morpheus.messages.memory.inference_memory.InferenceMemoryNLP
- class InferenceMemoryNLP(*, count, input_ids, input_mask, seq_ids)[source]
Bases:
morpheus.messages.memory.inference_memory.InferenceMemoryThis is a container class for data that needs to be submitted to the inference server for NLP category usecases.
- Parameters
- input_ids
- input_mask
- seq_ids
The token-ids for each string padded with 0s to max_length.
The mask for token-ids result where corresponding positions identify valid token-id values.
Ids used to index from an inference input to a message. Necessary since there can be more inference inputs than messages (i.e., if some messages get broken into multiple inference requests).
- Attributes
- input_ids
- input_mask
- seq_ids
- tensor_names
Methods
get_input(name)Get the tensor stored in the container identified by name.get_tensor(name)Get the Tensor stored in the container identified by name.get_tensors()Get the tensors contained by this instance. has_tensor(name)Returns True if a tensor with the requested name exists in the tensors object set_input(name, tensor)Update the input tensor identified by name.set_tensor(name, tensor)Update the tensor identified by name.set_tensors(tensors)Overwrite the tensors stored by this instance. - get_input(name)[source]
Get the tensor stored in the container identified by
name. Alias forInferenceMemory.get_tensor.- Parameters
- name
Key used to do lookup in inputs dict of the container.
- Returns
- cupy.ndarray
Inputs corresponding to name.
- Raises
- KeyError
If input name does not exist in the container.
- get_tensor(name)[source]
Get the Tensor stored in the container identified by
name.- Parameters
- name
Tensor key name.
- Returns
- cupy.ndarray
Tensor.
- Raises
- KeyError
If tensor name does not exist in the container.
- get_tensors()[source]
Get the tensors contained by this instance. It is important to note that when C++ execution is enabled the returned tensors will be a Python copy of the tensors stored in the C++ object. As such any changes made to the tensors will need to be updated with a call to
set_tensors.- Returns
- typing.Dict[str, cp.ndarray]
- has_tensor(name)[source]
Returns True if a tensor with the requested name exists in the tensors object
- Parameters
- name
Name to lookup
- Returns
- bool
True if the tensor was found
- set_input(name, tensor)[source]
Update the input tensor identified by
name. Alias forInferenceMemory.set_tensor- Parameters
- name
- tensor
Key used to do lookup in inputs dict of the container.
Tensor as a CuPy array.
- set_tensor(name, tensor)[source]
Update the tensor identified by
name.- Parameters
- name
- tensor
Tensor key name.
Tensor as a CuPy array.
- Raises
- ValueError
If the number of rows in
tensordoes not matchcount
- set_tensors(tensors)[source]
Overwrite the tensors stored by this instance. If the length of the tensors has changed, then the
countproperty should also be updated.- Parameters
- tensors
Collection of tensors uniquely identified by a name.