Models

class tensorrt_llm.models.BaichuanForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of BaichuanConfig

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create a BaichuanForCausalLM object from give parameters

classmethod quantize(

hf_model_dir: str,

output_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

*,

device: str = 'cuda',

calib_dataset: str = 'cnn_dailymail',

calib_batches: int = 512,

calib_batch_size: int = 1,

calib_max_seq_length: int = 512,

random_seed: int = 1234,

tokenizer_max_seq_length: int = 2048,

**kwargs,

)

class tensorrt_llm.models.BertForQuestionAnswering(*args, **kwargs)

Bases: BertBase

forward(

input_ids=None,

input_lengths=None,

token_type_ids=None,

position_ids=None,

hidden_states=None,

max_input_length=None,

)

class tensorrt_llm.models.BertForSequenceClassification(*args, **kwargs)

Bases: BertBase

forward(

input_ids,

input_lengths,

token_type_ids=None,

position_ids=None,

hidden_states=None,

max_input_length=None,

)

class tensorrt_llm.models.BertModel(*args, **kwargs)

Bases: BertBase

forward(

input_ids=None,

input_lengths=None,

position_ids=None,

token_type_ids=None,

hidden_states=None,

max_input_length=None,

)

class tensorrt_llm.models.BloomForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

class tensorrt_llm.models.BloomModel(

config: PretrainedConfig,

)

Bases: Module

forward(

input_ids: Tensor,

position_ids=None,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

prompt_embedding_table=None,

prompt_tasks=None,

prompt_vocab_size=None,

attention_params=None,

)

class tensorrt_llm.models.ChatGLMConfig(

*,

chatglm_version: str = 'chatglm3',

add_bias_linear: bool = False,

add_qkv_bias: bool = True,

apply_query_key_layer_scaling: bool = False,

apply_residual_connection_post_layernorm: bool = False,

rmsnorm: bool = True,

rotary_pct: float = 0.5,

rotary_base: float = 10000.0,

rotary_scaling: dict | None = None,

**kwargs,

)

Bases: PretrainedConfig

classmethod from_hugging_face(

hf_config_or_dir: str | transformers.PretrainedConfig,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

to_dict()

class tensorrt_llm.models.ChatGLMForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of ChatGLMConfig

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create a LLaMAForCausalLM object from give parameters

prepare_inputs(*args, **kwargs)

See PretrainedModel.prepare_inputs for the detailed parameter list.

classmethod quantize(

hf_model_dir: str,

output_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

*,

device: str = 'cuda',

calib_dataset: str = 'cnn_dailymail',

calib_batches: int = 512,

calib_batch_size: int = 1,

calib_max_seq_length: int = 512,

random_seed: int = 1234,

tokenizer_max_seq_length: int = 2048,

**kwargs,

)

class tensorrt_llm.models.ChatGLMModel(

config: ChatGLMConfig,

)

Bases: Module

forward(

input_ids: Tensor = None,

position_ids: Tensor = None,

use_cache: bool = False,

attention_mask: Tensor = None,

kv_cache_params: KeyValueCacheParams = None,

attention_params: AttentionParams = None,

)

class tensorrt_llm.models.CogVLMConfig(

*,

mlp_bias: bool = False,

attn_bias: bool = False,

rotary_base: float = 10000.0,

rotary_scaling: dict | None = None,

**kwargs,

)

Bases: PretrainedConfig

to_dict()

class tensorrt_llm.models.CogVLMForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM, TopModelMixin

config_class

alias of CogVLMConfig

default_plugin_config(**kwargs)

Return the default plugin config for this model, when the plugin_config value is not given in to_trt() call. If users need to set different plugin configs, they can start from the return object and change it.

classmethod from_hugging_face(

hf_model_dir,

dtype='float16',

mapping: Mapping | None = None,

quant_mode: QuantMode | None = None,

**kwargs,

)

Create LLM object and load weights from hugging face :param hf_model_dir: the hugging face model directory :param dtype: str, the default weights data type when loading from the hugging face model :param mapping: Mapping, specify the multi-gpu parallel strategy, when it’s None, single GPU is used

classmethod quantize(

hf_model_dir,

output_dir,

quant_config: QuantConfig,

*,

dtype='float16',

mapping: Mapping | None = None,

calib_batches=512,

calib_batch_size=1,

random_seed=1234,

tokenizer_max_seq_length=2048,

**kwargs,

)

class tensorrt_llm.models.CohereForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of CohereConfig

classmethod from_hugging_face(

hf_model_or_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create a CohereForCausalLM object from give parameters

class tensorrt_llm.models.DbrxConfig(

*,

bias: bool = False,

clip_qkv: float | None = None,

rotary_base: float = 500000.0,

rotary_scaling: dict | None = None,

moe: MoeConfig | dict | None = None,

**kwargs,

)

Bases: PretrainedConfig

to_dict()

class tensorrt_llm.models.DbrxForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of DbrxConfig

class tensorrt_llm.models.DecoderModel(*args, **kwargs)

Bases: PretrainedModel

check_config(

config: PretrainedConfig,

)

forward(

decoder_input_ids: Tensor,

encoder_output: Tensor,

position_ids=None,

token_type_ids=None,

use_cache=False,

attention_mask_params=None,

last_token_ids=None,

kv_cache_params=None,

attention_params=None,

hidden_states=None,

lora_params: LoraParams = None,

cross_kv_cache_gen: Tensor | None = None,

cross_kv_reuse: Tensor | None = None,

)

precompute_relative_attention_bias(build_config)

prepare_inputs(

max_batch_size,

max_beam_width,

max_decoder_input_len,

max_seq_len,

max_encoder_input_len,

gather_context_logits: bool = False,

gather_generation_logits: bool = False,

lora_target_modules: List[str] = None,

use_cache=True,

*args,

**kwargs,

)

@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the ranges of the dimensions of when using TRT dynamic shapes.

@return: a list contains values which can be fed into the self.forward()

use_lora(

lora_config: LoraConfig,

)

Load lora weights from the give config to the module :param lora_config: the lora config

class tensorrt_llm.models.DeepseekForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of DeepSeekV1Config

classmethod from_hugging_face(

model_dir,

dtype: str = 'auto',

mapping: Mapping | None = None,

override_fields={},

**kwargs,

)

Create LLM object and load weights from hugging face :param hf_model_dir: the hugging face model directory :param dtype: str, the default weights data type when loading from the hugging face model :param mapping: Mapping, specify the multi-gpu parallel strategy, when it’s None, single GPU is used

class tensorrt_llm.models.DeepseekV2ForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of DeepSeekV2Config

classmethod from_hugging_face(

hf_model,

model_dir,

dtype: str = 'auto',

mapping: Mapping | None = None,

override_fields={},

**kwargs,

)

Create LLM object and load weights from hugging face :param hf_model_dir: the hugging face model directory :param dtype: str, the default weights data type when loading from the hugging face model :param mapping: Mapping, specify the multi-gpu parallel strategy, when it’s None, single GPU is used

class tensorrt_llm.models.DiT(*args, **kwargs)

Bases: PretrainedModel

check_config(

config: PretrainedConfig,

)

forward(latent, timestep, label)

Forward pass of DiT. latent: (N, C, H, W) timestep: (N,) label: (N,)

forward_with_cfg(x, t, y)

Forward pass with classifier-free guidance.

forward_without_cfg(x, t, y)

Forward pass without classifier-free guidance.

prepare_inputs(max_batch_size, **kwargs)

@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the ranges of the dimensions of when using TRT dynamic shapes.

@return: a list contains values which can be fed into the self.forward()

unpatchify(x: Tensor)

class tensorrt_llm.models.EagleForCausalLM(*args, **kwargs)

Bases: LLaMAForCausalLM

config_class

alias of EagleConfig

forward(*args, **kwargs)

prepare_inputs(*args, **kwargs)

Inputs needed:

device_request_types: [bs] draft_tokens: [bs, max_draft_len] draft_lens: [bs] spec_decoding_generation_lengths: [bs] spec_decoding_position_offsets: [bs, max_gen_tokens] spec_decoding_packed_mask: [bs, max_draft_len, packed_length] ** eagle_temperature: [bs] rand_data_sample: [bs] rand_data_validation: [bs, max_draft_tokens]

** The mask is tricky since the boolean mask will need to be

packed in runtime. So, the last dim will be:

packed_length = ceil((max_draft_tokens+1)/32)

class tensorrt_llm.models.EncoderModel(*args, **kwargs)

Bases: PretrainedModel

check_config(

config: PretrainedConfig,

)

forward(

input_ids: Tensor,

input_lengths=None,

position_ids=None,

token_type_ids=None,

hidden_states=None,

max_input_length=None,

prompt_embedding_table=None,

prompt_tasks=None,

prompt_vocab_size=None,

attention_mask=None,

lora_params: LoraParams = None,

)

precompute_relative_attention_bias(build_config)

prepare_inputs(

max_batch_size,

max_input_len,

prompt_embedding_table_size: int = 0,

lora_target_modules: List[str] = None,

*args,

**kwargs,

)

@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the ranges of the dimensions of when using TRT dynamic shapes.

@return: a list contains values which can be fed into the self.forward()

use_lora(

lora_config: LoraConfig,

)

Load lora weights from the give config to the module :param lora_config: the lora config

use_prompt_tuning()

Enable p tuning when build the TRT engine, call this before to_trt

class tensorrt_llm.models.FalconConfig(

*,

bias: bool = False,

parallel_attention: bool = False,

num_ln_in_parallel_attn: int | None = None,

new_decoder_architecture: bool = False,

rotary_base: float = 10000.0,

**kwargs,

)

Bases: PretrainedConfig

classmethod from_hugging_face(

hf_config_or_dir: str | transformers.PretrainedConfig,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

to_dict()

class tensorrt_llm.models.FalconForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

check_config(config)

config_class

alias of FalconConfig

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create a FalconForCausalLM object from give parameters

class tensorrt_llm.models.FalconModel(config: FalconConfig)

Bases: Module

forward(

input_ids: Tensor,

position_ids=None,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

attention_params=None,

hidden_states=None,

)

class tensorrt_llm.models.GPTConfig(

*,

gpt_variant: str = 'gpt2',

bias: bool = True,

q_scaling: float = 1.0,

embedding_scale: float | None = None,

apply_query_key_layer_scaling: bool = False,

rotary_pct: float = 1.0,

rotary_base: float = 10000.0,

rotary_scaling: dict | None = None,

inner_layernorm: bool = False,

norm_before_bmm1: bool = False,

moe: MoeConfig | dict | None = None,

**kwargs,

)

Bases: PretrainedConfig

classmethod from_hugging_face(

hf_config_or_dir: str | transformers.PretrainedConfig,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

classmethod from_nemo(

nemo_ckpt_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

to_dict()

class tensorrt_llm.models.GPTForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of GPTConfig

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create a LLaMAForCausalLM object from give parameters

classmethod from_nemo(

nemo_ckpt_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

classmethod quantize(

hf_model_dir: str,

output_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

*,

device: str = 'cuda',

calib_dataset: str = 'cnn_dailymail',

calib_batches: int = 512,

calib_batch_size: int = 1,

calib_max_seq_length: int = 512,

random_seed: int = 1234,

tokenizer_max_seq_length: int = 2048,

**kwargs,

)

use_lora(

lora_config: LoraConfig,

)

Load lora weights from the give config to the module :param lora_config: the lora config

class tensorrt_llm.models.GPTJConfig(*, rotary_dim: int = 64, **kwargs)

Bases: PretrainedConfig

This is the configuration class to store the configuration of GPTJ model.

classmethod from_hugging_face(

hf_config_or_dir: str | transformers.PretrainedConfig,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

to_dict()

class tensorrt_llm.models.GPTJForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of GPTJConfig

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config=None,

**kwargs,

)

Create LLM object and load weights from hugging face :param hf_model_dir: the hugging face model directory :param dtype: str, the default weights data type when loading from the hugging face model :param mapping: Mapping, specify the multi-gpu parallel strategy, when it’s None, single GPU is used

class tensorrt_llm.models.GPTJModel(config: GPTJConfig)

Bases: Module

forward(

input_ids: Tensor,

position_ids=None,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

attention_params=None,

)

class tensorrt_llm.models.GPTModel(config: GPTConfig)

Bases: Module

forward(

input_ids,

position_ids,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

attention_params=None,

hidden_states=None,

prompt_embedding_table=None,

prompt_tasks=None,

prompt_vocab_size=None,

lora_params=None,

spec_decoding_params=None,

)

class tensorrt_llm.models.GPTNeoXForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

class tensorrt_llm.models.GPTNeoXModel(

config: PretrainedConfig,

)

Bases: Module

forward(

input_ids: Tensor,

position_ids=None,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

attention_params=None,

)

class tensorrt_llm.models.GemmaConfig(

*,

architecture: str,

rotary_base: float = 10000.0,

rotary_scaling: dict | None = None,

attn_bias: bool = False,

mlp_bias: bool = False,

position_embedding_type: PositionEmbeddingType = PositionEmbeddingType.rope_gpt_neox,

query_pre_attn_scalar: int | None = None,

final_logit_softcapping: float | None = None,

attn_logit_softcapping: float | None = None,

mapping: Mapping | dict | None = None,

**kwargs,

)

Bases: PretrainedConfig

GEMMA2_ADDED_FIELDS = {'attn_logit_softcapping', 'final_logit_softcapping', 'query_pre_attn_scalar'}

GEMMA_ADDED_FIELDS = {'attn_bias', 'inter_layernorms', 'mlp_bias', 'rotary_base', 'rotary_scaling'}

VERBATIM = {'attn_logit_softcapping', 'final_logit_softcapping', 'hidden_act', 'hidden_size', 'intermediate_size', 'max_position_embeddings', 'num_attention_heads', 'num_hidden_layers', 'query_pre_attn_scalar', 'use_parallel_embedding', 'vocab_size'}

classmethod from_hugging_face(

hf_config_or_dir: HfConfigOrDir,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

) → GemmaConfig

gemma2_config()

property is_gemma_2: bool

to_dict()

Serialize the fields added in GemmaConfig

class tensorrt_llm.models.GemmaForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

NATIVE_QUANT_FLOW = {QuantAlgo.W4A16, QuantAlgo.W8A16, QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN, QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN, QuantAlgo.W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN, QuantAlgo.W8A8_SQ_PER_TENSOR_PLUGIN}

classmethod assert_valid_quant_algo(

quant_algo: QuantAlgo | None,

)

config_class

alias of GemmaConfig

classmethod from_hugging_face(

hf_model_dir: HfConfigOrDir,

dtype='float16',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

load_model_on_cpu: bool = True,

**kwargs,

)

Create LLM object and load weights from hugging face :param hf_model_dir: the hugging face model directory :param dtype: str, the default weights data type when loading from the hugging face model :param mapping: Mapping, specify the multi-gpu parallel strategy, when it’s None, single GPU is used

classmethod quantize(

hf_model_dir: str,

output_dir: str,

dtype: str = 'float16',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

*,

gemma_config_kwargs: Dict[str, Any] = None,

**quantize_kwargs: Dict[str, Any],

)

class tensorrt_llm.models.LLaMAConfig(

*,

mlp_bias: bool = False,

attn_bias: bool = False,

rotary_base: float = 10000.0,

rotary_scaling: dict | None = None,

residual_mlp: bool = False,

disable_weight_only_quant_plugin: bool = False,

moe: MoeConfig | dict | None = None,

remove_duplicated_kv_heads: bool = False,

embedding_multiplier: float = 1.0,

attention_multiplier: float = 1.0,

residual_multiplier: float = 1.0,

output_multiplier_scale: float = 1.0,

**kwargs,

)

Bases: PretrainedConfig

classmethod from_hugging_face(

hf_config_or_dir: str | transformers.PretrainedConfig,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

classmethod from_meta_ckpt(

meta_ckpt_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

to_dict()

class tensorrt_llm.models.LLaMAForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of LLaMAConfig

default_plugin_config(**kwargs)

Return the default plugin config for this model, when the plugin_config value is not given in to_trt() call. If users need to set different plugin configs, they can start from the return object and change it.

classmethod from_hugging_face(

hf_model_or_dir: str | PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create a LLaMAForCausalLM object from give parameters

classmethod from_meta_ckpt(

meta_ckpt_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

classmethod quantize(

hf_model_dir: str,

output_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

*,

device: str = 'cuda',

calib_dataset: str = 'cnn_dailymail',

calib_batches: int = 512,

calib_batch_size: int = 1,

calib_max_seq_length: int = 512,

random_seed: int = 1234,

tokenizer_max_seq_length: int = 2048,

**kwargs,

)

use_lora(

lora_config: LoraConfig,

)

Load lora weights from the give config to the module :param lora_config: the lora config

class tensorrt_llm.models.LLaMAModel(config: LLaMAConfig)

Bases: Module

forward(

input_ids,

position_ids=None,

use_cache=False,

attention_mask=None,

spec_decoding_params=None,

kv_cache_params=None,

attention_params=None,

hidden_states=None,

hidden_states_for_embed=None,

prompt_embedding_table: Tensor | None = None,

prompt_tasks: Tensor | None = None,

prompt_vocab_size: Tensor | None = None,

lora_params=None,

)

class tensorrt_llm.models.MLLaMAForCausalLM(*args, **kwargs)

Bases: PretrainedModel

config_class

alias of MLLaMAConfig

forward(

decoder_input_ids: Tensor,

encoder_output: Tensor,

use_cache=False,

attention_mask_params=None,

last_token_ids=None,

kv_cache_params=None,

attention_params=None,

hidden_states=None,

lora_params: LoraParams = None,

cross_kv_cache_gen: Tensor | None = None,

cross_kv_reuse: Tensor | None = None,

prompt_embedding_table: Tensor | None = None,

prompt_tasks: Tensor | None = None,

prompt_vocab_size: Tensor | None = None,

skip_cross_attn_blocks: Tensor | None = None,

)

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create a MLLaMAForCausalLM object from give parameters

prepare_inputs(

max_batch_size,

max_beam_width,

max_decoder_input_len,

max_seq_len,

max_encoder_input_len,

gather_context_logits: bool = False,

gather_generation_logits: bool = False,

lora_target_modules: List[str] = None,

prompt_embedding_table_size: int = 0,

use_cache=True,

*args,

**kwargs,

)

@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the ranges of the dimensions of when using TRT dynamic shapes.

@return: a list contains values which can be fed into the self.forward()

use_lora(

lora_config: LoraConfig,

)

Load lora weights from the give config to the module :param lora_config: the lora config

class tensorrt_llm.models.MPTForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

check_config(config)

class tensorrt_llm.models.MPTModel(config: PretrainedConfig)

Bases: Module

forward(

input_ids,

position_ids,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

attention_params=None,

)

class tensorrt_llm.models.MambaForCausalLM(*args, **kwargs)

Bases: PretrainedModel

config_class

alias of MambaConfig

forward(

input_ids,

conv_states,

ssm_states,

host_request_types,

last_token_ids,

last_token_ids_for_logits,

host_context_lengths,

slot_mapping: Tensor | None = None,

)

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create LLM object and load weights from hugging face :param hf_model_dir: the hugging face model directory :param dtype: str, the default weights data type when loading from the hugging face model :param mapping: Mapping, specify the multi-gpu parallel strategy, when it’s None, single GPU is used

prepare_inputs(

max_batch_size,

max_input_len,

max_seq_len,

max_num_tokens,

use_cache,

max_beam_width: int = 1,

opt_num_tokens: int = None,

opt_batch_size: int = 0,

prompt_embedding_table_size: int = 0,

max_draft_len: int = 0,

gather_context_logits: bool = False,

gather_generation_logits: bool = False,

lora_target_modules: List[str] = None,

speculative_decoding_draft_tokens_external: bool = False,

)

@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the ranges of the dimensions of when using TRT dynamic shapes.

@return: a list contains values which can be fed into the self.forward()

class tensorrt_llm.models.MedusaConfig(

*,

num_medusa_heads: int = 4,

num_medusa_layers: int = 1,

max_draft_len: int = 63,

**kwargs,

)

Bases: PretrainedConfig

classmethod from_hugging_face(

hf_config_or_dir: str | transformers.PretrainedConfig,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

to_dict()

class tensorrt_llm.models.MedusaForCausalLm(*args, **kwargs)

Bases: PretrainedModel

config_class

alias of MedusaConfig

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create LLM object and load weights from hugging face :param hf_model_dir: the hugging face model directory :param dtype: str, the default weights data type when loading from the hugging face model :param mapping: Mapping, specify the multi-gpu parallel strategy, when it’s None, single GPU is used

class tensorrt_llm.models.OPTForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

check_config(config)

class tensorrt_llm.models.OPTModel(config: PretrainedConfig)

Bases: Module

forward(

input_ids: Tensor,

position_ids=None,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

attention_params=None,

prompt_embedding_table=None,

prompt_tasks=None,

prompt_vocab_size=None,

)

class tensorrt_llm.models.Phi3ForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

config_class

alias of Phi3Config

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create LLM object and load weights from hugging face :param hf_model_dir: the hugging face model directory :param dtype: str, the default weights data type when loading from the hugging face model :param mapping: Mapping, specify the multi-gpu parallel strategy, when it’s None, single GPU is used

use_lora(

lora_config: LoraConfig,

)

Load lora weights from the give config to the module :param lora_config: the lora config

class tensorrt_llm.models.Phi3Model(

config: PretrainedConfig,

)

Bases: Module

forward(

input_ids: Tensor,

position_ids=None,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

attention_params=None,

prompt_embedding_table=None,

prompt_tasks=None,

prompt_vocab_size=None,

lora_params=None,

)

class tensorrt_llm.models.PhiForCausalLM(*args, **kwargs)

Bases: DecoderModelForCausalLM

check_config(config)

config_class

alias of PhiConfig

classmethod from_hugging_face(

hf_model_or_dir: str | transformers.PreTrainedModel,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

**kwargs,

)

Create LLM object and load weights from hugging face :param hf_model_dir: the hugging face model directory :param dtype: str, the default weights data type when loading from the hugging face model :param mapping: Mapping, specify the multi-gpu parallel strategy, when it’s None, single GPU is used

class tensorrt_llm.models.PhiModel(config: PretrainedConfig)

Bases: Module

forward(

input_ids: Tensor,

position_ids=None,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

attention_params=None,

prompt_embedding_table=None,

prompt_tasks=None,

prompt_vocab_size=None,

)

class tensorrt_llm.models.PretrainedConfig(

*,

architecture: str,

dtype: str,

hidden_size: int,

num_hidden_layers: int,

num_attention_heads: int,

vocab_size: int | None = None,

hidden_act: str = 'gelu',

logits_dtype: str = 'float32',

norm_epsilon: float = 1e-05,

position_embedding_type: PositionEmbeddingType | str = PositionEmbeddingType.learned_absolute,

max_position_embeddings: int | None = None,

rotary_embedding_dim: int | None = None,

num_key_value_heads: int | None = None,

intermediate_size: int | None = None,

mapping: Mapping | dict | None = None,

quantization: QuantConfig | dict | None = None,

use_parallel_embedding: bool = False,

embedding_sharding_dim: int = 0,

head_size: int | None = None,

qk_layernorm: bool = False,

runtime_defaults: RuntimeDefaultsIn = None,

**kwargs,

)

Bases: object

static create_runtime_defaults(

defaults: RuntimeDefaultsIn = None,

) → RuntimeDefaults | None

for_each_rank() → Generator[Self, None, None]

classmethod from_checkpoint(ckpt_dir: str)

classmethod from_dict(config: dict)

classmethod from_json_file(config_file: str)

get_config_group(group_cls: Type[CG]) → CG

get_quant_cfg(module_name: str)

has_config_group(group_cls: Type[CG]) → bool

property kv_dtype

property quant_algo

property quant_mode

set_if_not_exist(key, value)

set_rank(rank: int)

to_dict()

to_json_file(config_file: str)

to_layer_quant_config(config_file: str)

class tensorrt_llm.models.PretrainedModel(*args, **kwargs)

Bases: Module, GenerationMixin, TopModelMixin

check_config(config)

classmethod from_checkpoint(

ckpt_dir: str,

rank: int | None = None,

config: PretrainedConfig | None = None,

*,

preprocess_weights_hook: Callable[[Dict[str, Tensor]], Dict[str, Tensor]] | None = None,

)

classmethod from_config(

config: PretrainedConfig,

)

load(weights, from_pruned=False)

prepare_inputs(

max_batch_size,

max_input_len,

max_seq_len,

max_num_tokens,

use_cache,

max_beam_width: int = 1,

opt_num_tokens: int = None,

prompt_embedding_table_size: int = 0,

position_encoding_2d: bool = False,

max_draft_len: int = 0,

speculative_decoding_draft_tokens_external: bool = False,

spec_decoding_is_generation_length_variable: bool = False,

gather_context_logits: bool = False,

gather_generation_logits: bool = False,

lora_target_modules: List[str] = None,

opt_batch_size: int = 0,

num_hidden_layers: int = None,

mrope_rotary_cos_sin_size: int = None,

)

@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the ranges of the dimensions of when using TRT dynamic shapes.

@return: a list contains values which can be fed into the self.forward()

classmethod quantize(

hf_model_dir: str,

output_dir: str,

dtype: str = 'auto',

mapping: Mapping | None = None,

quant_config: QuantConfig | None = None,

*,

device: str = 'cuda',

calib_dataset: str = 'cnn_dailymail',

calib_batches: int = 512,

calib_batch_size: int = 1,

calib_max_seq_length: int = 512,

random_seed: int = 1234,

tokenizer_max_seq_length: int = 2048,

**kwargs,

)

release()

save_checkpoint(output_dir, save_config=True)

class tensorrt_llm.models.ReDrafterForCausalLM(*args, **kwargs)

Bases: LLaMAForCausalLM

forward(*args, **kwargs)

0. run base model, get logits, hidden_states

prepare_inputs(*args, **kwargs)

Inputs needed:

Assuming, max_gen_tokens = 1 + nb*(bl - 1), counting true token device_request_types: [bs] draft_tokens: [bs, nb, bl] draft_indices: [bs, nb, bl] draft_probs: [bs, nb, bl-1, V] spec_decoding_generation_lengths: [bs] spec_decoding_position_offsets: [bs, max_gen_tokens] spec_decoding_packed_mask: [bs, max_gen_tokens, packed_length] ** redrafter_inverted_temperature: [bs] rand_data_sample: [bs] rand_data_validation: [bs, nb, bl-1]

** The mask is tricky since the boolean mask will need to be

packed in runtime. So, the last dim will be:

packed_length = ceil(max_gen_tokens/32)

class tensorrt_llm.models.RecurrentGemmaForCausalLM(*args, **kwargs)

Bases: PretrainedModel

forward(

input_ids,

position_ids=None,

use_cache=False,

attention_mask=None,

kv_cache_params=None,

attention_params=None,

conv_states=None,

rnn_states=None,

host_request_types=None,

last_token_ids=None,

last_token_ids_for_logits=None,

host_context_lengths=None,

slot_mapping=None,

)

prepare_inputs(

max_batch_size,

max_input_len,

max_seq_len,

max_num_tokens,

use_cache,

max_beam_width: int = 1,

opt_num_tokens: int = None,

opt_batch_size: int = 0,

prompt_embedding_table_size: int = 0,

max_draft_len: int = 0,

gather_context_logits: bool = False,

gather_generation_logits: bool = False,

lora_target_modules: List[str] = None,

speculative_decoding_draft_tokens_external: bool = False,

)

@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the ranges of the dimensions of when using TRT dynamic shapes.

@return: a list contains values which can be fed into the self.forward()

prepare_recurrent_inputs(

max_batch_size,

num_profiles,

mapping,

)

tensorrt_llm.models.RobertaForQuestionAnswering

alias of BertForQuestionAnswering

tensorrt_llm.models.RobertaForSequenceClassification

alias of BertForSequenceClassification

tensorrt_llm.models.RobertaModel

alias of BertModel

class tensorrt_llm.models.SpeculativeDecodingMode(

value,

names=<not given>,

*values,

module=None,

qualname=None,

type=None,

start=1,

boundary=None,

)

Bases: IntFlag

DRAFT_TOKENS_EXTERNAL = 2

EAGLE = 32

EXPLICIT_DRAFT_TOKENS = 16

LOOKAHEAD_DECODING = 8

MEDUSA = 4

NONE = 1

static from_arguments(args: Namespace)

class tensorrt_llm.models.WhisperEncoder(*args, **kwargs)

Bases: PretrainedModel

forward(

input_features: Tensor,

input_lengths=None,

position_ids=None,

)

precompute_relative_attention_bias(build_config)

prepare_inputs(max_batch_size=16)

@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the ranges of the dimensions of when using TRT dynamic shapes.

@return: a list contains values which can be fed into the self.forward()