vollo_compiler

class vollo_compiler.Config

A Vollo accelerator configuration.

Each Vollo bitstream contains a specific configuration of the Vollo accelerator, e.g. number of cores, size of each core, etc. Program s need to be compiled for the accelerator configuration that they will be run on.

For the bitstreams included in the Vollo SDK, use the preset configs ia_420f_c6b32(), ia_840f_c3b64().

static ia_420f_c6b32(): IA-420f configuration with 6 cores and block size 32

static ia_840f_c3b64(): IA-840f configuration with 3 cores and block size 64

save(json_path: str) → None: Save a hardware configuration to a JSON file

static load(json_path: str, check_version_matches: bool = True) → Config

Load a hardware configuration from a JSON file

Parameters:

json_path – Path to the JSON file
check_version_matches – if the provided JSON file is versioned, check that the version matches the current version

block_size: Size of the tensor blocks

num_cores: Number of cores

tensor_ram_depth: Amount of tensor RAM per-core (in blocks)

tensor_descriptor_count: Maximum number of tensors per core

weight_store_depth: Amount of weight store per-core (in blocks)

accum_store_depth: Amount of accumulator store per-core (in blocks)

cell_state_depth: Amount of LSTM cell state per-core (in blocks)

clamp_store_depth: Amount of clamp store per-core (i.e. the maximum number of different clamp configurations that can be used on a single core)

max_read_size: Maximum size of data that instructions can perform operations on (in blocks)

io_size: Minimum size of IO packet (in values)

class vollo_compiler.NNIR

Neural Network Intermediate Representation

The representation of neural networks in the Vollo compiler. It can be built from a PyTorch model using vollo_torch.fx.nnir.to_nnir(), or from an ONNX model.

static from_onnx(onnx_path: str, override_input_shapes: Sequence[int] | Sequence[Sequence[int]] | None) → NNIR

Load an ONNX model from a file and convert it to an NNIR graph

Parameters:

onnx_path – The path to the ONNX file.
override_input_shapes (Optional[Sequence[int] | Sequence[Sequence[int]]]) – If the model has dynamic input shapes, you must pass fixed input shapes. Can either be a Sequence[int] for single-input models, or a Sequence[Sequence[int]] for multi-input models (also works for single-input models). If this argument is used, you must provide a shape for each model input.

streaming_transform(streaming_axes: int | Sequence[int])

Performs the streaming transform, converting the NNIR to a streaming model

Parameters:: streaming_axes (int | Sequence[int]) – The dimensions over which to split the inputs into timesteps. Can either be an int for single-input models, or a sequence of ints for multi-input models (also works for single-input models). There should be one axis given for each model input.

Returns: - The transformed NNIR graph - The output streaming axis/es. This will either be an int for single-output models, or a tuple of ints for multi-output models

to_program(config: Config, name: str | None = None, *, optimize_transforms: bool = true, output_buffer_capacity: int = 64, write_queue_capacity: int = 32) → Program

Compile a NNIR graph to a Program.

Note that the NNIR model given must be a streaming model.

Parameters:

config – The hardware configuration to compile the program for
optimize_transforms – Whether to run the VM to decide whether to apply certain transformations or not
output_buffer_capacity – The size of the output buffer in the VM (only used when optimise_transforms is true)
write_queue_capacity – The size of the write queue in the VM (only used when optimise_transforms is true)
name – The name of the program

__new__(**kwargs)

class vollo_compiler.Program

A program which can be run on a VM, or can be used with the Vollo Runtime to put the program onto hardware.

compute_duration_per_inference_us(clock_mhz: int = 320, write_queue_capacity: int = 32, output_buffer_capacity: int = 64, model_index: int = 0) → float

Translate the program’s cycle count per inference to a figure in microseconds by dividing it by the clock speed.

Parameters:: clock_mhz – Clock speed of the Vollo frequency in MHz.

cycle_count_per_inference(write_queue_capacity: int = 32, output_buffer_capacity: int = 64, model_index: int = 0) → int: The number of cycles the program takes in one inference.

cycle_summary_per_inference(write_queue_capacity: int = 32, output_buffer_capacity: int = 64, model_index: int = 0)

A summary with the number of cycles the program takes from the first input word

Returns: A tuple containing (cycles_to_last_input, cycles_to_first_output, cycles_to_last_output)

hw_config() → Config

static io_only_test(config: Config, input_values: int, output_values: int) → Program: Make a new program that does no compute and aranges IO such that output only starts when all the input is available on the accelerator

static load(input_path: str) → Program

static load_bytes(data: bytes) → Program

metrics() → Metrics: Static Metrics

model_input_shape(model_index: int = 0, input_index: int = 0) → Tuple[int]: Get the shape of the input at input_index in model at model_index

model_input_streaming_dim(model_index: int = 0, input_index: int = 0) → int | None: Get the shape of the input at input_index in model at model_index

model_num_inputs(model_index: int = 0) → int: Get the number of inputs model at model_index uses.

model_num_outputs(model_index: int = 0) → int: Get the number of outputs model at model_index uses.

model_output_shape(model_index: int = 0, output_index: int = 0) → Tuple[int]: Get the shape of the output at output_index in model at model_index

model_output_streaming_dim(model_index: int = 0, output_index: int = 0) → int | None: Get the shape of the output at output_index in model at model_index

num_models() → int: The number of models in the program.

save(output_path: str)

save_bytes() → bytes

to_vm(write_queue_capacity: int = 32, output_buffer_capacity: int = 64, bit_accurate: bool = True, no_compute=False) → VM

Construct a stateful Virtual Machine for simulating a Vollo Program.

Parameters:

bit_accurate (bool) – Use a compute model that replicates the VOLLO accelerator with bit-accuracy. Disable to use single precision compute. Defaults to True.
no_compute (bool) – If True, the VM will not do any computations, and will just move data. (When set to True, the bit_accurate parameter is ignored.) This is useful for when you only need the cycle count of running a program as it is faster than doing the computations. Defaults to False.

transform_to_io_only_test() → Program: Make a new program that is IO compatible but does no compute, an IO only test

class vollo_compiler.ProgramBuilder

Tracks an internal list of NNIRs which can be compiled into a single multi-model program

add_nnir(nnir: NNIR, name: str | None = None, *, optimize_transforms: bool = false, output_buffer_capacity: int = 64, write_queue_capacity: int = 32)

Adds a model program compiled from an NNIR to the ProgramBuilder

Parameters:

nnir – The NNIR to add
optimize_transforms – Whether to run the VM to decide whether to apply certain transformations or not
output_buffer_capacity – The size of the output buffer in the VM (only used if optimise_transforms is true)
write_queue_capacity – The size of the write queue in the VM (only used if optimise_transforms is true)
name – The name of the model

to_program()

Builds a program the internal NNIRs

Parameters:: config – The config describing resources available for the final program

class vollo_compiler.Metrics

Static metrics of a program.

clamp_store_depth: Total amount of clamp store available on each core

clamp_store_used: Amount of clamp store used by the program on each core

input_bytes: Number of bytes input per-inference for each model

model_names: The name of each model if specified

num_instrs: Number of instructions on each core

output_bytes: Number of bytes output per-inference for each model

tensor_ram_depth: Total amount of tensor ram available on each core

tensor_ram_used: Tensor ram used by the program on each core

weight_store_depth: Total amount of weight store available on each core

weight_store_used: Amount of weight store used by the program on each core

class vollo_compiler.VM

compute_duration_us(clock_mhz: int = 320) → float

Translate the VM’s cycle count to a figure in microseconds by dividing it by the clock speed.

Parameters:: clock_mhz – Clock speed of the Vollo frequency in MHz.

cycle_count() → int: The number of cycles that have been performed so far on the VM across all inferences.

metrics() → Metrics: Get the static metrics of the program held by the VM

run(inputs: numpy.ndarray | Sequence[numpy.ndarray], model_index: int = 0, f32_round: bool = True) → numpy.ndarray | Tuple[numpy.ndarray, ...]

Run the VM on a shaped input.

Parameters:

inputs (numpy.ndarray | Sequence[numpy.ndarray]) – Can either be a numpy array for single-input models, or a sequence of numpy arrays for multi-input models (also works for single-input models).
model_index (int) – Which model to run. Defaults to 0.
f32_round (bool) – Only used for bit-accurate VMs. When True, f32 inputs will be converted to bf16 by rounding. When False, they will be converted by truncating. Defaults to True.

Returns:

Either an array for single-output models, or a tuple of arrays for multi-output models.

run_flat(inputs: numpy.ndarray | Sequence[numpy.ndarray], model_index: int = 0, f32_round: bool = True) → numpy.ndarray | Tuple[numpy.ndarray, ...]

Run the VM on a 1D input.

Parameters:

inputs (numpy.ndarray | Sequence[numpy.ndarray]) – Can either be a numpy array for single-input models, or a sequence of numpy arrays for multi-input models (also works for single-input models).
model_index (int) – Which model to run. Defaults to 0.
f32_round (bool) – Only used for bit-accurate VMs. When True, f32 inputs will be converted to bf16 by rounding. When False, they will be converted by truncating. Defaults to True.

Returns:

Either an array for single-output models, or a tuple of arrays for multi-output models.

run_flat_timesteps(inputs: numpy.ndarray | Sequence[numpy.ndarray], input_timestep_dims: int | Sequence[int], output_timestep_dims: int | Sequence[int], model_index: int = 0, f32_round: bool = True) → numpy.ndarray | Tuple[numpy.ndarray, ...]

Run the VM on multiple timesteps of inputs.

Parameters:

inputs (numpy.ndarray | Sequence[numpy.ndarray]) – Can either be a numpy array for single-input models, or a sequence of numpy arrays for multi-input models (also works for single-input models).
input_timestep_dims (int | Sequence[int]) – The dimensions over which to split the inputs into timesteps. Can either be an int for single-input models, or a sequence of ints for multi-input models. If it’s a sequence, then inputs should also be a sequence of the same length.
output_timestep_dims (int | Sequence[int]) – The dimension over which to build up the output timesteps, i.e. the timesteps are stacked along this dimension. Can either be an int for single-output models, or a sequence of ints for multi-output models.
model_index (int) – Which model to run. Defaults to 0.
f32_round (bool) – Only used for bit-accurate VMs. When True, f32 inputs will be converted to bf16 by rounding. When False, they will be converted by truncating. Defaults to True.

Returns:

Either an array for single-output models, or a tuple of arrays for multi-output models.

run_timesteps(inputs: numpy.ndarray | Sequence[numpy.ndarray], input_timestep_dims: int | Sequence[int], output_timestep_dims: int | Sequence[int], model_index: int, f32_round: bool = True) → numpy.ndarray | Tuple[numpy.ndarray, ...]

Run the VM on multiple timesteps with shaped inputs.

Parameters:

inputs (numpy.ndarray | Sequence[numpy.ndarray]) – Can either be a numpy array for single-input models, or a sequence of numpy arrays for multi-input models (also works for single-input models).
input_timestep_dims (int | Sequence[int]) – The dimensions over which to split the inputs into timesteps. Can either be an int for single-input models, or a sequence of ints for multi-input models. If it’s a sequence, then inputs should also be a sequence of the same length.
output_timestep_dims (int | Sequence[int]) – The dimension over which to build up the output timesteps, i.e. the timesteps are stacked along this dimension. Can either be an int for single-output models, or a sequence of ints for multi-output models.
model_index (int) – Which model to run. Defaults to 0.
f32_round (bool) – Only used for bit-accurate VMs. When True, f32 inputs will be converted to bf16 by rounding. When False, they will be converted by truncating. Defaults to True.

Returns:

Either an array for single-output models, or a tuple of arrays for multi-output models.

exception vollo_compiler.AllocationError

Failed to allocate memory during compilation.

This can happen if a model requires more space to store weights/activations, etc. than is available for the accelerator configuration.

exception vollo_compiler.SaveError: Failed to save program.

exception vollo_compiler.LoadError: Failed to load program.