200 lines
7.5 KiB
Python
200 lines
7.5 KiB
Python
"""
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Local users
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------------
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- 1 GPU:
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accelerate launch \
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--config_file scripts/accelerate_configs/ddp.yaml --num_processes 1 \
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examples/rnd/sft.py
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- 8 GPUs (DeepSpeed ZeRO-2):
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accelerate launch \
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--config_file scripts/accelerate_configs/zero2.yaml \
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examples/rnd/sft.py
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Slurm users
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# Note: run `mkdir logs` before running sbatch; and adjust
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# `partition` and `quotatype` in `scripts/train.slurm.sh` for your cluster.
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------------
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- 1 GPU:
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sbatch --gres=gpu:1 scripts/train.slurm.sh \
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--accelerate_config "single_gpu" \
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--script_path "examples/rnd/sft.py"
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- 2 Nodes, 16 GPUs (DeepSpeed ZeRO-2):
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sbatch --nodes=2 --gres=gpu:8 scripts/train.slurm.sh \
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--accelerate_config "zero2" \
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--script_path "examples/rnd/sft.py"
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"""
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import os
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from dataclasses import dataclass, field
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import transformers
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import accelerate
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import peft
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import datasets
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import dllm
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from dllm.pipelines import rnd
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@dataclass
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class ModelArguments(dllm.utils.ModelArguments):
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model_name_or_path: str = "radicalnumerics/RND1-Base-0910"
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moe_backend: str = "hf"
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attn_implementation: str = "sdpa"
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@dataclass
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class DataArguments(dllm.utils.DataArguments):
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dataset_args: str = "HuggingFaceTB/smoltalk[train:10000,test:1000]"
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truncation: str = "right"
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@dataclass
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class TrainingArguments(dllm.utils.TrainingArguments):
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output_dir: str = "models/RND1-SFT-0910/smoltalk[train:10000,test:1000]"
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# rnd specific
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group_by_length: bool = True
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mask_prompt_loss: bool = field(
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default=True,
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metadata={"help": "Whether to mask the loss on the prompt tokens"},
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)
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freeze_gate: bool = field(
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default=True,
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metadata={
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"help": "If True, freeze routing gate parameters (e.g., MoE router/gating layers)."
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},
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)
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freeze_embedding: bool = field(
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default=False,
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metadata={"help": "If True, freeze embedding parameters."},
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)
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def train():
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# ----- Argument parsing -------------------------------------------------------
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parser = transformers.HfArgumentParser(
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(ModelArguments, DataArguments, TrainingArguments)
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)
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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dllm.utils.print_args_main(model_args, data_args, training_args)
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dllm.utils.initial_training_setup(model_args, data_args, training_args)
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# ----- Model ------------------------------------------------------------------
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config = transformers.AutoConfig.from_pretrained(
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model_args.model_name_or_path,
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moe_backend=model_args.moe_backend,
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attn_implementation=model_args.attn_implementation,
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)
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model = dllm.utils.get_model(model_args=model_args, config=config)
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# ----- Tokenizer --------------------------------------------------------------
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tokenizer = dllm.utils.get_tokenizer(model_args=model_args)
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# ----- Optionally freeze modules ----------------------------------------------
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if not isinstance(model, peft.PeftModel):
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if getattr(training_args, "freeze_gate", False):
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for n, m in model.named_modules():
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if n.endswith(".gate"): # only router gate, not gate_proj
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for p in m.parameters(recurse=False):
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p.requires_grad_(False)
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if getattr(training_args, "freeze_embedding", False):
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# model.model.embed_tokens.requires_grad_(False)
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model.model.embed_tokens.weight.requires_grad_(False)
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# ----- Dataset ----------------------------------------------------------------
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def sft_map_fn(row) -> dict:
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prompt_tokens = tokenizer.apply_chat_template(
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row["messages"][:-1],
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tokenize=True,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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prompt_response_tokens = tokenizer.apply_chat_template(
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row["messages"], tokenize=True, add_generation_prompt=False
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)
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labels = prompt_response_tokens.copy()
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if training_args.mask_prompt_loss:
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# use -100 in labels to indicate positions where tokens should not be masked
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# and loss is ignored; all other positions match `input_ids`
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labels[: len(prompt_tokens)] = [-100] * len(prompt_tokens)
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else:
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# When training on all tokens, prepend a BOS token (if missing)
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# so the model can make predictions for the first mask token.
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if prompt_response_tokens[0] != tokenizer.bos_token_id:
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bos = [tokenizer.bos_token_id]
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prompt_response_tokens = bos + prompt_response_tokens
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prompt_tokens = bos + prompt_tokens
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labels = bos + labels
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labels[0] = -100 # ignore loss on the BOS token
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# `prompt_len` helps `post_process_dataset` truncate long sequences properly
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return {
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"input_ids": prompt_response_tokens,
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"labels": labels,
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# "attention_mask": [1.0] * len(prompt_response_tokens),
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"prompt_len": len(prompt_tokens),
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}
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if not data_args.load_from_disk:
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with accelerate.PartialState().local_main_process_first():
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dataset = dllm.data.load_sft_dataset(data_args.dataset_args)
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dataset = dataset.map(sft_map_fn, num_proc=data_args.num_proc)
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# truncate / filter long sequences if needed
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dataset = dllm.utils.post_process_dataset(dataset, data_args)
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else:
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dataset = datasets.load_from_disk(data_args.dataset_args)
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# truncate / filter long sequences if needed
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dataset = dllm.utils.post_process_dataset(dataset, data_args)
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# ----- Training --------------------------------------------------------------
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@dataclass
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class RNDSFTCollator(transformers.DataCollatorForSeq2Seq):
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def __call__(self, features, return_tensors=None):
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outputs = super().__call__(features, return_tensors)
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# RND is finetuned on padding <eos_token>
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outputs.pop("attention_mask")
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# temp fix here (`group_by_length=True` leads to shape mismatch)
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# clip seq_len (second dim) to the same for outputs `input_ids, labels`
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# TODO -> FIXED: clip all relevant tensors to a common seq_len
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# Determine common length across present tensors
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import torch
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keys_to_clip = [k for k in ("input_ids", "labels") if k in outputs]
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if keys_to_clip:
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# Get smallest seq_len to avoid out-of-bounds
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min_len = min(
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outputs[k].size(1)
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for k in keys_to_clip
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if isinstance(outputs[k], torch.Tensor)
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)
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for k in keys_to_clip:
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t = outputs[k]
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if isinstance(t, torch.Tensor) and t.size(1) != min_len:
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outputs[k] = t[:, :min_len]
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return outputs
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tokenizer.pad_token_id = tokenizer.mask_token_ids
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trainer = rnd.RNDTrainer(
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model=model,
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tokenizer=tokenizer,
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train_dataset=dataset["train"],
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eval_dataset=dataset["test"],
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args=training_args,
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data_collator=RNDSFTCollator(
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tokenizer,
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# pad_to_multiple_of=8,
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return_tensors="pt",
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padding=True,
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label_pad_token_id=-100,
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),
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)
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trainer.train()
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trainer.save_model(os.path.join(training_args.output_dir, "checkpoint-final"))
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trainer.processing_class.save_pretrained(
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os.path.join(training_args.output_dir, "checkpoint-final")
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)
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if __name__ == "__main__":
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train()
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