orcalib.orca_classification#

OrcaKnnClassifier #

OrcaKnnClassifier(
    num_classes,
    num_memories,
    label_column_name,
    weigh_memories=True,
    database=None,
    curate_enabled=False,
    memory_index_name=None,
    drop_exact_match=DropExactMatchOption.TRAINING_ONLY,
    exact_match_threshold=None,
)

Bases: OrcaLookupModule

A simple KNN layer that returns the average label of the K nearest memories to the input vector.

Examples:

import torch
from orcalib import OrcaModule, OrcaKnnClassifier

class MyModule(OrcaModule):
    def __init__(self):
        super().__init__()
        self.knn_head = OrcaKnnClassifier(
            memory_index_name="my_index",
            label_column_name="my_label",
            num_memories=10,
            num_classes=5,
        )

    def forward(self, x):
        logits = self.knn_head(x)
        return logits

Parameters:

num_classes (int) –

The size of the output vector.
num_memories (int) –

The number of memory vectors to be returned from the lookup.
weigh_memories (bool, default: True ) –

Whether to weigh the memories by their scores.
database (OrcaDatabase | str | None, default: None ) –

The OrcaDatabase instance to use for lookups and curate tracking.
curate_enabled (bool, default: False ) –

Whether Curate tracking is enabled.
memory_index_name (str | None, default: None ) –

The name of the index to use for lookups.
drop_exact_match (DropExactMatchOption, default: TRAINING_ONLY ) –

Choose to drop the exact match (if found) always, never, or only during training or inference.
exact_match_threshold (float | None, default: None ) –

Minimum similarity score for something to be considered the exact match
label_column_name (ColumnName) –

The name of the label column to return from the index.

lookup_result_transforms `property` `writable` #

1	`lookup_result_transforms`

A list of transforms to apply to the lookup result. NOTE: This will be applied even when lookup_result_override is set.

extra_lookup_column_names `property` `writable` #

1	`extra_lookup_column_names`

While set, all lookups will include these additional columns. They may inclue columns on the indexed table as well as index-specific columns, e.g., $score, $embedding.

lookup_query_override `property` `writable` #

1	`lookup_query_override`

The query to use instead of performing a lookup. NOTE: This will be ignored if lookup_result_override is also set.

lookup_result_override `property` `writable` #

1	`lookup_result_override`

The lookup result to use instead of performing a lookup.

lookup_database `property` `writable` #

1	`lookup_database`

The name of the database to use for looking up memories.

memory_index_name `property` `writable` #

1	`memory_index_name`

The name of the index to use for looking up memories.

lookup_column_names `property` `writable` #

1	`lookup_column_names`

The names of the columns to retrieve for each memory.

num_memories `property` `writable` #

1	`num_memories`

The number of memories to look up.

drop_exact_match `property` `writable` #

1	`drop_exact_match`

Whether to drop exact matches from the results.

exact_match_threshold `property` `writable` #

1	`exact_match_threshold`

The similarity threshold for exact matches.

shuffle_memories `property` `writable` #

1	`shuffle_memories`

Whether to shuffle the looked up memories.

curate_database `property` `writable` #

1	`curate_database`

The name of the database to use for saving curate tracking data.

curate_next_run_settings `property` `writable` #

1	`curate_next_run_settings`

The settings for the next curate model run.

curate_model_id `property` `writable` #

1	`curate_model_id`

The model id to associate with curated model runs.

curate_model_version `property` `writable` #

1	`curate_model_version`

The model version to associate with curated model runs.

curate_metadata `property` `writable` #

1	`curate_metadata`

The metadata to attach to curated model runs.

curate_tags `property` `writable` #

1	`curate_tags`

The tags to attach to the curated model runs.

curate_seq_id `property` `writable` #

1	`curate_seq_id`

The sequence id to associate with curated model runs.

curate_batch_size `property` `writable` #

1	`curate_batch_size`

The batch size of the model run to track curate data for, usually inferred automatically.

last_curate_run_ids `property` `writable` #

1	`last_curate_run_ids`

The run ids of the last model run for which curate tracking data was collected.

last_curate_run_settings `property` `writable` #

1	`last_curate_run_settings`

The settings of the last model run for which curate tracking data was collected.

get_effective_lookup_settings #

1	`get_effective_lookup_settings()`

Returns the effective lookup settings for this module, with any inherited settings applied.

Returns:

LookupSettings –

The effective lookup settings for this module. Practically, this be the lookup settings
LookupSettings –

set on this module. For any settings that are not set on this module, the inherited settings
LookupSettings –

will be used instead.

get_lookup_database_instance #

1	`get_lookup_database_instance()`

Returns the OrcaDatabase instance to use for looking up memories.

get_orca_modules_recursively #

get_orca_modules_recursively(
    max_depth=None, include_self=True, filter_type=None
)

Recursively yields all children of this module that are instances of the specified filter type.

All parent nodes will be processed before their children
This will search through all children —even those that are not a subclass of the filter_type— but it only returns children that are a subclass of filter_type.

Parameters:

max_depth (int | None, default: None ) –
The maximum depth to search.
- Setting this to 0 will only include this module.
- Setting this to 1 will include only this module and its children.
- Setting it to None (the default) will search through all modules.
- Modules that are not of filter_type or OrcaModule do not increment the depth.
include_self (bool, default: True ) –

Whether to include the current OrcaModule in the results.
filter_type (type[ORCA_MODULE_TYPE] | None, default: None ) –

The subtype of OrcaModule to filter for. If None, any subtypes of OrcaModule will be returned.

Yields:

ORCA_MODULE_TYPE –

modules of type filter_type that are used in the children of this module.

enable_curate #

enable_curate(recursive=True)

Enable Curate tracking for the model and (if recursive is True) for all its descendants.

Parameters:

recursive (bool, default: True ) –

Whether to enable Curate tracking recursively.

disable_curate #

disable_curate(recursive=True)

Disable Curate tracking for this module and (if recursive is True) for all its descendants.

Parameters:

recursive (bool, default: True ) –

Whether to disable Curate tracking recursively.

update_curate_settings #

update_curate_settings(
    model_id=None,
    model_version=None,
    tags=None,
    extra_tags=None,
    metadata=None,
    extra_metadata=None,
    batch_size=None,
    seq_id=None,
    enabled=None,
    enable_recursive=True,
)

Update curate tracking settings for the module and all its children.

Parameters:

model_id (str | None, default: None ) –

The ID of the model.
model_version (str | None, default: None ) –

The version of the model.
tags (Iterable[str] | None, default: None ) –

The new tags to be added to the model.
extra_tags (Iterable[str] | None, default: None ) –

The extra tags to be added to the model.
metadata (OrcaMetadataDict | None, default: None ) –

The new metadata to be added to the model.
extra_metadata (OrcaMetadataDict | None, default: None ) –

The extra metadata to be added to the model.
batch_size (int | None, default: None ) –

The batch size to be used for the model.
seq_id (UUID | None, default: None ) –

The sequence ID to be used for the model.

record_next_model_memory_lookups #

record_next_model_memory_lookups(
    tags=None, metadata=None, batch_size=None, seq_id=None
)

Sets up curate tracking for the memory lookups during the next forward pass only.

Parameters:

tags (Iterable[str] | None, default: None ) –

Additional tags to be recorded on the next model run.
metadata (OrcaMetadataDict | None, default: None ) –

Additional metadata to be recorded on the next model run.
batch_size (int | None, default: None ) –

The batch size to be used for the next model run.
seq_id (UUID | None, default: None ) –

The sequence ID to be used for the next model run.

record_model_feedback #

record_model_feedback(
    val, name="default", kind=FeedbackKind.CONTINUOUS
)

Records feedback for the last model runs for which memory lookups were recorded by curate.

Parameters:

val (list[float] | float | int | list[int]) –

The feedback to be recorded.
name (str, default: 'default' ) –

The name of the feedback.
kind (FeedbackKind, default: CONTINUOUS ) –

The kind of feedback.

record_model_input_output #

record_model_input_output(inputs, outputs)

Records the inputs and outputs of the last model runs for which memory lookups were recorded by curate.

Parameters:

inputs (list[Any] | Any) –

The inputs to be recorded.
outputs (list[Any] | Any) –

The outputs to be recorded.

get_lookup_setting_summary #

1	`get_lookup_setting_summary()`

Returns a summary of the lookup settings for each OrcaLookupLayer in this module and its descendants.

forward #

forward(x=None, ctx_labels=None, ctx_scores=None)

Generate logits based on the nearest neighbors of the input vector.

Parameters:

x (Tensor | None, default: None ) –

The input tensor of shape (batch_size, embedding_dim), can be omitted if labels and scores are provided directly.
ctx_labels (Tensor | None, default: None ) –

The memory label tensor of shape (batch_size, num_memories) contains integer labels. If this is None, the labels will be looked up from the index based on the input tensor.
ctx_scores (Tensor | None, default: None ) –

The memory score tensor of shape (batch_size, num_memories) contains float scores. If this is None, the scores will be looked up from the index based on the input tensor.

Returns:

Tensor –

The output tensor of shape (batch_size, num_classes), if neither x nor scores are provided the dtype will be float32, otherwise it will be the same as the scores or input tensor.

OrcaClassificationHead #

OrcaClassificationHead(
    model_dim,
    num_classes,
    num_memories,
    num_layers=1,
    num_heads=8,
    classification_mode=ClassificationMode.DIRECT,
    activation=F.relu,
    dropout=0.1,
    deep_residuals=True,
    split_retrieval_path=False,
    memory_guide_weight=0.0,
    single_lookup=True,
    database=None,
    curate_enabled=False,
    memory_index_name=None,
    drop_exact_match=None,
    exact_match_threshold=None,
    shuffle_memories=False,
    label_column_name=None,
)

Bases: OrcaLookupModule, LabelColumnNameMixin

A transformer decoder layer block that does cross attention with memory lookup

Examples:

import torch
from orcalib.orca_torch import OrcaModule, OrcaClassificationHead

class MyModule(OrcaModule):
    def __init__(self):
        super().__init__()
        self.trunk = torch.nn.Linear(10, 10)
        self.classifier = OrcaClassificationHead(model_dim=10, num_classes=5, "my_index", "my_label", num_memories=10)

    def forward(self, x):
        x = self.trunk(x) # N x 10
        x = self.classifier(x)
        return x # N x 5, e.g., where each row may become logits for a softmax

Parameters:

model_dim (int) –

The dimension of the input vector and hidden layers.
num_classes (int) –

The size of the output vector.
num_memories (int) –

The number of memory vectors to be returned from the lookup.
num_layers (int, default: 1 ) –

The number of attention blocks to be used, copies of OrcaClassificationCrossAttentionLayer.
num_heads (int, default: 8 ) –

The number of heads to be used in the multi-head attention layer.
classification_mode (ClassificationMode, default: DIRECT ) –

The mode of classification to be used.
activation (Callable[[Tensor], Tensor], default: relu ) –

The activation function.
dropout (float, default: 0.1 ) –

The dropout rate.
deep_residuals (bool, default: True ) –

Whether to use deep residuals.
split_retrieval_path (bool, default: False ) –

Whether to split the retrieval path.
memory_guide_weight (float, default: 0.0 ) –

The weight of the memory guide.
single_lookup (bool, default: True ) –

Whether to use a single lookup.
database (OrcaDatabase | str | None, default: None ) –

The OrcaDatabase instance to use for lookups and curate tracking.
curate_enabled (bool, default: False ) –

Whether Curate tracking is enabled.
memory_index_name (str | None, default: None ) –

The name of the index to use for lookups.
drop_exact_match (DropExactMatchOption | None, default: None ) –

Choose to drop the exact match (if found) always, never, or only during training or inference.
exact_match_threshold (float | None, default: None ) –

Minimum similarity score for something to be considered the exact match
shuffle_memories (bool, default: False ) –

Whether to shuffle the memories before returning them.
label_column_name (ColumnName | None, default: None ) –

The name of the label column to return from the index.

lookup_result_transforms `property` `writable` #

1	`lookup_result_transforms`

A list of transforms to apply to the lookup result. NOTE: This will be applied even when lookup_result_override is set.

extra_lookup_column_names `property` `writable` #

1	`extra_lookup_column_names`

While set, all lookups will include these additional columns. They may inclue columns on the indexed table as well as index-specific columns, e.g., $score, $embedding.

lookup_query_override `property` `writable` #

1	`lookup_query_override`

The query to use instead of performing a lookup. NOTE: This will be ignored if lookup_result_override is also set.

lookup_result_override `property` `writable` #

1	`lookup_result_override`

The lookup result to use instead of performing a lookup.

lookup_database `property` `writable` #

1	`lookup_database`

The name of the database to use for looking up memories.

memory_index_name `property` `writable` #

1	`memory_index_name`

The name of the index to use for looking up memories.

lookup_column_names `property` `writable` #

1	`lookup_column_names`

The names of the columns to retrieve for each memory.

num_memories `property` `writable` #

1	`num_memories`

The number of memories to look up.

drop_exact_match `property` `writable` #

1	`drop_exact_match`

Whether to drop exact matches from the results.

exact_match_threshold `property` `writable` #

1	`exact_match_threshold`

The similarity threshold for exact matches.

shuffle_memories `property` `writable` #

1	`shuffle_memories`

Whether to shuffle the looked up memories.

curate_database `property` `writable` #

1	`curate_database`

The name of the database to use for saving curate tracking data.

curate_next_run_settings `property` `writable` #

1	`curate_next_run_settings`

The settings for the next curate model run.

curate_model_id `property` `writable` #

1	`curate_model_id`

The model id to associate with curated model runs.

curate_model_version `property` `writable` #

1	`curate_model_version`

The model version to associate with curated model runs.

curate_metadata `property` `writable` #

1	`curate_metadata`

The metadata to attach to curated model runs.

curate_tags `property` `writable` #

1	`curate_tags`

The tags to attach to the curated model runs.

curate_seq_id `property` `writable` #

1	`curate_seq_id`

The sequence id to associate with curated model runs.

curate_batch_size `property` `writable` #

1	`curate_batch_size`

The batch size of the model run to track curate data for, usually inferred automatically.

last_curate_run_ids `property` `writable` #

1	`last_curate_run_ids`

The run ids of the last model run for which curate tracking data was collected.

last_curate_run_settings `property` `writable` #

1	`last_curate_run_settings`

The settings of the last model run for which curate tracking data was collected.

get_effective_lookup_settings #

1	`get_effective_lookup_settings()`

Returns the effective lookup settings for this module, with any inherited settings applied.

Returns:

LookupSettings –

The effective lookup settings for this module. Practically, this be the lookup settings
LookupSettings –

set on this module. For any settings that are not set on this module, the inherited settings
LookupSettings –

will be used instead.

get_lookup_database_instance #

1	`get_lookup_database_instance()`

Returns the OrcaDatabase instance to use for looking up memories.

get_orca_modules_recursively #

get_orca_modules_recursively(
    max_depth=None, include_self=True, filter_type=None
)

Recursively yields all children of this module that are instances of the specified filter type.

All parent nodes will be processed before their children
This will search through all children —even those that are not a subclass of the filter_type— but it only returns children that are a subclass of filter_type.

Parameters:

max_depth (int | None, default: None ) –
The maximum depth to search.
- Setting this to 0 will only include this module.
- Setting this to 1 will include only this module and its children.
- Setting it to None (the default) will search through all modules.
- Modules that are not of filter_type or OrcaModule do not increment the depth.
include_self (bool, default: True ) –

Whether to include the current OrcaModule in the results.
filter_type (type[ORCA_MODULE_TYPE] | None, default: None ) –

The subtype of OrcaModule to filter for. If None, any subtypes of OrcaModule will be returned.

Yields:

ORCA_MODULE_TYPE –

modules of type filter_type that are used in the children of this module.

enable_curate #

enable_curate(recursive=True)

Enable Curate tracking for the model and (if recursive is True) for all its descendants.

Parameters:

recursive (bool, default: True ) –

Whether to enable Curate tracking recursively.

disable_curate #

disable_curate(recursive=True)

Disable Curate tracking for this module and (if recursive is True) for all its descendants.

Parameters:

recursive (bool, default: True ) –

Whether to disable Curate tracking recursively.

update_curate_settings #

update_curate_settings(
    model_id=None,
    model_version=None,
    tags=None,
    extra_tags=None,
    metadata=None,
    extra_metadata=None,
    batch_size=None,
    seq_id=None,
    enabled=None,
    enable_recursive=True,
)

Update curate tracking settings for the module and all its children.

Parameters:

model_id (str | None, default: None ) –

The ID of the model.
model_version (str | None, default: None ) –

The version of the model.
tags (Iterable[str] | None, default: None ) –

The new tags to be added to the model.
extra_tags (Iterable[str] | None, default: None ) –

The extra tags to be added to the model.
metadata (OrcaMetadataDict | None, default: None ) –

The new metadata to be added to the model.
extra_metadata (OrcaMetadataDict | None, default: None ) –

The extra metadata to be added to the model.
batch_size (int | None, default: None ) –

The batch size to be used for the model.
seq_id (UUID | None, default: None ) –

The sequence ID to be used for the model.

record_next_model_memory_lookups #

record_next_model_memory_lookups(
    tags=None, metadata=None, batch_size=None, seq_id=None
)

Sets up curate tracking for the memory lookups during the next forward pass only.

Parameters:

tags (Iterable[str] | None, default: None ) –

Additional tags to be recorded on the next model run.
metadata (OrcaMetadataDict | None, default: None ) –

Additional metadata to be recorded on the next model run.
batch_size (int | None, default: None ) –

The batch size to be used for the next model run.
seq_id (UUID | None, default: None ) –

The sequence ID to be used for the next model run.

record_model_feedback #

record_model_feedback(
    val, name="default", kind=FeedbackKind.CONTINUOUS
)

Records feedback for the last model runs for which memory lookups were recorded by curate.

Parameters:

val (list[float] | float | int | list[int]) –

The feedback to be recorded.
name (str, default: 'default' ) –

The name of the feedback.
kind (FeedbackKind, default: CONTINUOUS ) –

The kind of feedback.

record_model_input_output #

record_model_input_output(inputs, outputs)

Records the inputs and outputs of the last model runs for which memory lookups were recorded by curate.

Parameters:

inputs (list[Any] | Any) –

The inputs to be recorded.
outputs (list[Any] | Any) –

The outputs to be recorded.

get_lookup_setting_summary #

1	`get_lookup_setting_summary()`

Returns a summary of the lookup settings for each OrcaLookupLayer in this module and its descendants.

forward #

forward(x, ctx=None, labels=None, memory_key=None)

Generate logits based on the input vector and memory context.

Parameters:

x (Tensor) –

The input tensor of shape (batch_size, embedding_dim)
ctx (Tensor | None, default: None ) –

The memory context tensor of shape (batch_size, num_memories, embedding_dim). If None, the memory context will be looked up based on the memory_key or input tensor.
labels (Tensor | None, default: None ) –

The memory label tensor of shape (batch_size, num_memories) containing integer labels. If None, the labels will be looked up from the index based on the memory_key or input tensor.
memory_key (Tensor | None, default: None ) –

The memory key tensor of shape (batch_size, embedding_dim) to use for lookup. If None, the input tensor will be used.

Returns:

Tensor –

The logits tensor of shape (batch_size, num_classes)

OrcaMoeClassificationHead #

OrcaMoeClassificationHead(
    model_dim,
    num_classes,
    num_memories,
    label_column_name,
    gate_layers=1,
    hidden_layers=0,
    activation=F.relu,
    dropout=0.1,
    database=None,
    curate_enabled=False,
    memory_index_name=None,
    drop_exact_match=DropExactMatchOption.TRAINING_ONLY,
    exact_match_threshold=None,
)

Bases: OrcaLookupModule

A mixture of experts classification head that combines a KNN classifier with a linear classifier.

Examples:

import torch
from orcalib import OrcaModel, OrcaMoeClassificationHead

class MyModule(OrcaModel):
    def __init__(self):
        super().__init__()
        self.trunk = torch.nn.Linear(10, 10)
        self.classifier = OrcaMoeClassificationHead(model_dim=10, num_classes=5, num_memories=10)

    def forward(self, x):
        x = self.trunk(x)
        x = self.classifier(x)
        return x

Parameters:

model_dim (int) –

The dimension of the input vector and hidden layers.
num_classes (int) –

The size of the output vector.
num_memories (int) –

The number of memory vectors to be returned from the lookup.
label_column_name (ColumnName) –

The name of the label column to return from the index.
gate_layers (int, default: 1 ) –

The number of layers to use in the gating network.
hidden_layers (int, default: 0 ) –

The number of hidden layers to use in the linear classifier.
activation (Callable[[Tensor], Tensor], default: relu ) –

The activation function.
dropout (float, default: 0.1 ) –

The dropout rate.
database (OrcaDatabase | str | None, default: None ) –

The OrcaDatabase instance to use for lookups and curate tracking.
curate_enabled (bool, default: False ) –

Whether Curate tracking is enabled.
memory_index_name (str | None, default: None ) –

The name of the index to use for lookups.
drop_exact_match (DropExactMatchOption, default: TRAINING_ONLY ) –

Choose to drop the exact match (if found) always, never, or only during training or inference.
exact_match_threshold (float | None, default: None ) –

Minimum similarity score for something to be considered the exact match

lookup_result_transforms `property` `writable` #

1	`lookup_result_transforms`

A list of transforms to apply to the lookup result. NOTE: This will be applied even when lookup_result_override is set.

extra_lookup_column_names `property` `writable` #

1	`extra_lookup_column_names`

While set, all lookups will include these additional columns. They may inclue columns on the indexed table as well as index-specific columns, e.g., $score, $embedding.

lookup_query_override `property` `writable` #

1	`lookup_query_override`

The query to use instead of performing a lookup. NOTE: This will be ignored if lookup_result_override is also set.

lookup_result_override `property` `writable` #

1	`lookup_result_override`

The lookup result to use instead of performing a lookup.

lookup_database `property` `writable` #

1	`lookup_database`

The name of the database to use for looking up memories.

memory_index_name `property` `writable` #

1	`memory_index_name`

The name of the index to use for looking up memories.

lookup_column_names `property` `writable` #

1	`lookup_column_names`

The names of the columns to retrieve for each memory.

num_memories `property` `writable` #

1	`num_memories`

The number of memories to look up.

drop_exact_match `property` `writable` #

1	`drop_exact_match`

Whether to drop exact matches from the results.

exact_match_threshold `property` `writable` #

1	`exact_match_threshold`

The similarity threshold for exact matches.

shuffle_memories `property` `writable` #

1	`shuffle_memories`

Whether to shuffle the looked up memories.

curate_database `property` `writable` #

1	`curate_database`

The name of the database to use for saving curate tracking data.

curate_next_run_settings `property` `writable` #

1	`curate_next_run_settings`

The settings for the next curate model run.

curate_model_id `property` `writable` #

1	`curate_model_id`

The model id to associate with curated model runs.

curate_model_version `property` `writable` #

1	`curate_model_version`

The model version to associate with curated model runs.

curate_metadata `property` `writable` #

1	`curate_metadata`

The metadata to attach to curated model runs.

curate_tags `property` `writable` #

1	`curate_tags`

The tags to attach to the curated model runs.

curate_seq_id `property` `writable` #

1	`curate_seq_id`

The sequence id to associate with curated model runs.

curate_batch_size `property` `writable` #

1	`curate_batch_size`

The batch size of the model run to track curate data for, usually inferred automatically.

last_curate_run_ids `property` `writable` #

1	`last_curate_run_ids`

The run ids of the last model run for which curate tracking data was collected.

last_curate_run_settings `property` `writable` #

1	`last_curate_run_settings`

The settings of the last model run for which curate tracking data was collected.

get_effective_lookup_settings #

1	`get_effective_lookup_settings()`

Returns the effective lookup settings for this module, with any inherited settings applied.

Returns:

LookupSettings –

The effective lookup settings for this module. Practically, this be the lookup settings
LookupSettings –

set on this module. For any settings that are not set on this module, the inherited settings
LookupSettings –

will be used instead.

get_lookup_database_instance #

1	`get_lookup_database_instance()`

Returns the OrcaDatabase instance to use for looking up memories.

get_orca_modules_recursively #

get_orca_modules_recursively(
    max_depth=None, include_self=True, filter_type=None
)

Recursively yields all children of this module that are instances of the specified filter type.

All parent nodes will be processed before their children
This will search through all children —even those that are not a subclass of the filter_type— but it only returns children that are a subclass of filter_type.

Parameters:

max_depth (int | None, default: None ) –
The maximum depth to search.
- Setting this to 0 will only include this module.
- Setting this to 1 will include only this module and its children.
- Setting it to None (the default) will search through all modules.
- Modules that are not of filter_type or OrcaModule do not increment the depth.
include_self (bool, default: True ) –

Whether to include the current OrcaModule in the results.
filter_type (type[ORCA_MODULE_TYPE] | None, default: None ) –

The subtype of OrcaModule to filter for. If None, any subtypes of OrcaModule will be returned.

Yields:

ORCA_MODULE_TYPE –

modules of type filter_type that are used in the children of this module.

enable_curate #

enable_curate(recursive=True)

Enable Curate tracking for the model and (if recursive is True) for all its descendants.

Parameters:

recursive (bool, default: True ) –

Whether to enable Curate tracking recursively.

disable_curate #

disable_curate(recursive=True)

Disable Curate tracking for this module and (if recursive is True) for all its descendants.

Parameters:

recursive (bool, default: True ) –

Whether to disable Curate tracking recursively.

update_curate_settings #

update_curate_settings(
    model_id=None,
    model_version=None,
    tags=None,
    extra_tags=None,
    metadata=None,
    extra_metadata=None,
    batch_size=None,
    seq_id=None,
    enabled=None,
    enable_recursive=True,
)

Update curate tracking settings for the module and all its children.

Parameters:

model_id (str | None, default: None ) –

The ID of the model.
model_version (str | None, default: None ) –

The version of the model.
tags (Iterable[str] | None, default: None ) –

The new tags to be added to the model.
extra_tags (Iterable[str] | None, default: None ) –

The extra tags to be added to the model.
metadata (OrcaMetadataDict | None, default: None ) –

The new metadata to be added to the model.
extra_metadata (OrcaMetadataDict | None, default: None ) –

The extra metadata to be added to the model.
batch_size (int | None, default: None ) –

The batch size to be used for the model.
seq_id (UUID | None, default: None ) –

The sequence ID to be used for the model.

record_next_model_memory_lookups #

record_next_model_memory_lookups(
    tags=None, metadata=None, batch_size=None, seq_id=None
)

Sets up curate tracking for the memory lookups during the next forward pass only.

Parameters:

tags (Iterable[str] | None, default: None ) –

Additional tags to be recorded on the next model run.
metadata (OrcaMetadataDict | None, default: None ) –

Additional metadata to be recorded on the next model run.
batch_size (int | None, default: None ) –

The batch size to be used for the next model run.
seq_id (UUID | None, default: None ) –

The sequence ID to be used for the next model run.

record_model_feedback #

record_model_feedback(
    val, name="default", kind=FeedbackKind.CONTINUOUS
)

Records feedback for the last model runs for which memory lookups were recorded by curate.

Parameters:

val (list[float] | float | int | list[int]) –

The feedback to be recorded.
name (str, default: 'default' ) –

The name of the feedback.
kind (FeedbackKind, default: CONTINUOUS ) –

The kind of feedback.

record_model_input_output #

record_model_input_output(inputs, outputs)

Records the inputs and outputs of the last model runs for which memory lookups were recorded by curate.

Parameters:

inputs (list[Any] | Any) –

The inputs to be recorded.
outputs (list[Any] | Any) –

The outputs to be recorded.

get_lookup_setting_summary #

1	`get_lookup_setting_summary()`

Returns a summary of the lookup settings for each OrcaLookupLayer in this module and its descendants.

forward #

forward(x, ctx_scores=None, ctx_labels=None)

Generate logits based on the input vector and memory context.

Parameters:

x (Tensor) –

The input tensor of shape (batch_size, embedding_dim).
ctx_scores (Tensor | None, default: None ) –

The memory scores tensor of shape (batch_size, num_memories). If this is None, the scores will be looked up from the index based on the input tensor.
ctx_labels (Tensor | None, default: None ) –

The memory labels tensor of shape (batch_size, num_memories). If this is None, the labels will be looked up from the index based on the input tensor

Returns:

Tensor –

The logits tensor of shape (batch_size, num_classes).

orcalib.orca_classification#

OrcaKnnClassifier #

lookup_result_transforms property writable #

extra_lookup_column_names property writable #

lookup_query_override property writable #

lookup_result_override property writable #

lookup_database property writable #

memory_index_name property writable #

lookup_column_names property writable #

num_memories property writable #

drop_exact_match property writable #

exact_match_threshold property writable #

shuffle_memories property writable #

curate_database property writable #

curate_next_run_settings property writable #

curate_model_id property writable #

curate_model_version property writable #

curate_metadata property writable #

curate_tags property writable #

curate_seq_id property writable #

curate_batch_size property writable #

last_curate_run_ids property writable #

last_curate_run_settings property writable #

get_effective_lookup_settings #

get_lookup_database_instance #

get_orca_modules_recursively #

enable_curate #

disable_curate #

update_curate_settings #

record_next_model_memory_lookups #

record_model_feedback #

record_model_input_output #

get_lookup_setting_summary #

forward #

OrcaClassificationHead #

lookup_result_transforms property writable #

extra_lookup_column_names property writable #

lookup_query_override property writable #

lookup_result_override property writable #

lookup_database property writable #

memory_index_name property writable #

lookup_column_names property writable #

num_memories property writable #

drop_exact_match property writable #

exact_match_threshold property writable #

shuffle_memories property writable #

curate_database property writable #

curate_next_run_settings property writable #

curate_model_id property writable #

curate_model_version property writable #

curate_metadata property writable #

curate_tags property writable #

curate_seq_id property writable #

curate_batch_size property writable #

last_curate_run_ids property writable #

last_curate_run_settings property writable #

get_effective_lookup_settings #

get_lookup_database_instance #

get_orca_modules_recursively #

enable_curate #

disable_curate #

update_curate_settings #

record_next_model_memory_lookups #

record_model_feedback #

record_model_input_output #

get_lookup_setting_summary #

forward #

OrcaMoeClassificationHead #

lookup_result_transforms property writable #

extra_lookup_column_names property writable #

lookup_query_override property writable #

lookup_result_override property writable #

lookup_database property writable #

memory_index_name property writable #

lookup_column_names property writable #

num_memories property writable #

drop_exact_match property writable #

exact_match_threshold property writable #

shuffle_memories property writable #

curate_database property writable #

lookup_result_transforms `property` `writable` #

extra_lookup_column_names `property` `writable` #

lookup_query_override `property` `writable` #

lookup_result_override `property` `writable` #

lookup_database `property` `writable` #

memory_index_name `property` `writable` #

lookup_column_names `property` `writable` #

num_memories `property` `writable` #

drop_exact_match `property` `writable` #

exact_match_threshold `property` `writable` #

shuffle_memories `property` `writable` #

curate_database `property` `writable` #

curate_next_run_settings `property` `writable` #

curate_model_id `property` `writable` #

curate_model_version `property` `writable` #

curate_metadata `property` `writable` #

curate_tags `property` `writable` #

curate_seq_id `property` `writable` #

curate_batch_size `property` `writable` #

last_curate_run_ids `property` `writable` #

last_curate_run_settings `property` `writable` #

lookup_result_transforms `property` `writable` #

extra_lookup_column_names `property` `writable` #

lookup_query_override `property` `writable` #

lookup_result_override `property` `writable` #

lookup_database `property` `writable` #

memory_index_name `property` `writable` #

lookup_column_names `property` `writable` #

num_memories `property` `writable` #

drop_exact_match `property` `writable` #

exact_match_threshold `property` `writable` #

shuffle_memories `property` `writable` #

curate_database `property` `writable` #

curate_next_run_settings `property` `writable` #

curate_model_id `property` `writable` #

curate_model_version `property` `writable` #

curate_metadata `property` `writable` #

curate_tags `property` `writable` #

curate_seq_id `property` `writable` #

curate_batch_size `property` `writable` #

last_curate_run_ids `property` `writable` #

last_curate_run_settings `property` `writable` #

lookup_result_transforms `property` `writable` #

extra_lookup_column_names `property` `writable` #

lookup_query_override `property` `writable` #

lookup_result_override `property` `writable` #

lookup_database `property` `writable` #

memory_index_name `property` `writable` #

lookup_column_names `property` `writable` #

num_memories `property` `writable` #

drop_exact_match `property` `writable` #

exact_match_threshold `property` `writable` #

shuffle_memories `property` `writable` #

curate_database `property` `writable` #

curate_next_run_settings `property` `writable` #

curate_model_id `property` `writable` #

curate_model_version `property` `writable` #

curate_metadata `property` `writable` #

curate_tags `property` `writable` #

curate_seq_id `property` `writable` #

curate_batch_size `property` `writable` #

last_curate_run_ids `property` `writable` #

last_curate_run_settings `property` `writable` #