[experiment] missing default_experiment-1-0.arrow
Hi !

The cache at `~/.cache/huggingface/metrics` stores the users data for metrics computations (hence the arrow files).

However python modules (i.e. dataset scripts, metric scripts) are stored in `~/.cache/huggingface/modules/datasets_modules`.

In particular the metrics are cached in `~/.cache/huggingface/modules/datasets_modules/metrics/`

Feel free to take a look at your cache and let me know if you find any issue that would help explaining why you had an issue with `rouge` with no connection. I'm doing some tests on my side to try to reproduce the issue you have
 Thank you for clarifying that the metrics files are to be found elsewhere, @lhoestq 

> The cache at ~/.cache/huggingface/metrics stores the users data for metrics computations (hence the arrow files).

could it be renamed to reflect that? otherwise it misleadingly suggests that it's the metrics. Perhaps `~/.cache/huggingface/metrics-user-data`?

And there are so many `.lock` files w/o corresponding files under `~/.cache/huggingface/metrics/`. Why are they there? 

for example after I wipe out the dir completely and do one training I end up with:
```
~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock
```
what is that lock file locking when nothing is running? The lock files come from an issue with filelock (see comment in the code [here](https://github.com/benediktschmitt/py-filelock/blob/master/filelock.py#L394-L398)). Basically on unix there're always .lock files left behind. I haven't dove into this issue are you sure you need an external lock file? if it's a single purpose locking in the same scope you can lock the caller `__file__` instead, e.g. here is how one can `flock` the script file itself to ensure atomic printing:

```
import fcntl
def printflock(*msgs):
    """ print in multiprocess env so that the outputs from different processes don't get interleaved """
    with open(__file__, "r") as fh:
        fcntl.flock(fh, fcntl.LOCK_EX)
        try:
            print(*msgs)
        finally:
            fcntl.flock(fh, fcntl.LOCK_UN)
```
 OK, this issue is not about caching but some internal conflict/race condition it seems, I have just run into it on my normal env:

```
Traceback (most recent call last):
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 356, in _finalize
    self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths]))
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py", line 236, in read_files
    pa_table = self._read_files(files, in_memory=in_memory)
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py", line 171, in _read_files
    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py", line 322, in read_table
    stream = stream_from(filename)
  File "pyarrow/io.pxi", line 782, in pyarrow.lib.memory_map
  File "pyarrow/io.pxi", line 743, in pyarrow.lib.MemoryMappedFile._open
  File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "examples/seq2seq/run_seq2seq.py", line 655, in <module>
    main()
  File "examples/seq2seq/run_seq2seq.py", line 619, in main
    test_results = trainer.predict(
  File "/mnt/nvme1/code/huggingface/transformers-master/src/transformers/trainer_seq2seq.py", line 121, in predict
    return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
  File "/mnt/nvme1/code/huggingface/transformers-master/src/transformers/trainer.py", line 1706, in predict
    output = self.prediction_loop(
  File "/mnt/nvme1/code/huggingface/transformers-master/src/transformers/trainer.py", line 1813, in prediction_loop
    metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))
  File "examples/seq2seq/run_seq2seq.py", line 556, in compute_metrics
    result = metric.compute(predictions=decoded_preds, references=decoded_labels)
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 388, in compute
    self._finalize()
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 358, in _finalize
    raise ValueError(
ValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.
```

I'm just running `run_seq2seq.py` under DeepSpeed:

```
export BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 deepspeed --num_gpus=2 examples/seq2seq/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps  --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000  --sortish_sampler --task translation_en_to_ro  --val_max_target_length 128 --warmup_steps 500 --max_train_samples 100 --max_val_samples 100 --max_test_samples 100 --dataset_name wmt16 --dataset_config ro-en  --source_prefix "translate English to Romanian: " --deepspeed examples/tests/deepspeed/ds_config.json
```

It finished the evaluation OK and crashed on the prediction part of the Trainer. But the eval / predict parts no longer run under Deepspeed, it's just plain ddp.

Is this some kind of race condition? It happens intermittently - there is nothing else running at the same time.

But if 2 independent instances  of the same script were to run at the same time it's clear to see that this problem would happen. Perhaps it'd help to create a unique hash which is shared between all processes in the group and use that as the default experiment id?
 When you're using metrics in a distributed setup, there are two cases:
1. you're doing two completely different experiments (two evaluations) and the 2 metrics jobs have nothing to do with each other
2. you're doing one experiment (one evaluation) but use multiple processes to feed the data to the metric.

In case 1. you just need to provide two different `experiment_id` so that the metrics don't collide.
In case 2. they must have the same experiment_id (or use the default one), but in this case you also need to provide the `num_processes` and `process_id`

If understand correctly you're in situation 2.

If so, you make sure that you instantiate the metrics with both the right `num_processes` and `process_id` parameters ?

If they're not set, then the cache files of the two metrics collide it can cause issues. For example if one metric finishes before the other, then the cache file is deleted and the other metric gets a FileNotFoundError
There's more information in the [documentation](https://huggingface.co/docs/datasets/loading_metrics.html#distributed-setups) if you want

Hope that helps ! Thank you for explaining that in a great way, @lhoestq 

So the bottom line is that the `transformers` examples are broken since they don't do any of that. At least `run_seq2seq.py` just does `metric = load_metric(metric_name)`

What test would you recommend to reliably reproduce this bug in `examples/seq2seq/run_seq2seq.py`? To give more context, we are just using the metrics for the `comput_metric` function and nothing else. Is there something else we can use that just applies the function to the full arrays of predictions and labels? Because that's all we need, all the gathering has already been done because the datasets Metric multiprocessing relies on file storage and thus does not work in a multi-node distributed setup (whereas the Trainer does).

Otherwise, we'll have to switch to something else to compute the metrics :-( OK, it definitely leads to a race condition in how it's used right now. Here is how you can reproduce it - by injecting a random  sleep time different for each process before the locks are acquired. 
```
--- a/src/datasets/metric.py
+++ b/src/datasets/metric.py
@@ -348,6 +348,16 @@ class Metric(MetricInfoMixin):

         elif self.process_id == 0:
             # Let's acquire a lock on each node files to be sure they are finished writing
+
+            import time
+            import random
+            import os
+            pid = os.getpid()
+            random.seed(pid)
+            secs = random.randint(1, 15)
+            time.sleep(secs)
+            print(f"sleeping {secs}")
+
             file_paths, filelocks = self._get_all_cache_files()

             # Read the predictions and references
@@ -385,7 +395,10 @@ class Metric(MetricInfoMixin):

         if predictions is not None:
             self.add_batch(predictions=predictions, references=references)
+        print("FINALIZE START")
+
         self._finalize()
+        print("FINALIZE END")

         self.cache_file_name = None
         self.filelock = None
```

then run with 2 procs: `python -m torch.distributed.launch --nproc_per_node=2`
```
export BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 examples/seq2seq/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps  --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000  --sortish_sampler --task translation_en_to_ro  --val_max_target_length 128 --warmup_steps 500 --max_train_samples 10 --max_val_samples 10 --max_test_samples 10  --dataset_name wmt16 --dataset_config ro-en --source_prefix "translate English to Romanian: "
```

```
***** Running Evaluation *****
  Num examples = 10
  Batch size = 16
  0%|                                                                                                                                      | 0/1 [00:00<?, ?it/s]FINALIZE START
FINALIZE START
sleeping 11
FINALIZE END
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:11<00:00, 11.06s/it]
sleeping 11
Traceback (most recent call last):
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 368, in _finalize
    self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths]))
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py", line 236, in read_files
    pa_table = self._read_files(files, in_memory=in_memory)
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py", line 171, in _read_files
    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py", line 322, in read_table
    stream = stream_from(filename)
  File "pyarrow/io.pxi", line 782, in pyarrow.lib.memory_map
  File "pyarrow/io.pxi", line 743, in pyarrow.lib.MemoryMappedFile._open
  File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "examples/seq2seq/run_seq2seq.py", line 645, in <module>
    main()
  File "examples/seq2seq/run_seq2seq.py", line 601, in main
    metrics = trainer.evaluate(
  File "/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer_seq2seq.py", line 74, in evaluate
    return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
  File "/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py", line 1703, in evaluate
    output = self.prediction_loop(
  File "/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py", line 1876, in prediction_loop
    metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))
  File "examples/seq2seq/run_seq2seq.py", line 556, in compute_metrics
    result = metric.compute(predictions=decoded_preds, references=decoded_labels)
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 402, in compute
    self._finalize()
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 370, in _finalize
    raise ValueError(
ValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.
``` I tried to adjust `run_seq2seq.py` and trainer to use the suggested dist env:
```
    import torch.distributed as dist
    metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())
```
and in `trainer.py` added to call just for rank 0:
```
        if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:
            metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))
```
and then the process hangs in a deadlock. 

Here is the tb:
```
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/filelock.py", line 275 in acquire
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 306 in _check_all_processes_locks
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 501 in _init_writer
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 440 in add_batch
  File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py", line 397 in compute
  File "examples/seq2seq/run_seq2seq.py", line 558 in compute_metrics
  File "/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py", line 1876 in prediction_loop
  File "/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py", line 1703 in evaluate
  File "/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer_seq2seq.py", line 74 in evaluate
  File "examples/seq2seq/run_seq2seq.py", line 603 in main
  File "examples/seq2seq/run_seq2seq.py", line 651 in <module>
```

But this sounds right, since in the above diff I set up a distributed metric and only called one process - so it's blocking on waiting for other processes to do the same.

So one working solution is to leave:

```
    metric = load_metric(metric_name)
```
alone, and only call `compute_metrics` from rank 0
```
        if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:
            metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))
```

so we now no longer use the distributed env as far as `datasets` is concerned, it's just a single process.

Are there any repercussions/side-effects to this proposed change in Trainer?  If it always gathers all inputs on rank 0 then this is how it should have been done in first place - i.e. only run for rank 0. It appears that currently it was re-calculating the metrics on all processes on the same data just to throw the results away other than for rank 0. Unless I missed something.
 But no, since 
`
 metric = load_metric(metric_name)
`
is called for each process, the race condition is still there. So still getting:

```
ValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.
```

i.e. the only way to fix this is to `load_metric` only for rank 0, but this requires huge changes in the code and all end users' code.
 OK, here is a workaround that works. The onus here is absolutely on the user:

```
diff --git a/examples/seq2seq/run_seq2seq.py b/examples/seq2seq/run_seq2seq.py
index 2a060dac5..c82fd83ea 100755
--- a/examples/seq2seq/run_seq2seq.py
+++ b/examples/seq2seq/run_seq2seq.py
@@ -520,7 +520,11 @@ def main():

     # Metric
     metric_name = "rouge" if data_args.task.startswith("summarization") else "sacrebleu"
-    metric = load_metric(metric_name)
+    import torch.distributed as dist
+    if dist.is_initialized():
+        metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())
+    else:
+        metric = load_metric(metric_name)

     def postprocess_text(preds, labels):
         preds = [pred.strip() for pred in preds]
@@ -548,12 +552,17 @@ def main():
         # Some simple post-processing
         decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)

+        kwargs = dict(predictions=decoded_preds, references=decoded_labels)
+        if metric_name == "rouge":
+            kwargs.update(use_stemmer=True)
+        result = metric.compute(**kwargs) # must call for all processes
+        if result is None: # only process with rank-0 will return metrics, others None
+            return {}
+
         if metric_name == "rouge":
-            result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
             # Extract a few results from ROUGE
             result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
         else:
-            result = metric.compute(predictions=decoded_preds, references=decoded_labels)
             result = {"bleu": result["score"]}

         prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
```

This is not user-friendly to say the least. And it's still wasteful as we don't need other processes to do anything.

But it solves the current race condition.

Clearly this calls for a design discussion as it's the responsibility of the Trainer to handle this and not user's. Perhaps in the `transformers` land? I don't see how this could be the responsibility of `Trainer`, who hasn't the faintest idea of what a `datasets.Metric` is. The trainer takes a function `compute_metrics` that goes from predictions + labels to metric results, there is nothing there. That computation is done on all processes  

The fact a `datasets.Metric` object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in `datasets`. Especially since, as I mentioned before, the multiprocessing part of `datasets.Metric` has a deep flaw since it can't work in a multinode environment. So you actually need to do the job of gather predictions and labels yourself.

The changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels `number_of_processes` times I believe, which is not going to make the metric computation any faster.

 Right, to clarify, I meant it'd be good to have it sorted on the library side and not requiring the user to figure it out. This is too complex and error-prone and if not coded correctly the bug will be intermittent which is even worse.

Oh I guess I wasn't clear in my message - in no way I'm proposing that we use this workaround code - I was just showing what I had to do to make it work.

We are on the same page.

> The changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels number_of_processes times I believe, which is not going to make the metric computation any faster.

And yes, this is another problem that my workaround introduces. Thank you for pointing it out, @sgugger 
 > The fact a datasets.Metric object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in datasets

Yes totally, this use case is supposed to be supported by `datasets`. And in this case there shouldn't be any collision between the metrics. I'm looking into it :)
My guess is that at one point the metric isn't using the right file name. It's supposed to use one with a unique uuid in order to avoid the collisions. I just opened #1966 to fix this :)
@stas00 if have a chance feel free to try it ! Thank you, @lhoestq - I will experiment and report back. 

edit: It works! Thank you
the original report was pretty bad and incomplete - my apologies!

Please see the complete version here: https://github.com/huggingface/datasets/issues/1942#issuecomment-786336481

------------

As mentioned here https://github.com/huggingface/datasets/issues/1939 metrics don't get cached, looking at my local `~/.cache/huggingface/metrics` - there are many `*.arrow.lock` files but zero metrics files.

w/o the network I get:
```
FileNotFoundError: [Errno 2] No such file or directory: '~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow
```
there is just `~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock`

I did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.

this is with master.

Thank you.