Add JetMoE model (#30005)

* init jetmoe code

* update archive maps

* remove flax import

* fix import error

* update README

* ruff fix

* update readme

* fix

* update config

* fix issue

* merge files

* fix model bug

* fix test

* auto fix

* model size

* add comments

* fix form

* add flash attention support

* fix attention head number

* fix init

* fix support list

* sort auto mapping

* fix test

* fix docs

* update test

* fix test

* fix test

* change variable name

* fix config

* fix init

* update format

* clean code

* fix config

* fix config

* change default config

* update config

* fix issues

* update formate

* update config argument

* update format

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* change to mixtral aux loss

* change to cache_position

* debug

* fix bugs

* debug

* fix format

* fix format

* fix copy

* fix format

* fix format

* fix sort

* fix sort

* fix sort

* add copy comment

* add copy from

* remove debug code

* revert readme update

* add copy

* debug

* remove debug code

* fix flash attention

* add comments

* clean code

* clean format

* fix format

* fix format

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* change variable name

* add copied from

* fix variable name

* remove deprecated functinos

* sync to llama implementation

* fix format

* fix copy

* fix format

* update format

* remove repr

* add comment for moe weight

* fix copy

* Update src/transformers/models/jetmoe/configuration_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
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* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* add comments and reformat config

* fix format

* fix format

* fix format

* update test

* update doc string in config

* Update src/transformers/models/jetmoe/modeling_jetmoe.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* update config doc

* update attention cache

* fix format

* fix copy

---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

docs/source/en/_toctree.yml

@@ -386,6 +386,8 @@
         title: I-BERT
       - local: model_doc/jamba
         title: Jamba
+      - local: model_doc/jetmoe
+        title: JetMoe
       - local: model_doc/jukebox
         title: Jukebox
       - local: model_doc/led

docs/source/en/index.md

@@ -166,6 +166,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                      [Informer](model_doc/informer)                      |       ✅        |         ❌         |      ❌      |
 |                  [InstructBLIP](model_doc/instructblip)                  |       ✅        |         ❌         |      ❌      |
 |                         [Jamba](model_doc/jamba)                         |       ✅        |         ❌         |      ❌      |
+|                        [JetMoe](model_doc/jetmoe)                        |       ✅        |         ❌         |      ❌      |
 |                       [Jukebox](model_doc/jukebox)                       |       ✅        |         ❌         |      ❌      |
 |                      [KOSMOS-2](model_doc/kosmos-2)                      |       ✅        |         ❌         |      ❌      |
 |                      [LayoutLM](model_doc/layoutlm)                      |       ✅        |         ✅         |      ❌      |

docs/source/en/model_doc/jetmoe.md

+<!--Copyright 2024 JetMoe team and 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# JetMoe
+
+## Overview
+
+**JetMoe-8B** is an 8B Mixture-of-Experts (MoE) language model developed by [Yikang Shen](https://scholar.google.com.hk/citations?user=qff5rRYAAAAJ) and [MyShell](https://myshell.ai/).
+JetMoe project aims to provide a LLaMA2-level performance and efficient language model with a limited budget.
+To achieve this goal, JetMoe uses a sparsely activated architecture inspired by the [ModuleFormer](https://arxiv.org/abs/2306.04640). 
+Each JetMoe block consists of two MoE layers: Mixture of Attention Heads and Mixture of MLP Experts.
+Given the input tokens, it activates a subset of its experts to process them.
+This sparse activation schema enables JetMoe to achieve much better training throughput than similar size dense models. 
+The training throughput of JetMoe-8B is around 100B tokens per day on a cluster of 96 H100 GPUs with a straightforward 3-way pipeline parallelism strategy.
+
+This model was contributed by [Yikang Shen](https://huggingface.co/YikangS).
+
+
+## JetMoeConfig
+
+[[autodoc]] JetMoeConfig
+
+## JetMoeModel
+
+[[autodoc]] JetMoeModel
+    - forward
+
+## JetMoeForCausalLM
+
+[[autodoc]] JetMoeForCausalLM
+    - forward
+
+## JetMoeForSequenceClassification
+
+[[autodoc]] JetMoeForSequenceClassification
+    - forward

docs/source/en/perf_infer_gpu_one.md

@@ -50,6 +50,7 @@ FlashAttention-2 is currently supported for the following architectures:
 * [GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj#transformers.GPTJModel)
 * [Idefics2](https://huggingface.co/docs/transformers/model_doc/idefics2#transformers.Idefics2Model)
 * [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
+* [JetMoe](https://huggingface.co/docs/transformers/model_doc/jetmoe#transformers.JetMoeModel)
 * [Jamba](https://huggingface.co/docs/transformers/model_doc/jamba#transformers.JambaModel)
 * [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
 * [Llava](https://huggingface.co/docs/transformers/model_doc/llava)
@@ -198,6 +199,7 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [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)
+* [JetMoe](https://huggingface.co/docs/transformers/model_doc/jetmoe#transformers.JetMoeModel)
 * [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)

src/transformers/__init__.py

@@ -448,6 +448,7 @@ _import_structure = {
         "InstructBlipVisionConfig",
     ],
     "models.jamba": ["JambaConfig"],
+    "models.jetmoe": ["JetMoeConfig"],
     "models.jukebox": [
         "JukeboxConfig",
         "JukeboxPriorConfig",
@@ -2202,6 +2203,14 @@ else:
             "JambaPreTrainedModel",
         ]
     )
+    _import_structure["models.jetmoe"].extend(
+        [
+            "JetMoeForCausalLM",
+            "JetMoeForSequenceClassification",
+            "JetMoeModel",
+            "JetMoePreTrainedModel",
+        ]
+    )
     _import_structure["models.jukebox"].extend(
         [
             "JukeboxModel",
@@ -4973,6 +4982,7 @@ if TYPE_CHECKING:
         InstructBlipVisionConfig,
     )
     from .models.jamba import JambaConfig
+    from .models.jetmoe import JetMoeConfig
     from .models.jukebox import (
         JukeboxConfig,
         JukeboxPriorConfig,
@@ -6591,6 +6601,12 @@ if TYPE_CHECKING:
             JambaModel,
             JambaPreTrainedModel,
         )
+        from .models.jetmoe import (
+            JetMoeForCausalLM,
+            JetMoeForSequenceClassification,
+            JetMoeModel,
+            JetMoePreTrainedModel,
+        )
         from .models.jukebox import (
             JukeboxModel,
             JukeboxPreTrainedModel,

src/transformers/models/__init__.py

@@ -117,6 +117,7 @@ from . import (
     informer,
     instructblip,
     jamba,
+    jetmoe,
     jukebox,
     kosmos2,
     layoutlm,

src/transformers/models/auto/configuration_auto.py

@@ -128,6 +128,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
         ("informer", "InformerConfig"),
         ("instructblip", "InstructBlipConfig"),
         ("jamba", "JambaConfig"),
+        ("jetmoe", "JetMoeConfig"),
         ("jukebox", "JukeboxConfig"),
         ("kosmos-2", "Kosmos2Config"),
         ("layoutlm", "LayoutLMConfig"),
@@ -399,6 +400,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("informer", "Informer"),
         ("instructblip", "InstructBLIP"),
         ("jamba", "Jamba"),
+        ("jetmoe", "JetMoe"),
         ("jukebox", "Jukebox"),
         ("kosmos-2", "KOSMOS-2"),
         ("layoutlm", "LayoutLM"),

src/transformers/models/auto/modeling_auto.py

@@ -125,6 +125,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("imagegpt", "ImageGPTModel"),
         ("informer", "InformerModel"),
         ("jamba", "JambaModel"),
+        ("jetmoe", "JetMoeModel"),
         ("jukebox", "JukeboxModel"),
         ("kosmos-2", "Kosmos2Model"),
         ("layoutlm", "LayoutLMModel"),
@@ -458,6 +459,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
         ("gpt_neox_japanese", "GPTNeoXJapaneseForCausalLM"),
         ("gptj", "GPTJForCausalLM"),
         ("jamba", "JambaForCausalLM"),
+        ("jetmoe", "JetMoeForCausalLM"),
         ("llama", "LlamaForCausalLM"),
         ("mamba", "MambaForCausalLM"),
         ("marian", "MarianForCausalLM"),
@@ -860,6 +862,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("gptj", "GPTJForSequenceClassification"),
         ("ibert", "IBertForSequenceClassification"),
         ("jamba", "JambaForSequenceClassification"),
+        ("jetmoe", "JetMoeForSequenceClassification"),
         ("layoutlm", "LayoutLMForSequenceClassification"),
         ("layoutlmv2", "LayoutLMv2ForSequenceClassification"),
         ("layoutlmv3", "LayoutLMv3ForSequenceClassification"),

src/transformers/models/auto/tokenization_auto.py

@@ -211,6 +211,13 @@ else:
                     "LlamaTokenizerFast" if is_tokenizers_available() else None,
                 ),
             ),
+            (
+                "jetmoe",
+                (
+                    "LlamaTokenizer" if is_sentencepiece_available() else None,
+                    "LlamaTokenizerFast" if is_tokenizers_available() else None,
+                ),
+            ),
             ("jukebox", ("JukeboxTokenizer", None)),
             (
                 "kosmos-2",

src/transformers/models/jetmoe/__init__.py

+# Copyright 2024 JetMoe AI and The HuggingFace Inc. 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.
+from typing import TYPE_CHECKING
+
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_jetmoe": ["JetMoeConfig"],
+}
+
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_jetmoe"] = [
+        "JetMoeForCausalLM",
+        "JetMoeModel",
+        "JetMoePreTrainedModel",
+        "JetMoeForSequenceClassification",
+    ]
+
+if TYPE_CHECKING:
+    from .configuration_jetmoe import JetMoeConfig
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_jetmoe import (
+            JetMoeForCausalLM,
+            JetMoeForSequenceClassification,
+            JetMoeModel,
+            JetMoePreTrainedModel,
+        )
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

src/transformers/models/jetmoe/configuration_jetmoe.py

+# coding=utf-8
+# Copyright 2024 JetMoe AI and the HuggingFace Inc. 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.
+"""JetMoe model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class JetMoeConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a
+    JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a configuration of the JetMoe-4B.
+
+    [jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the JetMoe model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`JetMoeModel`]
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each key and value in the Transformer encoder.
+        kv_channels (`int`, *optional*, defaults to 128):
+            Defines the number of channels for the key and value tensors.
+        intermediate_size (`int`, *optional*, defaults to 5632):
+            Dimension of the MLP representations.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with. JetMoe's attention allows sequence of
+            up to 4096 tokens.
+        activation_function (`string`, *optional*, defaults to `"silu"`):
+            Defines the activation function for MLP experts.
+        num_local_experts (`int`, *optional*, defaults to 8):
+            Defines the number of experts in the MoE and MoA.
+        num_experts_per_tok (`int, *optional*, defaults to 2):
+            The number of experts to route per-token and for MoE and MoA.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabeling this will also
+            allow the model to output the auxiliary loss.
+        aux_loss_coef (`float`, *optional*, defaults to 0.01):
+            The coefficient for the auxiliary loss.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        initializer_range (`float`, *optional*, defaults to 0.01):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import JetMoeModel, JetMoeConfig
+
+    >>> # Initializing a JetMoe 4B style configuration
+    >>> configuration = JetMoeConfig()
+
+    >>> # Initializing a model from the JetMoe 4B style configuration
+    >>> model = JetMoeModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "jetmoe"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=2048,
+        num_hidden_layers=12,
+        num_key_value_heads=16,
+        kv_channels=128,
+        intermediate_size=5632,
+        max_position_embeddings=4096,
+        activation_function="silu",
+        num_local_experts=8,
+        num_experts_per_tok=2,
+        output_router_logits=False,
+        aux_loss_coef=0.01,
+        use_cache=True,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        rms_norm_eps=1e-6,
+        initializer_range=0.01,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        if num_experts_per_tok > num_local_experts:
+            raise ValueError("`num_experts_per_tok` must be less than or equal to `num_local_experts`")
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_key_value_heads * num_experts_per_tok
+        self.num_key_value_heads = num_key_value_heads
+        self.kv_channels = kv_channels
+        self.intermediate_size = intermediate_size
+        self.max_position_embeddings = max_position_embeddings
+        self.activation_function = activation_function
+        self.num_local_experts = num_local_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.output_router_logits = output_router_logits
+        self.aux_loss_coef = aux_loss_coef
+        self.use_cache = use_cache
+        self.initializer_range = initializer_range
+        self.attention_dropout = attention_dropout
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        self.rope_theta = rope_theta
+        self.rms_norm_eps = rms_norm_eps
+
+        super().__init__(
+            bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -4334,6 +4334,34 @@ class JambaPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class JetMoeForCausalLM(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class JetMoeForSequenceClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class JetMoeModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class JetMoePreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class JukeboxModel(metaclass=DummyObject):
     _backends = ["torch"]