rixty/KoboldAI-Client
1
1
Fork 0
mirror of https://github.com/KoboldAI/KoboldAI-Client.git synced 2025-06-05 21:59:24 +02:00
Code Issues Projects Releases Wiki Activity

Added start to alternative multi-gen (linear instead of parallel). Non-functional

Browse Source 
Continued stub for in UI soft prompt training.
Removed old xls to preset file code
This commit is contained in:
ebolam
2022-12-05 13:50:49 -05:00
parent 457b7a46c4
commit 280c35b452
11 changed files with 246 additions and 205 deletions
Download Patch File Download Diff File Expand all files Collapse all files

63
prompt_tuner.py
View File

@@ -3,7 +3,7 @@ import os
import sys
import math
import numpy as np
import termcolor
from logger import logger
import contextlib
import traceback
import random
@@ -70,21 +70,24 @@ def patch_transformers_download():
class Send_to_socketio(object):
def write(self, bar):
bar = bar.replace("\r", "").replace("\n", "")
if bar != "":
if bar != "" and [ord(num) for num in bar] != [27, 91, 65]: #No idea why we're getting the 27, 1, 65 character set, just killing to so we can move on
try:
print(bar, end="\r")
if utils.emit is not None:
utils.emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True)
print('\r' + bar, end='')
socketio.emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True, room="UI_1")
eventlet.sleep(seconds=0)
except:
pass
def flush(self):
pass
def http_get(
url: str,
temp_file: transformers.utils.hub.BinaryIO,
temp_file,
proxies=None,
resume_size=0,
headers: transformers.utils.hub.Optional[transformers.utils.hub.Dict[str, str]] = None,
file_name: transformers.utils.hub.Optional[str] = None,
headers=None,
file_name=None,
):
"""
Download remote file. Do not gobble up errors.
@@ -108,13 +111,18 @@ def patch_transformers_download():
desc=f"Downloading {file_name}" if file_name is not None else "Downloading",
file=Send_to_socketio(),
)
koboldai_vars.status_message = "Download Model"
koboldai_vars.total_download_chunks = total
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
if url[-11:] != 'config.json':
progress.update(len(chunk))
koboldai_vars.downloaded_chunks += len(chunk)
temp_file.write(chunk)
if url[-11:] != 'config.json':
progress.close()
koboldai_vars.status_message = ""
transformers.utils.hub.http_get = http_get
@@ -195,18 +203,18 @@ def device_list(n_layers, primary=None, selected=None):
if(device_count < 2):
primary = None
gpu_blocks = breakmodel.gpu_blocks + (device_count - len(breakmodel.gpu_blocks))*[0]
print(f"{colors.YELLOW} DEVICE ID | LAYERS | DEVICE NAME{colors.END}")
logger.info(" DEVICE ID | LAYERS | DEVICE NAME{colors.END}")
for i in range(device_count):
name = torch.cuda.get_device_name(i)
if(len(name) > 47):
name = "..." + name[-44:]
row_color = colors.END
sep_color = colors.YELLOW
print(f"{row_color}{colors.YELLOW + '->' + row_color if i == selected else ' '} {'(primary)' if i == primary else ' '*9} {i:3} {sep_color}|{row_color} {gpu_blocks[i]:3} {sep_color}|{row_color} {name}{colors.END}")
logger.info(f"{'(primary)' if i == primary else ' '*9} {i:3} | {gpu_blocks[i]:3} | {name}")
row_color = colors.END
sep_color = colors.YELLOW
print(f"{row_color}{colors.YELLOW + '->' + row_color if -1 == selected else ' '} {' '*9} N/A {sep_color}|{row_color} {breakmodel.disk_blocks:3} {sep_color}|{row_color} (Disk cache){colors.END}")
print(f"{row_color} {' '*9} N/A {sep_color}|{row_color} {n_layers:3} {sep_color}|{row_color} (CPU){colors.END}")
logger.info(f" {' '*9} N/A | {breakmodel.disk_blocks:3} | (Disk cache)")
logger.info(f" {' '*9} N/A | {n_layers:3} | (CPU)")
def move_model_to_devices(model, usegpu, gpu_device):
@@ -440,12 +448,12 @@ class TrainerBase(abc.ABC):
@property
def lazy_load_spec(self):
print("WARNING: `TrainerData.lazy_load_spec` is currently unused", file=sys.stderr)
logger.warning("WARNING: `TrainerData.lazy_load_spec` is currently unused")
return self.__lazy_load_spec
@lazy_load_spec.setter
def lazy_load_spec(self, value: Optional[dict]):
print("WARNING: `TrainerData.lazy_load_spec` is currently unused", file=sys.stderr)
logger.warning("WARNING: `TrainerData.lazy_load_spec` is currently unused")
self.__lazy_load_spec = value
@property
@@ -465,7 +473,7 @@ class TrainerBase(abc.ABC):
self.data = self.TrainerData()
self._spmodule: Optional[str] = None
if universe is not None:
print("WARNING: The `universe` argument of `TrainerBase.__init__` is currently unused", file=sys.stderr)
logger.warning("WARNING: The `universe` argument of `TrainerBase.__init__` is currently unused")
def raise_configuration_error(self, msg, **kwargs):
if "quiet" not in kwargs:
@@ -608,14 +616,11 @@ class TrainerBase(abc.ABC):
self.data.params["max_batch_size"] - self.data.soft_in_dim,
)
assert batch_size >= 0
print(
termcolor.colored(
"\nIf you see a warning somewhere below about token indices, ignore it. That warning is normal.\n",
"magenta",
)
logger.info(
"\nIf you see a warning somewhere below about token indices, ignore it. That warning is normal.\n"
)
print("Batch size:", batch_size)
print(termcolor.colored("Tokenizing your dataset...\n", "magenta"))
logger.info("Batch size: {}".format(batch_size))
logger.info("Tokenizing your dataset...\n")
if not isinstance(dataset_path, str):
files = [dataset_path]
@@ -632,7 +637,7 @@ class TrainerBase(abc.ABC):
eos = tokenizer.decode(self.data.params["eos_token"])
for path in files:
if isinstance(path, str):
f = open(path)
f = open(path, 'r', encoding='utf-8')
else:
f = path
try:
@@ -645,7 +650,7 @@ class TrainerBase(abc.ABC):
if isinstance(path, str):
f.close()
print("Dataset size (in tokens):", len(tokens))
logger.info("Dataset size (in tokens): {}".format(len(tokens)))
if len(tokens) < batch_size + 1:
self.raise_configuration_error(
"Your dataset is too small! The number of tokens has to be greater than the batch size. Try increasing the epochs.",
@@ -653,7 +658,7 @@ class TrainerBase(abc.ABC):
)
tail = len(tokens) % (batch_size + 1)
if tail:
print(
logger.info(
f"We're removing the last {tail} tokens from your dataset to make the length a multiple of {batch_size+1}."
)
tokens = tokens[:-tail]
@@ -671,7 +676,7 @@ class TrainerBase(abc.ABC):
axis=0,
)
tokens = tokens[: math.ceil(epochs * sequences_per_epoch)]
print(f"Total sequences in your dataset: {tokens.shape[0]}")
logger.info(f"Total sequences in your dataset: {tokens.shape[0]}")
if isinstance(output_file, str):
f = open(output_file, "w")
@@ -698,7 +703,7 @@ class TrainerBase(abc.ABC):
self.data.params["max_batch_size"] = 2048
if not os.path.exists(self.data.save_file):
print("We are starting a brand new soft-tuning session.\n")
logger.info("We are starting a brand new soft-tuning session.\n")
self.startup(step=-1)
if self.data.soft_in_dim <= 0:
self.raise_configuration_error(
@@ -718,7 +723,7 @@ class TrainerBase(abc.ABC):
opt_state = z["opt_state"]
except AssertionError:
self.raise_configuration_error("MKUSP file is corrupted.", code=14)
print(f"We're resuming a previous soft-tuning session at step {step+1}.\n")
logger.info(f"We're resuming a previous soft-tuning session at step {step+1}.\n")
self.startup(step=step + 1)
soft_embeddings = z["tensor"]
@@ -785,7 +790,7 @@ class TrainerBase(abc.ABC):
num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs))
else:
num_tensors = len(device_map)
print(flush=True)
#print(flush=True)
utils.bar = tqdm(total=num_tensors, desc="Loading model tensors", file=Send_to_socketio())
with zipfile.ZipFile(f, "r") as z: