code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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|---|---|---|---|---|---|---|---|
def __mul__(self, other: SupportsIndex) -> "_lstr":
"""
Perform a multiplication operation between this lstr instance and an integer or another lstr,
tracing the operation by logging the operands and the result.
Args:
other (Union[SupportsIndex, "lstr"]): The right operand i... |
Perform a multiplication operation between this lstr instance and an integer or another lstr,
tracing the operation by logging the operands and the result.
Args:
other (Union[SupportsIndex, "lstr"]): The right operand in the multiplication operation.
Returns:
l... | __mul__ | python | MadcowD/ell | src/ell/types/_lstr.py | https://github.com/MadcowD/ell/blob/master/src/ell/types/_lstr.py | MIT |
def __getitem__(self, key: Union[SupportsIndex, slice]) -> "_lstr":
"""
Get a slice or index of this lstr instance.
Args:
key (Union[SupportsIndex, slice]): The index or slice to retrieve.
Returns:
lstr: A new lstr instance containing the sliced or indexed conte... |
Get a slice or index of this lstr instance.
Args:
key (Union[SupportsIndex, slice]): The index or slice to retrieve.
Returns:
lstr: A new lstr instance containing the sliced or indexed content, with theorigin_trace(s) preserved.
| __getitem__ | python | MadcowD/ell | src/ell/types/_lstr.py | https://github.com/MadcowD/ell/blob/master/src/ell/types/_lstr.py | MIT |
def __getattribute__(self, name: str) -> Union[Callable, Any]:
"""
Get an attribute from this lstr instance.
Args:
name (str): The name of the attribute to retrieve.
Returns:
Union[Callable, Any]: The requested attribute, which may be a method or a value.
... |
Get an attribute from this lstr instance.
Args:
name (str): The name of the attribute to retrieve.
Returns:
Union[Callable, Any]: The requested attribute, which may be a method or a value.
| __getattribute__ | python | MadcowD/ell | src/ell/types/_lstr.py | https://github.com/MadcowD/ell/blob/master/src/ell/types/_lstr.py | MIT |
def join(self, iterable: Iterable[Union[str, "_lstr"]]) -> "_lstr":
"""
Join a sequence of strings or lstr instances into a single lstr instance.
Args:
iterable (Iterable[Union[str, "lstr"]]): The sequence of strings or lstr instances to join.
Returns:
lstr: A n... |
Join a sequence of strings or lstr instances into a single lstr instance.
Args:
iterable (Iterable[Union[str, "lstr"]]): The sequence of strings or lstr instances to join.
Returns:
lstr: A new lstr instance containing the joined content, with theorigin_trace(s) updated... | join | python | MadcowD/ell | src/ell/types/_lstr.py | https://github.com/MadcowD/ell/blob/master/src/ell/types/_lstr.py | MIT |
def split(
self, sep: Optional[Union[str, "_lstr"]] = None, maxsplit: SupportsIndex = -1
) -> List["_lstr"]:
"""
Split this lstr instance into a list of lstr instances based on a separator.
Args:
sep (Optional[Union[str, "lstr"]], optional): The separator to split on. De... |
Split this lstr instance into a list of lstr instances based on a separator.
Args:
sep (Optional[Union[str, "lstr"]], optional): The separator to split on. Defaults to None.
maxsplit (SupportsIndex, optional): The maximum number of splits to perform. Defaults to -1.
Re... | split | python | MadcowD/ell | src/ell/types/_lstr.py | https://github.com/MadcowD/ell/blob/master/src/ell/types/_lstr.py | MIT |
def rsplit(
self, sep: Optional[Union[str, "_lstr"]] = None, maxsplit: SupportsIndex = -1
) -> List["_lstr"]:
"""
Split this lstr instance into a list of lstr instances based on a separator, starting from the right.
Args:
sep (Optional[Union[str, "lstr"]], optional): The... |
Split this lstr instance into a list of lstr instances based on a separator, starting from the right.
Args:
sep (Optional[Union[str, "lstr"]], optional): The separator to split on. Defaults to None.
maxsplit (SupportsIndex, optional): The maximum number of splits to perform. De... | rsplit | python | MadcowD/ell | src/ell/types/_lstr.py | https://github.com/MadcowD/ell/blob/master/src/ell/types/_lstr.py | MIT |
def splitlines(self, keepends: bool = False) -> List["_lstr"]:
"""
Split this lstr instance into a list of lstr instances based on line breaks.
Args:
keepends (bool, optional): Whether to include the line breaks in the resulting lstr instances. Defaults to False.
Returns:
... |
Split this lstr instance into a list of lstr instances based on line breaks.
Args:
keepends (bool, optional): Whether to include the line breaks in the resulting lstr instances. Defaults to False.
Returns:
List["lstr"]: A list of lstr instances containing the split con... | splitlines | python | MadcowD/ell | src/ell/types/_lstr.py | https://github.com/MadcowD/ell/blob/master/src/ell/types/_lstr.py | MIT |
def _partition_helper(
self, method, sep: Union[str, "_lstr"]
) -> Tuple["_lstr", "_lstr", "_lstr"]:
"""
Helper method for partitioning this lstr instance based on a separator.
Args:
method (Callable): The partitioning method to use (either partition or rpartition).
... |
Helper method for partitioning this lstr instance based on a separator.
Args:
method (Callable): The partitioning method to use (either partition or rpartition).
sep (Union[str, "lstr"]): The separator to partition on.
Returns:
Tuple["lstr", "lstr", "lstr"]... | _partition_helper | python | MadcowD/ell | src/ell/types/_lstr.py | https://github.com/MadcowD/ell/blob/master/src/ell/types/_lstr.py | MIT |
def _split_helper(
self,
method,
sep: Optional[Union[str, "_lstr"]] = None,
maxsplit: SupportsIndex = -1,
) -> List["_lstr"]:
"""
Helper method for splitting this lstr instance based on a separator.
Args:
method (Callable): The splitting method to... |
Helper method for splitting this lstr instance based on a separator.
Args:
method (Callable): The splitting method to use (either split or rsplit).
sep (Optional[Union[str, "lstr"]], optional): The separator to split on. Defaults to None.
maxsplit (SupportsIndex, op... | _split_helper | python | MadcowD/ell | src/ell/types/_lstr.py | https://github.com/MadcowD/ell/blob/master/src/ell/types/_lstr.py | MIT |
def lexical_closure(
func: Any,
already_closed: Set[int] = None,
initial_call: bool = False,
recursion_stack: list = None,
forced_dependencies: Optional[Dict[str, Any]] = None
) -> Tuple[str, Tuple[str, str], Set[str]]:
"""
Generate a lexical closure for a given function or callable.
Ar... |
Generate a lexical closure for a given function or callable.
Args:
func: The function or callable to process.
already_closed: Set of already processed function hashes.
initial_call: Whether this is the initial call to the function.
recursion_stack: Stack to keep track of the re... | lexical_closure | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def _format_source(source: str) -> str:
"""Format the source code using Black."""
try:
return black.format_str(source, mode=black.Mode())
except:
# If Black formatting fails, return the original source
return source | Format the source code using Black. | _format_source | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def _get_globals_and_frees(func: Callable) -> Dict[str, Dict]:
"""Get global and free variables for a function."""
globals_dict = collections.OrderedDict(globalvars(func))
frees_dict = collections.OrderedDict(dill.detect.freevars(func))
if isinstance(func, type):
for name, method in collect... | Get global and free variables for a function. | _get_globals_and_frees | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def _process_default_kwargs(func, dependencies, already_closed, recursion_stack, uses):
"""Process default keyword arguments and annotations of a function."""
ps = inspect.signature(func).parameters
for name, param in ps.items():
if param.default is not inspect.Parameter.empty:
_process_... | Process default keyword arguments and annotations of a function. | _process_default_kwargs | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def _process_callable(var_name, var_value, dependencies, already_closed, recursion_stack, uses):
"""Process a callable (function, method, or class)."""
try:
module_is_ell = 'ell' in inspect.getmodule(var_value).__name__
except:
module_is_ell = False
if var_name not in FORBIDDEN_NAMES a... | Process a callable (function, method, or class). | _process_callable | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def _process_other_variable(var_name, var_value, dependencies, uses):
"""Process variables that are not callables or modules."""
if isinstance(var_value, str) and '\n' in var_value:
dependencies.append(f"{var_name} = '''{var_value}'''")
elif is_immutable_variable(var_value):
dependencies.app... | Process variables that are not callables or modules. | _process_other_variable | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def _dereference_module_names(cur_src, mname, attrs_to_extract):
"""Dereference module names in the source code."""
for attr in attrs_to_extract:
cur_src = cur_src.replace(f"{mname}.{attr}", attr)
return cur_src | Dereference module names in the source code. | _dereference_module_names | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def _raise_error(message, exception, recursion_stack):
"""Raise an error with detailed information."""
error_msg = f"{message}. Error: {str(exception)}\n"
error_msg += f"Recursion stack: {' -> '.join(recursion_stack)}"
# print(error_msg)
raise Exception(error_msg) | Raise an error with detailed information. | _raise_error | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def get_referenced_names(code: str, module_name: str):
"""
This function takes a block of code and a module name as input. It parses the code into an Abstract Syntax Tree (AST)
and walks through the tree to find all instances where an attribute of the module is referenced in the code.
Parameters:
c... |
This function takes a block of code and a module name as input. It parses the code into an Abstract Syntax Tree (AST)
and walks through the tree to find all instances where an attribute of the module is referenced in the code.
Parameters:
code (str): The block of code to be parsed.
module_name (st... | get_referenced_names | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def lexically_closured_source(func, forced_dependencies: Optional[Dict[str, Any]] = None):
"""
Generate a lexically closured source for a given function.
This function takes a callable object (function, method, or class) and generates
a lexically closured source code. It captures all the dependencies, ... |
Generate a lexically closured source for a given function.
This function takes a callable object (function, method, or class) and generates
a lexically closured source code. It captures all the dependencies, including
global variables, free variables, and nested functions, to create a self-contained
... | lexically_closured_source | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def is_function_called(func_name, source_code):
"""
Check if a function is called in the given source code.
Parameters:
func_name (str): The name of the function to check.
source_code (str): The source code to check.
Returns:
bool: True if the function is called, False otherwise.
"""
... |
Check if a function is called in the given source code.
Parameters:
func_name (str): The name of the function to check.
source_code (str): The source code to check.
Returns:
bool: True if the function is called, False otherwise.
| is_function_called | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def globalvars(func, recurse=True, builtin=False):
"""get objects defined in global scope that are referred to by func
return a dict of {name:object}"""
while hasattr(func, "__ell_func__"):
func = func.__ell_func__
if inspect.ismethod(func): func = func.__func__
while hasattr(func, "__ell_f... | get objects defined in global scope that are referred to by func
return a dict of {name:object} | globalvars | python | MadcowD/ell | src/ell/util/closure.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure.py | MIT |
def should_import(module_name : str):
"""
This function checks if a module should be imported based on its origin.
It returns False if the module is in the local directory or if the module's spec is None.
Otherwise, it returns True.
Returns:
bool: True if the module should be imported, False ot... |
This function checks if a module should be imported based on its origin.
It returns False if the module is in the local directory or if the module's spec is None.
Otherwise, it returns True.
Returns:
bool: True if the module should be imported, False otherwise.
| should_import | python | MadcowD/ell | src/ell/util/closure_util.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/closure_util.py | MIT |
def plot_ascii(
image: Image.Image,
width: int = 100,
color: bool = True,
):
"""
Convert a PIL Image to ASCII art using pre-rendered character bitmaps and print it to the console with optional coloring.
"""
num_chars = len(ASCII_CHARS)
# Adjust the s... |
Convert a PIL Image to ASCII art using pre-rendered character bitmaps and print it to the console with optional coloring.
| plot_ascii | python | MadcowD/ell | src/ell/util/plot_ascii.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/plot_ascii.py | MIT |
def is_immutable_variable(value):
"""
Check if a value is immutable.
This function determines whether the given value is of an immutable type in Python.
Immutable types are objects whose state cannot be modified after they are created.
Args:
value: Any Python object to check for immutabili... |
Check if a value is immutable.
This function determines whether the given value is of an immutable type in Python.
Immutable types are objects whose state cannot be modified after they are created.
Args:
value: Any Python object to check for immutability.
Returns:
bool: True if t... | is_immutable_variable | python | MadcowD/ell | src/ell/util/serialization.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/serialization.py | MIT |
def should_import(module_name: str, raise_on_error: bool = False) -> bool:
"""
Determines whether a module should be imported based on its origin.
Excludes local modules and standard library modules.
Args:
module_name (str): The name of the module to check.
Returns:
bool: True if t... |
Determines whether a module should be imported based on its origin.
Excludes local modules and standard library modules.
Args:
module_name (str): The name of the module to check.
Returns:
bool: True if the module should be imported (i.e., it's a third-party module), False otherwise.
... | should_import | python | MadcowD/ell | src/ell/util/should_import.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/should_import.py | MIT |
def compute_color(invoking_lmp: LMP) -> str:
"""Compute and cache a consistent color for a given LMP."""
name_hash = hashlib.md5(invoking_lmp.__name__.encode()).hexdigest()
color_index = int(name_hash, 16) % len(ELL_COLORS)
return list(ELL_COLORS.values())[color_index] | Compute and cache a consistent color for a given LMP. | compute_color | python | MadcowD/ell | src/ell/util/verbosity.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/verbosity.py | MIT |
def format_arg(arg: Any, max_length: int = 8) -> str:
"""Format an argument for display with customizable max length."""
str_arg = str(arg)
return f"{Fore.MAGENTA}{str_arg[:max_length]}..{Style.RESET_ALL}" if len(str_arg) > max_length else f"{Fore.MAGENTA}{str_arg}{Style.RESET_ALL}" | Format an argument for display with customizable max length. | format_arg | python | MadcowD/ell | src/ell/util/verbosity.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/verbosity.py | MIT |
def get_terminal_width() -> int:
"""Get the terminal width, defaulting to 80 if it can't be determined."""
try:
return shutil.get_terminal_size((80, 20)).columns
except Exception:
logger.warning("Unable to determine terminal size. Defaulting to 80 columns.")
return 80 | Get the terminal width, defaulting to 80 if it can't be determined. | get_terminal_width | python | MadcowD/ell | src/ell/util/verbosity.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/verbosity.py | MIT |
def wrap_text_with_prefix(message, width: int, prefix: str, subsequent_prefix: str, text_color: str) -> List[str]:
"""Wrap text while preserving the prefix and color for each line."""
result = []
for i, content in enumerate(message.content):
wrapped_lines = []
if content.image and content.im... | Wrap text while preserving the prefix and color for each line. | wrap_text_with_prefix | python | MadcowD/ell | src/ell/util/verbosity.py | https://github.com/MadcowD/ell/blob/master/src/ell/util/verbosity.py | MIT |
def get_table_metadata(engine, exclude_tables=None):
"""Helper to get table metadata in a consistent format"""
inspector = inspect(engine)
metadata = {}
exclude_tables = exclude_tables or set()
for table_name in inspector.get_table_names():
if table_name in exclude_tables:
c... | Helper to get table metadata in a consistent format | get_table_metadata | python | MadcowD/ell | tests/test_migrations.py | https://github.com/MadcowD/ell/blob/master/tests/test_migrations.py | MIT |
def test_empty_db_migration(temp_db_url):
"""Test migrating an empty database creates same schema as SQLModel metadata"""
# Create engine for empty database
engine = create_engine(temp_db_url)
# Initialize database using migrations
init_or_migrate_database(engine)
# Get schema created ... | Test migrating an empty database creates same schema as SQLModel metadata | test_empty_db_migration | python | MadcowD/ell | tests/test_migrations.py | https://github.com/MadcowD/ell/blob/master/tests/test_migrations.py | MIT |
def test_existing_tables_no_alembic(temp_db_url):
"""Test database with existing tables but no alembic version table"""
engine = create_engine(temp_db_url)
# Create tables using SQLModel
SQLModel.metadata.create_all(engine)
# Initialize/migrate database
init_or_migrate_database(engine)... | Test database with existing tables but no alembic version table | test_existing_tables_no_alembic | python | MadcowD/ell | tests/test_migrations.py | https://github.com/MadcowD/ell/blob/master/tests/test_migrations.py | MIT |
def test_multiple_migrations(temp_db_url):
"""Test running multiple migrations in sequence"""
engine = create_engine(temp_db_url)
# Get alembic config
alembic_cfg = get_alembic_config(str(engine.url))
# Start with empty database
init_or_migrate_database(engine)
# Verify we're ... | Test running multiple migrations in sequence | test_multiple_migrations | python | MadcowD/ell | tests/test_migrations.py | https://github.com/MadcowD/ell/blob/master/tests/test_migrations.py | MIT |
def test_migration_idempotency(temp_db_url):
"""Test that running migrations multiple times is idempotent"""
engine = create_engine(temp_db_url)
# Initial migration
init_or_migrate_database(engine)
initial_metadata = get_table_metadata(engine)
# Run migration again
init_or_migrate_... | Test that running migrations multiple times is idempotent | test_migration_idempotency | python | MadcowD/ell | tests/test_migrations.py | https://github.com/MadcowD/ell/blob/master/tests/test_migrations.py | MIT |
def test_pure_migration_matches_metadata(temp_db_url):
"""
Test that running just the initial migration (without SQLModel.metadata)
creates the same schema as SQLModel.metadata
"""
# Create first empty database for migration
engine1 = create_engine(temp_db_url)
# Get alembic config and... |
Test that running just the initial migration (without SQLModel.metadata)
creates the same schema as SQLModel.metadata
| test_pure_migration_matches_metadata | python | MadcowD/ell | tests/test_migrations.py | https://github.com/MadcowD/ell/blob/master/tests/test_migrations.py | MIT |
def test_translate_to_provider_unregistered_model(self, provider, ell_call_params):
"""
Test that translate_to_provider does not fail when the model is not registered.
It should retain default streaming parameters.
"""
# Set up the EllCallParams with a model that is not registere... |
Test that translate_to_provider does not fail when the model is not registered.
It should retain default streaming parameters.
| test_translate_to_provider_unregistered_model | python | MadcowD/ell | tests/test_openai_provider.py | https://github.com/MadcowD/ell/blob/master/tests/test_openai_provider.py | MIT |
def mock_project_root(monkeypatch):
"""
Fixture to mock the ELL_PROJECT_ROOT environment variable.
"""
project_root = Path("/mock/project/root").resolve()
monkeypatch.setenv("ELL_PROJECT_ROOT", str(project_root))
return project_root |
Fixture to mock the ELL_PROJECT_ROOT environment variable.
| mock_project_root | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def mock_find_spec(module_name, raise_value):
"""
Helper function to create a mock spec object.
"""
mock_spec = MagicMock()
mock_spec.origin = raise_value["origin"]
mock_spec.has_location = raise_value["has_location"]
return mock_spec |
Helper function to create a mock spec object.
| mock_find_spec | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_exception_handling(mock_project_root, mock_sysconfig_paths, mock_site_packages, monkeypatch, capsys):
"""
Test the function's behavior when importlib.util.find_spec raises an exception.
"""
with patch("importlib.util.find_spec", side_effect=Exception("Test Exception")), \
... |
Test the function's behavior when importlib.util.find_spec raises an exception.
| test_should_import_exception_handling | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_raise_on_error(mock_project_root, mock_sysconfig_paths, mock_site_packages, monkeypatch):
"""
Test the function's behavior when raise_on_error is True and an exception is raised.
"""
with patch("importlib.util.find_spec", side_effect=Exception("Test Exception")), \
patch(... |
Test the function's behavior when raise_on_error is True and an exception is raised.
| test_should_import_raise_on_error | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_without_origin(mock_project_root, mock_sysconfig_paths, mock_site_packages, monkeypatch):
"""
Test behavior when the module spec exists but has no origin.
Typically for built-in modules.
"""
with patch("importlib.util.find_spec") as mock_find_spec_func, \
patch("site.... |
Test behavior when the module spec exists but has no origin.
Typically for built-in modules.
| test_should_import_without_origin | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_with_no_spec(mock_project_root, mock_sysconfig_paths, mock_site_packages, monkeypatch):
"""
Test behavior when find_spec returns None, indicating the module cannot be found.
"""
with patch("importlib.util.find_spec", return_value=None), \
patch("site.getsitepackages", ret... |
Test behavior when find_spec returns None, indicating the module cannot be found.
| test_should_import_with_no_spec | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_standard_library():
"""
Test importing a standard library module without mocking.
"""
import importlib
module_name = "json"
result = should_import(module_name)
assert result == True, f"Failed to import standard library module: {module_name}" |
Test importing a standard library module without mocking.
| test_should_import_standard_library | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_third_party():
"""
Test importing a third-party module without mocking.
Ensures that the module is installed in the environment.
"""
import importlib
module_name = "requests"
result = should_import(module_name)
assert result == True, f"Failed to import third-party... |
Test importing a third-party module without mocking.
Ensures that the module is installed in the environment.
| test_should_import_third_party | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_local_module():
"""
Test importing a local module within the project without mocking.
Assumes that 'src.ell.util.local_module' exists.
"""
module_name = "src.ell.util.local_module"
result = should_import(module_name)
assert result == False, f"Local module should not be... |
Test importing a local module within the project without mocking.
Assumes that 'src.ell.util.local_module' exists.
| test_should_import_local_module | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_nonexistent_module():
"""
Test importing a non-existent module without mocking.
"""
module_name = "this_module_does_not_exist"
result = should_import(module_name)
assert result == False, f"Non-existent module should not be imported: {module_name}" |
Test importing a non-existent module without mocking.
| test_should_import_nonexistent_module | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_multiple_standard_library_modules(module_name, expected):
"""
Test importing multiple standard library modules without mocking.
"""
result = should_import(module_name)
assert result == expected, f"Failed to import standard library module: {module_name}" |
Test importing multiple standard library modules without mocking.
| test_should_import_multiple_standard_library_modules | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_windows_specific_module():
"""
Test importing a Windows-specific module without mocking.
"""
module_name = "msvcrt" # Windows-specific module
result = should_import(module_name)
assert result == True, f"Failed to import Windows-specific module: {module_name}" |
Test importing a Windows-specific module without mocking.
| test_should_import_windows_specific_module | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def test_should_import_unix_specific_module():
"""
Test importing a Unix-specific module without mocking.
"""
module_name = "pwd" # Unix-specific module
result = should_import(module_name)
assert result == True, f"Failed to import Unix-specific module: {module_name}" |
Test importing a Unix-specific module without mocking.
| test_should_import_unix_specific_module | python | MadcowD/ell | tests/test_should_import.py | https://github.com/MadcowD/ell/blob/master/tests/test_should_import.py | MIT |
def extract_json_object(text, json_type="dict"):
"""
Extracts a JSON object from a text string.
Parameters:
- text (str): The text containing the JSON data.
- json_type (str): The type of JSON structure to look for ("dict" or "list").
Returns:
- dict or list: The extracted JSON object, or ... |
Extracts a JSON object from a text string.
Parameters:
- text (str): The text containing the JSON data.
- json_type (str): The type of JSON structure to look for ("dict" or "list").
Returns:
- dict or list: The extracted JSON object, or None if parsing fails.
| extract_json_object | python | X-PLUG/MobileAgent | Mobile-Agent-E/MobileAgentE/agents.py | https://github.com/X-PLUG/MobileAgent/blob/master/Mobile-Agent-E/MobileAgentE/agents.py | MIT |
def track_usage(res_json, api_key):
"""
{'id': 'chatcmpl-AbJIS3o0HMEW9CWtRjU43bu2Ccrdu', 'object': 'chat.completion', 'created': 1733455676, 'model': 'gpt-4o-2024-11-20', 'choices': [...], 'usage': {'prompt_tokens': 2731, 'completion_tokens': 235, 'total_tokens': 2966, 'prompt_tokens_details': {'cached_tokens':... |
{'id': 'chatcmpl-AbJIS3o0HMEW9CWtRjU43bu2Ccrdu', 'object': 'chat.completion', 'created': 1733455676, 'model': 'gpt-4o-2024-11-20', 'choices': [...], 'usage': {'prompt_tokens': 2731, 'completion_tokens': 235, 'total_tokens': 2966, 'prompt_tokens_details': {'cached_tokens': 0, 'audio_tokens': 0}, 'completion_tokens_... | track_usage | python | X-PLUG/MobileAgent | Mobile-Agent-E/MobileAgentE/api.py | https://github.com/X-PLUG/MobileAgent/blob/master/Mobile-Agent-E/MobileAgentE/api.py | MIT |
def save_screenshot_to_file(adb_path, file_path="screenshot.png"):
"""
Captures a screenshot from an Android device using ADB, saves it locally, and removes the screenshot from the device.
Args:
adb_path (str): The path to the adb executable.
Returns:
str: The path to the saved screens... |
Captures a screenshot from an Android device using ADB, saves it locally, and removes the screenshot from the device.
Args:
adb_path (str): The path to the adb executable.
Returns:
str: The path to the saved screenshot, or raises an exception on failure.
| save_screenshot_to_file | python | X-PLUG/MobileAgent | Mobile-Agent-E/MobileAgentE/controller.py | https://github.com/X-PLUG/MobileAgent/blob/master/Mobile-Agent-E/MobileAgentE/controller.py | MIT |
def box_iou(boxes1: np.ndarray, boxes2: np.ndarray) -> np.ndarray:
"""
Fast vectorized IOU implementation using only NumPy
boxes1: [N, 4] array of boxes
boxes2: [M, 4] array of boxes
Returns: [N, M] array of IOU values
"""
# Calculate areas of boxes1
area1 = (boxes1[:, 2] - boxes1[:, 0])... |
Fast vectorized IOU implementation using only NumPy
boxes1: [N, 4] array of boxes
boxes2: [M, 4] array of boxes
Returns: [N, M] array of IOU values
| box_iou | python | X-PLUG/MobileAgent | PC-Agent/pymac.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pymac.py | MIT |
def filter_ocr_elements(
self,
ocr_bboxes, # [(content, bbox), ]
preserved_nodes: List[Dict],
) -> Tuple[List[str], List[Dict]]:
"""
Add OCR-detected elements to the accessibility tree if they don't overlap with existing elements
Uses opti... |
Add OCR-detected elements to the accessibility tree if they don't overlap with existing elements
Uses optimized NumPy implementation
| filter_ocr_elements | python | X-PLUG/MobileAgent | PC-Agent/pymac.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pymac.py | MIT |
def click(
self,
element_id: int,
num_clicks: int = 1,
button_type: str = "left",
hold_keys: List = [],
):
"""Click on the element
Args:
element_id:int, ID of the element to click on
num_clicks:int, number of times to click the element
... | Click on the element
Args:
element_id:int, ID of the element to click on
num_clicks:int, number of times to click the element
button_type:str, which mouse button to press can be "left", "middle", or "right"
hold_keys:List, list of keys to hold while clicking
... | click | python | X-PLUG/MobileAgent | PC-Agent/pymac.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pymac.py | MIT |
def type(
self,
element_id: int = None,
text: str = "",
overwrite: bool = False,
enter: bool = False,
):
"""Type text into the element
Args:
element_id:int ID of the element to type into. If not provided, typing will start at the current cursor loc... | Type text into the element
Args:
element_id:int ID of the element to type into. If not provided, typing will start at the current cursor location.
text:str the text to type
overwrite:bool Assign it to True if the text should overwrite the existing text, otherwise assign it to... | type | python | X-PLUG/MobileAgent | PC-Agent/pymac.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pymac.py | MIT |
def drag_and_drop(self, drag_from_id: int, drop_on_id: int, hold_keys: List = []):
"""Drag element1 and drop it on element2.
Args:
drag_from_id:int ID of element to drag
drop_on_id:int ID of element to drop on
hold_keys:List list of keys to hold while dragging
... | Drag element1 and drop it on element2.
Args:
drag_from_id:int ID of element to drag
drop_on_id:int ID of element to drop on
hold_keys:List list of keys to hold while dragging
| drag_and_drop | python | X-PLUG/MobileAgent | PC-Agent/pymac.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pymac.py | MIT |
def scroll(self, element_id: int, clicks: int):
"""Scroll in the specified direction inside the specified element
Args:
element_id:int ID of the element to scroll in
clicks:int the number of clicks to scroll can be positive (up) or negative (down).
"""
try:
... | Scroll in the specified direction inside the specified element
Args:
element_id:int ID of the element to scroll in
clicks:int the number of clicks to scroll can be positive (up) or negative (down).
| scroll | python | X-PLUG/MobileAgent | PC-Agent/pymac.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pymac.py | MIT |
def hotkey(self, keys: List):
"""Press a hotkey combination
Args:
keys:List the keys to press in combination in a list format (e.g. ['shift', 'c'])
"""
# Normalize any 'cmd' to 'command'
keys = [_normalize_key(k) for k in keys]
# add quotes around the keys
... | Press a hotkey combination
Args:
keys:List the keys to press in combination in a list format (e.g. ['shift', 'c'])
| hotkey | python | X-PLUG/MobileAgent | PC-Agent/pymac.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pymac.py | MIT |
def hold_and_press(self, hold_keys: List, press_keys: List):
"""Hold a list of keys and press a list of keys
Args:
hold_keys:List, list of keys to hold
press_keys:List, list of keys to press in a sequence
"""
# Normalize any 'cmd' to 'command' in both lists
... | Hold a list of keys and press a list of keys
Args:
hold_keys:List, list of keys to hold
press_keys:List, list of keys to press in a sequence
| hold_and_press | python | X-PLUG/MobileAgent | PC-Agent/pymac.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pymac.py | MIT |
def type(
self,
element_id: int = None,
text: str = "",
overwrite: bool = False,
enter: bool = False,
):
"""Type text into the element
Args:
element_id:int ID of the element to type into. If not provided, typing will start at the current cursor loc... | Type text into the element
Args:
element_id:int ID of the element to type into. If not provided, typing will start at the current cursor location.
text:str the text to type
overwrite:bool Assign it to True if the text should overwrite the existing text, otherwise assign it to... | type | python | X-PLUG/MobileAgent | PC-Agent/pywin.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pywin.py | MIT |
def hotkey(self, keys: List[str]):
"""Press a hotkey combination
Args:
keys:List[str] the keys to press in combination in a list format (e.g. ['shift', 'c'])
"""
keys = [_normalize_key(k) for k in keys]
keys = [f"'{key}'" for key in keys]
command = f"import py... | Press a hotkey combination
Args:
keys:List[str] the keys to press in combination in a list format (e.g. ['shift', 'c'])
| hotkey | python | X-PLUG/MobileAgent | PC-Agent/pywin.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pywin.py | MIT |
def hold_and_press(self, hold_keys: List[str], press_keys: List[str]):
"""Hold a list of keys and press a list of keys
Args:
hold_keys:List[str], list of keys to hold
press_keys:List[str], list of keys to press in a sequence
"""
hold_keys = [_normalize_key(k) for ... | Hold a list of keys and press a list of keys
Args:
hold_keys:List[str], list of keys to hold
press_keys:List[str], list of keys to press in a sequence
| hold_and_press | python | X-PLUG/MobileAgent | PC-Agent/pywin.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/pywin.py | MIT |
def compute_iou(box1, box2):
"""
Compute the Intersection over Union (IoU) of two bounding boxes.
Parameters:
- box1: list or array [x1, y1, x2, y2]
- box2: list or array [x1, y1, x2, y2]
Returns:
- iou: float, IoU value
"""
x1_inter = max(box1[0], box2[0])
y1_inter = max(box1[... |
Compute the Intersection over Union (IoU) of two bounding boxes.
Parameters:
- box1: list or array [x1, y1, x2, y2]
- box2: list or array [x1, y1, x2, y2]
Returns:
- iou: float, IoU value
| compute_iou | python | X-PLUG/MobileAgent | PC-Agent/PCAgent/merge_strategy.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/PCAgent/merge_strategy.py | MIT |
def merge_boxes_and_texts(texts, boxes, iou_threshold=0):
"""
Merge bounding boxes and their corresponding texts based on IoU threshold.
Parameters:
- boxes: List of bounding boxes, with each box represented as [x1, y1, x2, y2].
- texts: List of texts corresponding to each bounding box.
- iou_t... |
Merge bounding boxes and their corresponding texts based on IoU threshold.
Parameters:
- boxes: List of bounding boxes, with each box represented as [x1, y1, x2, y2].
- texts: List of texts corresponding to each bounding box.
- iou_threshold: Intersection-over-Union threshold for merging boxes.
... | merge_boxes_and_texts | python | X-PLUG/MobileAgent | PC-Agent/PCAgent/merge_strategy.py | https://github.com/X-PLUG/MobileAgent/blob/master/PC-Agent/PCAgent/merge_strategy.py | MIT |
def get_parser(cls) -> ConfigParser:
"""Get a parser for post-training quantization of a diffusion model.
Returns:
`ConfigParser`:
A parser for post-training quantization of a diffusion model.
"""
parser = ConfigParser("Diffusion Run configuration")
D... | Get a parser for post-training quantization of a diffusion model.
Returns:
`ConfigParser`:
A parser for post-training quantization of a diffusion model.
| get_parser | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/config.py | Apache-2.0 |
def ptq( # noqa: C901
model: DiffusionModelStruct,
config: DiffusionQuantConfig,
cache: DiffusionPtqCacheConfig | None = None,
load_dirpath: str = "",
save_dirpath: str = "",
copy_on_save: bool = False,
save_model: bool = False,
) -> DiffusionModelStruct:
"""Post-training quantization o... | Post-training quantization of a diffusion model.
Args:
model (`DiffusionModelStruct`):
The diffusion model.
config (`DiffusionQuantConfig`):
The diffusion model post-training quantization configuration.
cache (`DiffusionPtqCacheConfig`, *optional*, defaults to `None`... | ptq | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/ptq.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/ptq.py | Apache-2.0 |
def main(config: DiffusionPtqRunConfig, logging_level: int = tools.logging.DEBUG) -> DiffusionPipeline:
"""Post-training quantization of a diffusion model.
Args:
config (`DiffusionPtqRunConfig`):
The diffusion model post-training quantization configuration.
logging_level (`int`, *op... | Post-training quantization of a diffusion model.
Args:
config (`DiffusionPtqRunConfig`):
The diffusion model post-training quantization configuration.
logging_level (`int`, *optional*, defaults to `logging.DEBUG`):
The logging level.
Returns:
`DiffusionPipeline`... | main | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/ptq.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/ptq.py | Apache-2.0 |
def info(
self,
name: str,
module: nn.Module,
tensors: dict[int | str, torch.Tensor],
cache: TensorsCache,
) -> None:
"""Update cache information.
Args:
name (`str`):
Module name.
module (`nn.Module`):
M... | Update cache information.
Args:
name (`str`):
Module name.
module (`nn.Module`):
Module.
tensors (`dict[int | str, torch.Tensor]`):
Tensors to cache.
cache (`TensorsCache`):
Cache.
| info | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/dataset/calib.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/dataset/calib.py | Apache-2.0 |
def __init__(self, config: DiffusionCalibCacheLoaderConfig) -> None:
"""Initialize the cache for the diffusion calibration dataset.
Args:
config (`DiffusionCalibCacheLoaderConfig`):
Configuration for the calibration cache loader.
"""
super().__init__(dataset=... | Initialize the cache for the diffusion calibration dataset.
Args:
config (`DiffusionCalibCacheLoaderConfig`):
Configuration for the calibration cache loader.
| __init__ | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/dataset/calib.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/dataset/calib.py | Apache-2.0 |
def _init_cache(self, name: str, module: nn.Module) -> IOTensorsCache:
"""Initialize cache.
Args:
name (`str`):
Module name.
module (`nn.Module`):
Module.
Returns:
`IOTensorsCache`:
Cache for inputs and outputs... | Initialize cache.
Args:
name (`str`):
Module name.
module (`nn.Module`):
Module.
Returns:
`IOTensorsCache`:
Cache for inputs and outputs.
| _init_cache | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/dataset/calib.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/dataset/calib.py | Apache-2.0 |
def _convert_layer_inputs(
self, m: nn.Module, args: tuple[tp.Any, ...], kwargs: dict[str, tp.Any], save_all: bool = False
) -> ModuleForwardInput:
"""Convert layer inputs to module forward input.
Args:
m (`nn.Module`):
Layer.
args (`tuple[Any, ...]`)... | Convert layer inputs to module forward input.
Args:
m (`nn.Module`):
Layer.
args (`tuple[Any, ...]`):
Layer input arguments.
kwargs (`dict[str, Any]`):
Layer input keyword arguments.
save_all (`bool`, *optional*, de... | _convert_layer_inputs | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/dataset/calib.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/dataset/calib.py | Apache-2.0 |
def _convert_layer_outputs(self, m: nn.Module, outputs: tp.Any) -> dict[str | int, tp.Any]:
"""Convert layer outputs to dictionary for updating the next layer inputs.
Args:
m (`nn.Module`):
Layer.
outputs (`Any`):
Layer outputs.
Returns:
... | Convert layer outputs to dictionary for updating the next layer inputs.
Args:
m (`nn.Module`):
Layer.
outputs (`Any`):
Layer outputs.
Returns:
`dict[str | int, Any]`:
Dictionary for updating the next layer inputs.
... | _convert_layer_outputs | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/dataset/calib.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/dataset/calib.py | Apache-2.0 |
def iter_layer_activations( # noqa: C901
self,
model: nn.Module | DiffusionModelStruct,
*args,
needs_inputs_fn: tp.Callable[[str, nn.Module], bool],
needs_outputs_fn: tp.Callable[[str, nn.Module], bool] | None = None,
action: CacheAction | None = None,
skip_pre_m... | Iterate over model activations in layers.
Args:
model (`nn.Module`):
Model.
action (`CacheAction`):
Action for caching activations.
needs_inputs_fn (`Callable[[str, nn.Module], bool]` or `bool` or `None`, *optional*, defaults to `True`):
... | iter_layer_activations | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/dataset/calib.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/dataset/calib.py | Apache-2.0 |
def categorize_metrics(metrics: tuple[str, ...]) -> tuple[list[str], list[str], list[str]]:
"""
Categorize metrics into multimodal, similarity, and other metrics.
Args:
metrics (tuple[str, ...]): List of metrics.
Returns:
tuple[list[str], list[str], list[str]]: Tuple of multimodal, sim... |
Categorize metrics into multimodal, similarity, and other metrics.
Args:
metrics (tuple[str, ...]): List of metrics.
Returns:
tuple[list[str], list[str], list[str]]: Tuple of multimodal, similarity, and other metrics.
| categorize_metrics | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/eval/metrics/__init__.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/eval/metrics/__init__.py | Apache-2.0 |
def replace_up_block_conv_with_concat_conv(model: nn.Module) -> None:
"""Replace up_block convolutions in UNet with ConcatConv."""
model_struct = DiffusionModelStruct.construct(model)
if not isinstance(model_struct, UNetStruct):
return
logger = tools.logging.getLogger(__name__)
logger.info("... | Replace up_block convolutions in UNet with ConcatConv. | replace_up_block_conv_with_concat_conv | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/nn/patch.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/nn/patch.py | Apache-2.0 |
def replace_fused_linear_with_concat_linear(model: nn.Module) -> None:
"""Replace fused Linear in FluxSingleTransformerBlock with ConcatLinear."""
logger = tools.logging.getLogger(__name__)
logger.info("Replacing fused Linear with ConcatLinear.")
tools.logging.Formatter.indent_inc()
for name, module... | Replace fused Linear in FluxSingleTransformerBlock with ConcatLinear. | replace_fused_linear_with_concat_linear | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/nn/patch.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/nn/patch.py | Apache-2.0 |
def shift_input_activations(model: nn.Module) -> None:
"""Shift input activations of convolutions and linear layers if their lowerbound is negative.
Args:
model (nn.Module): model to shift input activations.
"""
logger = tools.logging.getLogger(__name__)
model_struct = DiffusionModelStruct.... | Shift input activations of convolutions and linear layers if their lowerbound is negative.
Args:
model (nn.Module): model to shift input activations.
| shift_input_activations | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/nn/patch.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/nn/patch.py | Apache-2.0 |
def get_iter_layer_activations_args(
self, skip_pre_modules: bool, skip_post_modules: bool, **input_kwargs
) -> tuple[list[nn.Module], list[DiffusionModuleStruct | DiffusionBlockStruct], list[bool], list[bool]]:
"""
Get the arguments for iterating over the layers and their activations.
... |
Get the arguments for iterating over the layers and their activations.
Args:
skip_pre_modules (`bool`):
Whether to skip the pre-modules
skip_post_modules (`bool`):
Whether to skip the post-modules
Returns:
`tuple[list[nn.Modu... | get_iter_layer_activations_args | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/nn/struct.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/nn/struct.py | Apache-2.0 |
def _simplify_keys(keys: tp.Iterable[str], *, key_map: dict[str, set[str]]) -> list[str]:
"""Simplify the keys based on the key map.
Args:
keys (`Iterable[str]`):
The keys to simplify.
key_map (`dict[str, set[str]]`):
The key map.
Returns... | Simplify the keys based on the key map.
Args:
keys (`Iterable[str]`):
The keys to simplify.
key_map (`dict[str, set[str]]`):
The key map.
Returns:
`list[str]`:
The simplified keys.
| _simplify_keys | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/nn/struct.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/nn/struct.py | Apache-2.0 |
def filter_kwargs(self, kwargs: dict) -> dict:
"""Filter layer kwargs to attn kwargs."""
if isinstance(self.parent.module, BasicTransformerBlock):
if kwargs.get("cross_attention_kwargs", None) is None:
attn_kwargs = {}
else:
attn_kwargs = dict(kwar... | Filter layer kwargs to attn kwargs. | filter_kwargs | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/nn/struct.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/nn/struct.py | Apache-2.0 |
def build(
self, *, dtype: str | torch.dtype | None = None, device: str | torch.device | None = None
) -> DiffusionPipeline:
"""Build the diffusion pipeline.
Args:
dtype (`str` or `torch.dtype`, *optional*):
The data type of the pipeline.
device (`str... | Build the diffusion pipeline.
Args:
dtype (`str` or `torch.dtype`, *optional*):
The data type of the pipeline.
device (`str` or `torch.device`, *optional*):
The device of the pipeline.
Returns:
`DiffusionPipeline`:
The... | build | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/pipeline/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/pipeline/config.py | Apache-2.0 |
def extract_text_encoders(
self, pipeline: DiffusionPipeline, supported: tuple[type[PreTrainedModel], ...] = (T5EncoderModel,)
) -> list[tuple[str, PreTrainedModel, PreTrainedTokenizer]]:
"""Extract the text encoders and tokenizers from the pipeline.
Args:
pipeline (`DiffusionPi... | Extract the text encoders and tokenizers from the pipeline.
Args:
pipeline (`DiffusionPipeline`):
The diffusion pipeline.
supported (`tuple[type[PreTrainedModel], ...]`, *optional*, defaults to `(T5EncoderModel,)`):
The supported text encoder types. If no... | extract_text_encoders | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/pipeline/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/pipeline/config.py | Apache-2.0 |
def register_pipeline_factory(
cls,
names: str | tuple[str, ...],
/,
factory: tp.Callable[[str, str, torch.dtype, torch.device, bool], DiffusionPipeline],
*,
overwrite: bool = False,
) -> None:
"""Register a pipeline factory.
Args:
names (... | Register a pipeline factory.
Args:
names (`str` or `tuple[str, ...]`):
The name of the pipeline.
factory (`Callable[[str, str,torch.dtype, torch.device, bool], DiffusionPipeline]`):
The pipeline factory function.
overwrite (`bool`, *optional*,... | register_pipeline_factory | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/pipeline/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/pipeline/config.py | Apache-2.0 |
def register_text_extractor(
cls,
names: str | tuple[str, ...],
/,
extractor: tp.Callable[
[DiffusionPipeline, tuple[type[PreTrainedModel], ...]],
list[tuple[str, PreTrainedModel, PreTrainedTokenizer]],
],
*,
overwrite: bool = False,
) ... | Register a text extractor.
Args:
names (`str` or `tuple[str, ...]`):
The name of the pipeline.
extractor (`Callable[[DiffusionPipeline], list[tuple[str, PreTrainedModel, PreTrainedTokenizer]]`):
The text extractor function.
overwrite (`bool`, ... | register_text_extractor | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/pipeline/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/pipeline/config.py | Apache-2.0 |
def quantize_diffusion_block_activations( # noqa: C901
layer: DiffusionBlockStruct | DiffusionModuleStruct,
config: DiffusionQuantConfig,
quantizer_state_dict: dict[str, dict[str, torch.Tensor | float | None]],
layer_cache: dict[str, IOTensorsCache] | None = None,
layer_kwargs: dict[str, tp.Any] | ... | Quantize the activations of a diffusion model block.
Args:
layer (`DiffusionBlockStruct` or `DiffusionModuleStruct`):
The diffusion model block.
config (`DiffusionQuantConfig`):
The quantization configuration.
quantizer_state_dict (`dict[str, dict[str, torch.Tensor |... | quantize_diffusion_block_activations | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/activation.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/activation.py | Apache-2.0 |
def quantize_diffusion_activations(
model: nn.Module | DiffusionModelStruct,
config: DiffusionQuantConfig,
quantizer_state_dict: dict[str, dict[str, torch.Tensor | float | None]] | None = None,
orig_state_dict: dict[str, torch.Tensor] | None = None,
) -> dict[str, dict[str, torch.Tensor | float | None]]... | Quantize the activations of a diffusion model.
Args:
model (`nn.Module` or `DiffusionModelStruct`):
The diffusion model.
config (`DiffusionQuantConfig`):
The quantization configuration.
quantizer_state_dict (`dict[str, dict[str, torch.Tensor | float | None]]`, *optio... | quantize_diffusion_activations | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/activation.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/activation.py | Apache-2.0 |
def needs_acts_quantizer_cache(self) -> bool:
"""Whether to cache the activations quantizer settings."""
if self.enabled_ipts and self.ipts.needs_calib_data:
return True
if self.enabled_opts and self.opts.needs_calib_data:
return True
return False | Whether to cache the activations quantizer settings. | needs_acts_quantizer_cache | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/config.py | Apache-2.0 |
def generate_cache_dirpath(
self, *, root: str, shift: bool, default_dtype: torch.dtype = torch.float16
) -> DiffusionQuantCacheConfig: # noqa: C901
"""Generate the cache paths for the module quantization configuration."""
quant_names = self.generate_dirnames(default_dtype=default_dtype)
... | Generate the cache paths for the module quantization configuration. | generate_cache_dirpath | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/config.py | Apache-2.0 |
def generate_default_dirname(self) -> str: # noqa: C901
"""Generate output directory name for evaluating a large language model."""
key_map = type(self)._key_map
def simplify_skips(skips):
return set(
DiffusionQuantCacheConfig.simplify_path("skip.[{}]".format("+".jo... | Generate output directory name for evaluating a large language model. | generate_default_dirname | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/config.py | Apache-2.0 |
def organize(self) -> dict[str, bool]: # noqa: C901
"""Organize the flags for the diffusion model quantization configuration.
Returns:
dict[str, bool]: The organized flags.
"""
key_map = type(self)._key_map
wgts_skip_set, ipts_skip_set, opts_skip_set = set(), set(),... | Organize the flags for the diffusion model quantization configuration.
Returns:
dict[str, bool]: The organized flags.
| organize | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/config.py | Apache-2.0 |
def rotate_diffusion( # noqa: C901
model: DiffusionModelStruct, /, config: DiffusionQuantConfig
):
"""Rotate the weights of the diffusion model.
Args:
model (`PreTrainedModel` or `LlmStruct`):
Model to be rotated.
config (`QuantRotationConfig`):
Rotation configurati... | Rotate the weights of the diffusion model.
Args:
model (`PreTrainedModel` or `LlmStruct`):
Model to be rotated.
config (`QuantRotationConfig`):
Rotation configuration.
| rotate_diffusion | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/rotate.py | Apache-2.0 |
def smooth_diffusion_layer(
layer: DiffusionBlockStruct,
config: DiffusionQuantConfig,
smooth_cache: dict[str, torch.Tensor],
layer_cache: dict[str, IOTensorsCache] | None = None,
layer_kwargs: dict[str, tp.Any] | None = None,
) -> None:
"""Smooth a single diffusion model block.
Args:
... | Smooth a single diffusion model block.
Args:
layer (`DiffusionBlockStruct`):
The diffusion block.
config (`DiffusionQuantConfig`):
The quantization configuration.
smooth_cache (`dict[str, torch.Tensor]`):
The smoothing scales cache.
layer_cache (`... | smooth_diffusion_layer | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/smooth.py | Apache-2.0 |
def smooth_diffusion(
model: nn.Module | DiffusionModelStruct,
config: DiffusionQuantConfig,
smooth_cache: dict[str, torch.Tensor] | None = None,
) -> dict[str, torch.Tensor]:
"""Smooth the diffusion model.
Args:
model (`nn.Module` or `DiffusionModelStruct`):
The diffusion model... | Smooth the diffusion model.
Args:
model (`nn.Module` or `DiffusionModelStruct`):
The diffusion model.
config (`DiffusionQuantConfig`):
The quantization configuration.
smooth_cache (`dict[str, torch.Tensor]`, *optional*):
The smoothing scales cache.
R... | smooth_diffusion | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/smooth.py | Apache-2.0 |
def get_needs_inputs_fn(
model: DiffusionModelStruct, config: DiffusionQuantConfig
) -> tp.Callable[[str, nn.Module], bool]:
"""Get function that checks whether the module needs to cache inputs.
Args:
model (`DiffusionModelStruct`):
The diffused model.
config (`DiffusionQuantCon... | Get function that checks whether the module needs to cache inputs.
Args:
model (`DiffusionModelStruct`):
The diffused model.
config (`DiffusionQuantConfig`):
The quantization configuration.
Returns:
`Callable[[str, nn.Module], bool]`:
The function th... | get_needs_inputs_fn | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/utils.py | Apache-2.0 |
def get_needs_outputs_fn(
model: DiffusionModelStruct, config: DiffusionQuantConfig
) -> tp.Callable[[str, nn.Module], bool]:
"""Get function that checks whether the module needs to cache outputs.
Args:
model (`DiffusionModelStruct`):
The diffused model.
config (`DiffusionQuantC... | Get function that checks whether the module needs to cache outputs.
Args:
model (`DiffusionModelStruct`):
The diffused model.
config (`DiffusionQuantConfig`):
The quantization configuration.
Returns:
`Callable[[str, nn.Module], bool]`:
The function t... | get_needs_outputs_fn | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/utils.py | Apache-2.0 |
def calibrate_diffusion_block_low_rank_branch( # noqa: C901
layer: DiffusionModuleStruct | DiffusionBlockStruct,
config: DiffusionQuantConfig,
branch_state_dict: dict[str, dict[str, torch.Tensor]],
layer_cache: dict[str, IOTensorsCache] = None,
layer_kwargs: dict[str, tp.Any] = None,
) -> None:
... | Calibrate low-rank branches for a block of a diffusion model.
Args:
layer (`DiffusionModuleStruct` or `DiffusionBlockStruct`):
The block to calibrate.
config (`DiffusionQuantConfig`):
The quantization configuration.
branch_state_dict (`dict[str, dict[str, torch.Tenso... | calibrate_diffusion_block_low_rank_branch | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/weight.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/weight.py | Apache-2.0 |
def update_diffusion_block_weight_quantizer_state_dict(
layer: DiffusionModuleStruct | DiffusionBlockStruct,
config: DiffusionQuantConfig,
quantizer_state_dict: dict[str, dict[str, torch.Tensor | float | None]],
layer_cache: dict[str, IOTensorsCache],
layer_kwargs: dict[str, tp.Any],
):
"""Updat... | Update the state dict of the weight quantizers for a block of a diffusion model.
Args:
layer (`DiffusionModuleStruct` or `DiffusionBlockStruct`):
The block to update.
config (`DiffusionQuantConfig`):
The quantization configuration.
quantizer_state_dict (`dict[str, di... | update_diffusion_block_weight_quantizer_state_dict | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/weight.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/weight.py | Apache-2.0 |
def quantize_diffusion_block_weights(
layer: DiffusionModuleStruct | DiffusionBlockStruct,
config: DiffusionQuantConfig,
quantizer_state_dict: dict[str, dict[str, torch.Tensor | float | None]],
layer_cache: dict[str, IOTensorsCache] = None,
return_with_scale_state_dict: bool = False,
) -> dict[str, ... | Quantize the weights of a block of a diffusion model.
Args:
layer (`DiffusionModuleStruct` or `DiffusionBlockStruct`):
The block to quantize.
config (`DiffusionQuantConfig`):
The quantization configuration.
quantizer_state_dict (`dict[str, dict[str, torch.Tensor | fl... | quantize_diffusion_block_weights | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/weight.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/weight.py | Apache-2.0 |
def quantize_diffusion_weights(
model: nn.Module | DiffusionModelStruct,
config: DiffusionQuantConfig,
quantizer_state_dict: dict[str, dict[str, torch.Tensor | float | None]] | None = None,
branch_state_dict: dict[str, dict[str, torch.Tensor]] | None = None,
return_with_scale_state_dict: bool = Fals... | Quantize the weights of a diffusion model.
Args:
model (`nn.Module` or `DiffusionModelStruct`):
The diffusion model to quantize.
config (`DiffusionQuantConfig`):
The quantization configuration.
quantizer_state_dict (`dict[str, dict[str, torch.Tensor | float | None]]`... | quantize_diffusion_weights | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/weight.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/weight.py | Apache-2.0 |
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