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 __init__(self, function=None, name=None, description=None):
"""
Creates an attribute with `function`.
Adds a name and a description if it's specified.
"""
self.name = name
self.function = function
self.description = description |
Creates an attribute with `function`.
Adds a name and a description if it's specified.
| __init__ | python | simpleai-team/simpleai | simpleai/machine_learning/models.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/models.py | MIT |
def is_attribute(method, name=None):
"""
Decorator for methods that are attributes.
"""
if name is None:
name = method.__name__
method.is_attribute = True
method.name = name
return method |
Decorator for methods that are attributes.
| is_attribute | python | simpleai-team/simpleai | simpleai/machine_learning/models.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/models.py | MIT |
def boltzmann_exploration(actions, utilities, temperature, action_counter):
'''returns an action with a probability depending on utilities and temperature'''
utilities = [utilities[x] for x in actions]
temperature = max(temperature, 0.01)
_max = max(utilities)
_min = min(utilities)
if _max == _m... | returns an action with a probability depending on utilities and temperature | boltzmann_exploration | python | simpleai-team/simpleai | simpleai/machine_learning/reinforcement_learning.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/reinforcement_learning.py | MIT |
def make_exponential_temperature(initial_temperature, alpha):
'''returns a function like initial / exp(n * alpha)'''
def _function(n):
try:
return initial_temperature / math.exp(n * alpha)
except OverflowError:
return 0.01
return _function | returns a function like initial / exp(n * alpha) | make_exponential_temperature | python | simpleai-team/simpleai | simpleai/machine_learning/reinforcement_learning.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/machine_learning/reinforcement_learning.py | MIT |
def revise(domains, arc, constraints):
"""
Given the arc X, Y (variables), removes the values from X's domain that
do not meet the constraint between X and Y.
That is, given x1 in X's domain, x1 will be removed from the domain, if
there is no value y in Y's domain that makes constraint(X,Y) True, f... |
Given the arc X, Y (variables), removes the values from X's domain that
do not meet the constraint between X and Y.
That is, given x1 in X's domain, x1 will be removed from the domain, if
there is no value y in Y's domain that makes constraint(X,Y) True, for
those constraints affecting X and Y.
... | revise | python | simpleai-team/simpleai | simpleai/search/arc.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/arc.py | MIT |
def all_arcs(constraints):
"""
For each constraint ((X, Y), const) adds:
((X, Y), const)
((Y, X), const)
"""
arcs = set()
for neighbors, constraint in constraints:
if len(neighbors) == 2:
x, y = neighbors
list(map(arcs.add, ((x, y), (y, x))))
ret... |
For each constraint ((X, Y), const) adds:
((X, Y), const)
((Y, X), const)
| all_arcs | python | simpleai-team/simpleai | simpleai/search/arc.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/arc.py | MIT |
def arc_consistency_3(domains, constraints):
"""
Makes a CSP problem arc consistent.
Ignores any constraint that is not binary.
"""
arcs = list(all_arcs(constraints))
pending_arcs = set(arcs)
while pending_arcs:
x, y = pending_arcs.pop()
if revise(domains, (x, y), constrain... |
Makes a CSP problem arc consistent.
Ignores any constraint that is not binary.
| arc_consistency_3 | python | simpleai-team/simpleai | simpleai/search/arc.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/arc.py | MIT |
def backtrack(problem, variable_heuristic='', value_heuristic='', inference=True):
'''
Backtracking search.
variable_heuristic is the heuristic for variable choosing, can be
MOST_CONSTRAINED_VARIABLE, HIGHEST_DEGREE_VARIABLE, or blank for simple
ordered choosing.
value_heuristic is the heuristi... |
Backtracking search.
variable_heuristic is the heuristic for variable choosing, can be
MOST_CONSTRAINED_VARIABLE, HIGHEST_DEGREE_VARIABLE, or blank for simple
ordered choosing.
value_heuristic is the heuristic for value choosing, can be
LEAST_CONSTRAINING_VALUE or blank for simple ordered choo... | backtrack | python | simpleai-team/simpleai | simpleai/search/csp.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/csp.py | MIT |
def _most_constrained_variable_chooser(problem, variables, domains):
'''
Choose the variable that has less available values.
'''
# the variable with fewer values available
return sorted(variables, key=lambda v: len(domains[v]))[0] |
Choose the variable that has less available values.
| _most_constrained_variable_chooser | python | simpleai-team/simpleai | simpleai/search/csp.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/csp.py | MIT |
def _highest_degree_variable_chooser(problem, variables, domains):
'''
Choose the variable that is involved on more constraints.
'''
# the variable involved in more constraints
return sorted(variables, key=lambda v: problem.var_degrees[v], reverse=True)[0] |
Choose the variable that is involved on more constraints.
| _highest_degree_variable_chooser | python | simpleai-team/simpleai | simpleai/search/csp.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/csp.py | MIT |
def _find_conflicts(problem, assignment, variable=None, value=None):
'''
Find violated constraints on a given assignment, with the possibility
of specifying a new variable and value to add to the assignment before
checking.
'''
if variable is not None and value is not None:
assignment = ... |
Find violated constraints on a given assignment, with the possibility
of specifying a new variable and value to add to the assignment before
checking.
| _find_conflicts | python | simpleai-team/simpleai | simpleai/search/csp.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/csp.py | MIT |
def _least_constraining_values_sorter(problem, assignment, variable, domains):
'''
Sort values based on how many conflicts they generate if assigned.
'''
# the value that generates less conflicts
def update_assignment(value):
new_assignment = deepcopy(assignment)
new_assignment[varia... |
Sort values based on how many conflicts they generate if assigned.
| _least_constraining_values_sorter | python | simpleai-team/simpleai | simpleai/search/csp.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/csp.py | MIT |
def convert_to_binary(variables, domains, constraints):
"""
Returns new constraint list, all binary, using hidden variables.
You can use it as previous step when creating a problem.
"""
def wdiff(vars_):
def diff(variables, values):
hidden, other = variables
if hidd... |
Returns new constraint list, all binary, using hidden variables.
You can use it as previous step when creating a problem.
| convert_to_binary | python | simpleai-team/simpleai | simpleai/search/csp.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/csp.py | MIT |
def _all_expander(fringe, iteration, viewer):
'''
Expander that expands all nodes on the fringe.
'''
expanded_neighbors = [node.expand(local_search=True)
for node in fringe]
if viewer:
viewer.event('expanded', list(fringe), expanded_neighbors)
list(map(fringe.... |
Expander that expands all nodes on the fringe.
| _all_expander | python | simpleai-team/simpleai | simpleai/search/local.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/local.py | MIT |
def _first_expander(fringe, iteration, viewer):
'''
Expander that expands only the first node on the fringe.
'''
current = fringe[0]
neighbors = current.expand(local_search=True)
if viewer:
viewer.event('expanded', [current], [neighbors])
fringe.extend(neighbors) |
Expander that expands only the first node on the fringe.
| _first_expander | python | simpleai-team/simpleai | simpleai/search/local.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/local.py | MIT |
def _random_best_expander(fringe, iteration, viewer):
'''
Expander that expands one randomly chosen nodes on the fringe that
is better than the current (first) node.
'''
current = fringe[0]
neighbors = current.expand(local_search=True)
if viewer:
viewer.event('expanded', [current], [... |
Expander that expands one randomly chosen nodes on the fringe that
is better than the current (first) node.
| _random_best_expander | python | simpleai-team/simpleai | simpleai/search/local.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/local.py | MIT |
def _create_simulated_annealing_expander(schedule):
'''
Creates an expander that has a random chance to choose a node that is worse
than the current (first) node, but that chance decreases with time.
'''
def _expander(fringe, iteration, viewer):
T = schedule(iteration)
current = frin... |
Creates an expander that has a random chance to choose a node that is worse
than the current (first) node, but that chance decreases with time.
| _create_simulated_annealing_expander | python | simpleai-team/simpleai | simpleai/search/local.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/local.py | MIT |
def _create_genetic_expander(problem, mutation_chance):
'''
Creates an expander that expands the bests nodes of the population,
crossing over them.
'''
def _expander(fringe, iteration, viewer):
fitness = [x.value for x in fringe]
sampler = InverseTransformSampler(fitness, fringe)
... |
Creates an expander that expands the bests nodes of the population,
crossing over them.
| _create_genetic_expander | python | simpleai-team/simpleai | simpleai/search/local.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/local.py | MIT |
def _local_search(problem, fringe_expander, iterations_limit=0, fringe_size=1,
random_initial_states=False, stop_when_no_better=True,
viewer=None):
'''
Basic algorithm for all local search algorithms.
'''
if viewer:
viewer.event('started')
fringe = Bounde... |
Basic algorithm for all local search algorithms.
| _local_search | python | simpleai-team/simpleai | simpleai/search/local.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/local.py | MIT |
def path(self):
'''Path (list of nodes and actions) from root to this node.'''
node = self
path = []
while node:
path.append((node.action, node.state))
node = node.parent
return list(reversed(path)) | Path (list of nodes and actions) from root to this node. | path | python | simpleai-team/simpleai | simpleai/search/models.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/models.py | MIT |
def breadth_first(problem, graph_search=False, viewer=None):
'''
Breadth first search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result, and
SearchProblem.is_goal.
'''
return _search(problem,
FifoList(),
... |
Breadth first search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result, and
SearchProblem.is_goal.
| breadth_first | python | simpleai-team/simpleai | simpleai/search/traditional.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/traditional.py | MIT |
def depth_first(problem, graph_search=False, viewer=None):
'''
Depth first search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result, and
SearchProblem.is_goal.
'''
return _search(problem,
LifoList(),
... |
Depth first search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result, and
SearchProblem.is_goal.
| depth_first | python | simpleai-team/simpleai | simpleai/search/traditional.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/traditional.py | MIT |
def uniform_cost(problem, graph_search=False, viewer=None):
'''
Uniform cost search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result,
SearchProblem.is_goal, and SearchProblem.cost.
'''
return _search(problem,
... |
Uniform cost search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result,
SearchProblem.is_goal, and SearchProblem.cost.
| uniform_cost | python | simpleai-team/simpleai | simpleai/search/traditional.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/traditional.py | MIT |
def greedy(problem, graph_search=False, viewer=None):
'''
Greedy search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result,
SearchProblem.is_goal, SearchProblem.cost, and SearchProblem.heuristic.
'''
return _search(problem,
... |
Greedy search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result,
SearchProblem.is_goal, SearchProblem.cost, and SearchProblem.heuristic.
| greedy | python | simpleai-team/simpleai | simpleai/search/traditional.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/traditional.py | MIT |
def astar(problem, graph_search=False, viewer=None):
'''
A* search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result,
SearchProblem.is_goal, SearchProblem.cost, and SearchProblem.heuristic.
'''
return _search(problem,
... |
A* search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result,
SearchProblem.is_goal, SearchProblem.cost, and SearchProblem.heuristic.
| astar | python | simpleai-team/simpleai | simpleai/search/traditional.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/traditional.py | MIT |
def _search(problem, fringe, graph_search=False, depth_limit=None,
node_factory=SearchNode, graph_replace_when_better=False,
viewer=None):
'''
Basic search algorithm, base of all the other search algorithms.
'''
if viewer:
viewer.event('started')
memory = set()
i... |
Basic search algorithm, base of all the other search algorithms.
| _search | python | simpleai-team/simpleai | simpleai/search/traditional.py | https://github.com/simpleai-team/simpleai/blob/master/simpleai/search/traditional.py | MIT |
def test_target_in_attributes(self):
"""
If target in attributes precision is 1.0.
"""
self.problem.attributes = [self.target]
self.this = self.classifier(self.corpus, self.problem)
prec = evaluation.precision(self.this, self.test_set)
self.assertEqual(prec, 1.0) |
If target in attributes precision is 1.0.
| test_target_in_attributes | python | simpleai-team/simpleai | tests/machine_learning/test_classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/tests/machine_learning/test_classifiers.py | MIT |
def test_equal_classification(self):
"""
This checks that the three tree learning methods are equal.
"""
pseudo = DecisionTreeLearner(self.corpus, self.problem)
for test in self.test_set:
self.assertEqual(pseudo.classify(test), self.this.classify(test)) |
This checks that the three tree learning methods are equal.
| test_equal_classification | python | simpleai-team/simpleai | tests/machine_learning/test_classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/tests/machine_learning/test_classifiers.py | MIT |
def setup_dataset(self):
"""
Creates a corpus with the iris dataset. Returns the dataset,
the attributes getter and the target getter.
"""
dataset = []
with open(self.IRIS_PATH) as filehandler:
file_data = filehandler.read()
for line in file_data.spl... |
Creates a corpus with the iris dataset. Returns the dataset,
the attributes getter and the target getter.
| setup_dataset | python | simpleai-team/simpleai | tests/machine_learning/test_classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/tests/machine_learning/test_classifiers.py | MIT |
def setup_dataset(self):
"""
Creates a corpus with n k-bit examples of the parity problem:
k random bits followed by a 1 if an odd number of bits are 1, else 0
"""
k = 2
n = 100
dataset = []
for i in range(n):
# Pseudo random generation of bi... |
Creates a corpus with n k-bit examples of the parity problem:
k random bits followed by a 1 if an odd number of bits are 1, else 0
| setup_dataset | python | simpleai-team/simpleai | tests/machine_learning/test_classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/tests/machine_learning/test_classifiers.py | MIT |
def setup_dataset(self):
"""
Creates a corpus of primes. Returns the dataset,
the attributes getter and the target getter.
"""
size = 105 # Magic number, chosen to avoid an "error" that cannot be
# patched in Dtree Pseudo (with modifing the pseudocode).
... |
Creates a corpus of primes. Returns the dataset,
the attributes getter and the target getter.
| setup_dataset | python | simpleai-team/simpleai | tests/machine_learning/test_classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/tests/machine_learning/test_classifiers.py | MIT |
def isprime(self, number):
"""
Returns if a number is prime testing if
is divisible by any number from 0 to sqrt(number)
"""
if number < 2:
return False
if number == 2:
return True
if not number & 1:
return False
for i... |
Returns if a number is prime testing if
is divisible by any number from 0 to sqrt(number)
| isprime | python | simpleai-team/simpleai | tests/machine_learning/test_classifiers.py | https://github.com/simpleai-team/simpleai/blob/master/tests/machine_learning/test_classifiers.py | MIT |
def get_ray_directions(
H: int,
W: int,
focal: Union[float, Tuple[float, float]],
principal: Optional[Tuple[float, float]] = None,
use_pixel_centers: bool = True,
normalize: bool = True,
) -> torch.FloatTensor:
"""
Get ray directions for all pixels in camera coordinate.
Reference: ht... |
Get ray directions for all pixels in camera coordinate.
Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/
ray-tracing-generating-camera-rays/standard-coordinate-systems
Inputs:
H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal... | get_ray_directions | python | VAST-AI-Research/TripoSR | tsr/utils.py | https://github.com/VAST-AI-Research/TripoSR/blob/master/tsr/utils.py | MIT |
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
**cross_attention_kwargs,
) -> torch.Tensor:
r"""
The forward method of the `Attention` class.
... |
The forward method of the `Attention` class.
Args:
hidden_states (`torch.Tensor`):
The hidden states of the query.
encoder_hidden_states (`torch.Tensor`, *optional*):
The hidden states of the encoder.
attention_mask (`torch.Tensor`, *... | forward | python | VAST-AI-Research/TripoSR | tsr/models/transformer/attention.py | https://github.com/VAST-AI-Research/TripoSR/blob/master/tsr/models/transformer/attention.py | MIT |
def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
is the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`... |
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
is the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
Returns:
`torch.Ten... | batch_to_head_dim | python | VAST-AI-Research/TripoSR | tsr/models/transformer/attention.py | https://github.com/VAST-AI-Research/TripoSR/blob/master/tsr/models/transformer/attention.py | MIT |
def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
the number of heads initialized while constructing the `Attention` class.
Args:
... |
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
out_dim (`int`, *optional*, de... | head_to_batch_dim | python | VAST-AI-Research/TripoSR | tsr/models/transformer/attention.py | https://github.com/VAST-AI-Research/TripoSR/blob/master/tsr/models/transformer/attention.py | MIT |
def get_attention_scores(
self,
query: torch.Tensor,
key: torch.Tensor,
attention_mask: torch.Tensor = None,
) -> torch.Tensor:
r"""
Compute the attention scores.
Args:
query (`torch.Tensor`): The query tensor.
key (`torch.Tensor`): Th... |
Compute the attention scores.
Args:
query (`torch.Tensor`): The query tensor.
key (`torch.Tensor`): The key tensor.
attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
Returns:
`torch.Tensor`: T... | get_attention_scores | python | VAST-AI-Research/TripoSR | tsr/models/transformer/attention.py | https://github.com/VAST-AI-Research/TripoSR/blob/master/tsr/models/transformer/attention.py | MIT |
def prepare_attention_mask(
self,
attention_mask: torch.Tensor,
target_length: int,
batch_size: int,
out_dim: int = 3,
) -> torch.Tensor:
r"""
Prepare the attention mask for the attention computation.
Args:
attention_mask (`torch.Tensor`):... |
Prepare the attention mask for the attention computation.
Args:
attention_mask (`torch.Tensor`):
The attention mask to prepare.
target_length (`int`):
The target length of the attention mask. This is the length of the attention mask after padding... | prepare_attention_mask | python | VAST-AI-Research/TripoSR | tsr/models/transformer/attention.py | https://github.com/VAST-AI-Research/TripoSR/blob/master/tsr/models/transformer/attention.py | MIT |
def norm_encoder_hidden_states(
self, encoder_hidden_states: torch.Tensor
) -> torch.Tensor:
r"""
Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
`Attention` class.
Args:
encoder_hidden_states (`torch.Tensor`)... |
Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
`Attention` class.
Args:
encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
Returns:
`torch.Tensor`: The normalized encoder hidden states.
... | norm_encoder_hidden_states | python | VAST-AI-Research/TripoSR | tsr/models/transformer/attention.py | https://github.com/VAST-AI-Research/TripoSR/blob/master/tsr/models/transformer/attention.py | MIT |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
):
"""
The [`Transformer1DModel`] forward method.
... |
The [`Transformer1DModel`] forward method.
Args:
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
Input `hidden_states`.
encoder_hidden_s... | forward | python | VAST-AI-Research/TripoSR | tsr/models/transformer/transformer_1d.py | https://github.com/VAST-AI-Research/TripoSR/blob/master/tsr/models/transformer/transformer_1d.py | MIT |
def log_metric(
accelerator,
metrics: Dict,
train_time: float,
step: int,
epoch: int,
learning_rate: float = None,
prefix: str = "train",
):
"""Helper function to log all training/evaluation metrics with the correct prefixes and styling."""
log_metrics = {}
for k, v in metrics.it... | Helper function to log all training/evaluation metrics with the correct prefixes and styling. | log_metric | python | huggingface/distil-whisper | training/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_distillation.py | MIT |
def log_pred(
accelerator,
pred_str: List[str],
label_str: List[str],
norm_pred_str: List[str],
norm_label_str: List[str],
step: int,
prefix: str = "eval",
num_lines: int = 200000,
):
"""Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
if a... | Helper function to log target/predicted transcriptions to weights and biases (wandb). | log_pred | python | huggingface/distil-whisper | training/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_distillation.py | MIT |
def convert_dataset_str_to_list(
dataset_names,
dataset_config_names,
splits=None,
text_column_names=None,
dataset_samples=None,
default_split="train",
) -> List[Dict]:
"""
Given three lists of dataset names, configs and splits, this function groups the corresponding
names/configs/sp... |
Given three lists of dataset names, configs and splits, this function groups the corresponding
names/configs/splits. Each dataset is assigned a unique dictionary with these metadata values, and the
function returns a list of dictionaries, one for each dataset.
| convert_dataset_str_to_list | python | huggingface/distil-whisper | training/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_distillation.py | MIT |
def sorted_checkpoints(output_dir=None, checkpoint_prefix="checkpoint") -> List[str]:
"""Helper function to sort saved checkpoints from oldest to newest."""
ordering_and_checkpoint_path = []
glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{checkpoint_prefix}-*") if os.path.isdir(x)]
glob_ch... | Helper function to sort saved checkpoints from oldest to newest. | sorted_checkpoints | python | huggingface/distil-whisper | training/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_distillation.py | MIT |
def sorted_best_checkpoints(output_dir=None, checkpoint_prefix="checkpoint"):
"""Helper function to sort saved best checkpoints."""
ordering_and_checkpoint_path = []
glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{checkpoint_prefix}-*") if os.path.isdir(x)]
for path in glob_checkpoints:
... | Helper function to sort saved best checkpoints. | sorted_best_checkpoints | python | huggingface/distil-whisper | training/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_distillation.py | MIT |
def rotate_checkpoints(save_total_limit=None, output_dir=None, checkpoint_prefix="checkpoint", sorting_fn=sorted_checkpoints) -> None:
"""Helper function to delete old checkpoints."""
if save_total_limit is None or save_total_limit <= 0:
return
# Check if we should delete older checkpoint(s)
che... | Helper function to delete old checkpoints. | rotate_checkpoints | python | huggingface/distil-whisper | training/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_distillation.py | MIT |
def get_parameter_names(model, forbidden_layer_types, forbidden_module=None):
"""
Returns the names of the model parameters that are not inside a forbidden layer or forbidden module.
Can be used to get a subset of parameter names for decay masks, or to exclude parameters from an optimiser
(e.g. if the m... |
Returns the names of the model parameters that are not inside a forbidden layer or forbidden module.
Can be used to get a subset of parameter names for decay masks, or to exclude parameters from an optimiser
(e.g. if the module is frozen).
| get_parameter_names | python | huggingface/distil-whisper | training/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_distillation.py | MIT |
def prepare_train_dataset(batch):
"""
Pre-process the raw dataset in a three stage process:
1. Convert the audio arrays to log-mel spectrogram inputs
2. Possibly filter the timestamp tokens from the token ids (depending on the timestamp probability)
3. Possibly add pr... |
Pre-process the raw dataset in a three stage process:
1. Convert the audio arrays to log-mel spectrogram inputs
2. Possibly filter the timestamp tokens from the token ids (depending on the timestamp probability)
3. Possibly add prompt tokens if conditioning on previous text ... | prepare_train_dataset | python | huggingface/distil-whisper | training/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_distillation.py | MIT |
def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
"""
Shift label ids one token to the right.
"""
shifted_label_ids = np.zeros_like(label_ids)
shifted_label_ids[:, 1:] = label_ids[:, :-1]
shifted_label_ids[:, 0] = decoder_start_token_id
return shifted_l... |
Shift label ids one token to the right.
| shift_tokens_right | python | huggingface/distil-whisper | training/run_pseudo_labelling.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_pseudo_labelling.py | MIT |
def log_metric(
accelerator,
metrics: Dict,
train_time: float,
prefix: str = "eval",
):
"""Helper function to log all evaluation metrics with the correct prefixes and styling."""
log_metrics = {}
for k, v in metrics.items():
log_metrics[f"{prefix}/{k}"] = v
log_metrics[f"{prefix}... | Helper function to log all evaluation metrics with the correct prefixes and styling. | log_metric | python | huggingface/distil-whisper | training/run_pseudo_labelling.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_pseudo_labelling.py | MIT |
def log_pred(
accelerator,
pred_str: List[str],
label_str: List[str],
norm_pred_str: List[str],
norm_label_str: List[str],
prefix: str = "eval",
num_lines: int = 200000,
):
"""Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
if accelerator.is_m... | Helper function to log target/predicted transcriptions to weights and biases (wandb). | log_pred | python | huggingface/distil-whisper | training/run_pseudo_labelling.py | https://github.com/huggingface/distil-whisper/blob/master/training/run_pseudo_labelling.py | MIT |
def create_learning_rate_fn(
num_train_steps: int, lr_scheduler_type: str, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.array]:
"""Returns a linear warmup, linear_decay learning rate function."""
lr_scheduler_types = ("linear", "constant_with_warmup")
if lr_scheduler_type not in... | Returns a linear warmup, linear_decay learning rate function. | create_learning_rate_fn | python | huggingface/distil-whisper | training/flax/convert_train_state_to_hf.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/convert_train_state_to_hf.py | MIT |
def apply_gradients(self, *, grads, **kwargs):
"""Updates `step`, `params`, `opt_state` and `**kwargs` in return value, clipping the
gradients by the maximum grad norm.
Note that internally this function calls `.tx.update()` followed by a call
to `optax.apply_updates()` to update `param... | Updates `step`, `params`, `opt_state` and `**kwargs` in return value, clipping the
gradients by the maximum grad norm.
Note that internally this function calls `.tx.update()` followed by a call
to `optax.apply_updates()` to update `params` and `opt_state`.
Args:
grads: Gradie... | apply_gradients | python | huggingface/distil-whisper | training/flax/convert_train_state_to_hf.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/convert_train_state_to_hf.py | MIT |
def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
"""
Shift label ids one token to the right.
"""
shifted_label_ids = np.zeros_like(label_ids)
shifted_label_ids[:, 1:] = label_ids[:, :-1]
shifted_label_ids[:, 0] = decoder_start_token_id
return shifted_l... |
Shift label ids one token to the right.
| shift_tokens_right | python | huggingface/distil-whisper | training/flax/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_distillation.py | MIT |
def get_data_loader(
seed: int,
dataset: IterableDataset,
batch_size: int,
data_collator: FlaxDataCollatorSpeechSeq2SeqWithPadding,
shuffle: bool = True,
drop_last: bool = True,
dataloader_num_workers: int = 0,
skip_batches: int = 0,
pin_memory: bool = True,
prefetch_size: int = ... |
Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete,
and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`.
Args:
seed (int): Numpy seed for generating pseudo random numbers. Used if shuffling the datas... | get_data_loader | python | huggingface/distil-whisper | training/flax/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_distillation.py | MIT |
def apply_gradients(self, *, grads, to_dtype: to_fp32, **kwargs):
"""Updates `step`, `params`, `opt_state` and `**kwargs` in return value, clipping the
gradients by the maximum grad norm.
Note that internally this function calls `.tx.update()` followed by a call
to `optax.apply_updates(... | Updates `step`, `params`, `opt_state` and `**kwargs` in return value, clipping the
gradients by the maximum grad norm.
Note that internally this function calls `.tx.update()` followed by a call
to `optax.apply_updates()` to update `params` and `opt_state`.
Args:
grads: Gradie... | apply_gradients | python | huggingface/distil-whisper | training/flax/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_distillation.py | MIT |
def create(cls, *, apply_fn, params, tx, to_dtype: to_fp32, **kwargs):
"""Creates a new instance with `step=0` and initialized `opt_state`."""
# downcast optimizer state to bf16 if mixed-precision training
opt_state = tx.init(to_dtype(params))
return cls(
step=0,
... | Creates a new instance with `step=0` and initialized `opt_state`. | create | python | huggingface/distil-whisper | training/flax/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_distillation.py | MIT |
def create_learning_rate_fn(
num_train_steps: int, lr_scheduler_type: str, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.array]:
"""Returns a linear warmup, linear_decay learning rate function."""
lr_scheduler_types = ("linear", "constant_with_warmup")
if lr_scheduler_type not in... | Returns a linear warmup, linear_decay learning rate function. | create_learning_rate_fn | python | huggingface/distil-whisper | training/flax/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_distillation.py | MIT |
def get_layers_to_supervise(student_layers: int, teacher_layers: int) -> dict:
"""Helper function to map the student layer i to the teacher layer j whose output we'd like them to emulate. Used
for MSE loss terms in distillation (hidden-states and activations). Student layers are paired with teacher layers
i... | Helper function to map the student layer i to the teacher layer j whose output we'd like them to emulate. Used
for MSE loss terms in distillation (hidden-states and activations). Student layers are paired with teacher layers
in equal increments, e.g. for a 12-layer model distilled to a 3-layer model, student la... | get_layers_to_supervise | python | huggingface/distil-whisper | training/flax/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_distillation.py | MIT |
def _np_extract_fbank_features(self, waveform: np.array) -> np.ndarray:
"""
Compute the log-mel spectrogram of the provided audio using torch filters. Using the torch implementation
computes stft filter banks approx 5x faster than its numpy counterpart, which is the native implementation
... |
Compute the log-mel spectrogram of the provided audio using torch filters. Using the torch implementation
computes stft filter banks approx 5x faster than its numpy counterpart, which is the native implementation
in transformers, and matches to within 1e-5 abs tolerance.
| _np_extract_fbank_features | python | huggingface/distil-whisper | training/flax/run_distillation.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_distillation.py | MIT |
def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
"""
Shift label ids one token to the right.
"""
shifted_label_ids = np.zeros_like(label_ids)
shifted_label_ids[:, 1:] = label_ids[:, :-1]
shifted_label_ids[:, 0] = decoder_start_token_id
return shifted_l... |
Shift label ids one token to the right.
| shift_tokens_right | python | huggingface/distil-whisper | training/flax/run_eval.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_eval.py | MIT |
def get_data_loader(
dataset: Dataset,
batch_size: int,
data_collator: FlaxDataCollatorSpeechSeq2SeqWithPadding,
dataloader_num_workers: int = 0,
pin_memory: bool = True,
) -> DataLoader:
"""
Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final bat... |
Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete,
and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`.
Args:
dataset (Dataset): dataset from which to load the data.
batch_size (int): how ma... | get_data_loader | python | huggingface/distil-whisper | training/flax/run_eval.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_eval.py | MIT |
def _np_extract_fbank_features(self, waveform: np.array) -> np.ndarray:
"""
Compute the log-mel spectrogram of the provided audio using torch filters. Using the torch implementation
computes stft filter banks approx 5x faster than its numpy counterpart, which is the native implementation
... |
Compute the log-mel spectrogram of the provided audio using torch filters. Using the torch implementation
computes stft filter banks approx 5x faster than its numpy counterpart, which is the native implementation
in transformers, and matches to within 1e-5 abs tolerance.
| _np_extract_fbank_features | python | huggingface/distil-whisper | training/flax/run_eval.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_eval.py | MIT |
def loss_fn(logits, labels, label_smoothing_factor=0.0):
"""
The label smoothing implementation is adapted from Flax's official example:
https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
"""
vocab_size = logits.shape[-1]
... |
The label smoothing implementation is adapted from Flax's official example:
https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
| loss_fn | python | huggingface/distil-whisper | training/flax/run_eval.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_eval.py | MIT |
def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
"""
Shift label ids one token to the right.
"""
shifted_label_ids = np.zeros_like(label_ids)
shifted_label_ids[:, 1:] = label_ids[:, :-1]
shifted_label_ids[:, 0] = decoder_start_token_id
return shifted_l... |
Shift label ids one token to the right.
| shift_tokens_right | python | huggingface/distil-whisper | training/flax/run_finetuning.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_finetuning.py | MIT |
def get_data_loader(
rng: jax.random.PRNGKey,
dataset: Dataset,
batch_size: int,
data_collator: FlaxDataCollatorSpeechSeq2SeqWithPadding,
shuffle: bool = True,
drop_last: bool = True,
dataloader_num_workers: int = 0,
pin_memory: bool = True,
) -> DataLoader:
"""
Returns batches o... |
Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete,
and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`.
Args:
rng (List(int)): JAX rng for generating pseudo random numbers. Used if shuffling the dat... | get_data_loader | python | huggingface/distil-whisper | training/flax/run_finetuning.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_finetuning.py | MIT |
def create_learning_rate_fn(
num_train_steps: int, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.array]:
"""Returns a linear warmup, linear_decay learning rate function."""
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
... | Returns a linear warmup, linear_decay learning rate function. | create_learning_rate_fn | python | huggingface/distil-whisper | training/flax/run_finetuning.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_finetuning.py | MIT |
def loss_fn(logits, labels, label_smoothing_factor=0.0):
"""
The label smoothing implementation is adapted from Flax's official example:
https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
"""
vocab_size = logits.shape[-1]
... |
The label smoothing implementation is adapted from Flax's official example:
https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
| loss_fn | python | huggingface/distil-whisper | training/flax/run_finetuning.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_finetuning.py | MIT |
def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
"""
Shift label ids one token to the right.
"""
shifted_label_ids = np.zeros_like(label_ids)
shifted_label_ids[:, 1:] = label_ids[:, :-1]
shifted_label_ids[:, 0] = decoder_start_token_id
return shifted_l... |
Shift label ids one token to the right.
| shift_tokens_right | python | huggingface/distil-whisper | training/flax/run_pseudo_labelling_pt.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_pseudo_labelling_pt.py | MIT |
def log_metric(
accelerator,
metrics: Dict,
train_time: float,
prefix: str = "eval",
):
"""Helper function to log all evaluation metrics with the correct prefixes and styling."""
log_metrics = {}
for k, v in metrics.items():
log_metrics[f"{prefix}/{k}"] = v
log_metrics[f"{prefix}... | Helper function to log all evaluation metrics with the correct prefixes and styling. | log_metric | python | huggingface/distil-whisper | training/flax/run_pseudo_labelling_pt.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_pseudo_labelling_pt.py | MIT |
def log_pred(
accelerator,
pred_str: List[str],
label_str: List[str],
norm_pred_str: List[str],
norm_label_str: List[str],
prefix: str = "eval",
num_lines: int = 200000,
):
"""Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
if accelerator.is_m... | Helper function to log target/predicted transcriptions to weights and biases (wandb). | log_pred | python | huggingface/distil-whisper | training/flax/run_pseudo_labelling_pt.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/run_pseudo_labelling_pt.py | MIT |
def nd_dense_init(scale, mode, distribution):
"""Initializer with in_axis, out_axis set at call time."""
def init_fn(key, shape, dtype, in_axis, out_axis):
fn = variance_scaling(scale, mode, distribution, in_axis, out_axis)
return fn(key, shape, dtype)
return init_fn | Initializer with in_axis, out_axis set at call time. | nd_dense_init | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def __call__(
self,
inputs_q: Array,
inputs_kv: Array,
mask: Optional[Array] = None,
bias: Optional[Array] = None,
*,
decode: bool = False,
deterministic: bool = False,
) -> Array:
"""Applies multi-head dot product attention on the input data.
... | Applies multi-head dot product attention on the input data.
Projects the inputs into multi-headed query, key, and value vectors,
applies dot-product attention and project the results to an output vector.
There are two modes: decoding and non-decoding (e.g., training). The mode is
deter... | __call__ | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def __call__(self, inputs: Array) -> Array:
"""Applies a linear transformation to the inputs along multiple dimensions.
Args:
inputs: The nd-array to be transformed.
Returns:
The transformed input.
"""
features = _canonicalize_tuple(self.features)
ax... | Applies a linear transformation to the inputs along multiple dimensions.
Args:
inputs: The nd-array to be transformed.
Returns:
The transformed input.
| __call__ | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def _convert_to_activation_function(fn_or_string: Union[str, Callable]) -> Callable:
"""Convert a string to an activation function."""
if fn_or_string == "linear":
return lambda x: x
elif isinstance(fn_or_string, str):
return getattr(nn, fn_or_string)
elif callable(fn_or_string):
... | Convert a string to an activation function. | _convert_to_activation_function | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def __call__(self, inputs: Array) -> Array:
"""Embeds the inputs along the last dimension.
Args:
inputs: input data, all dimensions are considered batch dimensions.
Returns:
Output which is embedded input data. The output shape follows the input,
with an addition... | Embeds the inputs along the last dimension.
Args:
inputs: input data, all dimensions are considered batch dimensions.
Returns:
Output which is embedded input data. The output shape follows the input,
with an additional `features` dimension appended.
| __call__ | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def attend(self, query: Array) -> Array:
"""Attend over the embedding using a query array.
Args:
query: array with last dimension equal the feature depth `features` of the
embedding.
Returns:
An array with final dim `num_embeddings` corresponding to the batched
... | Attend over the embedding using a query array.
Args:
query: array with last dimension equal the feature depth `features` of the
embedding.
Returns:
An array with final dim `num_embeddings` corresponding to the batched
inner-product of the array of query vector... | attend | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""Translate relative position to a bucket number for relative attention.
The relative position is defined as memory_position - query_position, i.e.
the distance in tokens from the attending ... | Translate relative position to a bucket number for relative attention.
The relative position is defined as memory_position - query_position, i.e.
the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are
... | _relative_position_bucket | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def __call__(self, qlen, klen, bidirectional=True):
"""Produce relative position embedding attention biases.
Args:
qlen: attention query length.
klen: attention key length.
bidirectional: whether to allow positive memory-query relative position
embeddings.
... | Produce relative position embedding attention biases.
Args:
qlen: attention query length.
klen: attention key length.
bidirectional: whether to allow positive memory-query relative position
embeddings.
Returns:
output: `(1, len, q_len, k_len)` attent... | __call__ | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def __call__(self, x):
"""Applies layer normalization on the input.
Args:
x: the inputs
Returns:
Normalized inputs (the same shape as inputs).
"""
x = jnp.asarray(x, jnp.float32)
features = x.shape[-1]
mean = jnp.mean(x, axis=-1, keepdims=True)... | Applies layer normalization on the input.
Args:
x: the inputs
Returns:
Normalized inputs (the same shape as inputs).
| __call__ | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def make_attention_mask(
query_input: Array,
key_input: Array,
pairwise_fn: Callable = jnp.multiply,
extra_batch_dims: int = 0,
dtype: DType = jnp.float32,
) -> Array:
"""Mask-making helper for attention weights.
In case of 1d inputs (i.e., `[batch, len_q]`, `[batch, len_kv]`, the
atten... | Mask-making helper for attention weights.
In case of 1d inputs (i.e., `[batch, len_q]`, `[batch, len_kv]`, the
attention weights will be `[batch, heads, len_q, len_kv]` and this
function will produce `[batch, 1, len_q, len_kv]`.
Args:
query_input: a batched, flat input of query_length size
... | make_attention_mask | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def make_causal_mask(x: Array, extra_batch_dims: int = 0, dtype: DType = jnp.float32) -> Array:
"""Make a causal mask for self-attention.
In case of 1d inputs (i.e., `[batch, len]`, the self-attention weights
will be `[batch, heads, len, len]` and this function will produce a
causal mask of shape `[bat... | Make a causal mask for self-attention.
In case of 1d inputs (i.e., `[batch, len]`, the self-attention weights
will be `[batch, heads, len, len]` and this function will produce a
causal mask of shape `[batch, 1, len, len]`.
Note that a causal mask does not depend on the values of x; it only depends on
... | make_causal_mask | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def combine_masks(*masks: Optional[Array], dtype: DType = jnp.float32):
"""Combine attention masks.
Args:
*masks: set of attention mask arguments to combine, some can be None.
dtype: final mask dtype
Returns:
Combined mask, reduced by logical and, returns None if no masks given.
"""
... | Combine attention masks.
Args:
*masks: set of attention mask arguments to combine, some can be None.
dtype: final mask dtype
Returns:
Combined mask, reduced by logical and, returns None if no masks given.
| combine_masks | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def combine_biases(*masks: Optional[Array]):
"""Combine attention biases.
Args:
*masks: set of attention bias arguments to combine, some can be None.
Returns:
Combined mask, reduced by summation, returns None if no masks given.
"""
masks = [m for m in masks if m is not None]
if not... | Combine attention biases.
Args:
*masks: set of attention bias arguments to combine, some can be None.
Returns:
Combined mask, reduced by summation, returns None if no masks given.
| combine_biases | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def make_decoder_mask(
decoder_target_tokens: Array,
dtype: DType,
decoder_causal_attention: Optional[Array] = None,
decoder_segment_ids: Optional[Array] = None,
) -> Array:
"""Compute the self-attention mask for a decoder.
Decoder mask is formed by combining a causal mask, a padding mask and a... | Compute the self-attention mask for a decoder.
Decoder mask is formed by combining a causal mask, a padding mask and an
optional packing mask. If decoder_causal_attention is passed, it makes the
masking non-causal for positions that have value of 1.
A prefix LM is applied to a dataset which has a noti... | make_decoder_mask | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def canonicalize_padding(padding: PaddingLike, rank: int) -> LaxPadding:
""" "Canonicalizes conv padding to a jax.lax supported format."""
if isinstance(padding, str):
return padding
if isinstance(padding, int):
return [(padding, padding)] * rank
if isinstance(padding, Sequence) and len(... | "Canonicalizes conv padding to a jax.lax supported format. | canonicalize_padding | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def _conv_dimension_numbers(input_shape):
"""Computes the dimension numbers based on the input shape."""
ndim = len(input_shape)
lhs_spec = (0, ndim - 1) + tuple(range(1, ndim - 1))
rhs_spec = (ndim - 1, ndim - 2) + tuple(range(0, ndim - 2))
out_spec = lhs_spec
return lax.ConvDimensionNumbers(lh... | Computes the dimension numbers based on the input shape. | _conv_dimension_numbers | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def __call__(self, inputs: Array) -> Array:
"""Applies a (potentially unshared) convolution to the inputs.
Args:
inputs: input data with dimensions (*batch_dims, spatial_dims...,
features). This is the channels-last convention, i.e. NHWC for a 2d
convolution and NDHWC ... | Applies a (potentially unshared) convolution to the inputs.
Args:
inputs: input data with dimensions (*batch_dims, spatial_dims...,
features). This is the channels-last convention, i.e. NHWC for a 2d
convolution and NDHWC for a 3D convolution. Note: this is different from
... | __call__ | python | huggingface/distil-whisper | training/flax/distil_whisper/layers.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/layers.py | MIT |
def convert_unroll_to_scan(self, params: Union[Dict, FrozenDict]):
r"""
Convert a `PyTree` of unrolled model parameters to a scanned block of model parameters. This method can be used
to explicitly convert the model parameters to scanned format. This returns a new `params` tree and does not
... |
Convert a `PyTree` of unrolled model parameters to a scanned block of model parameters. This method can be used
to explicitly convert the model parameters to scanned format. This returns a new `params` tree and does not
convert the `params` in place.
To illustrate the workings of this ... | convert_unroll_to_scan | python | huggingface/distil-whisper | training/flax/distil_whisper/modeling_flax_whisper.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/modeling_flax_whisper.py | MIT |
def convert_scan_to_unroll(self, params: Union[Dict, FrozenDict]):
r"""
Convert a `PyTree` of scanned model parameters to an unrolled stack of model parameters. This method can be
used to explicitly convert the model parameters to unrolled format. This returns a new `params` tree and does
... |
Convert a `PyTree` of scanned model parameters to an unrolled stack of model parameters. This method can be
used to explicitly convert the model parameters to unrolled format. This returns a new `params` tree and does
not convert the `params` in place.
To illustrate the workings of thi... | convert_scan_to_unroll | python | huggingface/distil-whisper | training/flax/distil_whisper/modeling_flax_whisper.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/modeling_flax_whisper.py | MIT |
def init_cache(self, batch_size, max_length, encoder_outputs):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-r... |
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
... | init_cache | python | huggingface/distil-whisper | training/flax/distil_whisper/modeling_flax_whisper.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/modeling_flax_whisper.py | MIT |
def encode(
self,
input_features: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None... |
Returns:
Example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditiona... | encode | python | huggingface/distil-whisper | training/flax/distil_whisper/modeling_flax_whisper.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/modeling_flax_whisper.py | MIT |
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_atten... |
Returns:
Example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditiona... | decode | python | huggingface/distil-whisper | training/flax/distil_whisper/modeling_flax_whisper.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/modeling_flax_whisper.py | MIT |
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_atten... |
Returns:
Example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditiona... | decode | python | huggingface/distil-whisper | training/flax/distil_whisper/modeling_flax_whisper.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/modeling_flax_whisper.py | MIT |
def pjit_with_cpu_fallback(
fun: Callable, # pylint: disable=g-bare-generic
in_axis_resources,
out_axis_resources,
static_argnums: Union[int, Sequence[int]] = (),
donate_argnums: Union[int, Sequence[int]] = (),
backend: Optional[str] = None,
):
"""Wrapper for pjit that calls normal jit on c... | Wrapper for pjit that calls normal jit on cpu. | pjit_with_cpu_fallback | python | huggingface/distil-whisper | training/flax/distil_whisper/partitioner.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/partitioner.py | MIT |
def with_sharding_constraint(x, axis_resources):
"""Wrapper for pjit with_sharding_constraint, no-op on cpu or outside pjit."""
if jax.devices()[0].platform == "cpu" or not global_mesh_defined():
return x
else:
return jax.experimental.pjit.with_sharding_constraint(x, axis_resources) | Wrapper for pjit with_sharding_constraint, no-op on cpu or outside pjit. | with_sharding_constraint | python | huggingface/distil-whisper | training/flax/distil_whisper/partitioner.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/partitioner.py | MIT |
def bounds_from_last_device(last_device: JaxDevice) -> HardwareMesh:
"""Get the bound from the given last device."""
# Must be passed the device at the highest-coordinate corner of the
# relevant mesh, which is a requirement we know is satisfied by the last
# device in jax.devices().
if hasattr(last... | Get the bound from the given last device. | bounds_from_last_device | python | huggingface/distil-whisper | training/flax/distil_whisper/partitioner.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/partitioner.py | MIT |
def get_coords(device: JaxDevice) -> HardwareMesh:
"""Returns the coordinates of the given device."""
if hasattr(device, "coords"):
return (*device.coords, device.core_on_chip)
return (device.process_index, device.id % jax.local_device_count()) | Returns the coordinates of the given device. | get_coords | python | huggingface/distil-whisper | training/flax/distil_whisper/partitioner.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/partitioner.py | MIT |
def global_mesh_defined():
"""Checks if global xmap/pjit mesh resource environment is defined."""
maps_env = jax.experimental.maps.thread_resources.env
return maps_env.physical_mesh.devices.shape != () # pylint: disable=g-explicit-bool-comparison | Checks if global xmap/pjit mesh resource environment is defined. | global_mesh_defined | python | huggingface/distil-whisper | training/flax/distil_whisper/partitioner.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/partitioner.py | MIT |
def get_mesh(
model_parallel_submesh: HardwareMesh,
input_devices: Sequence[JaxDevice] = (),
input_local_devices: Sequence[JaxDevice] = (),
tile_by_host_if_needed: bool = True,
backend: Optional[str] = None,
) -> Mesh:
"""Construct an xmap/pjit Mesh for the given model-parallel submesh.
The... | Construct an xmap/pjit Mesh for the given model-parallel submesh.
The resulting mesh has two resource axes: 'model', with the provided submesh
shape, and 'data', which covers the rest of the mesh.
Args:
model_parallel_submesh: a HardwareMesh spec, namely (x,y,z,core) on TPU for
a single mode... | get_mesh | python | huggingface/distil-whisper | training/flax/distil_whisper/partitioner.py | https://github.com/huggingface/distil-whisper/blob/master/training/flax/distil_whisper/partitioner.py | MIT |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.