code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def test_n_dimensional_is_within_visible_spectrum(self) -> None:
"""
Test :func:`colour.volume.spectrum.is_within_visible_spectrum`
definition n-dimensional arrays support.
"""
a = np.array([0.3205, 0.4131, 0.5100])
b = is_within_visible_spectrum(a)
a = np.tile(... |
Test :func:`colour.volume.spectrum.is_within_visible_spectrum`
definition n-dimensional arrays support.
| test_n_dimensional_is_within_visible_spectrum | python | colour-science/colour | colour/volume/tests/test_spectrum.py | https://github.com/colour-science/colour/blob/master/colour/volume/tests/test_spectrum.py | BSD-3-Clause |
def test_nan_is_within_visible_spectrum(self) -> None:
"""
Test :func:`colour.volume.spectrum.is_within_visible_spectrum`
definition nan support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = np.array(list(set(product(cases, repeat=3))))
is_within... |
Test :func:`colour.volume.spectrum.is_within_visible_spectrum`
definition nan support.
| test_nan_is_within_visible_spectrum | python | colour-science/colour | colour/volume/tests/test_spectrum.py | https://github.com/colour-science/colour/blob/master/colour/volume/tests/test_spectrum.py | BSD-3-Clause |
def extract_todo_items(root_directory: str) -> dict:
"""
Extract the TODO items from given directory.
Parameters
----------
root_directory
Directory to extract the TODO items from.
Returns
-------
:class:`dict`
TODO items.
"""
todo_items = {}
for root, _dir... |
Extract the TODO items from given directory.
Parameters
----------
root_directory
Directory to extract the TODO items from.
Returns
-------
:class:`dict`
TODO items.
| extract_todo_items | python | colour-science/colour | utilities/export_todo.py | https://github.com/colour-science/colour/blob/master/utilities/export_todo.py | BSD-3-Clause |
def export_todo_items(todo_items: dict, file_path: str) -> None:
"""
Export TODO items to given file.
Parameters
----------
todo_items
TODO items.
file_path
File to write the TODO items to.
"""
todo_rst = []
for module, module_todo_items in todo_items.items():
... |
Export TODO items to given file.
Parameters
----------
todo_items
TODO items.
file_path
File to write the TODO items to.
| export_todo_items | python | colour-science/colour | utilities/export_todo.py | https://github.com/colour-science/colour/blob/master/utilities/export_todo.py | BSD-3-Clause |
def generate_documentation_plots(output_directory: str) -> None:
"""
Generate documentation plots.
Parameters
----------
output_directory
Output directory.
"""
filter_warnings()
colour_style()
np.random.seed(0)
# ******************************************************... |
Generate documentation plots.
Parameters
----------
output_directory
Output directory.
| generate_documentation_plots | python | colour-science/colour | utilities/generate_plots.py | https://github.com/colour-science/colour/blob/master/utilities/generate_plots.py | BSD-3-Clause |
def literalise(path_module_hints: str = PATH_MODULE_HINTS) -> None:
"""
Write various literals in the `colour.hints` module.
Parameters
----------
path_module_hints
Path to the hints module.
"""
with open(path_module_hints) as file_module_hints:
content = file_module_hints.... |
Write various literals in the `colour.hints` module.
Parameters
----------
path_module_hints
Path to the hints module.
| literalise | python | colour-science/colour | utilities/literalise.py | https://github.com/colour-science/colour/blob/master/utilities/literalise.py | BSD-3-Clause |
def unicode_to_ascii(root_directory: str) -> None:
"""
Recursively convert from unicode to ASCII *.py*, *.bib* and *.rst* files
in given directory.
Parameters
----------
root_directory
Directory to convert the files from unicode to ASCII.
"""
for root, _dirnames, filenames in o... |
Recursively convert from unicode to ASCII *.py*, *.bib* and *.rst* files
in given directory.
Parameters
----------
root_directory
Directory to convert the files from unicode to ASCII.
| unicode_to_ascii | python | colour-science/colour | utilities/unicode_to_ascii.py | https://github.com/colour-science/colour/blob/master/utilities/unicode_to_ascii.py | BSD-3-Clause |
def main(argv):
'''
examine.py is used to display environments and run policies.
For an example environment jsonnet, see
mujoco-worldgen/examples/example_env_examine.jsonnet
You can find saved policies and the in the 'examples' together with the environment they were
trained in and the hype... |
examine.py is used to display environments and run policies.
For an example environment jsonnet, see
mujoco-worldgen/examples/example_env_examine.jsonnet
You can find saved policies and the in the 'examples' together with the environment they were
trained in and the hyperparameters used. The n... | main | python | openai/multi-agent-emergence-environments | bin/examine.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/bin/examine.py | MIT |
def _get_obs(self, sim):
'''
Loops through modules, calls their observation_step functions, and
adds the result to the observation dictionary.
'''
obs = {}
for module in self.modules:
obs.update(module.observation_step(self, self.sim))
retu... |
Loops through modules, calls their observation_step functions, and
adds the result to the observation dictionary.
| _get_obs | python | openai/multi-agent-emergence-environments | mae_envs/envs/base.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/base.py | MIT |
def _get_sim(self, seed):
'''
Calls build_world_step and then modify_sim_step for each module. If
a build_world_step failed, then restarts.
'''
self.floor_size = np.random.uniform(self.floor_size_dist[0], self.floor_size_dist[1])
self.metadata['floor_size'] = self... |
Calls build_world_step and then modify_sim_step for each module. If
a build_world_step failed, then restarts.
| _get_sim | python | openai/multi-agent-emergence-environments | mae_envs/envs/base.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/base.py | MIT |
def make_env(n_substeps=5, horizon=250, deterministic_mode=False, n_agents=2,
n_boxes=2, n_ramps=1):
'''
This make_env function is not used anywhere; it exists to provide a simple, bare-bones
example of how to construct a multi-agent environment using the modules framework.
'''
... |
This make_env function is not used anywhere; it exists to provide a simple, bare-bones
example of how to construct a multi-agent environment using the modules framework.
| make_env | python | openai/multi-agent-emergence-environments | mae_envs/envs/base.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/base.py | MIT |
def _get_next_obj(self, obs):
'''
Return the next object that needs to be locked & the distance to it.
'''
agent_pos = obs[self.agent_key][:, :2]
if len(self.unlocked_objs) == 0:
next_obj = None
next_obj_dist = 0
elif self.task == 'order':
... |
Return the next object that needs to be locked & the distance to it.
| _get_next_obj | python | openai/multi-agent-emergence-environments | mae_envs/envs/box_locking.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/box_locking.py | MIT |
def _get_lock_reward(self, curr_objs_locked, old_objs_locked):
'''
Calculates the locking reward / unlocking penalty
'''
n_new_lock = np.sum(np.logical_and(curr_objs_locked == 1, old_objs_locked == 0))
n_new_unlock = np.sum(np.logical_and(curr_objs_locked == 0, old_objs_locke... |
Calculates the locking reward / unlocking penalty
| _get_lock_reward | python | openai/multi-agent-emergence-environments | mae_envs/envs/box_locking.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/box_locking.py | MIT |
def _get_shaped_reward(self, new_next_obj, new_next_obj_dist, new_spawn_pos_dist):
'''
Calculates the shaped reward based on the change in distance from the target
'''
rew = 0
if (self.next_obj is not None) and (new_next_obj == self.next_obj):
rew += (self.next_ob... |
Calculates the shaped reward based on the change in distance from the target
| _get_shaped_reward | python | openai/multi-agent-emergence-environments | mae_envs/envs/box_locking.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/box_locking.py | MIT |
def tri_placement(tri_room_idx):
'''
This function expects the wall scenario to be 'var_tri'
Returns a placement function that randomly places objects in the room
with index tri_room_idx
'''
def placement(grid, obj_size, metadata, random_state):
assert 'tri_room_grid_cell_ran... |
This function expects the wall scenario to be 'var_tri'
Returns a placement function that randomly places objects in the room
with index tri_room_idx
| tri_placement | python | openai/multi-agent-emergence-environments | mae_envs/envs/box_locking.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/box_locking.py | MIT |
def rotate_tri_placement(grid, obj_size, metadata, random_state):
'''
This function expects the wall scenario to be 'var_tri'.
It places objects equally among the three rooms, so that any room has
contains at most 1 more object than any other room.
'''
if 'tri_placement_rotation' not... |
This function expects the wall scenario to be 'var_tri'.
It places objects equally among the three rooms, so that any room has
contains at most 1 more object than any other room.
| rotate_tri_placement | python | openai/multi-agent-emergence-environments | mae_envs/envs/box_locking.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/box_locking.py | MIT |
def quadrant_placement(grid, obj_size, metadata, random_state):
'''
Places object within the bottom right quadrant of the playing field
'''
grid_size = len(grid)
qsize = metadata['quadrant_size']
pos = np.array([random_state.randint(grid_size - qsize, grid_size - obj_size[0] - 1),
... |
Places object within the bottom right quadrant of the playing field
| quadrant_placement | python | openai/multi-agent-emergence-environments | mae_envs/envs/hide_and_seek.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/hide_and_seek.py | MIT |
def outside_quadrant_placement(grid, obj_size, metadata, random_state):
'''
Places object outside of the bottom right quadrant of the playing field
'''
grid_size = len(grid)
qsize = metadata['quadrant_size']
poses = [
np.array([random_state.randint(1, grid_size - qsize - obj_size[0] ... |
Places object outside of the bottom right quadrant of the playing field
| outside_quadrant_placement | python | openai/multi-agent-emergence-environments | mae_envs/envs/hide_and_seek.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/envs/hide_and_seek.py | MIT |
def get_size_from_xml(obj):
'''
Args:
obj (worldgen.Obj): worldgen object
Returns: size of object annotation:outerbound if it exists, None if it doesn't
'''
outer_bound = None
for body in parse_file(obj._generate_xml_path())['worldbody']['body']:
if body.get('@name', ... |
Args:
obj (worldgen.Obj): worldgen object
Returns: size of object annotation:outerbound if it exists, None if it doesn't
| get_size_from_xml | python | openai/multi-agent-emergence-environments | mae_envs/modules/util.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/util.py | MIT |
def rejection_placement(env, placement_fn, floor_size, obj_size, num_tries=10):
'''
Args:
env (gym.Env): environment
placement_fn (function): Function that returns a position on a grid
Args:
grid (np.ndarray): 2D occupancy grid. 1's mean occupied
... |
Args:
env (gym.Env): environment
placement_fn (function): Function that returns a position on a grid
Args:
grid (np.ndarray): 2D occupancy grid. 1's mean occupied
obj_size_in_cells (int np.ndarray): number of cells in [x, y]
... | rejection_placement | python | openai/multi-agent-emergence-environments | mae_envs/modules/util.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/util.py | MIT |
def uniform_placement_middle(area_side_length_fraction):
'''
Creates a sampling function that samples object position uniformly within the
middle of the playing area. E.g. if the playing area is
------
|AAAA|
|ABBA|
|ABBA|
|AAAA|
----... |
Creates a sampling function that samples object position uniformly within the
middle of the playing area. E.g. if the playing area is
------
|AAAA|
|ABBA|
|ABBA|
|AAAA|
------
then uniform_placement_middle(0.5) will returned a fu... | uniform_placement_middle | python | openai/multi-agent-emergence-environments | mae_envs/modules/util.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/util.py | MIT |
def is_touching(self, pt):
'''
Is pt (tuple) touching this wall
'''
if self.is_vertical:
return pt[0] == self.pt1[0] and pt[1] >= self.pt1[1] and pt[1] <= self.pt2[1]
else:
return pt[1] == self.pt1[1] and pt[0] >= self.pt1[0] and pt[0] <= self.pt2[0] |
Is pt (tuple) touching this wall
| is_touching | python | openai/multi-agent-emergence-environments | mae_envs/modules/walls.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/walls.py | MIT |
def maybe_add_edge(self, wall):
'''
Check if wall is originating from this wall. If so add it to the list of edges.
'''
if self.is_vertical == wall.is_vertical:
return
if self.is_touching(wall.pt1):
self.right_edges.append(wall.pt1)
elif self.i... |
Check if wall is originating from this wall. If so add it to the list of edges.
| maybe_add_edge | python | openai/multi-agent-emergence-environments | mae_envs/modules/walls.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/walls.py | MIT |
def split_for_doors(self, num_doors=1, door_size=1, all_connect=False,
random_state=np.random.RandomState()):
'''
Split this wall into many walls with 'doors' in between.
Args:
num_doors (int): upper bound of number of doors to create
... |
Split this wall into many walls with 'doors' in between.
Args:
num_doors (int): upper bound of number of doors to create
door_size (int): door size in grid cells
all_connect (bool): create a door in every wall segment between pairs of points
... | split_for_doors | python | openai/multi-agent-emergence-environments | mae_envs/modules/walls.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/walls.py | MIT |
def connect_walls(wall1, wall2, min_dist_between, random_state=np.random.RandomState()):
'''
Draw a random new wall connecting wall1 and wall2. Return None if
the drawn wall was closer than min_dist_between to another wall
or the wall wasn't valid.
NOTE: This DOES NOT check if the cr... |
Draw a random new wall connecting wall1 and wall2. Return None if
the drawn wall was closer than min_dist_between to another wall
or the wall wasn't valid.
NOTE: This DOES NOT check if the created wall overlaps with any existing walls, that
should be done outside of this fun... | connect_walls | python | openai/multi-agent-emergence-environments | mae_envs/modules/walls.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/walls.py | MIT |
def choose_new_split(walls, min_dist_between, num_tries=10, random_state=np.random.RandomState()):
'''
Given a list of walls, choose a random wall and draw a new wall perpendicular to it.
NOTE: Right now this O(n_walls^2). We could probably get this to linear if we did
something smarter ... |
Given a list of walls, choose a random wall and draw a new wall perpendicular to it.
NOTE: Right now this O(n_walls^2). We could probably get this to linear if we did
something smarter with the occupancy grid. Until n_walls gets way bigger this
should be fine though.
Arg... | choose_new_split | python | openai/multi-agent-emergence-environments | mae_envs/modules/walls.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/walls.py | MIT |
def split_walls(walls, door_size, random_state=np.random.RandomState()):
'''
Add a door to each wall in walls. Return the new walls and doors.
Args:
walls (Wall list): walls
door_size (int): door size in grid cells
random_state (np.random.RandomState): random stat... |
Add a door to each wall in walls. Return the new walls and doors.
Args:
walls (Wall list): walls
door_size (int): door size in grid cells
random_state (np.random.RandomState): random state to use for sampling
| split_walls | python | openai/multi-agent-emergence-environments | mae_envs/modules/walls.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/walls.py | MIT |
def construct_door_obs(doors, floor_size, grid_size):
'''
Construct door observations in mujoco frame from door positions in grid frame.
Args:
doors ((n_doors, 2, 2) array): list of pairs of points of door edges.
floor_size (float): size of floor
grid_size (int): ... |
Construct door observations in mujoco frame from door positions in grid frame.
Args:
doors ((n_doors, 2, 2) array): list of pairs of points of door edges.
floor_size (float): size of floor
grid_size (int): size of placement grid
| construct_door_obs | python | openai/multi-agent-emergence-environments | mae_envs/modules/walls.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/walls.py | MIT |
def add_walls_to_grid(grid, walls):
'''
Draw walls onto a grid.
Args:
grid (np.ndarray): 2D occupancy grid
walls (Wall list): walls
'''
for wall in walls:
if wall.is_vertical:
grid[wall.pt1[0], wall.pt1[1]:wall.pt2[1] + 1] = 1
else:
... |
Draw walls onto a grid.
Args:
grid (np.ndarray): 2D occupancy grid
walls (Wall list): walls
| add_walls_to_grid | python | openai/multi-agent-emergence-environments | mae_envs/modules/walls.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/walls.py | MIT |
def walls_to_mujoco(floor, floor_size, grid_size, walls, friction=None):
'''
Take a list of walls in grid frame and add them to the floor in the worldgen frame.
Args:
floor (worldgen.Floor): floor
floor_size (float): size of floor
grid_size (int): size of placemen... |
Take a list of walls in grid frame and add them to the floor in the worldgen frame.
Args:
floor (worldgen.Floor): floor
floor_size (float): size of floor
grid_size (int): size of placement grid
walls (Wall list): list of walls
friction (float)... | walls_to_mujoco | python | openai/multi-agent-emergence-environments | mae_envs/modules/walls.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/modules/walls.py | MIT |
def dist_pt_to_cuboid(pt1, cuboid_center, cuboid_dims, cuboid_quat):
'''
This function calculates the shortest distance between test points
and cuboids at arbitrary locations, widths and rotations
Args:
pt1 (num points x 3): test point positions
cuboid_center (num cu... |
This function calculates the shortest distance between test points
and cuboids at arbitrary locations, widths and rotations
Args:
pt1 (num points x 3): test point positions
cuboid_center (num cuboids x 3): cuboid centers
cuboid_dims (num cuboids x 3): cuboid... | dist_pt_to_cuboid | python | openai/multi-agent-emergence-environments | mae_envs/util/geometry.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/util/geometry.py | MIT |
def add_weld_equality_constraint_transform(name, body_name1, body_name2):
'''
Creates a weld constraint that maintains relative position and orientation between
two objects
'''
def fun(xml_dict):
if 'equality' not in xml_dict:
xml_dict['equality'] = OrderedDict()
... |
Creates a weld constraint that maintains relative position and orientation between
two objects
| add_weld_equality_constraint_transform | python | openai/multi-agent-emergence-environments | mae_envs/util/transforms.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/util/transforms.py | MIT |
def set_joint_damping_transform(damping, joint_name):
''' Set joints damping to a single value.
Args:
damping (float): damping to set
joint_name (string): partial name of joint. Any joint with joint_name
as a substring will be affected.
'''
def closure(node):
... | Set joints damping to a single value.
Args:
damping (float): damping to set
joint_name (string): partial name of joint. Any joint with joint_name
as a substring will be affected.
| set_joint_damping_transform | python | openai/multi-agent-emergence-environments | mae_envs/util/transforms.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/util/transforms.py | MIT |
def remove_hinge_axis_transform(axis):
''' Removes specific hinge axis from the body. '''
def fun(xml_dict):
def closure(node):
if 'joint' in node:
node["joint"] = [j for j in node["joint"]
if j["@type"] != "hinge"
... | Removes specific hinge axis from the body. | remove_hinge_axis_transform | python | openai/multi-agent-emergence-environments | mae_envs/util/transforms.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/util/transforms.py | MIT |
def in_cone2d(origin_pts, origin_angles, cone_angle, target_pts):
'''
Computes whether 2D points target_pts are in the cones originating from
origin_pts at angle origin_angles with cone spread angle cone_angle.
Args:
origin_pts (np.ndarray): array with shape (n_points, 2) of ... |
Computes whether 2D points target_pts are in the cones originating from
origin_pts at angle origin_angles with cone spread angle cone_angle.
Args:
origin_pts (np.ndarray): array with shape (n_points, 2) of origin points
origin_angles (np.ndarray): array with shape (n... | in_cone2d | python | openai/multi-agent-emergence-environments | mae_envs/util/vision.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/util/vision.py | MIT |
def insight(sim, geom1_id, geom2_id=None, pt2=None, dist_thresh=np.inf, check_body=True):
'''
Check if geom2 or pt2 is in line of sight of geom1.
Args:
sim: Mujoco sim object
geom1 (int): geom id
geom2 (int): geom id
pt2 (tuple): xy point
d... |
Check if geom2 or pt2 is in line of sight of geom1.
Args:
sim: Mujoco sim object
geom1 (int): geom id
geom2 (int): geom id
pt2 (tuple): xy point
dist_thresh (float): Adds a distance threshold for vision. Objects beyond the threshold
... | insight | python | openai/multi-agent-emergence-environments | mae_envs/util/vision.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/util/vision.py | MIT |
def splitobs(obs, keepdims=True):
'''
Split obs into list of single agent obs.
Args:
obs: dictionary of numpy arrays where first dim in each array is agent dim
'''
n_agents = obs[list(obs.keys())[0]].shape[0]
return [{k: v[[i]] if keepdims else v[i] for k, v in obs.items()} f... |
Split obs into list of single agent obs.
Args:
obs: dictionary of numpy arrays where first dim in each array is agent dim
| splitobs | python | openai/multi-agent-emergence-environments | mae_envs/viewer/policy_viewer.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/viewer/policy_viewer.py | MIT |
def grab_obj(self, action):
'''
Implements object grabbing for all agents
Args:
action: Action dictionary
'''
action_pull = action['action_pull'][:, self.actual_body_slice]
sim = self.unwrapped.sim
agent_pos = sim.data.body_xpos[self.agent... |
Implements object grabbing for all agents
Args:
action: Action dictionary
| grab_obj | python | openai/multi-agent-emergence-environments | mae_envs/wrappers/manipulation.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/wrappers/manipulation.py | MIT |
def lock_obj(self, action_lock):
'''
Implements object gluing for all agents
Args:
lock: (n_agent, n_obj) boolean matrix
'''
sim = self.unwrapped.sim
action_lock = action_lock[self.agent_idx_allowed_to_lock]
action_lock = action_lock[:, sel... |
Implements object gluing for all agents
Args:
lock: (n_agent, n_obj) boolean matrix
| lock_obj | python | openai/multi-agent-emergence-environments | mae_envs/wrappers/manipulation.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/wrappers/manipulation.py | MIT |
def _process_self_matrix(self, self_matrix):
'''
self_matrix will be a (n_agent, n_agent) boolean matrix. Permute each row such that the matrix is consistent with
the circulant permutation used for self observations. E.g. this should be used for agent agent masks
'''
... |
self_matrix will be a (n_agent, n_agent) boolean matrix. Permute each row such that the matrix is consistent with
the circulant permutation used for self observations. E.g. this should be used for agent agent masks
| _process_self_matrix | python | openai/multi-agent-emergence-environments | mae_envs/wrappers/multi_agent.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/mae_envs/wrappers/multi_agent.py | MIT |
def construct_schemas_zero_state(spec, ob_space, scope=''):
'''
Takes a network spec (as specified in construct_tf_graph docstring) and returns
input schemas and zero states.
'''
schemas = OrderedDict()
zero_states = OrderedDict()
for i, layer in enumerate(spec):
layer = ... |
Takes a network spec (as specified in construct_tf_graph docstring) and returns
input schemas and zero states.
| construct_schemas_zero_state | python | openai/multi-agent-emergence-environments | ma_policy/graph_construct.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/graph_construct.py | MIT |
def entity_avg_pooling_masked(x, mask):
'''
Masks and pools x along the second to last dimension. Arguments have dimensions:
x: batch x time x n_entities x n_features
mask: batch x time x n_entities
'''
mask = tf.expand_dims(mask, -1)
masked = x * mask
summed = tf.... |
Masks and pools x along the second to last dimension. Arguments have dimensions:
x: batch x time x n_entities x n_features
mask: batch x time x n_entities
| entity_avg_pooling_masked | python | openai/multi-agent-emergence-environments | ma_policy/layers.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/layers.py | MIT |
def entity_concat(inps):
'''
Concat 4D tensors along the third dimension. If a 3D tensor is in the list
then treat it as a single entity and expand the third dimension
Args:
inps (list of tensors): tensors to concatenate
'''
with tf.variable_scope('concat_entities'):
... |
Concat 4D tensors along the third dimension. If a 3D tensor is in the list
then treat it as a single entity and expand the third dimension
Args:
inps (list of tensors): tensors to concatenate
| entity_concat | python | openai/multi-agent-emergence-environments | ma_policy/layers.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/layers.py | MIT |
def concat_entity_masks(inps, masks):
'''
Concats masks together. If mask is None, then it creates
a tensor of 1's with shape (BS, T, NE).
Args:
inps (list of tensors): tensors that masks apply to
masks (list of tensors): corresponding masks
'''
assert len... |
Concats masks together. If mask is None, then it creates
a tensor of 1's with shape (BS, T, NE).
Args:
inps (list of tensors): tensors that masks apply to
masks (list of tensors): corresponding masks
| concat_entity_masks | python | openai/multi-agent-emergence-environments | ma_policy/layers.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/layers.py | MIT |
def residual_sa_block(inp, mask, heads, n_embd,
layer_norm=False, post_sa_layer_norm=False,
n_mlp=1, qk_w=0.125, v_w=0.125, post_w=0.125,
mlp_w1=0.125, mlp_w2=0.125,
scope="residual_sa_block", reuse=False):
'''
Residual ... |
Residual self attention block for entities.
Notation:
T - Time
NE - Number entities
Args:
inp (tf): (BS, T, NE, f)
mask (tf): (BS, T, NE)
heads (int) -- number of attention heads
n_embd (int) -- dimension of queries, keys,... | residual_sa_block | python | openai/multi-agent-emergence-environments | ma_policy/layers.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/layers.py | MIT |
def self_attention(inp, mask, heads, n_embd, layer_norm=False, qk_w=1.0, v_w=0.01,
scope='', reuse=False):
'''
Self attention over entities.
Notation:
T - Time
NE - Number entities
Args:
inp (tf) -- tensor w/ shape (bs, T, NE, features)... |
Self attention over entities.
Notation:
T - Time
NE - Number entities
Args:
inp (tf) -- tensor w/ shape (bs, T, NE, features)
mask (tf) -- binary tensor with shape (bs, T, NE). For each batch x time,
nner matrix repres... | self_attention | python | openai/multi-agent-emergence-environments | ma_policy/layers.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/layers.py | MIT |
def stable_masked_softmax(logits, mask):
'''
Args:
logits (tf): tensor with shape (bs, T, heads, NE, NE)
mask (tf): tensor with shape(bs, T, 1, NE)
'''
with tf.variable_scope('stable_softmax'):
# Subtract a big number from the masked logits so they don't interfere wi... |
Args:
logits (tf): tensor with shape (bs, T, heads, NE, NE)
mask (tf): tensor with shape(bs, T, 1, NE)
| stable_masked_softmax | python | openai/multi-agent-emergence-environments | ma_policy/layers.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/layers.py | MIT |
def qkv_embed(inp, heads, n_embd, layer_norm=False, qk_w=1.0, v_w=0.01, reuse=False):
'''
Compute queries, keys, and values
Args:
inp (tf) -- tensor w/ shape (bs, T, NE, features)
heads (int) -- number of attention heads
n_embd (int) -- dimension of queries, keys,... |
Compute queries, keys, and values
Args:
inp (tf) -- tensor w/ shape (bs, T, NE, features)
heads (int) -- number of attention heads
n_embd (int) -- dimension of queries, keys, and values will be n_embd / heads
layer_norm (bool) -- normalize embedding prior... | qkv_embed | python | openai/multi-agent-emergence-environments | ma_policy/layers.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/layers.py | MIT |
def layernorm(x, scope, epsilon=1e-5, reuse=False):
'''
normalize state vector to be zero mean / unit variance + learned scale/shift
'''
with tf.variable_scope(scope, reuse=reuse):
n_state = x.get_shape()[-1]
gain = tf.get_variable('gain', [n_state], initializer=tf.constant_initializ... |
normalize state vector to be zero mean / unit variance + learned scale/shift
| layernorm | python | openai/multi-agent-emergence-environments | ma_policy/layers.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/layers.py | MIT |
def shape_list(x):
'''
deal with dynamic shape in tensorflow cleanly
'''
ps = x.get_shape().as_list()
ts = tf.shape(x)
return [ts[i] if ps[i] is None else ps[i] for i in range(len(ps))] |
deal with dynamic shape in tensorflow cleanly
| shape_list | python | openai/multi-agent-emergence-environments | ma_policy/load_policy.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/load_policy.py | MIT |
def load_policy(path, env=None, scope='policy'):
'''
Load a policy.
Args:
path (string): policy path
env (Gym.Env): This will update the observation space of the
policy that is returned
scope (string): The base scope for the policy variables
''... |
Load a policy.
Args:
path (string): policy path
env (Gym.Env): This will update the observation space of the
policy that is returned
scope (string): The base scope for the policy variables
| load_policy | python | openai/multi-agent-emergence-environments | ma_policy/load_policy.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/load_policy.py | MIT |
def _init(self, inputs, gaussian_fixed_var=True, **kwargs):
'''
Args:
inputs (dict): input dictionary containing tf tensors
gaussian_fixed_var (bool): If True the policies variance won't be conditioned on state
'''
taken_actions = {k: inputs[k] for k i... |
Args:
inputs (dict): input dictionary containing tf tensors
gaussian_fixed_var (bool): If True the policies variance won't be conditioned on state
| _init | python | openai/multi-agent-emergence-environments | ma_policy/ma_policy.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/ma_policy.py | MIT |
def process_observation_batch(self, obs):
'''
Batch obs together.
Args:
obs -- list of lists (batch, time), where elements are dictionary observations
'''
new_obs = deepcopy(obs)
# List tranpose -- now in (time, batch)
new_obs = list(map(l... |
Batch obs together.
Args:
obs -- list of lists (batch, time), where elements are dictionary observations
| process_observation_batch | python | openai/multi-agent-emergence-environments | ma_policy/ma_policy.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/ma_policy.py | MIT |
def prepare_input(self, observation, state_in, taken_action=None):
''' Add in time dimension to observations, assumes that first dimension of observation is
already the batch dimension and does not need to be added.'''
obs = deepcopy(observation)
obs.update(state_in)
if taken... | Add in time dimension to observations, assumes that first dimension of observation is
already the batch dimension and does not need to be added. | prepare_input | python | openai/multi-agent-emergence-environments | ma_policy/ma_policy.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/ma_policy.py | MIT |
def listdict2dictnp(l, keepdims=False):
'''
Convert a list of dicts of numpy arrays to a dict of numpy arrays.
If keepdims is False the new outer dimension in each dict element will be
the length of the list
If keepdims is True, then the new outdimension in each dict will be the ... |
Convert a list of dicts of numpy arrays to a dict of numpy arrays.
If keepdims is False the new outer dimension in each dict element will be
the length of the list
If keepdims is True, then the new outdimension in each dict will be the sum of the
outer dimensions of each... | listdict2dictnp | python | openai/multi-agent-emergence-environments | ma_policy/util.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/util.py | MIT |
def l2_loss(pred, label, std, mask):
'''
Masked L2 loss with a scaling paramter (std). We made the choice that
the loss would scale with the number of unmasked data points rather
than have the same magnitude regardless of how many samples came in.
TODO: Revisit whether th... |
Masked L2 loss with a scaling paramter (std). We made the choice that
the loss would scale with the number of unmasked data points rather
than have the same magnitude regardless of how many samples came in.
TODO: Revisit whether this is the right choice.
| l2_loss | python | openai/multi-agent-emergence-environments | ma_policy/util.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/util.py | MIT |
def __init__(self, shape, dtype):
"""Creates a schema for a variable used in policy.
Allows for symbolic definition of shape. Shape can consist of integers, as well as
strings BATCH and TIMESTEPS. This is taken advantage of in the optimizers, to
create placeholders or variables that asyn... | Creates a schema for a variable used in policy.
Allows for symbolic definition of shape. Shape can consist of integers, as well as
strings BATCH and TIMESTEPS. This is taken advantage of in the optimizers, to
create placeholders or variables that asynchronously prefetch the inputs.
Para... | __init__ | python | openai/multi-agent-emergence-environments | ma_policy/variable_schema.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/variable_schema.py | MIT |
def substitute(self, *, batch=BATCH, timesteps=TIMESTEPS):
"""Make a new VariableSchema with batch or timesteps optionally filled in."""
# Coerse None to default value.
batch = batch or BATCH
timesteps = timesteps or TIMESTEPS
shape = self._substituted_shape(batch, timesteps)
... | Make a new VariableSchema with batch or timesteps optionally filled in. | substitute | python | openai/multi-agent-emergence-environments | ma_policy/variable_schema.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/ma_policy/variable_schema.py | MIT |
def zero_action(ac_space):
'''
Define default zero action for when an agent dies such that it stays in place and doesn't do anything.
'''
ac = OrderedDict()
for ac_key, s in ac_space.spaces.items():
assert isinstance(s, gym.spaces.Tuple), f"space {s} is not a Tuple"
single_agent_... |
Define default zero action for when an agent dies such that it stays in place and doesn't do anything.
| zero_action | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/env_oasis.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/env_oasis.py | MIT |
def get_all_integer_partitions(n, min_team_size=1, max_team_size=np.inf):
'''
Return a list of all integer partitions of n.
Args:
n (int): number of entities.
min_team_size (int): minimum number of entities in a partition
max_team_size (int): maximum number of ent... |
Return a list of all integer partitions of n.
Args:
n (int): number of entities.
min_team_size (int): minimum number of entities in a partition
max_team_size (int): maximum number of entities in a partition
| get_all_integer_partitions | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | MIT |
def _partition_agents(self, n_agents, min_team_size, max_team_size):
'''
Return a random partition from the set of all integer partitions
'''
settings = (n_agents, min_team_size, max_team_size)
if settings not in self.cached_partitions:
self.cached_partitions[sett... |
Return a random partition from the set of all integer partitions
| _partition_agents | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | MIT |
def _generate_social_preferences(self, n_agents):
'''
Generate the relationship graph (without uncertainty)
'''
# Generate random partitions
if self.max_team_size != self.min_team_size:
random_partitions = self._partition_agents(n_agents, self.min_team_size, self.... |
Generate the relationship graph (without uncertainty)
| _generate_social_preferences | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | MIT |
def _generate_uncertainty(self, n_agents):
'''
Generate uncertainty levels and noise to be applied to the matrices
'''
self.noise_std = np.random.uniform(low=self.obs_noise_std_range[0],
high=self.obs_noise_std_range[1],
... |
Generate uncertainty levels and noise to be applied to the matrices
| _generate_uncertainty | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | MIT |
def _precompute_observations(self, n_agents):
'''
Precompute observations since they are static per episode.
'''
# We have independent noisy observations per agents, so we copy the reward matrix n_agents times and
# then add the noise matrices
rew_mats = np.repeat(n... |
Precompute observations since they are static per episode.
| _precompute_observations | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | MIT |
def _index_into_mats(key, *indices):
'''
Helper function to create 3 observation types with the same indices
'''
self.precomputed_obs[key] = rew_mats[indices] # Non-noisy version of the reward matrix
self.precomputed_obs[key + "_noisy"] = noisy_rew_mats[i... |
Helper function to create 3 observation types with the same indices
| _index_into_mats | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | MIT |
def _transpose_existing(new_key, existing_key):
'''
Helper function to transpose all 3 observations for an key. This is useful if an agent policy
or value function needs to observe what other agents observe about it.
'''
self.precomputed_obs[new_ke... |
Helper function to transpose all 3 observations for an key. This is useful if an agent policy
or value function needs to observe what other agents observe about it.
| _transpose_existing | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | MIT |
def add_rew_share_observation_keys(*, keys_self: List[str],
keys_additional_self_vf: List[str],
keys_other_agents: List[str],
keys_additional_other_agents_vf: List[str],
keys_self_... |
Determines how keys about the relationship graph should be observed.
Args:
keys_self: keys that the agent should observe about itself
keys_additional_self_vf: keys about an agent but only that the value function should observe
keys_other_agents: keys about other agen... | add_rew_share_observation_keys | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/wrappers_rusp.py | MIT |
def _get_target_actor(self, actor, action):
'''
Return the true index of the targeted agent. Indicies given by the action will be in a rotated space defined
based on how entities are presented to the policy, so we must map back to the underlying ordering.
If the index is... |
Return the true index of the targeted agent. Indicies given by the action will be in a rotated space defined
based on how entities are presented to the policy, so we must map back to the underlying ordering.
If the index is -1, this means no other agent was chosen.
| _get_target_actor | python | openai/multi-agent-emergence-environments | randomized_uncertain_social_preferences/rusp/wrappers_util.py | https://github.com/openai/multi-agent-emergence-environments/blob/master/randomized_uncertain_social_preferences/rusp/wrappers_util.py | MIT |
def dropout_condition_(sample: ConditionAttributes, condition_type: str, condition: str) -> None:
"""Utility function for nullifying an attribute inside a ConditionAttributes object.
Works in-place.
"""
valid_conditions = ConditionAttributes.condition_types()
if condition_type not in valid_condition... | Utility function for nullifying an attribute inside a ConditionAttributes object.
Works in-place.
| dropout_condition_ | python | kyutai-labs/moshi | moshi/moshi/conditioners/base.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/base.py | Apache-2.0 |
def dropout_all_conditions(attributes: tp.Sequence[ConditionAttributes]) -> list[ConditionAttributes]:
"""
Args:
attributes (list[ConditionAttributes]): All conditions attributes.
Returns:
list[ConditionAttributes]: Same with all conditions dropped.
"""
attributes = [attribute.copy()... |
Args:
attributes (list[ConditionAttributes]): All conditions attributes.
Returns:
list[ConditionAttributes]: Same with all conditions dropped.
| dropout_all_conditions | python | kyutai-labs/moshi | moshi/moshi/conditioners/base.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/base.py | Apache-2.0 |
def _collate_text(self, samples: tp.Sequence[ConditionAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]:
"""Given a list of ConditionAttributes objects, compile a dictionary where the keys
are the attributes and the values are the aggregated input per attribute.
For example:
Input:... | Given a list of ConditionAttributes objects, compile a dictionary where the keys
are the attributes and the values are the aggregated input per attribute.
For example:
Input:
[
ConditionAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=..... | _collate_text | python | kyutai-labs/moshi | moshi/moshi/conditioners/base.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/base.py | Apache-2.0 |
def _collate_tensors(self, samples: tp.Sequence[ConditionAttributes]) -> tp.Dict[str, TensorCondition]:
"""For each tensor attribute, collate the tensor from individual batch items.
Args:
samples (list of ConditionAttributes): List of ConditionAttributes samples.
Returns:
... | For each tensor attribute, collate the tensor from individual batch items.
Args:
samples (list of ConditionAttributes): List of ConditionAttributes samples.
Returns:
dict[str, TensorCondition]: A dictionary mapping an attribute name to tensor.
| _collate_tensors | python | kyutai-labs/moshi | moshi/moshi/conditioners/base.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/base.py | Apache-2.0 |
def prepare(self, inputs: tp.Sequence[ConditionAttributes]) -> tp.Dict[str, tp.Any]:
"""Match attributes/tensors with existing conditioners in self, and call `prepare` for each one.
This should be called before starting any real GPU work to avoid synchronization points.
This will return a dict m... | Match attributes/tensors with existing conditioners in self, and call `prepare` for each one.
This should be called before starting any real GPU work to avoid synchronization points.
This will return a dict matching conditioner names to their arbitrary prepared representations.
Args:
... | prepare | python | kyutai-labs/moshi | moshi/moshi/conditioners/base.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/base.py | Apache-2.0 |
def forward(self, prepared: tp.Dict[str, tp.Any]) -> tp.Dict[str, ConditionType]:
"""Compute pairs of `(embedding, mask)` using the configured conditioners and the prepared representations.
The output is for example:
{
"genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
... | Compute pairs of `(embedding, mask)` using the configured conditioners and the prepared representations.
The output is for example:
{
"genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
"description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])),
... | forward | python | kyutai-labs/moshi | moshi/moshi/conditioners/base.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/base.py | Apache-2.0 |
def get_cross(self, conditions: ConditionTensors) -> torch.Tensor | None:
"""Return the tensor to be provided for the cross attention."""
cross = None
for name in self.fuse2cond['cross']:
cond, _ = conditions[name]
if cross is None:
cross = cond
... | Return the tensor to be provided for the cross attention. | get_cross | python | kyutai-labs/moshi | moshi/moshi/conditioners/base.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/base.py | Apache-2.0 |
def get_sum(self, conditions: ConditionTensors) -> torch.Tensor | None:
"""Return the tensor to be provided as an extra sum offset shared for each step."""
sum = None
for name in self.fuse2cond['sum']:
cond, _ = conditions[name]
assert cond.shape[1] == 1, cond.shape
... | Return the tensor to be provided as an extra sum offset shared for each step. | get_sum | python | kyutai-labs/moshi | moshi/moshi/conditioners/base.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/base.py | Apache-2.0 |
def get_prepend(self, conditions: ConditionTensors) -> torch.Tensor | None:
"""Return the tensor to be prepended to the transformer."""
prepend = None
for name in self.fuse2cond['prepend']:
cond, _ = conditions[name]
if prepend is None:
prepend = cond
... | Return the tensor to be prepended to the transformer. | get_prepend | python | kyutai-labs/moshi | moshi/moshi/conditioners/base.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/base.py | Apache-2.0 |
def length_to_mask(lengths: torch.Tensor, max_len: tp.Optional[int] = None) -> torch.Tensor:
"""Utility function to convert a tensor of sequence lengths to a mask (useful when working on padded sequences).
For example: [3, 5] => [[1, 1, 1, 0, 0], [1, 1, 1, 1, 1]]
Args:
lengths (torch.Tensor): tenso... | Utility function to convert a tensor of sequence lengths to a mask (useful when working on padded sequences).
For example: [3, 5] => [[1, 1, 1, 0, 0], [1, 1, 1, 1, 1]]
Args:
lengths (torch.Tensor): tensor with lengths
max_len (int): can set the max length manually. Defaults to None.
Returns... | length_to_mask | python | kyutai-labs/moshi | moshi/moshi/conditioners/text.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/text.py | Apache-2.0 |
def hash_trick(word: str, vocab_size: int) -> int:
"""Hash trick to pair each word with an index
Args:
word (str): word we wish to convert to an index
vocab_size (int): size of the vocabulary
Returns:
int: index of the word in the embedding LUT
"""
hash = int(hashlib.sha256(... | Hash trick to pair each word with an index
Args:
word (str): word we wish to convert to an index
vocab_size (int): size of the vocabulary
Returns:
int: index of the word in the embedding LUT
| hash_trick | python | kyutai-labs/moshi | moshi/moshi/conditioners/text.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/conditioners/text.py | Apache-2.0 |
def _encode_to_unquantized_latent(self, x: torch.Tensor) -> torch.Tensor:
"""Projects a batch of waveforms to unquantized latent space.
Args:
x (torch.Tensor): Float tensor of shape [B, C, T].
Returns:
Unquantized embeddings.
"""
assert (
x.d... | Projects a batch of waveforms to unquantized latent space.
Args:
x (torch.Tensor): Float tensor of shape [B, C, T].
Returns:
Unquantized embeddings.
| _encode_to_unquantized_latent | python | kyutai-labs/moshi | moshi/moshi/models/compression.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/models/compression.py | Apache-2.0 |
def encode(self, x: torch.Tensor) -> torch.Tensor:
"""Encode the given input tensor to quantized representation.
Args:
x (torch.Tensor): Float tensor of shape [B, C, T]
Returns:
codes (torch.Tensor): an int tensor of shape [B, K, T]
with K the number of ... | Encode the given input tensor to quantized representation.
Args:
x (torch.Tensor): Float tensor of shape [B, C, T]
Returns:
codes (torch.Tensor): an int tensor of shape [B, K, T]
with K the number of codebooks used and T the timestep.
| encode | python | kyutai-labs/moshi | moshi/moshi/models/compression.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/models/compression.py | Apache-2.0 |
def encode_to_latent(self, x: torch.Tensor, quantize: bool = True) -> torch.Tensor:
"""Projects a batch of waveforms to latent space.
Args:
x (torch.Tensor): Float tensor of shape [B, C, T].
Returns:
Embeddings, either quantized or not.
"""
emb = self._e... | Projects a batch of waveforms to latent space.
Args:
x (torch.Tensor): Float tensor of shape [B, C, T].
Returns:
Embeddings, either quantized or not.
| encode_to_latent | python | kyutai-labs/moshi | moshi/moshi/models/compression.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/models/compression.py | Apache-2.0 |
def decode(self, codes: torch.Tensor):
"""Decode the given codes to a reconstructed representation.
Args:
codes (torch.Tensor): Int tensor of shape [B, K, T]
Returns:
out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
"""
state... | Decode the given codes to a reconstructed representation.
Args:
codes (torch.Tensor): Int tensor of shape [B, K, T]
Returns:
out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
| decode | python | kyutai-labs/moshi | moshi/moshi/models/compression.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/models/compression.py | Apache-2.0 |
def scatter_with_mask_(tensor: torch.Tensor, dim: int,
index: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> None:
"""Scatter but skipping the updates that are masked."""
old_value = tensor.gather(dim, index)
value = torch.where(mask, value, old_value)
tensor.scatter_(d... | Scatter but skipping the updates that are masked. | scatter_with_mask_ | python | kyutai-labs/moshi | moshi/moshi/models/lm.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/models/lm.py | Apache-2.0 |
def forward(
self, codes: torch.Tensor,
condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
"""Given an input tensor of codes [B, K, T] and list of conditions, returns the logits
along with masks indicating the valid positions at which to compute the loss.
... | Given an input tensor of codes [B, K, T] and list of conditions, returns the logits
along with masks indicating the valid positions at which to compute the loss.
The logits time steps are aligned with those in the input `code`.
Should only be used for training, not inference (use `LMGen` for tha... | forward | python | kyutai-labs/moshi | moshi/moshi/models/lm.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/models/lm.py | Apache-2.0 |
def _init_weights(self):
"""Initialization of the transformer module weights.
Mostly truncated gaussian, with `std = 1 / sqrt(dim_in)`.
Embeddings are also initialized with `1 / sqrt(dim)` rather than `1`.
Some layers are not going to be properly initialized:
- in_proj in MHA... | Initialization of the transformer module weights.
Mostly truncated gaussian, with `std = 1 / sqrt(dim_in)`.
Embeddings are also initialized with `1 / sqrt(dim)` rather than `1`.
Some layers are not going to be properly initialized:
- in_proj in MHA.
- depth transformer la... | _init_weights | python | kyutai-labs/moshi | moshi/moshi/models/lm.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/models/lm.py | Apache-2.0 |
def from_hf_repo(
hf_repo: str,
moshi_weights: Path | str | None = None,
mimi_weights: Path | str | None = None,
tokenizer: Path | str | None = None,
config_path: Path | str | None = None,
lora_weights: Path | str | None = None,
) -> "CheckpointInfo":
"""Downl... | Downloads the checkpoints from the given repo, along with its config.
Extra overrides are possible for each of Moshi, Mimi, or the text tokenizer,
which should be either a Path to a local file or a string representing a path
to a local file or starting with `hf://` for pointing to a file in ano... | from_hf_repo | python | kyutai-labs/moshi | moshi/moshi/models/loaders.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/models/loaders.py | Apache-2.0 |
def get_mimi(
filename: str | Path | None, device: torch.device | str = "cpu", num_codebooks: int = 8
) -> MimiModel:
"""Return a pretrained Mimi model, or unintialized if `filename` is None."""
encoder = SEANetEncoder(**_seanet_kwargs)
decoder = SEANetDecoder(**_seanet_kwargs)
encoder_transformer =... | Return a pretrained Mimi model, or unintialized if `filename` is None. | get_mimi | python | kyutai-labs/moshi | moshi/moshi/models/loaders.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/models/loaders.py | Apache-2.0 |
def test_conv1d(batch_size, in_channels, out_channels, seq_len, kernel_size):
"""Test that StreamingConv1d() calls are causal. Having new inputs does not change the previous output."""
assert seq_len > kernel_size
layer = StreamingConv1d(in_channels, out_channels, kernel_size, causal=True, norm="none", pad... | Test that StreamingConv1d() calls are causal. Having new inputs does not change the previous output. | test_conv1d | python | kyutai-labs/moshi | moshi/moshi/modules/conv_test.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/conv_test.py | Apache-2.0 |
def test_conv1d_streaming(batch_size, in_channels, out_channels, seq_len, kernel_size):
"""Test that StreamingConv1d() streaming works as expected."""
assert seq_len > kernel_size
layer = StreamingConv1d(in_channels, out_channels, kernel_size, causal=True, norm="none", pad_mode="constant")
generator =... | Test that StreamingConv1d() streaming works as expected. | test_conv1d_streaming | python | kyutai-labs/moshi | moshi/moshi/modules/conv_test.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/conv_test.py | Apache-2.0 |
def test_conv1d_transpose(batch_size, in_channels, out_channels, seq_len, kernel_size, stride):
"""Test that StreamingConvTranspose1d() calls are causal. Having new inputs does not change the previous output."""
assert seq_len > kernel_size
layer = StreamingConvTranspose1d(in_channels, out_channels, kernel... | Test that StreamingConvTranspose1d() calls are causal. Having new inputs does not change the previous output. | test_conv1d_transpose | python | kyutai-labs/moshi | moshi/moshi/modules/conv_test.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/conv_test.py | Apache-2.0 |
def test_conv1d_transpose_streaming(batch_size, in_channels, out_channels, seq_len, kernel_size, stride):
"""Test that StreamingConvTranspose1d() streaming works as expected."""
assert seq_len > kernel_size
layer = StreamingConvTranspose1d(in_channels, out_channels, kernel_size, stride, causal=True, norm="... | Test that StreamingConvTranspose1d() streaming works as expected. | test_conv1d_transpose_streaming | python | kyutai-labs/moshi | moshi/moshi/modules/conv_test.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/conv_test.py | Apache-2.0 |
def replace_all_linear_with_lora(module, rank: int, scaling: float, device=None, dtype=None):
""" Recursively replace all Linear layers with LoRALinear layers."""
for name, child in module.named_children():
if isinstance(child, nn.Linear):
if device is None:
this_device = chi... | Recursively replace all Linear layers with LoRALinear layers. | replace_all_linear_with_lora | python | kyutai-labs/moshi | moshi/moshi/modules/lora.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/lora.py | Apache-2.0 |
def replace_lora_with_linear(module):
"""Recursively replace all LoRALinear layers with Linear layers."""
for name, child in module.named_children():
if isinstance(child, LoRALinear):
# Compute merged weights: W' = W + scaling * B @ A
merged_weight = child.frozen_W.weight.data + ... | Recursively replace all LoRALinear layers with Linear layers. | replace_lora_with_linear | python | kyutai-labs/moshi | moshi/moshi/modules/lora.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/lora.py | Apache-2.0 |
def apply_rope(
q: torch.Tensor,
k: torch.Tensor,
offset: torch.Tensor,
max_period: float = 10_000,
time_before_heads: bool = False,
):
"""
Args:
q (torch.Tensor): queries, shape `[B, T, H, D]`.
k (torch.Tensor): keys, shape `[B, T, H, D]`.
offset (int): current offse... |
Args:
q (torch.Tensor): queries, shape `[B, T, H, D]`.
k (torch.Tensor): keys, shape `[B, T, H, D]`.
offset (int): current offset, e.g. when streaming.
max_period (float): maximum period for the cos and sin.
time_before_heads (bool): if True, expected [B, T, H, D], else [B,... | apply_rope | python | kyutai-labs/moshi | moshi/moshi/modules/rope.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/rope.py | Apache-2.0 |
def forward(
self,
q: torch.Tensor,
k: torch.Tensor,
offset: torch.Tensor,
time_before_heads: bool = False,
):
"""Apply rope rotation to query or key tensor."""
return apply_rope(q, k, offset, self.max_period, time_before_heads) | Apply rope rotation to query or key tensor. | forward | python | kyutai-labs/moshi | moshi/moshi/modules/rope.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/rope.py | Apache-2.0 |
def test_resnet(batch_size, dim, res_layer_index, seq_len, kernel_size):
"""Test that SEANetResnetBlock() calls are causal. Having new inputs does not change the previous output."""
assert seq_len > kernel_size
dilation_base = 2
layer = SEANetResnetBlock(dim=dim, dilations=[dilation_base**res_layer_ind... | Test that SEANetResnetBlock() calls are causal. Having new inputs does not change the previous output. | test_resnet | python | kyutai-labs/moshi | moshi/moshi/modules/seanet_test.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/seanet_test.py | Apache-2.0 |
def test_resnet_streaming(batch_size, dim, res_layer_index, seq_len, kernel_size):
"""Test that SEANetResnetBlock() streaming works as expected."""
assert seq_len > kernel_size
dilation_base = 2
layer = SEANetResnetBlock(dim=dim, dilations=[dilation_base**res_layer_index, 1], pad_mode="constant", causa... | Test that SEANetResnetBlock() streaming works as expected. | test_resnet_streaming | python | kyutai-labs/moshi | moshi/moshi/modules/seanet_test.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/seanet_test.py | Apache-2.0 |
def test_nonstreaming_causal_decode(num_timesteps, seanet_kwargs):
"""Test that the SEANetDecoder does not depend on future inputs."""
device = 'cuda' if torch.cuda.is_available() else 'cpu'
decoder = SEANetDecoder(**seanet_kwargs).to(device=device)
generator = torch.Generator(device=device)
gener... | Test that the SEANetDecoder does not depend on future inputs. | test_nonstreaming_causal_decode | python | kyutai-labs/moshi | moshi/moshi/modules/seanet_test.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/seanet_test.py | Apache-2.0 |
def streaming(self, batch_size: int) -> ExitStack:
"""Context manager to enter streaming mode. Reset streaming state on exit."""
exit_stack = ExitStack()
self._start_streaming(batch_size, exit_stack)
exit_stack.callback(self._stop_streaming)
return exit_stack | Context manager to enter streaming mode. Reset streaming state on exit. | streaming | python | kyutai-labs/moshi | moshi/moshi/modules/streaming.py | https://github.com/kyutai-labs/moshi/blob/master/moshi/moshi/modules/streaming.py | Apache-2.0 |
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