user stringlengths 3 28 | created_at timestamp[us]date 2020-04-01 09:48:12 2025-07-30 20:59:07 | body stringlengths 1 173k | issue_number int64 1 3.81k | __index_level_0__ int64 0 11.8k |
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jxmorris12 | 2025-06-10T19:32:44 | i have this problem too! Forced me to switch to VLLM. The relevant docs are here: https://huggingface.co/docs/trl/en/grpo_trainer#-option-2-colocate-mode | 3,034 | 61 |
AndreiCComan | 2025-03-10T16:23:51 | @JinyuanSun I had a similar issue in #2856 which has been fixed. Could you try to run the same MRE and the latest changes (i.e., learning rate etc.) I posted there? | 3,031 | 62 |
wyuzh | 2025-03-18T12:10:25 | Same issue.
#2856 is not the same issue, since we want to perform GRPO on a fine-tuned PeftModel, but not perform GRPO together with PEFT. | 3,031 | 63 |
DingZhenChen-code | 2025-03-20T12:51:56 | Same issue. How to continue training on a fine-tuned PeftModel which lora module is not merged.
Maybe resume from ckpt is helpful. | 3,031 | 64 |
cliang-huanglab | 2025-05-26T05:28:19 | Same issue. Have you found a solution? | 3,031 | 65 |
EttoreCaputo | 2025-06-10T09:45:01 | @JinyuanSun After many attempts, this worked for me:
```python
# LOAD THE BASE MODEL AND APPLY THE PREVIOUS FINE-TUNED (SFT in my case) PEFT MODEL
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "BASE MODEL HERE (unsloth in my case)",
max_seq_length = max_seq_length,
load_in_4bit = True,
... | 3,031 | 66 |
qgallouedec | 2025-03-11T14:07:37 | That's a good point.
That's also what's done in open-instruct: https://github.com/allenai/open-instruct/blob/6d5320539f23a6dd55c892fd35e7e86907569af1/open_instruct/grpo_vllm_thread_ray_gtrl.py#L777C9-L777C37
Ideally, we would like to have some curves to show this gap, so if someone has any, feel free to share. | 3,029 | 67 |
HuggingFaceDocBuilderDev | 2025-03-11T15:37:01 | The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/trl/pr_3029). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. | 3,029 | 68 |
deekshaVarshney | 2025-03-07T15:19:09 | @kashif | 3,027 | 69 |
qgallouedec | 2025-03-07T13:48:04 | If I summarize your article, the term KL doesn't seem correct to you because the sampling is done under $π_{\mathrm{old}}$ and not with $π_θ$?
Note that in practice (and this is the default setting), $μ=1$ (implies $π_{\mathrm{old}} = π_{\theta}$), this issue doesn’t arise.
In the general case, we would need to fin... | 3,025 | 70 |
zanghyu | 2025-03-07T16:41:07 | > If I summarize your article, the term KL doesn't seem correct to you because the sampling is done under π old and not with π θ ? Note that in practice (and this is the default setting), μ = 1 (implies π old = π θ ), this issue doesn’t arise. In the general case, we would need to find a way to perform importance sampl... | 3,025 | 71 |
qgallouedec | 2025-03-07T17:59:36 | In the DeepSeek Math paper, they use the same KL term, no? | 3,025 | 72 |
zanghyu | 2025-03-07T18:29:18 | > In the DeepSeek Math paper, they use the same KL term, no?
I got your point. Yeah, they use the same KL term, while the equation in their paper shows that their samples are from the old policy distribution. So the default implementation in this repo is okay ( as being on-policy), but it is hard to say how to impleme... | 3,025 | 73 |
qgallouedec | 2025-03-07T18:46:34 | Maybe with some kind of importance sampling? | 3,025 | 74 |
zanghyu | 2025-03-08T02:39:55 | > Maybe with some kind of importance sampling?
$$\nabla_\theta\mathbb{E}_{\pi_\theta}[\log\pi_\theta - \log\pi_\text{ref}]=\mathbb{E}_{\pi_\theta}[(\log\pi_\theta-\log\pi_\text{ref})\cdot \nabla_\theta \log\pi_\theta$$. So we only need to add the logprob difference between $$\log\pi_\theta$$ and $$\log\pi_\text{ref}$... | 3,025 | 75 |
qgallouedec | 2025-03-07T13:17:11 | Thanks for reporting. The easiest is indeed to turn it off. Another way is to call `LLM.llm_engine.reset_prefix_cache()` (suggested by @hmellor) after the new weights are loaded. If someone wants to try this and if it works, a PR would be welcome | 3,024 | 76 |
thepowerfuldeez | 2025-06-01T14:10:33 | Looks like this is already done: https://github.com/huggingface/trl/blob/7359ddcc6f80aaac7606ac1d9489909b054bbed9/trl/trainer/grpo_trainer.py#L944-L948
Would be nice though to support it by an argument, in cases where system prompt is long and it benefits prefix caching | 3,024 | 77 |
hmellor | 2025-06-03T09:48:39 | The linked code block shows that prefix caching is still enabled by default, but that the prefix cache is reset when the weights change.
@qgallouedec is this issue considered resolved now? | 3,024 | 78 |
qgallouedec | 2025-06-03T14:02:23 | Yes, this one can be closed | 3,024 | 79 |
HuggingFaceDocBuilderDev | 2025-03-07T11:25:59 | The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/trl/pr_3023). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. | 3,023 | 80 |
qgallouedec | 2025-04-10T15:26:41 | The most common approach is to use vLLM now, I'm closing this PR | 3,023 | 81 |
qgallouedec | 2025-03-07T16:25:40 | Can you share some code and results? | 3,021 | 82 |
JosephChenHub | 2025-04-17T10:42:21 | <img width="891" alt="Image" src="https://github.com/user-attachments/assets/88993907-21d6-4e3e-ace6-98889ffffbff" />
We have a similar observation. The above curves show two settings:
- GH200: per device batch size=16, gradient accumulation steps = 2, world size=8, num_generations=8 => batch size = 32
- A100: per de... | 3,021 | 83 |
JosephChenHub | 2025-04-17T11:03:33 | I noticed that the updated version is ```loss = ((per_token_loss * completion_mask).sum(-1) / completion_mask.sum(-1).clamp(min=1.0)).mean()```
maybe it has been resolved. | 3,021 | 84 |
loxs123 | 2025-05-02T02:19:39 | > I noticed that the updated version is `loss = ((per_token_loss * completion_mask).sum(-1) / completion_mask.sum(-1).clamp(min=1.0)).mean()`
>
> maybe it has been resolved.
```python
if self.loss_type == "grpo":
loss = ((per_token_loss * completion_mask).sum(-1) / completion_mask.sum(-1).clamp(min=1.0)).mean()
el... | 3,021 | 85 |
AMindToThink | 2025-03-06T20:44:01 | Here's the problem part of the documentation: [here](https://huggingface.co/docs/trl/en/sft_trainer#:~:text=dataset%20%3D%20load_dataset(%22lucasmccabe%2Dlmi/CodeAlpaca%2D20k%22%2C%20split%3D%22train%22) | 3,019 | 86 |
CloseChoice | 2025-05-05T18:11:35 | This is fixed | 3,019 | 87 |
tchang1997 | 2025-03-10T13:54:09 | +1 — As a hack, I've been getting around this by defining new reward functions and setting `reward_weight` to zero (so it still gets logged, but doesn't affect the "actual" reward). | 3,018 | 88 |
qgallouedec | 2025-03-06T15:53:58 | Let's say that you've 8 GPUs, in the limit you can have `per_device_batch_size=1` and `num_generations=8`. And set the number of gradient accumulation steps to any value.
> Currently `per_device_train_batch_size` must be a multiple of `num_generations` which can severely limit how large you can make it before
That's ... | 3,017 | 89 |
JamesBowerXanda | 2025-03-06T16:19:59 | Ah yes, sorry I forgot about number of devices. Though this doesn't change much right, we just amend my statement to
`num_devices * per_device_train_batch_size * gradient_accumulation_steps ` must be a multiple of `num_generations`.
Is it complicated because currently the prepare_inputs method does both the generatio... | 3,017 | 90 |
qgallouedec | 2025-03-06T18:06:00 |
> Is it complicated because currently the prepare_inputs method does both the generation and score calculation then the inputs are passed straight to the compute_loss method by the Trainer superclass?
Yes that's correct
> I was able to up the number of generations using smaller models and this seemed to be the big ... | 3,017 | 91 |
JamesBowerXanda | 2025-03-07T09:05:00 | Ok, I understand, thanks for your prompt responses.
Unfortunately I am most interested in using this on my personal gpu so I am not using multiple gpu clusters.
Thanks for your time, I am happy for the issue to be closed since it is not deemed feasible. | 3,017 | 92 |
qgallouedec | 2025-03-07T09:11:09 | With 1 GPU, the best you can do is to set `num_generations=per_device_train_batch_size`, and set the `gradient_accumulation_steps` depending on the desired effective batch size. Example:
```
per_device_train_batch_size = 8
num_generations = 8
gradient_accumulation_steps = 16
```
To have an effective batch size of 128 | 3,017 | 93 |
JamesBowerXanda | 2025-03-07T09:28:08 | I understand this but it doesn't solve the issue of the loss function being an estimation based on a sample size of 8.
Based on the GRPO loss formulation the expectation we estimate is conditional on the input prompt as are the... | 3,017 | 94 |
qgallouedec | 2025-03-07T10:20:17 | Then you should increase `num_generations`. By default it's 8, but in the DeepSeek Math paper, they use 64. Of course you'll be probably limited by the compute here if you've only have 1 GPU | 3,017 | 95 |
qgallouedec | 2025-03-07T10:25:20 | > I had just hoped it would be an easier adjustment
In fact, this is tricky, as it would involve sampling, generating and calculating the advantage for the whole batch, then iterating somehow over the batch. It's not impossible, but it adds an implementation complexity that I don't think is justified.
In my experience... | 3,017 | 96 |
JamesBowerXanda | 2025-03-07T11:00:04 | Forgive my naivety but would it not be as simple as overiding the `training_step` method for `GRPOTrainer` from the base `Trainer` one which is:
```
def training_step(
self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]], num_items_in_batch=None
) -> torch.Tensor:
"""
Per... | 3,017 | 97 |
JamesBowerXanda | 2025-03-07T11:04:13 | Sorry, I am not trying to be a pain. As I said previously I am happy for you to close this if it is just a no go. Just thought I would offer the suggestion in case it helped. | 3,017 | 98 |
qgallouedec | 2025-03-07T11:11:11 | It might work, but that's the complexity I want to avoid. Forking the repo might be the best option here. Or subclass `GRPOTrainer` to override the `training_step` method. | 3,017 | 99 |
JamesBowerXanda | 2025-03-07T11:17:52 | Ok, I am happy to do that. I won't bog you down anymore on this. | 3,017 | 100 |
ingambe | 2025-03-16T21:13:30 | Actually, being restricted on the minibatch size by the number of trajectories is very limiting.
Depending on the problem, if the variance is large or the reward is very sparse, 8 iterations will not cut it. | 3,017 | 101 |
jaeminSon | 2025-04-07T06:03:58 | If I understand correctly, per_device_train_batch_size is an integer, which means single GPU should be able to handle a backprop. H100 has roughly 80GB memory and I encountered GPU OOM with Qwen2-7B model. If I'm correct, this could be quite a constraint as bigger models cannot be run. | 3,017 | 102 |
jarrelscy | 2025-04-13T23:44:59 | Hi @JamesBowerXanda I ran into a similar thing as what you had and needed a larger generation batch size. I've implemented something which you can run using this. As mentioned above, I overwrote training_step within GRPOTrainer for this to work.
```
# train_grpo.py
from datasets import load_dataset
from trl import G... | 3,017 | 103 |
skoshx | 2025-03-06T14:47:19 | This is the offending code in `online_dpo_trainer.py`:
```py
def _generate(self, model, prompts):
eos_token_id = self.processing_class.eos_token_id
pad_token_id = self.processing_class.pad_token_id
# Apply chat template and tokenize the input. We do this on-the-fly to enable the use of reward ... | 3,016 | 104 |
skoshx | 2025-03-06T17:08:01 | 🎉 Update:
Quickly reading through the DeepSpeed codebase gave me the understanding that `DeepSpeedZeRoOffload` class automatically registered hooks upon instance creation, so I removed the `optimized_offload._register_hooks_recursively(optimizer_offload.module)` code line (`add_hooks` can completely be disregarded in... | 3,016 | 105 |
qgallouedec | 2025-03-06T17:59:42 | Is it related to #2963? | 3,016 | 106 |
skoshx | 2025-03-06T19:55:55 | The second part is related, but that won't fix the original "AttributeError: 'dict' object has no attribute 'is_encoder_decoder'" error.
Also, I see that PR was merged, but still I'm not convinced it's even needed to call `self._register_deepspeed_module(self.module)`, like they do in that PR, since it gets called aut... | 3,016 | 107 |
qgallouedec | 2025-03-06T08:00:54 | Thanks for reporting, how would you fix that? | 3,015 | 108 |
Boltzmachine | 2025-03-06T19:48:19 | I clamp it for now | 3,015 | 109 |
vagitablebirdcode | 2025-03-14T09:55:29 | I recommend implementing a similar `SoftClip` method in Pytorch as in TensorFlow Probability for truncation, its formula is similar to the following:
This activation function ensures that the output is smooth over the entire def... | 3,015 | 110 |
Alex-HaochenLi | 2025-05-18T10:34:56 | Hi @Boltzmachine, I met the same issue. I am wondering how you set the clamp value? | 3,015 | 111 |
August-murr | 2025-03-06T07:59:50 | @qgallouedec I'm gonna have to ask you to reproduce or at least rerun the code you used to train the https://github.com/huggingface/trl/pull/2873#issuecomment-2663793035 so I can calrify wether the problem is on my side and my script or TRL. | 3,013 | 112 |
AndreiCComan | 2025-03-06T17:47:02 | @August-murr I had a similar issue in #2856 which has been fixed. Could you try to run the same MRE I posted in #2856 and confirm you are facing the same issue? | 3,013 | 113 |
cuiyuhao1996 | 2025-03-18T02:52:42 | I ran into the same problem, even with the latest update. | 3,013 | 114 |
cuiyuhao1996 | 2025-03-18T02:54:40 | Have you solved the problem? :) | 3,013 | 115 |
August-murr | 2025-03-18T12:05:25 | > Have you solved the problem? :)
@qgallouedec said he was working on it
@qgallouedec any updates? | 3,013 | 116 |
Techie5879 | 2025-04-05T06:55:13 | Current setup:
vLLM model running on GPU0, and in another notebook, have set GPU 1 to be only visible (for training). This is from - https://github.com/huggingface/trl/blob/main/docs/source/speeding_up_training.md
```
training_args = GRPOConfig(
output_dir="Llama-3.2-1B-GRPO4",
logging_steps=1,
save_steps... | 3,013 | 117 |
qgallouedec | 2025-04-05T17:52:09 | We usually use a higher learning rate when using peft. Could you try this? | 3,013 | 118 |
Techie5879 | 2025-04-05T18:26:44 | @qgallouedec I've tried about 2e-5 with llama 3.2 1b. Using lora rank 64
Do you recommend something else/going higher? | 3,013 | 119 |
qgallouedec | 2025-03-07T16:36:09 | This can be considered; have you tried implementing it? | 3,010 | 120 |
radna0 | 2025-03-07T16:39:35 | @qgallouedec I’m still experimenting with LMDeploy for inference, so not yet. | 3,010 | 121 |
HuggingFaceDocBuilderDev | 2025-03-11T14:34:17 | The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/trl/pr_3009). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. | 3,009 | 122 |
qgallouedec | 2025-03-22T18:19:41 | ## Benchmark packing
```python
import timeit
import numpy as np
from datasets import Dataset
from trl.data_utils import pack_examples, pack_dataset
# Create a larger dataset with sequence lengths following a gamma distribution
num_samples = 10_000
# Generate sequence lengths following a gamma distribution... | 3,009 | 123 |
qgallouedec | 2025-03-22T18:22:40 | ## Benchmark truncate
```python
import timeit
import numpy as np
from datasets import Dataset
from trl.data_utils import truncate_dataset
def truncate_examples(example, max_length):
return {key: example[key][:max_length] for key in ["input_ids", "attention_mask"]}
# Create a larger dataset with se... | 3,009 | 124 |
qgallouedec | 2025-03-05T17:22:33 | Thanks for reporting. I can't reproduce right now. Can you try to provide the full code with a dataset and a model that allow to reproduce? Also, try downgrading to vLLM 0.7.2 and pull the latests commit from trl. Looking forward to know if it solves the issue. | 3,008 | 125 |
iamansinha | 2025-03-12T08:24:20 | @qgallouedec Thanks for your reply!
[Line 705 of grpo_trainer.py](https://github.com/huggingface/trl/blob/3f0695a4ca6f27bd1b7d0280c71960e7aff0d298/trl/trainer/grpo_trainer.py#L705):
`device = self.accelerator.device` was giving just `"cuda"`.
So, I was able to patch the error by manually setting `device = 'cuda:0'` bef... | 3,008 | 126 |
luckyyangrun | 2025-03-18T06:27:34 | i face the same issue with 2*4090 | 3,008 | 127 |
Vanchrn | 2025-03-22T03:26:38 | same | 3,008 | 128 |
HuggingFaceDocBuilderDev | 2025-03-03T18:28:14 | The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/trl/pr_3003). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. | 3,003 | 129 |
OctoSabercat | 2025-03-03T17:38:34 | @bot /style | 3,002 | 130 |
HelloWorldLTY | 2025-03-03T20:04:50 | Hi, did you try the model and have any ideas? Thanks. | 2,999 | 131 |
tastelikefeet | 2025-03-14T02:43:39 | May be you can try our framework based on trl: https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/full_vllm_qwenvl.sh
We support train a 72B model with 4 A100 GPUs:
https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/train_72b_4gpu.sh | 2,999 | 132 |
Wangbiao2 | 2025-03-15T10:27:54 | > May be you can try our framework based on trl: https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/full_vllm_qwenvl.sh We support train a 72B model with 4 A100 GPUs: https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/train_72b_4gpu.sh
Thank you! | 2,999 | 133 |
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