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Qwen2

Qwen2 모델군은 Alibaba Cloud에서 개발한 오픈소스 대규모 언어 모델입니다. RBLN NPU는 Optimum RBLN을 사용하여 Qwen2 모델 추론을 가속화할 수 있습니다.

주요 클래스

API 참조

Classes

RBLNQwen2ForCausalLM

Bases: RBLNDecoderOnlyModelForCausalLM

The Qwen2 Model transformer with a language modeling head (linear layer) on top. This model inherits from [RBLNDecoderOnlyModelForCausalLM]. Check the superclass documentation for the generic methods the library implements for all its models.

A class to convert and run pre-trained transformers based Qwen2ForCausalLM model on RBLN devices. It implements the methods to convert a pre-trained transformers Qwen2ForCausalLM model into a RBLN transformer model by: - transferring the checkpoint weights of the original into an optimized RBLN graph, - compiling the resulting graph using the RBLN compiler.

Configuration: This model uses [RBLNQwen2ForCausalLMConfig] for configuration. When calling methods like from_pretrained or from_model, the rbln_config parameter should be an instance of [RBLNQwen2ForCausalLMConfig] or a dictionary conforming to its structure.

See the [RBLNQwen2ForCausalLMConfig] class for all available configuration options.

Examples:

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from optimum.rbln import RBLNQwen2ForCausalLM

# Simple usage using rbln_* arguments
# `max_seq_len` is automatically inferred from the model config
model = RBLNQwen2ForCausalLM.from_pretrained(
    "Qwen/Qwen2-7B-Instruct",
    export=True,
    rbln_batch_size=1,
    rbln_tensor_parallel_size=4,
)


# Using a config dictionary
rbln_config = {
    "batch_size": 1,
    "max_seq_len": 4096,
    "tensor_parallel_size": 4,
}
model = RBLNQwen2ForCausalLM.from_pretrained(
    "Qwen/Qwen2-7B-Instruct",
    export=True,
    rbln_config=rbln_config
)


# Using a RBLNQwen2ForCausalLMConfig instance (recommended for type checking)
from optimum.rbln import RBLNQwen2ForCausalLMConfig

config = RBLNQwen2ForCausalLMConfig(
    batch_size=1,
    max_seq_len=4096,
    tensor_parallel_size=4
)
model = RBLNQwen2ForCausalLM.from_pretrained(
    "Qwen/Qwen2-7B-Instruct",
    export=True,
    rbln_config=config
)

Functions

from_pretrained(model_id, export=False, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name Type Description Default
model_id Union[str, Path]

The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.

 required
export bool

A boolean flag to indicate whether the model should be compiled.

 False
rbln_config Optional[Union[Dict, RBLNModelConfig]]

Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.

 None
kwargs Dict[str, Any]

Additional keyword arguments. Arguments with the prefix 'rbln_' are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.

 {}

Returns:

Type Description
Self

A RBLN model instance ready for inference on RBLN NPU devices.
from_model(model, *, rbln_config=None, **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name Type Description Default
model PreTrainedModel

The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.

 required
rbln_config Optional[Union[Dict, RBLNModelConfig]]

Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.

 None
kwargs Dict[str, Any]

Additional keyword arguments. Arguments with the prefix 'rbln_' are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.

 {}

The method performs the following steps:

  1. Compiles the PyTorch model into an optimized RBLN graph
  2. Configures the model for the specified NPU device
  3. Creates the necessary runtime objects if requested
  4. Saves the compiled model and configurations

Returns:

Type Description
Self

A RBLN model instance ready for inference on RBLN NPU devices.
save_pretrained(save_directory)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [from_pretrained] class method.

Parameters:

Name Type Description Default
save_directory Union[str, PathLike]

The directory to save the model and its configuration files. Will be created if it doesn't exist.

 required
generate(input_ids, attention_mask=None, max_length=None)

The generate function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to generate text from the model.

Parameters:

Name Type Description Default
input_ids LongTensor

The sequence used as a prompt for the generation

 required
attention_mask Optional[Tensor]

The attention mask to apply on the sequence

 None
max_length Optional[int]

The maximum length of the sequence to be generated

 None

Classes

RBLNQwen2ForCausalLMConfig

Bases: RBLNDecoderOnlyModelForCausalLMConfig

Configuration class for RBLN Qwen2 models.

This class is an alias of RBLNDecoderOnlyModelForCausalLMConfig.

Example usage: 

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from optimum.rbln import RBLNQwen2ForCausalLM, RBLNQwen2ForCausalLMConfig

# Create a configuration object
config = RBLNQwen2ForCausalLMConfig(
    batch_size=1,
    max_seq_len=4096,
    tensor_parallel_size=4
)

# Use the configuration with from_pretrained
model = RBLNQwen2ForCausalLM.from_pretrained(
    "Qwen/Qwen2-7B",
    export=True,
    rbln_config=config
)

Functions

__init__(batch_size=None, max_seq_len=None, use_inputs_embeds=None, use_attention_mask=None, attn_impl=None, kvcache_partition_len=None, kvcache_block_size=None, quantization=None, prefill_chunk_size=None, kvcache_num_blocks=None, **kwargs)

Parameters:

Name Type Description Default
batch_size Optional[int]

The batch size for inference. Defaults to 1.

 None
max_seq_len Optional[int]

The maximum sequence length supported by the model. If not provided, it attempts to infer from the model's configuration (max_position_embeddings or n_positions). Must be specified if not available in the model config.

 None
use_inputs_embeds Optional[bool]

Whether to use input embeddings (inputs_embeds) directly instead of input_ids. Defaults to False. Requires the model to be compiled with this option enabled.

 None
use_attention_mask Optional[bool]

Whether the model requires attention masks during inference. This is typically determined based on the target device and model architecture. Defaults are often set automatically based on the model and RBLN NPU.

 None
attn_impl Optional[str]

Specifies the attention implementation to use. See the "Attention Implementation (attn_impl)" section below for details.

 None
kvcache_partition_len Optional[int]

Defines the partition length for the KV cache when using "flash_attn". See the "KV Cache Partition Length (kvcache_partition_len)" section below for details.

 None
kvcache_block_size Optional[int]

Sets the size (in number of tokens) of each block in the PagedAttention KV cache. See the "KV Cache Block Size (kvcache_block_size)" section below for details.

 None
quantization Optional[Dict[str, Any]]

Configuration dictionary for applying model quantization. Specifies format, group sizes, etc.

 None
prefill_chunk_size Optional[int]

The chunk size used during the prefill phase for processing input sequences. Defaults to 128. Must be a positive integer divisible by 64. Affects prefill performance and memory usage.

 None
kvcache_num_blocks Optional[int]

The total number of blocks to allocate for the PagedAttention KV cache. See the "KV Cache Number of Blocks (kvcache_num_blocks)" section below for details.

 None
**kwargs Dict[str, Any]

Additional keyword arguments passed to the parent RBLNModelConfig.

 {}

Raises:

Type Description
ValueError

If batch_size is not a positive integer.
ValueError

If prefill_chunk_size is not a positive integer divisible by 64.
ValueError

If max_seq_len cannot be determined and is required.
ValueError

If attention parameter constraints are violated (e.g., max_seq_len vs kvcache_partition_len for flash attention).
Attention Implementation

attn_impl determines the underlying attention mechanism used by the model.

  • "eager" (Default if kvcache_partition_len is not set): Uses the standard PyTorch attention implementation. Suitable for sequences up to a certain limit (e.g., 32,768 tokens).
  • "flash_attn": Utilizes an optimized Flash Attention implementation, beneficial for longer sequences and potentially faster execution. Requires max_seq_len to be at least 8,192. If kvcache_partition_len is specified, attn_impl automatically defaults to "flash_attn". When using "flash_attn", kvcache_block_size must equal kvcache_partition_len.

The choice impacts performance and memory usage, especially for long sequences. Constraints related to max_seq_len and kvcache_partition_len apply when using "flash_attn".

KV Cache Partition Length

kvcache_partition_len is relevant only when attn_impl is "flash_attn".

  • It defines the length (number of tokens) of each partition within the Key-Value (KV) cache.
  • Must be between 4,096 and 32,768 (inclusive).
  • When using "flash_attn", max_seq_len must be a multiple of kvcache_partition_len and at least twice its value (max_seq_len >= 2 * kvcache_partition_len).
  • If attn_impl is "flash_attn" and kvcache_partition_len is None, it defaults to 16,384.
KV Cache Number of Blocks

kvcache_num_blocks controls the total number of memory blocks allocated for the PagedAttention KV cache. Each block holds kvcache_block_size tokens of Key and Value states.

  • Automatic Estimation (Default): If kvcache_num_blocks is None, the system estimates the maximum number of blocks that can fit into the available RBLN device memory. This calculation considers the model size (kernel memory), required buffer memory, the number of layers and heads, kvcache_block_size, tensor parallelism, and available RBLN NPU DRAM. This aims to maximize cache capacity for potentially better performance with long sequences or larger batches without manual tuning.
  • Manual Setting: You can explicitly set the number of blocks. This provides finer control but requires careful consideration of memory limits. Setting it too high may lead to compilation errors if it exceeds available memory. The system will issue warnings if your setting exceeds the estimated maximum.
  • Performance Impact: A larger number of blocks reduces the likelihood of cache eviction, which is beneficial for tasks involving many long sequences or large batch sizes, enabling higher throughput. However, allocating more blocks consumes more memory.
  • Minimum Requirement: The system requires a minimum number of blocks to function, calculated based on max_seq_len, kvcache_block_size, and batch_size. The number of allocated blocks must be sufficient to hold at least one full sequence length per item in the batch concurrently. The system will log warnings or raise errors if constraints are violated (e.g., if kvcache_num_blocks is less than batch_size when using Flash Attention).

The optimal value depends on the specific model, task, hardware, and desired trade-off between performance and memory usage. The automatic estimation provides a robust starting point.