Memory Efficient GRPO (#1773)
* Update __init__.py * Update loader.py * Update rl.py * Update rl.py * Update _utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Better TRL handling * Update rl.py * Update tokenizer_utils.py * Auto patching * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update rl.py * Update tokenizer_utils.py * Update rl.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update tokenizer_utils.py * Update rl.py * Update rl.py * Update rl.py * max seq length * Update rl.py * Update rl.py * Patching * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * NEFTune * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Extra replacements * Update rl_replacements.py * Update rl.py * extra RL replacements * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update llama.py * Update rl_replacements.py * Update _utils.py * Update loader_utils.py * Update rl.py * Update rl_replacements.py * Update rl_replacements.py * Update rl.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * autocast * Update rl_replacements.py * Update llama.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update llama.py * Update rl_replacements.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update rl_replacements.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update pyproject.toml * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update llama.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update llama.py * Update _utils.py * Update llama.py * Update _utils.py * Update rl_replacements.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update rl_replacements.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * GRPO optimized * Update rl.py * Update rl_replacements.py * Update rl_replacements.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Selective Log softmax * Fix GRPO bsz * Update rl.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Fix TRL * Metrics GRPO * Update rl_replacements.py * Update rl_replacements.py * No compile * Update rl.py * Remove docs * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl_replacements.py * Update rl.py * Update rl.py * Update rl_replacements.py * Update rl_replacements.py * llama-quantize on WINDOWS WSL error fix - edit save.py (gguf saving breaks) (#1649) * edit save.py to fix gguf saving breaks. * add check for .exe or not exe file extension for linux and windows * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update llama.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update llama.py * Update llama.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl.py * Update rl.py * Update rl_replacements.py * Update rl.py * Update rl.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * unsloth_num_chunks * Update rl.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl_replacements.py * Update rl_replacements.py * Update rl_replacements.py (#1754) Fix typo in comment: know -> now. This was printed when running the Llama3.1_(8B)-GRPO.ipynb example notebook, so I'd expect others to run into it as well. * Optional logits * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * Update rl_replacements.py * Update rl.py * Update rl.py * Update rl.py * Update rl.py * fix an import error (#1767) * fix an import error * Delete .gitignore * Update loader.py * Update save.py --------- Co-authored-by: Daniel Han <danielhanchen@gmail.com> * SamplingParams * Convert mask to float (#1762) * [Windows Support] Add latest `xformers` wheels to pyproject.toml (#1753) * Add latest xformers * Add a couple of lines to docs * vLLMSamplingParams * Update __init__.py * default num_chunks == -1 * Versioning --------- Co-authored-by: Gennadii Manzhos <105049664+everythingisc00l@users.noreply.github.com> Co-authored-by: Seth Weidman <seth@sethweidman.com> Co-authored-by: Nino Risteski <95188570+NinoRisteski@users.noreply.github.com> Co-authored-by: Edd <68678137+Erland366@users.noreply.github.com> Co-authored-by: Ben <6579034+versipellis@users.noreply.github.com>
This commit is contained in:
Daniel Han
GitHub
parent
25988683f8
commit
7b6cd79c2f
11 changed files with 206 additions and 101 deletions
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@ -193,7 +193,7 @@ print(f'pip install --upgrade pip && pip install "unsloth[{x}] @ git+https://git
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### Windows Installation
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To run Unsloth directly on Windows:
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- Install Triton from this Windows fork and follow the instructions: https://github.com/woct0rdho/triton-windows
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- Install Triton from this Windows fork and follow the instructions: https://github.com/woct0rdho/triton-windows (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12)
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- In the SFTTrainer, set `dataset_num_proc=1` to avoid a crashing issue:
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```python
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trainer = SFTTrainer(
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@ -202,12 +202,15 @@ trainer = SFTTrainer(
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)
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```
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### Advanced/Troubleshooting
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For **advanced installation instructions** or if you see weird errors during installations:
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1. Install `torch` and `triton`. Go to https://pytorch.org to install it. For example `pip install torch torchvision torchaudio triton`
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2. Confirm if CUDA is installated correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers.
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3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succeeded with `python -m xformers.info` Go to https://github.com/facebookresearch/xformers. Another option is to install `flash-attn` for Ampere GPUs.
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4. Finally, install `bitsandbytes` and check it with `python -m bitsandbytes`
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4. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix) may be useful.
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5. Finally, install `bitsandbytes` and check it with `python -m bitsandbytes`
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## 📜 [Documentation](https://docs.unsloth.ai)
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- Go to our official [Documentation](https://docs.unsloth.ai) for saving to GGUF, checkpointing, evaluation and more!
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@ -39,7 +39,7 @@ triton = [
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"triton @ https://github.com/woct0rdho/triton-windows/releases/download/v3.1.0-windows.post5/triton-3.1.0-cp312-cp312-win_amd64.whl ; python_version=='3.12' and platform_system == 'Windows'",
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]
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huggingface = [
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"unsloth_zoo>=2025.2.5",
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"unsloth_zoo>=2025.2.6",
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"packaging",
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"tyro",
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"transformers>=4.46.1,!=4.47.0",
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@ -196,6 +196,10 @@ cu126onlytorch260 = [
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"xformers @ https://download.pytorch.org/whl/cu126/xformers-0.0.29.post3-cp310-cp310-manylinux_2_28_x86_64.whl ; python_version=='3.10' and platform_system == 'Linux'",
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"xformers @ https://download.pytorch.org/whl/cu126/xformers-0.0.29.post3-cp311-cp311-manylinux_2_28_x86_64.whl ; python_version=='3.11' and platform_system == 'Linux'",
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"xformers @ https://download.pytorch.org/whl/cu126/xformers-0.0.29.post3-cp312-cp312-manylinux_2_28_x86_64.whl ; python_version=='3.12' and platform_system == 'Linux'",
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"xformers @ https://download.pytorch.org/whl/cu126/xformers-0.0.29.post3-cp39-cp39-win_amd64.whl ; python_version=='3.9' and platform_system == 'Windows'",
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"xformers @ https://download.pytorch.org/whl/cu126/xformers-0.0.29.post3-cp310-cp310-win_amd64.whl ; python_version=='3.10' and platform_system == 'Windows'",
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"xformers @ https://download.pytorch.org/whl/cu126/xformers-0.0.29.post3-cp311-cp311-win_amd64.whl ; python_version=='3.11' and platform_system == 'Windows'",
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"xformers @ https://download.pytorch.org/whl/cu126/xformers-0.0.29.post3-cp312-cp312-win_amd64.whl ; python_version=='3.12' and platform_system == 'Windows'",
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]
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cu118 = [
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"unsloth[huggingface]",
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@ -344,7 +348,7 @@ colab-ampere-torch220 = [
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"flash-attn>=2.6.3",
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]
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colab-new = [
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"unsloth_zoo>=2025.2.5",
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"unsloth_zoo>=2025.2.6",
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"packaging",
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"tyro",
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"transformers>=4.46.1,!=4.47.0",
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@ -196,7 +196,7 @@ pass
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# Check for unsloth_zoo
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try:
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unsloth_zoo_version = importlib_version("unsloth_zoo")
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if Version(unsloth_zoo_version) < Version("2025.2.4"):
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if Version(unsloth_zoo_version) < Version("2025.2.6"):
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try:
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os.system("pip install --upgrade --no-cache-dir --no-deps unsloth_zoo")
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except:
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@ -20,4 +20,4 @@ from .mistral import FastMistralModel
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from .qwen2 import FastQwen2Model
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from .dpo import PatchDPOTrainer, PatchKTOTrainer
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from ._utils import is_bfloat16_supported
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from .rl import PatchFastRL
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from .rl import PatchFastRL, vLLMSamplingParams
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@ -12,7 +12,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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__version__ = "2025.2.12"
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__version__ = "2025.2.13"
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__all__ = [
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"SUPPORTS_BFLOAT16",
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@ -700,6 +700,7 @@ def LlamaModel_fast_forward(
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elif inputs_requires_grad:
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inputs_embeds.requires_grad_(False)
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pass
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attention_mask = attention_mask[:,:self.max_seq_length] # Must resize!
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inputs_embeds *= attention_mask.unsqueeze(0).transpose(0, 1).transpose(1, 2)
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if inputs_requires_grad: inputs_embeds.requires_grad_(True)
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pass
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@ -774,9 +775,12 @@ def LlamaModel_fast_forward(
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self.SWA_mask = True
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self.GA_mask = False
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elif attention_mask is not None:
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# Fixes https://github.com/unslothai/unsloth/issues/853
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# Unsloth needs a 2D mask, not a [2, 1, n, n] mask!
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# https://github.com/pytorch/pytorch/issues/103749
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# Need to convert to float and not using bool
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attention_mask = (1.0 - attention_mask.float()) * torch.finfo(inputs_embeds.dtype).min
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dynamic_SWA_mask = _prepare_4d_causal_attention_mask_for_sdpa(
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attention_mask,
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(batch_size, seq_length),
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@ -1030,6 +1034,7 @@ def CausalLM_fast_forward(fast_forward_inference):
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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num_logits_to_keep: Optional[int] = 0,
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logits_to_keep: Optional[int] = 0,
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*args, **kwargs,
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) -> Union[Tuple, CausalLMOutputWithPast]:
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@ -1053,16 +1058,16 @@ def CausalLM_fast_forward(fast_forward_inference):
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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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self.model._has_no_labels = labels is None
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outputs = self.model(
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input_ids=input_ids,
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causal_mask=causal_mask,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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input_ids = input_ids,
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causal_mask = causal_mask,
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attention_mask = attention_mask,
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position_ids = position_ids,
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past_key_values = past_key_values,
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inputs_embeds = inputs_embeds,
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use_cache = use_cache,
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output_attentions = output_attentions,
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output_hidden_states = output_hidden_states,
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return_dict = return_dict,
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)
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pass
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hidden_states = outputs[0]
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@ -1072,6 +1077,20 @@ def CausalLM_fast_forward(fast_forward_inference):
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logit_softcapping = getattr(self.config, "final_logit_softcapping", 0)
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logit_scaling = getattr(self.config, "logit_scale", 0)
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dtype = lm_head.dtype
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num_logits_to_keep = max(num_logits_to_keep, logits_to_keep)
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# Output last hidden states without logits if asked
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if os.environ.get("UNSLOTH_RETURN_HIDDEN_STATES", "0") == "1":
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if num_logits_to_keep != 0:
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hidden_states = hidden_states[:, -num_logits_to_keep:, :]
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return CausalLMOutputWithPast(
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loss = None,
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logits = hidden_states,
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past_key_values = outputs.past_key_values,
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hidden_states = outputs.hidden_states,
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attentions= outputs.attentions,
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)
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pass
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if bsz == 1 and q_len == 1:
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logits = torch.mv(lm_head, hidden_states.ravel().to(dtype))
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@ -1166,11 +1185,11 @@ def CausalLM_fast_forward(fast_forward_inference):
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return (loss,) + output if loss is not None else output
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return CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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loss = loss,
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logits = logits,
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past_key_values = outputs.past_key_values,
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hidden_states = outputs.hidden_states,
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attentions= outputs.attentions,
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)
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pass
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return _CausalLM_fast_forward
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@ -1180,28 +1199,30 @@ pass
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@torch._disable_dynamo
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def PeftModelForCausalLM_fast_forward(
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self,
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input_ids=None,
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causal_mask=None,
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attention_mask=None,
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inputs_embeds=None,
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labels=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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task_ids=None,
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num_logits_to_keep=0,
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input_ids = None,
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causal_mask = None,
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attention_mask = None,
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inputs_embeds = None,
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labels = None,
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output_attentions = None,
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output_hidden_states = None,
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return_dict = None,
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task_ids = None,
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num_logits_to_keep = 0,
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logits_to_keep = 0,
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**kwargs,
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):
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return self.base_model(
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input_ids=input_ids,
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causal_mask=causal_mask,
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attention_mask=attention_mask,
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inputs_embeds=inputs_embeds,
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labels=labels,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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num_logits_to_keep=num_logits_to_keep,
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input_ids = input_ids,
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causal_mask = causal_mask,
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attention_mask = attention_mask,
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inputs_embeds = inputs_embeds,
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labels = labels,
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output_attentions = output_attentions,
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output_hidden_states = output_hidden_states,
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return_dict = return_dict,
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num_logits_to_keep = num_logits_to_keep,
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logits_to_keep = logits_to_keep,
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**kwargs,
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)
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pass
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@ -1694,9 +1715,9 @@ class FastLlamaModel:
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elif dtype == torch.bfloat16 and not SUPPORTS_BFLOAT16:
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logger.warning_once("Device does not support bfloat16. Will change to float16.")
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dtype = torch.float16
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elif dtype == torch.float16 and SUPPORTS_BFLOAT16:
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logger.warning_once("Device supports bfloat16 but you selected float16. Will change to bfloat16.")
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dtype = torch.bfloat16
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# elif dtype == torch.float16 and SUPPORTS_BFLOAT16:
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# logger.warning_once("Device supports bfloat16 but you selected float16. Will change to bfloat16.")
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# dtype = torch.bfloat16
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assert(dtype == torch.float16 or dtype == torch.bfloat16 or dtype == torch.float32)
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@ -24,10 +24,14 @@ from peft import PeftConfig, PeftModel
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from .loader_utils import get_model_name
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import os, contextlib, sys
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try:
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from huggingface_hub.utils import get_token
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from huggingface_hub import get_token
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except:
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# Old HF Hub versions <= 0.0.25
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from huggingface_hub.utils._token import get_token
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try:
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from huggingface_hub.utils import get_token
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except:
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# For older versions of huggingface_hub
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from huggingface_hub.utils._token import get_token
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pass
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pass
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from huggingface_hub import HfFileSystem
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import importlib.util
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@ -601,11 +601,6 @@ __INT_TO_FLOAT_MAPPER = \
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"Qwen/Qwen2.5-VL-72B-Instruct",
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"unsloth/Qwen2.5-VL-72B-Instruct-bnb-4bit",
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),
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"unsloth/DeepHermes-3-Llama-3-8B-Preview-unsloth-bnb-4bit" : (
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"unsloth/DeepHermes-3-Llama-3-8B-Preview",
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"NousResearch/DeepHermes-3-Llama-3-8B-Preview",
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"unsloth/DeepHermes-3-Llama-3-8B-Preview-bnb-4bit",
|
||||
),
|
||||
"unsloth/DeepScaleR-1.5B-Preview-unsloth-bnb-4bit" : (
|
||||
"unsloth/DeepHermes-3-Llama-3-8B-Preview",
|
||||
"agentica-org/DeepScaleR-1.5B-Preview",
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@
|
|||
|
||||
__all__ = [
|
||||
"PatchFastRL",
|
||||
"vLLMSamplingParams",
|
||||
]
|
||||
|
||||
import torch
|
||||
|
|
@ -36,12 +37,20 @@ selective_log_softmax = RL_REPLACEMENTS["selective_log_softmax"]
|
|||
|
||||
torch_compile_options = {
|
||||
"epilogue_fusion" : True,
|
||||
"max_autotune" : True,
|
||||
"max_autotune" : False, # Disable Triton mm kernels
|
||||
"shape_padding" : True,
|
||||
"trace.enabled" : False,
|
||||
"triton.cudagraphs" : False,
|
||||
}
|
||||
|
||||
|
||||
def vLLMSamplingParams(**kwargs):
|
||||
from vllm import SamplingParams
|
||||
sampling_params = SamplingParams(**kwargs)
|
||||
sampling_params._set_kwargs = kwargs
|
||||
return sampling_params
|
||||
pass
|
||||
|
||||
def PatchRL(FastLanguageModel):
|
||||
|
||||
from trl.models.utils import unwrap_model_for_generation
|
||||
|
|
@ -94,11 +103,12 @@ from typing import *
|
|||
from dataclasses import dataclass, field
|
||||
from packaging.version import Version
|
||||
import torch
|
||||
import numpy as np
|
||||
from contextlib import nullcontext
|
||||
from torch.nn import functional as F
|
||||
torch_compile_options = {{
|
||||
"epilogue_fusion" : True,
|
||||
"max_autotune" : True,
|
||||
"max_autotune" : False,
|
||||
"shape_padding" : True,
|
||||
"trace.enabled" : False,
|
||||
"triton.cudagraphs" : False,
|
||||
|
|
@ -112,16 +122,24 @@ class Unsloth{RLConfig_name}({RLConfig_name}):
|
|||
"""
|
||||
{__RLConfig_doc__}
|
||||
"""
|
||||
sampling_params: Optional[Any] = field(
|
||||
vllm_sampling_params: Optional[Any] = field(
|
||||
default = None,
|
||||
metadata = {{'help': 'vLLM SamplingParams'}},
|
||||
)
|
||||
unsloth_num_chunks : Optional[int] = field(
|
||||
default = -1,
|
||||
metadata = {{'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}},
|
||||
)
|
||||
def __init__({RLConfig_arguments},
|
||||
sampling_params = None,
|
||||
vllm_sampling_params = None,
|
||||
unsloth_num_chunks = -1,
|
||||
**kwargs,
|
||||
):
|
||||
{RLConfig_extra_args}
|
||||
super().__init__({RLConfig_call_args}{RLConfig_kwargs})
|
||||
assert(hasattr(vllm_sampling_params, '_set_kwargs'))
|
||||
self.vllm_sampling_params = vllm_sampling_params
|
||||
self.unsloth_num_chunks = unsloth_num_chunks
|
||||
pass
|
||||
|
||||
{RLTrainer_extras}
|
||||
|
|
@ -422,7 +440,9 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
|
|||
# Create full module
|
||||
exec(f"from trl.trainer import ({RLTrainer_name}, {RLConfig_name},)")
|
||||
__RLTrainer_doc__ = eval(f"trl.trainer.{RLTrainer_name}").__doc__
|
||||
if __RLTrainer_doc__ is None: __RLTrainer_doc__ = ""
|
||||
__RLConfig_doc__ = eval(f"trl.trainer.{RLConfig_name}") .__doc__
|
||||
if __RLConfig_doc__ is None: __RLConfig_doc__ = ""
|
||||
|
||||
# Get all pre-modules
|
||||
if trainer_file in RL_PRE_ITEMS:
|
||||
|
|
@ -431,6 +451,11 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
|
|||
RL_pre = ""
|
||||
pass
|
||||
|
||||
# Check if SamplingParams is in there
|
||||
if "SamplingParams" in old_RLTrainer_source:
|
||||
RL_pre = RL_pre + "\n" + inspect.getsource(vLLMSamplingParams)
|
||||
pass
|
||||
|
||||
# Selective log softmax
|
||||
selective_log_softmax_code = inspect.getsource(selective_log_softmax)
|
||||
|
||||
|
|
@ -457,6 +482,14 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
|
|||
selective_log_softmax_code = selective_log_softmax_code,
|
||||
)
|
||||
|
||||
# Remove multiple doc strings
|
||||
if __RLConfig_doc__ != "" and RLTrainer_source.count(__RLTrainer_doc__) == 2:
|
||||
RLTrainer_source = RLTrainer_source.replace(__RLTrainer_doc__, "", 1)
|
||||
pass
|
||||
|
||||
# Remove multiple newlines
|
||||
RLTrainer_source = re.sub(r"[\n]{3,}", "\n", RLTrainer_source)
|
||||
|
||||
# Create new function
|
||||
created_module = create_new_function(
|
||||
f"Unsloth{RLTrainer_name}",
|
||||
|
|
@ -501,7 +534,7 @@ def patch_functions(RLTrainer, trainer_file, RLTrainer_name, all_imports, import
|
|||
replacer = replacer[0]
|
||||
vllm_setter = "\n" + " "*8 + \
|
||||
"if hasattr(model, 'vllm_engine') and "\
|
||||
"getattr(args, 'use_vllm') and getattr(args, 'use_vllm', False): "\
|
||||
"hasattr(args, 'use_vllm') and (getattr(args, 'use_vllm', False) == False): "\
|
||||
"args.use_vllm = True\n"
|
||||
init = init.replace(replacer, replacer + vllm_setter)
|
||||
pass
|
||||
|
|
@ -529,14 +562,31 @@ def patch_functions(RLTrainer, trainer_file, RLTrainer_name, all_imports, import
|
|||
)
|
||||
if len(sampling_params) == 1:
|
||||
sampling_params = sampling_params[0]
|
||||
|
||||
# Fix guided_decoding
|
||||
sampling_params = sampling_params.replace(
|
||||
"guided_decoding=guided_decoding,",
|
||||
'guided_decoding='\
|
||||
'GuidedDecodingParams(backend="outlines", regex=args.vllm_guided_decoding_regex) '\
|
||||
'if getattr(args, "vllm_guided_decoding_regex", None) is not None else None,',
|
||||
)
|
||||
# Replace with our vLLM engine
|
||||
sampling_params = \
|
||||
" "*12 + "self.llm = model.vllm_engine; self._last_loaded_step = 0; " + \
|
||||
sampling_params # Add spaces
|
||||
|
||||
# Add extra arguments to SamplingParams
|
||||
extra = "**getattr(getattr(args, 'vllm_sampling_params', vLLMSamplingParams()), '_set_kwargs', {})"
|
||||
# Backwards replace
|
||||
to_replace = "," + extra + "," + ")"
|
||||
sampling_params = to_replace.join(sampling_params.rsplit(")", 1))
|
||||
# Strip multiple commas
|
||||
sampling_params = re.sub(r"[\,][\s]{0,}\,", ",", sampling_params)
|
||||
|
||||
new_vllm_part = \
|
||||
f"\n{' '*8}if {args}.use_vllm:\n{sampling_params} "\
|
||||
f"if getattr(args, 'sampling_params', None) is None else "\
|
||||
f"getattr(args, 'sampling_params', None)\n{' '*8}else:\n"
|
||||
f"\n{' '*8}if {args}.use_vllm:\n{sampling_params}"\
|
||||
f"\n{' '*8}else:\n"
|
||||
|
||||
init = init.replace(vllm_part, new_vllm_part)
|
||||
pass
|
||||
pass
|
||||
|
|
@ -607,6 +657,7 @@ def patch_functions(RLTrainer, trainer_file, RLTrainer_name, all_imports, import
|
|||
old, new = changed[function]
|
||||
RLTrainer_source = RLTrainer_source.replace(old, new)
|
||||
pass
|
||||
|
||||
RLTrainer_source = RLTrainer_source.replace(
|
||||
f"class {RLTrainer_name}", f"class _Unsloth{RLTrainer_name}", 1
|
||||
)
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ torch_compile_options = {
|
|||
}
|
||||
|
||||
# Check untrained tokens
|
||||
def sft_trainer_fix_untraiend_tokens(call_args, extra_args):
|
||||
def sft_trainer_fix_untrained_tokens(call_args, extra_args):
|
||||
if "model" in call_args and "train_dataset" in call_args:
|
||||
fix_tokenizer = \
|
||||
"IGNORED_TOKENIZER_NAMES = os.environ.get('UNSLOTH_IGNORED_TOKENIZER_NAMES', '').split('\\n')\n"\
|
||||
|
|
@ -52,7 +52,7 @@ def sft_trainer_fix_untraiend_tokens(call_args, extra_args):
|
|||
return fix_tokenizer
|
||||
return ""
|
||||
pass
|
||||
RL_EXTRA_ARGS["sft_trainer"].append(sft_trainer_fix_untraiend_tokens)
|
||||
RL_EXTRA_ARGS["sft_trainer"].append(sft_trainer_fix_untrained_tokens)
|
||||
|
||||
|
||||
# Remove DPO columns which might randomnly be tokenized
|
||||
|
|
@ -177,6 +177,7 @@ def grpo_trainer__get_per_token_logps(function_name, function):
|
|||
if function_name != "_get_per_token_logps": return function
|
||||
|
||||
def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep):
|
||||
return None # Unsloth efficient GRPO
|
||||
if not hasattr(self, '_autocast_dtype'):
|
||||
self._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16
|
||||
with torch.amp.autocast(device_type = 'cuda', dtype = self._autocast_dtype):
|
||||
|
|
@ -198,8 +199,12 @@ def grpo_trainer__get_per_token_logps(function_name, function):
|
|||
pass
|
||||
RL_FUNCTIONS["grpo_trainer"].append(grpo_trainer__get_per_token_logps)
|
||||
|
||||
grpo_compute_loss = RL_REPLACEMENTS["grpo_compute_loss"]
|
||||
grpo_compute_loss = RL_REPLACEMENTS["grpo_compute_loss"]
|
||||
UnslothEfficientGRPO = RL_REPLACEMENTS["UnslothEfficientGRPO"]
|
||||
grpo_accumulated_loss = RL_REPLACEMENTS["grpo_accumulated_loss"]
|
||||
RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(grpo_compute_loss))
|
||||
RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(UnslothEfficientGRPO))
|
||||
RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(grpo_accumulated_loss))
|
||||
|
||||
# Edit _get_per_token_logps to handle mixed precision
|
||||
def grpo_trainer_compute_loss(function_name, function):
|
||||
|
|
@ -213,10 +218,12 @@ def grpo_trainer_compute_loss(function_name, function):
|
|||
prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
|
||||
completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"]
|
||||
input_ids = torch.cat([prompt_ids, completion_ids], dim=1)
|
||||
bsz, qlen = input_ids.shape
|
||||
# attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
|
||||
attention_mask = None
|
||||
logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens
|
||||
|
||||
_input_ids = input_ids
|
||||
_logits_to_keep = logits_to_keep
|
||||
per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
|
||||
|
||||
# Compute the KL divergence between the model and the reference model
|
||||
|
|
@ -229,9 +236,16 @@ def grpo_trainer_compute_loss(function_name, function):
|
|||
# per_token_loss = -(per_token_loss - self.beta * per_token_kl)
|
||||
# loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
|
||||
input_ids = input_ids[:, -logits_to_keep:]
|
||||
loss, completion_length, mean_kl = grpo_compute_loss(
|
||||
ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages,
|
||||
)
|
||||
if False:#per_token_logps is not None:
|
||||
loss, completion_length, mean_kl = grpo_compute_loss(
|
||||
ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages,
|
||||
)
|
||||
else:
|
||||
loss, completion_length, mean_kl = grpo_accumulated_loss(
|
||||
self, _input_ids, logits_to_keep, completion_mask, advantages,
|
||||
n_chunks = self.args.unsloth_num_chunks,
|
||||
)
|
||||
|
||||
# Log the metrics
|
||||
# completion_length = self.accelerator.gather_for_metrics(completion_mask.sum(1)).float().mean().item()
|
||||
self._metrics["completion_length"].append(completion_length.item())
|
||||
|
|
@ -256,7 +270,7 @@ def grpo_trainer_fix_batch_size(RLTrainer_source, RLConfig_source):
|
|||
check_batch_size = \
|
||||
"div = per_device_train_batch_size // num_generations\n"\
|
||||
"if div * num_generations != per_device_train_batch_size:\n"\
|
||||
" print('Unsloth: We know expect `per_device_train_batch_size` to be a multiple of `num_generations`.\\n"\
|
||||
" print('Unsloth: We now expect `per_device_train_batch_size` to be a multiple of `num_generations`.\\n"\
|
||||
"We will change the batch size of ' + str(per_device_train_batch_size) + ' to the `num_generations` of ' + str(num_generations))\n"\
|
||||
" per_device_train_batch_size = num_generations\n"
|
||||
return check_batch_size
|
||||
|
|
|
|||
|
|
@ -31,10 +31,14 @@ from transformers.models.llama.modeling_llama import logger
|
|||
from .tokenizer_utils import fix_sentencepiece_gguf
|
||||
from huggingface_hub import HfApi
|
||||
try:
|
||||
from huggingface_hub.utils import get_token
|
||||
from huggingface_hub import get_token
|
||||
except:
|
||||
# Old HF Hub versions <= 0.0.25
|
||||
from huggingface_hub.utils._token import get_token
|
||||
try:
|
||||
from huggingface_hub.utils import get_token
|
||||
except:
|
||||
# For older versions of huggingface_hub
|
||||
from huggingface_hub.utils._token import get_token
|
||||
pass
|
||||
pass
|
||||
from pathlib import Path
|
||||
|
||||
|
|
@ -254,7 +258,7 @@ def unsloth_save_model(
|
|||
# First check for a token!
|
||||
if push_to_hub:
|
||||
from huggingface_hub import whoami
|
||||
try:
|
||||
try:
|
||||
username = whoami(token = token)["name"]
|
||||
except:
|
||||
raise RuntimeError(
|
||||
|
|
@ -385,7 +389,7 @@ def unsloth_save_model(
|
|||
else:
|
||||
internal_model = model
|
||||
pass
|
||||
|
||||
|
||||
# Cannot be converted properly!
|
||||
if (save_method == "merged_4bit") or (save_method == "lora") or (
|
||||
not hasattr(model, "model") or \
|
||||
|
|
@ -481,7 +485,7 @@ def unsloth_save_model(
|
|||
gb_found = re.match("([0-9]{1,})[\s]{0,}GB", max_shard_size, flags = re.IGNORECASE)
|
||||
mb_found = re.match("([0-9]{1,})[\s]{0,}MB", max_shard_size, flags = re.IGNORECASE)
|
||||
if gb_found: sharded_ram_usage = int(gb_found.group(1)) * 1024 * 1024 * 1024
|
||||
elif mb_found: sharded_ram_usage = int(mb_found.group(1)) * 1024 * 1024
|
||||
elif mb_found: sharded_ram_usage = int(mb_found.group(1)) * 1024 * 1024
|
||||
elif type(max_shard_size) is int:
|
||||
sharded_ram_usage = sharded_ram_usage
|
||||
pass
|
||||
|
|
@ -612,7 +616,7 @@ def unsloth_save_model(
|
|||
# Edit save_pretrained_settings
|
||||
# [TODO] _create_repo has errors due to **kwargs getting accepted
|
||||
save_pretrained_settings["state_dict"] = state_dict
|
||||
|
||||
|
||||
# commit_description does not seem to work?
|
||||
what_to_delete = ("use_temp_dir", "commit_message", "create_pr", "revision", "commit_description", "tags",) \
|
||||
if not push_to_hub else \
|
||||
|
|
@ -665,7 +669,7 @@ def unsloth_save_model(
|
|||
|
||||
# Revert back padding side
|
||||
tokenizer.padding_side = old_padding_side
|
||||
|
||||
|
||||
print(" Done.")
|
||||
else:
|
||||
print()
|
||||
|
|
@ -877,10 +881,15 @@ def install_llama_cpp_old(version = -10):
|
|||
pass
|
||||
|
||||
# Check if successful
|
||||
if not os.path.exists("llama.cpp/quantize") and not os.path.exists("llama.cpp/llama-quantize"):
|
||||
if not (
|
||||
os.path.exists("llama.cpp/llama-quantize.exe") or
|
||||
os.path.exists("llama.cpp/llama-quantize") or
|
||||
os.path.exists("llama.cpp/quantize.exe") or
|
||||
os.path.exists("llama.cpp/quantize")
|
||||
):
|
||||
raise RuntimeError(
|
||||
"Unsloth: The file 'llama.cpp/llama-quantize' or `llama.cpp/quantize` does not exist.\n"\
|
||||
"But we expect this file to exist! Maybe the llama.cpp developers changed the name?"
|
||||
"But we expect this file to exist! Maybe the llama.cpp developers changed the name or check extension of the llama-quantize file."
|
||||
)
|
||||
pass
|
||||
pass
|
||||
|
|
@ -957,7 +966,7 @@ def save_to_gguf(
|
|||
else:
|
||||
raise TypeError("Unsloth: quantization_method can only be a string or a list of strings")
|
||||
pass
|
||||
|
||||
|
||||
# Check if bfloat16 is supported
|
||||
if model_dtype == "bf16" and not torch.cuda.is_bf16_supported():
|
||||
logger.warning(
|
||||
|
|
@ -973,7 +982,7 @@ def save_to_gguf(
|
|||
pass
|
||||
|
||||
# Check I quants
|
||||
for quant_method in quantization_method:
|
||||
for quant_method in quantization_method:
|
||||
if quant_method.startswith("iq2"):
|
||||
raise RuntimeError("Unsloth: Currently iq2 type quantizations aren't supported yet - sorry!")
|
||||
pass
|
||||
|
|
@ -1026,9 +1035,9 @@ def save_to_gguf(
|
|||
pass
|
||||
|
||||
# Determine whether the system already has llama.cpp installed and the scripts are executable
|
||||
quantize_location = get_executable(["llama-quantize", "quantize"])
|
||||
quantize_location = get_executable(["llama-quantize", "quantize", "llama-quantize.exe", "quantize.exe"])
|
||||
convert_location = get_executable(["convert-hf-to-gguf.py", "convert_hf_to_gguf.py"])
|
||||
|
||||
|
||||
error = 0
|
||||
if quantize_location is not None and convert_location is not None:
|
||||
print("Unsloth: llama.cpp found in the system. We shall skip installation.")
|
||||
|
|
@ -1062,14 +1071,18 @@ def save_to_gguf(
|
|||
# and llama.cpp/main changed to llama.cpp/llama-cli
|
||||
# See https://github.com/ggerganov/llama.cpp/pull/7809
|
||||
quantize_location = None
|
||||
if os.path.exists("llama.cpp/quantize"):
|
||||
if os.path.exists("llama.cpp/quantize.exe"):
|
||||
quantize_location = "llama.cpp/quantize.exe"
|
||||
elif os.path.exists("llama.cpp/quantize"):
|
||||
quantize_location = "llama.cpp/quantize"
|
||||
elif os.path.exists("llama.cpp/llama-quantize.exe"):
|
||||
quantize_location = "llama.cpp/llama-quantize.exe"
|
||||
elif os.path.exists("llama.cpp/llama-quantize"):
|
||||
quantize_location = "llama.cpp/llama-quantize"
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Unsloth: The file 'llama.cpp/llama-quantize' or 'llama.cpp/quantize' does not exist.\n"\
|
||||
"But we expect this file to exist! Maybe the llama.cpp developers changed the name?"
|
||||
"Unsloth: The file ('llama.cpp/llama-quantize' or 'llama.cpp/llama-quantize.exe' if you are on Windows WSL) or 'llama.cpp/quantize' does not exist.\n"\
|
||||
"But we expect this file to exist! Maybe the llama.cpp developers changed the name or check extension of the llama-quantize file."
|
||||
)
|
||||
pass
|
||||
|
||||
|
|
@ -1150,7 +1163,7 @@ def save_to_gguf(
|
|||
# Concurrency from https://rentry.org/llama-cpp-conversions#merging-loras-into-a-model
|
||||
|
||||
final_location = str((Path(model_directory) / f"unsloth.{first_conversion.upper()}.gguf").absolute())
|
||||
|
||||
|
||||
print(f"Unsloth: [1] Converting model at {model_directory} into {first_conversion} GGUF format.\n"\
|
||||
f"The output location will be {final_location}\n"\
|
||||
"This might take 3 minutes...")
|
||||
|
|
@ -1217,7 +1230,7 @@ def save_to_gguf(
|
|||
|
||||
command = f"./{quantize_location} {full_precision_location} "\
|
||||
f"{final_location} {quant_method} {n_cpus}"
|
||||
|
||||
|
||||
try_execute([command,], force_complete = True)
|
||||
|
||||
# Check if quantization succeeded!
|
||||
|
|
@ -1378,7 +1391,7 @@ def _determine_username(save_directory, old_username, token):
|
|||
save_directory = save_directory.lstrip("./")
|
||||
if "/" not in save_directory:
|
||||
from huggingface_hub import whoami
|
||||
try:
|
||||
try:
|
||||
username = whoami(token = token)["name"]
|
||||
if type(old_username) is str and username != old_username:
|
||||
username = old_username
|
||||
|
|
@ -1412,7 +1425,7 @@ def create_huggingface_repo(
|
|||
repo_type = "model",
|
||||
exist_ok = False,
|
||||
private = private,
|
||||
)
|
||||
)
|
||||
|
||||
# Create model card
|
||||
from huggingface_hub import ModelCard
|
||||
|
|
@ -1453,7 +1466,7 @@ def upload_to_huggingface(
|
|||
repo_type = "model",
|
||||
exist_ok = False,
|
||||
private = private,
|
||||
)
|
||||
)
|
||||
|
||||
# Create model card
|
||||
from huggingface_hub import ModelCard
|
||||
|
|
@ -1527,7 +1540,7 @@ def fix_tokenizer_bos_token(tokenizer):
|
|||
# Check if BOS added already, then warn
|
||||
fix_bos_token = False
|
||||
chat_template = getattr(tokenizer, "chat_template", None)
|
||||
|
||||
|
||||
if (tokenizer("A").input_ids[0] == getattr(tokenizer, "bos_token_id", None)):
|
||||
if chat_template is not None and \
|
||||
(
|
||||
|
|
@ -1546,7 +1559,7 @@ def fix_tokenizer_bos_token(tokenizer):
|
|||
new_chat_template = re.sub(r"\{[\s]{0,}\{[\s]{0,}bos\_token[\s]{0,}\}[\s]{0,}\}", "", chat_template)
|
||||
# Remove {{bos_token +
|
||||
new_chat_template = re.sub(r"\{[\s]{0,}\{[\s]{0,}bos\_token[\s]{0,}\+[\s]{0,}", "", new_chat_template)
|
||||
|
||||
|
||||
tokenizer.chat_template = new_chat_template
|
||||
|
||||
pass
|
||||
|
|
@ -1580,7 +1593,7 @@ def create_ollama_modelfile(tokenizer, gguf_location):
|
|||
modelfile = modelfile\
|
||||
.replace(FILE_LOCATION_REPLACER, "{__FILE_LOCATION__}")\
|
||||
.replace(EOS_TOKEN_REPLACER, "{__EOS_TOKEN__}")
|
||||
|
||||
|
||||
if "__EOS_TOKEN__" in modelfile:
|
||||
modelfile = modelfile.format(
|
||||
__FILE_LOCATION__ = gguf_location,
|
||||
|
|
@ -1591,7 +1604,7 @@ def create_ollama_modelfile(tokenizer, gguf_location):
|
|||
__FILE_LOCATION__ = gguf_location,
|
||||
)
|
||||
pass
|
||||
|
||||
|
||||
modelfile = modelfile\
|
||||
.replace("⚫@✅#🦥", "{")\
|
||||
.replace("⚡@🦥#⛵", "}")\
|
||||
|
|
@ -1733,7 +1746,7 @@ def unsloth_save_pretrained_gguf(
|
|||
|
||||
# Save to GGUF
|
||||
all_file_locations, want_full_precision = save_to_gguf(
|
||||
model_type, model_dtype, is_sentencepiece_model,
|
||||
model_type, model_dtype, is_sentencepiece_model,
|
||||
new_save_directory, quantization_method, first_conversion, makefile,
|
||||
)
|
||||
|
||||
|
|
@ -1911,7 +1924,7 @@ def unsloth_push_to_hub_gguf(
|
|||
|
||||
# Save to GGUF
|
||||
all_file_locations, want_full_precision = save_to_gguf(
|
||||
model_type, model_dtype, is_sentencepiece_model,
|
||||
model_type, model_dtype, is_sentencepiece_model,
|
||||
new_save_directory, quantization_method, first_conversion, makefile,
|
||||
)
|
||||
|
||||
|
|
@ -1928,7 +1941,7 @@ def unsloth_push_to_hub_gguf(
|
|||
|
||||
# If not needing full precision, skip the first
|
||||
if not want_full_precision: all_file_locations = all_file_locations[1:]
|
||||
|
||||
|
||||
for file_location in all_file_locations:
|
||||
print("Unsloth: Uploading GGUF to Huggingface Hub...")
|
||||
username = upload_to_huggingface(
|
||||
|
|
@ -2044,8 +2057,8 @@ def unsloth_convert_lora_to_ggml_and_push_to_hub(
|
|||
|
||||
def unsloth_convert_lora_to_ggml_and_save_locally(
|
||||
self,
|
||||
save_directory: str, # Added parameter for the folder name
|
||||
tokenizer,
|
||||
save_directory: str, # Added parameter for the folder name
|
||||
tokenizer,
|
||||
temporary_location: str = "_unsloth_temporary_saved_buffers",
|
||||
maximum_memory_usage: float = 0.85,
|
||||
):
|
||||
|
|
@ -2162,7 +2175,7 @@ def unsloth_generic_save_pretrained_merged(
|
|||
tags : List[str] = None,
|
||||
temporary_location : str = "_unsloth_temporary_saved_buffers",
|
||||
maximum_memory_usage : float = 0.75,
|
||||
):
|
||||
):
|
||||
"""
|
||||
Same as .push_to_hub(...) except 4bit weights are auto
|
||||
converted to float16 with as few overhead as possible.
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue