Add metric logging #162

joecummings · 2024-01-09T20:37:45Z

Changelog

Add metric logger class
Add wandb logger
Add tensorboard logger
Update config and finetuning recipe

Test plan

run finetune, check wandb logs

- run finetune, check tensorboard logs

Docs

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joecummings · 2024-01-09T20:45:50Z

recipes/finetune_llm.py

+            pbar.set_description(f"{epoch+1}|{idx+1}|Loss: {loss.item()}")
+
+            # Log metrics
+            metric_logger.log_dict(


Currently, not configurable. This precedent is already set in HF, where if you have wandb logged in, it just automatically logged a bunch of stuff to your project. https://docs.wandb.ai/guides/integrations/huggingface

kartikayk · 2024-01-14T21:39:50Z

torchtune/utils/metric_logging.py

+        """
+        pass
+
+    def log_dict(self, payload: Mapping[str, Scalar], step: int) -> None:


I'm new to Mapping. Why use this instead of a Dict? When I look this up, most resources talk about using Mapping when you don't have a literal Dict. But seems like here we do?

Mapping has the following (small) advantages:

Allows for any type that supports a __getitem__ ,__len__ , __iter__ items.

Is immutable, so technically we can reason "stricter" about the values that come in.

For this case, it's not a huge difference. This interface is largely copied from TNT.

kartikayk · 2024-01-14T21:39:53Z

torchtune/utils/__init__.py

+    log_dir: Optional[str] = None,
+    project: Optional[str] = None,
+    **kwargs: Any
+) -> Optional[MetricLogger]:


Why does a function named get_.... return an Optional object? Shouldn't the caller make sure the function is called with reasonable values so we aren't just returning None?

More concretely why is metric_logger Optional? If this is None, just don't call this function?

kartikayk · 2024-01-14T21:40:02Z

recipes/finetune_llm.py

-    logger.addHandler(logging.StreamHandler())
-    logger.setLevel(logging.DEBUG)
-    return logger.info
+LOGGER = logging.Logger(__name__)


Sorry for being a noob, but I dont understand what this change is doing

This was handled by #180

kartikayk · 2024-01-14T21:42:58Z

Thanks for adding these capabilities! Some questions:

Should this change be accompanied with changes to requirements.txt?
How do we plan on catching downstream changes breaking our recipe if we don't have tests? For example, if the wandb or tensorboard module breaks, this will break our runs and we won't really capture it till we run a fine-tuning job?
How do we plan on capturing changes to the metric logger class which breaks this functionality?

I think this PR needs more testing and thought on how to prevent third-party breakages from breaking TorchTune. Let me know if I misunderstand.

rohan-varma

I know this is just a draft, but have significant concerns around the direction of this PR:

Usage of wandB by default (from what it seems like) + we need failsafes in place to ensure service unavailiblity doesn't affect the ability for users to run their finetunes.
Think that overall we should refocus the initial metric logging: lets log to stderr or a predefined file important statistics around training speed, the config used, memory utilization, etc). In particular, let's propose a set of initial metrics that are most important for our user cohorts, and simply support robust logging for those to a file. We can then follow up with wandB / TB, but I'm more concerned around getting the proper metrics logged first.

rohan-varma · 2024-01-22T08:41:58Z

recipes/configs/alpaca_llama2_finetune.yaml

@@ -19,3 +19,7 @@ output_dir: /tmp/alpaca-llama2-finetune
 device: cuda
 fsdp: False
 activation_checkpointing: False
+
+# Metrics arguments
+metric_logger: wandb


Largely due to what @kartikayk mentioned I'm a bit hesitant to have a third-party logger as a default. i.e. if wandb runs into errors, all our finetuning will just break, no? I think if this is the default, we should at least have some error checking + fallback in case wandB has issues.

Do you think the default should be a dead simple logger that logs metrics to an output file at each step?

rohan-varma · 2024-01-22T08:42:17Z

recipes/finetune_llm.py

-    logger.addHandler(logging.StreamHandler())
-    logger.setLevel(logging.DEBUG)
-    return logger.info
+LOGGER = logging.Logger(__name__)


rohan-varma · 2024-01-22T08:42:40Z

recipes/finetune_llm.py

@@ -69,7 +61,7 @@ def recipe(kwargs):
        else kwargs["device"].split(":")[0]
    )
    tokenizer = get_tokenizer(kwargs["tokenizer"], path=kwargs["tokenizer_checkpoint"])
-    logger(msg=f"Loaded tokenizer from {kwargs['tokenizer_checkpoint']}")
+    LOGGER.info(msg=f"Loaded tokenizer from {kwargs['tokenizer_checkpoint']}")


This stuff probably needs to be rebased.

rohan-varma · 2024-01-22T08:43:00Z

recipes/finetune_llm.py

+    LOGGER.info(msg=f"Loaded model from {kwargs['model_checkpoint']}")
+
+    # ---- Load tokenizer ---- #
+    tokenizer = get_tokenizer(kwargs["tokenizer"], path=kwargs["tokenizer_checkpoint"])


Why are we getting the tokenizer again?

This was moved. Changed in #180. Needs rebase.

rohan-varma · 2024-01-22T08:44:03Z

recipes/finetune_llm.py

+
+            # Log metrics at each step
+            # If no metric logger is specified, this is a no-op
+            metric_logger.log_dict(


Does the user need to do any sort of special configuration / registration with wandB then for our FT recipes to work OOTB? If necessary, having this on by default would be a no-go IMO.

WandB does need an init, but if you've already run a wandb.init() on your machine (which the majority of users probably have), then it'll work OOTB.

rohan-varma · 2024-01-22T08:45:38Z

torchtune/utils/__init__.py

+    Args:
+        metric_logger (Optional[str]): The metric logger to use.
+        log_dir (Optional[str]): The directory to log to.
+        project (Optional[str]): The project to log to.


Add the defaults in the docstr and what they mean.

rohan-varma · 2024-01-22T08:46:44Z

torchtune/utils/metric_logging.py

+        """Log scalar data.
+
+        Args:
+            name (string): tag name used to group scalars


Is "tag name used to group scalar" detailed enough for this arg? Could we add details such like - scalars loged w/ the same name will be aggregated together, IMO that might be a bit clearer?

Sure - this was copied from TNT. I can add more decriptions.

rohan-varma · 2024-01-22T08:47:34Z

torchtune/utils/metric_logging.py

+
+
+class WandBLogger(MetricLogger):
+    """WandB logger."""


Add details to this documentation and ensure all docs render properly.

rohan-varma · 2024-01-22T08:51:23Z

torchtune/utils/metric_logging.py

+        import wandb
+
+        self._wandb = wandb
+        self._wandb.init(


Some things that are not immediately clear to me:

Does this require the wandB service to be available and accessible through a defined endpoint? If so, have we considered scenaris where users are using torchtune completely offline, or through a proxy/firewall that could affect this?

Any way to check whether wandB service is available, and fail fast + inform the user to use a different logger? Overall, we want failures in the logging component due to third-party issues to not be in the critical paths of our finetunes.

rohan-varma · 2024-01-22T08:52:07Z

torchtune/utils/metric_logging.py

+        self._wandb.finish()
+
+
+class TensorBoardLogger(MetricLogger):


Could we have examples of usage, paste of expected outputs, etc for both Tensorboard and wandB logging?

These are listed in the PR. For "outputs", I'm not sure what you mean, b/c the outputs are images.

pbontrager

Looking good overall, just needs a getter and a default noop or terminal logger.

pbontrager · 2024-01-22T19:29:59Z

torchtune/utils/metric_logging.py

+        """
+        pass
+
+    def close(self) -> None:


Can this be called from a del method so it automatically gets called by garbage collection?

torchtune/utils/metric_logging.py

pbontrager · 2024-01-22T19:34:09Z

recipes/finetune_llm.py

+                    "lr": opt.param_groups[0]["lr"],
+                    "gpu_resources": torch.cuda.memory_allocated(),
+                },
+                step=idx,


step needs to be global step so step=epoch*len(dataloader) + idx .

pbontrager · 2024-01-22T19:40:24Z

An additional point I forgot, typically you only want metric logging on rank 0. We could either hard code that in or make it optional from init.

joecummings · 2024-01-22T21:18:01Z

tests/test_utils.py

+
+
+@contextmanager
+def captured_output() -> Generator[Tuple[TextIO, TextIO], None, None]:


From TorchTNT

pbontrager

I think we'll still need a no-op logger to be used by default and for higher ranks.

pbontrager · 2024-01-22T21:28:19Z

recipes/finetune_llm.py

@@ -37,11 +37,16 @@ def recipe(
    output_dir,
    run_generation,
    max_steps_per_epoch,
+    metric_logger,
+    project,
+    log_dir,


Can we reuse output-dir for this?

pbontrager · 2024-01-22T21:32:10Z

recipes/finetune_llm.py

+
+            # Log metrics at each step
+            # If no metric logger is specified, this is a no-op
+            if rank == 0:


I think metric_loggers should handle the rank functionality. They also need to only init on rank 0. One option is to create a class decorator that returns the given class on rank 0 and returns either a no-op class or terminal logging class on other ranks.

They also need to only init on rank 0

Should this then happen in the distributed utils instead of passing in rank information etc to the logger itself?

We could add a decorator in distributed utils called "run_on_rank_0(null_class)" that you pass to a class with a null_class that gets called on the other ranks. Or we could just build the logic into the logger and import get_world_size_and_rank so the rank wouldn't need to be passed in.

If you add a rank check on "get_metric_logger" I won't block on this, and better distributed support can be added in a followup PR.

pbontrager · 2024-01-22T21:34:33Z

torchtune/utils/metric_logging.py

+        return WandBLogger(project=project)
+    elif metric_logger == "tensorboard":
+        return TensorBoardLogger(log_dir=log_dir)
+    return StdoutLogger()


This needs an elif "stdout" and a final else to raise that the input isn't recognized. Since the options are just strings here, we probably don't need to accept None as an option.

pbontrager · 2024-01-22T21:37:51Z

recipes/finetune_llm.py

+    parser.add_argument(
+        "--metric-logger",
+        type=str,
+        default="stdout",


This default is going to flood a users terminal with logs and make tqdm useless. Maybe we should have a default no-op still?

Then we need a bunch of if statements, no?

@kartikayk Could you share your thoughts here?

Would it help to default to stderr?

torchtune/utils/metric_logging.py

pbontrager · 2024-01-22T21:47:50Z

dev-requirements.txt

@@ -1,3 +1,4 @@
 pytest
 pytest-cov
 pre-commit
+tensorboard


Can you move these to requirements.txt as the user needs access to. In general we need to add support for optional-dependencies as per this RFC. For now we could have a commented section of the requirements.txt for optional requirements that will get split off as actual optional requirements in the future.

Can you clarify your first sentence?

This would mean that until we specify these as optional, people will think we're taking a hard dependency on tensorboard and wandb. I really would like to avoid this as they are really only needed for tests. Anything else should be the users responsibility.

I agree that these probably should be optional dependencies. However, as stated in the RFC these fall under training utilities which means they would be in requirements.txt. (I am not saying we should put these in requirements.txt, in fact I think we should reconsider our framing there given that now the set of deps in our recipe but not in our training utilities is pretty much the empty set)

rohan-varma

LG overall. Main concern is around dependencies - if I can use wandB as a feature in torchtune, then it makes sense that this should be installable via requirements.txt. If we decide not to do this, then we should update the README documenting the installation requirements for our different logging utilities. Also have some more comments in line, will stamp after that

rohan-varma · 2024-01-23T01:26:49Z

dev-requirements.txt

@@ -1,3 +1,5 @@
 pytest
 pytest-cov
 pre-commit
+tensorboard
+wandb


Not sure why this and tb are dev requirements even after seeing the above discussion. If it's needed for a feature, why can't I just get it by installing from requirements.txt? cc @kartikayk

They aren't strictly needed for a feature. The default feature is stdout or a no-op, which are plain Python. Then, if a user wants to use wandb or tensorboard, they can, and our docs show how they can install and set it up. However, I don't want to take a hard dependency on these right now b/c they're really only needed for the tests.

Once we know how to add optional dependencies properly in requirements.txt, we can add them back. I'm trying to be very protective of our "hard" dependencies even if they are transitional.

rohan-varma · 2024-01-23T01:27:08Z

recipes/configs/alpaca_llama2_finetune.yaml

@@ -20,3 +20,6 @@ device: cuda
 dtype: fp32
 fsdp: False
 activation_checkpointing: False
+
+# Metrics arguments
+metric_logger: stdout


If this is a path instead of a logging abstraction, let's call it metric_logger_path?

It's not a path.

rohan-varma · 2024-01-23T01:27:44Z

recipes/finetune_llm.py

 ):
    # ---- Initialize components ---- #
    utils.init_distributed(fsdp)

    logger = utils.get_logger("DEBUG")
+    metric_logger = utils.get_metric_logger(metric_logger, project, output_dir)


Maybe ideally we'd only initialize this on rank 0, but not super important to me at the moment - can punt on it.

rohan-varma · 2024-01-23T01:28:08Z

recipes/finetune_llm.py

 ):
    # ---- Initialize components ---- #
    utils.init_distributed(fsdp)

    logger = utils.get_logger("DEBUG")
+    metric_logger = utils.get_metric_logger(metric_logger, project, output_dir)


also, this is confusing as we're creating a metric_logger name with an arg that is also metric_logger that is of a different type i think

Yup, this should be changed.

rohan-varma · 2024-01-23T01:28:54Z

recipes/finetune_llm.py

+        "--metric-logger",
+        type=str,
+        default="stdout",
+        choices=["wandb", "tensorboard", "stdout"],


should stderr also be a choice?

rohan-varma · 2024-01-23T01:29:07Z

recipes/finetune_llm.py

+    parser.add_argument(
+        "--metric-logger",
+        type=str,
+        default="stdout",


Would it help to default to stderr?

rohan-varma · 2024-01-23T01:29:43Z

recipes/finetune_llm.py

+        "--project",
+        type=str,
+        default=None,
+        help="Project name for WandB metric logger.",


let's add error checking / warning that this is ignored if not using wandB (can be done as follow up, or more generally as part of config validation cc @RdoubleA

I think this is part of a larger question of validation of configs

rohan-varma · 2024-01-23T01:31:05Z

recipes/finetune_llm.py

 ):
    # ---- Initialize components ---- #
    utils.init_distributed(fsdp)

    logger = utils.get_logger("DEBUG")
+    metric_logger = utils.get_metric_logger(metric_logger, project, output_dir)


output_dir docstring should also be updated to indicate that it's the TB log dir, or we should provide a separate arg that acts as this dir explicitly.

rohan-varma · 2024-01-23T01:32:26Z

torchtune/utils/metric_logging.py

+        )
+
+
+class MetricLogger(Protocol):


did you consider the use of abc.AbstractClass and how it could differ from Protocol? Assume protocol choice is from our design principles, but any details on how we're thinking about its usage here?

Bad answer but protocol is the only option "allowed" by our design principles.

That's still under discussion https://github.com/pytorch-labs/torchtune/pull/209/files#r1455763545 and I personally do not understand what a Protocol is. It seems to be related to type checking, so... something largely irrelevant here. I think that if we're going for Protocol instead of an standard ABC, it should be for clear good reasons that we all understand - like @rohan-varma , I'd love to learn more about this here. CC @kartikayk since I believe the original design principle comes from you

I'm fine with enforcing interfaces through abstract classes. But I don't fully understand why an abstract class is a better option here than a Protocol. Can someone help me understand this? Maybe I'm just being stupid.

My question is: What is a Protocol??

There are several online resources on this. But maybe look at this:
https://jellis18.github.io/post/2022-01-11-abc-vs-protocol/

Thanks for the resource. I'm still not sure why a Protocol is needed over an ABC here. That article confirmed that the Protocol is clearly focused on type checking, and none of that seems relevant here since both WandBLogger and TensorBoardLogger inherit from the base MetricLogger. An ABC would do just fine, the Protocol doesn't seem to bring anything.

But I don't fully understand why an abstract class is a better option here than a Protocol

I am personally much more familiar with ABCs than with Protocols. The first time I ever heard about Protocol was on Torchtune: it's a 3.8+ features and pytorch (and most other packages) only recently dropped support for 3.7. So the main benefit of ABC here is familiarity, IMHO. Out of fairness, let me return the question: why is a Protocol better than an ABC, for the purpose of Torchtune?

rohan-varma · 2024-01-23T01:33:09Z

torchtune/utils/metric_logging.py

+            print(f"{name}:{data} ", end="")
+        print("\n", end="")
+
+    def close(self) -> None:


Is it important to close resources in the case of errors? i.e. in a del method that @pbontrager mentioned?

cc @daniellepintz here b/c truthfully I'm not sure. They didn't include them in TNT.

ebsmothers · 2024-01-23T05:12:39Z

tests/torchtune/utils/test_metric_logging.py

+from tests.test_utils import captured_output
+
+
+class StdoutLoggerTest(unittest.TestCase):


ebsmothers · 2024-01-23T05:14:19Z

torchtune/utils/metric_logging.py

+    """
+
+    def __init__(self, log_dir: str, **kwargs):
+        from torch.utils.tensorboard import SummaryWriter


This is just in torch core, right? Why do we need to gate the import behind initialization of the TensorBoardLogger class?

no reason, just deferred import b/c someone might not want to use tensorboard.

ebsmothers · 2024-01-23T05:22:37Z

dev-requirements.txt

@@ -1,3 +1,4 @@
 pytest
 pytest-cov
 pre-commit
+tensorboard


I agree that these probably should be optional dependencies. However, as stated in the RFC these fall under training utilities which means they would be in requirements.txt. (I am not saying we should put these in requirements.txt, in fact I think we should reconsider our framing there given that now the set of deps in our recipe but not in our training utilities is pretty much the empty set)

rohan-varma

LGTM overall - I think the discussion on stdout/stderr/default logger can continue to evolve after this PR and it is a relatively easy change, we just need to make a decision. Pls close on open comments & let's have fast follow ons to add important metrics around performance, memory utilization, etc added.

NicolasHug · 2024-01-23T10:38:55Z

torchtune/utils/metric_logging.py

+        group: Optional[str] = None,
+        **kwargs,
+    ):
+        import wandb


This should probably be within a try: except: block with a nicer error message indicating users that they should be installing wandb, or optionally set the appropriate parameter to other options (like "stdout"). Same for tensorboard below. This could be done here, or more generally above in get_metric_logger

I think in here is good, will add now before submitting.

pbontrager

I also think it's good to land and then we can iterate from there. I only request that you pull out the list of metric loggers as mentioned in my last comment.

pbontrager · 2024-01-23T16:30:42Z

torchtune/utils/metric_logging.py

+        return StdoutLogger()
+    else:
+        raise ValueError(
+            f"Metric logger not recognized. Expected 'wandb', 'tensorboard', or 'stdout', received {metric_logger}."


All of the other unit tests handle their getters by having a global hidden list [wandb, tensorboard, stdout] and a list_metric_loggers function so you don't have to maintain this list in multiple places (e.g. Error messages, getter checks, choices).

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joecummings mentioned this pull request Jan 23, 2024

Metric logger should only log on rank 0 #233

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joecummings added 15 commits January 23, 2024 08:55

Add metric logging

1c5dde1

Add missing imports

9345ca4

Add tensorboard and wand docstrings and rst

8b3673f

Forgot to add utilities file

334bc12

Satisfy linter

83cd9f6

Add getter and tests

eba14cc

Add tensorboard to dev requirements

f8220e3

Update dev requirements

f5d8ef9

Update raise statement

4de6b14

log dir mistake

1560e8e

Address comments

d239672

Lint

928a2f1

Remove unittest

734b484

Address last comments

72b0f63

Add disk logger

6c2640c

joecummings force-pushed the add-logging branch from e1bb6b9 to 6c2640c Compare January 23, 2024 18:10

joecummings added 6 commits January 23, 2024 10:22

Remove faulty comments

a3c5b31

Add del function to stdout logger

10a8e78

Fix tests

099b197

Add kwargs

a8be048

Add patch

bb0f63c

Add different patching style

20e923d

joecummings merged commit 314edda into main Jan 23, 2024
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joecummings deleted the add-logging branch March 18, 2024 20:50



		@contextmanager
		def captured_output() -> Generator[Tuple[TextIO, TextIO], None, None]:

		from tests.test_utils import captured_output


		class StdoutLoggerTest(unittest.TestCase):

Add metric logging #162

Add metric logging #162

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joecummings commented Jan 9, 2024 • edited

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