Llama 3.1 (#797)
* Llama 3.1 * Update _utils.py * Llama 3.1 * Update _utils.py * Update llama.py * Update llama.py * hack for rotary * patch RoPE * refix rope * Update _utils.py * Update llama.py * Llama 3.1 check * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py
This commit is contained in:
Daniel Han
GitHub
parent
5798948c5a
commit
f037ccfaca
7 changed files with 258 additions and 14 deletions
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@ -22,7 +22,7 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
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| Unsloth supports | Free Notebooks | Performance | Memory use |
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|-----------|---------|--------|----------|
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| **Llama 3 (8B)** | [▶️ Start for free](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2x faster | 60% less |
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| **Llama 3.1 (8B)** | [▶️ Start for free](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2x faster | 60% less |
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| **Mistral Nemo (12B)** | [▶️ Start for free](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing) | 2x faster | 60% less |
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| **Gemma 2 (9B)** | [▶️ Start for free](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) | 2x faster | 63% less |
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| **Phi-3 (mini)** | [▶️ Start for free](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) | 2x faster | 50% less |
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@ -32,13 +32,14 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
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| **DPO Zephyr** | [▶️ Start for free](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 43% less |
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| **TinyLlama** | [▶️ Start for free](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
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- **Kaggle Notebooks** for [Llama 3 (8B)](https://www.kaggle.com/code/danielhanchen/kaggle-llama-3-8b-unsloth-notebook), [Gemma 2 (9B)](https://www.kaggle.com/code/danielhanchen/kaggle-gemma-7b-unsloth-notebook/), [Mistral (7B)](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)
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- **Kaggle Notebooks** for [Llama 3.1 (8B)](https://www.kaggle.com/code/danielhanchen/kaggle-llama-3-8b-unsloth-notebook), [Gemma 2 (9B)](https://www.kaggle.com/code/danielhanchen/kaggle-gemma-7b-unsloth-notebook/), [Mistral (7B)](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)
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- Run [Llama 3 conversational notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) and [Mistral v0.3 ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing)
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- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for continued pretraining / raw text
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- This [continued pretraining notebook](https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing) is for learning another language
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- Click [here](https://github.com/unslothai/unsloth/wiki) for detailed documentation for Unsloth.
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## 🦥 Unsloth.ai News
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- 📣 NEW! [Llama 3.1 8b, 70b](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) both Base and Instruct now supported
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- 📣 NEW! [Mistral Nemo-12b](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing) both Base and Instruct now supported
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- 📣 NEW! [Gemma-2-9b](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) and Gemma-2-27b now supported
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- 📣 UPDATE! [Phi-3 mini](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing) model updated. [Phi-3 Medium](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing) 2x faster finetuning.
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@ -35,7 +35,7 @@ exclude = ["images*"]
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huggingface = [
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"packaging",
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"tyro",
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"transformers>=4.42.3",
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"transformers>=4.43.1",
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"datasets>=2.16.0",
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"sentencepiece>=0.2.0",
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"tqdm",
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@ -188,7 +188,7 @@ colab-ampere-torch220 = [
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colab-new = [
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"packaging",
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"tyro",
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"transformers>=4.42.3",
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"transformers>=4.43.1",
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"datasets>=2.16.0",
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"sentencepiece>=0.2.0",
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"tqdm",
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@ -33,6 +33,7 @@ __all__ = [
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"unsloth_offloaded_gradient_checkpoint",
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"torch_compile_options",
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"patch_linear_scaling",
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"patch_llama_rope_scaling",
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"check_nvidia",
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"create_boolean_mask",
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"torch_amp_custom_fwd",
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@ -332,7 +333,13 @@ def patch_tokenizer(model, tokenizer):
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Check if pad_token is not the same as eos_token otherwise the loss will ignore it!!
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Fixes https://github.com/unslothai/unsloth/issues/5
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"""
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possible_reserved_tokens = ("<|reserved", "<|placeholder", "[control")
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possible_reserved_tokens = (
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"<|reserved", # Llama-3
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"<|placeholder", # Phi-3
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"[control", # Forgot where lol
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"<pad>", # Mistral Nemo
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"<|finetune_right_pad_id|>", # Llama-3.1
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)
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if model is not None:
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model.config.update({"unsloth_version" : __version__})
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@ -745,7 +752,7 @@ def patch_linear_scaling(
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fix_rope_function = """
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if getattr(self.config, "rope_scaling", None) is None:
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self.rotary_emb = {rope_function}(
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self.head_dim,
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dim = self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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base=self.rope_theta,
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)
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@ -754,7 +761,7 @@ def patch_linear_scaling(
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scaling_factor = self.config.rope_scaling["factor"]
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if scaling_type == "linear":
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self.rotary_emb = {scaled_rope_function}(
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self.head_dim,
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dim = self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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@ -779,6 +786,91 @@ def patch_linear_scaling(
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pass
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# Patches for Llama-3 LlamaExtendedRotaryEmbedding
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def patch_llama_rope_scaling(
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model_name = "llama",
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rope_module = None,
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scaled_rope_module = None,
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extended_rope_module = None,
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attention_module = None,
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):
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assert(\
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rope_module is not None and \
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scaled_rope_module is not None and \
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extended_rope_module is not None
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)
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assert(attention_module is not None)
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rope_name = rope_module.__name__
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scaled_rope_name = scaled_rope_module.__name__
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model_filepath = f"transformers.models.{model_name}.modeling_{model_name}"
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exec_code = \
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f"import torch.nn as nn\n"\
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f"from typing import Union, Optional, List, Any, Callable, Tuple\n"\
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f"from {model_filepath} import logger, "\
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f"{model_name.title()}Attention, {model_name.title()}Config"
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try:
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function = inspect.getsource(attention_module.__init__)
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except:
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# Most likely already patched!
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return None, None
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where = function.find("def")
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function = function.split("\n")
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function = "\n".join(x[where:] for x in function)
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init_name = f"{model_name.title()}Attention__init__"
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function = function.replace("def __init__", f"def {init_name}")
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function = function.replace(
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"super().__init__()",
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f"super({model_name.title()}Attention, self).__init__()",
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)
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fix_rope_function = """
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if getattr(self.config, "rope_scaling", None) is None:
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self.rotary_emb = {rope_function}(
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dim = self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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base=self.rope_theta,
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)
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else:
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scaling_type1 = self.config.rope_scaling.get("type", None)
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scaling_type2 = self.config.rope_scaling.get("rope_type", None)
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scaling_type = scaling_type1 if scaling_type1 is not None else scaling_type2
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scaling_factor = self.config.rope_scaling.get("factor")
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if scaling_type == "linear":
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self.rotary_emb = {scaled_rope_function}(
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dim = self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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)
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elif scaling_type == "llama3":
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self.rotary_emb = {extended_rope_function}(
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dim = self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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base=self.rope_theta,
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)
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else:
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raise ValueError(f"Unknown RoPE scaling type {{scaling_type}}")
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pass
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"""
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fix_rope_function = fix_rope_function.format(
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rope_function = rope_module.__name__,
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scaled_rope_function = scaled_rope_module.__name__,
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extended_rope_function = extended_rope_module.__name__,
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)
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rotary_emb = re.findall(
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"self.rotary_emb = .+?\)", function,
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flags = re.DOTALL | re.MULTILINE,
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)
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if len(rotary_emb) == 0: return None, function
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rotary_emb = rotary_emb[0]
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function = function.replace(rotary_emb, fix_rope_function, 1)
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function = exec_code + "\n\n" + function
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return init_name, function
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pass
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def check_nvidia():
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# Unsloth doesn't work yet on AMD devices - we're working on it!
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output = np.array([0,])
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@ -205,8 +205,11 @@ class GemmaFixedRotaryEmbedding(torch.nn.Module):
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# Fixes https://github.com/huggingface/transformers/pull/28837
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# https://github.com/microsoft/DeepSpeed/issues/4932
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# The precision of RoPE buffers is not correct, so we cast to int64.
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=None,
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config = None, # [TODO] Hack to pass in config - need to remove later
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):
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super().__init__()
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if config is not None: return # [TODO] Hack to pass in config - need to remove later
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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@ -264,9 +267,11 @@ class GemmaFixedLinearScalingRotaryEmbedding(GemmaFixedRotaryEmbedding):
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# Fixes https://github.com/huggingface/transformers/pull/28837
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# https://github.com/microsoft/DeepSpeed/issues/4932
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# The precision of RoPE buffers is not correct, so we cast to int64.
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0,
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config = None, # [TODO] Hack to pass in config - need to remove later
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):
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self.scaling_factor = scaling_factor
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super().__init__(dim, max_position_embeddings, base, device)
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super().__init__(dim = dim, max_position_embeddings = max_position_embeddings, base = base, device = device, config = config)
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pass
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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@ -977,8 +977,19 @@ class LlamaRotaryEmbedding(torch.nn.Module):
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# Fixes https://github.com/huggingface/transformers/pull/28837
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# https://github.com/microsoft/DeepSpeed/issues/4932
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# The precision of RoPE buffers is not correct, so we cast to int64.
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=None,
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config = None, # [TODO] Hack to pass in config - need to remove later
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):
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super().__init__()
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if config is not None:
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# [TODO] Hack to pass in config - need to remove later
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base = config.rope_theta
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partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
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dim = int((config.hidden_size // config.num_attention_heads))
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device = "cuda"
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max_position_embeddings = config.max_position_embeddings
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pass
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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@ -1030,9 +1041,11 @@ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
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# Fixes https://github.com/huggingface/transformers/pull/28837
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# https://github.com/microsoft/DeepSpeed/issues/4932
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# The precision of RoPE buffers is not correct, so we cast to int64.
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0,
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config = None, # [TODO] Hack to pass in config - need to remove later
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):
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self.scaling_factor = scaling_factor
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super().__init__(dim, max_position_embeddings, base, device)
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super().__init__(dim = dim, max_position_embeddings = max_position_embeddings, base = base, device = device, config = config)
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pass
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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@ -1052,6 +1065,99 @@ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
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pass
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# See https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/rotary_embedding.py#L736
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# For Llama 3.1
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class LlamaExtendedRotaryEmbedding(torch.nn.Module):
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def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=None,
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config = None, # [TODO] Hack to pass in config - need to remove later
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):
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super().__init__()
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if config is not None:
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# [TODO] Hack to pass in config - need to remove later
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base = config.rope_theta
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partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
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dim = int((config.hidden_size // config.num_attention_heads))
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device = "cuda"
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max_position_embeddings = config.max_position_embeddings
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pass
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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# Dynamic RoPE we first set it to a max of 4 * 8192 tokens then we iteratively grow this
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self.current_rope_size = min(4 * 8192, self.max_position_embeddings)
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# Normal Llama-3 RoPE
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inv_freq = 1.0 / (
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self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device="cpu").float() / self.dim)
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)
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inv_freq = self.apply_scaling(inv_freq)
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self.register_buffer("inv_freq", inv_freq, persistent = False)
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# Build here to make `torch.jit.trace` work.
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self._set_cos_sin_cache(seq_len=self.current_rope_size, device=device, dtype=torch.get_default_dtype())
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pass
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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# Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
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# in FP32. They are applied (multiplied) in FP32 as well.
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self.current_rope_size = seq_len
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t = torch.arange(self.current_rope_size, device="cpu", dtype=torch.int64).float()
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
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pass
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# From https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/api/model.py#L41
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def apply_scaling(self, freqs: torch.Tensor):
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# Values obtained from grid search
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scale_factor = 8
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low_freq_factor = 1
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high_freq_factor = 4
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old_context_len = 8192 # original llama3 length
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low_freq_wavelen = old_context_len / low_freq_factor
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high_freq_wavelen = old_context_len / high_freq_factor
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new_freqs = []
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for freq in freqs:
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wavelen = 2 * math.pi / freq
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if wavelen < high_freq_wavelen:
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new_freqs.append(freq)
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elif wavelen > low_freq_wavelen:
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new_freqs.append(freq / scale_factor)
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else:
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assert low_freq_wavelen != high_freq_wavelen
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smooth = (old_context_len / wavelen - low_freq_factor) / (
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high_freq_factor - low_freq_factor
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)
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new_freqs.append((1 - smooth) * freq / scale_factor + smooth * freq)
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return torch.tensor(new_freqs, dtype=freqs.dtype, device=freqs.device)
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pass
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def forward(self, x, position_ids=None, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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if seq_len > self.current_rope_size:
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self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
||||
|
||||
return (
|
||||
self.cos_cached[:seq_len].to(dtype = x.dtype),
|
||||
self.sin_cached[:seq_len].to(dtype = x.dtype),
|
||||
)
|
||||
pass
|
||||
|
||||
def extend_rope_embedding(self, x, seq_len):
|
||||
if seq_len <= self.current_rope_size: return
|
||||
# Iteratively grow by increments of 8192
|
||||
self.current_rope_size = int(round(seq_len / 8192)) * 8192
|
||||
self._set_cos_sin_cache(self.current_rope_size, device = "cuda:0", dtype = x.dtype)
|
||||
pass
|
||||
pass
|
||||
|
||||
|
||||
def _wrap_fast_inference(generate, device_type, dtype, model):
|
||||
# Wraps inference with bfloat16 / float16
|
||||
@torch.inference_mode
|
||||
|
|
@ -1108,6 +1214,17 @@ class FastLlamaModel:
|
|||
|
||||
@staticmethod
|
||||
def pre_patch():
|
||||
init_name, function = patch_llama_rope_scaling(
|
||||
model_name = "llama",
|
||||
rope_module = LlamaRotaryEmbedding,
|
||||
scaled_rope_module = LlamaLinearScalingRotaryEmbedding,
|
||||
extended_rope_module = LlamaExtendedRotaryEmbedding,
|
||||
attention_module = LlamaAttention,
|
||||
)
|
||||
if init_name is not None:
|
||||
exec(function, globals())
|
||||
LlamaAttention.__init__ = eval(init_name)
|
||||
pass
|
||||
LlamaAttention .forward = LlamaAttention_fast_forward
|
||||
LlamaSdpaAttention .forward = LlamaAttention_fast_forward
|
||||
LlamaFlashAttention2.forward = LlamaAttention_fast_forward
|
||||
|
|
|
|||
|
|
@ -27,6 +27,7 @@ transformers_version = Version(transformers_version)
|
|||
SUPPORTS_FOURBIT = transformers_version >= Version("4.37")
|
||||
SUPPORTS_GEMMA = transformers_version >= Version("4.38")
|
||||
SUPPORTS_GEMMA2 = transformers_version >= Version("4.42")
|
||||
SUPPORTS_LLAMA31 = transformers_version >= Version("4.43.1")
|
||||
if SUPPORTS_GEMMA:
|
||||
from .gemma import FastGemmaModel
|
||||
if SUPPORTS_GEMMA2:
|
||||
|
|
@ -130,7 +131,19 @@ class FastLanguageModel(FastLlamaModel):
|
|||
|
||||
model_type = model_config.model_type
|
||||
|
||||
if model_type == "llama": dispatch_model = FastLlamaModel
|
||||
if model_type == "llama":
|
||||
scaling_type1 = model_config.rope_scaling.get("type", None)
|
||||
scaling_type2 = model_config.rope_scaling.get("rope_type", None)
|
||||
scaling_type = scaling_type1 if scaling_type1 is not None else scaling_type2
|
||||
|
||||
if scaling_type == "llama3" and not SUPPORTS_LLAMA31:
|
||||
raise ImportError(
|
||||
f"Unsloth: Your transformers version of {transformers_version} does not support Llama 3.1.\n"\
|
||||
f"The minimum required version is 4.43.1\n"\
|
||||
f'Try `pip install --upgrade "transformers>=4.43.1"`\n'\
|
||||
f"to obtain the latest transformers build, then restart this session."\
|
||||
)
|
||||
dispatch_model = FastLlamaModel
|
||||
elif model_type == "mistral": dispatch_model = FastMistralModel
|
||||
elif model_type == "gemma":
|
||||
if not SUPPORTS_GEMMA:
|
||||
|
|
|
|||
|
|
@ -218,6 +218,22 @@ __INT_TO_FLOAT_MAPPER = \
|
|||
"unsloth/Mistral-Nemo-Base-2407",
|
||||
"mistralai/Mistral-Nemo-Base-2407",
|
||||
),
|
||||
"unsloth/Meta-Llama-3.1-8B-bnb-4bit" : (
|
||||
"unsloth/Meta-Llama-3.1-8B",
|
||||
"meta-llama/Meta-Llama-3.1-8B",
|
||||
),
|
||||
"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit" : (
|
||||
"unsloth/Meta-Llama-3.1-8B-Instruct",
|
||||
"meta-llama/Meta-Llama-3.1-8B-Instruct",
|
||||
),
|
||||
"unsloth/Meta-Llama-3.1-70B-bnb-4bit" : (
|
||||
"unsloth/Meta-Llama-3.1-70B",
|
||||
"meta-llama/Meta-Llama-3.1-70B",
|
||||
),
|
||||
"unsloth/Meta-Llama-3.1-70B-Instruct-bnb-4bit" : (
|
||||
"unsloth/Meta-Llama-3.1-70B-Instruct",
|
||||
"meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
),
|
||||
}
|
||||
|
||||
INT_TO_FLOAT_MAPPER = {}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue