Fix Gemma GGUF (#234)
* Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * revert * revert * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * rope * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * llama * Update llama.py * gemma * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update save.py * RoPE * Update llama.py * Update llama.py * Update llama.py * Update gemma.py * correct_dtype * Update gemma.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Chat Templates * Update README.md * Update README.md * Update llama.py * DoRA * Update _utils.py * Update chat_templates.py * Update llama.py * Hotfix - fix DoRA, Gemma prompt template (#202) (#203) * Update save.py * saving * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update __init__.py * Update save.py * Update save.py * Update save.py * save * trainer * spaces * original * Gemma * Update pyproject.toml * Update mapper.py * Update fast_lora.py * FastGemmaModel * model_type * Update llama.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update llama.py * Update fast_lora.py * Update llama.py * Update llama.py * Update cross_entropy_loss.py * Update llama.py * Update llama.py * gemma * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update fast_lora.py * Update fast_lora.py * Fast CE Loss * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * CE * Update llama.py * Update llama.py * Update cross_entropy_loss.py * Update geglu.py * Update cross_entropy_loss.py * revert * Update llama.py * Update llama.py * norm * Update gemma.py * Update gemma.py * position_ids * Update gemma.py * Update gemma.py * pos * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * revert * revert * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * rope * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * llama * Update llama.py * gemma * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update save.py * RoPE * Update llama.py * Update llama.py * Update llama.py * Update gemma.py * correct_dtype * Update gemma.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Chat Templates * Update README.md * Update README.md * Update llama.py * DoRA * Update _utils.py * Update chat_templates.py * Update pyproject.toml * Small fixes * Update pyproject.toml * Approx gelu * Update geglu.py * Approx gelu * Update llama.py * Update __init__.py * Update __init__.py * Update _utils.py * Update geglu.py * Update gemma.py * Update rms_layernorm.py * Update rms_layernorm.py * Update rms_layernorm.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Fix Gemma merging * Update rms_layernorm.py * Update gemma.py * Update pyproject.toml * Layernorms * Gemma precision * Update gemma.py * sqrt * Update gemma.py * Update save.py * RoPE and Gemma precision * Update rms_layernorm.py * Fix warning * Update chat_templates.py * Update chat_templates.py * Update save.py * Update save.py * Update save.py * Update chat_templates.py * Update llama.py * model_name * Update loader.py * Tokenizer overwritten * Update llama.py * Update llama.py * Update llama.py * Update save.py * Accuracy * Revert * Update save.py * Update fast_lora.py * Update fast_lora.py * Update fast_lora.py * Update fast_lora.py * Update fast_lora.py * Update chat_templates.py * Update save.py * Update save.py * Update llama.py * Update llama.py * Account for DoRA * Update llama.py * Update save.py * GGUF incorrect * Update save.py * Update pyproject.toml * kaggle new * Update pyproject.toml * Update pyproject.toml * upcasting * Fix Colab * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml
This commit is contained in:
Daniel Han
GitHub
parent
32223779c4
commit
2c5c5bb4bb
6 changed files with 92 additions and 36 deletions
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@ -59,7 +59,7 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
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- No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow.
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- Works on **Linux** and **Windows** via WSL.
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- Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
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- Open source trains 5x faster - see [Unsloth Pro](https://unsloth.ai/) for **30x faster training**!
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- Open source trains 5x faster - see [Unsloth Pro](https://unsloth.ai/) for up to **30x faster training**!
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- If you trained a model with 🦥Unsloth, you can use this cool sticker! <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" height="50" align="center" />
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@ -33,17 +33,17 @@ exclude = ["images*"]
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[project.optional-dependencies]
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huggingface = [
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"tyro",
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"transformers>=4.38.2",
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"datasets>=2.16.0",
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"sentencepiece",
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"accelerate>=0.26.1",
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"trl>=0.7.9",
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"peft>=0.7.1",
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"tqdm",
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"psutil",
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"wheel>=0.42.0",
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"numpy",
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"triton",
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"accelerate>=0.26.1",
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"trl>=0.7.9",
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"peft>=0.7.1",
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]
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cu118only = [
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"xformers @ https://download.pytorch.org/whl/cu118/xformers-0.0.22.post7%2Bcu118-cp39-cp39-manylinux2014_x86_64.whl ; python_version=='3.9'",
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@ -128,18 +128,13 @@ cu121-torch220 = [
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kaggle = [
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"unsloth[huggingface]",
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]
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kaggle-new = [
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"unsloth[huggingface]",
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"bitsandbytes",
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]
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conda = [
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"unsloth[huggingface]",
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]
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colab = [
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"unsloth[cu121]",
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]
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colab-ampere = [
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"unsloth[cu121]",
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"packaging",
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"ninja",
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"flash-attn",
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]
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colab-torch211 = [
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"unsloth[huggingface]",
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"bitsandbytes",
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@ -166,6 +161,29 @@ colab-ampere-torch220 = [
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"ninja",
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"flash-attn",
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]
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colab-new = [
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"tyro",
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"transformers>=4.38.2",
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"datasets>=2.16.0",
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"sentencepiece",
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"tqdm",
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"psutil",
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"wheel>=0.42.0",
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"numpy",
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]
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colab-no-deps = [
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"accelerate>=0.26.1",
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"trl>=0.7.9",
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"peft>=0.7.1",
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"xformers",
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"bitsandbytes",
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]
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colab-ampere = [
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"unsloth[colab-ampere-torch220]",
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"packaging",
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"ninja",
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"flash-attn",
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]
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cu118-ampere = [
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"unsloth[huggingface]",
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"bitsandbytes",
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@ -259,8 +259,10 @@ def get_chat_template(
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assert("Unsloth: Can only map new tokens to EOS for now. Adding new tokens is not yet supported.")
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pass
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if tokenizer.__class__.__name__.startswith("Gemma") and chat_template == "chatml":
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chat_template = "gemma_chatml"
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IS_GEMMA = False
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if tokenizer.__class__.__name__.startswith("Gemma"):
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if chat_template == "chatml": chat_template = "gemma_chatml"
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IS_GEMMA = True
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pass
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old_padding_side = tokenizer.padding_side
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@ -338,6 +340,12 @@ def get_chat_template(
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.replace("'user'", "'" + mapping["user"] + "'")\
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.replace("'assistant'", "'" + mapping["assistant"] + "'")
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# Careful on Gemma
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# bos_token is a must or else losses become too high
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if IS_GEMMA and not chat_template.startswith("{{ bos_token }}"):
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chat_template = "{{ bos_token }}" + chat_template
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pass
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_, tokenizer = patch_tokenizer(model = None, tokenizer = tokenizer)
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tokenizer.padding_side = old_padding_side
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tokenizer.chat_template = chat_template
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@ -154,6 +154,7 @@ def GemmaModel_fast_forward_inference(
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out_weight = torch.empty_like(self.layers[0].input_layernorm.weight, dtype = torch.float32, device = "cuda")
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hidden_states = self.embed_tokens(input_ids)
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hidden_states = hidden_states.to(self.config.torch_dtype)
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# 3072**0.5 = 55.5000 in bfloat16, whilst 55.4256 in float32
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# 2048**0.5 = 45.2500 in bfloat16, whilst 45.2548 in float32
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hidden_states *= torch.tensor(math_sqrt(self.config.hidden_size), dtype = hidden_states.dtype)
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@ -509,7 +509,10 @@ def LlamaModel_fast_forward(
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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# Mormalized from Gemma
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# Downcast to the correct dtype ie float32 to float16
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inputs_embeds = inputs_embeds.to(self.config.torch_dtype)
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# Normalized from Gemma
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IS_GEMMA = self.config.model_type == "gemma"
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train_embed_tokens = self.embed_tokens.weight.requires_grad
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@ -665,6 +668,7 @@ def LlamaModel_fast_forward_inference(
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input_ids = input_ids[:,:self.max_seq_length]
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hidden_states = self.embed_tokens(input_ids)
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hidden_states = hidden_states.to(self.config.torch_dtype)
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next_decoder_cache = []
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for idx, decoder_layer in enumerate(self.layers):
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@ -1334,6 +1338,7 @@ class FastLlamaModel:
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"We shall do it for you!"
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)
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train_lm_head = True
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model.model.embed_tokens.to(torch.float32, non_blocking = True)
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elif module == "embed_tokens":
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logger.warning_once(
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@ -1341,6 +1346,7 @@ class FastLlamaModel:
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"We shall do it for you!"
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)
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train_embed_tokens = True
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model.lm_head.to(torch.float32, non_blocking = True)
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else:
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assert(module in accepted_modules)
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@ -1477,12 +1483,12 @@ class FastLlamaModel:
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if hasattr(gate_proj, "lora_A") and \
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hasattr( up_proj, "lora_A") and \
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hasattr(down_proj, "lora_A") and \
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((gate_proj.base_layer if hasattr(gate_proj, "base_layer") else gate_proj).bias is None) and \
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(( up_proj.base_layer if hasattr( up_proj, "base_layer") else up_proj).bias is None) and \
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((down_proj.base_layer if hasattr(down_proj, "base_layer") else down_proj).bias is None) and \
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((gate_proj.lora_magnitude_vector if hasattr(gate_proj, "lora_magnitude_vector") else None) is None) and \
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(( up_proj.lora_magnitude_vector if hasattr( up_proj, "lora_magnitude_vector") else None) is None) and \
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((down_proj.lora_magnitude_vector if hasattr(down_proj, "lora_magnitude_vector") else None) is None):
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(getattr(gate_proj, "base_layer", gate_proj).bias is None) and \
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(getattr( up_proj, "base_layer", up_proj).bias is None) and \
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(getattr(down_proj, "base_layer", down_proj).bias is None) and \
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(getattr(gate_proj, "lora_magnitude_vector", None) is None) and \
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(getattr( up_proj, "lora_magnitude_vector", None) is None) and \
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(getattr(down_proj, "lora_magnitude_vector", None) is None):
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# https://stackoverflow.com/questions/50599045/python-replacing-a-function-within-a-class-of-a-module
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layer.mlp.forward = types.MethodType(apply_lora_mlp, layer.mlp)
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@ -1501,12 +1507,12 @@ class FastLlamaModel:
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if hasattr(q_proj, "lora_A") and \
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hasattr(k_proj, "lora_A") and \
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hasattr(v_proj, "lora_A") and \
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((q_proj.base_layer if hasattr(q_proj, "base_layer") else q_proj).bias is None) and \
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((k_proj.base_layer if hasattr(k_proj, "base_layer") else k_proj).bias is None) and \
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((v_proj.base_layer if hasattr(v_proj, "base_layer") else v_proj).bias is None) and \
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((q_proj.lora_magnitude_vector if hasattr(q_proj, "lora_magnitude_vector") else None) is None) and \
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((k_proj.lora_magnitude_vector if hasattr(k_proj, "lora_magnitude_vector") else None) is None) and \
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((v_proj.lora_magnitude_vector if hasattr(v_proj, "lora_magnitude_vector") else None) is None):
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(getattr(q_proj, "base_layer", q_proj).bias is None) and \
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(getattr(q_proj, "base_layer", k_proj).bias is None) and \
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(getattr(q_proj, "base_layer", v_proj).bias is None) and \
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(getattr(q_proj, "lora_magnitude_vector", None) is None) and \
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(getattr(k_proj, "lora_magnitude_vector", None) is None) and \
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(getattr(v_proj, "lora_magnitude_vector", None) is None):
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layer.self_attn.apply_qkv = apply_lora_qkv
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n_qkv += 1
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@ -1520,8 +1526,8 @@ class FastLlamaModel:
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# O attention patching
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o_proj = layer.self_attn.o_proj
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if hasattr(o_proj, "lora_A") and \
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((o_proj.base_layer if hasattr(o_proj, "base_layer") else o_proj).bias is None) and \
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((o_proj.lora_magnitude_vector if hasattr(o_proj, "lora_magnitude_vector") else None) is None):
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(getattr(o_proj, "base_layer", o_proj).bias is None) and \
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(getattr(o_proj, "lora_magnitude_vector", None) is None):
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layer.self_attn.apply_o = apply_lora_o
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n_o += 1
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@ -632,6 +632,7 @@ pass
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def save_to_gguf(
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model_type : str,
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model_directory : str = "unsloth_finetuned_model",
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quantization_method : str = "fast_quantized",
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first_conversion : str = "f16",
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@ -639,10 +640,18 @@ def save_to_gguf(
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):
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from transformers.models.llama.modeling_llama import logger
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# Careful convert.py is only for Llama / Mistral based archs
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use_fast_convert = False
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if model_type == "llama": use_fast_convert = True
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elif model_type == "mistral": use_fast_convert = True
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pass
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logger.warning_once(f"Unsloth: Converting {model_type} model. Can use fast conversion = {use_fast_convert}.")
|
||||
|
||||
if quantization_method == "not_quantized": quantization_method = "f16"
|
||||
elif quantization_method == "fast_quantized": quantization_method = "q8_0"
|
||||
elif quantization_method == "quantized": quantization_method = "q4_k_m"
|
||||
elif quantization_method is None: quantization_method = "q8_0"
|
||||
pass
|
||||
|
||||
if quantization_method not in ALLOWED_QUANTS.keys():
|
||||
error = f"Unsloth: Quant method = [{quantization_method}] not supported. Choose from below:\n"
|
||||
|
|
@ -692,6 +701,12 @@ def save_to_gguf(
|
|||
pass
|
||||
pass
|
||||
|
||||
# Non llama/mistral needs can only use f32 or f16
|
||||
if not use_fast_convert and (first_conversion != "f16" or first_conversion != "f32"):
|
||||
logger.warning_once("Unsloth: We must use f16 for non Llama and Mistral models.")
|
||||
first_conversion = "f16"
|
||||
pass
|
||||
|
||||
n_cpus = psutil.cpu_count()
|
||||
if n_cpus is None: n_cpus = 1
|
||||
n_cpus *= 2
|
||||
|
|
@ -703,9 +718,15 @@ def save_to_gguf(
|
|||
f"The output location will be {final_location}\n"\
|
||||
"This will take 3 minutes...")
|
||||
|
||||
command = f"python llama.cpp/convert.py {model_directory} "\
|
||||
f"--outfile {final_location} --vocab-type hfft "\
|
||||
f"--outtype {first_conversion} --concurrency {n_cpus}"
|
||||
if use_fast_convert:
|
||||
command = f"python llama.cpp/convert.py {model_directory} "\
|
||||
f"--outfile {final_location} --vocab-type hfft "\
|
||||
f"--outtype {first_conversion} --concurrency {n_cpus}"
|
||||
else:
|
||||
command = f"python llama.cpp/convert-hf-to-gguf.py {model_directory} "\
|
||||
f"--outfile {final_location} "\
|
||||
f"--outtype {first_conversion}"
|
||||
pass
|
||||
|
||||
with subprocess.Popen(command, shell = True, stdout = subprocess.PIPE, stderr = subprocess.PIPE, bufsize = 1) as sp:
|
||||
for line in sp.stdout:
|
||||
|
|
@ -1054,7 +1075,8 @@ def unsloth_save_pretrained_gguf(
|
|||
for _ in range(3):
|
||||
gc.collect()
|
||||
|
||||
file_location = save_to_gguf(new_save_directory, quantization_method, first_conversion, makefile)
|
||||
model_type = self.config.model_type
|
||||
file_location = save_to_gguf(model_type, new_save_directory, quantization_method, first_conversion, makefile)
|
||||
|
||||
if push_to_hub:
|
||||
print("Unsloth: Uploading GGUF to Huggingface Hub...")
|
||||
|
|
@ -1154,7 +1176,8 @@ def unsloth_push_to_hub_gguf(
|
|||
for _ in range(3):
|
||||
gc.collect()
|
||||
|
||||
file_location = save_to_gguf(new_save_directory, quantization_method, first_conversion, makefile)
|
||||
model_type = self.config.model_type
|
||||
file_location = save_to_gguf(model_type, new_save_directory, quantization_method, first_conversion, makefile)
|
||||
|
||||
print("Unsloth: Uploading GGUF to Huggingface Hub...")
|
||||
username = upload_to_huggingface(
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue