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Add DBRX Model (#29921)

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* wip

* fix __init__.py

* add docs

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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* address comments 1

* work on make fixup

* pass configs down

* add sdpa attention

* remove DbrxBlock

* add to configuration_auto

* docstring now passes formatting test

* fix style

* update READMEs

* add dbrx to modeling_auto

* make fix-copies generated this

* add DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP

* config docstring passes formatting test

* rename moe_loss_weight to router_aux_loss_coef

* add to flash-attn documentation

* fix model-path in tests

* Explicitly make `"suli"` the default `ffn_act_fn`

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* default to using router_aux_loss_coef over ffn_config[moe_loss_weight]

* fix _flash_attn_uses_top_left_mask and is_causal

* fix tests path

* don't use token type IDs

* follow Llama and remove token_type_ids from test

* init ConfigTester differently so tests pass

* remove multiple choice test

* remove question + answer test

* remove sequence classification test

* remove token classification test

* copy Llama tests and remove token_type_ids from test inputs

* do not test pruning or headmasking; style code

* add _tied_weights_keys parameter to pass test

* add type hints

* fix type check

* update config tester

* remove masked_lm test

* remove encoder tests

* initialize DbrxModelTester with correct params

* style

* torch_dtype does not rely on torch

* run make fixup, fix-copies

* use https://huggingface.co/v2ray/dbrx-base-fixed/blob/main/modeling_dbrx.py

* add copyright info

* fix imports and DbrxRotaryEmbedding

* update DbrxModel docstring

* use copies

* change model path in docstring

* use config in DbrxFFN

* fix flashattention2, sdpaattention

* input config to DbrXAttention, DbrxNormAttentionNorm

* more fixes

* fix

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* change doc-test

* fix doc-test

* fix docstring

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* make defaults match dbrx-instruct

* replace `router_aux_loss_coef` with `moe_loss_weight`

* is_decoder=True

* remove is_decoder from configtester

* implement sdpa properly

* make is_decoder pass tests

* start on the GenerationTesterMixin tests

* add dbrx to sdpa documentation

* skip weight typing test

* style

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Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Add DBRX to toctree

* skip test_new_cache_format

* make config defaults smaller again

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* remove pad_token_id from config

* Remove all references to DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP

* Update src/transformers/models/dbrx/__init__.py

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* Update docs/source/en/model_doc/dbrx.md

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* update docs, fix configuration_auto.py

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* Update modeling_dbrx.py

* Update src/transformers/models/dbrx/modeling_dbrx.py

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* replace "Gemma" with "Dbrx"

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* Update src/transformers/models/dbrx/modeling_dbrx.py

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* address some comments

* small model

* add dbrx to tokenization_auto

* More docstrings with add_start_docstrings

* Dbrx for now

* add PipelineTesterMixin

* Update src/transformers/models/dbrx/configuration_dbrx.py

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* remove flash-attn2 import error

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* add useage example

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* fix ffn_act_fn

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* change "dbrx" to "DBRX" for display purposes.

* fix __init__.py?

* fix __init__.py

* fix README

* return the aux_loss

* remove extra spaces

* fix configuration_auto.py

* fix format in tokenization_auto

* remove new line

* add more useage examples

---------

Co-authored-by: Abhi Venigalla <abhi.venigalla@databricks.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Eitan Turok <eitan.turok@databricks.com>
Co-authored-by: Eitan Turok <150733043+eitanturok@users.noreply.github.com>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Eitan Turok <eitanturok@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Mihir Patel <mihir.v.patel7@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Abhi Venigalla
2024-04-18 06:18:52 -07:00
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docs/source/en/_toctree.yml
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@@ -320,6 +320,8 @@
title: CPMANT
- local: model_doc/ctrl
title: CTRL
- local: model_doc/dbrx
title: DBRX
- local: model_doc/deberta
title: DeBERTa
- local: model_doc/deberta-v2

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@@ -107,6 +107,7 @@ Flax), PyTorch, and/or TensorFlow.
| [Data2VecAudio](model_doc/data2vec) | ✅ | ❌ | ❌ |
| [Data2VecText](model_doc/data2vec) | ✅ | ❌ | ❌ |
| [Data2VecVision](model_doc/data2vec) | ✅ | ✅ | ❌ |
| [DBRX](model_doc/dbrx) | ✅ | ❌ | ❌ |
| [DeBERTa](model_doc/deberta) | ✅ | ✅ | ❌ |
| [DeBERTa-v2](model_doc/deberta-v2) | ✅ | ✅ | ❌ |
| [Decision Transformer](model_doc/decision_transformer) | ✅ | ❌ | ❌ |

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@@ -0,0 +1,120 @@
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# DBRX
## Overview
DBRX is a [transformer-based](https://www.isattentionallyouneed.com/) decoder-only large language model (LLM) that was trained using next-token prediction.
It uses a *fine-grained* mixture-of-experts (MoE) architecture with 132B total parameters of which 36B parameters are active on any input.
It was pre-trained on 12T tokens of text and code data.
Compared to other open MoE models like Mixtral-8x7B and Grok-1, DBRX is fine-grained, meaning it uses a larger number of smaller experts. DBRX has 16 experts and chooses 4, while Mixtral-8x7B and Grok-1 have 8 experts and choose 2.
This provides 65x more possible combinations of experts and we found that this improves model quality.
DBRX uses rotary position encodings (RoPE), gated linear units (GLU), and grouped query attention (GQA).
It is a BPE based model and uses the GPT-4 tokenizer as described in the [tiktoken](https://github.com/openai/tiktoken) repository.
We made these choices based on exhaustive evaluation and scaling experiments.
DBRX was pretrained on 12T tokens of carefully curated data and a maximum context length of 32K tokens.
We estimate that this data is at least 2x better token-for-token than the data we used to pretrain the MPT family of models.
This new dataset was developed using the full suite of Databricks tools, including Apache Spark™ and Databricks notebooks for data processing, and Unity Catalog for data management and governance.
We used curriculum learning for pretraining, changing the data mix during training in ways we found to substantially improve model quality.
More detailed information about DBRX Instruct and DBRX Base can be found in our [technical blog post](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm).
This model was contributed by [eitan-turok](https://huggingface.co/eitanturok) and [abhi-db](https://huggingface.co/abhi-db). The original code can be found [here](https://github.com/databricks/dbrx-instruct).
## Usage Examples
The `generate()` method can be used to generate text using DBRX. You can generate using the standard attention implementation, flash-attention, and the PyTorch scaled dot product attention. The last two attention implementations give speed ups.
```python
from transformers import DbrxForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct", token="YOUR_HF_TOKEN")
model = DbrxForCausalLM.from_pretrained(
"databricks/dbrx-instruct",
device_map="auto",
torch_dtype=torch.bfloat16,
token="YOUR_HF_TOKEN",
)
input_text = "What does it take to build a great LLM?"
messages = [{"role": "user", "content": input_text}]
input_ids = tokenizer.apply_chat_template(messages, return_dict=True, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))
```
If you have flash-attention installed (`pip install flash-attn`), it is possible to generate faster. (The HuggingFace documentation for flash-attention can be found [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2).)
```python
from transformers import DbrxForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct", token="YOUR_HF_TOKEN")
model = DbrxForCausalLM.from_pretrained(
"databricks/dbrx-instruct",
device_map="auto",
torch_dtype=torch.bfloat16,
token="YOUR_HF_TOKEN",
attn_implementation="flash_attention_2",
)
input_text = "What does it take to build a great LLM?"
messages = [{"role": "user", "content": input_text}]
input_ids = tokenizer.apply_chat_template(messages, return_dict=True, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))
```
You can also generate faster using the PyTorch scaled dot product attention. (The HuggingFace documentation for scaled dot product attention can be found [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#pytorch-scaled-dot-product-attention).)
```python
from transformers import DbrxForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct", token="YOUR_HF_TOKEN")
model = DbrxForCausalLM.from_pretrained(
"databricks/dbrx-instruct",
device_map="auto",
torch_dtype=torch.bfloat16,
token="YOUR_HF_TOKEN",
attn_implementation="sdpa",
)
input_text = "What does it take to build a great LLM?"
messages = [{"role": "user", "content": input_text}]
input_ids = tokenizer.apply_chat_template(messages, return_dict=True, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))
```
## DbrxConfig
[[autodoc]] DbrxConfig
## DbrxModel
[[autodoc]] DbrxModel
- forward
## DbrxForCausalLM
[[autodoc]] DbrxForCausalLM
- forward

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@@ -40,6 +40,7 @@ FlashAttention-2 is currently supported for the following architectures:
* [Bark](https://huggingface.co/docs/transformers/model_doc/bark#transformers.BarkModel)
* [Bart](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartModel)
* [Cohere](https://huggingface.co/docs/transformers/model_doc/cohere#transformers.CohereModel)
* [Dbrx](https://huggingface.co/docs/transformers/model_doc/dbrx#transformers.DbrxModel)
* [DistilBert](https://huggingface.co/docs/transformers/model_doc/distilbert#transformers.DistilBertModel)
* [Gemma](https://huggingface.co/docs/transformers/model_doc/gemma#transformers.GemmaModel)
* [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)
@@ -184,9 +185,10 @@ PyTorch's [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.o
For now, Transformers supports SDPA inference and training for the following architectures:
* [Bart](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartModel)
* [Cohere](https://huggingface.co/docs/transformers/model_doc/cohere#transformers.CohereModel)
* [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode#transformers.GPTBigCodeModel)
* [Dbrx](https://huggingface.co/docs/transformers/model_doc/dbrx#transformers.DbrxModel)
* [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
* [Gemma](https://huggingface.co/docs/transformers/model_doc/gemma#transformers.GemmaModel)
* [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode#transformers.GPTBigCodeModel)
* [Jamba](https://huggingface.co/docs/transformers/model_doc/jamba#transformers.JambaModel)
* [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
* [OLMo](https://huggingface.co/docs/transformers/model_doc/olmo#transformers.OlmoModel)

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@@ -37,7 +37,7 @@ You can finetune other architectures for causal language modeling following the
Choose one of the following architectures:
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeLlama](../model_doc/code_llama), [CodeGen](../model_doc/codegen), [Cohere](../model_doc/cohere), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [Falcon](../model_doc/falcon), [Fuyu](../model_doc/fuyu), [Gemma](../model_doc/gemma), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [Jamba](../model_doc/jamba), [LLaMA](../model_doc/llama), [Mamba](../model_doc/mamba), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MPT](../model_doc/mpt), [MusicGen](../model_doc/musicgen), [MusicGen Melody](../model_doc/musicgen_melody), [MVP](../model_doc/mvp), [OLMo](../model_doc/olmo), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Qwen2MoE](../model_doc/qwen2_moe), [RecurrentGemma](../model_doc/recurrent_gemma), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [StableLm](../model_doc/stablelm), [Starcoder2](../model_doc/starcoder2), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [Whisper](../model_doc/whisper), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)
[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeLlama](../model_doc/code_llama), [CodeGen](../model_doc/codegen), [Cohere](../model_doc/cohere), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [DBRX](../model_doc/dbrx), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [Falcon](../model_doc/falcon), [Fuyu](../model_doc/fuyu), [Gemma](../model_doc/gemma), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [Jamba](../model_doc/jamba), [LLaMA](../model_doc/llama), [Mamba](../model_doc/mamba), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MPT](../model_doc/mpt), [MusicGen](../model_doc/musicgen), [MusicGen Melody](../model_doc/musicgen_melody), [MVP](../model_doc/mvp), [OLMo](../model_doc/olmo), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Qwen2MoE](../model_doc/qwen2_moe), [RecurrentGemma](../model_doc/recurrent_gemma), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [StableLm](../model_doc/stablelm), [Starcoder2](../model_doc/starcoder2), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [Whisper](../model_doc/whisper), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)