions_buckets"):
checkpoint["opt"].relative_positions_buckets = 0
if not hasattr(checkpoint["opt"], "parallel_mode"):
checkpoint["opt"].parallel_mode = "data_parallel"
if not hasattr(checkpoint["opt"], "norm_eps"):
checkpoint["opt"].norm_eps = 1e-6
# fix v2 compatibility
if "generator" in checkpoint.keys() and checkpoint["generator"]:
if "0.weight" in checkpoint["generator"]:
checkpoint["generator"]["weight"] = checkpoint["generator"].pop(
"0.weight"
)
if "0.bias" in checkpoint["generator"]:
checkpoint["generator"]["bias"] = checkpoint["generator"].pop("0.bias")
# end of patch for backward compatibility
return checkpoint
class ModelSaverBase(object):
"""Base class for model saving operations
Inherited classes must implement private methods:
* `_save`
* `_rm_checkpoint
"""
def __init__(
self,
base_path,
model,
model_opt,
vocabs,
optim,
keep_checkpoint=-1,
save_format="pytorch",
device_id=0,
):
self.base_path = base_path
self.model = model
self.model_opt = model_opt
self.vocabs = vocabs
self.optim = optim
self.last_saved_step = None
self.keep_checkpoint = keep_checkpoint
self.save_format = save_format
self.device_id = device_id
if keep_checkpoint > 0:
self.checkpoint_queue = deque([], maxlen=keep_checkpoint)
if save_format == "safetensors":
self.model_queue = deque([], maxlen=keep_checkpoint)
def save(self, step, moving_average=None):
"""Main entry point for model saver
It wraps the `_save` method with checks and apply `keep_checkpoint`
related logic
"""
if self.keep_checkpoint == 0 or step == self.last_saved_step:
return
save_model = self.model
if moving_average:
model_params_data = []
for avg, param in zip(moving_average, save_model.parameters()):
model_params_data.append(param.data)
param.data = avg.data
if self.save_format == "pytorch":
ckpt_path, _ = self._save(step, save_model)
elif self.save_format == "safetensors":
ckpt_path, model_path = self._st_save(step, save_model)
self.last_saved_step = step
if moving_average:
for param_data, param in zip(model_params_data, save_model.parameters()):
param.data = param_data
if ckpt_path is not None: # not None when process id 0
if self.keep_checkpoint > 0:
if len(self.checkpoint_queue) == self.checkpoint_queue.maxlen:
todel = self.checkpoint_queue.popleft()
self._rm_checkpoint(todel)
if self.save_format == "safetensors":
todel = self.model_queue.popleft()
self._rm_checkpoint(todel)
self.checkpoint_queue.append(ckpt_path)
if self.save_format == "safetensors":
self.model_queue.append(model_path)
def _save(self, step, model):
"""Save a resumable checkpoint.
Args:
step (int): step number
model (nn.Module): torch model to save
Returns:
(str, str):
* checkpoint_name: name (or path) of the saved checkpoint
* model_name: name (or path) of the saved safetensors weights if applicable
"""
raise NotImplementedError()
def _rm_checkpoint(self, name):
"""Remove a checkpoint
Args:
name(str): name that indentifies the checkpoint
(it may be a filepath)
"""
raise NotImplementedError()
class ModelSaver(ModelSaverBase):
"""Simple model saver to filesystem"""
def _save(self, step, model):
if (
hasattr(self.model_opt, "lora_layers")
and len(self.model_opt.lora_layers) > 0
) or (
hasattr(self.model_opt, "lora_embedding") and self.model_opt.lora_embedding
):
model_state_dict = lora_state_dict(model, bias="lora_only")
generator_state_dict = None
else:
model_state_dict = model.state_dict()
model_state_dict = {
k: v for k, v in model_state_dict.items() if "generator" not in k
}
generator_state_dict = model.generator.state_dict()
if torch.distributed.is_initialized():
ws = torch.distributed.get_world_size()
else:
ws = 1
if ws > 1:
full_model = [None for _ in range(ws)]
for key, value in model_state_dict.items():
model_state_dict[key] = value.cpu()
torch.distributed.all_gather_object(full_model, model_state_dict)
fm_sd = {}
for key in full_model[0].keys():
if key.split(".")[-1] == "lora_A":
if key.split(".")[-2] in [
"linear_keys",
"linear_values",
"linear_query",
"w_1",
"w_3",
]:
fm_sd[key] = (
sum([full_model[i][key].cpu() for i in range(ws)]) / ws
)
elif key.split(".")[-2] in ["final_linear", "w_2"]:
fm_sd[key] = torch.cat(
[full_model[i][key].cpu() for i in range(ws)], 1
)
elif key.split(".")[-1] == "lora_B":
if key.split(".")[-2] in [
"linear_keys",
"linear_values",
"linear_query",
"w_1",
"w_3",
]:
fm_sd[key] = torch.cat(
[full_model[i][key].cpu() for i in range(ws)], 0
)
elif key.split(".")[-2] in ["final_linear", "w_2"]:
fm_sd[key] = (
sum([full_model[i][key].cpu() for i in range(ws)]) / ws
)
elif key.split(".")[-1] in [
"linear_keys",
"linear_values",
"linear_query",
"w_1",
"w_3",
]:
fm_sd[key] = torch.cat(
[full_model[i][key].cpu() for i in range(ws)], 0
)
elif key.split(".")[-1] in ["final_linear", "w_2"]:
fm_sd[key] = torch.cat(
[full_model[i][key].cpu() for i in range(ws)], 1
)
else:
fm_sd[key] = full_model[0][key]
model_state_dict = fm_sd
checkpoint = {
"model": model_state_dict,
"generator": generator_state_dict,
"vocab": vocabs_to_dict(self.vocabs),
"opt": self.model_opt,
"optim": self.optim.state_dict(),
}
if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
logger.info("Saving checkpoint %s_step_%d.pt" % (self.base_path, step))
ckpt_path = "%s_step_%d.pt" % (self.base_path, step)
torch.save(checkpoint, ckpt_path)
else:
ckpt_path = None
if torch.distributed.is_initialized():
torch.distributed.barrier()
return ckpt_path, None
def _st_save(self, step, model):
try:
from safetensors.torch import save_file
except ImportError:
raise ImportError("run: pip install safetensors, to use safetensors")
if (
hasattr(self.model_opt, "lora_layers")
and len(self.model_opt.lora_layers) > 0
) or (
hasattr(self.model_opt, "lora_embedding") and self.model_opt.lora_embedding
):
model_state_dict = lora_state_dict(model, bias="lora_only")
else:
model_state_dict = model.state_dict()
if torch.distributed.is_initialized():
ws = torch.distributed.get_world_size()
else:
ws = 1
if ws > 1:
full_model = [None for _ in range(ws)]
for key, value in model_state_dict.items():
model_state_dict[key] = value.cpu()
torch.distributed.all_gather_object(full_model, model_state_dict)
fm_sd = {}
for key in full_model[0].keys():
if key.split(".")[-1] == "lora_A":
if key.split(".")[-2] in [
"linear_keys",
"linear_values",
"linear_query",
"w_1",
"w_3",
]:
fm_sd[key] = (
sum([full_model[i][key].cpu() for i in range(ws)]) / ws
)
elif key.split(".")[-2] in ["final_linear", "w_2"]:
fm_sd[key] = torch.cat(
[full_model[i][key].cpu() for i in range(ws)], 1
)
elif key.split(".")[-1] == "lora_B":
if key.split(".")[-2] in [
"linear_keys",
"linear_values",
"linear_query",
"w_1",
"w_3",
]:
fm_sd[key] = torch.cat(
[full_model[i][key].cpu() for i in range(ws)], 0
)
elif key.split(".")[-2] in ["final_linear", "w_2"]:
fm_sd[key] = (
sum([full_model[i][key].cpu() for i in range(ws)]) / ws
)
elif key.split(".")[-1] in [
"linear_keys",
"linear_values",
"linear_query",
"w_1",
"w_3",
]:
fm_sd[key] = torch.cat(
[full_model[i][key].cpu() for i in range(ws)], 0
)
elif key.split(".")[-1] in ["final_linear", "w_2"]:
fm_sd[key] = torch.cat(
[full_model[i][key].cpu() for i in range(ws)], 1
)
else:
fm_sd[key] = full_model[0][key]
model_state_dict = fm_sd
checkpoint = {
"vocab": vocabs_to_dict(self.vocabs),
"opt": self.model_opt,
"optim": self.optim.state_dict(),
}
if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
logger.info("Saving checkpoint %s_step_%d.pt" % (self.base_path, step))
ckpt_path = "%s_step_%d.pt" % (self.base_path, step)
torch.save(checkpoint, ckpt_path)
logger.info("Saving safetensors %s_step_%d.pt" % (self.base_path, step))
model_path = "%s_step_%d.safetensors" % (self.base_path, step)
save_file(model_state_dict, model_path)
else:
ckpt_path = None
model_path = None
if torch.distributed.is_initialized():
torch.distributed.barrier()
return ckpt_path, model_path
def _rm_checkpoint(self, name):
if os.path.exists(name):
os.remove(name)
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