code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def MetersToTile(
self,
mx,
my,
zoom,
):
'''Returns tile for given mercator coordinates'''
(px, py) = self.MetersToPixels(mx, my, zoom)
return self.PixelsToTile(px, py) | Returns tile for given mercator coordinates | MetersToTile | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def TileBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile in EPSG:900913 coordinates'''
(minx, miny) = self.PixelsToMeters(tx * self.tileSize, ty
* self.tileSize, zoom)
(maxx, maxy) = self.PixelsToMeters((tx + 1) * self.ti... | Returns bounds of the given tile in EPSG:900913 coordinates | TileBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def TileLatLonBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile in latutude/longitude using WGS84 datum'''
bounds = self.TileBounds(tx, ty, zoom)
(minLat, minLon) = self.MetersToLatLon(bounds[0], bounds[1])
(maxLat, maxLon) = self... | Returns bounds of the given tile in latutude/longitude using WGS84 datum | TileLatLonBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def Resolution(self, zoom):
'''Resolution (meters/pixel) for given zoom level (measured at Equator)'''
# return (2 * math.pi * 6378137) / (self.tileSize * 2**zoom)
return self.initialResolution / 2 ** zoom | Resolution (meters/pixel) for given zoom level (measured at Equator) | Resolution | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def ZoomForPixelSize(self, pixelSize):
'''Maximal scaledown zoom of the pyramid closest to the pixelSize.'''
for i in range(MAXZOOMLEVEL):
if pixelSize > self.Resolution(i):
if i != 0:
return i - 1
else:
return 0 # We ... | Maximal scaledown zoom of the pyramid closest to the pixelSize. | ZoomForPixelSize | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def GoogleTile(
self,
tx,
ty,
zoom,
):
'''Converts TMS tile coordinates to Google Tile coordinates'''
# coordinate origin is moved from bottom-left to top-left corner of the extent
return (tx, 2 ** zoom - 1 - ty) | Converts TMS tile coordinates to Google Tile coordinates | GoogleTile | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def QuadTree(
self,
tx,
ty,
zoom,
):
'''Converts TMS tile coordinates to Microsoft QuadTree'''
quadKey = ''
ty = 2 ** zoom - 1 - ty
for i in range(zoom, 0, -1):
digit = 0
mask = 1 << i - 1
if tx & mask != 0:
... | Converts TMS tile coordinates to Microsoft QuadTree | QuadTree | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def LonLatToPixels(
self,
lon,
lat,
zoom,
):
'''Converts lon/lat to pixel coordinates in given zoom of the EPSG:4326 pyramid'''
res = self.resFact / 2 ** zoom
px = (180 + lon) / res
py = (90 + lat) / res
return (px, py) | Converts lon/lat to pixel coordinates in given zoom of the EPSG:4326 pyramid | LonLatToPixels | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def PixelsToTile(self, px, py):
'''Returns coordinates of the tile covering region in pixel coordinates'''
tx = int(math.ceil(px / float(self.tileSize)) - 1)
ty = int(math.ceil(py / float(self.tileSize)) - 1)
return (tx, ty) | Returns coordinates of the tile covering region in pixel coordinates | PixelsToTile | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def LonLatToTile(
self,
lon,
lat,
zoom,
):
'''Returns the tile for zoom which covers given lon/lat coordinates'''
(px, py) = self.LonLatToPixels(lon, lat, zoom)
return self.PixelsToTile(px, py) | Returns the tile for zoom which covers given lon/lat coordinates | LonLatToTile | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def Resolution(self, zoom):
'''Resolution (arc/pixel) for given zoom level (measured at Equator)'''
return self.resFact / 2 ** zoom
# return 180 / float( 1 << (8+zoom) ) | Resolution (arc/pixel) for given zoom level (measured at Equator) | Resolution | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def TileBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile'''
res = self.resFact / 2 ** zoom
return (tx * self.tileSize * res - 180, ty * self.tileSize
* res - 90, (tx + 1) * self.tileSize * res - 180, (ty
+... | Returns bounds of the given tile | TileBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def TileLatLonBounds(
self,
tx,
ty,
zoom,
):
'''Returns bounds of the given tile in the SWNE form'''
b = self.TileBounds(tx, ty, zoom)
return (b[1], b[0], b[3], b[2]) | Returns bounds of the given tile in the SWNE form | TileLatLonBounds | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def __init__(
self,
width,
height,
tilesize=256,
tileformat='jpg',
):
"""Initialization of the Zoomify tile tree"""
self.tilesize = tilesize
self.tileformat = tileformat
imagesize = (width, height)
tiles = (math.ceil(width / tilesi... | Initialization of the Zoomify tile tree | __init__ | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def tilefilename(
self,
x,
y,
z,
):
"""Returns filename for tile with given coordinates"""
tileIndex = x + y * self.tierSizeInTiles[z][0] \
+ self.tileCountUpToTier[z]
return os.path.join('TileGroup%.0f' % math.floor(tileIndex
... | Returns filename for tile with given coordinates | tilefilename | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def process(self):
"""The main processing function, runs all the main steps of processing"""
# Opening and preprocessing of the input file
self.open_input()
# Generation of main metadata files and HTML viewers
self.generate_metadata()
# Generation of the lowest tiles... | The main processing function, runs all the main steps of processing | process | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def error(self, msg, details=''):
"""Print an error message and stop the processing"""
if details:
self.parser.error(msg + '''
''' + details)
else:
self.parser.error(msg) | Print an error message and stop the processing | error | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def optparse_init(self):
"""Prepare the option parser for input (argv)"""
from optparse import OptionParser, OptionGroup
usage = 'Usage: %prog [options] input_file(s) [output]'
p = OptionParser(usage, version='%prog ' + __version__)
p.add_option(
'-p',
'-... | Prepare the option parser for input (argv) | optparse_init | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def open_input(self):
"""Initialization of the input raster, reprojection if necessary"""
gdal.UseExceptions()
gdal.AllRegister()
if not self.options.verbose:
gdal.PushErrorHandler('CPLQuietErrorHandler')
# Initialize necessary GDAL drivers
self.out_drv = g... | Initialization of the input raster, reprojection if necessary | open_input | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_metadata(self):
"""Generation of main metadata files and HTML viewers (metadata related to particular tiles are generated during the tile processing)."""
if not os.path.exists(self.output):
os.makedirs(self.output)
if self.options.profile == 'mercator':
(s... | Generation of main metadata files and HTML viewers (metadata related to particular tiles are generated during the tile processing). | generate_metadata | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_base_tiles(self, cpu):
"""Generation of the base tiles (the lowest in the pyramid) directly from the input raster"""
if self.options.verbose:
# mx, my = self.out_gt[0], self.out_gt[3] # OriginX, OriginY
# px, py = self.mercator.MetersToPixels( mx, my, self.tmaxz)
... | Generation of the base tiles (the lowest in the pyramid) directly from the input raster | generate_base_tiles | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_overview_tiles(self, cpu, tz):
"""Generation of the overview tiles (higher in the pyramid) based on existing tiles"""
tilebands = self.dataBandsCount + 1
# Usage of existing tiles: from 4 underlying tiles generate one as overview.
tcount = 0
for z in range(self.tm... | Generation of the overview tiles (higher in the pyramid) based on existing tiles | generate_overview_tiles | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def geo_query(
self,
ds,
ulx,
uly,
lrx,
lry,
querysize=0,
):
"""For given dataset and query in cartographic coordinates
returns parameters for ReadRaster() in raster coordinates and
x/y shifts (for border tiles). If the querysize is... | For given dataset and query in cartographic coordinates
returns parameters for ReadRaster() in raster coordinates and
x/y shifts (for border tiles). If the querysize is not given, the
extent is returned in the native resolution of dataset ds. | geo_query | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def scale_query_to_tile(
self,
dsquery,
dstile,
tilefilename='',
):
"""Scales down query dataset to the tile dataset"""
querysize = dsquery.RasterXSize
tilesize = dstile.RasterXSize
tilebands = dstile.RasterCount
if self.options.resamplin... | Scales down query dataset to the tile dataset | scale_query_to_tile | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_tilemapresource(self):
"""
Template for tilemapresource.xml. Returns filled string. Expected variables:
title, north, south, east, west, isepsg4326, projection, publishurl,
zoompixels, tilesize, tileformat, profile
"""
args = {}
args['title'] = s... |
Template for tilemapresource.xml. Returns filled string. Expected variables:
title, north, south, east, west, isepsg4326, projection, publishurl,
zoompixels, tilesize, tileformat, profile
| generate_tilemapresource | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_kml(
self,
tx,
ty,
tz,
children=[],
**args
):
"""
Template for the KML. Returns filled string.
"""
(args['tx'], args['ty'], args['tz']) = (tx, ty, tz)
args['tileformat'] = self.tileext
if 'tilesize' not... |
Template for the KML. Returns filled string.
| generate_kml | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_googlemaps(self):
"""
Template for googlemaps.html implementing Overlay of tiles for 'mercator' profile.
It returns filled string. Expected variables:
title, googlemapskey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
"""
args... |
Template for googlemaps.html implementing Overlay of tiles for 'mercator' profile.
It returns filled string. Expected variables:
title, googlemapskey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
| generate_googlemaps | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def generate_openlayers(self):
"""
Template for openlayers.html implementing overlay of available Spherical Mercator layers.
It returns filled string. Expected variables:
title, bingkey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
"""
ar... |
Template for openlayers.html implementing overlay of available Spherical Mercator layers.
It returns filled string. Expected variables:
title, bingkey, north, south, east, west, minzoom, maxzoom, tilesize, tileformat, publishurl
| generate_openlayers | python | commenthol/gdal2tiles-leaflet | gdal2tiles-multiprocess.py | https://github.com/commenthol/gdal2tiles-leaflet/blob/master/gdal2tiles-multiprocess.py | MIT |
def build_dataset(tokenizer, config):
'''
We assume that we have preprocessed the dataset appropriately such that the sample is organized as follows:
{"positive": prompt + answer_positive, "negative": prompt + answer_negative}, where the positive response is preferred.
'''
def tokenize(sample):
... |
We assume that we have preprocessed the dataset appropriately such that the sample is organized as follows:
{"positive": prompt + answer_positive, "negative": prompt + answer_negative}, where the positive response is preferred.
| build_dataset | python | OptimalScale/LMFlow | contrib/rlhflow/reward_modeling.py | https://github.com/OptimalScale/LMFlow/blob/master/contrib/rlhflow/reward_modeling.py | Apache-2.0 |
def tokenize(
self,
dataset,
add_special_tokens=True,
*args,
**kwargs
) -> Dataset:
"""
Tokenize the full dataset.
Parameters
------------
dataset : lmflow.datasets.Dataset.
args : Optional.
Positional argu... |
Tokenize the full dataset.
Parameters
------------
dataset : lmflow.datasets.Dataset.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
------------
tokenized_d... | tokenize | python | OptimalScale/LMFlow | contrib/tool-finetune/function_call_finetune.py | https://github.com/OptimalScale/LMFlow/blob/master/contrib/tool-finetune/function_call_finetune.py | Apache-2.0 |
def update_ema(target_params, source_params, rate=0.99):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA ra... |
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
| update_ema | python | OptimalScale/LMFlow | experimental/LISA-diffusion/diffusion_dpo/train_diffusion_dpo_lisa.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/diffusion_dpo/train_diffusion_dpo_lisa.py | Apache-2.0 |
def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
"""Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
lower case (Default va... | Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
lower case (Default value = True)
| group_by_keys_nothrow | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | Apache-2.0 |
def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these timesteps
embedding_dim (... |
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these timesteps
embedding_dim (`int`, *optional*, defaults to 512):
dimension of the embeddings to ... | guidance_scale_embedding | python | OptimalScale/LMFlow | experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | https://github.com/OptimalScale/LMFlow/blob/master/experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py | Apache-2.0 |
def parse_argument(sys_argv):
"""Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '-... | Parses arguments from command line.
Args:
sys_argv: the list of arguments (strings) from command line.
Returns:
A struct whose member corresponds to the required (optional) variable.
For example,
```
args = parse_argument(['main.py' '--input', 'a.txt', '--num', '10'])
... | parse_argument | python | OptimalScale/LMFlow | scripts/data_preprocess/add_end_mark.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/add_end_mark.py | Apache-2.0 |
def raw2textonly(fin):
"""
Converts raw text to text-only format.
Args:
fin: the input file description of the raw text file.
Returns:
a dict with "text-only" format.
"""
data_dict = {
"type": "text_only",
"instances": [ { "text": line.strip() } for line in fin ]... |
Converts raw text to text-only format.
Args:
fin: the input file description of the raw text file.
Returns:
a dict with "text-only" format.
| raw2textonly | python | OptimalScale/LMFlow | scripts/data_preprocess/raw2textonly.py | https://github.com/OptimalScale/LMFlow/blob/master/scripts/data_preprocess/raw2textonly.py | Apache-2.0 |
def _check_instance_format(self):
"""
Checks if data (instances) have required fields.
Raises messages with hints if not matched.
"""
fields = self.backend_dataset.features
correct_fields = INSTANCE_FIELDS_MAP[self.type]
if not set(correct_fields).issubset(set(fi... |
Checks if data (instances) have required fields.
Raises messages with hints if not matched.
| _check_instance_format | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def from_dict(self, dict_obj: dict, *args, **kwargs):
r"""
Create a Dataset object from a dictionary.
Return a Dataset given a dict with format:
{
"type": TYPE,
"instances": [
{
"key_1": VALUE_1.1,
... |
Create a Dataset object from a dictionary.
Return a Dataset given a dict with format:
{
"type": TYPE,
"instances": [
{
"key_1": VALUE_1.1,
"key_2": VALUE_1.2,
...
... | from_dict | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def create_from_dict(cls, dict_obj, *args, **kwargs):
r"""
Returns
--------
Returns a Dataset object given a dict.
"""
empty_data_args = DatasetArguments(dataset_path=None)
dataset = Dataset(empty_data_args)
return dataset.from_dict(dict_obj) |
Returns
--------
Returns a Dataset object given a dict.
| create_from_dict | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def to_dict(self):
r"""
Returns
---------
Return a dict represents the dataset:
{
"type": TYPE,
"instances": [
{
"key_1": VALUE_1.1,
"key_2": VALUE_1.2,
... |
Returns
---------
Return a dict represents the dataset:
{
"type": TYPE,
"instances": [
{
"key_1": VALUE_1.1,
"key_2": VALUE_1.2,
...
},
... | to_dict | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def map(self, *args, **kwargs):
r"""
Parameters
------------
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
---------
self : Dataset object.
"""
# If the dataset uses ... |
Parameters
------------
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
---------
self : Dataset object.
| map | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def save(
self,
file_path: str,
format: str="json"
):
r"""
Save the dataset to a json file.
Parameters
------------
file_path : str.
The path to the file where the dataset will be saved.
"""
if format == "json":
... |
Save the dataset to a json file.
Parameters
------------
file_path : str.
The path to the file where the dataset will be saved.
| save | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def sample(self, n: int, seed: int=42):
r"""
Sample n instances from the dataset.
Parameters
------------
n : int.
The number of instances to sample from the dataset.
Returns
---------
sample_dataset : Dataset object.
A new datas... |
Sample n instances from the dataset.
Parameters
------------
n : int.
The number of instances to sample from the dataset.
Returns
---------
sample_dataset : Dataset object.
A new dataset object containing the sampled instances.
| sample | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def train_test_split(self, test_size: float=0.2, shuffle: bool=True, seed: int=42):
r"""
Split the dataset into training and testing sets.
Parameters
------------
test_size : float, default=0.2.
The proportion of the dataset that will be used for testing.
Re... |
Split the dataset into training and testing sets.
Parameters
------------
test_size : float, default=0.2.
The proportion of the dataset that will be used for testing.
Returns
---------
train_dataset : Dataset object.
A new dataset objec... | train_test_split | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def drop_instances(self, indices: list):
r"""
Drop instances from the dataset.
Parameters
------------
indices : list.
A list of indices of the instances to drop from the dataset.
"""
if self.backend == "huggingface":
self.backend_dataset ... |
Drop instances from the dataset.
Parameters
------------
indices : list.
A list of indices of the instances to drop from the dataset.
| drop_instances | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def sanity_check(
self,
drop_invalid: bool=True,
):
r"""
Perform a sanity check on the dataset.
"""
if self.backend == "huggingface":
self.hf_dataset_sanity_check(drop_invalid)
else:
raise NotImplementedError(
f'Current... |
Perform a sanity check on the dataset.
| sanity_check | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def hf_dataset_sanity_check(
self,
drop_invalid: bool=True,
):
r"""
Perform a sanity check on the HuggingFace dataset.
"""
if self.backend_dataset is None or len(self.backend_dataset) == 0:
raise ValueError("Dataset is empty.")
if self.type == 'te... |
Perform a sanity check on the HuggingFace dataset.
| hf_dataset_sanity_check | python | OptimalScale/LMFlow | src/lmflow/datasets/dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/dataset.py | Apache-2.0 |
def preprocess_llama_from_llava_plain(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False):
"""
This function just add the image in the front of text.
And don't add any prompt.
Args:
sources: The input data with text and image.
tokenizer: The tokeni... |
This function just add the image in the front of text.
And don't add any prompt.
Args:
sources: The input data with text and image.
tokenizer: The tokenizer to process text.
has_image: Whether the input data has image.
Returns:
The input_ids and labels for the model.
... | preprocess_llama_from_llava_plain | python | OptimalScale/LMFlow | src/lmflow/datasets/multi_modal_dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/multi_modal_dataset.py | Apache-2.0 |
def preprocess_llama_from_llava_v1(
sources,
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False):
"""
This function add the prompt and then put the image after the prompt.
So it needs additional code to generate the target label.
Args:
sources: The input data with t... |
This function add the prompt and then put the image after the prompt.
So it needs additional code to generate the target label.
Args:
sources: The input data with text and image.
tokenizer: The tokenizer to process text.
has_image: Whether the input data has image.
Returns:
... | preprocess_llama_from_llava_v1 | python | OptimalScale/LMFlow | src/lmflow/datasets/multi_modal_dataset.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/datasets/multi_modal_dataset.py | Apache-2.0 |
def __init__(
self,
model_args,
tune_strategy='normal',
ds_config=None,
device="gpu",
use_accelerator=False,
*args,
**kwargs
):
"""
Initializes a HFDecoderModel instance.
:param model_args: dictionary with model arguments such a... |
Initializes a HFDecoderModel instance.
:param model_args: dictionary with model arguments such as model name, path, revision, etc.
:param tune_strategy: tuning strategy: normal, none, lora or adapter
:param ds_config: deepspeed configuration for distributed training
| __init__ | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def encode(self, input: Union[str, List[str]], *args, **kwargs ) -> Union[List[int], List[List[int]]]:
"""
Perform encoding process of the tokenizer.
Parameters
------------
inputs : str or list.
The text sequence.
args : Optional.
... |
Perform encoding process of the tokenizer.
Parameters
------------
inputs : str or list.
The text sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Re... | encode | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def decode(self, input, *args, **kwargs ) -> Union[str, List[str]]:
"""
Perform decoding process of the tokenizer.
Parameters
------------
inputs : list or tensor.
The token sequence.
args : Optional.
Positional arguments.
... |
Perform decoding process of the tokenizer.
Parameters
------------
inputs : list or tensor.
The token sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
... | decode | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def inference(
self,
inputs,
release_gpu: bool = False,
use_vllm: bool = False,
**kwargs
):
"""
Perform generation process of the model.
Parameters
------------
inputs :
The sequence used as a prompt for the generatio... |
Perform generation process of the model.
Parameters
------------
inputs :
The sequence used as a prompt for the generation or as model inputs to the model.
When using vllm inference, this should be a string or a list of strings.
When using normal... | inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def __inference(self, inputs, *args, **kwargs):
"""
Perform generation process of the model.
Parameters
------------
inputs :
The **tokenized** sequence used as a prompt for the generation or as model inputs to the model.
args : Optional.
... |
Perform generation process of the model.
Parameters
------------
inputs :
The **tokenized** sequence used as a prompt for the generation or as model inputs to the model.
args : Optional.
Positional arguments.
kwargs : Op... | __inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def __vllm_inference(
self,
inputs: Union[str, List[str]],
sampling_params: Optional['SamplingParams'] = None,
**kwargs,
) -> List[VLLMInferenceResultWithInput]:
"""Perform VLLM inference process of the model.
Parameters
----------
inputs : Union[str... | Perform VLLM inference process of the model.
Parameters
----------
inputs : Union[str, List[str]]
Prompt(s), string or a list of strings.
sampling_params : Optional[SamplingParams], optional
vllm SamplingParams object, by default None.
Returns
--... | __vllm_inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def prepare_inputs_for_inference(
self,
dataset: Dataset,
apply_chat_template: bool = True,
enable_distributed_inference: bool = False,
use_vllm: bool = False,
**kwargs,
) -> Union[List[str], "ray.data.Dataset", Dict[str, torch.Tensor]]:
"""
Prepare in... |
Prepare inputs for inference.
Parameters
------------
dataset : lmflow.datasets.Dataset.
The dataset used for inference.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
... | prepare_inputs_for_inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def save(self, dir, save_full_model=False, *args, **kwargs):
"""
Perform generation process of the model.
Parameters
------------
dir :
The directory to save model and tokenizer
save_full_model : Optional.
Whether to save full mod... |
Perform generation process of the model.
Parameters
------------
dir :
The directory to save model and tokenizer
save_full_model : Optional.
Whether to save full model.
kwargs : Optional.
Keyword arguments. ... | save | python | OptimalScale/LMFlow | src/lmflow/models/hf_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_decoder_model.py | Apache-2.0 |
def encode(self, input: Union[str, List[str]], *args, **kwargs ) -> Union[List[int], List[List[int]]]:
"""
Perform encoding process of the tokenizer.
Parameters
------------
inputs : str or list.
The text sequence.
args : Optional.
Positional arg... |
Perform encoding process of the tokenizer.
Parameters
------------
inputs : str or list.
The text sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
------------
o... | encode | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def decode(self, input, *args, **kwargs ) -> Union[str, List[str]]:
"""
Perform decoding process of the tokenizer.
Parameters
------------
inputs : list.
The token sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
... |
Perform decoding process of the tokenizer.
Parameters
------------
inputs : list.
The token sequence.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.
Returns
------------
outputs... | decode | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def inference(self, inputs, *args, **kwargs):
"""
Perform generation process of the model.
Parameters
------------
inputs :
The sequence used as a prompt for the generation or as model inputs to the model.
args : Optional.
Positional arguments.
... |
Perform generation process of the model.
Parameters
------------
inputs :
The sequence used as a prompt for the generation or as model inputs to the model.
args : Optional.
Positional arguments.
kwargs : Optional.
Keyword arguments.... | inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def save(self, dir, save_full_model=False, *args, **kwargs):
"""
Perform generation process of the model.
Parameters
------------
dir :
The directory to save model and tokenizer
save_full_model : Optional.
Whether to save full model.
kwa... |
Perform generation process of the model.
Parameters
------------
dir :
The directory to save model and tokenizer
save_full_model : Optional.
Whether to save full model.
kwargs : Optional.
Keyword arguments.
Returns
... | save | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def get_max_length(self):
"""
Return max acceptable input length in terms of tokens.
"""
if "tokenizer" not in self.tokenizer.__dict__:
return self.tokenizer.model_max_length
else:
# for the multi-modality processor,
# the max length is stored ... |
Return max acceptable input length in terms of tokens.
| get_max_length | python | OptimalScale/LMFlow | src/lmflow/models/hf_encoder_decoder_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_encoder_decoder_model.py | Apache-2.0 |
def __init__(
self,
model_args: ModelArguments,
do_train: bool,
ds_config=None,
device: Optional[str]="gpu",
use_accelerator: bool=False,
hf_auto_model_additional_args: Optional[Dict]=None,
*args,
**kwargs
):
"""Initializes a HFModel in... | Initializes a HFModel instance.
Parameters
----------
model_args :
Dictionary with model arguments such as model name, path, revision, etc.
do_train : bool
To prepare the model for training or inference.
ds_config : optional
Deepspeed configu... | __init__ | python | OptimalScale/LMFlow | src/lmflow/models/hf_model_mixin.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_model_mixin.py | Apache-2.0 |
def __prepare_model_config(
self,
model_args: ModelArguments,
hf_auto_model_additional_args: Optional[Dict]=None,
):
"""Prepare model configuration for hf auto register,
Parameters
----------
model_args : ModelArguments
LMFlow model arguments.
... | Prepare model configuration for hf auto register,
Parameters
----------
model_args : ModelArguments
LMFlow model arguments.
hf_auto_model_additional_args : Optional[Dict], optional
Special configurations such as `num_labels` in `AutoModelForSequenceClassification`... | __prepare_model_config | python | OptimalScale/LMFlow | src/lmflow/models/hf_model_mixin.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_model_mixin.py | Apache-2.0 |
def __model_module_inject(
self,
model_args: ModelArguments,
) -> None:
"""Override some model modules with custom implementations.
Current implementations:
- Position interpolation (model_args.do_rope_scaling):
replace llama embeddings with condense emb... | Override some model modules with custom implementations.
Current implementations:
- Position interpolation (model_args.do_rope_scaling):
replace llama embeddings with condense embeddings.
| __model_module_inject | python | OptimalScale/LMFlow | src/lmflow/models/hf_model_mixin.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_model_mixin.py | Apache-2.0 |
def deactivate_model_for_inference(
self,
use_vllm: bool=False,
):
"""Deactivate the model and release the resources.
NOTE: Currently, VLLM doesn't have an official way to do this, and the
implementation below cannot release all gpu resources by our observation.
... | Deactivate the model and release the resources.
NOTE: Currently, VLLM doesn't have an official way to do this, and the
implementation below cannot release all gpu resources by our observation.
Thus this method is just a placeholder for future implementation. See:
[Github issue]... | deactivate_model_for_inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_model_mixin.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_model_mixin.py | Apache-2.0 |
def __init__(
self,
model_args: ModelArguments,
tune_strategy: str='normal',
ds_config=None,
device="gpu",
use_accelerator=False,
*args,
**kwargs
):
"""
Initializes a HFTextRegressionModel instance.
:param model_args: dictionary... |
Initializes a HFTextRegressionModel instance.
:param model_args: dictionary with model arguments such as model name, path, revision, etc.
:param tune_strategy: tuning strategy: normal, none, lora or adapter
:param ds_config: deepspeed configuration for distributed training
| __init__ | python | OptimalScale/LMFlow | src/lmflow/models/hf_text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_text_regression_model.py | Apache-2.0 |
def __inference(
self,
inputs,
**kwargs
):
"""
Perform generation process of the model.
Parameters
------------
inputs :
The **tokenized** sequence used as a prompt for the generation or as model inputs to the model.
kwargs :... |
Perform generation process of the model.
Parameters
------------
inputs :
The **tokenized** sequence used as a prompt for the generation or as model inputs to the model.
kwargs : Optional.
Keyword arguments.
Returns
-----... | __inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_text_regression_model.py | Apache-2.0 |
def __vllm_inference(
self,
inputs: Union[str, List[str]],
sampling_params: Optional['SamplingParams'] = None,
**kwargs,
) -> Union[List[List[str]], List[List[List[int]]]]:
"""Perform VLLM inference process of the model.
Parameters
----------
inputs ... | Perform VLLM inference process of the model.
Parameters
----------
inputs : Union[str, List[str]]
Prompt(s), string or a list of strings.
sampling_params : Optional[SamplingParams], optional
vllm SamplingParams object, by default None.
Returns
--... | __vllm_inference | python | OptimalScale/LMFlow | src/lmflow/models/hf_text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/hf_text_regression_model.py | Apache-2.0 |
def __init__(
self,
model_args,
*args,
**kwargs
):
"""
Initializes a TextRegressionModel instance.
:param model_args: dictionary with model arguments such as model name, path, revision, etc.
"""
self.inference_func = None |
Initializes a TextRegressionModel instance.
:param model_args: dictionary with model arguments such as model name, path, revision, etc.
| __init__ | python | OptimalScale/LMFlow | src/lmflow/models/text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/text_regression_model.py | Apache-2.0 |
def inference(self, inputs: Dataset):
"""
Gets regression results of a given dataset.
:inputs: Dataset object, only accept type "text_only".
"""
if self.inference_func is not None:
return self.inference_func(inputs)
else:
pass |
Gets regression results of a given dataset.
:inputs: Dataset object, only accept type "text_only".
| inference | python | OptimalScale/LMFlow | src/lmflow/models/text_regression_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/text_regression_model.py | Apache-2.0 |
def __init__(self,
config: Blip2Config,
image_encoder_name_or_path=None,
qformer_name_or_path=None,
language_model_name_or_path=None,
low_resource=False,):
'''
TODO update the docs
Args:
config:
... |
TODO update the docs
Args:
config:
# the below varaible are used to overwrite the model in config
image_encoder_name_or_path:
qformer_name_or_path:
language_model_name_or_path:
Returns:
| __init__ | python | OptimalScale/LMFlow | src/lmflow/models/vision2seq_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision2seq_model.py | Apache-2.0 |
def register_prompt_cache(self, prompt_ids, prompt_keys_values):
"""
Udpate the prompt id and embedding for reuse in the future
Args:
prompt_ids (torch.LongTensor): The id of the prompt.
prompt_keys_values (torch.FloatTensor): The embedding of the prompt.
Return... |
Udpate the prompt id and embedding for reuse in the future
Args:
prompt_ids (torch.LongTensor): The id of the prompt.
prompt_keys_values (torch.FloatTensor): The embedding of the prompt.
Returns:
None
| register_prompt_cache | python | OptimalScale/LMFlow | src/lmflow/models/vision2seq_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision2seq_model.py | Apache-2.0 |
def save_prompt_cache(self, path):
"""
Save prompt embedding and id.
Args:
path: The path to save the prompt embedding and id.
Returns:
None
"""
torch.save(
dict(
prompt_ids=self.prompt_ids,
prompt_key... |
Save prompt embedding and id.
Args:
path: The path to save the prompt embedding and id.
Returns:
None
| save_prompt_cache | python | OptimalScale/LMFlow | src/lmflow/models/vision2seq_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision2seq_model.py | Apache-2.0 |
def generate(
self,
pixel_values: torch.FloatTensor,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
image_token_indexes: Optional[List] = [0],
one_sample_multiple_images: Optional[bool] = False,
images: Optional[to... |
Overrides `generate` function to be able to use the model as a conditional generator.
Args:
pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)):
Input images to be processed.
input_ids (`torch.LongTensor` of shape (batch_size, s... | generate | python | OptimalScale/LMFlow | src/lmflow/models/vision2seq_model.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision2seq_model.py | Apache-2.0 |
def prepare_inputs_labels_for_multimodal(
self, input_ids, attention_mask, past_key_values, labels, images,
language_projection=None,
language_model=None,
**kwargs
):
'''
Copy from the LLAVA code base.
Should be polished.
'''
vision_tower = sel... |
Copy from the LLAVA code base.
Should be polished.
| prepare_inputs_labels_for_multimodal | python | OptimalScale/LMFlow | src/lmflow/models/vision_encoder/clip_encoder.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/models/vision_encoder/clip_encoder.py | Apache-2.0 |
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for ... | Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/adabelief.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/adabelief.py | Apache-2.0 |
def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group, base_lr in zip(self.param_... | Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/adabound.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/adabound.py | Apache-2.0 |
def step(self, closure: Callable=None):
"""
Performs a single optimization step.
Arguments:
closure (:obj:`Callable`, `optional`): A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
... |
Performs a single optimization step.
Arguments:
closure (:obj:`Callable`, `optional`): A closure that reevaluates the model and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/dummy.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/dummy.py | Apache-2.0 |
def zeropower_via_newtonschulz5(G: Tensor, steps: int) -> Tensor:
"""
Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a
quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose
of minimizing steps, it turns out to be emp... |
Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a
quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose
of minimizing steps, it turns out to be empirically effective to keep increasing the slope at
zero even beyond t... | zeropower_via_newtonschulz5 | python | OptimalScale/LMFlow | src/lmflow/optim/muon.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/muon.py | Apache-2.0 |
def step(self, closure=None):
"""
Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
... |
Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
| step | python | OptimalScale/LMFlow | src/lmflow/optim/muon.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/optim/muon.py | Apache-2.0 |
def convert_to_paired_dataset(
self,
source_dataset: Dataset,
sampling_paired_method: str="random",
length_penalty: float=0.0,
margin_scale: float=1.0,
use_fast: bool=False,
) -> Dataset:
"""Convert a scored one to multiple (text_to_scored_textlist) to a paire... | Convert a scored one to multiple (text_to_scored_textlist) to a paired dataset by rejection sampling.
| convert_to_paired_dataset | python | OptimalScale/LMFlow | src/lmflow/pipeline/dpov2_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/dpov2_aligner.py | Apache-2.0 |
def _calc_reward_with_length_penalty(
self,
rewards: List[float],
lengths: List[int],
length_penalty: float,
) -> List[float]:
"""When length_penalty > 0, penalize the longer sequence by subtracting
length_penalty * length from the reward. Vice versa when length_pe... | When length_penalty > 0, penalize the longer sequence by subtracting
length_penalty * length from the reward. Vice versa when length_penalty < 0.
| _calc_reward_with_length_penalty | python | OptimalScale/LMFlow | src/lmflow/pipeline/dpov2_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/dpov2_aligner.py | Apache-2.0 |
def sampling_paired_idx_from_rewards(
self,
rewards: List[float],
sampling_paired_method: str="random",
use_fast: bool=False,
) -> Tuple[int, int]:
"""Prepare the dataset for DPO training by rejection sampling.
We implement different strategies to select pairs, includ... | Prepare the dataset for DPO training by rejection sampling.
We implement different strategies to select pairs, including
random: randomly select two instances
max_min: best v.s. worst
max_max: best v.s. second best
max_random: best v.s. random from the remaining
| sampling_paired_idx_from_rewards | python | OptimalScale/LMFlow | src/lmflow/pipeline/dpov2_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/dpov2_aligner.py | Apache-2.0 |
def get_paired_dataset(
data_root: str,
data_dir: str,
sanity_check: bool = False,
cache_dir: Optional[str] = None,
num_proc=24,
) -> Dataset:
"""Load dataset and convert it to the necessary format.
The dataset is converted to a dictionary with the following structure:
... | Load dataset and convert it to the necessary format.
The dataset is converted to a dictionary with the following structure:
{
'prompt': List[str],
'chosen': List[str],
'rejected': List[str],
}
Prompts are structured as follows:
"Question: " + <prompt> + "
Answer: "
| get_paired_dataset | python | OptimalScale/LMFlow | src/lmflow/pipeline/dpo_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/dpo_aligner.py | Apache-2.0 |
def evaluate(
self,
model,
dataset: Dataset,
metric = "accuracy",
verbose=True,
):
"""
Perform Evaluation for a model
Parameters
------------
model : TunableModel object.
TunableModel to perform inference
dataset :... |
Perform Evaluation for a model
Parameters
------------
model : TunableModel object.
TunableModel to perform inference
dataset : Dataset object.
| evaluate | python | OptimalScale/LMFlow | src/lmflow/pipeline/evaluator.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/evaluator.py | Apache-2.0 |
def _evaluate_nll(
self,
model,
dataset: Dataset,
verbose=True,
):
"""
Evaluates negative log likelihood of the model over a dataset.
NLL = -1/N sum_{i=1}^N sum_{j=1}^|w_i| ln(p(w_{i,j}|context_window)),
where N is the number of data samples, w_{i,j}... |
Evaluates negative log likelihood of the model over a dataset.
NLL = -1/N sum_{i=1}^N sum_{j=1}^|w_i| ln(p(w_{i,j}|context_window)),
where N is the number of data samples, w_{i,j} is the j-th token in
i-th sample. Here "context_window" = p(w_{i,start}, w_{i,start+1}, ...,
p_{i... | _evaluate_nll | python | OptimalScale/LMFlow | src/lmflow/pipeline/evaluator.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/evaluator.py | Apache-2.0 |
def group_text(self, tokenized_datasets, model_max_length):
"""
Groups texts together to form blocks of maximum length `model_max_length` and returns the processed data as
a dictionary.
"""
data_args = self.data_args
finetuner_args = self.finetuner_args
if data_a... |
Groups texts together to form blocks of maximum length `model_max_length` and returns the processed data as
a dictionary.
| group_text | python | OptimalScale/LMFlow | src/lmflow/pipeline/finetuner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/finetuner.py | Apache-2.0 |
def tune(self,
model: Union[HFDecoderModel, HFTextRegressionModel, HFEncoderDecoderModel],
dataset: Dataset,
transform_dataset_in_place=True,
data_collator=None):
"""
Perform tuning for a model
Parameters
------------
model : T... |
Perform tuning for a model
Parameters
------------
model : TunableModel object.
TunableModel to perform tuning.
dataset:
dataset to train model.
| tune | python | OptimalScale/LMFlow | src/lmflow/pipeline/finetuner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/finetuner.py | Apache-2.0 |
def create_dataloader(self, dataset: Dataset):
r"""Batchlize dataset and format it to dataloader.
Args:
dataset (Dataset): the dataset object
Output:
dataloader (batchlize): the dataloader object
dataset_size (int): the length of the dataset
"""
... | Batchlize dataset and format it to dataloader.
Args:
dataset (Dataset): the dataset object
Output:
dataloader (batchlize): the dataloader object
dataset_size (int): the length of the dataset
| create_dataloader | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def inference(
self,
model,
dataset: Dataset,
max_new_tokens: int=100,
temperature: float=0.0,
prompt_structure: str='{input}',
remove_image_flag: bool=False,
chatbot_type: str="mini_gpt",
):
"""
Perform inference for a model
P... |
Perform inference for a model
Parameters
------------
model : TunableModel object.
TunableModel to perform inference
dataset : Dataset object.
Returns:
output_dataset: Dataset object.
| inference | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def score_to_prob(scores: torch.Tensor,
temperature: float = 0.,
top_p: float = 1.,) -> torch.Tensor:
"""Convert scores (NOT softmaxed tensor) to probabilities with support for temperature, top-p sampling, and argmax.
Parameters
----------
sc... | Convert scores (NOT softmaxed tensor) to probabilities with support for temperature, top-p sampling, and argmax.
Parameters
----------
scores : torch.Tensor
Input scores.
temperature : float, optional
Temperature parameter for controlling randomness. Higher value... | score_to_prob | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def predict_next_token(model: HFDecoderModel, input_ids: torch.Tensor, num_new_tokens: int = 1):
"""Predict the next token given the input_ids.
"""
output = model.inference(input_ids,
use_accelerator=True,
max_new_tokens=num_new... | Predict the next token given the input_ids.
| predict_next_token | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def autoregressive_sampling(self,
input_ids: torch.Tensor,
model: HFDecoderModel,
temperature: float = 0.,
num_new_tokens: int = 5) -> Dict:
"""Ref: [arXiv:2211.17192v2](https://ar... | Ref: [arXiv:2211.17192v2](https://arxiv.org/abs/2211.17192) Section 2.2
| autoregressive_sampling | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def inference(
self,
model: HFDecoderModel,
input: str,
max_new_tokens: int=1024,
):
"""
Perform inference for a model
Parameters
------------
model : HFDecoderModel object.
TunableModel to perform inference
input : str.
... |
Perform inference for a model
Parameters
------------
model : HFDecoderModel object.
TunableModel to perform inference
input : str.
The input text (i.e., the prompt) for the model.
max_new_tokens : int.
The maximum numb... | inference | python | OptimalScale/LMFlow | src/lmflow/pipeline/inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/inferencer.py | Apache-2.0 |
def _initialize_trainer(self, model, tokenizer, training_args):
"""
This function takes the model and tokenizer as the input and initialize the trainer.
"""
trainer = RaftTrainer(
model=model,
args=training_args,
train_dataset=Dataset.from_dict({"text"... |
This function takes the model and tokenizer as the input and initialize the trainer.
| _initialize_trainer | python | OptimalScale/LMFlow | src/lmflow/pipeline/raft_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/raft_aligner.py | Apache-2.0 |
def _load_dataset(
self,
selected_dataset,
model,
tokenizer,
model_args,
data_args,
training_args,
):
'''
This function prepares the dataset for every iteration.
'''
raw_datasets = selected_dataset
if training_args.do_t... |
This function prepares the dataset for every iteration.
| _load_dataset | python | OptimalScale/LMFlow | src/lmflow/pipeline/raft_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/raft_aligner.py | Apache-2.0 |
def _load_input_dataset(self, dataset, tokenizer):
"""
Load input dataset (i.e. prompt/question dataset) for training.
Args:
dataset: A Dataset object.
The dataset to be loaded.
Returns:
dataloader (`torch.utils.data.DataLoader`):
... |
Load input dataset (i.e. prompt/question dataset) for training.
Args:
dataset: A Dataset object.
The dataset to be loaded.
Returns:
dataloader (`torch.utils.data.DataLoader`):
The dataloader for the dataset.
| _load_input_dataset | python | OptimalScale/LMFlow | src/lmflow/pipeline/raft_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/raft_aligner.py | Apache-2.0 |
def align(self, model, dataset, reward_model):
"""
Perform alignment for a model
Parameters
------------
model : BaseModel object.
dataset: Dataset object.
Input dataset for model to generate outputs. The input and output
will then be feed int... |
Perform alignment for a model
Parameters
------------
model : BaseModel object.
dataset: Dataset object.
Input dataset for model to generate outputs. The input and output
will then be feed into reward model to get the reward for
align... | align | python | OptimalScale/LMFlow | src/lmflow/pipeline/raft_aligner.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/raft_aligner.py | Apache-2.0 |
def __call__(self, batch: Dict[str, np.ndarray]):
"""batch: Dict[str, np.ndarray]
Example (batch size=2):
{'input': array(['...','...'], dtype=object),
'output': array([array(["...", "..."], dtype=object), array(['...','...'], dtype=object)], dtype=object... | batch: Dict[str, np.ndarray]
Example (batch size=2):
{'input': array(['...','...'], dtype=object),
'output': array([array(["...", "..."], dtype=object), array(['...','...'], dtype=object)], dtype=object),
'input_ids': array([[[128000, 128006, 882, ...... | __call__ | python | OptimalScale/LMFlow | src/lmflow/pipeline/rm_inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/rm_inferencer.py | Apache-2.0 |
def inference(
self,
model: HFDecoderModel,
dataset: Dataset,
enable_decode_inference_result: bool = True,
release_gpu: bool = False,
inference_args: Optional[InferencerArguments] = None,
enable_distributed_inference: bool = False,
**kwargs,
) -> Lis... | Perform inference using the provided model and dataset. Will save inference results if
`save_results` is set to True in `inferencer_args`.
Parameters
----------
model : HFDecoderModel
LMFlow HFDecoderModel object
dataset : Dataset
LMFlow Dataset object
... | inference | python | OptimalScale/LMFlow | src/lmflow/pipeline/vllm_inferencer.py | https://github.com/OptimalScale/LMFlow/blob/master/src/lmflow/pipeline/vllm_inferencer.py | Apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.