matlok/python-copilot-training-from-many-repos-large

do_log

TextLogger

NullLogger

true

self,arg

null

def do_log(self, arg): sys.stdout.write(str(arg))

["def","do_log","(","self",",","arg",")",":","sys.stdout.write","(","str","(","arg",")",")"]

39

40

null

logger.py

turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py

import sys

15

2

1

0

1

4

1

Use image node_id 2 for calling the TextLogger obj's underlying member method code with example usage: obj.do_log(arg) without return types

139

node_id 2

2,276,655

interesting

TextLogger

NullLogger

true

self,source_name

null

True

def interesting(self, source_name): return True

["def","interesting","(","self",",","source_name",")",":","return","True"]

42

43

null

logger.py

turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py

import sys

15

2

1

0

1

4

1

Use image node_id 3 for calling the TextLogger obj's underlying member method code with example usage: obj.interesting(source_name) and returns: True

149

node_id 3

2,276,656

test_autolog_log_models_configuration

global

null

false

log_models

null

def test_autolog_log_models_configuration(log_models): mlflow.paddle.autolog(log_models=log_models) with mlflow.start_run() as run: train_model() artifacts = MlflowClient().list_artifacts(run.info.run_id) assert any(x.path == "model" for x in artifacts) == log_models

["def","test_autolog_log_models_configuration","(","log_models",")",":","mlflow.paddle.autolog","(","log_models=log_models",")","with","mlflow.start_run","(",")","as","run",":","train_model","(",")","artifacts","=","MlflowClient","(",")",".list_artifacts","(","run.info.run_id",")","assert","any","(","x.path","==","``","model","''","for","x","in","artifacts",")","==","log_models"]

86

93

null

test_paddle_autolog.py

mlflow/tests/paddle/test_paddle_autolog.py

import paddle import pytest import mlflow from mlflow import MlflowClient

15

null

4

7

null

Use image node_id 5 for calling a global function with example usage: test_autolog_log_models_configuration(log_models) without return types

140

node_id 5

1,356,430

test_autolog_early_stopping_callback

global

null

false

null

def test_autolog_early_stopping_callback(): mlflow.paddle.autolog() early_stopping = paddle.callbacks.EarlyStopping( "loss", mode="min", patience=1, min_delta=0 ) with mlflow.start_run() as run: train_model(callbacks=[early_stopping]) client = MlflowClient() data = client.get_run(run.info.run_id).data for param_key in ["monitor", "patience", "min_delta", "baseline"]: assert param_key in data.params assert data.params[param_key] == str( getattr(early_stopping, param_key) ) for metric_key in ["stopped_epoch", "best_value"]: assert metric_key in data.metrics assert float(data.metrics[metric_key]) == getattr( early_stopping, metric_key ) for metric_key in ["loss", "step"]: assert metric_key in data.metrics metric_history = client.get_metric_history( run.info.run_id, metric_key ) assert len(metric_history) == NUM_EPOCHS

["def","test_autolog_early_stopping_callback","(",")",":","mlflow.paddle.autolog","(",")","early_stopping","=","paddle.callbacks.EarlyStopping","(","``","loss","''",",","mode=","''","min","''",",","patience=1",",","min_delta=0",")","with","mlflow.start_run","(",")","as","run",":","train_model","(","callbacks=","[","early_stopping","]",")","client","=","MlflowClient","(",")","data","=","client.get_run","(","run.info.run_id",")",".data","for","param_key","in","[","``","monitor","''",",","``","patience","''",",","``","min_delta","''",",","``","baseline","''","]",":","assert","param_key","in","data.params","assert","data.params","[","param_key","]","==","str","(","getattr","(","early_stopping",",","param_key",")",")","for","metric_key","in","[","``","stopped_epoch","''",",","``","best_value","''","]",":","assert","metric_key","in","data.metrics","assert","float","(","data.metrics","[","metric_key","]",")","==","getattr","(","early_stopping",",","metric_key",")","for","metric_key","in","[","``","loss","''",",","``","step","''","]",":","assert","metric_key","in","data.metrics","metric_history","=","client.get_metric_history","(","run.info.run_id",",","metric_key",")","assert","len","(","metric_history",")","==","NUM_EPOCHS"]

61

82

null

test_paddle_autolog.py

mlflow/tests/paddle/test_paddle_autolog.py

import paddle import pytest import mlflow from mlflow import MlflowClient

15

null

4

7

null

Use image node_id 4 for calling a global function with example usage: test_autolog_early_stopping_callback() without return types

129

node_id 4

1,356,429

test_get_files_from_dir

TestRetrieveUtils

null

true

self

null

def test_get_files_from_dir(self): files = get_files_from_dir(test_dir, recursive=False) assert all(os.path.isfile(file) for file in files) pdf_file_path = os.path.join(test_dir, "example.pdf") txt_file_path = os.path.join(test_dir, "example.txt") files = get_files_from_dir([pdf_file_path, txt_file_path]) assert all(os.path.isfile(file) for file in files) files = get_files_from_dir( [ pdf_file_path, txt_file_path, os.path.join(test_dir, "..", "..", "website/docs"), "https://raw.githubusercontent.com/microsoft/autogen/main/README.md", ], recursive=True, ) assert all(os.path.isfile(file) for file in files) files = get_files_from_dir( [ pdf_file_path, txt_file_path, os.path.join(test_dir, "..", "..", "website/docs"), "https://raw.githubusercontent.com/microsoft/autogen/main/README.md", ], recursive=True, types=["pdf", "txt"], ) assert all(os.path.isfile(file) for file in files) assert len(files) == 3

["def","test_get_files_from_dir","(","self",")",":","files","=","get_files_from_dir","(","test_dir",",","recursive=False",")","assert","all","(","os.path.isfile","(","file",")","for","file","in","files",")","pdf_file_path","=","os.path.join","(","test_dir",",","``","example.pdf","''",")","txt_file_path","=","os.path.join","(","test_dir",",","``","example.txt","''",")","files","=","get_files_from_dir","(","[","pdf_file_path",",","txt_file_path","]",")","assert","all","(","os.path.isfile","(","file",")","for","file","in","files",")","files","=","get_files_from_dir","(","[","pdf_file_path",",","txt_file_path",",","os.path.join","(","test_dir",",","``","..","''",",","``","..","''",",","``","website\/docs","''",")",",","``","https",":","\/\/raw.githubusercontent.com\/microsoft\/autogen\/main\/README.md","''",",","]",",","recursive=True",",",")","assert","all","(","os.path.isfile","(","file",")","for","file","in","files",")","files","=","get_files_from_dir","(","[","pdf_file_path",",","txt_file_path",",","os.path.join","(","test_dir",",","``","..","''",",","``","..","''",",","``","website\/docs","''",")",",","``","https",":","\/\/raw.githubusercontent.com\/microsoft\/autogen\/main\/README.md","''",",","]",",","recursive=True",",","types=","[","``","pdf","''",",","``","txt","''","]",",",")","assert","all","(","os.path.isfile","(","file",")","for","file","in","files",")","assert","len","(","files",")","==","3"]

62

90

null

test_retrieve_utils.py

autogen/test/test_retrieve_utils.py

import pytest import os

15

1

2

0

12

null

Use image node_id 5 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_get_files_from_dir() without return types

160

node_id 5

319,450

_transform_search_space

SearchSpaceToChoice

Transform

true

self,search_space

Replaces the search space with a single choice parameter, whose values are the signatures of the arms observed in the data. This transform is meant to be used with ThompsonSampler. Choice parameter will be unordered unless config["use_ordered"] specifies otherwise. Transform is done in-place.

["Replaces","the","search","space","with","a","single","choice","parameter",",","whose","values","are","the","signatures","of","the","arms","observed","in","the","data",".","This","transform","is","meant","to","be","used","with","ThompsonSampler",".","Choice","parameter","will","be","unordered","unless","config","[","``","use_ordered","''","]","specifies","otherwise",".","Transform","is","done","in-place","."]

null

SearchSpace

def _transform_search_space( self, search_space: SearchSpace ) -> SearchSpace: values = list(self.signature_to_parameterization.keys()) if len(values) > 1: parameter = ChoiceParameter( name=self.parameter_name, parameter_type=ParameterType.STRING, values=values, is_ordered=checked_cast( bool, self.config.get("use_ordered", False) ), sort_values=False, ) else: parameter = FixedParameter( name=self.parameter_name, parameter_type=ParameterType.STRING, value=values[0], ) return SearchSpace(parameters=[parameter])

["def","_transform_search_space","(","self",",","search_space",":","SearchSpace",")","-",">","SearchSpace",":","values","=","list","(","self.signature_to_parameterization.keys","(",")",")","if","len","(","values",")",">","1",":","parameter","=","ChoiceParameter","(","name=self.parameter_name",",","parameter_type=ParameterType.STRING",",","values=values",",","is_ordered=checked_cast","(","bool",",","self.config.get","(","``","use_ordered","''",",","False",")",")",",","sort_values=False",",",")","else",":","parameter","=","FixedParameter","(","name=self.parameter_name",",","parameter_type=ParameterType.STRING",",","value=values","[","0","]",",",")","return","SearchSpace","(","parameters=","[","parameter","]",")"]

65

81

null

search_space_to_choice.py

Ax/ax/modelbridge/transforms/search_space_to_choice.py

from typing import List, Optional, TYPE_CHECKING from ax.core.arm import Arm from ax.core.observation import Observation, ObservationFeatures from ax.core.parameter import ChoiceParameter, FixedParameter, ParameterType from ax.core.search_space import RobustSearchSpace, SearchSpace from ax.exceptions.core import UnsupportedError from ax.modelbridge.transforms.base import Transform from ax.models.types import TConfig from ax.utils.common.typeutils import checked_cast

15

1

9

0

1

4

1

Use image node_id 2 for calling the SearchSpaceToChoice obj's underlying member method code with example usage: obj._transform_search_space(search_space) and returns: SearchSpace

178

node_id 2

9,097

test_df_to_sql_no_dtype

TestRedshiftDbEngineSpec

TestDbEngineSpec

true

self

null

def test_df_to_sql_no_dtype(self): mock_database = mock.MagicMock() mock_database.get_df.return_value.empty = False table_name = "foobar" data = [ ("foo", "bar", pd.NA, None), ("foo", "bar", pd.NA, True), ("foo", "bar", pd.NA, None), ] numpy_dtype = [ ("id", "object"), ("value", "object"), ("num", "object"), ("bool", "object"), ] column_names = ["id", "value", "num", "bool"] test_array = np.array(data, dtype=numpy_dtype) df = pd.DataFrame(test_array, columns=column_names) df.to_sql = mock.MagicMock() with app.app_context(): RedshiftEngineSpec.df_to_sql( mock_database, Table(table=table_name), df, to_sql_kwargs={}, ) assert df.to_sql.call_args[1]["dtype"] == {}

["def","test_df_to_sql_no_dtype","(","self",")",":","mock_database","=","mock.MagicMock","(",")","mock_database.get_df.return_value.empty","=","False","table_name","=","``","foobar","''","data","=","[","(","``","foo","''",",","``","bar","''",",","pd.NA",",","None",")",",","(","``","foo","''",",","``","bar","''",",","pd.NA",",","True",")",",","(","``","foo","''",",","``","bar","''",",","pd.NA",",","None",")",",","]","numpy_dtype","=","[","(","``","id","''",",","``","object","''",")",",","(","``","value","''",",","``","object","''",")",",","(","``","num","''",",","``","object","''",")",",","(","``","bool","''",",","``","object","''",")",",","]","column_names","=","[","``","id","''",",","``","value","''",",","``","num","''",",","``","bool","''","]","test_array","=","np.array","(","data",",","dtype=numpy_dtype",")","df","=","pd.DataFrame","(","test_array",",","columns=column_names",")","df.to_sql","=","mock.MagicMock","(",")","with","app.app_context","(",")",":","RedshiftEngineSpec.df_to_sql","(","mock_database",",","Table","(","table=table_name",")",",","df",",","to_sql_kwargs=","{","}",",",")","assert","df.to_sql.call_args","[","1","]","[","``","dtype","''","]","==","{","}"]

194

221

null

redshift_tests.py

superset/tests/integration_tests/db_engine_specs/redshift_tests.py

import unittest.mock from textwrap import dedent import numpy import pandas from sqlalchemy.types import NVARCHAR from superset.db_engine_specs.redshift import RedshiftEngineSpec from superset.errors import ErrorLevel, SupersetError, SupersetErrorType from superset.sql_parse import Table from tests.integration_tests.db_engine_specs.base_tests import TestDbEngineSpec from tests.integration_tests.test_app import app

15

1

10

0

1

3

1

Use image node_id 2 for calling the TestRedshiftDbEngineSpec obj's underlying member method code with example usage: obj.test_df_to_sql_no_dtype() without return types

167

node_id 2

2,027,365

test_df_to_sql_with_string_dtype

TestRedshiftDbEngineSpec

TestDbEngineSpec

true

self

null

def test_df_to_sql_with_string_dtype(self): mock_database = mock.MagicMock() mock_database.get_df.return_value.empty = False table_name = "foobar" data = [ ("foo", "bar", pd.NA, None), ("foo", "bar", pd.NA, True), ("foo", "bar", pd.NA, None), ] column_names = ["id", "value", "num", "bool"] df = pd.DataFrame(data, columns=column_names) df = df.astype(dtype={"value": "string"}) df.to_sql = mock.MagicMock() with app.app_context(): RedshiftEngineSpec.df_to_sql( mock_database, Table(table=table_name), df, to_sql_kwargs={}, ) # varchar string length should be 65535 dtype = df.to_sql.call_args[1]["dtype"] assert isinstance(dtype["value"], NVARCHAR) assert dtype["value"].length == 65535

["def","test_df_to_sql_with_string_dtype","(","self",")",":","mock_database","=","mock.MagicMock","(",")","mock_database.get_df.return_value.empty","=","False","table_name","=","``","foobar","''","data","=","[","(","``","foo","''",",","``","bar","''",",","pd.NA",",","None",")",",","(","``","foo","''",",","``","bar","''",",","pd.NA",",","True",")",",","(","``","foo","''",",","``","bar","''",",","pd.NA",",","None",")",",","]","column_names","=","[","``","id","''",",","``","value","''",",","``","num","''",",","``","bool","''","]","df","=","pd.DataFrame","(","data",",","columns=column_names",")","df","=","df.astype","(","dtype=","{","``","value","''",":","``","string","''","}",")","df.to_sql","=","mock.MagicMock","(",")","with","app.app_context","(",")",":","RedshiftEngineSpec.df_to_sql","(","mock_database",",","Table","(","table=table_name",")",",","df",",","to_sql_kwargs=","{","}",",",")","#","varchar","string","length","should","be","65535","dtype","=","df.to_sql.call_args","[","1","]","[","``","dtype","''","]","assert","isinstance","(","dtype","[","``","value","''","]",",","NVARCHAR",")","assert","dtype","[","``","value","''","]",".length","==","65535"]

223

246

null

redshift_tests.py

superset/tests/integration_tests/db_engine_specs/redshift_tests.py

import unittest.mock from textwrap import dedent import numpy import pandas from sqlalchemy.types import NVARCHAR from superset.db_engine_specs.redshift import RedshiftEngineSpec from superset.errors import ErrorLevel, SupersetError, SupersetErrorType from superset.sql_parse import Table from tests.integration_tests.db_engine_specs.base_tests import TestDbEngineSpec from tests.integration_tests.test_app import app

15

1

10

0

1

3

1

Use image node_id 3 for calling the TestRedshiftDbEngineSpec obj's underlying member method code with example usage: obj.test_df_to_sql_with_string_dtype() without return types

176

node_id 3

2,027,366

differint

global

null

false

ctx,f,x,n,x0

null

unknown

def differint(ctx, f, x, n=1, x0=0): r""" Calculates the Riemann-Liouville differintegral, or fractional derivative, defined by .. math :: \,_{x_0}{\mathbb{D}}^n_xf(x) = \frac{1}{\Gamma(m-n)} \frac{d^m}{dx^m} \int_{x_0}^{x}(x-t)^{m-n-1}f(t)dt where `f` is a given (presumably well-behaved) function, `x` is the evaluation point, `n` is the order, and `x_0` is the reference point of integration (`m` is an arbitrary parameter selected automatically). With `n = 1`, this is just the standard derivative `f'(x)`; with `n = 2`, the second derivative `f''(x)`, etc. With `n = -1`, it gives `\int_{x_0}^x f(t) dt`, with `n = -2` it gives `\int_{x_0}^x \left( \int_{x_0}^t f(u) du \right) dt`, etc. As `n` is permitted to be any number, this operator generalizes iterated differentiation and iterated integration to a single operator with a continuous order parameter. **Examples** There is an exact formula for the fractional derivative of a monomial `x^p`, which may be used as a reference. For example, the following gives a half-derivative (order 0.5):: >>> from mpmath import * >>> mp.dps = 15; mp.pretty = True >>> x = mpf(3); p = 2; n = 0.5 >>> differint(lambda t: t**p, x, n) 7.81764019044672 >>> gamma(p+1)/gamma(p-n+1) * x**(p-n) 7.81764019044672 Another useful test function is the exponential function, whose integration / differentiation formula easy generalizes to arbitrary order. Here we first compute a third derivative, and then a triply nested integral. (The reference point `x_0` is set to `-\infty` to avoid nonzero endpoint terms.):: >>> differint(lambda x: exp(pi*x), -1.5, 3) 0.278538406900792 >>> exp(pi*-1.5) * pi**3 0.278538406900792 >>> differint(lambda x: exp(pi*x), 3.5, -3, -inf) 1922.50563031149 >>> exp(pi*3.5) / pi**3 1922.50563031149 However, for noninteger `n`, the differentiation formula for the exponential function must be modified to give the same result as the Riemann-Liouville differintegral:: >>> x = mpf(3.5) >>> c = pi >>> n = 1+2*j >>> differint(lambda x: exp(c*x), x, n) (-123295.005390743 + 140955.117867654j) >>> x**(-n) * exp(c)**x * (x*c)**n * gammainc(-n, 0, x*c) / gamma(-n) (-123295.005390743 + 140955.117867654j) """ m = max(int(ctx.ceil(ctx.re(n))) + 1, 1) r = m - n - 1 g = lambda x: ctx.quad(lambda t: (x - t) ** r * f(t), [x0, x]) return ctx.diff(g, x, m) / ctx.gamma(m - n)

449

519

null

differentiation.py

catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py

from ..libmp.backend import xrange from .calculus import defun

15

null

2

13

null

Use image node_id 10 for calling a global function with example usage: differint(ctx, f, x, n, x0) and returns: unknown

119

node_id 10

407,221

diffun

global

null

false

ctx,f,n

null

g,f,ctx

def diffun(ctx, f, n=1, **options): r""" Given a function `f`, returns a function `g(x)` that evaluates the nth derivative `f^{(n)}(x)`:: >>> from mpmath import * >>> mp.dps = 15; mp.pretty = True >>> cos2 = diffun(sin) >>> sin2 = diffun(sin, 4) >>> cos(1.3), cos2(1.3) (0.267498828624587, 0.267498828624587) >>> sin(1.3), sin2(1.3) (0.963558185417193, 0.963558185417193) The function `f` must support arbitrary precision evaluation. See :func:`~mpmath.diff` for additional details and supported keyword options. """ if n == 0: return f def g(x): return ctx.diff(f, x, n, **options) return g

["def","diffun","(","ctx",",","f",",","n=1",",","*","*","options",")",":","r","''","''","''","Given","a","function","`","f","`",",","returns","a","function","`","g","(","x",")","`","that","evaluates","the","nth","derivative","`","f^","{","(","n",")","}","(","x",")","`",":",":",">",">",">","from","mpmath","import","*",">",">",">","mp.dps","=","15",";","mp.pretty","=","True",">",">",">","cos2","=","diffun","(","sin",")",">",">",">","sin2","=","diffun","(","sin",",","4",")",">",">",">","cos","(","1.3",")",",","cos2","(","1.3",")","(","0.267498828624587",",","0.267498828624587",")",">",">",">","sin","(","1.3",")",",","sin2","(","1.3",")","(","0.963558185417193",",","0.963558185417193",")","The","function","`","f","`","must","support","arbitrary","precision","evaluation",".","See",":","func",":","`","~mpmath.diff","`","for","additional","details","and","supported","keyword","options.","``","''","''","if","n","==","0",":","return","f","def","g","(","x",")",":","return","ctx.diff","(","f",",","x",",","n",",","*","*","options",")","return","g"]

522

544

null

differentiation.py

catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py

from ..libmp.backend import xrange from .calculus import defun

15

null

2

13

null

Use image node_id 11 for calling a global function with example usage: diffun(ctx, f, n) and returns: g, f, ctx

111

node_id 11

407,222

taylor

global

null

false

ctx,f,x,n

null

unknown,unknown

def taylor(ctx, f, x, n, **options): r""" Produces a degree-`n` Taylor polynomial around the point `x` of the given function `f`. The coefficients are returned as a list. >>> from mpmath import * >>> mp.dps = 15; mp.pretty = True >>> nprint(chop(taylor(sin, 0, 5))) [0.0, 1.0, 0.0, -0.166667, 0.0, 0.00833333] The coefficients are computed using high-order numerical differentiation. The function must be possible to evaluate to arbitrary precision. See :func:`~mpmath.diff` for additional details and supported keyword options. Note that to evaluate the Taylor polynomial as an approximation of `f`, e.g. with :func:`~mpmath.polyval`, the coefficients must be reversed, and the point of the Taylor expansion must be subtracted from the argument: >>> p = taylor(exp, 2.0, 10) >>> polyval(p[::-1], 2.5 - 2.0) 12.1824939606092 >>> exp(2.5) 12.1824939607035 """ gen = enumerate(ctx.diffs(f, x, n, **options)) if options.get("chop", True): return [ctx.chop(d) / ctx.factorial(i) for i, d in gen] else: return [d / ctx.factorial(i) for i, d in gen]

["def","taylor","(","ctx",",","f",",","x",",","n",",","*","*","options",")",":","r","''","''","''","Produces","a","degree-","`","n","`","Taylor","polynomial","around","the","point","`","x","`","of","the","given","function","`","f","`",".","The","coefficients","are","returned","as","a","list",".",">",">",">","from","mpmath","import","*",">",">",">","mp.dps","=","15",";","mp.pretty","=","True",">",">",">","nprint","(","chop","(","taylor","(","sin",",","0",",","5",")",")",")","[","0.0",",","1.0",",","0.0",",","-0.166667",",","0.0",",","0.00833333","]","The","coefficients","are","computed","using","high-order","numerical","differentiation",".","The","function","must","be","possible","to","evaluate","to","arbitrary","precision",".","See",":","func",":","`","~mpmath.diff","`","for","additional","details","and","supported","keyword","options",".","Note","that","to","evaluate","the","Taylor","polynomial","as","an","approximation","of","`","f","`",",","e.g",".","with",":","func",":","`","~mpmath.polyval","`",",","the","coefficients","must","be","reversed",",","and","the","point","of","the","Taylor","expansion","must","be","subtracted","from","the","argument",":",">",">",">","p","=","taylor","(","exp",",","2.0",",","10",")",">",">",">","polyval","(","p","[",":",":-1","]",",","2.5","-","2.0",")","12.1824939606092",">",">",">","exp","(","2.5",")","12.1824939607035","``","''","''","gen","=","enumerate","(","ctx.diffs","(","f",",","x",",","n",",","*","*","options",")",")","if","options.get","(","``","chop","''",",","True",")",":","return","[","ctx.chop","(","d",")","\/","ctx.factorial","(","i",")","for","i",",","d","in","gen","]","else",":","return","[","d","\/","ctx.factorial","(","i",")","for","i",",","d","in","gen","]"]

547

578

null

differentiation.py

catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py

from ..libmp.backend import xrange from .calculus import defun

15

null

2

13

null

Use image node_id 12 for calling a global function with example usage: taylor(ctx, f, x, n) and returns: unknown, unknown

121

node_id 12

407,223

__init__

DmsTaskBaseSensor

AwsBaseSensor

true

self,replication_task_arn,target_statuses,termination_statuses

Contains general sensor behavior for DMS task. Subclasses should set ``target_statuses`` and ``termination_statuses`` fields. :param replication_task_arn: AWS DMS replication task ARN :param target_statuses: the target statuses, sensor waits until the task reaches any of these states :param termination_statuses: the termination statuses, sensor fails when the task reaches any of these states :param aws_conn_id: The Airflow connection used for AWS credentials. If this is ``None`` or empty then the default boto3 behaviour is used. If running Airflow in a distributed manner and aws_conn_id is None or empty, then default boto3 configuration would be used (and must be maintained on each worker node). :param region_name: AWS region_name. If not specified then the default boto3 behaviour is used. :param verify: Whether or not to verify SSL certificates. See: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html :param botocore_config: Configuration dictionary (key-values) for botocore client. See: https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html

["Contains","general","sensor","behavior","for","DMS","task",".","Subclasses","should","set","``","target_statuses","``","and","``","termination_statuses","``","fields",".",":","param","replication_task_arn",":","AWS","DMS","replication","task","ARN",":","param","target_statuses",":","the","target","statuses",",","sensor","waits","until","the","task","reaches","any","of","these","states",":","param","termination_statuses",":","the","termination","statuses",",","sensor","fails","when","the","task","reaches","any","of","these","states",":","param","aws_conn_id",":","The","Airflow","connection","used","for","AWS","credentials",".","If","this","is","``","None","``","or","empty","then","the","default","boto3","behaviour","is","used",".","If","running","Airflow","in","a","distributed","manner","and","aws_conn_id","is","None","or","empty",",","then","default","boto3","configuration","would","be","used","(","and","must","be","maintained","on","each","worker","node",")",".",":","param","region_name",":","AWS","region_name",".","If","not","specified","then","the","default","boto3","behaviour","is","used",".",":","param","verify",":","Whether","or","not","to","verify","SSL","certificates",".","See",":","https",":","\/\/boto3.amazonaws.com\/v1\/documentation\/api\/latest\/reference\/core\/session.html",":","param","botocore_config",":","Configuration","dictionary","(","key-values",")","for","botocore","client",".","See",":","https",":","\/\/botocore.amazonaws.com\/v1\/documentation\/api\/latest\/reference\/config.html"]

null

DmsTaskBaseSensor

def __init__( self, replication_task_arn: str, target_statuses: Iterable[str] | None = None, termination_statuses: Iterable[str] | None = None, **kwargs, ): super().__init__(**kwargs) self.replication_task_arn = replication_task_arn self.target_statuses: Iterable[str] = target_statuses or [] self.termination_statuses: Iterable[str] = ( termination_statuses or [] )

["def","__init__","(","self",",","replication_task_arn",":","str",",","target_statuses",":","Iterable","[","str","]","|","None","=","None",",","termination_statuses",":","Iterable","[","str","]","|","None","=","None",",","*","*","kwargs",",",")",":","super","(",")",".__init__","(","*","*","kwargs",")","self.replication_task_arn","=","replication_task_arn","self.target_statuses",":","Iterable","[","str","]","=","target_statuses","or","[","]","self.termination_statuses",":","Iterable","[","str","]","=","(","termination_statuses","or","[","]",")"]

59

69

null

dms.py

airflow/airflow/providers/amazon/aws/sensors/dms.py

from __future__ import annotations from typing import TYPE_CHECKING, Iterable, Sequence from deprecated import deprecated from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning, AirflowSkipException from airflow.providers.amazon.aws.hooks.dms import DmsHook from airflow.providers.amazon.aws.sensors.base_aws import AwsBaseSensor from airflow.providers.amazon.aws.utils.mixins import aws_template_fields

15

2

7

0

2

3

1

Use image node_id 1 to create a new DmsTaskBaseSensor object from inherited base classes: AwsBaseSensor with example: obj = DmsTaskBaseSensor(replication_task_arn, target_statuses, termination_statuses)

202

node_id 1

248,289

get_hook

DmsTaskBaseSensor

AwsBaseSensor

true

self

Contains general sensor behavior for DMS task. Subclasses should set ``target_statuses`` and ``termination_statuses`` fields. :param replication_task_arn: AWS DMS replication task ARN :param target_statuses: the target statuses, sensor waits until the task reaches any of these states :param termination_statuses: the termination statuses, sensor fails when the task reaches any of these states :param aws_conn_id: The Airflow connection used for AWS credentials. If this is ``None`` or empty then the default boto3 behaviour is used. If running Airflow in a distributed manner and aws_conn_id is None or empty, then default boto3 configuration would be used (and must be maintained on each worker node). :param region_name: AWS region_name. If not specified then the default boto3 behaviour is used. :param verify: Whether or not to verify SSL certificates. See: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html :param botocore_config: Configuration dictionary (key-values) for botocore client. See: https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html

["Contains","general","sensor","behavior","for","DMS","task",".","Subclasses","should","set","``","target_statuses","``","and","``","termination_statuses","``","fields",".",":","param","replication_task_arn",":","AWS","DMS","replication","task","ARN",":","param","target_statuses",":","the","target","statuses",",","sensor","waits","until","the","task","reaches","any","of","these","states",":","param","termination_statuses",":","the","termination","statuses",",","sensor","fails","when","the","task","reaches","any","of","these","states",":","param","aws_conn_id",":","The","Airflow","connection","used","for","AWS","credentials",".","If","this","is","``","None","``","or","empty","then","the","default","boto3","behaviour","is","used",".","If","running","Airflow","in","a","distributed","manner","and","aws_conn_id","is","None","or","empty",",","then","default","boto3","configuration","would","be","used","(","and","must","be","maintained","on","each","worker","node",")",".",":","param","region_name",":","AWS","region_name",".","If","not","specified","then","the","default","boto3","behaviour","is","used",".",":","param","verify",":","Whether","or","not","to","verify","SSL","certificates",".","See",":","https",":","\/\/boto3.amazonaws.com\/v1\/documentation\/api\/latest\/reference\/core\/session.html",":","param","botocore_config",":","Configuration","dictionary","(","key-values",")","for","botocore","client",".","See",":","https",":","\/\/botocore.amazonaws.com\/v1\/documentation\/api\/latest\/reference\/config.html"]

Get DmsHook.

["Get","DmsHook","."]

self

def get_hook(self) -> DmsHook: """Get DmsHook.""" return self.hook

["def","get_hook","(","self",")","-",">","DmsHook",":","``","''","''","Get","DmsHook",".","''","''","''","return","self.hook"]

72

74

null

dms.py

airflow/airflow/providers/amazon/aws/sensors/dms.py

from __future__ import annotations from typing import TYPE_CHECKING, Iterable, Sequence from deprecated import deprecated from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning, AirflowSkipException from airflow.providers.amazon.aws.hooks.dms import DmsHook from airflow.providers.amazon.aws.sensors.base_aws import AwsBaseSensor from airflow.providers.amazon.aws.utils.mixins import aws_template_fields

15

2

7

0

2

3

1

Use image node_id 2 for calling the DmsTaskBaseSensor obj's underlying member method code with example usage: obj.get_hook() and returns: self

142

node_id 2

248,290

pade

global

null

false

ctx,a,L,M

null

p, q,list, list,a, list

def pade(ctx, a, L, M): r""" Computes a Pade approximation of degree `(L, M)` to a function. Given at least `L+M+1` Taylor coefficients `a` approximating a function `A(x)`, :func:`~mpmath.pade` returns coefficients of polynomials `P, Q` satisfying .. math :: P = \sum_{k=0}^L p_k x^k Q = \sum_{k=0}^M q_k x^k Q_0 = 1 A(x) Q(x) = P(x) + O(x^{L+M+1}) `P(x)/Q(x)` can provide a good approximation to an analytic function beyond the radius of convergence of its Taylor series (example from G.A. Baker 'Essentials of Pade Approximants' Academic Press, Ch.1A):: >>> from mpmath import * >>> mp.dps = 15; mp.pretty = True >>> one = mpf(1) >>> def f(x): ... return sqrt((one + 2*x)/(one + x)) ... >>> a = taylor(f, 0, 6) >>> p, q = pade(a, 3, 3) >>> x = 10 >>> polyval(p[::-1], x)/polyval(q[::-1], x) 1.38169105566806 >>> f(x) 1.38169855941551 """ # To determine L+1 coefficients of P and M coefficients of Q # L+M+1 coefficients of A must be provided if len(a) < L + M + 1: raise ValueError("L+M+1 Coefficients should be provided") if M == 0: if L == 0: return [ctx.one], [ctx.one] else: return a[: L + 1], [ctx.one] # Solve first # a[L]*q[1] + ... + a[L-M+1]*q[M] = -a[L+1] # ... # a[L+M-1]*q[1] + ... + a[L]*q[M] = -a[L+M] A = ctx.matrix(M) for j in range(M): for i in range(min(M, L + j + 1)): A[j, i] = a[L + j - i] v = -ctx.matrix(a[(L + 1) : (L + M + 1)]) x = ctx.lu_solve(A, v) q = [ctx.one] + list(x) # compute p p = [0] * (L + 1) for i in range(L + 1): s = a[i] for j in range(1, min(M, i) + 1): s += q[j] * a[i - j] p[i] = s return p, q

["def","pade","(","ctx",",","a",",","L",",","M",")",":","r","''","''","''","Computes","a","Pade","approximation","of","degree","`","(","L",",","M",")","`","to","a","function",".","Given","at","least","`","L+M+1","`","Taylor","coefficients","`","a","`","approximating","a","function","`","A","(","x",")","`",",",":","func",":","`","~mpmath.pade","`","returns","coefficients","of","polynomials","`","P",",","Q","`","satisfying","..","math",":",":","P","=","\\sum_","{","k=0","}","^L","p_k","x^k","Q","=","\\sum_","{","k=0","}","^M","q_k","x^k","Q_0","=","1","A","(","x",")","Q","(","x",")","=","P","(","x",")","+","O","(","x^","{","L+M+1","}",")","`","P","(","x",")","\/Q","(","x",")","`","can","provide","a","good","approximation","to","an","analytic","function","beyond","the","radius","of","convergence","of","its","Taylor","series","(","example","from","G.A",".","Baker","'Essentials","of","Pade","Approximants","'","Academic","Press",",","Ch.1A",")",":",":",">",">",">","from","mpmath","import","*",">",">",">","mp.dps","=","15",";","mp.pretty","=","True",">",">",">","one","=","mpf","(","1",")",">",">",">","def","f","(","x",")",":","...","return","sqrt","(","(","one","+","2","*","x",")","\/","(","one","+","x",")",")","...",">",">",">","a","=","taylor","(","f",",","0",",","6",")",">",">",">","p",",","q","=","pade","(","a",",","3",",","3",")",">",">",">","x","=","10",">",">",">","polyval","(","p","[",":",":-1","]",",","x",")","\/polyval","(","q","[",":",":-1","]",",","x",")","1.38169105566806",">",">",">","f","(","x",")","1.38169855941551","``","''","''","#","To","determine","L+1","coefficients","of","P","and","M","coefficients","of","Q","#","L+M+1","coefficients","of","A","must","be","provided","if","len","(","a",")","<","L","+","M","+","1",":","raise","ValueError","(","``","L+M+1","Coefficients","should","be","provided","''",")","if","M","==","0",":","if","L","==","0",":","return","[","ctx.one","]",",","[","ctx.one","]","else",":","return","a","[",":","L","+","1","]",",","[","ctx.one","]","#","Solve","first","#","a","[","L","]","*","q","[","1","]","+","...","+","a","[","L-M+1","]","*","q","[","M","]","=","-a","[","L+1","]","#","...","#","a","[","L+M-1","]","*","q","[","1","]","+","...","+","a","[","L","]","*","q","[","M","]","=","-a","[","L+M","]","A","=","ctx.matrix","(","M",")","for","j","in","range","(","M",")",":","for","i","in","range","(","min","(","M",",","L","+","j","+","1",")",")",":","A","[","j",",","i","]","=","a","[","L","+","j","-","i","]","v","=","-ctx.matrix","(","a","[","(","L","+","1",")",":","(","L","+","M","+","1",")","]",")","x","=","ctx.lu_solve","(","A",",","v",")","q","=","[","ctx.one","]","+","list","(","x",")","#","compute","p","p","=","[","0","]","*","(","L","+","1",")","for","i","in","range","(","L","+","1",")",":","s","=","a","[","i","]","for","j","in","range","(","1",",","min","(","M",",","i",")","+","1",")",":","s","+=","q","[","j","]","*","a","[","i","-","j","]","p","[","i","]","=","s","return","p",",","q"]

581

647

null

differentiation.py

catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py

from ..libmp.backend import xrange from .calculus import defun

15

null

2

13

null

Use image node_id 13 for calling a global function with example usage: pade(ctx, a, L, M) and returns: p, q, list, list, a, list

131

node_id 13

407,224

run

TfxRunner

null

true

self,pipeline,run_options

Base runner class for TFX. This is the base class for every TFX runner.

["Base","runner","class","for","TFX",".","This","is","the","base","class","for","every","TFX","runner","."]

Runs a TFX pipeline on a specific platform. Args: pipeline: a pipeline.Pipeline instance representing a pipeline definition. run_options: an Optional pipeline.RunOptions object. See the class definition pipeline.RunOptions for details. If None, runs the full pipeline. **kwargs: extra orchestrator backend-specific keyword arguments. Returns: Optional platform-specific object.

["Runs","a","TFX","pipeline","on","a","specific","platform",".","Args",":","pipeline",":","a","pipeline.Pipeline","instance","representing","a","pipeline","definition",".","run_options",":","an","Optional","pipeline.RunOptions","object",".","See","the","class","definition","pipeline.RunOptions","for","details",".","If","None",",","runs","the","full","pipeline",".","*","*","kwargs",":","extra","orchestrator","backend-specific","keyword","arguments",".","Returns",":","Optional","platform-specific","object","."]

null

def run( self, pipeline: pipeline_py.Pipeline, run_options: Optional[pipeline_py.RunOptions] = None, **kwargs: Any, ) -> Optional[Any]: """Runs a TFX pipeline on a specific platform. Args: pipeline: a pipeline.Pipeline instance representing a pipeline definition. run_options: an Optional pipeline.RunOptions object. See the class definition pipeline.RunOptions for details. If None, runs the full pipeline. **kwargs: extra orchestrator backend-specific keyword arguments. Returns: Optional platform-specific object. """ pass

["def","run","(","self",",","pipeline",":","pipeline_py.Pipeline",",","run_options",":","Optional","[","pipeline_py.RunOptions","]","=","None",",","*","*","kwargs",":","Any",",",")","-",">","Optional","[","Any","]",":","``","''","''","Runs","a","TFX","pipeline","on","a","specific","platform",".","Args",":","pipeline",":","a","pipeline.Pipeline","instance","representing","a","pipeline","definition",".","run_options",":","an","Optional","pipeline.RunOptions","object",".","See","the","class","definition","pipeline.RunOptions","for","details",".","If","None",",","runs","the","full","pipeline",".","*","*","kwargs",":","extra","orchestrator","backend-specific","keyword","arguments",".","Returns",":","Optional","platform-specific","object.","``","''","''","pass"]

33

51

null

tfx_runner.py

tfx/tfx/orchestration/portable/tfx_runner.py

import abc from typing import Any, Optional from tfx.dsl.compiler import compiler from tfx.dsl.components.base import base_component from tfx.orchestration import pipeline from tfx.proto.orchestration import pipeline_pb2 from tfx.utils import doc_controls

15

2

7

2

1

null

Use image node_id 1 for calling the TfxRunner obj's underlying member method code with example usage: obj.run(pipeline, run_options) without return types

153

node_id 1

2,199,013

test_split_text_to_chunks_raises_on_invalid_chunk_mode

TestRetrieveUtils

null

true

self

null

def test_split_text_to_chunks_raises_on_invalid_chunk_mode(self): with pytest.raises(AssertionError): split_text_to_chunks( "A" * 10000, chunk_mode="bogus_chunk_mode" )

["def","test_split_text_to_chunks_raises_on_invalid_chunk_mode","(","self",")",":","with","pytest.raises","(","AssertionError",")",":","split_text_to_chunks","(","``","A","''","*","10000",",","chunk_mode=","''","bogus_chunk_mode","''",")"]

45

47

null

test_retrieve_utils.py

autogen/test/test_retrieve_utils.py

import pytest import os

15

1

2

0

12

null

Use image node_id 2 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_split_text_to_chunks_raises_on_invalid_chunk_mode() without return types

191

node_id 2

319,447

run_with_ir

IrBasedRunner

TfxRunner

true

self,pipeline,run_options

Base class for IR-based TFX runners.

["Base","class","for","IR-based","TFX","runners","."]

Runs a TFX pipeline on a specific platform. Args: pipeline: a pipeline_pb2.Pipeline instance representing a pipeline definition. run_options: Optional args for the run. **kwargs: extra orchestrator backend-specific keyword arguments. Returns: Optional platform-specific object.

["Runs","a","TFX","pipeline","on","a","specific","platform",".","Args",":","pipeline",":","a","pipeline_pb2.Pipeline","instance","representing","a","pipeline","definition",".","run_options",":","Optional","args","for","the","run",".","*","*","kwargs",":","extra","orchestrator","backend-specific","keyword","arguments",".","Returns",":","Optional","platform-specific","object","."]

null

def run_with_ir( self, pipeline: pipeline_pb2.Pipeline, run_options: Optional[pipeline_pb2.RunOptions] = None, **kwargs: Any, ) -> Optional[Any]: """Runs a TFX pipeline on a specific platform. Args: pipeline: a pipeline_pb2.Pipeline instance representing a pipeline definition. run_options: Optional args for the run. **kwargs: extra orchestrator backend-specific keyword arguments. Returns: Optional platform-specific object. """ pass

["def","run_with_ir","(","self",",","pipeline",":","pipeline_pb2.Pipeline",",","run_options",":","Optional","[","pipeline_pb2.RunOptions","]","=","None",",","*","*","kwargs",":","Any",",",")","-",">","Optional","[","Any","]",":","``","''","''","Runs","a","TFX","pipeline","on","a","specific","platform",".","Args",":","pipeline",":","a","pipeline_pb2.Pipeline","instance","representing","a","pipeline","definition",".","run_options",":","Optional","args","for","the","run",".","*","*","kwargs",":","extra","orchestrator","backend-specific","keyword","arguments",".","Returns",":","Optional","platform-specific","object.","``","''","''","pass"]

93

110

null

tfx_runner.py

tfx/tfx/orchestration/portable/tfx_runner.py

import abc from typing import Any, Optional from tfx.dsl.compiler import compiler from tfx.dsl.components.base import base_component from tfx.orchestration import pipeline from tfx.proto.orchestration import pipeline_pb2 from tfx.utils import doc_controls

15

2

7

2

1

2

1

Use image node_id 1 for calling the IrBasedRunner obj's underlying member method code with example usage: obj.run_with_ir(pipeline, run_options) without return types

165

node_id 1

2,199,014

run

IrBasedRunner

TfxRunner

true

self,pipeline,run_options

Base class for IR-based TFX runners.

["Base","class","for","IR-based","TFX","runners","."]

See TfxRunner.

["See","TfxRunner","."]

self

def run( self, pipeline: pipeline_py.Pipeline, run_options: Optional[pipeline_py.RunOptions] = None, **kwargs: Any, ) -> Optional[Any]: """See TfxRunner.""" pipeline_pb = _make_pipeline_proto(pipeline) if run_options: run_options_pb = _run_opts_to_proto(run_options) else: run_options_pb = None return self.run_with_ir( pipeline_pb, run_options=run_options_pb, **kwargs )

["def","run","(","self",",","pipeline",":","pipeline_py.Pipeline",",","run_options",":","Optional","[","pipeline_py.RunOptions","]","=","None",",","*","*","kwargs",":","Any",",",")","-",">","Optional","[","Any","]",":","``","''","''","See","TfxRunner",".","''","''","''","pipeline_pb","=","_make_pipeline_proto","(","pipeline",")","if","run_options",":","run_options_pb","=","_run_opts_to_proto","(","run_options",")","else",":","run_options_pb","=","None","return","self.run_with_ir","(","pipeline_pb",",","run_options=run_options_pb",",","*","*","kwargs",")"]

112

124

null

tfx_runner.py

tfx/tfx/orchestration/portable/tfx_runner.py

import abc from typing import Any, Optional from tfx.dsl.compiler import compiler from tfx.dsl.components.base import base_component from tfx.orchestration import pipeline from tfx.proto.orchestration import pipeline_pb2 from tfx.utils import doc_controls

15

2

7

2

1

2

1

Use image node_id 2 for calling the IrBasedRunner obj's underlying member method code with example usage: obj.run(pipeline, run_options) and returns: self

154

node_id 2

2,199,015

get_param_names

Ridge

BaseEstimator,SyncFitMixinLinearModel,DelayedPredictionMixin

true

self

Ridge extends LinearRegression by providing L2 regularization on the coefficients when predicting response y with a linear combination of the predictors in X. It can reduce the variance of the predictors, and improves the conditioning of the problem. cuML's dask Ridge (multi-node multi-gpu) expects dask cuDF DataFrame and provides an algorithms, Eig, to fit a linear model. And provides an eigendecomposition-based algorithm to fit a linear model. (SVD, which is more stable than eig, will be added in an upcoming version) Eig algorithm is usually preferred when the X is a tall and skinny matrix. As the number of features in X increases, the accuracy of Eig algorithm drops. This is an experimental implementation of dask Ridge Regression. It supports input X that has more than one column. Single column input X will be supported after SVD algorithm is added in an upcoming version. Parameters ---------- alpha : float (default = 1.0) Regularization strength - must be a positive float. Larger values specify stronger regularization. Array input will be supported later. solver : {'eig'} Eig uses a eigendecomposition of the covariance matrix, and is much faster. Other solvers will be supported in the future. fit_intercept : boolean (default = True) If True, Ridge adds an additional term c to correct for the global mean of y, modeling the response as "x * beta + c". If False, the model expects that you have centered the data. normalize : boolean (default = False) If True, the predictors in X will be normalized by dividing by it's L2 norm. If False, no scaling will be done. Attributes ---------- coef_ : array, shape (n_features) The estimated coefficients for the linear regression model. intercept_ : array The independent term. If `fit_intercept` is False, will be 0.

["Ridge","extends","LinearRegression","by","providing","L2","regularization","on","the","coefficients","when","predicting","response","y","with","a","linear","combination","of","the","predictors","in","X",".","It","can","reduce","the","variance","of","the","predictors",",","and","improves","the","conditioning","of","the","problem",".","cuML","'s","dask","Ridge","(","multi-node","multi-gpu",")","expects","dask","cuDF","DataFrame","and","provides","an","algorithms",",","Eig",",","to","fit","a","linear","model",".","And","provides","an","eigendecomposition-based","algorithm","to","fit","a","linear","model",".","(","SVD",",","which","is","more","stable","than","eig",",","will","be","added","in","an","upcoming","version",")","Eig","algorithm","is","usually","preferred","when","the","X","is","a","tall","and","skinny","matrix",".","As","the","number","of","features","in","X","increases",",","the","accuracy","of","Eig","algorithm","drops",".","This","is","an","experimental","implementation","of","dask","Ridge","Regression",".","It","supports","input","X","that","has","more","than","one","column",".","Single","column","input","X","will","be","supported","after","SVD","algorithm","is","added","in","an","upcoming","version",".","Parameters","--","--","--","--","--","alpha",":","float","(","default","=","1.0",")","Regularization","strength","-","must","be","a","positive","float",".","Larger","values","specify","stronger","regularization",".","Array","input","will","be","supported","later",".","solver",":","{","'eig","'","}","Eig","uses","a","eigendecomposition","of","the","covariance","matrix",",","and","is","much","faster",".","Other","solvers","will","be","supported","in","the","future",".","fit_intercept",":","boolean","(","default","=","True",")","If","True",",","Ridge","adds","an","additional","term","c","to","correct","for","the","global","mean","of","y",",","modeling","the","response","as","``","x","*","beta","+","c","''",".","If","False",",","the","model","expects","that","you","have","centered","the","data",".","normalize",":","boolean","(","default","=","False",")","If","True",",","the","predictors","in","X","will","be","normalized","by","dividing","by","it","'s","L2","norm",".","If","False",",","no","scaling","will","be","done",".","Attributes","--","--","--","--","--","coef_",":","array",",","shape","(","n_features",")","The","estimated","coefficients","for","the","linear","regression","model",".","intercept_",":","array","The","independent","term",".","If","`","fit_intercept","`","is","False",",","will","be","0","."]

null

list

def get_param_names(self): return list(self.kwargs.keys())

["def","get_param_names","(","self",")",":","return","list","(","self.kwargs.keys","(",")",")"]

117

118

null

ridge.py

cuml/python/cuml/dask/linear_model/ridge.py

from cuml.dask.common.base import BaseEstimator from cuml.dask.common.base import DelayedPredictionMixin from cuml.dask.common.base import mnmg_import from cuml.dask.common.base import SyncFitMixinLinearModel from raft_dask.common.comms import get_raft_comm_state from dask.distributed import get_worker

15

1

6

0

3

5

3

Use image node_id 4 for calling the Ridge obj's underlying member method code with example usage: obj.get_param_names() and returns: list

137

node_id 4

688,429

__call__

ClipToTensor

object

true

self,clip

Convert a list of m (H x W x C) numpy.ndarrays in the range [0, 255] to a torch.FloatTensor of shape (C x m x H x W) in the range [0, 1.0]

["Convert","a","list","of","m","(","H","x","W","x","C",")","numpy.ndarrays","in","the","range","[","0",",","255","]","to","a","torch.FloatTensor","of","shape","(","C","x","m","x","H","x","W",")","in","the","range","[","0",",","1.0","]"]

Args: clip (list of numpy.ndarray): clip (list of images) to be converted to tensor.

["Args",":","clip","(","list","of","numpy.ndarray",")",":","clip","(","list","of","images",")","to","be","converted","to","tensor","."]

np_clip,tensor_clip

def __call__(self, clip): """ Args: clip (list of numpy.ndarray): clip (list of images) to be converted to tensor. """ # Retrieve shape if isinstance(clip[0], np.ndarray): h, w, ch = clip[0].shape assert ( ch == self.channel_nb ), "Got {0} instead of 3 channels".format(ch) elif isinstance(clip[0], Image.Image): w, h = clip[0].size else: raise TypeError( "Expected numpy.ndarray or PIL.Image\ but got list of {0}".format( type(clip[0]) ) ) np_clip = np.zeros([self.channel_nb, len(clip), int(h), int(w)]) # Convert for img_idx, img in enumerate(clip): if isinstance(img, np.ndarray): pass elif isinstance(img, Image.Image): img = np.array(img, copy=False) else: raise TypeError( "Expected numpy.ndarray or PIL.Image\ but got list of {0}".format( type(clip[0]) ) ) img = imageutils.convert_img(img) np_clip[:, img_idx, :, :] = img if self.numpy: if self.div_255: np_clip = np_clip / 255.0 return np_clip else: tensor_clip = torch.from_numpy(np_clip) if not isinstance(tensor_clip, torch.FloatTensor): tensor_clip = tensor_clip.float() if self.div_255: tensor_clip = torch.div(tensor_clip, 255) return tensor_clip

["def","__call__","(","self",",","clip",")",":","``","''","''","Args",":","clip","(","list","of","numpy.ndarray",")",":","clip","(","list","of","images",")","to","be","converted","to","tensor.","``","''","''","#","Retrieve","shape","if","isinstance","(","clip","[","0","]",",","np.ndarray",")",":","h",",","w",",","ch","=","clip","[","0","]",".shape","assert","(","ch","==","self.channel_nb",")",",","``","Got","{","0","}","instead","of","3","channels","''",".format","(","ch",")","elif","isinstance","(","clip","[","0","]",",","Image.Image",")",":","w",",","h","=","clip","[","0","]",".size","else",":","raise","TypeError","(","``","Expected","numpy.ndarray","or","PIL.Image\\","but","got","list","of","{","0","}","''",".format","(","type","(","clip","[","0","]",")",")",")","np_clip","=","np.zeros","(","[","self.channel_nb",",","len","(","clip",")",",","int","(","h",")",",","int","(","w",")","]",")","#","Convert","for","img_idx",",","img","in","enumerate","(","clip",")",":","if","isinstance","(","img",",","np.ndarray",")",":","pass","elif","isinstance","(","img",",","Image.Image",")",":","img","=","np.array","(","img",",","copy=False",")","else",":","raise","TypeError","(","``","Expected","numpy.ndarray","or","PIL.Image\\","but","got","list","of","{","0","}","''",".format","(","type","(","clip","[","0","]",")",")",")","img","=","imageutils.convert_img","(","img",")","np_clip","[",":",",","img_idx",",",":",",",":","]","=","img","if","self.numpy",":","if","self.div_255",":","np_clip","=","np_clip","\/","255.0","return","np_clip","else",":","tensor_clip","=","torch.from_numpy","(","np_clip",")","if","not","isinstance","(","tensor_clip",",","torch.FloatTensor",")",":","tensor_clip","=","tensor_clip.float","(",")","if","self.div_255",":","tensor_clip","=","torch.div","(","tensor_clip",",","255",")","return","tensor_clip"]

19

60

null

volume_transforms.py

gluon-cv/gluoncv/torch/data/transforms/videotransforms/volume_transforms.py

import numpy from PIL import Image import torch from .utils import images

15

3

4

0

3

2

1

Use image node_id 2 for calling the ClipToTensor obj's underlying member method code with example usage: obj.__call__(clip) and returns: np_clip, tensor_clip

157

node_id 2

1,095,764

__init__

ClipToTensor_K

object

true

self,channel_nb,div_255,numpy

Convert a list of m (H x W x C) numpy.ndarrays in the range [0, 255] to a torch.FloatTensor of shape (C x m x H x W) in the range [0, 1.0]

["Convert","a","list","of","m","(","H","x","W","x","C",")","numpy.ndarrays","in","the","range","[","0",",","255","]","to","a","torch.FloatTensor","of","shape","(","C","x","m","x","H","x","W",")","in","the","range","[","0",",","1.0","]"]

null

ClipToTensor_K

def __init__(self, channel_nb=3, div_255=True, numpy=False): self.channel_nb = channel_nb self.div_255 = div_255 self.numpy = numpy

["def","__init__","(","self",",","channel_nb=3",",","div_255=True",",","numpy=False",")",":","self.channel_nb","=","channel_nb","self.div_255","=","div_255","self.numpy","=","numpy"]

69

72

null

volume_transforms.py

gluon-cv/gluoncv/torch/data/transforms/videotransforms/volume_transforms.py

import numpy from PIL import Image import torch from .utils import images

15

3

4

0

3

2

1

Use image node_id 1 to create a new ClipToTensor_K object from inherited base classes: object with example: obj = ClipToTensor_K(channel_nb, div_255, numpy)

156

node_id 1

1,095,765

__call__

ClipToTensor_K

object

true

self,clip

Convert a list of m (H x W x C) numpy.ndarrays in the range [0, 255] to a torch.FloatTensor of shape (C x m x H x W) in the range [0, 1.0]

["Convert","a","list","of","m","(","H","x","W","x","C",")","numpy.ndarrays","in","the","range","[","0",",","255","]","to","a","torch.FloatTensor","of","shape","(","C","x","m","x","H","x","W",")","in","the","range","[","0",",","1.0","]"]

Args: clip (list of numpy.ndarray): clip (list of images) to be converted to tensor.

["Args",":","clip","(","list","of","numpy.ndarray",")",":","clip","(","list","of","images",")","to","be","converted","to","tensor","."]

np_clip,tensor_clip

def __call__(self, clip): """ Args: clip (list of numpy.ndarray): clip (list of images) to be converted to tensor. """ # Retrieve shape if isinstance(clip[0], np.ndarray): h, w, ch = clip[0].shape assert ( ch == self.channel_nb ), "Got {0} instead of 3 channels".format(ch) elif isinstance(clip[0], Image.Image): w, h = clip[0].size else: raise TypeError( "Expected numpy.ndarray or PIL.Image\ but got list of {0}".format( type(clip[0]) ) ) np_clip = np.zeros([self.channel_nb, len(clip), int(h), int(w)]) # Convert for img_idx, img in enumerate(clip): if isinstance(img, np.ndarray): pass elif isinstance(img, Image.Image): img = np.array(img, copy=False) else: raise TypeError( "Expected numpy.ndarray or PIL.Image\ but got list of {0}".format( type(clip[0]) ) ) img = imageutils.convert_img(img) np_clip[:, img_idx, :, :] = img if self.numpy: if self.div_255: np_clip = (np_clip - 127.5) / 127.5 return np_clip else: tensor_clip = torch.from_numpy(np_clip) if not isinstance(tensor_clip, torch.FloatTensor): tensor_clip = tensor_clip.float() if self.div_255: tensor_clip = torch.div( torch.sub(tensor_clip, 127.5), 127.5 ) return tensor_clip

["def","__call__","(","self",",","clip",")",":","``","''","''","Args",":","clip","(","list","of","numpy.ndarray",")",":","clip","(","list","of","images",")","to","be","converted","to","tensor.","``","''","''","#","Retrieve","shape","if","isinstance","(","clip","[","0","]",",","np.ndarray",")",":","h",",","w",",","ch","=","clip","[","0","]",".shape","assert","(","ch","==","self.channel_nb",")",",","``","Got","{","0","}","instead","of","3","channels","''",".format","(","ch",")","elif","isinstance","(","clip","[","0","]",",","Image.Image",")",":","w",",","h","=","clip","[","0","]",".size","else",":","raise","TypeError","(","``","Expected","numpy.ndarray","or","PIL.Image\\","but","got","list","of","{","0","}","''",".format","(","type","(","clip","[","0","]",")",")",")","np_clip","=","np.zeros","(","[","self.channel_nb",",","len","(","clip",")",",","int","(","h",")",",","int","(","w",")","]",")","#","Convert","for","img_idx",",","img","in","enumerate","(","clip",")",":","if","isinstance","(","img",",","np.ndarray",")",":","pass","elif","isinstance","(","img",",","Image.Image",")",":","img","=","np.array","(","img",",","copy=False",")","else",":","raise","TypeError","(","``","Expected","numpy.ndarray","or","PIL.Image\\","but","got","list","of","{","0","}","''",".format","(","type","(","clip","[","0","]",")",")",")","img","=","imageutils.convert_img","(","img",")","np_clip","[",":",",","img_idx",",",":",",",":","]","=","img","if","self.numpy",":","if","self.div_255",":","np_clip","=","(","np_clip","-","127.5",")","\/","127.5","return","np_clip","else",":","tensor_clip","=","torch.from_numpy","(","np_clip",")","if","not","isinstance","(","tensor_clip",",","torch.FloatTensor",")",":","tensor_clip","=","tensor_clip.float","(",")","if","self.div_255",":","tensor_clip","=","torch.div","(","torch.sub","(","tensor_clip",",","127.5",")",",","127.5",")","return","tensor_clip"]

74

115

null

volume_transforms.py

gluon-cv/gluoncv/torch/data/transforms/videotransforms/volume_transforms.py

import numpy from PIL import Image import torch from .utils import images

15

3

4

0

3

2

1

Use image node_id 2 for calling the ClipToTensor_K obj's underlying member method code with example usage: obj.__call__(clip) and returns: np_clip, tensor_clip

159

node_id 2

1,095,766

__call__

ToTensor

object

true

self,array

Converts numpy array to tensor

["Converts","numpy","array","to","tensor"]

null

tensor

def __call__(self, array): tensor = torch.from_numpy(array) return tensor

["def","__call__","(","self",",","array",")",":","tensor","=","torch.from_numpy","(","array",")","return","tensor"]

122

124

null

volume_transforms.py

gluon-cv/gluoncv/torch/data/transforms/videotransforms/volume_transforms.py

import numpy from PIL import Image import torch from .utils import images

15

3

4

0

3

1

Use image node_id 1 for calling the ToTensor obj's underlying member method code with example usage: obj.__call__(array) and returns: tensor

140

node_id 1

1,095,767

__init__

VideoClsDataset

Dataset

true

self,anno_path,data_path,mode,clip_len,frame_sample_rate,crop_size,short_side_size,new_height,new_width,keep_aspect_ratio,num_segment,num_crop,test_num_segment,test_num_crop,use_multigrid

Load your own video classification dataset.

["Load","your","own","video","classification","dataset","."]

null

VideoClsDataset

def __init__( self, anno_path, data_path, mode="train", clip_len=8, frame_sample_rate=2, crop_size=224, short_side_size=256, new_height=256, new_width=340, keep_aspect_ratio=False, num_segment=1, num_crop=1, test_num_segment=10, test_num_crop=3, use_multigrid=False, ): self.anno_path = anno_path self.data_path = data_path self.mode = mode self.clip_len = clip_len self.frame_sample_rate = frame_sample_rate self.crop_size = crop_size self.short_side_size = short_side_size self.new_height = new_height self.new_width = new_width self.keep_aspect_ratio = keep_aspect_ratio self.num_segment = num_segment self.test_num_segment = test_num_segment self.num_crop = num_crop self.test_num_crop = test_num_crop self.use_multigrid = use_multigrid and (mode == "train") if VideoReader is None: raise ImportError( "Unable to import `decord` which is required to read videos." ) import pandas as pd cleaned = pd.read_csv(self.anno_path, header=None, delimiter=" ") self.dataset_samples = list(cleaned.values[:, 0]) self.label_array = list(cleaned.values[:, 2]) if mode == "train": if self.use_multigrid: self.mg_helper = multiGridHelper() self.data_transform = [] for alpha in range(self.mg_helper.mod_long): tmp = [] for beta in range(self.mg_helper.mod_short): info = self.mg_helper.get_resize(alpha, beta) scale_s = info[1] tmp.append( video_transforms.Compose( [ video_transforms.Resize( int( self.short_side_size / scale_s ), interpolation="bilinear", ), # TODO: multiscale corner cropping video_transforms.RandomResize( ratio=(1, 1.25), interpolation="bilinear", ), video_transforms.RandomCrop( size=( int(self.crop_size / scale_s), int(self.crop_size / scale_s), ) ), ] ) ) self.data_transform.append(tmp) else: self.data_transform = video_transforms.Compose( [ video_transforms.Resize( int(self.short_side_size), interpolation="bilinear", ), video_transforms.RandomResize( ratio=(1, 1.25), interpolation="bilinear" ), video_transforms.RandomCrop( size=( int(self.crop_size), int(self.crop_size), ) ), ] ) self.data_transform_after = video_transforms.Compose( [ video_transforms.RandomHorizontalFlip(), volume_transforms.ClipToTensor(), video_transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ), ] ) elif mode == "validation": self.data_transform = video_transforms.Compose( [ video_transforms.Resize( self.short_side_size, interpolation="bilinear" ), video_transforms.CenterCrop( size=(self.crop_size, self.crop_size) ), volume_transforms.ClipToTensor(), video_transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ), ] ) elif mode == "test": self.data_resize = video_transforms.Compose( [ video_transforms.Resize( size=(short_side_size), interpolation="bilinear" ) ] ) self.data_transform = video_transforms.Compose( [ volume_transforms.ClipToTensor(), video_transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ), ] ) self.test_seg = [] self.test_dataset = [] self.test_label_array = [] for ck in range(self.test_num_segment): for cp in range(self.test_num_crop): for idx in range(len(self.label_array)): sample_label = self.label_array[idx] self.test_label_array.append(sample_label) self.test_dataset.append( self.dataset_samples[idx] ) self.test_seg.append((ck, cp))

24

111

null

dataset_classification.py

gluon-cv/gluoncv/torch/data/video_cls/dataset_classification.py

import os import warnings import numpy import torch from torch.utils.data import Dataset from ..transforms.videotransforms import video_transforms, volume_transforms from .multigrid_helper import multiGridHelper, MultiGridBatchSampler

15

1

7

2

1

4

1

Use image node_id 1 to create a new VideoClsDataset object from inherited base classes: Dataset with example: obj = VideoClsDataset(anno_path, data_path, mode, clip_len, frame_sample_rate, crop_size, short_side_size, new_height, new_width, keep_aspect_ratio, num_segment, num_crop, test_num_segment, test_num_crop, use_multigrid)

329

node_id 1

1,095,768

on_startup

global

null

false

null

def on_startup(): create_db_and_tables()

["def","on_startup","(",")",":","create_db_and_tables","(",")"]

38

39

null

tutorial001_py310.py

sqlmodel/docs_src/tutorial/fastapi/read_one/tutorial001_py310.py

from fastapi import FastAPI, HTTPException from sqlmodel import Field, Session, SQLModel, create_engine, select

15

null

2

5

null

Use image node_id 2 for calling a global function with example usage: on_startup() without return types

103

node_id 2

1,989,840

create_hero

global

null

false

hero

null

db_hero

def create_hero(hero: HeroCreate): with Session(engine) as session: db_hero = Hero.model_validate(hero) session.add(db_hero) session.commit() session.refresh(db_hero) return db_hero

["def","create_hero","(","hero",":","HeroCreate",")",":","with","Session","(","engine",")","as","session",":","db_hero","=","Hero.model_validate","(","hero",")","session.add","(","db_hero",")","session.commit","(",")","session.refresh","(","db_hero",")","return","db_hero"]

43

49

null

tutorial001_py310.py

sqlmodel/docs_src/tutorial/fastapi/read_one/tutorial001_py310.py

from fastapi import FastAPI, HTTPException from sqlmodel import Field, Session, SQLModel, create_engine, select

15

null

2

5

null

Use image node_id 3 for calling a global function with example usage: create_hero(hero) and returns: db_hero

108

node_id 3

1,989,841

transform_observation_features

SearchSpaceToChoice

Transform

true

self,observation_features

Replaces the search space with a single choice parameter, whose values are the signatures of the arms observed in the data. This transform is meant to be used with ThompsonSampler. Choice parameter will be unordered unless config["use_ordered"] specifies otherwise. Transform is done in-place.

["Replaces","the","search","space","with","a","single","choice","parameter",",","whose","values","are","the","signatures","of","the","arms","observed","in","the","data",".","This","transform","is","meant","to","be","used","with","ThompsonSampler",".","Choice","parameter","will","be","unordered","unless","config","[","``","use_ordered","''","]","specifies","otherwise",".","Transform","is","done","in-place","."]

null

observation_features

def transform_observation_features( self, observation_features: List[ObservationFeatures] ) -> List[ObservationFeatures]: for obsf in observation_features: obsf.parameters = { self.parameter_name: Arm( parameters=obsf.parameters ).signature } return observation_features

["def","transform_observation_features","(","self",",","observation_features",":","List","[","ObservationFeatures","]",")","-",">","List","[","ObservationFeatures","]",":","for","obsf","in","observation_features",":","obsf.parameters","=","{","self.parameter_name",":","Arm","(","parameters=obsf.parameters",")",".signature","}","return","observation_features"]

83

90

null

search_space_to_choice.py

Ax/ax/modelbridge/transforms/search_space_to_choice.py

from typing import List, Optional, TYPE_CHECKING from ax.core.arm import Arm from ax.core.observation import Observation, ObservationFeatures from ax.core.parameter import ChoiceParameter, FixedParameter, ParameterType from ax.core.search_space import RobustSearchSpace, SearchSpace from ax.exceptions.core import UnsupportedError from ax.modelbridge.transforms.base import Transform from ax.models.types import TConfig from ax.utils.common.typeutils import checked_cast

15

1

9

0

1

4

1

Use image node_id 3 for calling the SearchSpaceToChoice obj's underlying member method code with example usage: obj.transform_observation_features(observation_features) and returns: observation_features

202

node_id 3

9,098

_create_model

Ridge

BaseEstimator,SyncFitMixinLinearModel,DelayedPredictionMixin

true

sessionId,datatype

Ridge extends LinearRegression by providing L2 regularization on the coefficients when predicting response y with a linear combination of the predictors in X. It can reduce the variance of the predictors, and improves the conditioning of the problem. cuML's dask Ridge (multi-node multi-gpu) expects dask cuDF DataFrame and provides an algorithms, Eig, to fit a linear model. And provides an eigendecomposition-based algorithm to fit a linear model. (SVD, which is more stable than eig, will be added in an upcoming version) Eig algorithm is usually preferred when the X is a tall and skinny matrix. As the number of features in X increases, the accuracy of Eig algorithm drops. This is an experimental implementation of dask Ridge Regression. It supports input X that has more than one column. Single column input X will be supported after SVD algorithm is added in an upcoming version. Parameters ---------- alpha : float (default = 1.0) Regularization strength - must be a positive float. Larger values specify stronger regularization. Array input will be supported later. solver : {'eig'} Eig uses a eigendecomposition of the covariance matrix, and is much faster. Other solvers will be supported in the future. fit_intercept : boolean (default = True) If True, Ridge adds an additional term c to correct for the global mean of y, modeling the response as "x * beta + c". If False, the model expects that you have centered the data. normalize : boolean (default = False) If True, the predictors in X will be normalized by dividing by it's L2 norm. If False, no scaling will be done. Attributes ---------- coef_ : array, shape (n_features) The estimated coefficients for the linear regression model. intercept_ : array The independent term. If `fit_intercept` is False, will be 0.

["Ridge","extends","LinearRegression","by","providing","L2","regularization","on","the","coefficients","when","predicting","response","y","with","a","linear","combination","of","the","predictors","in","X",".","It","can","reduce","the","variance","of","the","predictors",",","and","improves","the","conditioning","of","the","problem",".","cuML","'s","dask","Ridge","(","multi-node","multi-gpu",")","expects","dask","cuDF","DataFrame","and","provides","an","algorithms",",","Eig",",","to","fit","a","linear","model",".","And","provides","an","eigendecomposition-based","algorithm","to","fit","a","linear","model",".","(","SVD",",","which","is","more","stable","than","eig",",","will","be","added","in","an","upcoming","version",")","Eig","algorithm","is","usually","preferred","when","the","X","is","a","tall","and","skinny","matrix",".","As","the","number","of","features","in","X","increases",",","the","accuracy","of","Eig","algorithm","drops",".","This","is","an","experimental","implementation","of","dask","Ridge","Regression",".","It","supports","input","X","that","has","more","than","one","column",".","Single","column","input","X","will","be","supported","after","SVD","algorithm","is","added","in","an","upcoming","version",".","Parameters","--","--","--","--","--","alpha",":","float","(","default","=","1.0",")","Regularization","strength","-","must","be","a","positive","float",".","Larger","values","specify","stronger","regularization",".","Array","input","will","be","supported","later",".","solver",":","{","'eig","'","}","Eig","uses","a","eigendecomposition","of","the","covariance","matrix",",","and","is","much","faster",".","Other","solvers","will","be","supported","in","the","future",".","fit_intercept",":","boolean","(","default","=","True",")","If","True",",","Ridge","adds","an","additional","term","c","to","correct","for","the","global","mean","of","y",",","modeling","the","response","as","``","x","*","beta","+","c","''",".","If","False",",","the","model","expects","that","you","have","centered","the","data",".","normalize",":","boolean","(","default","=","False",")","If","True",",","the","predictors","in","X","will","be","normalized","by","dividing","by","it","'s","L2","norm",".","If","False",",","no","scaling","will","be","done",".","Attributes","--","--","--","--","--","coef_",":","array",",","shape","(","n_features",")","The","estimated","coefficients","for","the","linear","regression","model",".","intercept_",":","array","The","independent","term",".","If","`","fit_intercept","`","is","False",",","will","be","0","."]

null

RidgeMG

def _create_model(sessionId, datatype, **kwargs): from cuml.linear_model.ridge_mg import RidgeMG handle = get_raft_comm_state(sessionId, get_worker())["handle"] return RidgeMG(handle=handle, output_type=datatype, **kwargs)

["def","_create_model","(","sessionId",",","datatype",",","*","*","kwargs",")",":","from","cuml.linear_model.ridge_mg","import","RidgeMG","handle","=","get_raft_comm_state","(","sessionId",",","get_worker","(",")",")","[","``","handle","''","]","return","RidgeMG","(","handle=handle",",","output_type=datatype",",","*","*","kwargs",")"]

122

126

null

ridge.py

cuml/python/cuml/dask/linear_model/ridge.py

from cuml.dask.common.base import BaseEstimator from cuml.dask.common.base import DelayedPredictionMixin from cuml.dask.common.base import mnmg_import from cuml.dask.common.base import SyncFitMixinLinearModel from raft_dask.common.comms import get_raft_comm_state from dask.distributed import get_worker

15

1

6

0

3

5

3

Use image node_id 5 for calling the Ridge obj's underlying member method code with example usage: obj._create_model(sessionId, datatype) and returns: RidgeMG

157

node_id 5

688,430

init_states

GPT2Decoder

BaseStepDecoder

true

self,batch_size,ctx

null

self

def init_states(self, batch_size, ctx): return self._gpt2_lm_model.init_states(batch_size, ctx)

["def","init_states","(","self",",","batch_size",",","ctx",")",":","return","self._gpt2_lm_model.init_states","(","batch_size",",","ctx",")"]

51

52

null

interactive_conditional_gpt2_samples.py

gluon-nlp/scripts/generation/interactive_conditional_gpt2_samples.py

import os import mxnet import argparse from gluonnlp.utils import set_seed from gluonnlp.sequence_sampler import BeamSearchSampler, BaseStepDecoder from gluonnlp.models.gpt2 import GPT2ForLM, list_pretrained_gpt2, get_pretrained_gpt2

15

1

6

2

1

5

1

Use image node_id 4 for calling the GPT2Decoder obj's underlying member method code with example usage: obj.init_states(batch_size, ctx) and returns: self

154

node_id 4

1,097,717

diffs_exp

global

null

false

ctx,fdiffs

null

def diffs_exp(ctx, fdiffs): r""" Given an iterable or generator yielding `f(x), f'(x), f''(x), \ldots` generate `g(x), g'(x), g''(x), \ldots` where `g(x) = \exp(f(x))`. At high precision and for large orders, this is typically more efficient than numerical differentiation if the derivatives of `f(x)` admit direct computation. Note: This function does not increase the working precision internally, so guard digits may have to be added externally for full accuracy. **Examples** The derivatives of the gamma function can be computed using logarithmic differentiation:: >>> from mpmath import * >>> mp.dps = 15; mp.pretty = True >>> >>> def diffs_loggamma(x): ... yield loggamma(x) ... i = 0 ... while 1: ... yield psi(i,x) ... i += 1 ... >>> u = diffs_exp(diffs_loggamma(3)) >>> v = diffs(gamma, 3) >>> next(u); next(v) 2.0 2.0 >>> next(u); next(v) 1.84556867019693 1.84556867019693 >>> next(u); next(v) 2.49292999190269 2.49292999190269 >>> next(u); next(v) 3.44996501352367 3.44996501352367 """ fn = iterable_to_function(fdiffs) f0 = ctx.exp(fn(0)) yield f0 i = 1 while 1: s = ctx.mpf(0) for powers, c in iteritems(dpoly(i)): s += c * ctx.fprod( fn(k + 1) ** p for (k, p) in enumerate(powers) if p ) yield s * f0 i += 1

["def","diffs_exp","(","ctx",",","fdiffs",")",":","r","''","''","''","Given","an","iterable","or","generator","yielding","`","f","(","x",")",",","f","'","(","x",")",",","f","''","(","x",")",",","\\ldots","`","generate","`","g","(","x",")",",","g","'","(","x",")",",","g","''","(","x",")",",","\\ldots","`","where","`","g","(","x",")","=","\\exp","(","f","(","x",")",")","`",".","At","high","precision","and","for","large","orders",",","this","is","typically","more","efficient","than","numerical","differentiation","if","the","derivatives","of","`","f","(","x",")","`","admit","direct","computation",".","Note",":","This","function","does","not","increase","the","working","precision","internally",",","so","guard","digits","may","have","to","be","added","externally","for","full","accuracy",".","*","*","Examples","*","*","The","derivatives","of","the","gamma","function","can","be","computed","using","logarithmic","differentiation",":",":",">",">",">","from","mpmath","import","*",">",">",">","mp.dps","=","15",";","mp.pretty","=","True",">",">",">",">",">",">","def","diffs_loggamma","(","x",")",":","...","yield","loggamma","(","x",")","...","i","=","0","...","while","1",":","...","yield","psi","(","i",",","x",")","...","i","+=","1","...",">",">",">","u","=","diffs_exp","(","diffs_loggamma","(","3",")",")",">",">",">","v","=","diffs","(","gamma",",","3",")",">",">",">","next","(","u",")",";","next","(","v",")","2.0","2.0",">",">",">","next","(","u",")",";","next","(","v",")","1.84556867019693","1.84556867019693",">",">",">","next","(","u",")",";","next","(","v",")","2.49292999190269","2.49292999190269",">",">",">","next","(","u",")",";","next","(","v",")","3.44996501352367","3.44996501352367","``","''","''","fn","=","iterable_to_function","(","fdiffs",")","f0","=","ctx.exp","(","fn","(","0",")",")","yield","f0","i","=","1","while","1",":","s","=","ctx.mpf","(","0",")","for","powers",",","c","in","iteritems","(","dpoly","(","i",")",")",":","s","+=","c","*","ctx.fprod","(","fn","(","k","+","1",")","*","*","p","for","(","k",",","p",")","in","enumerate","(","powers",")","if","p",")","yield","s","*","f0","i","+=","1"]

394

446

null

differentiation.py

catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py

from ..libmp.backend import xrange from .calculus import defun

15

null

2

13

null

Use image node_id 9 for calling a global function with example usage: diffs_exp(ctx, fdiffs) without return types

113

node_id 9

407,220

test_split_files_to_chunks

TestRetrieveUtils

null

true

self

null

def test_split_files_to_chunks(self): pdf_file_path = os.path.join(test_dir, "example.pdf") txt_file_path = os.path.join(test_dir, "example.txt") chunks = split_files_to_chunks([pdf_file_path, txt_file_path]) assert all( isinstance(chunk, str) and "AutoGen is an advanced tool designed to assist developers" in chunk.strip() for chunk in chunks )

["def","test_split_files_to_chunks","(","self",")",":","pdf_file_path","=","os.path.join","(","test_dir",",","``","example.pdf","''",")","txt_file_path","=","os.path.join","(","test_dir",",","``","example.txt","''",")","chunks","=","split_files_to_chunks","(","[","pdf_file_path",",","txt_file_path","]",")","assert","all","(","isinstance","(","chunk",",","str",")","and","``","AutoGen","is","an","advanced","tool","designed","to","assist","developers","''","in","chunk.strip","(",")","for","chunk","in","chunks",")"]

53

60

null

test_retrieve_utils.py

autogen/test/test_retrieve_utils.py

import pytest import os

15

1

2

0

12

null

Use image node_id 4 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_split_files_to_chunks() without return types

163

node_id 4

319,449

main

global

null

false

null

def main(): """Convert standard rttm to sample-based result""" args = get_parser().parse_args() # logging info if args.verbose > 1: logging.basicConfig( level=logging.DEBUG, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) elif args.verbose > 0: logging.basicConfig( level=logging.INFO, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) else: logging.basicConfig( level=logging.WARN, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) logging.warning("Skip DEBUG/INFO messages") sampling_rate = humanfriendly.parse_size(args.sampling_rate) convert_rttm_text( args.rttm, args.wavscp, sampling_rate, args.output_path ) logging.info("Successfully finished RTTM converting.")

["def","main","(",")",":","``","''","''","Convert","standard","rttm","to","sample-based","result","''","''","''","args","=","get_parser","(",")",".parse_args","(",")","#","logging","info","if","args.verbose",">","1",":","logging.basicConfig","(","level=logging.DEBUG",",","format=","''","%","(","asctime",")","s","(","%","(","module",")","s",":","%","(","lineno",")","d",")","%","(","levelname",")","s",":","%","(","message",")","s","''",",",")","elif","args.verbose",">","0",":","logging.basicConfig","(","level=logging.INFO",",","format=","''","%","(","asctime",")","s","(","%","(","module",")","s",":","%","(","lineno",")","d",")","%","(","levelname",")","s",":","%","(","message",")","s","''",",",")","else",":","logging.basicConfig","(","level=logging.WARN",",","format=","''","%","(","asctime",")","s","(","%","(","module",")","s",":","%","(","lineno",")","d",")","%","(","levelname",")","s",":","%","(","message",")","s","''",",",")","logging.warning","(","``","Skip","DEBUG\/INFO","messages","''",")","sampling_rate","=","humanfriendly.parse_size","(","args.sampling_rate",")","convert_rttm_text","(","args.rttm",",","args.wavscp",",","sampling_rate",",","args.output_path",")","logging.info","(","``","Successfully","finished","RTTM","converting",".","''",")"]

111

136

null

convert_rttm.py

espnet/egs2/wsj0_2mix/mixit_enh1/pyscripts/utils/convert_rttm.py

import argparse import collections.abc import logging import os import re from pathlib import Path from typing import Union import humanfriendly import numpy import soundfile from typeguard import check_argument_types from espnet2.utils.types import str_or_int

15

null

12

3

null

Use image node_id 3 for calling a global function with example usage: main() without return types

97

node_id 3

998,881

test_extract_text_from_pdf

TestRetrieveUtils

null

true

self

null

def test_extract_text_from_pdf(self): pdf_file_path = os.path.join(test_dir, "example.pdf") assert "".join(expected_text.split()) == "".join( extract_text_from_pdf(pdf_file_path).strip().split() )

["def","test_extract_text_from_pdf","(","self",")",":","pdf_file_path","=","os.path.join","(","test_dir",",","``","example.pdf","''",")","assert","``","''",".join","(","expected_text.split","(",")",")","==","``","''",".join","(","extract_text_from_pdf","(","pdf_file_path",")",".strip","(",")",".split","(",")",")"]

49

51

null

test_retrieve_utils.py

autogen/test/test_retrieve_utils.py

import pytest import os

15

1

2

0

12

null

Use image node_id 3 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_extract_text_from_pdf() without return types

163

node_id 3

319,448

_to_request_dict

FileSource

object

true

self

Accepts file source parameters for conversion to request dict.

["Accepts","file","source","parameters","for","conversion","to","request","dict","."]

Generates a request dictionary using the parameters provided to the class.

["Generates","a","request","dictionary","using","the","parameters","provided","to","the","class","."]

file_source_request

def _to_request_dict(self): """Generates a request dictionary using the parameters provided to the class.""" file_source_request = {"S3Uri": self.s3_uri} if self.content_digest is not None: file_source_request["ContentDigest"] = self.content_digest if self.content_type is not None: file_source_request["ContentType"] = self.content_type return file_source_request

["def","_to_request_dict","(","self",")",":","``","''","''","Generates","a","request","dictionary","using","the","parameters","provided","to","the","class",".","''","''","''","file_source_request","=","{","``","S3Uri","''",":","self.s3_uri","}","if","self.content_digest","is","not","None",":","file_source_request","[","``","ContentDigest","''","]","=","self.content_digest","if","self.content_type","is","not","None",":","file_source_request","[","``","ContentType","''","]","=","self.content_type","return","file_source_request"]

153

160

null

model_metrics.py

sagemaker-python-sdk/src/sagemaker/model_metrics.py

from __future__ import absolute_import from typing import Optional, Union from sagemaker.workflow.entities import PipelineVariable

15

3

0

3

2

1

Use image node_id 2 for calling the FileSource obj's underlying member method code with example usage: obj._to_request_dict() and returns: file_source_request

158

node_id 2

1,845,785

__init__

FileSource

object

true

self,s3_uri,content_digest,content_type

Accepts file source parameters for conversion to request dict.

["Accepts","file","source","parameters","for","conversion","to","request","dict","."]

Initialize a ``FileSource`` instance and turn parameters into dict. Args: s3_uri (str or PipelineVariable): The S3 URI of the metric content_digest (str or PipelineVariable): The digest of the metric (default: None) content_type (str or PipelineVariable): Specifies the type of content in S3 URI (default: None)

["Initialize","a","``","FileSource","``","instance","and","turn","parameters","into","dict",".","Args",":","s3_uri","(","str","or","PipelineVariable",")",":","The","S3","URI","of","the","metric","content_digest","(","str","or","PipelineVariable",")",":","The","digest","of","the","metric","(","default",":","None",")","content_type","(","str","or","PipelineVariable",")",":","Specifies","the","type","of","content","in","S3","URI","(","default",":","None",")"]

FileSource

def __init__( self, s3_uri: Union[str, PipelineVariable], content_digest: Optional[Union[str, PipelineVariable]] = None, content_type: Optional[Union[str, PipelineVariable]] = None, ): """Initialize a ``FileSource`` instance and turn parameters into dict. Args: s3_uri (str or PipelineVariable): The S3 URI of the metric content_digest (str or PipelineVariable): The digest of the metric (default: None) content_type (str or PipelineVariable): Specifies the type of content in S3 URI (default: None) """ self.content_type = content_type self.s3_uri = s3_uri self.content_digest = content_digest

["def","__init__","(","self",",","s3_uri",":","Union","[","str",",","PipelineVariable","]",",","content_digest",":","Optional","[","Union","[","str",",","PipelineVariable","]","]","=","None",",","content_type",":","Optional","[","Union","[","str",",","PipelineVariable","]","]","=","None",",",")",":","``","''","''","Initialize","a","``","FileSource","``","instance","and","turn","parameters","into","dict",".","Args",":","s3_uri","(","str","or","PipelineVariable",")",":","The","S3","URI","of","the","metric","content_digest","(","str","or","PipelineVariable",")",":","The","digest","of","the","metric","(","default",":","None",")","content_type","(","str","or","PipelineVariable",")",":","Specifies","the","type","of","content","in","S3","URI","(","default",":","None",")","``","''","''","self.content_type","=","content_type","self.s3_uri","=","s3_uri","self.content_digest","=","content_digest"]

134

151

null

model_metrics.py

sagemaker-python-sdk/src/sagemaker/model_metrics.py

from __future__ import absolute_import from typing import Optional, Union from sagemaker.workflow.entities import PipelineVariable

15

3

0

3

2

1

Use image node_id 1 to create a new FileSource object from inherited base classes: object with example: obj = FileSource(s3_uri, content_digest, content_type)

158

node_id 1

1,845,784

diffs

global

null

false

ctx,f,x,n

null

def diffs(ctx, f, x, n=None, **options): r""" Returns a generator that yields the sequence of derivatives .. math :: f(x), f'(x), f''(x), \ldots, f^{(k)}(x), \ldots With ``method='step'``, :func:`~mpmath.diffs` uses only `O(k)` function evaluations to generate the first `k` derivatives, rather than the roughly `O(k^2)` evaluations required if one calls :func:`~mpmath.diff` `k` separate times. With `n < \infty`, the generator stops as soon as the `n`-th derivative has been generated. If the exact number of needed derivatives is known in advance, this is further slightly more efficient. Options are the same as for :func:`~mpmath.diff`. **Examples** >>> from mpmath import * >>> mp.dps = 15 >>> nprint(list(diffs(cos, 1, 5))) [0.540302, -0.841471, -0.540302, 0.841471, 0.540302, -0.841471] >>> for i, d in zip(range(6), diffs(cos, 1)): ... print("%s %s" % (i, d)) ... 0 0.54030230586814 1 -0.841470984807897 2 -0.54030230586814 3 0.841470984807897 4 0.54030230586814 5 -0.841470984807897 """ if n is None: n = ctx.inf else: n = int(n) if options.get("method", "step") != "step": k = 0 while k < n + 1: yield ctx.diff(f, x, k, **options) k += 1 return singular = options.get("singular") if singular: yield ctx.diff(f, x, 0, singular=True) else: yield f(ctx.convert(x)) if n < 1: return if n == ctx.inf: A, B = 1, 2 else: A, B = 1, n + 1 while 1: callprec = ctx.prec y, norm, workprec = hsteps(ctx, f, x, B, callprec, **options) for k in xrange(A, B): try: ctx.prec = workprec d = ctx.difference(y, k) / norm**k finally: ctx.prec = callprec yield +d if k >= n: return A, B = B, int(A * 1.4 + 1) B = min(B, n)

["def","diffs","(","ctx",",","f",",","x",",","n=None",",","*","*","options",")",":","r","''","''","''","Returns","a","generator","that","yields","the","sequence","of","derivatives","..","math",":",":","f","(","x",")",",","f","'","(","x",")",",","f","''","(","x",")",",","\\ldots",",","f^","{","(","k",")","}","(","x",")",",","\\ldots","With","``","method='step","'","``",",",":","func",":","`","~mpmath.diffs","`","uses","only","`","O","(","k",")","`","function","evaluations","to","generate","the","first","`","k","`","derivatives",",","rather","than","the","roughly","`","O","(","k^2",")","`","evaluations","required","if","one","calls",":","func",":","`","~mpmath.diff","`","`","k","`","separate","times",".","With","`","n","<","\\infty","`",",","the","generator","stops","as","soon","as","the","`","n","`","-th","derivative","has","been","generated",".","If","the","exact","number","of","needed","derivatives","is","known","in","advance",",","this","is","further","slightly","more","efficient",".","Options","are","the","same","as","for",":","func",":","`","~mpmath.diff","`",".","*","*","Examples","*","*",">",">",">","from","mpmath","import","*",">",">",">","mp.dps","=","15",">",">",">","nprint","(","list","(","diffs","(","cos",",","1",",","5",")",")",")","[","0.540302",",","-0.841471",",","-0.540302",",","0.841471",",","0.540302",",","-0.841471","]",">",">",">","for","i",",","d","in","zip","(","range","(","6",")",",","diffs","(","cos",",","1",")",")",":","...","print","(","``","%","s","%","s","''","%","(","i",",","d",")",")","...","0","0.54030230586814","1","-0.841470984807897","2","-0.54030230586814","3","0.841470984807897","4","0.54030230586814","5","-0.841470984807897","``","''","''","if","n","is","None",":","n","=","ctx.inf","else",":","n","=","int","(","n",")","if","options.get","(","``","method","''",",","``","step","''",")","!","=","``","step","''",":","k","=","0","while","k","<","n","+","1",":","yield","ctx.diff","(","f",",","x",",","k",",","*","*","options",")","k","+=","1","return","singular","=","options.get","(","``","singular","''",")","if","singular",":","yield","ctx.diff","(","f",",","x",",","0",",","singular=True",")","else",":","yield","f","(","ctx.convert","(","x",")",")","if","n","<","1",":","return","if","n","==","ctx.inf",":","A",",","B","=","1",",","2","else",":","A",",","B","=","1",",","n","+","1","while","1",":","callprec","=","ctx.prec","y",",","norm",",","workprec","=","hsteps","(","ctx",",","f",",","x",",","B",",","callprec",",","*","*","options",")","for","k","in","xrange","(","A",",","B",")",":","try",":","ctx.prec","=","workprec","d","=","ctx.difference","(","y",",","k",")","\/","norm","*","*","k","finally",":","ctx.prec","=","callprec","yield","+d","if","k",">","=","n",":","return","A",",","B","=","B",",","int","(","A","*","1.4","+","1",")","B","=","min","(","B",",","n",")"]

224

295

null

differentiation.py

catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py

from ..libmp.backend import xrange from .calculus import defun

15

null

2

13

null

Use image node_id 5 for calling a global function with example usage: diffs(ctx, f, x, n) without return types

110

node_id 5

407,216

iterable_to_function

global

null

false

gen

null

f,data

def iterable_to_function(gen): gen = iter(gen) data = [] def f(k): for i in xrange(len(data), k + 1): data.append(next(gen)) return data[k] return f

["def","iterable_to_function","(","gen",")",":","gen","=","iter","(","gen",")","data","=","[","]","def","f","(","k",")",":","for","i","in","xrange","(","len","(","data",")",",","k","+","1",")",":","data.append","(","next","(","gen",")",")","return","data","[","k","]","return","f"]

297

304

null

differentiation.py

catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py

from ..libmp.backend import xrange from .calculus import defun

15

null

2

13

null

Use image node_id 6 for calling a global function with example usage: iterable_to_function(gen) and returns: f, data

116

node_id 6

407,217

diffs_prod

global

null

false

ctx,factors

null

def diffs_prod(ctx, factors): r""" Given a list of `N` iterables or generators yielding `f_k(x), f'_k(x), f''_k(x), \ldots` for `k = 1, \ldots, N`, generate `g(x), g'(x), g''(x), \ldots` where `g(x) = f_1(x) f_2(x) \cdots f_N(x)`. At high precision and for large orders, this is typically more efficient than numerical differentiation if the derivatives of each `f_k(x)` admit direct computation. Note: This function does not increase the working precision internally, so guard digits may have to be added externally for full accuracy. **Examples** >>> from mpmath import * >>> mp.dps = 15; mp.pretty = True >>> f = lambda x: exp(x)*cos(x)*sin(x) >>> u = diffs(f, 1) >>> v = mp.diffs_prod([diffs(exp,1), diffs(cos,1), diffs(sin,1)]) >>> next(u); next(v) 1.23586333600241 1.23586333600241 >>> next(u); next(v) 0.104658952245596 0.104658952245596 >>> next(u); next(v) -5.96999877552086 -5.96999877552086 >>> next(u); next(v) -12.4632923122697 -12.4632923122697 """ N = len(factors) if N == 1: for c in factors[0]: yield c else: u = iterable_to_function(ctx.diffs_prod(factors[: N // 2])) v = iterable_to_function(ctx.diffs_prod(factors[N // 2 :])) n = 0 while 1: # yield sum(binomial(n,k)*u(n-k)*v(k) for k in xrange(n+1)) s = u(n) * v(0) a = 1 for k in xrange(1, n + 1): a = a * (n - k + 1) // k s += a * u(n - k) * v(k) yield s n += 1

["def","diffs_prod","(","ctx",",","factors",")",":","r","''","''","''","Given","a","list","of","`","N","`","iterables","or","generators","yielding","`","f_k","(","x",")",",","f'_k","(","x",")",",","f","''","_k","(","x",")",",","\\ldots","`","for","`","k","=","1",",","\\ldots",",","N","`",",","generate","`","g","(","x",")",",","g","'","(","x",")",",","g","''","(","x",")",",","\\ldots","`","where","`","g","(","x",")","=","f_1","(","x",")","f_2","(","x",")","\\cdots","f_N","(","x",")","`",".","At","high","precision","and","for","large","orders",",","this","is","typically","more","efficient","than","numerical","differentiation","if","the","derivatives","of","each","`","f_k","(","x",")","`","admit","direct","computation",".","Note",":","This","function","does","not","increase","the","working","precision","internally",",","so","guard","digits","may","have","to","be","added","externally","for","full","accuracy",".","*","*","Examples","*","*",">",">",">","from","mpmath","import","*",">",">",">","mp.dps","=","15",";","mp.pretty","=","True",">",">",">","f","=","lambda","x",":","exp","(","x",")","*","cos","(","x",")","*","sin","(","x",")",">",">",">","u","=","diffs","(","f",",","1",")",">",">",">","v","=","mp.diffs_prod","(","[","diffs","(","exp,1",")",",","diffs","(","cos,1",")",",","diffs","(","sin,1",")","]",")",">",">",">","next","(","u",")",";","next","(","v",")","1.23586333600241","1.23586333600241",">",">",">","next","(","u",")",";","next","(","v",")","0.104658952245596","0.104658952245596",">",">",">","next","(","u",")",";","next","(","v",")","-5.96999877552086","-5.96999877552086",">",">",">","next","(","u",")",";","next","(","v",")","-12.4632923122697","-12.4632923122697","``","''","''","N","=","len","(","factors",")","if","N","==","1",":","for","c","in","factors","[","0","]",":","yield","c","else",":","u","=","iterable_to_function","(","ctx.diffs_prod","(","factors","[",":","N","\/\/","2","]",")",")","v","=","iterable_to_function","(","ctx.diffs_prod","(","factors","[","N","\/\/","2",":","]",")",")","n","=","0","while","1",":","#","yield","sum","(","binomial","(","n",",","k",")","*","u","(","n-k",")","*","v","(","k",")","for","k","in","xrange","(","n+1",")",")","s","=","u","(","n",")","*","v","(","0",")","a","=","1","for","k","in","xrange","(","1",",","n","+","1",")",":","a","=","a","*","(","n","-","k","+","1",")","\/\/","k","s","+=","a","*","u","(","n","-","k",")","*","v","(","k",")","yield","s","n","+=","1"]

307

358

null

differentiation.py

catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py

from ..libmp.backend import xrange from .calculus import defun

15

null

2

13

null

Use image node_id 7 for calling a global function with example usage: diffs_prod(ctx, factors) without return types

115

node_id 7

407,218

test_ascent

TestDatasets

null

true

self

null

def test_ascent(self): assert_equal(ascent().shape, (512, 512)) # hash check assert _has_hash( os.path.join(data_dir, "ascent.dat"), registry["ascent.dat"] )

["def","test_ascent","(","self",")",":","assert_equal","(","ascent","(",")",".shape",",","(","512",",","512",")",")","#","hash","check","assert","_has_hash","(","os.path.join","(","data_dir",",","``","ascent.dat","''",")",",","registry","[","``","ascent.dat","''","]",")"]

41

46

null

test_data.py

scipy/scipy/datasets/tests/test_data.py

from scipy.datasets._registry import registry from scipy.datasets._fetchers import data_fetcher from scipy.datasets._utils import _clear_cache from scipy.datasets import ascent, face, electrocardiogram, download_all from numpy.testing import assert_equal, assert_almost_equal import os import pytest

15

1

7

2

0

5

null

Use image node_id 3 for calling the TestDatasets obj's underlying member method code with example usage: obj.test_ascent() without return types

143

node_id 3

1,884,889

_to_request_dict

MetricsSource

object

true

self

Accepts metrics source parameters for conversion to request dict.

["Accepts","metrics","source","parameters","for","conversion","to","request","dict","."]

Generates a request dictionary using the parameters provided to the class.

["Generates","a","request","dictionary","using","the","parameters","provided","to","the","class","."]

metrics_source_request

def _to_request_dict(self): """Generates a request dictionary using the parameters provided to the class.""" metrics_source_request = { "ContentType": self.content_type, "S3Uri": self.s3_uri, } if self.content_digest is not None: metrics_source_request["ContentDigest"] = self.content_digest return metrics_source_request

["def","_to_request_dict","(","self",")",":","``","''","''","Generates","a","request","dictionary","using","the","parameters","provided","to","the","class",".","''","''","''","metrics_source_request","=","{","``","ContentType","''",":","self.content_type",",","``","S3Uri","''",":","self.s3_uri",",","}","if","self.content_digest","is","not","None",":","metrics_source_request","[","``","ContentDigest","''","]","=","self.content_digest","return","metrics_source_request"]

123

128

null

model_metrics.py

sagemaker-python-sdk/src/sagemaker/model_metrics.py

from __future__ import absolute_import from typing import Optional, Union from sagemaker.workflow.entities import PipelineVariable

15

3

0

3

2

1

Use image node_id 2 for calling the MetricsSource obj's underlying member method code with example usage: obj._to_request_dict() and returns: metrics_source_request

164

node_id 2

1,845,783

leiden

global

null

false

input_graph,max_iter,resolution,random_state,theta

null

ddf, mod_score

def leiden( input_graph: Graph, max_iter: int = 100, resolution: int = 1.0, random_state: int = None, theta: int = 1.0, ) -> Tuple[dask_cudf.DataFrame, float]: """ Compute the modularity optimizing partition of the input graph using the Leiden method Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. doi: 10.1038/s41598-019-41695-z Parameters ---------- G : cugraph.Graph The graph descriptor should contain the connectivity information and weights. The adjacency list will be computed if not already present. The current implementation only supports undirected graphs. max_iter : integer, optional (default=100) This controls the maximum number of levels/iterations of the Leiden algorithm. When specified the algorithm will terminate after no more than the specified number of iterations. No error occurs when the algorithm terminates early in this manner. resolution: float, optional (default=1.0) Called gamma in the modularity formula, this changes the size of the communities. Higher resolutions lead to more smaller communities, lower resolutions lead to fewer larger communities. Defaults to 1. random_state: int, optional(default=None) Random state to use when generating samples. Optional argument, defaults to a hash of process id, time, and hostname. theta: float, optional (default=1.0) Called theta in the Leiden algorithm, this is used to scale modularity gain in Leiden refinement phase, to compute the probability of joining a random leiden community. Returns ------- parts : dask_cudf.DataFrame GPU data frame of size V containing two columns the vertex id and the partition id it is assigned to. ddf['vertex'] : cudf.Series Contains the vertex identifiers ddf['partition'] : cudf.Series Contains the partition assigned to the vertices modularity_score : float a floating point number containing the global modularity score of the partitioning. Examples -------- >>> import cugraph.dask as dcg >>> import dask_cudf >>> # ... Init a DASK Cluster >>> # see https://docs.rapids.ai/api/cugraph/stable/dask-cugraph.html >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/.. >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv") >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv", ... chunksize=chunksize, delimiter=" ", ... names=["src", "dst", "value"], ... dtype=["int32", "int32", "float32"]) >>> dg = cugraph.Graph() >>> dg.from_dask_cudf_edgelist(ddf, source='src', destination='dst') >>> parts, modularity_score = dcg.leiden(dg) """ if input_graph.is_directed(): raise ValueError("input graph must be undirected") # Return a client if one has started client = default_client() do_expensive_check = False result = [ client.submit( _call_plc_leiden, Comms.get_session_id(), input_graph._plc_graph[w], max_iter, resolution, random_state, theta, do_expensive_check, workers=[w], allow_other_workers=False, ) for w in Comms.get_workers() ] wait(result) part_mod_score = [ client.submit(convert_to_cudf, r) for r in result ] wait(part_mod_score) vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0] empty_df = cudf.DataFrame( { "vertex": numpy.empty(shape=0, dtype=vertex_dtype), "partition": numpy.empty(shape=0, dtype="int32"), } ) part_mod_score = [ delayed(lambda x: x, nout=2)(r) for r in part_mod_score ] ddf = dask_cudf.from_delayed( [r[0] for r in part_mod_score], meta=empty_df, verify_meta=False, ).persist() mod_score = dask.array.from_delayed( part_mod_score[0][1], shape=(1,), dtype=float ).compute() wait(ddf) wait(mod_score) wait([r.release() for r in part_mod_score]) if input_graph.renumbered: ddf = input_graph.unrenumber(ddf, "vertex") return ddf, mod_score

67

199

null

leiden.py

cugraph/python/cugraph/cugraph/dask/community/leiden.py

from __future__ import annotations from dask.distributed import wait, default_client import cugraph.dask.comms.comms import dask_cudf import dask from dask import delayed import cudf from pylibcugraph import ResourceHandle from pylibcugraph import leiden import numpy import cupy from typing import Tuple, TYPE_CHECKING

15

null

12

3

null

Use image node_id 3 for calling a global function with example usage: leiden(input_graph, max_iter, resolution, random_state, theta) and returns: ddf, mod_score

161

node_id 3

686,170

test_face

TestDatasets

null

true

self

null

def test_face(self): assert_equal(face().shape, (768, 1024, 3)) # hash check assert _has_hash( os.path.join(data_dir, "face.dat"), registry["face.dat"] )

["def","test_face","(","self",")",":","assert_equal","(","face","(",")",".shape",",","(","768",",","1024",",","3",")",")","#","hash","check","assert","_has_hash","(","os.path.join","(","data_dir",",","``","face.dat","''",")",",","registry","[","``","face.dat","''","]",")"]

48

53

null

test_data.py

scipy/scipy/datasets/tests/test_data.py

from scipy.datasets._registry import registry from scipy.datasets._fetchers import data_fetcher from scipy.datasets._utils import _clear_cache from scipy.datasets import ascent, face, electrocardiogram, download_all from numpy.testing import assert_equal, assert_almost_equal import os import pytest

15

1

7

2

0

5

null

Use image node_id 4 for calling the TestDatasets obj's underlying member method code with example usage: obj.test_face() without return types

141

node_id 4

1,884,890

_no_op

global

null

false

null

args, kwargs

def _no_op(*args, **kwargs) -> Any: """no_op A function that returns its arguments :return: whatever was passed in :rtype: Any """ return args, kwargs

["def","_no_op","(","*","args",",","*","*","kwargs",")","-",">","Any",":","``","''","''","no_op","A","function","that","returns","its","arguments",":","return",":","whatever","was","passed","in",":","rtype",":","Any","``","''","''","return","args",",","kwargs"]

38

44

null

test_consume.py

airflow/tests/providers/apache/kafka/operators/test_consume.py

from __future__ import annotations import json import logging from typing import Any from unittest import mock import pytest from airflow.models import Connection from airflow.providers.apache.kafka.operators.consume import ConsumeFromTopicOperator from airflow.utils import db

15

null

9

1

null

Use image node_id 1 for calling a global function with example usage: _no_op() and returns: args, kwargs

105

node_id 1

263,603

create_db_and_tables

global

null

false

null

def create_db_and_tables(): SQLModel.metadata.create_all(engine)

["def","create_db_and_tables","(",")",":","SQLModel.metadata.create_all","(","engine",")"]

30

31

null

tutorial001_py310.py

sqlmodel/docs_src/tutorial/fastapi/read_one/tutorial001_py310.py

from fastapi import FastAPI, HTTPException from sqlmodel import Field, Session, SQLModel, create_engine, select

15

null

2

5

null

Use image node_id 1 for calling a global function with example usage: create_db_and_tables() without return types

113

node_id 1

1,989,839

_to_request_dict

ModelMetrics

object

true

self

Accepts model metrics parameters for conversion to request dict.

["Accepts","model","metrics","parameters","for","conversion","to","request","dict","."]

Generates a request dictionary using the parameters provided to the class.

["Generates","a","request","dictionary","using","the","parameters","provided","to","the","class","."]

model_metrics_request

def _to_request_dict(self): """Generates a request dictionary using the parameters provided to the class.""" model_metrics_request = {} model_quality = {} if self.model_statistics is not None: model_quality[ "Statistics" ] = self.model_statistics._to_request_dict() if self.model_constraints is not None: model_quality[ "Constraints" ] = self.model_constraints._to_request_dict() if model_quality: model_metrics_request["ModelQuality"] = model_quality model_data_quality = {} if self.model_data_statistics is not None: model_data_quality[ "Statistics" ] = self.model_data_statistics._to_request_dict() if self.model_data_constraints is not None: model_data_quality[ "Constraints" ] = self.model_data_constraints._to_request_dict() if model_data_quality: model_metrics_request["ModelDataQuality"] = model_data_quality bias = {} if self.bias is not None: bias["Report"] = self.bias._to_request_dict() if self.bias_pre_training is not None: bias[ "PreTrainingReport" ] = self.bias_pre_training._to_request_dict() if self.bias_post_training is not None: bias[ "PostTrainingReport" ] = self.bias_post_training._to_request_dict() model_metrics_request["Bias"] = bias explainability = {} if self.explainability is not None: explainability[ "Report" ] = self.explainability._to_request_dict() model_metrics_request["Explainability"] = explainability return model_metrics_request

["def","_to_request_dict","(","self",")",":","``","''","''","Generates","a","request","dictionary","using","the","parameters","provided","to","the","class",".","''","''","''","model_metrics_request","=","{","}","model_quality","=","{","}","if","self.model_statistics","is","not","None",":","model_quality","[","``","Statistics","''","]","=","self.model_statistics._to_request_dict","(",")","if","self.model_constraints","is","not","None",":","model_quality","[","``","Constraints","''","]","=","self.model_constraints._to_request_dict","(",")","if","model_quality",":","model_metrics_request","[","``","ModelQuality","''","]","=","model_quality","model_data_quality","=","{","}","if","self.model_data_statistics","is","not","None",":","model_data_quality","[","``","Statistics","''","]","=","self.model_data_statistics._to_request_dict","(",")","if","self.model_data_constraints","is","not","None",":","model_data_quality","[","``","Constraints","''","]","=","self.model_data_constraints._to_request_dict","(",")","if","model_data_quality",":","model_metrics_request","[","``","ModelDataQuality","''","]","=","model_data_quality","bias","=","{","}","if","self.bias","is","not","None",":","bias","[","``","Report","''","]","=","self.bias._to_request_dict","(",")","if","self.bias_pre_training","is","not","None",":","bias","[","``","PreTrainingReport","''","]","=","self.bias_pre_training._to_request_dict","(",")","if","self.bias_post_training","is","not","None",":","bias","[","``","PostTrainingReport","''","]","=","self.bias_post_training._to_request_dict","(",")","model_metrics_request","[","``","Bias","''","]","=","bias","explainability","=","{","}","if","self.explainability","is","not","None",":","explainability","[","``","Report","''","]","=","self.explainability._to_request_dict","(",")","model_metrics_request","[","``","Explainability","''","]","=","explainability","return","model_metrics_request"]

64

98

null

model_metrics.py

sagemaker-python-sdk/src/sagemaker/model_metrics.py

from __future__ import absolute_import from typing import Optional, Union from sagemaker.workflow.entities import PipelineVariable

15

3

0

3

2

1

Use image node_id 2 for calling the ModelMetrics obj's underlying member method code with example usage: obj._to_request_dict() and returns: model_metrics_request

162

node_id 2

1,845,781

__init__

ModelMetrics

object

true

self,model_statistics,model_constraints,model_data_statistics,model_data_constraints,bias,explainability,bias_pre_training,bias_post_training

Accepts model metrics parameters for conversion to request dict.

["Accepts","model","metrics","parameters","for","conversion","to","request","dict","."]

Initialize a ``ModelMetrics`` instance and turn parameters into dict. Args: model_statistics (MetricsSource): A metric source object that represents model statistics (default: None). model_constraints (MetricsSource): A metric source object that represents model constraints (default: None). model_data_statistics (MetricsSource): A metric source object that represents model data statistics (default: None). model_data_constraints (MetricsSource): A metric source object that represents model data constraints (default: None). bias (MetricsSource): A metric source object that represents bias report (default: None). explainability (MetricsSource): A metric source object that represents explainability report (default: None). bias_pre_training (MetricsSource): A metric source object that represents Pre-training report (default: None). bias_post_training (MetricsSource): A metric source object that represents Post-training report (default: None).

["Initialize","a","``","ModelMetrics","``","instance","and","turn","parameters","into","dict",".","Args",":","model_statistics","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","statistics","(","default",":","None",")",".","model_constraints","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","constraints","(","default",":","None",")",".","model_data_statistics","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","data","statistics","(","default",":","None",")",".","model_data_constraints","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","data","constraints","(","default",":","None",")",".","bias","(","MetricsSource",")",":","A","metric","source","object","that","represents","bias","report","(","default",":","None",")",".","explainability","(","MetricsSource",")",":","A","metric","source","object","that","represents","explainability","report","(","default",":","None",")",".","bias_pre_training","(","MetricsSource",")",":","A","metric","source","object","that","represents","Pre-training","report","(","default",":","None",")",".","bias_post_training","(","MetricsSource",")",":","A","metric","source","object","that","represents","Post-training","report","(","default",":","None",")","."]

ModelMetrics

def __init__( self, model_statistics: Optional["MetricsSource"] = None, model_constraints: Optional["MetricsSource"] = None, model_data_statistics: Optional["MetricsSource"] = None, model_data_constraints: Optional["MetricsSource"] = None, bias: Optional["MetricsSource"] = None, explainability: Optional["MetricsSource"] = None, bias_pre_training: Optional["MetricsSource"] = None, bias_post_training: Optional["MetricsSource"] = None, ): """Initialize a ``ModelMetrics`` instance and turn parameters into dict. Args: model_statistics (MetricsSource): A metric source object that represents model statistics (default: None). model_constraints (MetricsSource): A metric source object that represents model constraints (default: None). model_data_statistics (MetricsSource): A metric source object that represents model data statistics (default: None). model_data_constraints (MetricsSource): A metric source object that represents model data constraints (default: None). bias (MetricsSource): A metric source object that represents bias report (default: None). explainability (MetricsSource): A metric source object that represents explainability report (default: None). bias_pre_training (MetricsSource): A metric source object that represents Pre-training report (default: None). bias_post_training (MetricsSource): A metric source object that represents Post-training report (default: None). """ self.model_statistics = model_statistics self.model_constraints = model_constraints self.model_data_statistics = model_data_statistics self.model_data_constraints = model_data_constraints self.bias = bias self.bias_pre_training = bias_pre_training self.bias_post_training = bias_post_training self.explainability = explainability

["def","__init__","(","self",",","model_statistics",":","Optional","[","``","MetricsSource","''","]","=","None",",","model_constraints",":","Optional","[","``","MetricsSource","''","]","=","None",",","model_data_statistics",":","Optional","[","``","MetricsSource","''","]","=","None",",","model_data_constraints",":","Optional","[","``","MetricsSource","''","]","=","None",",","bias",":","Optional","[","``","MetricsSource","''","]","=","None",",","explainability",":","Optional","[","``","MetricsSource","''","]","=","None",",","bias_pre_training",":","Optional","[","``","MetricsSource","''","]","=","None",",","bias_post_training",":","Optional","[","``","MetricsSource","''","]","=","None",",",")",":","``","''","''","Initialize","a","``","ModelMetrics","``","instance","and","turn","parameters","into","dict",".","Args",":","model_statistics","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","statistics","(","default",":","None",")",".","model_constraints","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","constraints","(","default",":","None",")",".","model_data_statistics","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","data","statistics","(","default",":","None",")",".","model_data_constraints","(","MetricsSource",")",":","A","metric","source","object","that","represents","model","data","constraints","(","default",":","None",")",".","bias","(","MetricsSource",")",":","A","metric","source","object","that","represents","bias","report","(","default",":","None",")",".","explainability","(","MetricsSource",")",":","A","metric","source","object","that","represents","explainability","report","(","default",":","None",")",".","bias_pre_training","(","MetricsSource",")",":","A","metric","source","object","that","represents","Pre-training","report","(","default",":","None",")",".","bias_post_training","(","MetricsSource",")",":","A","metric","source","object","that","represents","Post-training","report","(","default",":","None",")",".","``","''","''","self.model_statistics","=","model_statistics","self.model_constraints","=","model_constraints","self.model_data_statistics","=","model_data_statistics","self.model_data_constraints","=","model_data_constraints","self.bias","=","bias","self.bias_pre_training","=","bias_pre_training","self.bias_post_training","=","bias_post_training","self.explainability","=","explainability"]

24

62

null

model_metrics.py

sagemaker-python-sdk/src/sagemaker/model_metrics.py

from __future__ import absolute_import from typing import Optional, Union from sagemaker.workflow.entities import PipelineVariable

15

3

0

3

2

1

Use image node_id 1 to create a new ModelMetrics object from inherited base classes: object with example: obj = ModelMetrics(model_statistics, model_constraints, model_data_statistics, model_data_constraints, bias, explainability, bias_pre_training, bias_post_training)

269

node_id 1

1,845,780

untransform_observation_features

SearchSpaceToChoice

Transform

true

self,observation_features

Replaces the search space with a single choice parameter, whose values are the signatures of the arms observed in the data. This transform is meant to be used with ThompsonSampler. Choice parameter will be unordered unless config["use_ordered"] specifies otherwise. Transform is done in-place.

["Replaces","the","search","space","with","a","single","choice","parameter",",","whose","values","are","the","signatures","of","the","arms","observed","in","the","data",".","This","transform","is","meant","to","be","used","with","ThompsonSampler",".","Choice","parameter","will","be","unordered","unless","config","[","``","use_ordered","''","]","specifies","otherwise",".","Transform","is","done","in-place","."]

null

observation_features

def untransform_observation_features( self, observation_features: List[ObservationFeatures] ) -> List[ObservationFeatures]: for obsf in observation_features: signature = obsf.parameters[self.parameter_name] obsf.parameters = self.signature_to_parameterization[ signature ] return observation_features

["def","untransform_observation_features","(","self",",","observation_features",":","List","[","ObservationFeatures","]",")","-",">","List","[","ObservationFeatures","]",":","for","obsf","in","observation_features",":","signature","=","obsf.parameters","[","self.parameter_name","]","obsf.parameters","=","self.signature_to_parameterization","[","signature","]","return","observation_features"]

92

98

null

search_space_to_choice.py

Ax/ax/modelbridge/transforms/search_space_to_choice.py

from typing import List, Optional, TYPE_CHECKING from ax.core.arm import Arm from ax.core.observation import Observation, ObservationFeatures from ax.core.parameter import ChoiceParameter, FixedParameter, ParameterType from ax.core.search_space import RobustSearchSpace, SearchSpace from ax.exceptions.core import UnsupportedError from ax.modelbridge.transforms.base import Transform from ax.models.types import TConfig from ax.utils.common.typeutils import checked_cast

15

1

9

0

1

4

1

Use image node_id 4 for calling the SearchSpaceToChoice obj's underlying member method code with example usage: obj.untransform_observation_features(observation_features) and returns: observation_features

204

node_id 4

9,099

get_parser

global

null

false

null

parser

def get_parser() -> argparse.Namespace: """Get argument parser.""" parser = argparse.ArgumentParser( description="Convert standard rttm file to ESPnet format" ) parser.add_argument( "--rttm", required=True, type=str, help="Path of rttm file" ) parser.add_argument( "--wavscp", required=True, type=str, help="Path of corresponding scp file", ) parser.add_argument( "--output_path", required=True, type=str, help="Output directory to storry espnet_rttm", ) parser.add_argument( "--sampling_rate", type=str_or_int, default=16000, help="Sampling rate of the audio", ) parser.add_argument( "--verbose", default=1, type=int, help="Verbosity level. Higher is more logging.", ) return parser

["def","get_parser","(",")","-",">","argparse.Namespace",":","``","''","''","Get","argument","parser",".","''","''","''","parser","=","argparse.ArgumentParser","(","description=","''","Convert","standard","rttm","file","to","ESPnet","format","''",")","parser.add_argument","(","``","--","rttm","''",",","required=True",",","type=str",",","help=","''","Path","of","rttm","file","''",")","parser.add_argument","(","``","--","wavscp","''",",","required=True",",","type=str",",","help=","''","Path","of","corresponding","scp","file","''",",",")","parser.add_argument","(","``","--","output_path","''",",","required=True",",","type=str",",","help=","''","Output","directory","to","storry","espnet_rttm","''",",",")","parser.add_argument","(","``","--","sampling_rate","''",",","type=str_or_int",",","default=16000",",","help=","''","Sampling","rate","of","the","audio","''",",",")","parser.add_argument","(","``","--","verbose","''",",","default=1",",","type=int",",","help=","''","Verbosity","level",".","Higher","is","more","logging",".","``",",",")","return","parser"]

78

108

null

convert_rttm.py

espnet/egs2/wsj0_2mix/mixit_enh1/pyscripts/utils/convert_rttm.py

import argparse import collections.abc import logging import os import re from pathlib import Path from typing import Union import humanfriendly import numpy import soundfile from typeguard import check_argument_types from espnet2.utils.types import str_or_int

15

null

12

3

null

Use image node_id 2 for calling a global function with example usage: get_parser() and returns: parser

102

node_id 2

998,880

convert_rttm_text

global

null

false

path,wavscp_path,sampling_rate,output_path

null

def convert_rttm_text( path: Union[Path, str], wavscp_path: Union[Path, str], sampling_rate: int, output_path: Union[Path, str], ) -> None: """Convert a RTTM file Note: only support speaker information now """ output_handler = Path( os.path.join(output_path, "espnet_rttm") ).open("w", encoding="utf-8") assert check_argument_types() utt_ids = set() with Path(path).open("r", encoding="utf-8") as f: for linenum, line in enumerate(f, 1): sps = re.split(" +", line.rstrip()) # RTTM format must have exactly 9 fields assert ( len(sps) == 9 ), "{} does not have exactly 9 fields".format(path) ( label_type, utt_id, channel, start, duration, _, _, spk_id, _, ) = sps # Only support speaker label now assert label_type == "SPEAKER" utt_ids.add(utt_id) start = int(np.rint(float(start) * sampling_rate)) end = start + int( np.rint(float(duration) * sampling_rate) ) output_handler.write( "{} {} {} {} {} <NA> <NA> {} <NA>\n".format( label_type, utt_id, channel, start, end, spk_id ) ) with Path(wavscp_path).open("r", encoding="utf-8") as f: for linenum, line in enumerate(f, 1): sps = re.split("[ \t]+", line.rstrip()) utt_id, wav_path = sps assert ( utt_id in utt_ids ), "{} is not in corresponding rttm {}".foramt( utt_id, path ) sf = soundfile.SoundFile(wav_path) assert sf.samplerate == sampling_rate output_handler.write( ( "{} {} <NA> <NA> {} <NA> <NA> <NA> <NA>\n".format( "END", utt_id, sf.frames ) ) ) output_handler.close()

["def","convert_rttm_text","(","path",":","Union","[","Path",",","str","]",",","wavscp_path",":","Union","[","Path",",","str","]",",","sampling_rate",":","int",",","output_path",":","Union","[","Path",",","str","]",",",")","-",">","None",":","``","''","''","Convert","a","RTTM","file","Note",":","only","support","speaker","information","now","``","''","''","output_handler","=","Path","(","os.path.join","(","output_path",",","``","espnet_rttm","''",")",")",".open","(","``","w","''",",","encoding=","''","utf-8","''",")","assert","check_argument_types","(",")","utt_ids","=","set","(",")","with","Path","(","path",")",".open","(","``","r","''",",","encoding=","''","utf-8","''",")","as","f",":","for","linenum",",","line","in","enumerate","(","f",",","1",")",":","sps","=","re.split","(","``","+","''",",","line.rstrip","(",")",")","#","RTTM","format","must","have","exactly","9","fields","assert","(","len","(","sps",")","==","9",")",",","``","{","}","does","not","have","exactly","9","fields","''",".format","(","path",")","(","label_type",",","utt_id",",","channel",",","start",",","duration",",","_",",","_",",","spk_id",",","_",",",")","=","sps","#","Only","support","speaker","label","now","assert","label_type","==","``","SPEAKER","''","utt_ids.add","(","utt_id",")","start","=","int","(","np.rint","(","float","(","start",")","*","sampling_rate",")",")","end","=","start","+","int","(","np.rint","(","float","(","duration",")","*","sampling_rate",")",")","output_handler.write","(","``","{","}","{","}","{","}","{","}","{","}","<","NA",">","<","NA",">","{","}","<","NA",">","\\n","''",".format","(","label_type",",","utt_id",",","channel",",","start",",","end",",","spk_id",")",")","with","Path","(","wavscp_path",")",".open","(","``","r","''",",","encoding=","''","utf-8","''",")","as","f",":","for","linenum",",","line","in","enumerate","(","f",",","1",")",":","sps","=","re.split","(","``","[","\\t","]","+","''",",","line.rstrip","(",")",")","utt_id",",","wav_path","=","sps","assert","(","utt_id","in","utt_ids",")",",","``","{","}","is","not","in","corresponding","rttm","{","}","''",".foramt","(","utt_id",",","path",")","sf","=","soundfile.SoundFile","(","wav_path",")","assert","sf.samplerate","==","sampling_rate","output_handler.write","(","(","``","{","}","{","}","<","NA",">","<","NA",">","{","}","<","NA",">","<","NA",">","<","NA",">","<","NA",">","\\n","''",".format","(","``","END","''",",","utt_id",",","sf.frames",")",")",")","output_handler.close","(",")"]

19

75

null

convert_rttm.py

espnet/egs2/wsj0_2mix/mixit_enh1/pyscripts/utils/convert_rttm.py

import argparse import collections.abc import logging import os import re from pathlib import Path from typing import Union import humanfriendly import numpy import soundfile from typeguard import check_argument_types from espnet2.utils.types import str_or_int

15

null

12

3

null

Use image node_id 1 for calling a global function with example usage: convert_rttm_text(path, wavscp_path, sampling_rate, output_path) without return types

155

node_id 1

998,879

data_batch_axis

GPT2Decoder

BaseStepDecoder

true

self

null

unknown

def data_batch_axis(self): return 0 if self._layout == "NT" else 1

["def","data_batch_axis","(","self",")",":","return","0","if","self._layout","==","``","NT","''","else","1"]

48

49

null

interactive_conditional_gpt2_samples.py

gluon-nlp/scripts/generation/interactive_conditional_gpt2_samples.py

import os import mxnet import argparse from gluonnlp.utils import set_seed from gluonnlp.sequence_sampler import BeamSearchSampler, BaseStepDecoder from gluonnlp.models.gpt2 import GPT2ForLM, list_pretrained_gpt2, get_pretrained_gpt2

15

1

6

2

1

5

1

Use image node_id 3 for calling the GPT2Decoder obj's underlying member method code with example usage: obj.data_batch_axis() and returns: unknown

146

node_id 3

1,097,716

dpoly

global

null

false

n,_cache

null

_cache,_cache

def dpoly(n, _cache={}): """ nth differentiation polynomial for exp (Faa di Bruno's formula). TODO: most exponents are zero, so maybe a sparse representation would be better. """ if n in _cache: return _cache[n] if not _cache: _cache[0] = {(0,): 1} R = dpoly(n - 1) R = dict((c + (0,), v) for (c, v) in iteritems(R)) Ra = {} for powers, count in iteritems(R): powers1 = (powers[0] + 1,) + powers[1:] if powers1 in Ra: Ra[powers1] += count else: Ra[powers1] = count for powers, count in iteritems(R): if not sum(powers): continue for k, p in enumerate(powers): if p: powers2 = ( powers[:k] + (p - 1, powers[k + 1] + 1) + powers[k + 2 :] ) if powers2 in Ra: Ra[powers2] += p * count else: Ra[powers2] = p * count _cache[n] = Ra return _cache[n]

["def","dpoly","(","n",",","_cache=","{","}",")",":","``","''","''","nth","differentiation","polynomial","for","exp","(","Faa","di","Bruno","'s","formula",")",".","TODO",":","most","exponents","are","zero",",","so","maybe","a","sparse","representation","would","be","better.","``","''","''","if","n","in","_cache",":","return","_cache","[","n","]","if","not","_cache",":","_cache","[","0","]","=","{","(","0",",",")",":","1","}","R","=","dpoly","(","n","-","1",")","R","=","dict","(","(","c","+","(","0",",",")",",","v",")","for","(","c",",","v",")","in","iteritems","(","R",")",")","Ra","=","{","}","for","powers",",","count","in","iteritems","(","R",")",":","powers1","=","(","powers","[","0","]","+","1",",",")","+","powers","[","1",":","]","if","powers1","in","Ra",":","Ra","[","powers1","]","+=","count","else",":","Ra","[","powers1","]","=","count","for","powers",",","count","in","iteritems","(","R",")",":","if","not","sum","(","powers",")",":","continue","for","k",",","p","in","enumerate","(","powers",")",":","if","p",":","powers2","=","(","powers","[",":","k","]","+","(","p","-","1",",","powers","[","k","+","1","]","+","1",")","+","powers","[","k","+","2",":","]",")","if","powers2","in","Ra",":","Ra","[","powers2","]","+=","p","*","count","else",":","Ra","[","powers2","]","=","p","*","count","_cache","[","n","]","=","Ra","return","_cache","[","n","]"]

360

391

null

differentiation.py

catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py

from ..libmp.backend import xrange from .calculus import defun

15

null

2

13

null

Use image node_id 8 for calling a global function with example usage: dpoly(n, _cache) and returns: _cache, _cache

114

node_id 8

407,219

test_extract_errors

TestRedshiftDbEngineSpec

TestDbEngineSpec

true

self

null

Test that custom error messages are extracted correctly.

["Test","that","custom","error","messages","are","extracted","correctly","."]

null

def test_extract_errors(self): """ Test that custom error messages are extracted correctly. """ msg = ( 'FATAL: password authentication failed for user "wronguser"' ) result = RedshiftEngineSpec.extract_errors(Exception(msg)) assert result == [ SupersetError( error_type=SupersetErrorType.CONNECTION_ACCESS_DENIED_ERROR, message='Either the username "wronguser" or the password is incorrect.', level=ErrorLevel.ERROR, extra={ "invalid": ["username", "password"], "engine_name": "Amazon Redshift", "issue_codes": [ { "code": 1014, "message": "Issue 1014 - Either the username " "or the password is wrong.", }, { "code": 1015, "message": "Issue 1015 - Either the database is " "spelled incorrectly or does not exist.", }, ], }, ) ] msg = ( 'redshift: error: could not translate host name "badhost" ' "to address: nodename nor servname provided, or not known" ) result = RedshiftEngineSpec.extract_errors(Exception(msg)) assert result == [ SupersetError( error_type=SupersetErrorType.CONNECTION_INVALID_HOSTNAME_ERROR, message='The hostname "badhost" cannot be resolved.', level=ErrorLevel.ERROR, extra={ "invalid": ["host"], "engine_name": "Amazon Redshift", "issue_codes": [ { "code": 1007, "message": "Issue 1007 - The hostname provided " "can't be resolved.", } ], }, ) ] msg = dedent( """ psql: error: could not connect to server: Connection refused Is the server running on host "localhost" (::1) and accepting TCP/IP connections on port 12345? could not connect to server: Connection refused Is the server running on host "localhost" (127.0.0.1) and accepting TCP/IP connections on port 12345? """ ) result = RedshiftEngineSpec.extract_errors(Exception(msg)) assert result == [ SupersetError( error_type=SupersetErrorType.CONNECTION_PORT_CLOSED_ERROR, message='Port 12345 on hostname "localhost" refused the connection.', level=ErrorLevel.ERROR, extra={ "invalid": ["host", "port"], "engine_name": "Amazon Redshift", "issue_codes": [ { "code": 1008, "message": "Issue 1008 - The port is closed.", } ], }, ) ] msg = dedent( """ psql: error: could not connect to server: Operation timed out Is the server running on host "example.com" (93.184.216.34) and accepting TCP/IP connections on port 12345? """ ) result = RedshiftEngineSpec.extract_errors(Exception(msg)) assert result == [ SupersetError( error_type=SupersetErrorType.CONNECTION_HOST_DOWN_ERROR, message=( 'The host "example.com" might be down, ' "and can't be reached on port 12345." ), level=ErrorLevel.ERROR, extra={ "engine_name": "Amazon Redshift", "issue_codes": [ { "code": 1009, "message": "Issue 1009 - The host might be down, " "and can't be reached on the provided port.", } ], "invalid": ["host", "port"], }, ) ] # response with IP only msg = dedent( """ psql: error: could not connect to server: Operation timed out Is the server running on host "93.184.216.34" and accepting TCP/IP connections on port 12345? """ ) result = RedshiftEngineSpec.extract_errors(Exception(msg)) assert result == [ SupersetError( error_type=SupersetErrorType.CONNECTION_HOST_DOWN_ERROR, message=( 'The host "93.184.216.34" might be down, ' "and can't be reached on port 12345." ), level=ErrorLevel.ERROR, extra={ "engine_name": "Amazon Redshift", "issue_codes": [ { "code": 1009, "message": "Issue 1009 - The host might be down, " "and can't be reached on the provided port.", } ], "invalid": ["host", "port"], }, ) ] msg = 'database "badDB" does not exist' result = RedshiftEngineSpec.extract_errors(Exception(msg)) assert result == [ SupersetError( error_type=SupersetErrorType.CONNECTION_UNKNOWN_DATABASE_ERROR, message='We were unable to connect to your database named "badDB".' " Please verify your database name and try again.", level=ErrorLevel.ERROR, extra={ "engine_name": "Amazon Redshift", "issue_codes": [ { "code": 10015, "message": "Issue 1015 - Either the database is " "spelled incorrectly or does not exist.", } ], "invalid": ["database"], }, ) ]

32

192

null

redshift_tests.py

superset/tests/integration_tests/db_engine_specs/redshift_tests.py

import unittest.mock from textwrap import dedent import numpy import pandas from sqlalchemy.types import NVARCHAR from superset.db_engine_specs.redshift import RedshiftEngineSpec from superset.errors import ErrorLevel, SupersetError, SupersetErrorType from superset.sql_parse import Table from tests.integration_tests.db_engine_specs.base_tests import TestDbEngineSpec from tests.integration_tests.test_app import app

15

1

10

0

1

3

1

Use image node_id 1 for calling the TestRedshiftDbEngineSpec obj's underlying member method code with example usage: obj.test_extract_errors() without return types

163

node_id 1

2,027,364

__init__

MetricsSource

object

true

self,content_type,s3_uri,content_digest

Accepts metrics source parameters for conversion to request dict.

["Accepts","metrics","source","parameters","for","conversion","to","request","dict","."]

Initialize a ``MetricsSource`` instance and turn parameters into dict. Args: content_type (str or PipelineVariable): Specifies the type of content in S3 URI s3_uri (str or PipelineVariable): The S3 URI of the metric content_digest (str or PipelineVariable): The digest of the metric (default: None)

["Initialize","a","``","MetricsSource","``","instance","and","turn","parameters","into","dict",".","Args",":","content_type","(","str","or","PipelineVariable",")",":","Specifies","the","type","of","content","in","S3","URI","s3_uri","(","str","or","PipelineVariable",")",":","The","S3","URI","of","the","metric","content_digest","(","str","or","PipelineVariable",")",":","The","digest","of","the","metric","(","default",":","None",")"]

MetricsSource

def __init__( self, content_type: Union[str, PipelineVariable], s3_uri: Union[str, PipelineVariable], content_digest: Optional[Union[str, PipelineVariable]] = None, ): """Initialize a ``MetricsSource`` instance and turn parameters into dict. Args: content_type (str or PipelineVariable): Specifies the type of content in S3 URI s3_uri (str or PipelineVariable): The S3 URI of the metric content_digest (str or PipelineVariable): The digest of the metric (default: None) """ self.content_type = content_type self.s3_uri = s3_uri self.content_digest = content_digest

["def","__init__","(","self",",","content_type",":","Union","[","str",",","PipelineVariable","]",",","s3_uri",":","Union","[","str",",","PipelineVariable","]",",","content_digest",":","Optional","[","Union","[","str",",","PipelineVariable","]","]","=","None",",",")",":","``","''","''","Initialize","a","``","MetricsSource","``","instance","and","turn","parameters","into","dict",".","Args",":","content_type","(","str","or","PipelineVariable",")",":","Specifies","the","type","of","content","in","S3","URI","s3_uri","(","str","or","PipelineVariable",")",":","The","S3","URI","of","the","metric","content_digest","(","str","or","PipelineVariable",")",":","The","digest","of","the","metric","(","default",":","None",")","``","''","''","self.content_type","=","content_type","self.s3_uri","=","s3_uri","self.content_digest","=","content_digest"]

104

121

null

model_metrics.py

sagemaker-python-sdk/src/sagemaker/model_metrics.py

from __future__ import absolute_import from typing import Optional, Union from sagemaker.workflow.entities import PipelineVariable

15

3

0

3

2

1

Use image node_id 1 to create a new MetricsSource object from inherited base classes: object with example: obj = MetricsSource(content_type, s3_uri, content_digest)

164

node_id 1

1,845,782

ihilbert

global

null

false

x

null

unknown

def ihilbert(x): """ Return inverse Hilbert transform of a periodic sequence x. If ``x_j`` and ``y_j`` are Fourier coefficients of periodic functions x and y, respectively, then:: y_j = -sqrt(-1)*sign(j) * x_j y_0 = 0 """ return -hilbert(x)

["def","ihilbert","(","x",")",":","``","''","''","Return","inverse","Hilbert","transform","of","a","periodic","sequence","x",".","If","``","x_j","``","and","``","y_j","``","are","Fourier","coefficients","of","periodic","functions","x","and","y",",","respectively",",","then",":",":","y_j","=","-sqrt","(","-1",")","*","sign","(","j",")","*","x_j","y_0","=","0","``","''","''","return","-hilbert","(","x",")"]

265

276

null

pseudo_diffs.py

catboost/contrib/python/scipy/py2/scipy/fftpack/pseudo_diffs.py

from __future__ import division, print_function, absolute_import from numpy import pi, asarray, sin, cos, sinh, cosh, tanh, iscomplexobj from .None import convolve from scipy.fftpack.basic import _datacopied import atexit

15

null

5

10

null

Use image node_id 5 for calling a global function with example usage: ihilbert(x) and returns: unknown

102

node_id 5

523,401

__init__

NullLogger

null

true

self

null

NullLogger

def __init__(self): self.indent_ = ""

["def","__init__","(","self",")",":","self.indent_","=","``","''"]

8

9

null

logger.py

turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py

import sys

15

2

1

0

1

7

null

Use image node_id 1 to create a new NullLogger object with example: obj = NullLogger()

87

node_id 1

2,276,647

update_already_pruned

SensitivityAnalysis

null

true

self,layername,ratio

null

Set the already pruned ratio for the target layer.

["Set","the","already","pruned","ratio","for","the","target","layer","."]

null

def update_already_pruned(self, layername, ratio): """ Set the already pruned ratio for the target layer. """ self.already_pruned[layername] = ratio

["def","update_already_pruned","(","self",",","layername",",","ratio",")",":","``","''","''","Set","the","already","pruned","ratio","for","the","target","layer.","``","''","''","self.already_pruned","[","layername","]","=","ratio"]

240

244

null

sensitivity_analysis.py

auptimizer/src/aup/compression/torch/utils/sensitivity_analysis.py

import copy import csv import logging from collections import OrderedDict import numpy import torch.nn

15

1

6

0

8

null

Use image node_id 7 for calling the SensitivityAnalysis obj's underlying member method code with example usage: obj.update_already_pruned(layername, ratio) without return types

176

node_id 7

315,483

setup

global

null

false

app

null

def setup(app): app.add_autodocumenter(HasTraitsDocumenter) app.add_autodocumenter(TraitDocumenter)

["def","setup","(","app",")",":","app.add_autodocumenter","(","HasTraitsDocumenter",")","app.add_autodocumenter","(","TraitDocumenter",")"]

99

101

null

autodoc_traits.py

pythreejs/docs/sphinxext/autodoc_traits.py

from collections import OrderedDict from traitlets import TraitType, Undefined, Container, Dict, Any, HasTraits from sphinx.ext.autodoc import ClassDocumenter, AttributeDocumenter

15

null

3

null

Use image node_id 3 for calling a global function with example usage: setup(app) without return types

101

node_id 3

1,691,047

get_retro_decoder_block_spec

global

null

false

config,use_transformer_engine

null

block_spec

def get_retro_decoder_block_spec( config: RetroConfig, use_transformer_engine: bool ) -> TransformerBlockSubmodules: """Retro decoder block spec. Retro decoder block implementation details: - The retro decoder block consists of interleaved GPT layers and customized Retro decoder layers. - The Retro decoder layers are spaced three layers apart, and start on layer 6 or 9 (depending on the total number of layers). - The first decoder layer instantiates an encoder block, and it therefore passes in an encoder_block_spec. Arguments: config (RetroConfig): Retro config. use_transformer_engine (bool): If True, use Transformer Engine (instead of local modules. """ # Num layers. assert ( parallel_state.get_pipeline_model_parallel_world_size() == 1 ), "retro does not currently support pipeline parallelism." assert ( parallel_state.get_virtual_pipeline_model_parallel_world_size() is None ), "retro does not currently support virtual pipeline parallelism." num_layers = get_num_layers_to_build(config) # Retro layer numbers. retro_layer_start = 6 if num_layers <= 15 else 9 retro_layer_numbers = list( range(retro_layer_start, num_layers + 1, 3) ) # Layer specs. gpt_layer_spec = ( get_gpt_layer_with_transformer_engine_spec() if use_transformer_engine else get_gpt_layer_local_spec() ) get_retro_decoder_layer_spec = ( get_retro_decoder_layer_te_spec if use_transformer_engine else get_retro_decoder_layer_local_spec ) retro_layer_spec = get_retro_decoder_layer_spec() retro_layer_spec_with_retriever = get_retro_decoder_layer_spec( get_retro_encoder_block_spec(config, use_transformer_engine) ) layer_specs = [] for layer_number in range(1, num_layers + 1): if layer_number == retro_layer_numbers[0]: layer_specs.append(retro_layer_spec_with_retriever) elif layer_number in retro_layer_numbers: layer_specs.append(retro_layer_spec) else: layer_specs.append(gpt_layer_spec) # Block spec. block_spec = TransformerBlockSubmodules(layer_specs=layer_specs) return block_spec

["def","get_retro_decoder_block_spec","(","config",":","RetroConfig",",","use_transformer_engine",":","bool",")","-",">","TransformerBlockSubmodules",":","``","''","''","Retro","decoder","block","spec",".","Retro","decoder","block","implementation","details",":","-","The","retro","decoder","block","consists","of","interleaved","GPT","layers","and","customized","Retro","decoder","layers",".","-","The","Retro","decoder","layers","are","spaced","three","layers","apart",",","and","start","on","layer","6","or","9","(","depending","on","the","total","number","of","layers",")",".","-","The","first","decoder","layer","instantiates","an","encoder","block",",","and","it","therefore","passes","in","an","encoder_block_spec",".","Arguments",":","config","(","RetroConfig",")",":","Retro","config",".","use_transformer_engine","(","bool",")",":","If","True",",","use","Transformer","Engine","(","instead","of","local","modules.","``","''","''","#","Num","layers",".","assert","(","parallel_state.get_pipeline_model_parallel_world_size","(",")","==","1",")",",","``","retro","does","not","currently","support","pipeline","parallelism",".","''","assert","(","parallel_state.get_virtual_pipeline_model_parallel_world_size","(",")","is","None",")",",","``","retro","does","not","currently","support","virtual","pipeline","parallelism",".","''","num_layers","=","get_num_layers_to_build","(","config",")","#","Retro","layer","numbers",".","retro_layer_start","=","6","if","num_layers","<","=","15","else","9","retro_layer_numbers","=","list","(","range","(","retro_layer_start",",","num_layers","+","1",",","3",")",")","#","Layer","specs",".","gpt_layer_spec","=","(","get_gpt_layer_with_transformer_engine_spec","(",")","if","use_transformer_engine","else","get_gpt_layer_local_spec","(",")",")","get_retro_decoder_layer_spec","=","(","get_retro_decoder_layer_te_spec","if","use_transformer_engine","else","get_retro_decoder_layer_local_spec",")","retro_layer_spec","=","get_retro_decoder_layer_spec","(",")","retro_layer_spec_with_retriever","=","get_retro_decoder_layer_spec","(","get_retro_encoder_block_spec","(","config",",","use_transformer_engine",")",")","layer_specs","=","[","]","for","layer_number","in","range","(","1",",","num_layers","+","1",")",":","if","layer_number","==","retro_layer_numbers","[","0","]",":","layer_specs.append","(","retro_layer_spec_with_retriever",")","elif","layer_number","in","retro_layer_numbers",":","layer_specs.append","(","retro_layer_spec",")","else",":","layer_specs.append","(","gpt_layer_spec",")","#","Block","spec",".","block_spec","=","TransformerBlockSubmodules","(","layer_specs=layer_specs",")","return","block_spec"]

89

152

null

decoder_spec.py

megatron-lm/megatron/core/models/retro/decoder_spec.py

from megatron.core import parallel_state from megatron.core.fusions.fused_layer_norm import FusedLayerNorm from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec, get_gpt_layer_with_transformer_engine_spec from megatron.core.models.retro.config import RetroConfig from megatron.core.models.retro.decoder_attention import RetroDecoderBiasDropoutAdd, RetroDecoderCrossAttention from megatron.core.models.retro.encoder_spec import get_retro_encoder_block_spec from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear from megatron.core.transformer import ModuleSpec from megatron.core.transformer.attention import CrossAttentionSubmodules from megatron.core.transformer.custom_layers.transformer_engine import TEColumnParallelLinear, TEDotProductAttention, TENorm, TERowParallelLinear from megatron.core.transformer.dot_product_attention import DotProductAttention from megatron.core.transformer.transformer_block import TransformerBlockSubmodules, get_num_layers_to_build

15

null

12

3

null

Use image node_id 3 for calling a global function with example usage: get_retro_decoder_block_spec(config, use_transformer_engine) and returns: block_spec

154

node_id 3

1,324,188

time_map_coordinates

NdimageInterpolation

Benchmark

true

self,shape,order,mode

null

def time_map_coordinates(self, shape, order, mode): coords = np.meshgrid( *[np.arange(0, s, 2) + 0.3 for s in self.x.shape] ) map_coordinates(self.x, coords, order=order, mode=mode)

["def","time_map_coordinates","(","self",",","shape",",","order",",","mode",")",":","coords","=","np.meshgrid","(","*","[","np.arange","(","0",",","s",",","2",")","+","0.3","for","s","in","self.x.shape","]",")","map_coordinates","(","self.x",",","coords",",","order=order",",","mode=mode",")"]

60

62

null

ndimage_interpolation.py

scipy/benchmarks/benchmarks/ndimage_interpolation.py

import numpy from .common import Benchmark

15

1

2

1

9

1

Use image node_id 7 for calling the NdimageInterpolation obj's underlying member method code with example usage: obj.time_map_coordinates(shape, order, mode) without return types

178

node_id 7

1,883,762

time_geometric_transform_mapping

NdimageInterpolation

Benchmark

true

self,shape,order,mode

null

def time_geometric_transform_mapping(self, shape, order, mode): if self.x.ndim == 2: mapping = shift_func_2d if self.x.ndim == 3: mapping = shift_func_3d geometric_transform(self.x, mapping, order=order, mode=mode)

["def","time_geometric_transform_mapping","(","self",",","shape",",","order",",","mode",")",":","if","self.x.ndim","==","2",":","mapping","=","shift_func_2d","if","self.x.ndim","==","3",":","mapping","=","shift_func_3d","geometric_transform","(","self.x",",","mapping",",","order=order",",","mode=mode",")"]

53

58

null

ndimage_interpolation.py

scipy/benchmarks/benchmarks/ndimage_interpolation.py

import numpy from .common import Benchmark

15

1

2

1

9

1

Use image node_id 6 for calling the NdimageInterpolation obj's underlying member method code with example usage: obj.time_geometric_transform_mapping(shape, order, mode) without return types

190

node_id 6

1,883,761

time_zoom

NdimageInterpolation

Benchmark

true

self,shape,order,mode

null

def time_zoom(self, shape, order, mode): zoom(self.x, (1.3,) * self.x.ndim, order=order, mode=mode)

["def","time_zoom","(","self",",","shape",",","order",",","mode",")",":","zoom","(","self.x",",","(","1.3",",",")","*","self.x.ndim",",","order=order",",","mode=mode",")"]

50

51

null

ndimage_interpolation.py

scipy/benchmarks/benchmarks/ndimage_interpolation.py

import numpy from .common import Benchmark

15

1

2

1

9

1

Use image node_id 5 for calling the NdimageInterpolation obj's underlying member method code with example usage: obj.time_zoom(shape, order, mode) without return types

167

node_id 5

1,883,760

time_shift

NdimageInterpolation

Benchmark

true

self,shape,order,mode

null

def time_shift(self, shape, order, mode): shift(self.x, (-2.5,) * self.x.ndim, order=order, mode=mode)

["def","time_shift","(","self",",","shape",",","order",",","mode",")",":","shift","(","self.x",",","(","-2.5",",",")","*","self.x.ndim",",","order=order",",","mode=mode",")"]

47

48

null

ndimage_interpolation.py

scipy/benchmarks/benchmarks/ndimage_interpolation.py

import numpy from .common import Benchmark

15

1

2

1

9

1

Use image node_id 4 for calling the NdimageInterpolation obj's underlying member method code with example usage: obj.time_shift(shape, order, mode) without return types

168

node_id 4

1,883,759

time_rotate

NdimageInterpolation

Benchmark

true

self,shape,order,mode

null

def time_rotate(self, shape, order, mode): rotate(self.x, 15, order=order, mode=mode)

["def","time_rotate","(","self",",","shape",",","order",",","mode",")",":","rotate","(","self.x",",","15",",","order=order",",","mode=mode",")"]

44

45

null

ndimage_interpolation.py

scipy/benchmarks/benchmarks/ndimage_interpolation.py

import numpy from .common import Benchmark

15

1

2

1

9

1

Use image node_id 3 for calling the NdimageInterpolation obj's underlying member method code with example usage: obj.time_rotate(shape, order, mode) without return types

169

node_id 3

1,883,758

resample_poly

global

null

false

x,up,down,axis,window,padtype,cval

null

y,x

def resample_poly( x, up, down, axis=0, window=("kaiser", 5.0), padtype="constant", cval=None, ): """ Resample `x` along the given axis using polyphase filtering. The signal `x` is upsampled by the factor `up`, a zero-phase low-pass FIR filter is applied, and then it is downsampled by the factor `down`. The resulting sample rate is ``up / down`` times the original sample rate. Values beyond the boundary of the signal are assumed to be zero during the filtering step. Parameters ---------- x : array_like The data to be resampled. up : int The upsampling factor. down : int The downsampling factor. axis : int, optional The axis of `x` that is resampled. Default is 0. window : string, tuple, or array_like, optional Desired window to use to design the low-pass filter, or the FIR filter coefficients to employ. See below for details. padtype : string, optional `constant`, `line`, `mean`, `median`, `maximum`, `minimum` or any of the other signal extension modes supported by `cupyx.scipy.signal.upfirdn`. Changes assumptions on values beyond the boundary. If `constant`, assumed to be `cval` (default zero). If `line` assumed to continue a linear trend defined by the first and last points. `mean`, `median`, `maximum` and `minimum` work as in `cupy.pad` and assume that the values beyond the boundary are the mean, median, maximum or minimum respectively of the array along the axis. cval : float, optional Value to use if `padtype='constant'`. Default is zero. Returns ------- resampled_x : array The resampled array. See Also -------- decimate : Downsample the signal after applying an FIR or IIR filter. resample : Resample up or down using the FFT method. Notes ----- This polyphase method will likely be faster than the Fourier method in `cusignal.resample` when the number of samples is large and prime, or when the number of samples is large and `up` and `down` share a large greatest common denominator. The length of the FIR filter used will depend on ``max(up, down) // gcd(up, down)``, and the number of operations during polyphase filtering will depend on the filter length and `down` (see `cusignal.upfirdn` for details). The argument `window` specifies the FIR low-pass filter design. If `window` is an array_like it is assumed to be the FIR filter coefficients. Note that the FIR filter is applied after the upsampling step, so it should be designed to operate on a signal at a sampling frequency higher than the original by a factor of `up//gcd(up, down)`. This function's output will be centered with respect to this array, so it is best to pass a symmetric filter with an odd number of samples if, as is usually the case, a zero-phase filter is desired. For any other type of `window`, the functions `cusignal.get_window` and `cusignal.firwin` are called to generate the appropriate filter coefficients. The first sample of the returned vector is the same as the first sample of the input vector. The spacing between samples is changed from ``dx`` to ``dx * down / float(up)``. Examples -------- Note that the end of the resampled data rises to meet the first sample of the next cycle for the FFT method, and gets closer to zero for the polyphase method: >>> import cupy >>> import cupyx.scipy.signal import resample, resample_poly >>> x = cupy.linspace(0, 10, 20, endpoint=False) >>> y = cupy.cos(-x**2/6.0) >>> f_fft = resample(y, 100) >>> f_poly = resample_poly(y, 100, 20) >>> xnew = cupy.linspace(0, 10, 100, endpoint=False) >>> import matplotlib.pyplot as plt >>> plt.plot(cupy.asnumpy(xnew), cupy.asnumpy(f_fft), 'b.-', \ cupy.asnumpy(xnew), cupy.asnumpy(f_poly), 'r.-') >>> plt.plot(cupy.asnumpy(x), cupy.asnumpy(y), 'ko-') >>> plt.plot(10, cupy.asnumpy(y[0]), 'bo', 10, 0., 'ro') # boundaries >>> plt.legend(['resample', 'resamp_poly', 'data'], loc='best') >>> plt.show() """ if padtype != "constant" or cval is not None: raise ValueError( "padtype and cval arguments are not supported by upfirdn" ) x = cupy.asarray(x) up = int(up) down = int(down) if up < 1 or down < 1: raise ValueError("up and down must be >= 1") # Determine our up and down factors # Use a rational approimation to save computation time on really long # signals g_ = gcd(up, down) up //= g_ down //= g_ if up == down == 1: return x.copy() n_out = x.shape[axis] * up n_out = n_out // down + bool(n_out % down) if isinstance(window, (list, cupy.ndarray)): window = cupy.asarray(window) if window.ndim > 1: raise ValueError("window must be 1-D") half_len = (window.size - 1) // 2 h = up * window else: half_len = 10 * max(up, down) h = up * _design_resample_poly(up, down, window) # Zero-pad our filter to put the output samples at the center n_pre_pad = down - half_len % down n_post_pad = 0 n_pre_remove = (half_len + n_pre_pad) // down # We should rarely need to do this given our filter lengths... while ( _output_len( len(h) + n_pre_pad + n_post_pad, x.shape[axis], up, down ) < n_out + n_pre_remove ): n_post_pad += 1 h = cupy.concatenate( ( cupy.zeros(n_pre_pad, h.dtype), h, cupy.zeros(n_post_pad, h.dtype), ) ) n_pre_remove_end = n_pre_remove + n_out # filter then remove excess y = upfirdn(h, x, up, down, axis) keep = [slice(None)] * x.ndim keep[axis] = slice(n_pre_remove, n_pre_remove_end) return y[tuple(keep)]

407

556

null

_resample.py

cupy/cupyx/scipy/signal/_resample.py

import operator from math import gcd import cupy from cupyx.scipy.fft import fft, rfft, fftfreq, ifft, irfft, ifftshift from cupyx.scipy.signal._iir_filter_design import cheby1 from cupyx.scipy.signal._fir_filter_design import firwin from cupyx.scipy.signal._iir_filter_conversions import zpk2sos from cupyx.scipy.signal._ltisys import dlti from cupyx.scipy.signal._upfirdn import upfirdn, _output_len from cupyx.scipy.signal._signaltools import sosfiltfilt, filtfilt, sosfilt, lfilter from cupyx.scipy.signal.windows._windows import get_window

15

null

11

4

null

Use image node_id 4 for calling a global function with example usage: resample_poly(x, up, down, axis, window, padtype, cval) and returns: y, x

143

node_id 4

692,584

global_gc

global

null

false

null

def global_gc(): """Trigger gc.collect() on all workers in the cluster.""" worker = ray._private.worker.global_worker worker.core_worker.global_gc()

["def","global_gc","(",")",":","``","''","''","Trigger","gc.collect","(",")","on","all","workers","in","the","cluster",".","''","''","''","worker","=","ray._private.worker.global_worker","worker.core_worker.global_gc","(",")"]

14

18

null

internal_api.py

ray/python/ray/_private/internal_api.py

import ray import ray._private.profiling import ray._private.services import ray._private.utils import ray._private.worker from ray._private import ray_constants from ray._private.state import GlobalState from ray._raylet import GcsClientOptions

15

null

8

7

null

Use image node_id 1 for calling a global function with example usage: global_gc() without return types

102

node_id 1

1,801,716

get_state_from_address

global

null

false

address

null

state

def get_state_from_address(address=None): address = services.canonicalize_bootstrap_address_or_die(address) state = GlobalState() options = GcsClientOptions.from_gcs_address(address) state._initialize_global_state(options) return state

["def","get_state_from_address","(","address=None",")",":","address","=","services.canonicalize_bootstrap_address_or_die","(","address",")","state","=","GlobalState","(",")","options","=","GcsClientOptions.from_gcs_address","(","address",")","state._initialize_global_state","(","options",")","return","state"]

21

27

null

internal_api.py

ray/python/ray/_private/internal_api.py

import ray import ray._private.profiling import ray._private.services import ray._private.utils import ray._private.worker from ray._private import ray_constants from ray._private.state import GlobalState from ray._raylet import GcsClientOptions

15

null

8

7

null

Use image node_id 2 for calling a global function with example usage: get_state_from_address(address) and returns: state

120

node_id 2

1,801,717

resample

global

null

false

x,num,t,axis,window,domain

null

y,y, new_t

def resample(x, num, t=None, axis=0, window=None, domain="time"): """ Resample `x` to `num` samples using Fourier method along the given axis. The resampled signal starts at the same value as `x` but is sampled with a spacing of ``len(x) / num * (spacing of x)``. Because a Fourier method is used, the signal is assumed to be periodic. Parameters ---------- x : array_like The data to be resampled. num : int The number of samples in the resampled signal. t : array_like, optional If `t` is given, it is assumed to be the sample positions associated with the signal data in `x`. axis : int, optional The axis of `x` that is resampled. Default is 0. window : array_like, callable, string, float, or tuple, optional Specifies the window applied to the signal in the Fourier domain. See below for details. domain : string, optional A string indicating the domain of the input `x`: ``time`` Consider the input `x` as time-domain. (Default) ``freq`` Consider the input `x` as frequency-domain. Returns ------- resampled_x or (resampled_x, resampled_t) Either the resampled array, or, if `t` was given, a tuple containing the resampled array and the corresponding resampled positions. See Also -------- decimate : Downsample the signal after applying an FIR or IIR filter. resample_poly : Resample using polyphase filtering and an FIR filter. Notes ----- The argument `window` controls a Fourier-domain window that tapers the Fourier spectrum before zero-padding to alleviate ringing in the resampled values for sampled signals you didn't intend to be interpreted as band-limited. If `window` is a function, then it is called with a vector of inputs indicating the frequency bins (i.e. fftfreq(x.shape[axis]) ). If `window` is an array of the same length as `x.shape[axis]` it is assumed to be the window to be applied directly in the Fourier domain (with dc and low-frequency first). For any other type of `window`, the function `cusignal.get_window` is called to generate the window. The first sample of the returned vector is the same as the first sample of the input vector. The spacing between samples is changed from ``dx`` to ``dx * len(x) / num``. If `t` is not None, then it represents the old sample positions, and the new sample positions will be returned as well as the new samples. As noted, `resample` uses FFT transformations, which can be very slow if the number of input or output samples is large and prime; see `scipy.fftpack.fft`. Examples -------- Note that the end of the resampled data rises to meet the first sample of the next cycle: >>> import cupy as cp >>> import cupyx.scipy.signal import resample >>> x = cupy.linspace(0, 10, 20, endpoint=False) >>> y = cupy.cos(-x**2/6.0) >>> f = resample(y, 100) >>> xnew = cupy.linspace(0, 10, 100, endpoint=False) >>> import matplotlib.pyplot as plt >>> plt.plot(cupy.asnumpy(x), cupy.asnumpy(y), 'go-', cupy.asnumpy(xnew), \ cupy.asnumpy(f), '.-', 10, cupy.asnumpy(y[0]), 'ro') >>> plt.legend(['data', 'resampled'], loc='best') >>> plt.show() """ if domain not in ("time", "freq"): raise ValueError( "Acceptable domain flags are 'time' or" " 'freq', not domain={}".format(domain) ) x = cupy.asarray(x) Nx = x.shape[axis] # Check if we can use faster real FFT real_input = cupy.isrealobj(x) if domain == "time": # Forward transform if real_input: X = rfft(x, axis=axis) else: # Full complex FFT X = fft(x, axis=axis) else: # domain == 'freq' X = x # Apply window to spectrum if window is not None: if callable(window): W = window(fftfreq(Nx)) elif isinstance(window, cupy.ndarray): if window.shape != (Nx,): raise ValueError( "window must have the same length as data" ) W = window else: W = ifftshift(get_window(window, Nx)) newshape_W = [1] * x.ndim newshape_W[axis] = X.shape[axis] if real_input: # Fold the window back on itself to mimic complex behavior W_real = W.copy() W_real[1:] += W_real[-1:0:-1] W_real[1:] *= 0.5 X *= W_real[: newshape_W[axis]].reshape(newshape_W) else: X *= W.reshape(newshape_W) # Copy each half of the original spectrum to the output spectrum, either # truncating high frequences (downsampling) or zero-padding them # (upsampling) # Placeholder array for output spectrum newshape = list(x.shape) if real_input: newshape[axis] = num // 2 + 1 else: newshape[axis] = num Y = cupy.zeros(newshape, X.dtype) # Copy positive frequency components (and Nyquist, if present) N = min(num, Nx) nyq = N // 2 + 1 # Slice index that includes Nyquist if present sl = [slice(None)] * x.ndim sl[axis] = slice(0, nyq) Y[tuple(sl)] = X[tuple(sl)] if not real_input: # Copy negative frequency components if ( N > 2 ): # (slice expression doesn't collapse to empty array) sl[axis] = slice(nyq - N, None) Y[tuple(sl)] = X[tuple(sl)] # Split/join Nyquist component(s) if present # So far we have set Y[+N/2]=X[+N/2] if N % 2 == 0: if num < Nx: # downsampling if real_input: sl[axis] = slice(N // 2, N // 2 + 1) Y[tuple(sl)] *= 2.0 else: # select the component of Y at frequency +N/2, # add the component of X at -N/2 sl[axis] = slice(-N // 2, -N // 2 + 1) Y[tuple(sl)] += X[tuple(sl)] elif Nx < num: # upsampling # select the component at frequency +N/2 and halve it sl[axis] = slice(N // 2, N // 2 + 1) Y[tuple(sl)] *= 0.5 if not real_input: temp = Y[tuple(sl)] # set the component at -N/2 equal to the component at +N/2 sl[axis] = slice(num - N // 2, num - N // 2 + 1) Y[tuple(sl)] = temp # Inverse transform if real_input: y = irfft(Y, num, axis=axis) else: y = ifft(Y, axis=axis, overwrite_x=True) y *= float(num) / float(Nx) if t is None: return y else: new_t = ( cupy.arange(0, num) * (t[1] - t[0]) * Nx / float(num) + t[0] ) return y, new_t

215

404

null

_resample.py

cupy/cupyx/scipy/signal/_resample.py

import operator from math import gcd import cupy from cupyx.scipy.fft import fft, rfft, fftfreq, ifft, irfft, ifftshift from cupyx.scipy.signal._iir_filter_design import cheby1 from cupyx.scipy.signal._fir_filter_design import firwin from cupyx.scipy.signal._iir_filter_conversions import zpk2sos from cupyx.scipy.signal._ltisys import dlti from cupyx.scipy.signal._upfirdn import upfirdn, _output_len from cupyx.scipy.signal._signaltools import sosfiltfilt, filtfilt, sosfilt, lfilter from cupyx.scipy.signal.windows._windows import get_window

15

null

11

4

null

Use image node_id 3 for calling a global function with example usage: resample(x, num, t, axis, window, domain) and returns: y, y, new_t

137

node_id 3

692,583

time_affine_transform

NdimageInterpolation

Benchmark

true

self,shape,order,mode

null

def time_affine_transform(self, shape, order, mode): if self.x.ndim == 2: matrix = self.matrix_2d else: matrix = self.matrix_3d affine_transform(self.x, matrix, order=order, mode=mode)

["def","time_affine_transform","(","self",",","shape",",","order",",","mode",")",":","if","self.x.ndim","==","2",":","matrix","=","self.matrix_2d","else",":","matrix","=","self.matrix_3d","affine_transform","(","self.x",",","matrix",",","order=order",",","mode=mode",")"]

37

42

null

ndimage_interpolation.py

scipy/benchmarks/benchmarks/ndimage_interpolation.py

import numpy from .common import Benchmark

15

1

2

1

9

1

Use image node_id 2 for calling the NdimageInterpolation obj's underlying member method code with example usage: obj.time_affine_transform(shape, order, mode) without return types

179

node_id 2

1,883,757

get_retro_encoder_layer_te_spec

global

null

false

null

spec

def get_retro_encoder_layer_te_spec() -> ModuleSpec: """Retro encoder TE spec (uses Transformer Engine components). A Retro encoder layer uses custom attention, bias-dropout-add, and layernorm operators to encode neighboring chunks that are retrieved from the chunk database. Each operator is responsible for iterating the retrieved chunks and processing them individually. """ spec = get_gpt_layer_with_transformer_engine_spec() spec.submodules.pre_cross_attn_layernorm = TENorm spec.submodules.cross_attention = ModuleSpec( module=RetroEncoderCrossAttention, params={ "attn_mask_type": AttnMaskType.padding, }, submodules=CrossAttentionSubmodules( linear_q=TEColumnParallelLinear, linear_kv=TEColumnParallelLinear, core_attention=TEDotProductAttention, linear_proj=TERowParallelLinear, ), ) spec.submodules.cross_attn_bda = ModuleSpec( module=RetroEncoderBiasDropoutAdd ) spec.submodules.pre_mlp_layernorm = ModuleSpec( module=RetroEncoderLayerNorm, submodules=TENorm, ) spec.submodules.mlp = ModuleSpec( module=MLP, submodules=MLPSubmodules( linear_fc1=TEColumnParallelLinear, linear_fc2=TERowParallelLinear, ), ) return spec

["def","get_retro_encoder_layer_te_spec","(",")","-",">","ModuleSpec",":","``","''","''","Retro","encoder","TE","spec","(","uses","Transformer","Engine","components",")",".","A","Retro","encoder","layer","uses","custom","attention",",","bias-dropout-add",",","and","layernorm","operators","to","encode","neighboring","chunks","that","are","retrieved","from","the","chunk","database",".","Each","operator","is","responsible","for","iterating","the","retrieved","chunks","and","processing","them","individually.","``","''","''","spec","=","get_gpt_layer_with_transformer_engine_spec","(",")","spec.submodules.pre_cross_attn_layernorm","=","TENorm","spec.submodules.cross_attention","=","ModuleSpec","(","module=RetroEncoderCrossAttention",",","params=","{","``","attn_mask_type","''",":","AttnMaskType.padding",",","}",",","submodules=CrossAttentionSubmodules","(","linear_q=TEColumnParallelLinear",",","linear_kv=TEColumnParallelLinear",",","core_attention=TEDotProductAttention",",","linear_proj=TERowParallelLinear",",",")",",",")","spec.submodules.cross_attn_bda","=","ModuleSpec","(","module=RetroEncoderBiasDropoutAdd",")","spec.submodules.pre_mlp_layernorm","=","ModuleSpec","(","module=RetroEncoderLayerNorm",",","submodules=TENorm",",",")","spec.submodules.mlp","=","ModuleSpec","(","module=MLP",",","submodules=MLPSubmodules","(","linear_fc1=TEColumnParallelLinear",",","linear_fc2=TERowParallelLinear",",",")",",",")","return","spec"]

29

57

null

encoder_spec.py

megatron-lm/megatron/core/models/retro/encoder_spec.py

from megatron.core.fusions.fused_layer_norm import FusedLayerNorm from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec, get_gpt_layer_with_transformer_engine_spec from megatron.core.models.retro.config import RetroConfig from megatron.core.models.retro.encoder_attention import RetroEncoderBiasDropoutAdd, RetroEncoderCrossAttention, RetroEncoderLayerNorm from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear from megatron.core.transformer import ModuleSpec from megatron.core.transformer.attention import CrossAttentionSubmodules from megatron.core.transformer.custom_layers.transformer_engine import TEColumnParallelLinear, TEDotProductAttention, TENorm, TERowParallelLinear from megatron.core.transformer.dot_product_attention import DotProductAttention from megatron.core.transformer.enums import AttnMaskType from megatron.core.transformer.mlp import MLP, MLPSubmodules from megatron.core.transformer.transformer_block import TransformerBlockSubmodules

15

null

12

3

null

Use image node_id 1 for calling a global function with example usage: get_retro_encoder_layer_te_spec() and returns: spec

121

node_id 1

1,324,195

get_retro_encoder_layer_local_spec

global

null

false

null

spec

def get_retro_encoder_layer_local_spec() -> ModuleSpec: """Retro encoder local spec (uses Megatron-Core components). A Retro encoder layer uses custom attention, bias-dropout-add, and layernorm operators to encode neighboring chunks that are retrieved from the chunk database. Each operator is responsible for iterating the retrieved chunks and processing them individually. """ spec = get_gpt_layer_local_spec() spec.submodules.pre_cross_attn_layernorm = FusedLayerNorm spec.submodules.cross_attention = ModuleSpec( module=RetroEncoderCrossAttention, params={ "attn_mask_type": AttnMaskType.padding, }, submodules=CrossAttentionSubmodules( linear_q=ColumnParallelLinear, linear_kv=ColumnParallelLinear, core_attention=DotProductAttention, linear_proj=RowParallelLinear, ), ) spec.submodules.cross_attn_bda = ModuleSpec( module=RetroEncoderBiasDropoutAdd ) spec.submodules.pre_mlp_layernorm = ModuleSpec( module=RetroEncoderLayerNorm, submodules=FusedLayerNorm, ) spec.submodules.mlp = ModuleSpec( module=MLP, submodules=MLPSubmodules( linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear, ), ) return spec

["def","get_retro_encoder_layer_local_spec","(",")","-",">","ModuleSpec",":","``","''","''","Retro","encoder","local","spec","(","uses","Megatron-Core","components",")",".","A","Retro","encoder","layer","uses","custom","attention",",","bias-dropout-add",",","and","layernorm","operators","to","encode","neighboring","chunks","that","are","retrieved","from","the","chunk","database",".","Each","operator","is","responsible","for","iterating","the","retrieved","chunks","and","processing","them","individually.","``","''","''","spec","=","get_gpt_layer_local_spec","(",")","spec.submodules.pre_cross_attn_layernorm","=","FusedLayerNorm","spec.submodules.cross_attention","=","ModuleSpec","(","module=RetroEncoderCrossAttention",",","params=","{","``","attn_mask_type","''",":","AttnMaskType.padding",",","}",",","submodules=CrossAttentionSubmodules","(","linear_q=ColumnParallelLinear",",","linear_kv=ColumnParallelLinear",",","core_attention=DotProductAttention",",","linear_proj=RowParallelLinear",",",")",",",")","spec.submodules.cross_attn_bda","=","ModuleSpec","(","module=RetroEncoderBiasDropoutAdd",")","spec.submodules.pre_mlp_layernorm","=","ModuleSpec","(","module=RetroEncoderLayerNorm",",","submodules=FusedLayerNorm",",",")","spec.submodules.mlp","=","ModuleSpec","(","module=MLP",",","submodules=MLPSubmodules","(","linear_fc1=ColumnParallelLinear",",","linear_fc2=RowParallelLinear",",",")",",",")","return","spec"]

60

88

null

encoder_spec.py

megatron-lm/megatron/core/models/retro/encoder_spec.py

from megatron.core.fusions.fused_layer_norm import FusedLayerNorm from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec, get_gpt_layer_with_transformer_engine_spec from megatron.core.models.retro.config import RetroConfig from megatron.core.models.retro.encoder_attention import RetroEncoderBiasDropoutAdd, RetroEncoderCrossAttention, RetroEncoderLayerNorm from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear from megatron.core.transformer import ModuleSpec from megatron.core.transformer.attention import CrossAttentionSubmodules from megatron.core.transformer.custom_layers.transformer_engine import TEColumnParallelLinear, TEDotProductAttention, TENorm, TERowParallelLinear from megatron.core.transformer.dot_product_attention import DotProductAttention from megatron.core.transformer.enums import AttnMaskType from megatron.core.transformer.mlp import MLP, MLPSubmodules from megatron.core.transformer.transformer_block import TransformerBlockSubmodules

15

null

12

3

null

Use image node_id 2 for calling a global function with example usage: get_retro_encoder_layer_local_spec() and returns: spec

124

node_id 2

1,324,196

get_retro_encoder_block_spec

global

null

false

config,use_transformer_engine

null

block_spec

def get_retro_encoder_block_spec( config: RetroConfig, use_transformer_engine: bool ) -> TransformerBlockSubmodules: """Retro encoder block spec. The retro encoder block consists of one customized Retro encoder layer (layer 1), and all of the following layers are standard GPT layers. Arguments: config (RetroConfig): Retro config. use_transformer_engine (bool): If True, use Transformer Engine (instead of local modules. """ # Num layers. num_layers = config.retro_encoder_num_layers retro_layer_numbers = [1] # Layer specs. gpt_layer_spec = ( get_gpt_layer_with_transformer_engine_spec() if use_transformer_engine else get_gpt_layer_local_spec() ) get_retro_encoder_layer_spec = ( get_retro_encoder_layer_te_spec if use_transformer_engine else get_retro_encoder_layer_local_spec ) retro_layer_spec = get_retro_encoder_layer_spec() for spec in (gpt_layer_spec, retro_layer_spec): spec.params[ "hidden_dropout" ] = config.retro_encoder_hidden_dropout spec.submodules.self_attention.params[ "attn_mask_type" ] = AttnMaskType.padding spec.submodules.self_attention.submodules.core_attention = ModuleSpec( module=TEDotProductAttention if use_transformer_engine else DotProductAttention, params={ "attention_dropout": config.retro_encoder_attention_dropout, }, ) layer_specs = [] for layer_number in range(1, num_layers + 1): if layer_number in retro_layer_numbers: layer_specs.append(retro_layer_spec) else: layer_specs.append(gpt_layer_spec) # Block spec. block_spec = TransformerBlockSubmodules(layer_specs=layer_specs) return block_spec

["def","get_retro_encoder_block_spec","(","config",":","RetroConfig",",","use_transformer_engine",":","bool",")","-",">","TransformerBlockSubmodules",":","``","''","''","Retro","encoder","block","spec",".","The","retro","encoder","block","consists","of","one","customized","Retro","encoder","layer","(","layer","1",")",",","and","all","of","the","following","layers","are","standard","GPT","layers",".","Arguments",":","config","(","RetroConfig",")",":","Retro","config",".","use_transformer_engine","(","bool",")",":","If","True",",","use","Transformer","Engine","(","instead","of","local","modules.","``","''","''","#","Num","layers",".","num_layers","=","config.retro_encoder_num_layers","retro_layer_numbers","=","[","1","]","#","Layer","specs",".","gpt_layer_spec","=","(","get_gpt_layer_with_transformer_engine_spec","(",")","if","use_transformer_engine","else","get_gpt_layer_local_spec","(",")",")","get_retro_encoder_layer_spec","=","(","get_retro_encoder_layer_te_spec","if","use_transformer_engine","else","get_retro_encoder_layer_local_spec",")","retro_layer_spec","=","get_retro_encoder_layer_spec","(",")","for","spec","in","(","gpt_layer_spec",",","retro_layer_spec",")",":","spec.params","[","``","hidden_dropout","''","]","=","config.retro_encoder_hidden_dropout","spec.submodules.self_attention.params","[","``","attn_mask_type","''","]","=","AttnMaskType.padding","spec.submodules.self_attention.submodules.core_attention","=","ModuleSpec","(","module=TEDotProductAttention","if","use_transformer_engine","else","DotProductAttention",",","params=","{","``","attention_dropout","''",":","config.retro_encoder_attention_dropout",",","}",",",")","layer_specs","=","[","]","for","layer_number","in","range","(","1",",","num_layers","+","1",")",":","if","layer_number","in","retro_layer_numbers",":","layer_specs.append","(","retro_layer_spec",")","else",":","layer_specs.append","(","gpt_layer_spec",")","#","Block","spec",".","block_spec","=","TransformerBlockSubmodules","(","layer_specs=layer_specs",")","return","block_spec"]

91

141

null

encoder_spec.py

megatron-lm/megatron/core/models/retro/encoder_spec.py

from megatron.core.fusions.fused_layer_norm import FusedLayerNorm from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec, get_gpt_layer_with_transformer_engine_spec from megatron.core.models.retro.config import RetroConfig from megatron.core.models.retro.encoder_attention import RetroEncoderBiasDropoutAdd, RetroEncoderCrossAttention, RetroEncoderLayerNorm from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear from megatron.core.transformer import ModuleSpec from megatron.core.transformer.attention import CrossAttentionSubmodules from megatron.core.transformer.custom_layers.transformer_engine import TEColumnParallelLinear, TEDotProductAttention, TENorm, TERowParallelLinear from megatron.core.transformer.dot_product_attention import DotProductAttention from megatron.core.transformer.enums import AttnMaskType from megatron.core.transformer.mlp import MLP, MLPSubmodules from megatron.core.transformer.transformer_block import TransformerBlockSubmodules

15

null

12

3

null

Use image node_id 3 for calling a global function with example usage: get_retro_encoder_block_spec(config, use_transformer_engine) and returns: block_spec

154

node_id 3

1,324,197

memory_summary

global

null

false

address,redis_password,group_by,sort_by,units,line_wrap,stats_only,num_entries

null

unknown,store_stats_summary

def memory_summary( address=None, redis_password=ray_constants.REDIS_DEFAULT_PASSWORD, group_by="NODE_ADDRESS", sort_by="OBJECT_SIZE", units="B", line_wrap=True, stats_only=False, num_entries=None, ): from ray.dashboard.memory_utils import memory_summary state = get_state_from_address(address) reply = get_memory_info_reply(state) if stats_only: return store_stats_summary(reply) return memory_summary( state, group_by, sort_by, line_wrap, units, num_entries ) + store_stats_summary(reply)

["def","memory_summary","(","address=None",",","redis_password=ray_constants.REDIS_DEFAULT_PASSWORD",",","group_by=","''","NODE_ADDRESS","''",",","sort_by=","''","OBJECT_SIZE","''",",","units=","''","B","''",",","line_wrap=True",",","stats_only=False",",","num_entries=None",",",")",":","from","ray.dashboard.memory_utils","import","memory_summary","state","=","get_state_from_address","(","address",")","reply","=","get_memory_info_reply","(","state",")","if","stats_only",":","return","store_stats_summary","(","reply",")","return","memory_summary","(","state",",","group_by",",","sort_by",",","line_wrap",",","units",",","num_entries",")","+","store_stats_summary","(","reply",")"]

30

49

null

internal_api.py

ray/python/ray/_private/internal_api.py

import ray import ray._private.profiling import ray._private.services import ray._private.utils import ray._private.worker from ray._private import ray_constants from ray._private.state import GlobalState from ray._raylet import GcsClientOptions

15

null

8

7

null

Use image node_id 3 for calling a global function with example usage: memory_summary(address, redis_password, group_by, sort_by, units, line_wrap, stats_only, num_entries) and returns: unknown, store_stats_summary

213

node_id 3

1,801,718

get_memory_info_reply

global

null

false

state,node_manager_address,node_manager_port

null

reply

def get_memory_info_reply( state, node_manager_address=None, node_manager_port=None ): """Returns global memory info.""" from ray.core.generated import ( node_manager_pb2, node_manager_pb2_grpc, ) # We can ask any Raylet for the global memory info, that Raylet internally # asks all nodes in the cluster for memory stats. if node_manager_address is None or node_manager_port is None: # We should ask for a raylet that is alive. raylet = None for node in state.node_table(): if node["Alive"]: raylet = node break assert raylet is not None, "Every raylet is dead" raylet_address = "{}:{}".format( raylet["NodeManagerAddress"], raylet["NodeManagerPort"] ) else: raylet_address = "{}:{}".format( node_manager_address, node_manager_port ) channel = utils.init_grpc_channel( raylet_address, options=[ ("grpc.max_send_message_length", MAX_MESSAGE_LENGTH), ("grpc.max_receive_message_length", MAX_MESSAGE_LENGTH), ], ) stub = node_manager_pb2_grpc.NodeManagerServiceStub(channel) reply = stub.FormatGlobalMemoryInfo( node_manager_pb2.FormatGlobalMemoryInfoRequest( include_memory_info=False ), timeout=60.0, ) return reply

["def","get_memory_info_reply","(","state",",","node_manager_address=None",",","node_manager_port=None",")",":","``","''","''","Returns","global","memory","info",".","''","''","''","from","ray.core.generated","import","(","node_manager_pb2",",","node_manager_pb2_grpc",",",")","#","We","can","ask","any","Raylet","for","the","global","memory","info",",","that","Raylet","internally","#","asks","all","nodes","in","the","cluster","for","memory","stats",".","if","node_manager_address","is","None","or","node_manager_port","is","None",":","#","We","should","ask","for","a","raylet","that","is","alive",".","raylet","=","None","for","node","in","state.node_table","(",")",":","if","node","[","``","Alive","''","]",":","raylet","=","node","break","assert","raylet","is","not","None",",","``","Every","raylet","is","dead","''","raylet_address","=","``","{","}",":","{","}","''",".format","(","raylet","[","``","NodeManagerAddress","''","]",",","raylet","[","``","NodeManagerPort","''","]",")","else",":","raylet_address","=","``","{","}",":","{","}","''",".format","(","node_manager_address",",","node_manager_port",")","channel","=","utils.init_grpc_channel","(","raylet_address",",","options=","[","(","``","grpc.max_send_message_length","''",",","MAX_MESSAGE_LENGTH",")",",","(","``","grpc.max_receive_message_length","''",",","MAX_MESSAGE_LENGTH",")",",","]",",",")","stub","=","node_manager_pb2_grpc.NodeManagerServiceStub","(","channel",")","reply","=","stub.FormatGlobalMemoryInfo","(","node_manager_pb2.FormatGlobalMemoryInfoRequest","(","include_memory_info=False",")",",","timeout=60.0",",",")","return","reply"]

52

86

null

internal_api.py

ray/python/ray/_private/internal_api.py

import ray import ray._private.profiling import ray._private.services import ray._private.utils import ray._private.worker from ray._private import ray_constants from ray._private.state import GlobalState from ray._raylet import GcsClientOptions

15

null

8

7

null

Use image node_id 4 for calling a global function with example usage: get_memory_info_reply(state, node_manager_address, node_manager_port) and returns: reply

158

node_id 4

1,801,719

node_stats

global

null

false

node_manager_address,node_manager_port,include_memory_info

null

node_stats

def node_stats( node_manager_address=None, node_manager_port=None, include_memory_info=True, ): """Returns NodeStats object describing memory usage in the cluster.""" from ray.core.generated import ( node_manager_pb2, node_manager_pb2_grpc, ) # We can ask any Raylet for the global memory info. assert ( node_manager_address is not None and node_manager_port is not None ) raylet_address = "{}:{}".format( node_manager_address, node_manager_port ) channel = utils.init_grpc_channel( raylet_address, options=[ ("grpc.max_send_message_length", MAX_MESSAGE_LENGTH), ("grpc.max_receive_message_length", MAX_MESSAGE_LENGTH), ], ) stub = node_manager_pb2_grpc.NodeManagerServiceStub(channel) node_stats = stub.GetNodeStats( node_manager_pb2.GetNodeStatsRequest( include_memory_info=include_memory_info ), timeout=30.0, ) return node_stats

["def","node_stats","(","node_manager_address=None",",","node_manager_port=None",",","include_memory_info=True",",",")",":","``","''","''","Returns","NodeStats","object","describing","memory","usage","in","the","cluster",".","''","''","''","from","ray.core.generated","import","(","node_manager_pb2",",","node_manager_pb2_grpc",",",")","#","We","can","ask","any","Raylet","for","the","global","memory","info",".","assert","(","node_manager_address","is","not","None","and","node_manager_port","is","not","None",")","raylet_address","=","``","{","}",":","{","}","''",".format","(","node_manager_address",",","node_manager_port",")","channel","=","utils.init_grpc_channel","(","raylet_address",",","options=","[","(","``","grpc.max_send_message_length","''",",","MAX_MESSAGE_LENGTH",")",",","(","``","grpc.max_receive_message_length","''",",","MAX_MESSAGE_LENGTH",")",",","]",",",")","stub","=","node_manager_pb2_grpc.NodeManagerServiceStub","(","channel",")","node_stats","=","stub.GetNodeStats","(","node_manager_pb2.GetNodeStatsRequest","(","include_memory_info=include_memory_info",")",",","timeout=30.0",",",")","return","node_stats"]

89

112

null

internal_api.py

ray/python/ray/_private/internal_api.py

import ray import ray._private.profiling import ray._private.services import ray._private.utils import ray._private.worker from ray._private import ray_constants from ray._private.state import GlobalState from ray._raylet import GcsClientOptions

15

null

8

7

null

Use image node_id 5 for calling a global function with example usage: node_stats(node_manager_address, node_manager_port, include_memory_info) and returns: node_stats

166

node_id 5

1,801,720

store_stats_summary

global

null

false

reply

null

store_summary

def store_stats_summary(reply): """Returns formatted string describing object store stats in all nodes.""" store_summary = ( "--- Aggregate object store stats across all nodes ---\n" ) # TODO(ekl) it would be nice if we could provide a full memory usage # breakdown by type (e.g., pinned by worker, primary, etc.) store_summary += ( "Plasma memory usage {} MiB, {} objects, {}% full, {}% " "needed\n".format( int( reply.store_stats.object_store_bytes_used / (1024 * 1024) ), reply.store_stats.num_local_objects, round( 100 * reply.store_stats.object_store_bytes_used / reply.store_stats.object_store_bytes_avail, 2, ), round( 100 * reply.store_stats.object_store_bytes_primary_copy / reply.store_stats.object_store_bytes_avail, 2, ), ) ) if reply.store_stats.object_store_bytes_fallback > 0: store_summary += ( "Plasma filesystem mmap usage: {} MiB\n".format( int( reply.store_stats.object_store_bytes_fallback / (1024 * 1024) ) ) ) if reply.store_stats.spill_time_total_s > 0: store_summary += "Spilled {} MiB, {} objects, avg write throughput {} MiB/s\n".format( int( reply.store_stats.spilled_bytes_total / (1024 * 1024) ), reply.store_stats.spilled_objects_total, int( reply.store_stats.spilled_bytes_total / (1024 * 1024) / reply.store_stats.spill_time_total_s ), ) if reply.store_stats.restore_time_total_s > 0: store_summary += "Restored {} MiB, {} objects, avg read throughput {} MiB/s\n".format( int( reply.store_stats.restored_bytes_total / (1024 * 1024) ), reply.store_stats.restored_objects_total, int( reply.store_stats.restored_bytes_total / (1024 * 1024) / reply.store_stats.restore_time_total_s ), ) if reply.store_stats.consumed_bytes > 0: store_summary += ( "Objects consumed by Ray tasks: {} MiB.\n".format( int(reply.store_stats.consumed_bytes / (1024 * 1024)) ) ) if reply.store_stats.object_pulls_queued: store_summary += ( "Object fetches queued, waiting for available memory." ) return store_summary

["def","store_stats_summary","(","reply",")",":","``","''","''","Returns","formatted","string","describing","object","store","stats","in","all","nodes",".","''","''","''","store_summary","=","(","``","--","-","Aggregate","object","store","stats","across","all","nodes","--","-\\n","''",")","#","TODO","(","ekl",")","it","would","be","nice","if","we","could","provide","a","full","memory","usage","#","breakdown","by","type","(","e.g.",",","pinned","by","worker",",","primary",",","etc",".",")","store_summary","+=","(","``","Plasma","memory","usage","{","}","MiB",",","{","}","objects",",","{","}","%","full",",","{","}","%","``","``","needed\\n","''",".format","(","int","(","reply.store_stats.object_store_bytes_used","\/","(","1024","*","1024",")",")",",","reply.store_stats.num_local_objects",",","round","(","100","*","reply.store_stats.object_store_bytes_used","\/","reply.store_stats.object_store_bytes_avail",",","2",",",")",",","round","(","100","*","reply.store_stats.object_store_bytes_primary_copy","\/","reply.store_stats.object_store_bytes_avail",",","2",",",")",",",")",")","if","reply.store_stats.object_store_bytes_fallback",">","0",":","store_summary","+=","(","``","Plasma","filesystem","mmap","usage",":","{","}","MiB\\n","''",".format","(","int","(","reply.store_stats.object_store_bytes_fallback","\/","(","1024","*","1024",")",")",")",")","if","reply.store_stats.spill_time_total_s",">","0",":","store_summary","+=","``","Spilled","{","}","MiB",",","{","}","objects",",","avg","write","throughput","{","}","MiB\/s\\n","''",".format","(","int","(","reply.store_stats.spilled_bytes_total","\/","(","1024","*","1024",")",")",",","reply.store_stats.spilled_objects_total",",","int","(","reply.store_stats.spilled_bytes_total","\/","(","1024","*","1024",")","\/","reply.store_stats.spill_time_total_s",")",",",")","if","reply.store_stats.restore_time_total_s",">","0",":","store_summary","+=","``","Restored","{","}","MiB",",","{","}","objects",",","avg","read","throughput","{","}","MiB\/s\\n","''",".format","(","int","(","reply.store_stats.restored_bytes_total","\/","(","1024","*","1024",")",")",",","reply.store_stats.restored_objects_total",",","int","(","reply.store_stats.restored_bytes_total","\/","(","1024","*","1024",")","\/","reply.store_stats.restore_time_total_s",")",",",")","if","reply.store_stats.consumed_bytes",">","0",":","store_summary","+=","(","``","Objects","consumed","by","Ray","tasks",":","{","}","MiB.\\n","''",".format","(","int","(","reply.store_stats.consumed_bytes","\/","(","1024","*","1024",")",")",")",")","if","reply.store_stats.object_pulls_queued",":","store_summary","+=","(","``","Object","fetches","queued",",","waiting","for","available","memory",".","''",")","return","store_summary"]

115

174

null

internal_api.py

ray/python/ray/_private/internal_api.py

import ray import ray._private.profiling import ray._private.services import ray._private.utils import ray._private.worker from ray._private import ray_constants from ray._private.state import GlobalState from ray._raylet import GcsClientOptions

15

null

8

7

null

Use image node_id 6 for calling a global function with example usage: store_stats_summary(reply) and returns: store_summary

123

node_id 6

1,801,721

free

global

null

false

object_refs,local_only

null

def free(object_refs: list, local_only: bool = False): """Free a list of IDs from the in-process and plasma object stores. This function is a low-level API which should be used in restricted scenarios. If local_only is false, the request will be send to all object stores. This method will not return any value to indicate whether the deletion is successful or not. This function is an instruction to the object store. If some of the objects are in use, the object stores will delete them later when the ref count is down to 0. Examples: .. testcode:: import ray @ray.remote def f(): return 0 obj_ref = f.remote() ray.get(obj_ref) # wait for object to be created first free([obj_ref]) # unpin & delete object globally Args: object_refs (List[ObjectRef]): List of object refs to delete. local_only: Whether only deleting the list of objects in local object store or all object stores. """ worker = ray._private.worker.global_worker if isinstance(object_refs, ray.ObjectRef): object_refs = [object_refs] if not isinstance(object_refs, list): raise TypeError( "free() expects a list of ObjectRef, got {}".format( type(object_refs) ) ) # Make sure that the values are object refs. for object_ref in object_refs: if not isinstance(object_ref, ray.ObjectRef): raise TypeError( "Attempting to call `free` on the value {}, " "which is not an ray.ObjectRef.".format(object_ref) ) worker.check_connected() with profiling.profile("ray.free"): if len(object_refs) == 0: return worker.core_worker.free_objects(object_refs, local_only)

["def","free","(","object_refs",":","list",",","local_only",":","bool","=","False",")",":","``","''","''","Free","a","list","of","IDs","from","the","in-process","and","plasma","object","stores",".","This","function","is","a","low-level","API","which","should","be","used","in","restricted","scenarios",".","If","local_only","is","false",",","the","request","will","be","send","to","all","object","stores",".","This","method","will","not","return","any","value","to","indicate","whether","the","deletion","is","successful","or","not",".","This","function","is","an","instruction","to","the","object","store",".","If","some","of","the","objects","are","in","use",",","the","object","stores","will","delete","them","later","when","the","ref","count","is","down","to","0",".","Examples",":","..","testcode",":",":","import","ray","@","ray.remote","def","f","(",")",":","return","0","obj_ref","=","f.remote","(",")","ray.get","(","obj_ref",")","#","wait","for","object","to","be","created","first","free","(","[","obj_ref","]",")","#","unpin","&","delete","object","globally","Args",":","object_refs","(","List","[","ObjectRef","]",")",":","List","of","object","refs","to","delete",".","local_only",":","Whether","only","deleting","the","list","of","objects","in","local","object","store","or","all","object","stores.","``","''","''","worker","=","ray._private.worker.global_worker","if","isinstance","(","object_refs",",","ray.ObjectRef",")",":","object_refs","=","[","object_refs","]","if","not","isinstance","(","object_refs",",","list",")",":","raise","TypeError","(","``","free","(",")","expects","a","list","of","ObjectRef",",","got","{","}","''",".format","(","type","(","object_refs",")",")",")","#","Make","sure","that","the","values","are","object","refs",".","for","object_ref","in","object_refs",":","if","not","isinstance","(","object_ref",",","ray.ObjectRef",")",":","raise","TypeError","(","``","Attempting","to","call","`","free","`","on","the","value","{","}",",","``","``","which","is","not","an","ray.ObjectRef",".","``",".format","(","object_ref",")",")","worker.check_connected","(",")","with","profiling.profile","(","``","ray.free","''",")",":","if","len","(","object_refs",")","==","0",":","return","worker.core_worker.free_objects","(","object_refs",",","local_only",")"]

177

232

null

internal_api.py

ray/python/ray/_private/internal_api.py

import ray import ray._private.profiling import ray._private.services import ray._private.utils import ray._private.worker from ray._private import ray_constants from ray._private.state import GlobalState from ray._raylet import GcsClientOptions

15

null

8

7

null

Use image node_id 7 for calling a global function with example usage: free(object_refs, local_only) without return types

120

node_id 7

1,801,722

drop_block_fast_2d

global

null

false

x,drop_prob,block_size,gamma_scale,with_noise,inplace

null

x

def drop_block_fast_2d( x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, ): """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid block mask at edges. """ B, C, H, W = x.shape total_size = W * H clipped_block_size = min(block_size, min(W, H)) gamma = ( gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((W - block_size + 1) * (H - block_size + 1)) ) block_mask = torch.empty_like(x).bernoulli_(gamma) block_mask = F.max_pool2d( block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2, ) if with_noise: normal_noise = torch.empty_like(x).normal_() if inplace: x.mul_(1.0 - block_mask).add_(normal_noise * block_mask) else: x = x * (1.0 - block_mask) + normal_noise * block_mask else: block_mask = 1 - block_mask normalize_scale = ( block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6) ).to(dtype=x.dtype) if inplace: x.mul_(block_mask * normalize_scale) else: x = x * block_mask * normalize_scale return x

["def","drop_block_fast_2d","(","x",":","torch.Tensor",",","drop_prob",":","float","=","0.1",",","block_size",":","int","=","7",",","gamma_scale",":","float","=","1.0",",","with_noise",":","bool","=","False",",","inplace",":","bool","=","False",",",")",":","``","''","''","DropBlock",".","See","https",":","\/\/arxiv.org\/pdf\/1810.12890.pdf","DropBlock","with","an","experimental","gaussian","noise","option",".","Simplied","from","above","without","concern","for","valid","block","mask","at","edges.","``","''","''","B",",","C",",","H",",","W","=","x.shape","total_size","=","W","*","H","clipped_block_size","=","min","(","block_size",",","min","(","W",",","H",")",")","gamma","=","(","gamma_scale","*","drop_prob","*","total_size","\/","clipped_block_size","*","*","2","\/","(","(","W","-","block_size","+","1",")","*","(","H","-","block_size","+","1",")",")",")","block_mask","=","torch.empty_like","(","x",")",".bernoulli_","(","gamma",")","block_mask","=","F.max_pool2d","(","block_mask.to","(","x.dtype",")",",","kernel_size=clipped_block_size",",","stride=1",",","padding=clipped_block_size","\/\/","2",",",")","if","with_noise",":","normal_noise","=","torch.empty_like","(","x",")",".normal_","(",")","if","inplace",":","x.mul_","(","1.0","-","block_mask",")",".add_","(","normal_noise","*","block_mask",")","else",":","x","=","x","*","(","1.0","-","block_mask",")","+","normal_noise","*","block_mask","else",":","block_mask","=","1","-","block_mask","normalize_scale","=","(","block_mask.numel","(",")","\/","block_mask.to","(","dtype=torch.float32",")",".sum","(",")",".add","(","1e-6",")",")",".to","(","dtype=x.dtype",")","if","inplace",":","x.mul_","(","block_mask","*","normalize_scale",")","else",":","x","=","x","*","block_mask","*","normalize_scale","return","x"]

70

101

null

drop.py

pytorch-image-models/timm/layers/drop.py

import torch import torch.nn import torch.nn.functional

15

null

3

null

Use image node_id 2 for calling a global function with example usage: drop_block_fast_2d(x, drop_prob, block_size, gamma_scale, with_noise, inplace) and returns: x

163

node_id 2

1,692,292

extended_trait_info

global

null

false

trait

null

trait,dict_info,str,str

def extended_trait_info(trait): if isinstance(trait, Dict): return dict_info(trait) elif isinstance(trait, Container): if trait._trait is None: return "{} of any type".format(trait.info()) return "{} with values that are: {}".format( trait.info(), trait._trait.info() ) return trait.info()

["def","extended_trait_info","(","trait",")",":","if","isinstance","(","trait",",","Dict",")",":","return","dict_info","(","trait",")","elif","isinstance","(","trait",",","Container",")",":","if","trait._trait","is","None",":","return","``","{","}","of","any","type","''",".format","(","trait.info","(",")",")","return","``","{","}","with","values","that","are",":","{","}","''",".format","(","trait.info","(",")",",","trait._trait.info","(",")",")","return","trait.info","(",")"]

32

39

null

autodoc_traits.py

pythreejs/docs/sphinxext/autodoc_traits.py

from collections import OrderedDict from traitlets import TraitType, Undefined, Container, Dict, Any, HasTraits from sphinx.ext.autodoc import ClassDocumenter, AttributeDocumenter

15

null

3

null

Use image node_id 2 for calling a global function with example usage: extended_trait_info(trait) and returns: trait, dict_info, str, str

136

node_id 2

1,691,046

decimate

global

null

false

x,q,n,ftype,axis,zero_phase

null

y

def decimate(x, q, n=None, ftype="iir", axis=-1, zero_phase=True): """ Downsample the signal after applying an anti-aliasing filter. By default, an order 8 Chebyshev type I filter is used. A 30 point FIR filter with Hamming window is used if `ftype` is 'fir'. Parameters ---------- x : array_like The signal to be downsampled, as an N-dimensional array. q : int The downsampling factor. When using IIR downsampling, it is recommended to call `decimate` multiple times for downsampling factors higher than 13. n : int, optional The order of the filter (1 less than the length for 'fir'). Defaults to 8 for 'iir' and 20 times the downsampling factor for 'fir'. ftype : str {'iir', 'fir'} or ``dlti`` instance, optional If 'iir' or 'fir', specifies the type of lowpass filter. If an instance of an `dlti` object, uses that object to filter before downsampling. axis : int, optional The axis along which to decimate. zero_phase : bool, optional Prevent phase shift by filtering with `filtfilt` instead of `lfilter` when using an IIR filter, and shifting the outputs back by the filter's group delay when using an FIR filter. The default value of ``True`` is recommended, since a phase shift is generally not desired. Returns ------- y : ndarray The down-sampled signal. See Also -------- resample : Resample up or down using the FFT method. resample_poly : Resample using polyphase filtering and an FIR filter. """ x = cupy.asarray(x) q = operator.index(q) if n is not None: n = operator.index(n) result_type = x.dtype if ( not cupy.issubdtype(result_type, cupy.inexact) or result_type.type == cupy.float16 ): # upcast integers and float16 to float64 result_type = cupy.float64 if ftype == "fir": if n is None: half_len = ( 10 * q ) # reasonable cutoff for our sinc-like function n = 2 * half_len b, a = firwin(n + 1, 1.0 / q, window="hamming"), 1.0 b = cupy.asarray(b, dtype=result_type) a = cupy.asarray(a, dtype=result_type) elif ftype == "iir": iir_use_sos = True if n is None: n = 8 sos = cheby1(n, 0.05, 0.8 / q, output="sos") sos = cupy.asarray(sos, dtype=result_type) elif isinstance(ftype, dlti): system = ftype._as_zpk() if system.poles.shape[0] == 0: # FIR system = ftype._as_tf() b, a = system.num, system.den ftype = "fir" elif ( any(cupy.iscomplex(system.poles)) or any(cupy.iscomplex(system.poles)) or cupy.iscomplex(system.gain) ): # sosfilt & sosfiltfilt don't handle complex coeffs iir_use_sos = False system = ftype._as_tf() b, a = system.num, system.den else: iir_use_sos = True sos = zpk2sos(system.zeros, system.poles, system.gain) sos = cupy.asarray(sos, dtype=result_type) else: raise ValueError("invalid ftype") sl = [slice(None)] * x.ndim if ftype == "fir": b = b / a if zero_phase: y = resample_poly(x, 1, q, axis=axis, window=b) else: # upfirdn is generally faster than lfilter by a factor equal to the # downsampling factor, since it only calculates the needed outputs n_out = x.shape[axis] // q + bool(x.shape[axis] % q) y = upfirdn(b, x, up=1, down=q, axis=axis) sl[axis] = slice(None, n_out, None) else: # IIR case if zero_phase: if iir_use_sos: y = sosfiltfilt(sos, x, axis=axis) else: y = filtfilt(b, a, x, axis=axis) else: if iir_use_sos: y = sosfilt(sos, x, axis=axis) else: y = lfilter(b, a, x, axis=axis) sl[axis] = slice(None, None, q) return y[tuple(sl)]

99

212

null

_resample.py

cupy/cupyx/scipy/signal/_resample.py

import operator from math import gcd import cupy from cupyx.scipy.fft import fft, rfft, fftfreq, ifft, irfft, ifftshift from cupyx.scipy.signal._iir_filter_design import cheby1 from cupyx.scipy.signal._fir_filter_design import firwin from cupyx.scipy.signal._iir_filter_conversions import zpk2sos from cupyx.scipy.signal._ltisys import dlti from cupyx.scipy.signal._upfirdn import upfirdn, _output_len from cupyx.scipy.signal._signaltools import sosfiltfilt, filtfilt, sosfilt, lfilter from cupyx.scipy.signal.windows._windows import get_window

15

null

11

4

null

Use image node_id 2 for calling a global function with example usage: decimate(x, q, n, ftype, axis, zero_phase) and returns: y

127

node_id 2

692,582

dict_info

global

null

false

trait

null

str

def dict_info(trait): try: trait_base = trait._value_trait except AttributeError: trait_base = trait._trait try: traits = trait._per_key_traits except AttributeError: traits = trait._traits if traits is None and ( trait_base is None or isinstance(trait_base, Any) ): value_string = "elements of any type" else: parts = [] if traits: parts.append( "the following types: %r" % {k: v.info() for k, v in traits} ) if trait_base: parts.append("values that are: %s" % trait_base.info()) value_string = "elements with " + ", and ".join(parts) return "{} with {}".format(trait.info(), value_string)

["def","dict_info","(","trait",")",":","try",":","trait_base","=","trait._value_trait","except","AttributeError",":","trait_base","=","trait._trait","try",":","traits","=","trait._per_key_traits","except","AttributeError",":","traits","=","trait._traits","if","traits","is","None","and","(","trait_base","is","None","or","isinstance","(","trait_base",",","Any",")",")",":","value_string","=","``","elements","of","any","type","''","else",":","parts","=","[","]","if","traits",":","parts.append","(","``","the","following","types",":","%","r","''","%","{","k",":","v.info","(",")","for","k",",","v","in","traits","}",")","if","trait_base",":","parts.append","(","``","values","that","are",":","%","s","''","%","trait_base.info","(",")",")","value_string","=","``","elements","with","``","+","``",",","and","``",".join","(","parts",")","return","``","{","}","with","{","}","''",".format","(","trait.info","(",")",",","value_string",")"]

9

29

null

autodoc_traits.py

pythreejs/docs/sphinxext/autodoc_traits.py

from collections import OrderedDict from traitlets import TraitType, Undefined, Container, Dict, Any, HasTraits from sphinx.ext.autodoc import ClassDocumenter, AttributeDocumenter

15

null

3

null

Use image node_id 1 for calling a global function with example usage: dict_info(trait) and returns: str

103

node_id 1

1,691,045

format_name

TraitDocumenter

AttributeDocumenter

true

self

null

self

def format_name(self): return self.objpath[-1]

["def","format_name","(","self",")",":","return","self.objpath","[","-1","]"]

83

84

null

autodoc_traits.py

pythreejs/docs/sphinxext/autodoc_traits.py

from collections import OrderedDict from traitlets import TraitType, Undefined, Container, Dict, Any, HasTraits from sphinx.ext.autodoc import ClassDocumenter, AttributeDocumenter

15

2

3

2

3

1

Use image node_id 2 for calling the TraitDocumenter obj's underlying member method code with example usage: obj.format_name() and returns: self

143

node_id 2

1,691,043

_fetch_lfw_people

global

null

false

data_folder_path,slice_,color,resize,min_faces_per_person

null

faces, target, target_names

def _fetch_lfw_people( data_folder_path, slice_=None, color=False, resize=None, min_faces_per_person=0, ): """Perform the actual data loading for the lfw people dataset This operation is meant to be cached by a joblib wrapper. """ # scan the data folder content to retain people with more that # `min_faces_per_person` face pictures person_names, file_paths = [], [] for person_name in sorted(listdir(data_folder_path)): folder_path = join(data_folder_path, person_name) if not isdir(folder_path): continue paths = [ join(folder_path, f) for f in sorted(listdir(folder_path)) ] n_pictures = len(paths) if n_pictures >= min_faces_per_person: person_name = person_name.replace("_", " ") person_names.extend([person_name] * n_pictures) file_paths.extend(paths) n_faces = len(file_paths) if n_faces == 0: raise ValueError( "min_faces_per_person=%d is too restrictive" % min_faces_per_person ) target_names = np.unique(person_names) target = np.searchsorted(target_names, person_names) faces = _load_imgs(file_paths, slice_, color, resize) # shuffle the faces with a deterministic RNG scheme to avoid having # all faces of the same person in a row, as it would break some # cross validation and learning algorithms such as SGD and online # k-means that make an IID assumption indices = np.arange(n_faces) np.random.RandomState(42).shuffle(indices) faces, target = faces[indices], target[indices] return faces, target, target_names

["def","_fetch_lfw_people","(","data_folder_path",",","slice_=None",",","color=False",",","resize=None",",","min_faces_per_person=0",",",")",":","``","''","''","Perform","the","actual","data","loading","for","the","lfw","people","dataset","This","operation","is","meant","to","be","cached","by","a","joblib","wrapper.","``","''","''","#","scan","the","data","folder","content","to","retain","people","with","more","that","#","`","min_faces_per_person","`","face","pictures","person_names",",","file_paths","=","[","]",",","[","]","for","person_name","in","sorted","(","listdir","(","data_folder_path",")",")",":","folder_path","=","join","(","data_folder_path",",","person_name",")","if","not","isdir","(","folder_path",")",":","continue","paths","=","[","join","(","folder_path",",","f",")","for","f","in","sorted","(","listdir","(","folder_path",")",")","]","n_pictures","=","len","(","paths",")","if","n_pictures",">","=","min_faces_per_person",":","person_name","=","person_name.replace","(","``","_","''",",","``","``",")","person_names.extend","(","[","person_name","]","*","n_pictures",")","file_paths.extend","(","paths",")","n_faces","=","len","(","file_paths",")","if","n_faces","==","0",":","raise","ValueError","(","``","min_faces_per_person=","%","d","is","too","restrictive","''","%","min_faces_per_person",")","target_names","=","np.unique","(","person_names",")","target","=","np.searchsorted","(","target_names",",","person_names",")","faces","=","_load_imgs","(","file_paths",",","slice_",",","color",",","resize",")","#","shuffle","the","faces","with","a","deterministic","RNG","scheme","to","avoid","having","#","all","faces","of","the","same","person","in","a","row",",","as","it","would","break","some","#","cross","validation","and","learning","algorithms","such","as","SGD","and","online","#","k-means","that","make","an","IID","assumption","indices","=","np.arange","(","n_faces",")","np.random.RandomState","(","42",")",".shuffle","(","indices",")","faces",",","target","=","faces","[","indices","]",",","target","[","indices","]","return","faces",",","target",",","target_names"]

192

232

null

_lfw.py

catboost/contrib/python/scikit-learn/py3/sklearn/datasets/_lfw.py

import logging from numbers import Integral, Real from os import PathLike, listdir, makedirs, remove from os.path import exists, isdir, join import numpy from joblib import Memory from ..utils import Bunch from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params from ._base import RemoteFileMetadata, _fetch_remote, get_data_home, load_descr

15

null

9

6

null

Use image node_id 3 for calling a global function with example usage: _fetch_lfw_people(data_folder_path, slice_, color, resize, min_faces_per_person) and returns: faces, target, target_names

193

node_id 3

520,101

__init__

AudioToSlowFastFusionBuilder

null

true

self,slowfast_channel_reduction_ratio,slowfast_audio_reduction_ratio,conv_fusion_channel_ratio,conv_kernel_size,conv_kernel_size_a,conv_stride,conv_stride_a,conv_fusion_channel_interm_dim,conv_num_a,norm,norm_eps,norm_momentum,activation,max_stage_idx

null

Given a list of two tensors from Slow pathway and Fast pathway, fusion information from the Fast pathway to the Slow on through a convolution followed by a concatenation, then return the fused list of tensors from Slow and Fast pathway in order. Args: slowfast_channel_reduction_ratio (int): Reduction ratio from the stage dimension. Used to compute conv_dim_in = fusion_dim_in // slowfast_channel_reduction_ratio slowfast_audio_reduction_ratio (int): Audio Reduction ratio from the stage dimension. Used to compute conv_dim_in_a = fusion_dim_in // slowfast_audio_reduction_ratio conv_fusion_channel_ratio (int): channel ratio for the convolution used to fuse from Fast pathway to Slow pathway. conv_kernel_size (int): kernel size of the convolution used to fuse from Fast pathway to Slow pathway. conv_kernel_size_a (int): kernel size of the convolution used to fuse from Audio pathway to FastSlow pathway. conv_stride (int): stride size of the convolution used to fuse from Fast pathway to Slow pathway. Optionally indexed by stage. conv_stride_a (int): stride size of the convolution used to fuse from Audio pathway to FastSlow pathway. Optionally indexed by stage. conv_fusion_channel_interm_dim (Union[int, float]): When conv_num_a > 1 this value controls the dimensions of the intermediate conv conv_num_a (int): Number of intermediate conv for audio channel norm (callable): a callable that constructs normalization layer, examples include nn.BatchNorm3d, None (not performing normalization). norm_eps (float): normalization epsilon. norm_momentum (float): normalization momentum. activation (callable): a callable that constructs activation layer, examples include: nn.ReLU, nn.Softmax, nn.Sigmoid, and None (not performing activation). max_stage_idx (int): Returns identity module if we exceed this

["Given","a","list","of","two","tensors","from","Slow","pathway","and","Fast","pathway",",","fusion","information","from","the","Fast","pathway","to","the","Slow","on","through","a","convolution","followed","by","a","concatenation",",","then","return","the","fused","list","of","tensors","from","Slow","and","Fast","pathway","in","order",".","Args",":","slowfast_channel_reduction_ratio","(","int",")",":","Reduction","ratio","from","the","stage","dimension",".","Used","to","compute","conv_dim_in","=","fusion_dim_in","\/\/","slowfast_channel_reduction_ratio","slowfast_audio_reduction_ratio","(","int",")",":","Audio","Reduction","ratio","from","the","stage","dimension",".","Used","to","compute","conv_dim_in_a","=","fusion_dim_in","\/\/","slowfast_audio_reduction_ratio","conv_fusion_channel_ratio","(","int",")",":","channel","ratio","for","the","convolution","used","to","fuse","from","Fast","pathway","to","Slow","pathway",".","conv_kernel_size","(","int",")",":","kernel","size","of","the","convolution","used","to","fuse","from","Fast","pathway","to","Slow","pathway",".","conv_kernel_size_a","(","int",")",":","kernel","size","of","the","convolution","used","to","fuse","from","Audio","pathway","to","FastSlow","pathway",".","conv_stride","(","int",")",":","stride","size","of","the","convolution","used","to","fuse","from","Fast","pathway","to","Slow","pathway",".","Optionally","indexed","by","stage",".","conv_stride_a","(","int",")",":","stride","size","of","the","convolution","used","to","fuse","from","Audio","pathway","to","FastSlow","pathway",".","Optionally","indexed","by","stage",".","conv_fusion_channel_interm_dim","(","Union","[","int",",","float","]",")",":","When","conv_num_a",">","1","this","value","controls","the","dimensions","of","the","intermediate","conv","conv_num_a","(","int",")",":","Number","of","intermediate","conv","for","audio","channel","norm","(","callable",")",":","a","callable","that","constructs","normalization","layer",",","examples","include","nn.BatchNorm3d",",","None","(","not","performing","normalization",")",".","norm_eps","(","float",")",":","normalization","epsilon",".","norm_momentum","(","float",")",":","normalization","momentum",".","activation","(","callable",")",":","a","callable","that","constructs","activation","layer",",","examples","include",":","nn.ReLU",",","nn.Softmax",",","nn.Sigmoid",",","and","None","(","not","performing","activation",")",".","max_stage_idx","(","int",")",":","Returns","identity","module","if","we","exceed","this"]

AudioToSlowFastFusionBuilder

def __init__( self, slowfast_channel_reduction_ratio: int, slowfast_audio_reduction_ratio: int, conv_fusion_channel_ratio: float, conv_kernel_size: Tuple[int], conv_kernel_size_a: Tuple[int], conv_stride: Union[Tuple[int], Tuple[Tuple[int]]], conv_stride_a: Union[Tuple[int], Tuple[Tuple[int]]], conv_fusion_channel_interm_dim: Union[ int, float ] = 0.25, # also, 64 conv_num_a: int = 2, norm: Callable = nn.BatchNorm3d, norm_eps: float = 1e-5, norm_momentum: float = 0.1, activation: Callable = nn.ReLU, max_stage_idx: int = 3, ) -> None: """ Given a list of two tensors from Slow pathway and Fast pathway, fusion information from the Fast pathway to the Slow on through a convolution followed by a concatenation, then return the fused list of tensors from Slow and Fast pathway in order. Args: slowfast_channel_reduction_ratio (int): Reduction ratio from the stage dimension. Used to compute conv_dim_in = fusion_dim_in // slowfast_channel_reduction_ratio slowfast_audio_reduction_ratio (int): Audio Reduction ratio from the stage dimension. Used to compute conv_dim_in_a = fusion_dim_in // slowfast_audio_reduction_ratio conv_fusion_channel_ratio (int): channel ratio for the convolution used to fuse from Fast pathway to Slow pathway. conv_kernel_size (int): kernel size of the convolution used to fuse from Fast pathway to Slow pathway. conv_kernel_size_a (int): kernel size of the convolution used to fuse from Audio pathway to FastSlow pathway. conv_stride (int): stride size of the convolution used to fuse from Fast pathway to Slow pathway. Optionally indexed by stage. conv_stride_a (int): stride size of the convolution used to fuse from Audio pathway to FastSlow pathway. Optionally indexed by stage. conv_fusion_channel_interm_dim (Union[int, float]): When conv_num_a > 1 this value controls the dimensions of the intermediate conv conv_num_a (int): Number of intermediate conv for audio channel norm (callable): a callable that constructs normalization layer, examples include nn.BatchNorm3d, None (not performing normalization). norm_eps (float): normalization epsilon. norm_momentum (float): normalization momentum. activation (callable): a callable that constructs activation layer, examples include: nn.ReLU, nn.Softmax, nn.Sigmoid, and None (not performing activation). max_stage_idx (int): Returns identity module if we exceed this """ set_attributes(self, locals())

["def","__init__","(","self",",","slowfast_channel_reduction_ratio",":","int",",","slowfast_audio_reduction_ratio",":","int",",","conv_fusion_channel_ratio",":","float",",","conv_kernel_size",":","Tuple","[","int","]",",","conv_kernel_size_a",":","Tuple","[","int","]",",","conv_stride",":","Union","[","Tuple","[","int","]",",","Tuple","[","Tuple","[","int","]","]","]",",","conv_stride_a",":","Union","[","Tuple","[","int","]",",","Tuple","[","Tuple","[","int","]","]","]",",","conv_fusion_channel_interm_dim",":","Union","[","int",",","float","]","=","0.25",",","#","also",",","64","conv_num_a",":","int","=","2",",","norm",":","Callable","=","nn.BatchNorm3d",",","norm_eps",":","float","=","1e-5",",","norm_momentum",":","float","=","0.1",",","activation",":","Callable","=","nn.ReLU",",","max_stage_idx",":","int","=","3",",",")","-",">","None",":","``","''","''","Given","a","list","of","two","tensors","from","Slow","pathway","and","Fast","pathway",",","fusion","information","from","the","Fast","pathway","to","the","Slow","on","through","a","convolution","followed","by","a","concatenation",",","then","return","the","fused","list","of","tensors","from","Slow","and","Fast","pathway","in","order",".","Args",":","slowfast_channel_reduction_ratio","(","int",")",":","Reduction","ratio","from","the","stage","dimension",".","Used","to","compute","conv_dim_in","=","fusion_dim_in","\/\/","slowfast_channel_reduction_ratio","slowfast_audio_reduction_ratio","(","int",")",":","Audio","Reduction","ratio","from","the","stage","dimension",".","Used","to","compute","conv_dim_in_a","=","fusion_dim_in","\/\/","slowfast_audio_reduction_ratio","conv_fusion_channel_ratio","(","int",")",":","channel","ratio","for","the","convolution","used","to","fuse","from","Fast","pathway","to","Slow","pathway",".","conv_kernel_size","(","int",")",":","kernel","size","of","the","convolution","used","to","fuse","from","Fast","pathway","to","Slow","pathway",".","conv_kernel_size_a","(","int",")",":","kernel","size","of","the","convolution","used","to","fuse","from","Audio","pathway","to","FastSlow","pathway",".","conv_stride","(","int",")",":","stride","size","of","the","convolution","used","to","fuse","from","Fast","pathway","to","Slow","pathway",".","Optionally","indexed","by","stage",".","conv_stride_a","(","int",")",":","stride","size","of","the","convolution","used","to","fuse","from","Audio","pathway","to","FastSlow","pathway",".","Optionally","indexed","by","stage",".","conv_fusion_channel_interm_dim","(","Union","[","int",",","float","]",")",":","When","conv_num_a",">","1","this","value","controls","the","dimensions","of","the","intermediate","conv","conv_num_a","(","int",")",":","Number","of","intermediate","conv","for","audio","channel","norm","(","callable",")",":","a","callable","that","constructs","normalization","layer",",","examples","include","nn.BatchNorm3d",",","None","(","not","performing","normalization",")",".","norm_eps","(","float",")",":","normalization","epsilon",".","norm_momentum","(","float",")",":","normalization","momentum",".","activation","(","callable",")",":","a","callable","that","constructs","activation","layer",",","examples","include",":","nn.ReLU",",","nn.Softmax",",","nn.Sigmoid",",","and","None","(","not","performing","activation",")",".","max_stage_idx","(","int",")",":","Returns","identity","module","if","we","exceed","this","``","''","''","set_attributes","(","self",",","locals","(",")",")"]

241

290

null

audio_visual_slowfast.py

pytorchvideo/pytorchvideo/models/audio_visual_slowfast.py

from typing import Callable, Tuple, Union import torch import torch.nn from pytorchvideo.layers.utils import set_attributes from pytorchvideo.models.resnet import create_acoustic_bottleneck_block, create_bottleneck_block from pytorchvideo.models.slowfast import create_slowfast from pytorchvideo.models.stem import create_acoustic_res_basic_stem, create_res_basic_stem

15

2

7

1

2

null

Use image node_id 1 to create a new AudioToSlowFastFusionBuilder object with example: obj = AudioToSlowFastFusionBuilder(slowfast_channel_reduction_ratio, slowfast_audio_reduction_ratio, conv_fusion_channel_ratio, conv_kernel_size, conv_kernel_size_a, conv_stride, conv_stride_a, conv_fusion_channel_interm_dim, conv_num_a, norm, norm_eps, norm_momentum, activation, max_stage_idx)

382

node_id 1

1,777,572

_load_imgs

global

null

false

file_paths,slice_,color,resize

null

faces

def _load_imgs(file_paths, slice_, color, resize): """Internally used to load images""" try: from PIL import Image except ImportError: raise ImportError( "The Python Imaging Library (PIL) is required to load data " "from jpeg files. Please refer to " "https://pillow.readthedocs.io/en/stable/installation.html " "for installing PIL." ) # compute the portion of the images to load to respect the slice_ parameter # given by the caller default_slice = (slice(0, 250), slice(0, 250)) if slice_ is None: slice_ = default_slice else: slice_ = tuple( s or ds for s, ds in zip(slice_, default_slice) ) h_slice, w_slice = slice_ h = (h_slice.stop - h_slice.start) // (h_slice.step or 1) w = (w_slice.stop - w_slice.start) // (w_slice.step or 1) if resize is not None: resize = float(resize) h = int(resize * h) w = int(resize * w) # allocate some contiguous memory to host the decoded image slices n_faces = len(file_paths) if not color: faces = np.zeros((n_faces, h, w), dtype=np.float32) else: faces = np.zeros((n_faces, h, w, 3), dtype=np.float32) # iterate over the collected file path to load the jpeg files as numpy # arrays for i, file_path in enumerate(file_paths): if i % 1000 == 0: logger.debug("Loading face #%05d / %05d", i + 1, n_faces) # Checks if jpeg reading worked. Refer to issue #3594 for more # details. pil_img = Image.open(file_path) pil_img = pil_img.crop( (w_slice.start, h_slice.start, w_slice.stop, h_slice.stop) ) if resize is not None: pil_img = pil_img.resize((w, h)) face = np.asarray(pil_img, dtype=np.float32) if face.ndim == 0: raise RuntimeError( "Failed to read the image file %s, " "Please make sure that libjpeg is installed" % file_path ) face /= ( 255.0 # scale uint8 coded colors to the [0.0, 1.0] floats ) if not color: # average the color channels to compute a gray levels # representation face = face.mean(axis=2) faces[i, ...] = face return faces

["def","_load_imgs","(","file_paths",",","slice_",",","color",",","resize",")",":","``","''","''","Internally","used","to","load","images","''","''","''","try",":","from","PIL","import","Image","except","ImportError",":","raise","ImportError","(","``","The","Python","Imaging","Library","(","PIL",")","is","required","to","load","data","``","``","from","jpeg","files",".","Please","refer","to","``","``","https",":","\/\/pillow.readthedocs.io\/en\/stable\/installation.html","``","``","for","installing","PIL",".","''",")","#","compute","the","portion","of","the","images","to","load","to","respect","the","slice_","parameter","#","given","by","the","caller","default_slice","=","(","slice","(","0",",","250",")",",","slice","(","0",",","250",")",")","if","slice_","is","None",":","slice_","=","default_slice","else",":","slice_","=","tuple","(","s","or","ds","for","s",",","ds","in","zip","(","slice_",",","default_slice",")",")","h_slice",",","w_slice","=","slice_","h","=","(","h_slice.stop","-","h_slice.start",")","\/\/","(","h_slice.step","or","1",")","w","=","(","w_slice.stop","-","w_slice.start",")","\/\/","(","w_slice.step","or","1",")","if","resize","is","not","None",":","resize","=","float","(","resize",")","h","=","int","(","resize","*","h",")","w","=","int","(","resize","*","w",")","#","allocate","some","contiguous","memory","to","host","the","decoded","image","slices","n_faces","=","len","(","file_paths",")","if","not","color",":","faces","=","np.zeros","(","(","n_faces",",","h",",","w",")",",","dtype=np.float32",")","else",":","faces","=","np.zeros","(","(","n_faces",",","h",",","w",",","3",")",",","dtype=np.float32",")","#","iterate","over","the","collected","file","path","to","load","the","jpeg","files","as","numpy","#","arrays","for","i",",","file_path","in","enumerate","(","file_paths",")",":","if","i","%","1000","==","0",":","logger.debug","(","``","Loading","face","#","%","05d","\/","%","05d","''",",","i","+","1",",","n_faces",")","#","Checks","if","jpeg","reading","worked",".","Refer","to","issue","#","3594","for","more","#","details",".","pil_img","=","Image.open","(","file_path",")","pil_img","=","pil_img.crop","(","(","w_slice.start",",","h_slice.start",",","w_slice.stop",",","h_slice.stop",")",")","if","resize","is","not","None",":","pil_img","=","pil_img.resize","(","(","w",",","h",")",")","face","=","np.asarray","(","pil_img",",","dtype=np.float32",")","if","face.ndim","==","0",":","raise","RuntimeError","(","``","Failed","to","read","the","image","file","%","s",",","``","``","Please","make","sure","that","libjpeg","is","installed","''","%","file_path",")","face","\/=","(","255.0","#","scale","uint8","coded","colors","to","the","[","0.0",",","1.0","]","floats",")","if","not","color",":","#","average","the","color","channels","to","compute","a","gray","levels","#","representation","face","=","face.mean","(","axis=2",")","faces","[","i",",","...","]","=","face","return","faces"]

118

184

null

_lfw.py

catboost/contrib/python/scikit-learn/py3/sklearn/datasets/_lfw.py

import logging from numbers import Integral, Real from os import PathLike, listdir, makedirs, remove from os.path import exists, isdir, join import numpy from joblib import Memory from ..utils import Bunch from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params from ._base import RemoteFileMetadata, _fetch_remote, get_data_home, load_descr

15

null

9

6

null

Use image node_id 2 for calling a global function with example usage: _load_imgs(file_paths, slice_, color, resize) and returns: faces

134

node_id 2

520,100

itilbert

global

null

false

x,h,period,_cache

null

convolve,unknown,int,unknown

def itilbert(x, h, period=None, _cache=_cache): """ Return inverse h-Tilbert transform of a periodic sequence x. If ``x_j`` and ``y_j`` are Fourier coefficients of periodic functions x and y, respectively, then:: y_j = -sqrt(-1)*tanh(j*h*2*pi/period) * x_j y_0 = 0 For more details, see `tilbert`. """ tmp = asarray(x) if iscomplexobj(tmp): return itilbert(tmp.real, h, period) + 1j * itilbert( tmp.imag, h, period ) if period is not None: h = h * 2 * pi / period n = len(x) omega = _cache.get((n, h)) if omega is None: if len(_cache) > 20: while _cache: _cache.popitem() def kernel(k, h=h): if k: return -tanh(h * k) return 0 omega = convolve.init_convolution_kernel(n, kernel, d=1) _cache[(n, h)] = omega overwrite_x = _datacopied(tmp, x) return convolve.convolve( tmp, omega, swap_real_imag=1, overwrite_x=overwrite_x )

["def","itilbert","(","x",",","h",",","period=None",",","_cache=_cache",")",":","``","''","''","Return","inverse","h-Tilbert","transform","of","a","periodic","sequence","x",".","If","``","x_j","``","and","``","y_j","``","are","Fourier","coefficients","of","periodic","functions","x","and","y",",","respectively",",","then",":",":","y_j","=","-sqrt","(","-1",")","*","tanh","(","j","*","h","*","2","*","pi\/period",")","*","x_j","y_0","=","0","For","more","details",",","see","`","tilbert","`",".","``","''","''","tmp","=","asarray","(","x",")","if","iscomplexobj","(","tmp",")",":","return","itilbert","(","tmp.real",",","h",",","period",")","+","1j","*","itilbert","(","tmp.imag",",","h",",","period",")","if","period","is","not","None",":","h","=","h","*","2","*","pi","\/","period","n","=","len","(","x",")","omega","=","_cache.get","(","(","n",",","h",")",")","if","omega","is","None",":","if","len","(","_cache",")",">","20",":","while","_cache",":","_cache.popitem","(",")","def","kernel","(","k",",","h=h",")",":","if","k",":","return","-tanh","(","h","*","k",")","return","0","omega","=","convolve.init_convolution_kernel","(","n",",","kernel",",","d=1",")","_cache","[","(","n",",","h",")","]","=","omega","overwrite_x","=","_datacopied","(","tmp",",","x",")","return","convolve.convolve","(","tmp",",","omega",",","swap_real_imag=1",",","overwrite_x=overwrite_x",")"]

159

192

null

pseudo_diffs.py

catboost/contrib/python/scipy/py2/scipy/fftpack/pseudo_diffs.py

from __future__ import division, print_function, absolute_import from numpy import pi, asarray, sin, cos, sinh, cosh, tanh, iscomplexobj from .None import convolve from scipy.fftpack.basic import _datacopied import atexit

15

null

5

10

null

Use image node_id 3 for calling a global function with example usage: itilbert(x, h, period, _cache) and returns: convolve, unknown, int, unknown

145

node_id 3

523,399

tilbert

global

null

false

x,h,period,_cache

null

convolve,unknown,int,unknown

def tilbert(x, h, period=None, _cache=_cache): """ Return h-Tilbert transform of a periodic sequence x. If x_j and y_j are Fourier coefficients of periodic functions x and y, respectively, then:: y_j = sqrt(-1)*coth(j*h*2*pi/period) * x_j y_0 = 0 Parameters ---------- x : array_like The input array to transform. h : float Defines the parameter of the Tilbert transform. period : float, optional The assumed period of the sequence. Default period is ``2*pi``. Returns ------- tilbert : ndarray The result of the transform. Notes ----- If ``sum(x, axis=0) == 0`` and ``n = len(x)`` is odd then ``tilbert(itilbert(x)) == x``. If ``2 * pi * h / period`` is approximately 10 or larger, then numerically ``tilbert == hilbert`` (theoretically oo-Tilbert == Hilbert). For even ``len(x)``, the Nyquist mode of ``x`` is taken zero. """ tmp = asarray(x) if iscomplexobj(tmp): return tilbert(tmp.real, h, period) + 1j * tilbert( tmp.imag, h, period ) if period is not None: h = h * 2 * pi / period n = len(x) omega = _cache.get((n, h)) if omega is None: if len(_cache) > 20: while _cache: _cache.popitem() def kernel(k, h=h): if k: return 1.0 / tanh(h * k) return 0 omega = convolve.init_convolution_kernel(n, kernel, d=1) _cache[(n, h)] = omega overwrite_x = _datacopied(tmp, x) return convolve.convolve( tmp, omega, swap_real_imag=1, overwrite_x=overwrite_x )

["def","tilbert","(","x",",","h",",","period=None",",","_cache=_cache",")",":","``","''","''","Return","h-Tilbert","transform","of","a","periodic","sequence","x",".","If","x_j","and","y_j","are","Fourier","coefficients","of","periodic","functions","x","and","y",",","respectively",",","then",":",":","y_j","=","sqrt","(","-1",")","*","coth","(","j","*","h","*","2","*","pi\/period",")","*","x_j","y_0","=","0","Parameters","--","--","--","--","--","x",":","array_like","The","input","array","to","transform",".","h",":","float","Defines","the","parameter","of","the","Tilbert","transform",".","period",":","float",",","optional","The","assumed","period","of","the","sequence",".","Default","period","is","``","2","*","pi","``",".","Returns","--","--","--","-","tilbert",":","ndarray","The","result","of","the","transform",".","Notes","--","--","-","If","``","sum","(","x",",","axis=0",")","==","0","``","and","``","n","=","len","(","x",")","``","is","odd","then","``","tilbert","(","itilbert","(","x",")",")","==","x","``",".","If","``","2","*","pi","*","h","\/","period","``","is","approximately","10","or","larger",",","then","numerically","``","tilbert","==","hilbert","``","(","theoretically","oo-Tilbert","==","Hilbert",")",".","For","even","``","len","(","x",")","``",",","the","Nyquist","mode","of","``","x","``","is","taken","zero.","``","''","''","tmp","=","asarray","(","x",")","if","iscomplexobj","(","tmp",")",":","return","tilbert","(","tmp.real",",","h",",","period",")","+","1j","*","tilbert","(","tmp.imag",",","h",",","period",")","if","period","is","not","None",":","h","=","h","*","2","*","pi","\/","period","n","=","len","(","x",")","omega","=","_cache.get","(","(","n",",","h",")",")","if","omega","is","None",":","if","len","(","_cache",")",">","20",":","while","_cache",":","_cache.popitem","(",")","def","kernel","(","k",",","h=h",")",":","if","k",":","return","1.0","\/","tanh","(","h","*","k",")","return","0","omega","=","convolve.init_convolution_kernel","(","n",",","kernel",",","d=1",")","_cache","[","(","n",",","h",")","]","=","omega","overwrite_x","=","_datacopied","(","tmp",",","x",")","return","convolve.convolve","(","tmp",",","omega",",","swap_real_imag=1",",","overwrite_x=overwrite_x",")"]

89

150

null

pseudo_diffs.py

catboost/contrib/python/scipy/py2/scipy/fftpack/pseudo_diffs.py

from __future__ import division, print_function, absolute_import from numpy import pi, asarray, sin, cos, sinh, cosh, tanh, iscomplexobj from .None import convolve from scipy.fftpack.basic import _datacopied import atexit

15

null

5

10

null

Use image node_id 2 for calling a global function with example usage: tilbert(x, h, period, _cache) and returns: convolve, unknown, int, unknown

144

node_id 2

523,398

diff

global

null

false

x,order,period,_cache

null

convolve,tmp,unknown,int,pow

def diff(x, order=1, period=None, _cache=_cache): """ Return k-th derivative (or integral) of a periodic sequence x. If x_j and y_j are Fourier coefficients of periodic functions x and y, respectively, then:: y_j = pow(sqrt(-1)*j*2*pi/period, order) * x_j y_0 = 0 if order is not 0. Parameters ---------- x : array_like Input array. order : int, optional The order of differentiation. Default order is 1. If order is negative, then integration is carried out under the assumption that ``x_0 == 0``. period : float, optional The assumed period of the sequence. Default is ``2*pi``. Notes ----- If ``sum(x, axis=0) = 0`` then ``diff(diff(x, k), -k) == x`` (within numerical accuracy). For odd order and even ``len(x)``, the Nyquist mode is taken zero. """ tmp = asarray(x) if order == 0: return tmp if iscomplexobj(tmp): return diff(tmp.real, order, period) + 1j * diff( tmp.imag, order, period ) if period is not None: c = 2 * pi / period else: c = 1.0 n = len(x) omega = _cache.get((n, order, c)) if omega is None: if len(_cache) > 20: while _cache: _cache.popitem() def kernel(k, order=order, c=c): if k: return pow(c * k, order) return 0 omega = convolve.init_convolution_kernel( n, kernel, d=order, zero_nyquist=1 ) _cache[(n, order, c)] = omega overwrite_x = _datacopied(tmp, x) return convolve.convolve( tmp, omega, swap_real_imag=order % 2, overwrite_x=overwrite_x )

["def","diff","(","x",",","order=1",",","period=None",",","_cache=_cache",")",":","``","''","''","Return","k-th","derivative","(","or","integral",")","of","a","periodic","sequence","x",".","If","x_j","and","y_j","are","Fourier","coefficients","of","periodic","functions","x","and","y",",","respectively",",","then",":",":","y_j","=","pow","(","sqrt","(","-1",")","*","j","*","2","*","pi\/period",",","order",")","*","x_j","y_0","=","0","if","order","is","not","0",".","Parameters","--","--","--","--","--","x",":","array_like","Input","array",".","order",":","int",",","optional","The","order","of","differentiation",".","Default","order","is","1",".","If","order","is","negative",",","then","integration","is","carried","out","under","the","assumption","that","``","x_0","==","0","``",".","period",":","float",",","optional","The","assumed","period","of","the","sequence",".","Default","is","``","2","*","pi","``",".","Notes","--","--","-","If","``","sum","(","x",",","axis=0",")","=","0","``","then","``","diff","(","diff","(","x",",","k",")",",","-k",")","==","x","``","(","within","numerical","accuracy",")",".","For","odd","order","and","even","``","len","(","x",")","``",",","the","Nyquist","mode","is","taken","zero.","``","''","''","tmp","=","asarray","(","x",")","if","order","==","0",":","return","tmp","if","iscomplexobj","(","tmp",")",":","return","diff","(","tmp.real",",","order",",","period",")","+","1j","*","diff","(","tmp.imag",",","order",",","period",")","if","period","is","not","None",":","c","=","2","*","pi","\/","period","else",":","c","=","1.0","n","=","len","(","x",")","omega","=","_cache.get","(","(","n",",","order",",","c",")",")","if","omega","is","None",":","if","len","(","_cache",")",">","20",":","while","_cache",":","_cache.popitem","(",")","def","kernel","(","k",",","order=order",",","c=c",")",":","if","k",":","return","pow","(","c","*","k",",","order",")","return","0","omega","=","convolve.init_convolution_kernel","(","n",",","kernel",",","d=order",",","zero_nyquist=1",")","_cache","[","(","n",",","order",",","c",")","]","=","omega","overwrite_x","=","_datacopied","(","tmp",",","x",")","return","convolve.convolve","(","tmp",",","omega",",","swap_real_imag=order","%","2",",","overwrite_x=overwrite_x",")"]

26

80

null

pseudo_diffs.py

catboost/contrib/python/scipy/py2/scipy/fftpack/pseudo_diffs.py

from __future__ import division, print_function, absolute_import from numpy import pi, asarray, sin, cos, sinh, cosh, tanh, iscomplexobj from .None import convolve from scipy.fftpack.basic import _datacopied import atexit

15

null

5

10

null

Use image node_id 1 for calling a global function with example usage: diff(x, order, period, _cache) and returns: convolve, tmp, unknown, int, pow

146

node_id 1

523,397

betweenness_centrality

global

null

false

input_graph,k,normalized,weight,endpoints,random_state

null

ddf,input_graph

def betweenness_centrality( input_graph, k: Union[ int, list, cudf.Series, cudf.DataFrame, dask_cudf.Series, dask_cudf.DataFrame, ] = None, normalized: bool = True, weight: cudf.DataFrame = None, endpoints: bool = False, random_state: int = None, ) -> dask_cudf.DataFrame: """ Compute the betweenness centrality for all vertices of the graph G. Betweenness centrality is a measure of the number of shortest paths that pass through a vertex. A vertex with a high betweenness centrality score has more paths passing through it and is therefore believed to be more important. To improve performance. rather than doing an all-pair shortest path, a sample of k starting vertices can be used. CuGraph does not currently support 'weight' parameters. Parameters ---------- input_graph: cuGraph.Graph The graph can be either directed (Graph(directed=True)) or undirected. The current implementation uses a parallel variation of the Brandes Algorithm (2001) to compute exact or approximate betweenness. If weights are provided in the edgelist, they will not be used. k : int, list or (dask)cudf object or None, optional (default=None) If k is not None, use k node samples to estimate betweenness. Higher values give better approximation. If k is either a list, a cudf DataFrame, or a dask_cudf DataFrame, then its contents are assumed to be vertex identifiers to be used for estimation. If k is None (the default), all the vertices are used to estimate betweenness. Vertices obtained through sampling or defined as a list will be used as sources for traversals inside the algorithm. normalized : bool, optional (default=True) If True, normalize the resulting betweenness centrality values by __2 / ((n - 1) * (n - 2))__ for undirected Graphs, and __1 / ((n - 1) * (n - 2))__ for directed Graphs where n is the number of nodes in G. Normalization will ensure that values are in [0, 1], this normalization scales for the highest possible value where one node is crossed by every single shortest path. weight : (dask)cudf.DataFrame, optional (default=None) Specifies the weights to be used for each edge. Should contain a mapping between edges and weights. (Not Supported) endpoints : bool, optional (default=False) If true, include the endpoints in the shortest path counts. random_state : int, optional (default=None) if k is specified and k is an integer, use random_state to initialize the random number generator. Using None defaults to a hash of process id, time, and hostname If k is either None or list or cudf objects: random_state parameter is ignored. Returns ------- betweenness_centrality : dask_cudf.DataFrame GPU distributed data frame containing two dask_cudf.Series of size V: the vertex identifiers and the corresponding betweenness centrality values. ddf['vertex'] : dask_cudf.Series Contains the vertex identifiers ddf['betweenness_centrality'] : dask_cudf.Series Contains the betweenness centrality of vertices Examples -------- >>> import cugraph.dask as dcg >>> import dask_cudf >>> # ... Init a DASK Cluster >>> # see https://docs.rapids.ai/api/cugraph/stable/dask-cugraph.html >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/.. >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv") >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv", ... chunksize=chunksize, delimiter=" ", ... names=["src", "dst", "value"], ... dtype=["int32", "int32", "float32"]) >>> dg = cugraph.Graph(directed=True) >>> dg.from_dask_cudf_edgelist(ddf, source='src', destination='dst') >>> pr = dcg.betweenness_centrality(dg) """ if input_graph.store_transposed is True: warning_msg = ( "Betweenness centrality expects the 'store_transposed' flag " "to be set to 'False' for optimal performance during " "the graph creation" ) warnings.warn(warning_msg, UserWarning) if weight is not None: raise NotImplementedError( "weighted implementation of betweenness " "centrality not currently supported" ) if not isinstance(k, (dask_cudf.DataFrame, dask_cudf.Series)): if isinstance(k, (cudf.DataFrame, cudf.Series, list)): if isinstance(k, list): k_dtype = input_graph.nodes().dtype k = cudf.Series(k, dtype=k_dtype) if isinstance(k, (cudf.Series, cudf.DataFrame)): splits = cp.array_split( cp.arange(len(k)), len(Comms.get_workers()) ) k = { w: [k.iloc[splits[i]]] for i, w in enumerate(Comms.get_workers()) } else: if k is not None: k = get_distributed_data(k) wait(k) k = k.worker_to_parts if input_graph.renumbered: if isinstance(k, dask_cudf.DataFrame): tmp_col_names = k.columns elif isinstance(k, dask_cudf.Series): tmp_col_names = None if isinstance(k, (dask_cudf.DataFrame, dask_cudf.Series)): k = input_graph.lookup_internal_vertex_id( k, tmp_col_names ) # FIXME: should we add this parameter as an option? do_expensive_check = False client = get_client() ddf = _mg_call_plc_betweenness_centrality( input_graph=input_graph, client=client, sID=Comms.get_session_id(), k=k, random_state=random_state, normalized=normalized, endpoints=endpoints, do_expensive_check=do_expensive_check, ) if input_graph.renumbered: return input_graph.unrenumber(ddf, "vertex") return ddf

123

275

null

betweenness_centrality.py

cugraph/python/cugraph/cugraph/dask/centrality/betweenness_centrality.py

from dask.distributed import wait, get_client from pylibcugraph import ResourceHandle, betweenness_centrality, edge_betweenness_centrality import cugraph.dask.comms.comms from cugraph.dask.common.input_utils import get_distributed_data import dask_cudf import cudf import cupy import warnings import dask from typing import Union

15

null

10

5

null

Use image node_id 4 for calling a global function with example usage: betweenness_centrality(input_graph, k, normalized, weight, endpoints, random_state) and returns: ddf, input_graph

183

node_id 4

686,101

edge_betweenness_centrality

global

null

false

input_graph,k,normalized,weight,random_state

null

ddf,input_graph

def edge_betweenness_centrality( input_graph, k: Union[ int, list, cudf.Series, cudf.DataFrame, dask_cudf.Series, dask_cudf.DataFrame, ] = None, normalized: bool = True, weight: cudf.DataFrame = None, random_state: int = None, ) -> dask_cudf.DataFrame: """ Compute the edge betweenness centrality for all edges of the graph G. Betweenness centrality is a measure of the number of shortest paths that pass over an edge. An edge with a high betweenness centrality score has more paths passing over it and is therefore believed to be more important. To improve performance. rather than doing an all-pair shortest path, a sample of k starting vertices can be used. CuGraph does not currently support the 'weight' parameter. Parameters ---------- input_graph: cuGraph.Graph The graph can be either directed (Graph(directed=True)) or undirected. The current implementation uses a parallel variation of the Brandes Algorithm (2001) to compute exact or approximate betweenness. If weights are provided in the edgelist, they will not be used. k : int, list or (dask)cudf object or None, optional (default=None) If k is not None, use k node samples to estimate betweenness. Higher values give better approximation. If k is either a list, a cudf DataFrame, or a dask_cudf DataFrame, then its contents are assumed to be vertex identifiers to be used for estimation. If k is None (the default), all the vertices are used to estimate betweenness. Vertices obtained through sampling or defined as a list will be used as sources for traversals inside the algorithm. normalized : bool, optional (default=True) If True, normalize the resulting betweenness centrality values by __2 / (n * (n - 1))__ for undirected Graphs, and __1 / (n * (n - 1))__ for directed Graphs where n is the number of nodes in G. Normalization will ensure that values are in [0, 1], this normalization scales for the highest possible value where one edge is crossed by every single shortest path. weight : (dask)cudf.DataFrame, optional (default=None) Specifies the weights to be used for each edge. Should contain a mapping between edges and weights. (Not Supported) random_state : int, optional (default=None) if k is specified and k is an integer, use random_state to initialize the random number generator. Using None defaults to a hash of process id, time, and hostname If k is either None or list or cudf objects: random_state parameter is ignored. Returns ------- betweenness_centrality : dask_cudf.DataFrame GPU distributed data frame containing two dask_cudf.Series of size V: the vertex identifiers and the corresponding betweenness centrality values. ddf['src'] : dask_cudf.Series Contains the vertex identifiers of the source of each edge ddf['dst'] : dask_cudf.Series Contains the vertex identifiers of the destination of each edge ddf['betweenness_centrality'] : dask_cudf.Series Contains the betweenness centrality of edges ddf["edge_id"] : dask_cudf.Series Contains the edge ids of edges if present. Examples -------- >>> import cugraph.dask as dcg >>> import dask_cudf >>> # ... Init a DASK Cluster >>> # see https://docs.rapids.ai/api/cugraph/stable/dask-cugraph.html >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/.. >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv") >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv", ... chunksize=chunksize, delimiter=" ", ... names=["src", "dst", "value"], ... dtype=["int32", "int32", "float32"]) >>> dg = cugraph.Graph(directed=True) >>> dg.from_dask_cudf_edgelist(ddf, source='src', destination='dst') >>> pr = dcg.edge_betweenness_centrality(dg) """ if input_graph.store_transposed is True: warning_msg = ( "Betweenness centrality expects the 'store_transposed' flag " "to be set to 'False' for optimal performance during " "the graph creation" ) warnings.warn(warning_msg, UserWarning) if weight is not None: raise NotImplementedError( "weighted implementation of edge betweenness " "centrality not currently supported" ) if not isinstance(k, (dask_cudf.DataFrame, dask_cudf.Series)): if isinstance(k, (cudf.DataFrame, cudf.Series, list)): if isinstance(k, list): k_dtype = input_graph.nodes().dtype k = cudf.Series(k, dtype=k_dtype) if isinstance(k, (cudf.Series, cudf.DataFrame)): splits = cp.array_split( cp.arange(len(k)), len(Comms.get_workers()) ) k = { w: [k.iloc[splits[i]]] for i, w in enumerate(Comms.get_workers()) } else: if k is not None: k = get_distributed_data(k) wait(k) k = k.worker_to_parts if input_graph.renumbered: if isinstance(k, dask_cudf.DataFrame): tmp_col_names = k.columns elif isinstance(k, dask_cudf.Series): tmp_col_names = None if isinstance(k, (dask_cudf.DataFrame, dask_cudf.Series)): k = input_graph.lookup_internal_vertex_id( k, tmp_col_names ) # FIXME: should we add this parameter as an option? do_expensive_check = False client = get_client() ddf = _mg_call_plc_betweenness_centrality( input_graph=input_graph, client=client, sID=Comms.get_session_id(), k=k, random_state=random_state, normalized=normalized, do_expensive_check=do_expensive_check, edge_bc=True, ) if input_graph.renumbered: return input_graph.unrenumber(ddf, "vertex") if input_graph.is_directed() is False: # swap the src and dst vertices for the lower triangle only. Because # this is a symmeterized graph, this operation results in a df with # multiple src/dst entries. ddf["src"], ddf["dst"] = ddf[["src", "dst"]].min(axis=1), ddf[ ["src", "dst"] ].max(axis=1) # overwrite the df with the sum of the values for all alike src/dst # vertex pairs, resulting in half the edges of the original df from the # symmeterized graph. ddf = ddf.groupby(by=["src", "dst"]).sum().reset_index() return ddf

278

445

null

betweenness_centrality.py

cugraph/python/cugraph/cugraph/dask/centrality/betweenness_centrality.py

from dask.distributed import wait, get_client from pylibcugraph import ResourceHandle, betweenness_centrality, edge_betweenness_centrality import cugraph.dask.comms.comms from cugraph.dask.common.input_utils import get_distributed_data import dask_cudf import cudf import cupy import warnings import dask from typing import Union

15

null

10

5

null

Use image node_id 5 for calling a global function with example usage: edge_betweenness_centrality(input_graph, k, normalized, weight, random_state) and returns: ddf, input_graph

177

node_id 5

686,102

_check_fetch_lfw

global

null

false

data_home,funneled,download_if_missing

null

lfw_home, data_folder_path

def _check_fetch_lfw( data_home=None, funneled=True, download_if_missing=True ): """Helper function to download any missing LFW data""" data_home = get_data_home(data_home=data_home) lfw_home = join(data_home, "lfw_home") if not exists(lfw_home): makedirs(lfw_home) for target in TARGETS: target_filepath = join(lfw_home, target.filename) if not exists(target_filepath): if download_if_missing: logger.info( "Downloading LFW metadata: %s", target.url ) _fetch_remote(target, dirname=lfw_home) else: raise OSError("%s is missing" % target_filepath) if funneled: data_folder_path = join(lfw_home, "lfw_funneled") archive = FUNNELED_ARCHIVE else: data_folder_path = join(lfw_home, "lfw") archive = ARCHIVE if not exists(data_folder_path): archive_path = join(lfw_home, archive.filename) if not exists(archive_path): if download_if_missing: logger.info( "Downloading LFW data (~200MB): %s", archive.url ) _fetch_remote(archive, dirname=lfw_home) else: raise OSError("%s is missing" % archive_path) import tarfile logger.debug( "Decompressing the data archive to %s", data_folder_path ) tarfile.open(archive_path, "r:gz").extractall(path=lfw_home) remove(archive_path) return lfw_home, data_folder_path

["def","_check_fetch_lfw","(","data_home=None",",","funneled=True",",","download_if_missing=True",")",":","``","''","''","Helper","function","to","download","any","missing","LFW","data","''","''","''","data_home","=","get_data_home","(","data_home=data_home",")","lfw_home","=","join","(","data_home",",","``","lfw_home","''",")","if","not","exists","(","lfw_home",")",":","makedirs","(","lfw_home",")","for","target","in","TARGETS",":","target_filepath","=","join","(","lfw_home",",","target.filename",")","if","not","exists","(","target_filepath",")",":","if","download_if_missing",":","logger.info","(","``","Downloading","LFW","metadata",":","%","s","''",",","target.url",")","_fetch_remote","(","target",",","dirname=lfw_home",")","else",":","raise","OSError","(","``","%","s","is","missing","''","%","target_filepath",")","if","funneled",":","data_folder_path","=","join","(","lfw_home",",","``","lfw_funneled","''",")","archive","=","FUNNELED_ARCHIVE","else",":","data_folder_path","=","join","(","lfw_home",",","``","lfw","''",")","archive","=","ARCHIVE","if","not","exists","(","data_folder_path",")",":","archive_path","=","join","(","lfw_home",",","archive.filename",")","if","not","exists","(","archive_path",")",":","if","download_if_missing",":","logger.info","(","``","Downloading","LFW","data","(","~200MB",")",":","%","s","''",",","archive.url",")","_fetch_remote","(","archive",",","dirname=lfw_home",")","else",":","raise","OSError","(","``","%","s","is","missing","''","%","archive_path",")","import","tarfile","logger.debug","(","``","Decompressing","the","data","archive","to","%","s","''",",","data_folder_path",")","tarfile.open","(","archive_path",",","``","r",":","gz","''",")",".extractall","(","path=lfw_home",")","remove","(","archive_path",")","return","lfw_home",",","data_folder_path"]

75

115

null

_lfw.py

catboost/contrib/python/scikit-learn/py3/sklearn/datasets/_lfw.py

import logging from numbers import Integral, Real from os import PathLike, listdir, makedirs, remove from os.path import exists, isdir, join import numpy from joblib import Memory from ..utils import Bunch from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params from ._base import RemoteFileMetadata, _fetch_remote, get_data_home, load_descr

15

null

9

6

null

Use image node_id 1 for calling a global function with example usage: _check_fetch_lfw(data_home, funneled, download_if_missing) and returns: lfw_home, data_folder_path

169

node_id 1

520,099

_random_samples

global

null

false

lb,ub,npts

null

pts

def _random_samples(lb, ub, npts=10000): """ generate npts random samples between given lb & ub Inputs: lower bounds -- a list of the lower bounds upper bounds -- a list of the upper bounds npts -- number of sample points [default = 10000]""" from mystic.tools import random_state dim = len(lb) pts = random_state(module="numpy.random").rand(dim, npts) for i in range(dim): pts[i] = (pts[i] * abs(ub[i] - lb[i])) + lb[i] return pts

["def","_random_samples","(","lb",",","ub",",","npts=10000",")",":","``","''","''","generate","npts","random","samples","between","given","lb","&","ub","Inputs",":","lower","bounds","--","a","list","of","the","lower","bounds","upper","bounds","--","a","list","of","the","upper","bounds","npts","--","number","of","sample","points","[","default","=","10000","]","''","''","''","from","mystic.tools","import","random_state","dim","=","len","(","lb",")","pts","=","random_state","(","module=","''","numpy.random","''",")",".rand","(","dim",",","npts",")","for","i","in","range","(","dim",")",":","pts","[","i","]","=","(","pts","[","i","]","*","abs","(","ub","[","i","]","-","lb","[","i","]",")",")","+","lb","[","i","]","return","pts"]

17

31

null

samples.py

mystic/mystic/math/samples.py

15

null

0

15

null

Use image node_id 1 for calling a global function with example usage: _random_samples(lb, ub, npts) and returns: pts

116

node_id 1

1,407,021

random_samples

global

null

false

lb,ub,npts,dist,clip

null

pts,_random_samples,pts

def random_samples(lb, ub, npts=10000, dist=None, clip=False): """ generate npts samples from the given distribution between given lb & ub Inputs: dist -- a mystic.tools.Distribution instance (or list of Distributions) lower bounds -- a list of the lower bounds upper bounds -- a list of the upper bounds npts -- number of sample points [default = 10000] clip -- if True, clip at bounds, else resample [default = False] """ if dist is None: return _random_samples(lb, ub, npts) import numpy as np if hasattr(dist, "__len__"): # FIXME: isiterable pts = np.array(tuple(di(npts) for di in dist)).T else: pts = dist((npts, len(lb))) # transpose of desired shape dist = (dist,) * len(lb) pts = np.clip(pts, lb, ub).T if clip: return pts # XXX: returns a numpy.array bad = ((pts.T == lb) + (pts.T == ub)).T new = bad.sum(-1) _n, n = 1, 1000 # FIXME: fixed number of max tries while any(new): if _n == n: # XXX: slows the while loop... raise RuntimeError( "bounds could not be applied in %s iterations" % n ) for i, inew in enumerate( new ): # XXX: slows... but enables iterable dist if inew: pts[i][bad[i]] = dist[i](inew) pts = np.clip(pts.T, lb, ub).T bad = ((pts.T == lb) + (pts.T == ub)).T new = bad.sum(-1) _n += 1 return pts

["def","random_samples","(","lb",",","ub",",","npts=10000",",","dist=None",",","clip=False",")",":","``","''","''","generate","npts","samples","from","the","given","distribution","between","given","lb","&","ub","Inputs",":","dist","--","a","mystic.tools.Distribution","instance","(","or","list","of","Distributions",")","lower","bounds","--","a","list","of","the","lower","bounds","upper","bounds","--","a","list","of","the","upper","bounds","npts","--","number","of","sample","points","[","default","=","10000","]","clip","--","if","True",",","clip","at","bounds",",","else","resample","[","default","=","False","]","``","''","''","if","dist","is","None",":","return","_random_samples","(","lb",",","ub",",","npts",")","import","numpy","as","np","if","hasattr","(","dist",",","``","__len__","''",")",":","#","FIXME",":","isiterable","pts","=","np.array","(","tuple","(","di","(","npts",")","for","di","in","dist",")",")",".T","else",":","pts","=","dist","(","(","npts",",","len","(","lb",")",")",")","#","transpose","of","desired","shape","dist","=","(","dist",",",")","*","len","(","lb",")","pts","=","np.clip","(","pts",",","lb",",","ub",")",".T","if","clip",":","return","pts","#","XXX",":","returns","a","numpy.array","bad","=","(","(","pts.T","==","lb",")","+","(","pts.T","==","ub",")",")",".T","new","=","bad.sum","(","-1",")","_n",",","n","=","1",",","1000","#","FIXME",":","fixed","number","of","max","tries","while","any","(","new",")",":","if","_n","==","n",":","#","XXX",":","slows","the","while","loop","...","raise","RuntimeError","(","``","bounds","could","not","be","applied","in","%","s","iterations","''","%","n",")","for","i",",","inew","in","enumerate","(","new",")",":","#","XXX",":","slows","...","but","enables","iterable","dist","if","inew",":","pts","[","i","]","[","bad","[","i","]","]","=","dist","[","i","]","(","inew",")","pts","=","np.clip","(","pts.T",",","lb",",","ub",")",".T","bad","=","(","(","pts.T","==","lb",")","+","(","pts.T","==","ub",")",")",".T","new","=","bad.sum","(","-1",")","_n","+=","1","return","pts"]

35

68

null

samples.py

mystic/mystic/math/samples.py

15

null

0

15

null

Use image node_id 2 for calling a global function with example usage: random_samples(lb, ub, npts, dist, clip) and returns: pts, _random_samples, pts

149

node_id 2

1,407,022

sample

global

null

false

f,lb,ub,npts,map

null

failure, success

def sample(f, lb, ub, npts=10000, map=None): """ return number of failures and successes for some boolean function f Inputs: f -- a function that returns True for 'success' and False for 'failure' lb -- a list of lower bounds ub -- a list of upper bounds npts -- the number of points to sample [Default is npts=10000] map -- the mapping function [Default is builtins.map]""" if map is None: from builtins import map from numpy import transpose, atleast_2d pts = _random_samples(lb, ub, npts) results = list(map(f, atleast_2d(transpose(pts)).tolist())) failure = results.count(False) success = len(results) - failure return failure, success

["def","sample","(","f",",","lb",",","ub",",","npts=10000",",","map=None",")",":","``","''","''","return","number","of","failures","and","successes","for","some","boolean","function","f","Inputs",":","f","--","a","function","that","returns","True","for","'success","'","and","False","for","'failure'","lb","--","a","list","of","lower","bounds","ub","--","a","list","of","upper","bounds","npts","--","the","number","of","points","to","sample","[","Default","is","npts=10000","]","map","--","the","mapping","function","[","Default","is","builtins.map","]","''","''","''","if","map","is","None",":","from","builtins","import","map","from","numpy","import","transpose",",","atleast_2d","pts","=","_random_samples","(","lb",",","ub",",","npts",")","results","=","list","(","map","(","f",",","atleast_2d","(","transpose","(","pts",")",")",".tolist","(",")",")",")","failure","=","results.count","(","False",")","success","=","len","(","results",")","-","failure","return","failure",",","success"]

71

90

null

samples.py

mystic/mystic/math/samples.py

15

null

0

15

null

Use image node_id 3 for calling a global function with example usage: sample(f, lb, ub, npts, map) and returns: failure, success

129

node_id 3

1,407,023

__init__

MockTorchCSCTensor

null

true

self,edge_index,edge_attr,size

null

MockTorchCSCTensor

def __init__( self, edge_index: Tensor, edge_attr: Optional[Tensor] = None, size: Optional[Union[int, Tuple[int, int]]] = None, ): self.edge_index = edge_index self.edge_attr = edge_attr self.size = size

["def","__init__","(","self",",","edge_index",":","Tensor",",","edge_attr",":","Optional","[","Tensor","]","=","None",",","size",":","Optional","[","Union","[","int",",","Tuple","[","int",",","int","]","]","]","=","None",",",")",":","self.edge_index","=","edge_index","self.edge_attr","=","edge_attr","self.size","=","size"]

253

261

null

typing.py

pytorch_geometric/torch_geometric/typing.py

import inspect import os import sys import warnings from typing import Any, Dict, List, Optional, Tuple, Union import numpy import torch from torch import Tensor

15

2

8

0

1

2

null

Use image node_id 1 to create a new MockTorchCSCTensor object with example: obj = MockTorchCSCTensor(edge_index, edge_attr, size)

130

node_id 1

1,775,551

t

MockTorchCSCTensor

null

true

self

null

to_torch_csr_tensor

def t(self) -> Tensor: # Only support accessing its transpose: from torch_geometric.utils import to_torch_csr_tensor size = self.size return to_torch_csr_tensor( self.edge_index.flip([0]), self.edge_attr, size[::-1] if isinstance(size, (tuple, list)) else size, )

["def","t","(","self",")","-",">","Tensor",":","#","Only","support","accessing","its","transpose",":","from","torch_geometric.utils","import","to_torch_csr_tensor","size","=","self.size","return","to_torch_csr_tensor","(","self.edge_index.flip","(","[","0","]",")",",","self.edge_attr",",","size","[",":",":-1","]","if","isinstance","(","size",",","(","tuple",",","list",")",")","else","size",",",")"]

263

270

null

typing.py

pytorch_geometric/torch_geometric/typing.py

import inspect import os import sys import warnings from typing import Any, Dict, List, Optional, Tuple, Union import numpy import torch from torch import Tensor

15

2

8

0

1

2

null

Use image node_id 2 for calling the MockTorchCSCTensor obj's underlying member method code with example usage: obj.t() and returns: to_torch_csr_tensor

151

node_id 2

1,775,552

__call__

_Fitness

object

true

self

A metric to measure the fitness of a program. This object is able to be called with NumPy vectorized arguments and return a resulting floating point score quantifying the quality of the program's representation of the true relationship. Parameters ---------- function : callable A function with signature function(y, y_pred, sample_weight) that returns a floating point number. Where `y` is the input target y vector, `y_pred` is the predicted values from the genetic program, and sample_weight is the sample_weight vector. greater_is_better : bool Whether a higher value from `function` indicates a better fit. In general this would be False for metrics indicating the magnitude of the error, and True for metrics indicating the quality of fit.

["A","metric","to","measure","the","fitness","of","a","program",".","This","object","is","able","to","be","called","with","NumPy","vectorized","arguments","and","return","a","resulting","floating","point","score","quantifying","the","quality","of","the","program's","representation","of","the","true","relationship",".","Parameters","--","--","--","--","--","function",":","callable","A","function","with","signature","function","(","y",",","y_pred",",","sample_weight",")","that","returns","a","floating","point","number",".","Where","`","y","`","is","the","input","target","y","vector",",","`","y_pred","`","is","the","predicted","values","from","the","genetic","program",",","and","sample_weight","is","the","sample_weight","vector",".","greater_is_better",":","bool","Whether","a","higher","value","from","`","function","`","indicates","a","better","fit",".","In","general","this","would","be","False","for","metrics","indicating","the","magnitude","of","the","error",",","and","True","for","metrics","indicating","the","quality","of","fit","."]

null

self

def __call__(self, *args): return self.function(*args)

["def","__call__","(","self",",","*","args",")",":","return","self.function","(","*","args",")"]

48

49

null

fitness.py

gplearn/gplearn/fitness.py

import numbers import numpy from joblib import wrap_non_picklable_objects from scipy.stats import rankdata

15

1

4

7

1

2

1

Use image node_id 2 for calling the _Fitness obj's underlying member method code with example usage: obj.__call__() and returns: self

133

node_id 2

1,106,029

peakmem_rotate

NdimageInterpolation

Benchmark

true

self,shape,order,mode

null

def peakmem_rotate(self, shape, order, mode): rotate(self.x, 15, order=order, mode=mode)

["def","peakmem_rotate","(","self",",","shape",",","order",",","mode",")",":","rotate","(","self.x",",","15",",","order=order",",","mode=mode",")"]

64

65

null

ndimage_interpolation.py

scipy/benchmarks/benchmarks/ndimage_interpolation.py

import numpy from .common import Benchmark

15

1

2

1

9

1

Use image node_id 8 for calling the NdimageInterpolation obj's underlying member method code with example usage: obj.peakmem_rotate(shape, order, mode) without return types

172

node_id 8

1,883,763

get_retro_decoder_layer_te_spec

global

null

false

encoder_block_spec

null

spec

def get_retro_decoder_layer_te_spec( encoder_block_spec: ModuleSpec = None, ) -> ModuleSpec: """Retro decoder TE spec (uses Transformer Engine components). A Retro decoder layer uses custom attention and bias-dropout-add operators to perform chunked-cross attention. Additionally, the first Retro decoder layer instantiates an entire encoder transformer block. As such, the decoder cross attention module takes an optional encoder block spec, which is only provided for the first Retro decoder layer. Arguments: encoder_block_spec (ModuleSpec): Retro encoder block spec, to be provided for the first Retro decoder layer. """ spec = get_gpt_layer_with_transformer_engine_spec() spec.submodules.pre_cross_attn_layernorm = TENorm spec.submodules.cross_attention = ModuleSpec( module=RetroDecoderCrossAttention, params={ "encoder_block_spec": encoder_block_spec, }, submodules=CrossAttentionSubmodules( linear_q=TEColumnParallelLinear, linear_kv=TEColumnParallelLinear, core_attention=TEDotProductAttention, linear_proj=TERowParallelLinear, ), ) spec.submodules.cross_attn_bda = ModuleSpec( module=RetroDecoderBiasDropoutAdd ) return spec

["def","get_retro_decoder_layer_te_spec","(","encoder_block_spec",":","ModuleSpec","=","None",",",")","-",">","ModuleSpec",":","``","''","''","Retro","decoder","TE","spec","(","uses","Transformer","Engine","components",")",".","A","Retro","decoder","layer","uses","custom","attention","and","bias-dropout-add","operators","to","perform","chunked-cross","attention",".","Additionally",",","the","first","Retro","decoder","layer","instantiates","an","entire","encoder","transformer","block",".","As","such",",","the","decoder","cross","attention","module","takes","an","optional","encoder","block","spec",",","which","is","only","provided","for","the","first","Retro","decoder","layer",".","Arguments",":","encoder_block_spec","(","ModuleSpec",")",":","Retro","encoder","block","spec",",","to","be","provided","for","the","first","Retro","decoder","layer.","``","''","''","spec","=","get_gpt_layer_with_transformer_engine_spec","(",")","spec.submodules.pre_cross_attn_layernorm","=","TENorm","spec.submodules.cross_attention","=","ModuleSpec","(","module=RetroDecoderCrossAttention",",","params=","{","``","encoder_block_spec","''",":","encoder_block_spec",",","}",",","submodules=CrossAttentionSubmodules","(","linear_q=TEColumnParallelLinear",",","linear_kv=TEColumnParallelLinear",",","core_attention=TEDotProductAttention",",","linear_proj=TERowParallelLinear",",",")",",",")","spec.submodules.cross_attn_bda","=","ModuleSpec","(","module=RetroDecoderBiasDropoutAdd",")","return","spec"]

31

57

null

decoder_spec.py

megatron-lm/megatron/core/models/retro/decoder_spec.py

from megatron.core import parallel_state from megatron.core.fusions.fused_layer_norm import FusedLayerNorm from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec, get_gpt_layer_with_transformer_engine_spec from megatron.core.models.retro.config import RetroConfig from megatron.core.models.retro.decoder_attention import RetroDecoderBiasDropoutAdd, RetroDecoderCrossAttention from megatron.core.models.retro.encoder_spec import get_retro_encoder_block_spec from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear from megatron.core.transformer import ModuleSpec from megatron.core.transformer.attention import CrossAttentionSubmodules from megatron.core.transformer.custom_layers.transformer_engine import TEColumnParallelLinear, TEDotProductAttention, TENorm, TERowParallelLinear from megatron.core.transformer.dot_product_attention import DotProductAttention from megatron.core.transformer.transformer_block import TransformerBlockSubmodules, get_num_layers_to_build

15

null

12

3

null

Use image node_id 1 for calling a global function with example usage: get_retro_decoder_layer_te_spec(encoder_block_spec) and returns: spec

139

node_id 1

1,324,186

get_retro_decoder_layer_local_spec

global

null

false

encoder_block_spec

null

spec

def get_retro_decoder_layer_local_spec( encoder_block_spec: ModuleSpec = None, ) -> ModuleSpec: """Retro decoder local spec (uses Megatron-Core components). A Retro decoder layer uses custom attention and bias-dropout-add operators to perform chunked-cross attention. Additionally, the first Retro decoder layer instantiates an entire encoder transformer block. As such, the decoder cross attention module takes an optional encoder block spec, which is only provided for the first Retro decoder layer. Arguments: encoder_block_spec (ModuleSpec): Retro encoder block spec, to be provided for the first Retro decoder layer. """ spec = get_gpt_layer_local_spec() spec.submodules.pre_cross_attn_layernorm = FusedLayerNorm spec.submodules.cross_attention = ModuleSpec( module=RetroDecoderCrossAttention, params={ "encoder_block_spec": encoder_block_spec, }, submodules=CrossAttentionSubmodules( linear_q=ColumnParallelLinear, linear_kv=ColumnParallelLinear, core_attention=DotProductAttention, linear_proj=RowParallelLinear, ), ) spec.submodules.cross_attn_bda = ModuleSpec( module=RetroDecoderBiasDropoutAdd ) return spec

["def","get_retro_decoder_layer_local_spec","(","encoder_block_spec",":","ModuleSpec","=","None",",",")","-",">","ModuleSpec",":","``","''","''","Retro","decoder","local","spec","(","uses","Megatron-Core","components",")",".","A","Retro","decoder","layer","uses","custom","attention","and","bias-dropout-add","operators","to","perform","chunked-cross","attention",".","Additionally",",","the","first","Retro","decoder","layer","instantiates","an","entire","encoder","transformer","block",".","As","such",",","the","decoder","cross","attention","module","takes","an","optional","encoder","block","spec",",","which","is","only","provided","for","the","first","Retro","decoder","layer",".","Arguments",":","encoder_block_spec","(","ModuleSpec",")",":","Retro","encoder","block","spec",",","to","be","provided","for","the","first","Retro","decoder","layer.","``","''","''","spec","=","get_gpt_layer_local_spec","(",")","spec.submodules.pre_cross_attn_layernorm","=","FusedLayerNorm","spec.submodules.cross_attention","=","ModuleSpec","(","module=RetroDecoderCrossAttention",",","params=","{","``","encoder_block_spec","''",":","encoder_block_spec",",","}",",","submodules=CrossAttentionSubmodules","(","linear_q=ColumnParallelLinear",",","linear_kv=ColumnParallelLinear",",","core_attention=DotProductAttention",",","linear_proj=RowParallelLinear",",",")",",",")","spec.submodules.cross_attn_bda","=","ModuleSpec","(","module=RetroDecoderBiasDropoutAdd",")","return","spec"]

60

86

null

decoder_spec.py

megatron-lm/megatron/core/models/retro/decoder_spec.py

from megatron.core import parallel_state from megatron.core.fusions.fused_layer_norm import FusedLayerNorm from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec, get_gpt_layer_with_transformer_engine_spec from megatron.core.models.retro.config import RetroConfig from megatron.core.models.retro.decoder_attention import RetroDecoderBiasDropoutAdd, RetroDecoderCrossAttention from megatron.core.models.retro.encoder_spec import get_retro_encoder_block_spec from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear from megatron.core.transformer import ModuleSpec from megatron.core.transformer.attention import CrossAttentionSubmodules from megatron.core.transformer.custom_layers.transformer_engine import TEColumnParallelLinear, TEDotProductAttention, TENorm, TERowParallelLinear from megatron.core.transformer.dot_product_attention import DotProductAttention from megatron.core.transformer.transformer_block import TransformerBlockSubmodules, get_num_layers_to_build

15

null

12

3

null

Use image node_id 2 for calling a global function with example usage: get_retro_decoder_layer_local_spec(encoder_block_spec) and returns: spec

142

node_id 2

1,324,187

can_document_member

HasTraitsDocumenter

ClassDocumenter

true

cls,member,membername,isattr,parent

Specialized Documenter subclass for traits

["Specialized","Documenter","subclass","for","traits"]

null

isinstance

def can_document_member(cls, member, membername, isattr, parent): return isinstance(member, HasTraits)

["def","can_document_member","(","cls",",","member",",","membername",",","isattr",",","parent",")",":","return","isinstance","(","member",",","HasTraits",")"]

48

49

null

autodoc_traits.py

pythreejs/docs/sphinxext/autodoc_traits.py

from collections import OrderedDict from traitlets import TraitType, Undefined, Container, Dict, Any, HasTraits from sphinx.ext.autodoc import ClassDocumenter, AttributeDocumenter

15

2

3

2

1

Use image node_id 1 for calling the HasTraitsDocumenter obj's underlying member method code with example usage: obj.can_document_member(cls, member, membername, isattr, parent) and returns: isinstance

200

node_id 1

1,691,040