Python Class Documentation & Reference¶
This section provides a breakdown of the Python classes and what each of their functions provide. Below is a diagram that provides insights on the relationship between Vulkan Kompute objects and Vulkan resources, which primarily encompass ownership of either CPU and/or GPU memory.
Manager¶
The Kompute Manager provides a high level interface to simplify interaction with underlying kp.Sequence of Operations.
-
class
kp.Manager¶ -
destroy(*args, **kwargs)¶ Overloaded function.
destroy(self: kp.Manager, tensor: kp.Tensor) -> None
Destroy owned Vulkan GPU resources and free GPU memory for single tensor.
@param tensors Single tensor to rebuild
destroy(self: kp.Manager, tensors: List[kp.Tensor]) -> None
Destroy owned Vulkan GPU resources and free GPU memory for vector of tensors.
@param tensors Single tensor to rebuild
destroy(self: kp.Manager, sequences: List[kp.Sequence]) -> None
Destroy owned Vulkan GPU resources and free GPU memory for vector of sequences. Destroying by sequence name is more efficent and hence recommended instead of by object.
@param sequences Vector for shared ptrs with sequences to destroy
destroy(self: kp.Manager, sequence: kp.Sequence) -> None
Destroy owned Vulkan GPU resources and free GPU memory for single sequence. Destroying by sequence name is more efficent and hence recommended instead of by object.
@param sequences Single sequence to rebuild
destroy(self: kp.Manager, sequenceName: str) -> None
Destroy owned Vulkan GPU resources and free GPU memory for sequence by name.
@param sequenceName Single name of named sequence to destroy
destroy(self: kp.Manager, sequenceNames: List[str]) -> None
Destroy owned Vulkan GPU resources and free GPU memory for sequences using vector of named sequence names.
@param sequenceName Vector of sequence names to destroy
-
eval_algo_data(self: kp.Manager, tensors: List[kp.Tensor], sequence_name: str, bytes: bytes, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → None¶ Evaluates an operation using a custom shader provided as spirv bytes with explicitly named Sequence
-
eval_algo_data_def(self: kp.Manager, tensors: List[kp.Tensor], bytes: bytes, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → None¶ Evaluates an operation using a custom shader provided as spirv bytes with new anonymous Sequence
-
eval_algo_file(self: kp.Manager, tensors: List[kp.Tensor], sequence_name: str, data: str, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → None¶ Evaluates an operation using a custom shader provided from a shader path with explicitly named Sequence
-
eval_algo_file_def(self: kp.Manager, tensors: List[kp.Tensor], data: str, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → None¶ Evaluates an operation using a custom shader provided from a shader path with new anonymous Sequence
-
eval_async_algo_data(self: kp.Manager, tensors: List[kp.Tensor], sequence_name: str, bytes: bytes, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → None¶ Evaluates asynchronously an operation using a custom shader provided as raw string or spirv bytes with explicitly named Sequence
-
eval_async_algo_data_def(self: kp.Manager, tensors: List[kp.Tensor], bytes: bytes, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → None¶ Evaluates asynchronously an operation using a custom shader provided as raw string or spirv bytes with anonymous Sequence
-
eval_async_algo_file(self: kp.Manager, tensors: List[kp.Tensor], sequence_name: str, data: str, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → None¶ Evaluates asynchronously an operation using a custom shader provided from a shader path with explicitly named Sequence
-
eval_async_algo_file_def(self: kp.Manager, tensors: List[kp.Tensor], data: str, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → None¶ Evaluates asynchronously an operation using a custom shader provided from a shader path with anonymous Sequence
-
eval_async_tensor_copy(self: kp.Manager, arg0: List[kp.Tensor], arg1: str) → None¶ Evaluates asynchronously operation to copy one tensor to one or many tensors with explicitly named Sequence
-
eval_async_tensor_copy_def(self: kp.Manager, arg0: List[kp.Tensor]) → None¶ Evaluates asynchronously operation to copy one tensor to one or many tensors with anonymous Sequence
-
eval_async_tensor_sync_device(self: kp.Manager, arg0: List[kp.Tensor], arg1: str) → None¶ Evaluates asynchronously operation to sync tensor from local memory to GPU memory with explicitly named Sequence
-
eval_async_tensor_sync_device_def(self: kp.Manager, arg0: List[kp.Tensor]) → None¶ Evaluates asynchronously operation to sync tensor from local memory to GPU memory with anonymous Sequence
-
eval_async_tensor_sync_local(self: kp.Manager, arg0: List[kp.Tensor], arg1: str) → None¶ Evaluates asynchronously operation to sync tensor(s) from GPU memory to local memory with explicitly named Sequence
-
eval_async_tensor_sync_local_def(self: kp.Manager, arg0: List[kp.Tensor]) → None¶ Evaluates asynchronously operation to sync tensor(s) from GPU memory to local memory with anonymous Sequence
-
eval_await(self: kp.Manager, sequenceName: str, waitFor: int = 18446744073709551615) → None¶ Awaits for asynchronous operation on a named Sequence
-
eval_await_def(self: kp.Manager, waitFor: int = 18446744073709551615) → None¶ Awaits for asynchronous operation on the last anonymous Sequence created
-
eval_tensor_copy(self: kp.Manager, arg0: List[kp.Tensor], arg1: str) → None¶ Evaluates operation to copy one tensor to one or many tensors with explicitly named Sequence
-
eval_tensor_copy_def(self: kp.Manager, arg0: List[kp.Tensor]) → None¶ Evaluates operation to copy one tensor to one or many tensors with new anonymous Sequence
-
eval_tensor_create_def(self: kp.Manager, tensors: List[kp.Tensor], syncDataToGPU: bool = True) → None¶ Temporary backwards compatibility for tensor creation function which will be removed in the next version.
-
eval_tensor_sync_device(self: kp.Manager, arg0: List[kp.Tensor], arg1: str) → None¶ Evaluates operation to sync tensor from local memory to GPU memory with explicitly named Sequence
-
eval_tensor_sync_device_def(self: kp.Manager, arg0: List[kp.Tensor]) → None¶ Evaluates operation to sync tensor from local memory to GPU memory with new anonymous Sequence
-
eval_tensor_sync_local(self: kp.Manager, arg0: List[kp.Tensor], arg1: str) → None¶ Evaluates operation to sync tensor(s) from GPU memory to local memory with explicitly named Sequence
-
eval_tensor_sync_local_def(self: kp.Manager, arg0: List[kp.Tensor]) → None¶ Evaluates operation to sync tensor(s) from GPU memory to local memory with new anonymous Sequence
-
rebuild(*args, **kwargs)¶ Overloaded function.
rebuild(self: kp.Manager, tensors: List[kp.Tensor], syncDataToGPU: bool = True) -> None
Build and initialise list of tensors
rebuild(self: kp.Manager, tensor: kp.Tensor, syncDataToGPU: bool = True) -> None
Build and initialise tensor
-
sequence(self: kp.Manager, name: str = '', queueIndex: int = 0) → kp.Sequence¶ Get or create a sequence with specific name and specified index of available queues
-
Sequence¶
The Kompute Sequence consists of batches of Kompute Operations, which are executed on a respective GPU queue. The execution of sequences can be synchronous or asynchronous, and it can be coordinated through its respective Vulkan Fence.
-
class
kp.Sequence¶ -
begin(self: kp.Sequence) → bool¶ Begins recording commands for commands to be submitted into the command buffer.
@return Boolean stating whether execution was successful.
-
end(self: kp.Sequence) → bool¶ Ends the recording and stops recording commands when the record command is sent.
@return Boolean stating whether execution was successful.
-
eval(self: kp.Sequence) → bool¶ Eval sends all the recorded and stored operations in the vector of operations into the gpu as a submit job with a barrier.
@return Boolean stating whether execution was successful.
-
eval_async(self: kp.Sequence) → bool¶ Eval Async sends all the recorded and stored operations in the vector of operations into the gpu as a submit job with a barrier. EvalAwait() must be called after to ensure the sequence is terminated correctly.
@return Boolean stating whether execution was successful.
-
eval_await(self: kp.Sequence, arg0: int) → bool¶ Eval Await waits for the fence to finish processing and then once it finishes, it runs the postEval of all operations.
@param waitFor Number of milliseconds to wait before timing out. @return Boolean stating whether execution was successful.
-
init(self: kp.Sequence) → None¶ Initialises sequence including the creation of the command pool and the command buffer.
-
is_init(self: kp.Sequence) → bool¶ Returns true if the sequence has been successfully initialised.
@return Boolean stating if sequence has been initialised.
-
is_rec(self: kp.Sequence) → bool¶ Returns true if the sequence is currently in recording activated.
@return Boolean stating if recording ongoing.
-
is_running(self: kp.Sequence) → bool¶ Returns true if the sequence is currently running - mostly used for async workloads.
@return Boolean stating if currently running.
-
record_algo_data(self: kp.Sequence, tensors: List[kp.Tensor], bytes: bytes, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → bool¶ Records an operation using a custom shader provided as spirv bytes
-
record_algo_file(self: kp.Sequence, tensors: List[kp.Tensor], data: str, workgroup: List[int[3]] = [0, 0, 0], constants: List[float] = []) → bool¶ Records an operation using a custom shader provided from a shader path
-
record_tensor_copy(self: kp.Sequence, arg0: List[kp.Tensor]) → bool¶ Record function for operation to be added to the GPU queue in batch. This template requires classes to be derived from the OpBase class. This function also requires the Sequence to be recording, otherwise it will not be able to add the operation.
@param tensors Vector of tensors to use for the operation @param TArgs Template parameters that are used to initialise operation which allows for extensible configurations on initialisation.
-
record_tensor_sync_device(self: kp.Sequence, arg0: List[kp.Tensor]) → bool¶ Records operation to sync tensor from local memory to GPU memory
-
record_tensor_sync_local(self: kp.Sequence, arg0: List[kp.Tensor]) → bool¶ Records operation to sync tensor(s) from GPU memory to local memory
-
Tensor¶
The Kompute Tensor is the atomic unit in Kompute, and it is used primarily for handling Host and GPU Device data.
-
class
kp.Tensor¶ Structured data used in GPU operations.
Tensors are the base building block in Kompute to perform operations across GPUs. Each tensor would have a respective Vulkan memory and buffer, which would be used to store their respective data. The tensors can be used for GPU data storage or transfer.
-
data(self: kp.Tensor) → List[float]¶ Returns the vector of data currently contained by the Tensor. It is important to ensure that there is no out-of-sync data with the GPU memory.
@return Reference to vector of elements representing the data in the tensor.
-
set_data(self: kp.Tensor, arg0: numpy.ndarray[numpy.float32]) → None¶ Overrides the data in the local Tensor memory.
-
tensor_type(self: kp.Tensor) → kp.TensorTypes¶ Retreves the memory type of the tensor.
-
TensorType¶
-
class
kp.Shader¶ Shader class
-
static
compile_source(source: str, entryPoint: str = 'main', definitions: List[Tuple[str, str]] = []) → bytes¶ Compiles string source provided and returns the value in bytes
-
static
compile_sources(sources: List[str], files: List[str] = [], entryPoint: str = 'main', definitions: List[Tuple[str, str]] = []) → bytes¶ Compiles sources provided with file names and returns the value in bytes
-
static
-
class
kp.Tensor¶ Structured data used in GPU operations.
Tensors are the base building block in Kompute to perform operations across GPUs. Each tensor would have a respective Vulkan memory and buffer, which would be used to store their respective data. The tensors can be used for GPU data storage or transfer.
-
data(self: kp.Tensor) → List[float]¶ Returns the vector of data currently contained by the Tensor. It is important to ensure that there is no out-of-sync data with the GPU memory.
@return Reference to vector of elements representing the data in the tensor.
-
set_data(self: kp.Tensor, arg0: numpy.ndarray[numpy.float32]) → None¶ Overrides the data in the local Tensor memory.
-
tensor_type(self: kp.Tensor) → kp.TensorTypes¶ Retreves the memory type of the tensor.
-
-
class
kp.TensorTypes¶ Type for tensors created: Device allows memory to be transferred from staging buffers. Staging are host memory visible. Storage are device visible but are not set up to transfer or receive data (only for shader storage).
Members:
device : Tensor holding data in GPU memory.
host : Tensor used for CPU visible GPU data.
storage : Tensor with host visible gpu memory.
-
property
name¶
-
property