how to postpone filter init till it's running

[stas00]

So it appears that currently I can't instantiate a model on a gpu because the filter object is created by the launcher, which either doesn't have a gpu, or it is most likely the wrong gpu even if it has one, since we would need a dedicated gpu(s) for each task.

Is it possible to add a 2nd init which would be the user init that will run on the actual job?

The filter task is simple - instantiate a model on a gpu and then run filter using it - of course we don't want model to be re-instantiated on every filter call.

Needing to import torch inside the filter is super-weird as well, but I get that it's due to pickle - but perhaps we can have two inits - one of the framework - and then another of the user.

So when a job is launched the first thing the framework runs is user defined init if any, and then proceeds normally.

I guess I will try to overcome this meanwhile using @functools.cache or something similar.

Thank you!

tag: @guipenedo

[stas00]

I'm trying:

    @functools.cached_property
    def device(self):
        return torch.device('cuda:0')

    @functools.cached_property
    def model(self):
        return ClassifierHead.from_pretrained(mname).to(self.device)

    @functools.cached_property
    def tokenizer(self):
        return AutoTokenizer.from_pretrained(mname)

    @functools.cached_property
    def config(self):
        return AutoConfig.from_pretrained(mname)

and then inside the filter only the first items gets hit by this init.

edit: hmm, this approach seems to hang. strace showing it's stuck trying to read from fd=4

so I got to:

+ export PYTHONUNBUFFERED=TRUE
+ PYTHONUNBUFFERED=TRUE
+ srun -l launch_pickled_pipeline /data/stas/classify2/data/logs/slurm_processing/executor.pik
0: 2024-07-09 01:18:35.831 | INFO     | datatrove.utils.logging:add_task_logger:58 - Launching pipeline for rank=0
0: 2024-07-09 01:18:35.831 | INFO     | datatrove.utils.logging:log_pipeline:90 -
0: --- 🛠️ PIPELINE 🛠
0: 📖 - READER: 🤗 HuggingFace
0: 🔻 - FILTER: Classifier Filter
0: 💽 - WRITER: 🐿 Jsonl

and then nothing happens.

I set the reader to limit=10, so it should be real fast.

Must be something pickle-related

Do you by chance have an example of a working filter that uses a gpu given by the srun task?

[stas00]

update: If I run the same job as a local executor it works fine, it hangs on the first sample w/ slurm, so it must be some pickle related issue.

When I scancel the job it shows the buffered up part

srun: Job step aborted: Waiting up to 32 seconds for job step to finish.
 10%|█         | 1/10 [00:06<00:54,  6.00s/it]slurmstepd: error: *** STEP 101728.0 ON -5 CANCELLED AT 2024-07-09T03:51:10 ***
slurmstepd: error: *** JOB 101728 ON 5 CANCELLED AT 2024-07-09T03:51:10 ***

[guipenedo]

can you share the full class so I can try to reproduce the issue?

[stas00]

Yes, of course

import functools
import torch
from transformers import AutoConfig, AutoModel, AutoTokenizer

class ClassifierFilter(BaseFilter):
    name = "Classifier Filter"

    def __init__(
        self,
        exclusion_writer: DiskWriter = None,
    ):
        super().__init__(exclusion_writer)

    @functools.cached_property
    def device(self):
        return torch.device('cuda')

    @functools.cached_property
    def model(self):
        return ClassifierHead.from_pretrained("nvidia/domain-classifier").to(self.device)

    @functools.cached_property
    def tokenizer(self):
        return AutoTokenizer.from_pretrained("nvidia/domain-classifier")

    @functools.cached_property
    def config(self):
        return AutoConfig.from_pretrained("nvidia/domain-classifier")

    def filter(self, doc) -> bool | tuple[bool, str]:
        import torch  # noqa - pickle quirk

        inputs = self.tokenizer(
            [doc.text], return_tensors="pt", padding="longest", truncation=True
        ).to(self.device)
        outputs = self.model(inputs["input_ids"], inputs["attention_mask"])

        predicted_classes = torch.argmax(outputs, dim=1)
        predicted_domains = [
            self.config.id2label[class_idx.item()]
            for class_idx in predicted_classes.cpu().numpy()
        ]
        if predicted_domains[0] == "Health":
            return True
        else:
            return False, predicted_domains[0]

or perhaps if you have an example of a filter that works on running something on cuda that could help too.

[stas00]

Is there a plan for another way of passing the jobs instead of pickle?

The hanging happens because of functool.cached_property - so can't use it it seems.

I came up with the following workaround, creating my own post-un-pickle-init via to_device:

class ClassifierFilter(BaseFilter):
    name = "Classifier Filter"

    def __init__(
        self,
        exclusion_writer: DiskWriter = None,
    ):
        super().__init__(exclusion_writer)

        self.device = None
        self.config = AutoConfig.from_pretrained("nvidia/domain-classifier")
        self.tokenizer = AutoTokenizer.from_pretrained("nvidia/domain-classifier")
        self.model = ClassifierHead.from_pretrained("nvidia/domain-classifier")

    def to_device(self):
        import torch
        if self.device is not None:
            return
        self.device = torch.device('cuda')
        self.model = self.model.to(self.device)

    def filter(self, doc) -> bool | tuple[bool, str]:
        import torch  # noqa - pickle quirk

        self.to_device()

        inputs = self.tokenizer(
            [doc.text], return_tensors="pt", padding="longest", truncation=True
        ).to(self.device)
        outputs = self.model(inputs["input_ids"], inputs["attention_mask"])

        predicted_classes = torch.argmax(outputs, dim=1)
        predicted_domains = [
            self.config.id2label[class_idx.item()]
            for class_idx in predicted_classes.cpu().numpy()
        ]
        if predicted_domains[0] == "Health":
            return True
        else:
            return False, predicted_domains[0]