API Reference

class tensorrt_llm.hlapi.LLM(model: str, tokenizer: str | Path | PreTrainedTokenizerBase | TokenizerBase | None = None, skip_tokenizer_init: bool = False, tensor_parallel_size: int = 1, dtype: str = 'auto', trust_remote_code: bool = False, revision: str | None = None, tokenizer_revision: str | None = None, **kwargs: Any)

Bases: object

LLM class is the main class for running a LLM model.

Parameters:

• model (str) – The model name or a local path to the model directory. It could be a HuggingFace(HF) model name, a local path to the HF model, or a local path to the TRT-LLM engine or checkpoint.

• tokenizer (Optional[Union[str, Path, TokenizerBase, PreTrainedTokenizerBase]]) – The tokenizer name or a local path to the tokenizer directory.

• skip_tokenizer_init – If true, skip initialization of tokenizer and detokenizer. generate and generate_async will accept prompt token ids as input only.

• tensor_parallel_size (int) – The number of processes for tensor parallelism.

• dtype (str) – The data type for the model weights and activations.

• trust_remote_code (bool, default=False) – Download the model and tokenizer from trust remote code (e.g, Hugging Face)

• revision (Optional[str]) – The revision of the model.

• tokenzier_revision (Optional[str]) – The revision of the tokenizer.

• auto_parallel (bool, default=False) – Enable auto parallel mode.

• pipeline_parallel_size (int, default=1) – The pipeline parallel size.

• enable_lora (bool, default=False) – Enable LoRA adapters.

• max_lora_rank (int, default=None) – Maximum LoRA rank. If specified, it overrides build_config.lora_config.max_lora_rank.

• max_loras (int, default=4) – Maximum number of LoRA adapters to be stored in GPU memory.

• max_cpu_loras (int, default=4) – Maximum number of LoRA adapters to be stored in CPU memory.

• build_config (BuildConfig, default=BuildConfig()) – The build configuration for the model. Default is an empty BuildConfig instance.

• quant_config (QuantConfig, default=QuantConfig()) – The quantization configuration for the model. Default is an empty QuantConfig instance.

• calib_config (CalibConfig, default=CalibConfig()) – The calibration configuration for the model.

• embedding_parallel_mode (str, default="SHARDING_ALONG_VOCAB") – The parallel mode for embeddings.

• share_embedding_table (bool, default=False) – Whether to share the embedding table.

• kv_cache_config (KvCacheConfig, optional) – The key-value cache configuration for the model. Default is None.

• peft_cache_config (PeftCacheConfig, optional) – The PEFT cache configuration for the model. Default is None.

• decoding_config (DecodingConfig, optional) – The decoding configuration for the model. Default is None.

• logits_post_processor_map (Dict[str, Callable], optional) – A map of logit post-processing functions. Default is None.

• scheduler_config (SchedulerConfig, default=SchedulerConfig()) – The scheduler configuration for the model. Default is an empty SchedulerConfig instance.

• normalize_log_probs (bool, default=False) – Whether to normalize log probabilities for the model.

• iter_stats_max_iterations (int, optional) – The maximum number of iterations for iteration statistics. Default is None.

• request_stats_max_iterations (int, optional) – The maximum number of iterations for request statistics. Default is None.

• batching_type (BatchingType, optional) – The batching type for the model. Default is None.

• enable_build_cache (bool or BuildCacheConfig, optional) – Whether to enable build caching for the model. Default is None.

• enable_tqdm (bool, default=False) – Whether to display a progress bar during model building.

• trust_remote_code – Whether to trust remote code when downloading model and tokenizer from Hugging Face.

__init__(model: str, tokenizer: str | Path | PreTrainedTokenizerBase | TokenizerBase | None = None, skip_tokenizer_init: bool = False, tensor_parallel_size: int = 1, dtype: str = 'auto', trust_remote_code: bool = False, revision: str | None = None, tokenizer_revision: str | None = None, **kwargs: Any)

generate(inputs: str | List[int] | Sequence[str | List[int]], sampling_params: SamplingParams | List[SamplingParams] | None = None, use_tqdm: bool = True, lora_request: LoRARequest | Sequence[LoRARequest] | None = None) → RequestOutput | List[RequestOutput]

Generate output for the given prompts in the synchronous mode. Synchronous generation accepts either single prompt or batched prompts.

Parameters:

• inputs (Union[PromptInputs, Sequence[PromptInputs]]) – The prompt text or token ids. Note, it must be single prompt or batched prompts.

• sampling_params (Optional[Union[SamplingParams, List[SamplingParams]]]) – The sampling params for the generation, a default one will be used if not provided.

• use_tqdm (bool) – Whether to use tqdm to display the progress bar.

• lora_request (Optional[Union[LoRARequest, Sequence[LoRARequest]]]) – LoRA request to use for generation, if any.

Returns:

The output data of the completion request to the LLM.

Return type:

Union[RequestOutput, List[RequestOutput]]

generate_async(inputs: str | List[int], sampling_params: SamplingParams | None = None, lora_request: LoRARequest | None = None, streaming: bool = False) → RequestOutput

Generate output for the given prompt in the asynchronous mode. Asynchronous generation accepts single prompt only.

Parameters:

• inputs (PromptInputs) – The prompt text or token ids; must be single prompt.

• sampling_params (Optional[SamplingParams]) – The sampling params for the generation, a default one will be used if not provided.

• lora_request (Optional[LoRARequest]) – LoRA request to use for generation, if any.

• streaming (bool) – Whether to use the streaming mode for the generation.

Returns:

The output data of the completion request to the LLM.

Return type:

RequestOutput

save(engine_dir: str)

Save the built engine to the given path.

Parameters:

engine_dir (str) – The path to save the engine.

Returns:

None

property tokenizer: TokenizerBase | None

property workspace: Path

class tensorrt_llm.hlapi.RequestOutput(generation_result: GenerationResult, prompt: str | None = None, tokenizer: TokenizerBase | None = None)

Bases: GenerationResult

The output data of a completion request to the LLM.

Fields:

request_id (int): The unique ID of the request. prompt (str): The prompt string of the request. prompt_token_ids (List[int]): The token ids of the prompt. outputs (List[CompletionOutput]): The output sequences of the request. context_logits (torch.Tensor): The logits on the prompt token ids. finished (bool): Whether the whole request is finished.

__init__(generation_result: GenerationResult, prompt: str | None = None, tokenizer: TokenizerBase | None = None) → None

handle_response(response)

class tensorrt_llm.hlapi.SamplingParams(*, end_id: int | None = None, pad_id: int | None = None, max_tokens: int = 32, max_new_tokens: int | None = None, bad: List[str] | str | None = None, bad_token_ids: List[int] | None = None, stop: List[str] | str | None = None, stop_token_ids: List[int] | None = None, include_stop_str_in_output: bool = False, embedding_bias: Tensor | None = None, external_draft_tokens_config: ExternalDraftTokensConfig | None = None, prompt_tuning_config: PromptTuningConfig | None = None, logits_post_processor_name: str | None = None, beam_width: int = 1, top_k: int | None = None, top_p: float | None = None, top_p_min: float | None = None, top_p_reset_ids: int | None = None, top_p_decay: float | None = None, seed: int | None = None, random_seed: int | None = None, temperature: float | None = None, min_tokens: int | None = None, min_length: int | None = None, beam_search_diversity_rate: float | None = None, repetition_penalty: float | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, length_penalty: float | None = None, early_stopping: int | None = None, no_repeat_ngram_size: int | None = None, return_log_probs: bool = False, return_context_logits: bool = False, return_generation_logits: bool = False, exclude_input_from_output: bool = True, return_encoder_output: bool = False, add_special_tokens: bool = True)

Bases: object

Sampling parameters for text generation.

Parameters:

• end_id (int) – The end token id.

• pad_id (int) – The pad token id.

• max_tokens (int) – The maximum number of tokens to generate.

• max_new_tokens (int) – The maximum number of tokens to generate. This argument is being deprecated; please use max_tokens instead.

• bad (Union[str, List[str]]) – A string or a list of strings that redirect the generation when they are generated, so that the bad strings are excluded from the returned output.

• bad_token_ids (List[int]) – A list of token ids that redirect the generation when they are generated, so that the bad ids are excluded from the returned output.

• stop (Union[str, List[str]]) – A string or a list of strings that stop the generation when they are generated. The returned output will not contain the stop strings unless include_stop_str_in_output is True.

• stop_token_ids (List[int]) – A list of token ids that stop the generation when they are generated.

• include_stop_str_in_output (bool) – Whether to include the stop strings in output text. Defaults to False.

• embedding_bias (torch.Tensor) – The embedding bias tensor. Expected type is kFP32 and shape is [vocab_size].

• external_draft_tokens_config (ExternalDraftTokensConfig) – The speculative decoding configuration.

• prompt_tuning_config (PromptTuningConfig) – The prompt tuning configuration.

• logits_post_processor_name (str) – The logits postprocessor name. Must correspond to one of the logits postprocessor name provided to the ExecutorConfig.

• beam_width (int) – The beam width. Default is 1 which disables beam search.

• top_k (int) – Controls number of logits to sample from. Default is 0 (all logits).

• top_p (float) – Controls the top-P probability to sample from. Default is 0.f

• top_p_min (float) – Controls decay in the top-P algorithm. topPMin is lower-bound. Default is 1.e-6.

• top_p_reset_ids (int) – Controls decay in the top-P algorithm. Indicates where to reset the decay. Default is 1.

• top_p_decay (float) – Controls decay in the top-P algorithm. The decay value. Default is 1.f

• seed (int) – Controls the random seed used by the random number generator in sampling

• random_seed (int) – Controls the random seed used by the random number generator in sampling. This argument is being deprecated; please use seed instead.

• temperature (float) – Controls the modulation of logits when sampling new tokens. It can have values > 0.f. Default is 1.0f

• min_tokens (int) – Lower bound on the number of tokens to generate. Values < 1 have no effect. Default is 1.

• min_length (int) – Lower bound on the number of tokens to generate. Values < 1 have no effect. Default is 1. This argument is being deprecated; please use min_tokens instead.

• beam_search_diversity_rate (float) – Controls the diversity in beam search.

• repetition_penalty (float) – Used to penalize tokens based on how often they appear in the sequence. It can have any value > 0.f. Values < 1.f encourages repetition, values > 1.f discourages it. Default is 1.f

• presence_penalty (float) – Used to penalize tokens already present in the sequence (irrespective of the number of appearances). It can have any values. Values < 0.f encourage repetition, values > 0.f discourage it. Default is 0.f

• frequency_penalty (float) – Used to penalize tokens already present in the sequence (dependent on the number of appearances). It can have any values. Values < 0.f encourage repetition, values > 0.f discourage it. Default is 0.f

• length_penalty (float) – Controls how to penalize longer sequences in beam search. Default is 0.f

• early_stopping (int) – Controls whether the generation process finishes once beamWidth sentences are generated (ends with end_token)

• no_repeat_ngram_size (int) – Controls how many repeat ngram size are acceptable. Default is 1 << 30.

• return_log_probs (bool) – Controls if Result should contain log probabilities. Default is false.

• return_context_logits (bool) – Controls if Result should contain the context logits. Default is false.

• return_generation_logits (bool) – Controls if Result should contain the generation logits. Default is false.

• exclude_input_from_output (bool) – Controls if output tokens in Result should include the input tokens. Default is true.

• return_encoder_output (bool) – Controls if Result should contain encoder output hidden states (for encoder-only and encoder-decoder models). Default is false.

• add_special_tokens (bool) – Whether to add special tokens to the prompt.

__init__(*, end_id: int | None = None, pad_id: int | None = None, max_tokens: int = 32, max_new_tokens: int | None = None, bad: List[str] | str | None = None, bad_token_ids: List[int] | None = None, stop: List[str] | str | None = None, stop_token_ids: List[int] | None = None, include_stop_str_in_output: bool = False, embedding_bias: Tensor | None = None, external_draft_tokens_config: ExternalDraftTokensConfig | None = None, prompt_tuning_config: PromptTuningConfig | None = None, logits_post_processor_name: str | None = None, beam_width: int = 1, top_k: int | None = None, top_p: float | None = None, top_p_min: float | None = None, top_p_reset_ids: int | None = None, top_p_decay: float | None = None, seed: int | None = None, random_seed: int | None = None, temperature: float | None = None, min_tokens: int | None = None, min_length: int | None = None, beam_search_diversity_rate: float | None = None, repetition_penalty: float | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, length_penalty: float | None = None, early_stopping: int | None = None, no_repeat_ngram_size: int | None = None, return_log_probs: bool = False, return_context_logits: bool = False, return_generation_logits: bool = False, exclude_input_from_output: bool = True, return_encoder_output: bool = False, add_special_tokens: bool = True) → None

add_special_tokens: bool

bad: List[str] | str | None

bad_token_ids: List[int] | None

beam_search_diversity_rate: float | None

beam_width: int

early_stopping: int | None

embedding_bias: Tensor | None

end_id: int | None

exclude_input_from_output: bool

external_draft_tokens_config: ExternalDraftTokensConfig | None

frequency_penalty: float | None

include_stop_str_in_output: bool

length_penalty: float | None

logits_post_processor_name: str | None

max_new_tokens: int | None

max_tokens: int

min_length: int | None

min_tokens: int | None

no_repeat_ngram_size: int | None

pad_id: int | None

presence_penalty: float | None

prompt_tuning_config: PromptTuningConfig | None

random_seed: int | None

repetition_penalty: float | None

return_context_logits: bool

return_encoder_output: bool

return_generation_logits: bool

return_log_probs: bool

seed: int | None

setup(tokenizer, add_special_tokens: bool = False) → SamplingParams

stop: List[str] | str | None

stop_token_ids: List[int] | None

temperature: float | None

top_k: int | None

top_p: float | None

top_p_decay: float | None

top_p_min: float | None

top_p_reset_ids: int | None

class tensorrt_llm.hlapi.KvCacheConfig

Bases: pybind11_object

__init__(self: tensorrt_llm.bindings.executor.KvCacheConfig, enable_block_reuse: bool = False, max_tokens: int | None = None, max_attention_window: list[int] | None = None, sink_token_length: int | None = None, free_gpu_memory_fraction: float | None = None, host_cache_size: int | None = None, onboard_blocks: bool = True, cross_kv_cache_fraction: float | None = None) → None

property cross_kv_cache_fraction

property enable_block_reuse

property free_gpu_memory_fraction

property host_cache_size

property max_attention_window

property max_tokens

property onboard_blocks

property sink_token_length

class tensorrt_llm.hlapi.SchedulerConfig

Bases: pybind11_object

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: tensorrt_llm.bindings.executor.SchedulerConfig, capacity_scheduler_policy: tensorrt_llm.bindings.executor.CapacitySchedulerPolicy = CapacitySchedulerPolicy.GUARANTEED_NO_EVICT) -> None

2. __init__(self: tensorrt_llm.bindings.executor.SchedulerConfig, capacity_scheduler_policy: tensorrt_llm.bindings.executor.CapacitySchedulerPolicy, context_chunking_policy: Optional[tensorrt_llm.bindings.executor.ContextChunkingPolicy]) -> None

property capacity_scheduler_policy

property context_chunking_policy

class tensorrt_llm.hlapi.CapacitySchedulerPolicy

Bases: pybind11_object

Members:

MAX_UTILIZATION

GUARANTEED_NO_EVICT

STATIC_BATCH

GUARANTEED_NO_EVICT = <CapacitySchedulerPolicy.GUARANTEED_NO_EVICT: 1>

MAX_UTILIZATION = <CapacitySchedulerPolicy.MAX_UTILIZATION: 0>

STATIC_BATCH = <CapacitySchedulerPolicy.STATIC_BATCH: 2>

__init__(self: tensorrt_llm.bindings.executor.CapacitySchedulerPolicy, value: int) → None

property name

property value

class tensorrt_llm.hlapi.BuildConfig(max_input_len: int = 256, max_seq_len: int = 512, opt_batch_size: int = 8, max_batch_size: int = 8, max_beam_width: int = 1, max_num_tokens: Optional[int] = None, opt_num_tokens: Optional[int] = None, max_prompt_embedding_table_size: int = 0, kv_cache_type: tensorrt_llm.bindings.KVCacheType = None, gather_context_logits: int = False, gather_generation_logits: int = False, strongly_typed: bool = True, force_num_profiles: Optional[int] = None, profiling_verbosity: str = 'layer_names_only', enable_debug_output: bool = False, max_draft_len: int = 0, speculative_decoding_mode: tensorrt_llm.models.modeling_utils.SpeculativeDecodingMode = <SpeculativeDecodingMode.NONE: 1>, use_refit: bool = False, input_timing_cache: str = None, output_timing_cache: str = None, lora_config: tensorrt_llm.lora_manager.LoraConfig = <factory>, auto_parallel_config: tensorrt_llm.auto_parallel.config.AutoParallelConfig = <factory>, weight_sparsity: bool = False, weight_streaming: bool = False, plugin_config: tensorrt_llm.plugin.plugin.PluginConfig = <factory>, use_strip_plan: bool = False, max_encoder_input_len: int = 1, use_fused_mlp: bool = False, dry_run: bool = False, visualize_network: bool = False)

Bases: object

__init__(max_input_len: int = 256, max_seq_len: int = 512, opt_batch_size: int = 8, max_batch_size: int = 8, max_beam_width: int = 1, max_num_tokens: int | None = None, opt_num_tokens: int | None = None, max_prompt_embedding_table_size: int = 0, kv_cache_type: ~tensorrt_llm.bindings.KVCacheType | None = None, gather_context_logits: int = False, gather_generation_logits: int = False, strongly_typed: bool = True, force_num_profiles: int | None = None, profiling_verbosity: str = 'layer_names_only', enable_debug_output: bool = False, max_draft_len: int = 0, speculative_decoding_mode: ~tensorrt_llm.models.modeling_utils.SpeculativeDecodingMode = <SpeculativeDecodingMode.NONE: 1>, use_refit: bool = False, input_timing_cache: str | None = None, output_timing_cache: str | None = None, lora_config: ~tensorrt_llm.lora_manager.LoraConfig = <factory>, auto_parallel_config: ~tensorrt_llm.auto_parallel.config.AutoParallelConfig = <factory>, weight_sparsity: bool = False, weight_streaming: bool = False, plugin_config: ~tensorrt_llm.plugin.plugin.PluginConfig = <factory>, use_strip_plan: bool = False, max_encoder_input_len: int = 1, use_fused_mlp: bool = False, dry_run: bool = False, visualize_network: bool = False) → None

auto_parallel_config: AutoParallelConfig

dry_run: bool = False

enable_debug_output: bool = False

force_num_profiles: int | None = None

classmethod from_dict(config, plugin_config=None)

classmethod from_json_file(config_file, plugin_config=None)

gather_context_logits: int = False

gather_generation_logits: int = False

input_timing_cache: str = None

kv_cache_type: KVCacheType = None

lora_config: LoraConfig

max_batch_size: int = 8

max_beam_width: int = 1

max_draft_len: int = 0

max_encoder_input_len: int = 1

max_input_len: int = 256

max_num_tokens: int | None = None

max_prompt_embedding_table_size: int = 0

max_seq_len: int = 512

opt_batch_size: int = 8

opt_num_tokens: int | None = None

output_timing_cache: str = None

plugin_config: PluginConfig

profiling_verbosity: str = 'layer_names_only'

speculative_decoding_mode: SpeculativeDecodingMode = 1

strongly_typed: bool = True

to_dict()

update(**kwargs)

update_from_dict(config: dict)

update_kv_cache_type(model_architecture: str)

use_fused_mlp: bool = False

use_refit: bool = False

use_strip_plan: bool = False

visualize_network: bool = False

weight_sparsity: bool = False

weight_streaming: bool = False

class tensorrt_llm.hlapi.QuantConfig(quant_algo: QuantAlgo | None = None, kv_cache_quant_algo: QuantAlgo | None = None, group_size: int | None = 128, smoothquant_val: float = 0.5, clamp_val: List[float] | None = None, has_zero_point: bool | None = False, pre_quant_scale: bool | None = False, exclude_modules: List[str] | None = None)

Bases: object

Serializable quantization configuration class, part of the PretrainedConfig

__init__(quant_algo: QuantAlgo | None = None, kv_cache_quant_algo: QuantAlgo | None = None, group_size: int | None = 128, smoothquant_val: float = 0.5, clamp_val: List[float] | None = None, has_zero_point: bool | None = False, pre_quant_scale: bool | None = False, exclude_modules: List[str] | None = None) → None

clamp_val: List[float] | None = None

exclude_modules: List[str] | None = None

classmethod from_dict(config: dict)

get_modelopt_kv_cache_dtype()

get_modelopt_qformat()

get_quant_cfg(module_name=None)

group_size: int | None = 128

has_zero_point: bool | None = False

kv_cache_quant_algo: QuantAlgo | None = None

property layer_quant_mode: QuantMode

pre_quant_scale: bool | None = False

quant_algo: QuantAlgo | None = None

property quant_mode: QuantModeWrapper

property requires_calibration

property requires_modelopt_quantization

smoothquant_val: float = 0.5

to_dict()

property use_plugin_sq

class tensorrt_llm.hlapi.QuantAlgo(value)

Bases: StrEnum

An enumeration.

FP8 = 'FP8'

FP8_PER_CHANNEL_PER_TOKEN = 'FP8_PER_CHANNEL_PER_TOKEN'

INT8 = 'INT8'

MIXED_PRECISION = 'MIXED_PRECISION'

NO_QUANT = 'NO_QUANT'

W4A16 = 'W4A16'

W4A16_AWQ = 'W4A16_AWQ'

W4A16_GPTQ = 'W4A16_GPTQ'

W4A8_AWQ = 'W4A8_AWQ'

W8A16 = 'W8A16'

W8A8_SQ_PER_CHANNEL = 'W8A8_SQ_PER_CHANNEL'

W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN = 'W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN'

W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN = 'W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN'

W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN = 'W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN'

W8A8_SQ_PER_TENSOR_PLUGIN = 'W8A8_SQ_PER_TENSOR_PLUGIN'

class tensorrt_llm.hlapi.CalibConfig(device: Literal['cuda', 'cpu'] = '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)

Bases: object

Calibration configuration.

Parameters:

• device (Literal['cuda', 'cpu'], default='cuda') – The device to run calibration.

• calib_dataset (str, default='cnn_dailymail') – The name or local path of calibration dataset.

• calib_batches (int, default=512) – The number of batches that the calibration runs.

• calib_batch_size (int, default=1) – The batch size that the calibration runs.

• calib_max_seq_length (int, default=512) – The maximum sequence length that the calibration runs.

• random_seed (int, default=1234) – The random seed used for calibration.

• tokenizer_max_seq_length (int, default=2048) – The maximum sequence length to initialize tokenizer for calibration.

__init__(device: Literal['cuda', 'cpu'] = '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) → None

calib_batch_size: int

calib_batches: int

calib_dataset: str

calib_max_seq_length: int

device: Literal['cuda', 'cpu']

classmethod from_dict(config: dict)

random_seed: int

to_dict()

tokenizer_max_seq_length: int

class tensorrt_llm.hlapi.BuildCacheConfig(cache_root: Path | None = None, max_records: int = 10, max_cache_storage_gb: float = 256)

Bases: object

Configuration for the build cache.

cache_root

The root directory for the build cache.

Type:

str

max_records

The maximum number of records to store in the cache.

Type:

int

max_cache_storage_gb

The maximum amount of storage (in GB) to use for the cache.

Type:

float

Note

The build-cache assumes the weights of the model are not changed during the execution. If the weights are changed, you should remove the caches manually.

__init__(cache_root: Path | None = None, max_records: int = 10, max_cache_storage_gb: float = 256)

property cache_root: Path

property max_cache_storage_gb: float

property max_records: int

class tensorrt_llm.hlapi.RequestError

Bases: RuntimeError

The error raised when the request is failed.