GPT inference model. More...

Inheritance diagram for Mila::Dnn::GptModel< TDeviceType, TPrecision >:

Public Types
using	GptTransformerType = GptTransformer<TDeviceType, TPrecision>
using	ModelBase = LanguageModel<TDeviceType, TPrecision>
using	MR = typename DeviceTypeTraits<TDeviceType>::memory_resource
using	TensorType = Tensor<TPrecision, MR>
using	TokenIndexType = Tensor<dtype_t::INT32, MR>
Public Types inherited from Mila::Dnn::LanguageModel< TDeviceType, TPrecision >
using	Base = Model<TDeviceType, TPrecision>
Public Types inherited from Mila::Dnn::Model< TDeviceType, TPrecision >
using	NetworkType = Network<TDeviceType, TPrecision>

Public Member Functions
	GptModel (const GptModel &)=delete
	GptModel (GptModel &&)=default
const GptConfig &	getConfig () const noexcept
GptModel &	operator= (const GptModel &)=delete
GptModel &	operator= (GptModel &&)=default
void	saveCheckpoint (const std::filesystem::path &path, SerializationMode mode=SerializationMode::Checkpoint) const
	Write a Mila-native archive that fromCheckpoint() can restore.
std::string	toString () const override
	Human-readable summary of this model's configuration.
Public Member Functions inherited from Mila::Dnn::LanguageModel< TDeviceType, TPrecision >
	LanguageModel (const LanguageModel &)=delete
	LanguageModel (LanguageModel &&)=default
GenerateStatus	generate (std::span< const int32_t > prompt_tokens, const std::function< void(int32_t)> &on_token, const GenerateParams &params={}, std::stop_token stop={})
	Generate tokens from a prompt, streaming each through on_token.
LanguageModel &	operator= (const LanguageModel &)=delete
LanguageModel &	operator= (LanguageModel &&)=default
void	savePretrained (const std::filesystem::path &path) const
	Write this model's live weights as a safetensors artifact.
void	seedSampler (uint64_t seed)
	Seed the sampler's RNG for reproducible generation.
Public Member Functions inherited from Mila::Dnn::Model< TDeviceType, TPrecision >
	Model (const Model &)=delete
	Model (Model &&)=default
DeviceId	getDeviceId () const noexcept
	The device this model runs on.
MemoryStats	getMemoryStats () const
	Current memory allocation breakdown for this model.
RuntimeMode	getRuntimeMode () const noexcept
	The runtime mode this model was constructed for.
std::size_t	getScratchHighWaterBytes () const
	Context-owned scratch device memory, in bytes, at its high-water mark.
bool	isEval () const noexcept
	True if this model is currently in eval sub-state.
bool	isInferenceMode () const noexcept
	True if this model was constructed for inference.
bool	isTrainingMode () const noexcept
	True if this model was constructed for training.
Model &	operator= (const Model &)=delete
Model &	operator= (Model &&)=default
void	setEval (bool eval)
	Toggle eval sub-state for this model.
void	train ()
	Run the training loop for this model.

Static Public Member Functions
static std::unique_ptr< GptModel >	fromCheckpoint (const std::filesystem::path &path, DeviceId device_id=DeviceId{ TDeviceType, 0 }, dim_t context_length=0)
	Load from a Mila-native serialized artifact.
static std::unique_ptr< GptModel >	fromPretrained (const std::filesystem::path &path, dim_t context_length, DeviceId device_id=DeviceId{ TDeviceType, 0 }, bool strict=true)
	Load from third-party pretrained weights.

Protected Member Functions
int32_t	eosToken () const noexcept override
	GPT-2 end-of-text token id.
dim_t	maxSequenceLength () const noexcept override
	Maximum sequence length from GPT config.
GenerateStatus	onGenerating (std::span< const int32_t > prompt_tokens, const std::function< void(int32_t)> &on_token, const GenerateParams &params, std::stop_token stop) override
	Prefill + KV-cache decode loop with per-token streaming.
void	onTraining () override
	Training loop – not yet implemented for GptModel.
dim_t	vocabSize () const noexcept override
	Vocabulary size from GPT config.
Protected Member Functions inherited from Mila::Dnn::LanguageModel< TDeviceType, TPrecision >
	LanguageModel (std::unique_ptr< LanguageNetwork< TDeviceType, TPrecision > > network, RuntimeMode runtime_mode, Serialization::PretrainedMetadata source_metadata={}, WeightQuantization weight_quantization=WeightQuantization::None)
int32_t	awaitSampledToken ()
	Block until the last enqueueSampleNext()'s token id is host-visible.
void	enqueueSampleNext (const TensorType &logits, TokenTensor &token_out, const SamplingParams &params)
	Enqueue a sampling step without waiting for the host readback.
virtual float	finalLogitSoftcap () const noexcept
	Optional final-logit softcap the sampler applies (0 disables).
const LanguageNetwork< TDeviceType, TPrecision > &	getLanguageNetwork () const noexcept
LanguageNetwork< TDeviceType, TPrecision > &	getLanguageNetwork () noexcept
int32_t	sampleNext (const TensorType &logits, TokenTensor &token_out, const SamplingParams &params)
	Sample the next token from a logits row on the device.
virtual std::unordered_set< int32_t >	stopTokens () const
Protected Member Functions inherited from Mila::Dnn::Model< TDeviceType, TPrecision >
	Model (std::unique_ptr< NetworkType > network, RuntimeMode runtime_mode)
	Construct with a fully built network and runtime mode.

Additional Inherited Members
Protected Types inherited from Mila::Dnn::LanguageModel< TDeviceType, TPrecision >
using	MR = typename DeviceTypeTraits<TDeviceType>::memory_resource
using	TensorType = Tensor<TPrecision, MR>
using	TokenTensor = Tensor<TensorDataType::INT32, MR>
Protected Attributes inherited from Mila::Dnn::LanguageModel< TDeviceType, TPrecision >
Serialization::PretrainedMetadata	source_metadata_
	The loaded artifact's metadata, carried so savePretrained can write it back verbatim.
WeightQuantization	weight_quantization_ { WeightQuantization::None }
	What the live weights are, not what the source file was.
Protected Attributes inherited from Mila::Dnn::Model< TDeviceType, TPrecision >
std::unique_ptr< NetworkType >	network_
	The owned Network instance.

Detailed Description

template<DeviceType TDeviceType, TensorDataType TPrecision>
requires PrecisionSupportedOnDevice<TPrecision, TDeviceType>
class Mila::Dnn::GptModel< TDeviceType, TPrecision >

GPT inference model.

Owns a loaded, built GptTransformer and exposes generate() for autoregressive text generation.

Construction is only possible via fromPretrained() or fromCheckpoint(). The network is always in a built, weights-loaded, inference-mode state when generation is called.

Member Function Documentation

◆ eosToken()

template<DeviceType TDeviceType, TensorDataType TPrecision>

int32_t Mila::Dnn::GptModel< TDeviceType, TPrecision >::eosToken ( ) const

inlineoverrideprotectedvirtualnoexcept

GPT-2 end-of-text token id.

Implements Mila::Dnn::LanguageModel< TDeviceType, TPrecision >.

◆ fromCheckpoint()

template<DeviceType TDeviceType, TensorDataType TPrecision>

std::unique_ptr< GptModel > Mila::Dnn::GptModel< TDeviceType, TPrecision >::fromCheckpoint	(	const std::filesystem::path &	path,
		DeviceId	device_id = DeviceId{ TDeviceType, 0 },
		dim_t	context_length = 0 )

inlinestatic

Load from a Mila-native serialized artifact.

Reads a checkpoint or weights-only artifact produced by GptTransformer::save() via ModelArchive.

Parameters

path	Path to the Mila archive.
device_id	Target device.
context_length	Deployment context length. Zero takes the geometry the checkpoint was built with, falling back to the trained maximum.

Returns: Inference-ready GptModel.

◆ fromPretrained()

template<DeviceType TDeviceType, TensorDataType TPrecision>

std::unique_ptr< GptModel > Mila::Dnn::GptModel< TDeviceType, TPrecision >::fromPretrained	(	const std::filesystem::path &	path,
		dim_t	context_length,
		DeviceId	device_id = DeviceId{ TDeviceType, 0 },
		bool	strict = true )

inlinestatic

Load from third-party pretrained weights.

Reads weights from a Mila-compatible pretrained artifact produced by converting third-party checkpoints (e.g. HuggingFace GPT-2) via PretrainedModelReader.

Parameters

path	Path to the pretrained artifact.
context_length	Maximum sequence length to build for.
device_id	Target device.
strict	Throws on unknown parameter names if true.

Returns: Inference-ready GptModel.

◆ maxSequenceLength()

template<DeviceType TDeviceType, TensorDataType TPrecision>

dim_t Mila::Dnn::GptModel< TDeviceType, TPrecision >::maxSequenceLength ( ) const

inlineoverrideprotectedvirtualnoexcept

Maximum sequence length from GPT config.

Implements Mila::Dnn::LanguageModel< TDeviceType, TPrecision >.

◆ onGenerating()

template<DeviceType TDeviceType, TensorDataType TPrecision>

GenerateStatus Mila::Dnn::GptModel< TDeviceType, TPrecision >::onGenerating	(	std::span< const int32_t >	prompt_tokens,
		const std::function< void(int32_t)> &	on_token,
		const GenerateParams &	params,
		std::stop_token	stop )

inlineoverrideprotectedvirtual

Prefill + KV-cache decode loop with per-token streaming.

Implements Mila::Dnn::LanguageModel< TDeviceType, TPrecision >.

◆ onTraining()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::GptModel< TDeviceType, TPrecision >::onTraining ( )

inlineoverrideprotectedvirtual

Training loop – not yet implemented for GptModel.

Exceptions

std::runtime_error always.

Implements Mila::Dnn::Model< TDeviceType, TPrecision >.

◆ saveCheckpoint()

template<DeviceType TDeviceType, TensorDataType TPrecision>

void Mila::Dnn::GptModel< TDeviceType, TPrecision >::saveCheckpoint	(	const std::filesystem::path &	path,
		SerializationMode	mode = SerializationMode::Checkpoint ) const

inline

Write a Mila-native archive that fromCheckpoint() can restore.

Writes the network config, the component graph, and one blob per parameter. Weights only – optimizer state belongs to the trainer, which owns its own archive scope.

Parameters

path	Destination archive path (overwritten if it exists).
mode	Serialization mode recorded in the archive.

Exceptions

std::runtime_error if the archive cannot be opened or a component cannot serialize its parameters.

◆ toString()

template<DeviceType TDeviceType, TensorDataType TPrecision>

std::string Mila::Dnn::GptModel< TDeviceType, TPrecision >::toString ( ) const

inlineoverridevirtual

Human-readable summary of this model's configuration.

Implements Mila::Dnn::Model< TDeviceType, TPrecision >.

◆ vocabSize()

template<DeviceType TDeviceType, TensorDataType TPrecision>

dim_t Mila::Dnn::GptModel< TDeviceType, TPrecision >::vocabSize ( ) const

inlineoverrideprotectedvirtualnoexcept

Vocabulary size from GPT config.

Implements Mila::Dnn::LanguageModel< TDeviceType, TPrecision >.

The documentation for this class was generated from the following file:

Mila/Src/Dnn/Models/GptModel.ixx

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ eosToken()

◆ fromCheckpoint()

◆ fromPretrained()

◆ maxSequenceLength()

◆ onGenerating()

◆ onTraining()

◆ saveCheckpoint()

◆ toString()

◆ vocabSize()