TorchNeuron: รันโมเดล Hugging Face บน AWS Trainium ง่ายกว่าเดิม

import os
 
import torch
import torch.distributed as dist
import torch_neuronx  # noqa: F401
from torch.distributed import DeviceMesh
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from transformers.distributed import DistributedConfig
from transformers.generation.configuration_utils import CompileConfig
 
MODEL_ID = "Qwen/Qwen3.8-27B"
MAX_SEQ_LEN = 1024
PREFILL_CHUNK_SIZE = 512
 
dist.init_process_group(backend="neuron")
world_size = dist.get_world_size()
 
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    dtype=torch.bfloat16,
    distributed_config=DistributedConfig(tp_size=world_size),
    device_mesh=DeviceMesh("neuron", list(range(world_size))),
).eval()
 
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
 
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": "The future of AI is"}],
    add_generation_prompt=True,
    padding=True,
    tokenizer_kwargs={"pad_to_multiple_of": PREFILL_CHUNK_SIZE},
    return_tensors="pt",
)
 
generation_config = GenerationConfig(
    max_new_tokens=128,
    do_sample=False,
    use_cache=True,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    cache_implementation="static",
    max_cache_len=MAX_SEQ_LEN,
    prefill_chunk_size=PREFILL_CHUNK_SIZE,
    compile_config=CompileConfig(backend="neuron", fullgraph=False, dynamic=False),
)
 
with torch.no_grad():
    output_ids = model.generate(
        inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        generation_config=generation_config,
    )
 
if dist.get_rank() == 0:
    prompt_len = inputs["input_ids"].shape[1]
    print(tokenizer.decode(output_ids[0, prompt_len:], skip_special_tokens=True))

import os
 
import torch
from datasets import load_dataset
from transformers.distributed import DistributedConfig
 
from trl import ModelConfig, ScriptArguments, SFTConfig, SFTTrainer, TrlParser
 
def main(script_args, training_args, model_args):
    # The Neuron backend can't compile Dynamo's automatic dynamic shapes yet (https://github.com/huggingface/trl/issues/7660)
    torch._dynamo.config.automatic_dynamic_shapes = False
 
training_args.model_init_kwargs = {
        "revision": model_args.model_revision,
        "trust_remote_code": training_args.trust_remote_code,
        "attn_implementation": model_args.attn_implementation,
        "dtype": model_args.dtype,
        "device_map": None,
    }
    if int(os.environ.get("WORLD_SIZE", "1")) > 1:
        # The model is sharded on the TP x FSDP grid as its weights are loaded
        training_args.model_init_kwargs["distributed_config"] = DistributedConfig(
            tp_size=int(os.environ.get("TP_SIZE", "1")),
            fsdp_size=int(os.environ.get("FSDP_SIZE", "1")),
        )
 
# Prepare dataset
    train_dataset = load_dataset(
        script_args.dataset_name,
        name=script_args.dataset_config,
        split=script_args.dataset_train_split,
    )
    # UltraChat also has a string `prompt` column, which makes TRL treat it as a prompt-completion dataset.
    train_dataset = train_dataset.select_columns(["messages"])
 
# Training
    trainer = SFTTrainer(
        model=model_args.model_name_or_path,
        args=training_args,
        train_dataset=train_dataset,
    )
 
trainer.train()
    trainer.save_model(training_args.output_dir)
    trainer.end()
 
if __name__ == "__main__":
    parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
    script_args, training_args, model_args = parser.parse_args_and_config()
    main(script_args, training_args, model_args)

TP_SIZE=4 FSDP_SIZE=8 HF_DEACTIVATE_ASYNC_LOAD=1 uv run torchrun --nproc_per_node=32 sft_neuron_example.py \
    --model_name_or_path Qwen/Qwen3-8B \
    --dataset_name HuggingFaceH4/ultrachat_200k \
    --dataset_train_split train_sft \
    --output_dir Qwen3-8B-sft-tp4-fsdp8 \
    --dtype bfloat16 \
    --max_length 2048 \
    --packing true \
    --packing_strategy wrapped \
    --learning_rate 2e-5 \
    --warmup_steps 0.03 \
    --num_train_epochs 1 \
    --logging_steps 2 \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 4 \
    --save_only_model true \
    --torch_compile true

import torch, torch.distributed as dist, torch_neuronx # TP
from torch.distributed.device_mesh import DeviceMesh # TP
from diffusers import QwenImage21Pipeline, QwenImage21Transformer2DModel, TensorParallelConfig
 
# Distributed devices initialization
dist.init_process_group(backend="neuron") # TP
dev = torch.neuron.current_device()
mesh = DeviceMesh("neuron", list(range(8))) # TP=8
 
# Pipeline initialization
model = "Qwen/Qwen-Image-2.1"
transformer = QwenImage21Transformer2DModel.from_pretrained(
    model,
    subfolder="transformer",
    torch_dtype=torch.bfloat16,
    parallel_config=TensorParallelConfig(mesh=mesh), # TP
)
pipe = QwenImage21Pipeline.from_pretrained(
    model,
    transformer=transformer,
    torch_dtype=torch.bfloat16,
)
pipe.vae.to(dev)
pipe.transformer.compile_repeated_blocks(backend="neuron", fullgraph=True, dynamic=False)
 
# Image generation
emb, mask, _ = pipe.encode_prompt(prompt="A capybara wearing a wizard hat, oil painting", device="cpu")  # text encoder stays on CPU
image = pipe(
    prompt_embeds=emb.to(dev),
    prompt_embeds_mask=mask.to(dev) if mask is not None else None,
    height=1024,
    width=1024,
    num_inference_steps=40,
    generator=torch.Generator("cpu").manual_seed(0), # same noise on every rank
).images[0]
if dist.get_rank() == 0:
    image.save("out.png")