[Add Mixtral] Adds support for the Mixtral MoE (#27942)
* up * up * test * logits ok * up * up * few fixes * conversion script * up * nits * nits * update * nuke * more updates * nites * fix many issues * nit * scatter * nit * nuke megablocks * nits * fix conversion script * nit * remove * nits * nit * update * oupsssss * change * nits device * nits * fixup * update * merge * add copied from * fix the copy mentions * update tests * more fixes * nits * conversion script * add parts of the readme * Update tests/models/mixtral/test_modeling_mixtral.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * new test + conversion script * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Apply suggestions from code review * fix * fix copies * fix copies * ooops * fix config * Apply suggestions from code review * fix nits * nit * add copies * add batched tests * docs * fix flash attention * let's add more verbose * add correct outputs * support router ouptus * ignore copies where needed * fix * cat list if list is given for now * nits * Update docs/source/en/model_doc/mixtral.md * finish router refactoring * fix forward * fix expected values * nits * fixup * fix * fix bug * fix * fix dtype mismatch * fix * grrr grrr I support item assignment * fix CI * docs * fixup * remove some copied form * fix weird diff * skip doctest fast on the config and modeling * mark that is supports flash attention in the doc * update * Update src/transformers/models/mixtral/modeling_mixtral.py Co-authored-by: Lysandre Debut <hi@lysand.re> * Update docs/source/en/model_doc/mixtral.md Co-authored-by: Lysandre Debut <hi@lysand.re> * revert router logits config issue * update doc accordingly * Update src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py * nits * use torch testing asssert close * fixup * doc nits --------- Co-authored-by: younesbelkada <younesbelkada@gmail.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Lysandre Debut <hi@lysand.re>
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Arthur
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<!--Copyright 2023 Mistral AI and The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# Mixtral
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## Overview
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Mixtral-8x7B is Mistral AI's second Large Language Model (LLM).
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The Mixtral model was proposed in the by the [Mistral AI](https://mistral.ai/) team.
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It was introduced in the [Mixtral of Experts blogpost](https://mistral.ai/news/mixtral-of-experts/) with the following introduction:
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*Today, the team is proud to release Mixtral 8x7B, a high-quality sparse mixture of experts models (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT3.5 on most standard benchmarks.*
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Tips:
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- The model needs to be converted using the [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py).
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- If the model is quantized to 4bits, a single A100 is enough to fit the entire 84B model.
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This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArthurZ) .
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The original code can be found [here](https://github.com/mistralai/mistral-src).
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### Model Details
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Mixtral-84B is a decoder-based LM with the following architectural choices:
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* Mixtral is a Mixture of Expert (MOE) model with 8 experts per MLP, with a total of 85B paramateres but the compute required is the same as a 14B model. This is because even though each experts have to be loaded in RAM (70B like ram requirement) each token from the hidden states are dipatched twice (top 2 routing) and thus the compute (the operation required at each foward computation) is just 2 X sequence_length.
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The following implementation details are shared with Mistral AI's first model [mistral](~models/doc/mistral):
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* Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
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* GQA (Grouped Query Attention) - allowing faster inference and lower cache size.
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* Byte-fallback BPE tokenizer - ensures that characters are never mapped to out of vocabulary tokens.
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They also provide an instruction fine-tuned model: `mistralai/Mixtral-8x7B-v0.1` which can be used for chat-based inference.
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For more details please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/)
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### License
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`Mixtral-8x7B` is released under the Apache 2.0 license.
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## Usage tips
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`Mixtral-8x7B` can be found on the [Huggingface Hub](https://huggingface.co/mistralai)
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These ready-to-use checkpoints can be downloaded and used via the HuggingFace Hub:
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```python
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer
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>>> device = "cuda" # the device to load the model onto
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>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
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>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-8x7B")
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>>> prompt = "My favourite condiment is"
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>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
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>>> model.to(device)
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>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
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>>> tokenizer.batch_decode(generated_ids)[0]
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"The expected output"
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```
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To use the raw checkpoints with HuggingFace you can use the `convert_mixtral_weights_to_hf.py` script to convert them to the HuggingFace format:
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```bash
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python src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py \
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--input_dir /path/to/downloaded/mistral/weights --output_dir /output/path
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```
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You can then load the converted model from the `output/path`:
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```python
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from transformers import MixtralForCausalLM, LlamaTokenizer
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tokenizer = LlamaTokenizer.from_pretrained("/output/path")
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model = MixtralForCausalLM.from_pretrained("/output/path")
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```
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## Combining Mixtral and Flash Attention 2
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First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.
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```bash
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pip install -U flash-attn --no-build-isolation
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```
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Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of [`flash-attn`](https://github.com/Dao-AILab/flash-attention) repository. Make also sure to load your model in half-precision (e.g. `torch.float16`)
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To load and run a model using Flash Attention 2, refer to the snippet below:
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```python
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>>> import torch
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer
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>>> device = "cuda" # the device to load the model onto
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>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
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>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
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>>> prompt = "My favourite condiment is"
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>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
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>>> model.to(device)
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>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
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>>> tokenizer.batch_decode(generated_ids)[0]
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"The expected output"
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```
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### Expected speedups
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Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using `mistralai/Mixtral-8x7B-v0.1` checkpoint and the Flash Attention 2 version of the model.
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<div style="text-align: center">
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<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/mixtral-7b-inference-large-seqlen.png">
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</div>
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### Sliding window Attention
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The current implementation supports the sliding window attention mechanism and memory efficient cache management.
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To enable sliding window attention, just make sure to have a `flash-attn` version that is compatible with sliding window attention (`>=2.3.0`).
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The Flash Attention-2 model uses also a more memory efficient cache slicing mechanism - as recommended per the official implementation of Mistral model that use rolling cache mechanism we keep the cache size fixed (`self.config.sliding_window`), support batched generation only for `padding_side="left"` and use the absolute position of the current token to compute the positional embedding.
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## The Mistral Team
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Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
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## MixtralConfig
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[[autodoc]] MixtralConfig
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## MixtralModel
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[[autodoc]] MixtralModel
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- forward
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## MixtralForCausalLM
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[[autodoc]] MixtralForCausalLM
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- forward
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## MixtralForSequenceClassification
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[[autodoc]] MixtralForSequenceClassification
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- forward
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