aphrodite-engine/aphrodite/modeling/model_loader/neuron.py

"""Utilities for selecting and loading neuron models."""
import importlib
import os
from typing import Dict, List, Optional, Tuple

import torch
import torch.nn as nn
import transformers
from transformers import PretrainedConfig

from aphrodite.common.config import (ModelConfig, ParallelConfig,
                                     SchedulerConfig)
from aphrodite.modeling.layers.logits_processor import LogitsProcessor
from aphrodite.modeling.layers.sampler import Sampler, SamplerOutput
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.quantization import get_quantization_config

TORCH_DTYPE_TO_NEURON_AMP = {
    "auto": "f32",
    "half": "f16",
    "float16": "f16",
    "bfloat16": "bf16",
    "float": "f32",
    "float32": "f32",
    torch.float16: "f16",
    torch.bfloat16: "bf16",
    torch.float32: "f32",
}

# Models supported by Neuron.
_NEURON_SUPPORTED_MODELS: Dict[str, Tuple[str, str, str]] = {
    "LlamaForCausalLM": ("transformers_neuronx.llama.model",
                         "LlamaForSampling", "LlamaForCausalLM"),
    "MistralForCausalLM": ("transformers_neuronx.mistral.model",
                           "MistralForSampling", "MistralForCausalLM")
}


class NeuronCasualLM(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
    ) -> None:
        super().__init__()
        self.config = config
        self.logits_processor = LogitsProcessor(config.vocab_size,
                                                logits_as_input=True)
        self.sampler = Sampler()

        # Lazy initialized
        self.model: nn.Module

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        input_block_ids: torch.Tensor,
    ) -> torch.Tensor:
        logits = self.model(input_ids,
                            cache_ids=positions,
                            start_ids=input_block_ids)
        return logits

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        logits = self.logits_processor(None, hidden_states, sampling_metadata)
        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, model_name_or_path: str, **kwargs):
        arch = _get_model_architecture(self.config)
        neuronx_module_path, neuronx_model_cls_name, hf_model_cls_name = (
            _NEURON_SUPPORTED_MODELS[arch])
        neuronx_module = importlib.import_module(neuronx_module_path)
        neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls_name)

        split_model_dir = f"{model_name_or_path}-split"
        if _is_pretrained_neuron_checkpoint(model_name_or_path):
            split_model_dir = model_name_or_path
        elif not os.path.exists(f"{model_name_or_path}-split"):
            hf_model_cls = getattr(transformers, hf_model_cls_name)
            from transformers_neuronx.module import save_pretrained_split

            hf_model = hf_model_cls.from_pretrained(model_name_or_path,
                                                    low_cpu_mem_usage=True)
            save_pretrained_split(hf_model, f"{model_name_or_path}-split")

        self.model = neuronx_model_cls.from_pretrained(split_model_dir,
                                                       **kwargs)
        self.model.to_neuron()


def _is_pretrained_neuron_checkpoint(model_name_or_path: str) -> bool:
    # Checking if the neuron checkpoint is saved in the old format.
    if os.path.isdir(os.path.join(model_name_or_path, "pytorch_model.bin")):
        return True
    # Checking if the neuron checkpoint is saved in the new format.
    pretrained_split_files = ["config.json", "generation_config.json"]
    pretrained_split_format = ".safetensors"
    for file in pretrained_split_files:
        file_path = os.path.join(model_name_or_path, file)
        if not os.path.isfile(file_path):
            return False
    for file in os.listdir(model_name_or_path):
        if file.endswith(pretrained_split_format):
            return True
    return False


def _get_model_architecture(config: PretrainedConfig) -> str:
    architectures = getattr(config, "architectures", [])
    for arch in architectures:
        if arch in _NEURON_SUPPORTED_MODELS:
            return arch
    raise ValueError(
        f"Model architectures {architectures} are not supported on Neuron "
        f"for now. Supported architectures: "
        f"{list(_NEURON_SUPPORTED_MODELS.keys())}")


def _get_buckets(env: str, default_value: List[int]) -> List[int]:
    env_value = os.getenv(env)
    if env_value is None:
        return default_value
    buckets_remove_empty = filter(
        lambda x: x is not None and len(x.strip()) > 0, env_value.split(","))
    buckets_int = map(int, buckets_remove_empty)
    buckets_list = list(buckets_int)
    return buckets_list


def _get_default_neuron_config(model_config: ModelConfig,
                               parallel_config: ParallelConfig,
                               scheduler_config: SchedulerConfig):
    from transformers_neuronx.config import ContinuousBatchingConfig
    from transformers_neuronx.constants import LAYOUT_BSH
    continuous_batching_config = ContinuousBatchingConfig(
        batch_size_for_shared_caches=scheduler_config.max_num_seqs)
    quant_config = dict(
        dequant_dtype=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
        quantize_method="vector_dynamic")
    neuron_quantization_config_builder = lambda quant: get_quantization_config(
        quant).from_config(quant_config).get_quant_method(None, "")
    # TODO: Add Paged attention config to the default neuron arguments.
    default_neuron_args = dict(
        collectives_layout=LAYOUT_BSH,
        attention_layout=LAYOUT_BSH,
        fuse_qkv=True,
        quant=neuron_quantization_config_builder(model_config.quantization)
        if model_config.quantization else None,
        continuous_batching=continuous_batching_config,
        weight_tiling=bool(model_config.quantization))
    return default_neuron_args


def _get_neuron_config_after_override(default_neuron_config,
                                      overridden_neuron_config):
    from transformers_neuronx.config import NeuronConfig
    overridden_neuron_config = overridden_neuron_config or {}
    default_neuron_config.update(overridden_neuron_config)
    return NeuronConfig(**default_neuron_config)


def get_neuron_model(model_config: ModelConfig,
                     parallel_config: ParallelConfig,
                     scheduler_config: SchedulerConfig) -> nn.Module:

    # Create a model instance.
    model = NeuronCasualLM(model_config.hf_config)

    default_neuron_config_args = _get_default_neuron_config(
        model_config, parallel_config, scheduler_config)
    neuron_config = _get_neuron_config_after_override(
        default_neuron_config_args, model_config.override_neuron_config)

    context_length_estimates = _get_buckets("NEURON_CONTEXT_LENGTH_BUCKETS",
                                            [scheduler_config.max_model_len])
    n_positions = _get_buckets("NEURON_TOKEN_GEN_BUCKETS",
                               [scheduler_config.max_model_len])

    # Load the weights from the cached or downloaded files.
    model.load_weights(model_config.model,
                       tp_degree=parallel_config.tensor_parallel_size,
                       amp=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
                       neuron_config=neuron_config,
                       context_length_estimate=context_length_estimates,
                       n_positions=n_positions,
                       batch_size=scheduler_config.max_num_seqs)

    return model.eval()