run_generate.py

"""
Nanotron Inference Script

Usage:
```
export CUDA_DEVICE_MAX_CONNECTIONS=1 # important for some distributed operations
torchrun --nproc_per_node=1 run_generate.py --ckpt-path ./pretrained/MiniCPM-2B-dpo-bf16
```
"""

import argparse
import os
from pathlib import Path

import torch
from nanotron import distributed as dist
from nanotron import logging
from nanotron.config import GenerationArgs, LoggingArgs, ParallelismArgs, get_config_from_file
from nanotron.generation.decode import GenerationInput, TokenizerConfig, decode_text, decode_tokenized
from nanotron.logging import log_rank, set_logger_verbosity_format
from nanotron.models import build_model
from nanotron.parallel import ParallelContext
from nanotron.parallel.parameters import sanity_check
from nanotron.parallel.pipeline_parallel.engine import (
    OneForwardOneBackwardPipelineEngine,
)
from nanotron.parallel.pipeline_parallel.tensor_pointer import TensorPointer
from nanotron.parallel.tensor_parallel.enum import TensorParallelLinearMode
from nanotron.random import (
    RandomStates,
    get_current_random_state,
    get_synced_random_state,
    set_random_seed,
)
from nanotron.serialize import (
    load_weights,
)
from nanotron.trainer import CONFIG_TO_MODEL_CLASS, mark_tied_parameters

from brrr.config import BrrrConfig
from config_minicpm import MiniCPMConfig
from modeling_minicpm import MiniCPMForTraining

try:
    from transformers import AutoTokenizer
except ImportError:
    AutoTokenizer = None

logger = logging.get_logger(__name__)


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--ckpt-path", type=Path, required=True, help="Checkpoint path")
    parser.add_argument("--dp", type=int, default=1)
    parser.add_argument("--pp", type=int, default=1)
    parser.add_argument("--tp", type=int, default=1)
    parser.add_argument("--max-new-tokens", type=int, default=128, help="Maximum number of new tokens to generate")
    return parser.parse_args()


def main():
    args = get_args()

    assert args.ckpt_path.exists(), f"Checkpoint path {args.ckpt_path} does not exist"

    config = get_config_from_file((args.ckpt_path / "config.yaml").as_posix(), config_class=BrrrConfig, model_config_class=MiniCPMConfig)
    model_config = config.model.model_config
    tokenizer_path = config.tokenizer.tokenizer_name_or_path

    parallel_config = ParallelismArgs(
        dp=args.dp or config.parallelism.dp,
        pp=args.pp or config.parallelism.pp,
        tp=args.tp or config.parallelism.tp,
        pp_engine=OneForwardOneBackwardPipelineEngine(),
        tp_mode=TensorParallelLinearMode.ALL_REDUCE,
        recompute_granularity=None,
        tp_linear_async_communication=False,
    )

    # Initialise all process groups
    parallel_context = ParallelContext(
        data_parallel_size=parallel_config.dp,
        pipeline_parallel_size=parallel_config.pp,
        tensor_parallel_size=parallel_config.tp,
    )

    # Set log levels
    logging_config = LoggingArgs(
        log_level="info",
        log_level_replica="info",
    )

    if dist.get_rank(parallel_context.world_pg) == 0:
        if logging_config.log_level is not None:
            set_logger_verbosity_format(logging_config.log_level, parallel_context=parallel_context)
    else:
        if logging_config.log_level_replica is not None:
            set_logger_verbosity_format(logging_config.log_level_replica, parallel_context=parallel_context)

    log_rank(f"model_config: {model_config}", logger=logger, level=logging.INFO, rank=0)
    log_rank(f"tokenizer_path: {tokenizer_path}", logger=logger, level=logging.INFO, rank=0)

    dtype = torch.bfloat16

    # Set random states
    set_random_seed(42)

    # Get synchronized random states
    if parallel_config.tp_mode is TensorParallelLinearMode.ALL_REDUCE:
        random_states = RandomStates(
            {"tp_synced": get_synced_random_state(random_state=get_current_random_state(), pg=parallel_context.tp_pg)}
        )
    else:
        # We don't need to sync across TP when using sequence parallel (REDUCE_SCATTER)
        random_states = RandomStates({})

    model = build_model(
        model_builder=lambda: MiniCPMForTraining(
            config=model_config,
            parallel_context=parallel_context,
            parallel_config=parallel_config,
            random_states=random_states,
        ),
        dtype=dtype,
        parallel_context=parallel_context,
    )

    # Mark some parameters as tied
    # TODO @nouamane: this is only needed for training, can we just mark params as NanotronParameter instead?
    mark_tied_parameters(model=model, parallel_context=parallel_context, parallel_config=parallel_config)

    # Sanity check model
    sanity_check(root_module=model)

    # Load checkpoint
    checkpoint_path = args.ckpt_path
    log_rank(
        f"Loading checkpoint from {checkpoint_path}:",
        logger=logger,
        level=logging.INFO,
        rank=0,
    )
    load_weights(model=model, parallel_context=parallel_context, root_folder=checkpoint_path)

    model.eval()
    if AutoTokenizer is not None:
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
        # tokenizer.pad_token_id = tokenizer.eos_token_id
        if tokenizer.pad_token_id is None:
            if tokenizer.eos_token_id is not None:
                tokenizer.pad_token_id = tokenizer.eos_token_id
            elif getattr(model.config, "pad_token_id", None) is not None:
                tokenizer.pad_token_id = int(model.config.pad_token_id)
            elif getattr(model.config, "eos_token_id", None) is not None:
                tokenizer.pad_token_id = int(model.config.eos_token_id)
            else:
                tokenizer.add_special_tokens({"pad_token": "[PAD]"})
        tokenizer.padding_side = "left"
        tokenizer.truncation_side = "left"  # TODO @nouamane: do we want this?
        dummy_inputs = [
            # "Passage: Daniel went back to the garden. Mary travelled to the kitchen. Sandra journeyed to the kitchen. Sandra went to the hallway. John went to the bedroom. Mary went back to the garden. Where is Mary?\nAnswer:",
            # "def fib(n)",
            "This film was probably inspired by Godzilla",
        ]

        outputs = decode_text(
            input_iter=(GenerationInput(text=text) for text in dummy_inputs),
            tokenizer=tokenizer,
            # TODO @thomasw21: From ModelWithLoss extract the model.
            model=model.model,
            parallel_context=parallel_context,
            max_new_tokens=args.max_new_tokens,
            max_micro_batch_size=2,
            generation_config=GenerationArgs(sampler="greedy", use_cache=True),
            tokenizer_config=TokenizerConfig(max_input_length=None),
            is_bench=os.environ.get("USE_BENCH", "0") == "1",
        )
        for output in outputs:
            input_ids = output.input_ids
            generated_ids = output.generation_ids
            if isinstance(input_ids, TensorPointer):
                assert isinstance(generated_ids, TensorPointer)
                continue
            assert isinstance(generated_ids, torch.Tensor)

            log_rank(
                f"input: {tokenizer.decode(input_ids, clean_up_tokenization_spaces=False)[:1000]}",
                logger=logger,
                level=logging.INFO,
                rank=0,
            )

            log_rank(
                f"generation: {tokenizer.decode(generated_ids[len(input_ids) :], clean_up_tokenization_spaces=False)}",
                logger=logger,
                level=logging.INFO,
                rank=0,
            )

            log_rank(
                "--------------------------------------------------",
                logger=logger,
                level=logging.INFO,
                rank=0,
            )
    else:
        outputs = decode_tokenized(
            input_ids=torch.zeros(1, 1).to(dtype=torch.int64, device="cuda"),
            input_mask=torch.ones(1, 1).to(dtype=torch.bool, device="cuda"),
            model=model.model,
            parallel_context=parallel_context,
            generation_config=GenerationArgs(sampler="greedy", use_cache=True),
            max_micro_batch_size=1,
            max_new_tokens=12,
            returns_logits=False,
        )
        for output in outputs:
            input_ids = output.input_ids
            generated_ids = output.generation_ids
            if isinstance(input_ids, TensorPointer):
                assert isinstance(generated_ids, TensorPointer)
                continue
            assert isinstance(generated_ids, torch.Tensor)
            log_rank(
                f"generation: {generated_ids[len(input_ids) :]}",
                logger=logger,
                level=logging.INFO,
                rank=0,
            )

            log_rank(
                "--------------------------------------------------",
                logger=logger,
                level=logging.INFO,
                rank=0,
            )

    dist.barrier()


if __name__ == "__main__":
    main()