Add Zamba (#30950)

* Update index.md

* Rebase

* Rebase

* Updates from make fixup

* Update zamba.md

* Batched inference

* Update

* Fix tests

* Fix tests

* Fix tests

* Fix tests

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

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

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

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

* Update configuration_zamba.py

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Update modeling_zamba.py

* Update modeling_zamba.py

* Update modeling_zamba.py

* Update configuration_zamba.py

* Update modeling_zamba.py

* Update modeling_zamba.py

* Merge branch 'main' of https://github.com/Zyphra/transformers_zamba

* Update ZambaForCausalLM

* Update ZambaForCausalLM

* Describe diffs with original mamba layer

* Moved mamba init into `_init_weights`

* Update index.md

* Rebase

* Rebase

* Updates from make fixup

* Update zamba.md

* Batched inference

* Update

* Fix tests

* Fix tests

* Fix tests

* Fix tests

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

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

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

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

* Update configuration_zamba.py

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Update modeling_zamba.py

* Update modeling_zamba.py

* Update modeling_zamba.py

* Update configuration_zamba.py

* Update modeling_zamba.py

* Update modeling_zamba.py

* Merge branch 'main' of https://github.com/Zyphra/transformers_zamba

* Update ZambaForCausalLM

* Moved mamba init into `_init_weights`

* Update ZambaForCausalLM

* Describe diffs with original mamba layer

* make fixup fixes

* quality test fixes

* Fix Zamba model path

* circleci fixes

* circleci fixes

* circleci fixes

* circleci fixes

* circleci fixes

* circleci fixes

* circleci fixes

* circleci fixes

* circleci fixes

* Update

* circleci fixes

* fix zamba test from merge

* fix ValueError for disabling mamba kernels

* add HF copyright

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

* shared_transf --> shared_transformer

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Fixes

* Move attention head dim to config

* Fix circle/ci tests

* Update modeling_zamba.py

* apply GenerationMixin inheritance change from upstream

* apply import ordering

* update needed transformers version for zamba

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

* add contribution author

* add @slow to avoid CI

* Update src/transformers/models/zamba/modeling_zamba.py

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

* Define attention_hidden_size

* Added doc for attention_head_size

* trigger CI

* Fix doc of attention_hidden_size

* [run-slow] zamba

* Fixed shared layer logic, swapped up<->gate in mlp

* shared_transformer -> shared_transf

* reformat HybridLayer __init__

* fix docstrings in zamba config

* added definition of _get_input_ids_and_config

* fixed formatting of _get_input_ids_and_config

---------

Co-authored-by: root <root@node-4.us-southcentral1-a.compute.internal>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: root <root@node-1.us-southcentral1-a.compute.internal>
Co-authored-by: Quentin Anthony <qganthony@yahoo.com>

docs/source/en/_toctree.yml

@@ -711,6 +711,8 @@
         title: ViTMSN
       - local: model_doc/yolos
         title: YOLOS
+      - local: model_doc/zamba
+        title: Zamba
       - local: model_doc/zoedepth
         title: ZoeDepth
       title: Vision models

docs/source/en/index.md

@@ -361,6 +361,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                 [XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)                 |       ✅        |         ✅         |      ✅      |
 |                         [YOLOS](model_doc/yolos)                         |       ✅        |         ❌         |      ❌      |
 |                          [YOSO](model_doc/yoso)                          |       ✅        |         ❌         |      ❌      |
+|                         [Zamba](model_doc/zamba)                         |       ✅        |         ❌         |      ❌      |
 |                      [ZoeDepth](model_doc/zoedepth)                      |       ✅        |         ❌         |      ❌      |
 
 <!-- End table-->

docs/source/en/model_doc/zamba.md

@@ -0,0 +1,100 @@
+<!--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.
+
+⚠️ 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.
+
+-->
+# Zamba
+
+Zamba is a large language model (LLM) trained by Zyphra, and made available under an Apache 2.0 license. Please see the [Zyphra Hugging Face](https://huggingface.co/collections/zyphra/) repository for model weights.
+
+This model was contributed by [pglo](https://huggingface.co/pglo).
+
+
+## Model details
+
+Zamba-7B-v1 is a hybrid between state-space models (Specifically [Mamba](https://github.com/state-spaces/mamba)) and transformer, and was trained using next-token prediction. Zamba uses a shared transformer layer after every 6 mamba blocks. It uses the [Mistral v0.1 tokenizer](https://huggingface.co/mistralai/Mistral-7B-v0.1). We came to this architecture after a series of ablations at small scales. Zamba-7B-v1 was pre-trained on 1T tokens of text and code data.
+
+<img src=https://github.com/user-attachments/assets/c2cff209-b901-483c-87aa-774b82a0769f width=30% height=40% />
+
+## Quick start
+
+
+### Presequities
+
+Zamba requires you use `transformers` version 4.46.0 or higher:
+```bash
+pip install transformers>=4.45.0
+```
+
+In order to run optimized Mamba implementations, you first need to install `mamba-ssm` and `causal-conv1d`:
+```bash
+pip install mamba-ssm causal-conv1d>=1.2.0
+```
+You also have to have the model on a CUDA device.
+
+You can run the model not using the optimized Mamba kernels, but it is **not** recommended as it will result in significantly lower latencies. In order to do that, you'll need to specify `use_mamba_kernels=False` when loading the model.
+
+
+## Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba-7B-v1")
+model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba-7B-v1", device_map="auto", torch_dtype=torch.bfloat16)
+
+input_text = "A funny prompt would be "
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=100)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+## Model card
+
+The model cards can be found at:
+* [Zamba-7B](MODEL_CARD_ZAMBA-7B-v1.md)
+
+
+## Issues
+For issues with model output, or community discussion, please use the Hugging Face community [forum](https://huggingface.co/zyphra/zamba-7b)
+
+
+## License
+
+The model weights are open-sourced via an Apache 2.0 license.
+
+
+## ZambaConfig
+
+[[autodoc]] ZambaConfig
+
+
+## ZambaModel
+
+[[autodoc]] ZambaModel
+    - forward
+
+
+## ZambaForCausalLM
+
+[[autodoc]] ZambaForCausalLM
+    - forward
+
+
+## ZambaForSequenceClassification
+
+[[autodoc]] transformers.ZambaForSequenceClassification
+    - forward

src/transformers/__init__.py

@@ -847,6 +847,7 @@ _import_structure = {
     "models.xmod": ["XmodConfig"],
     "models.yolos": ["YolosConfig"],
     "models.yoso": ["YosoConfig"],
+    "models.zamba": ["ZambaConfig"],
     "models.zoedepth": ["ZoeDepthConfig"],
     "onnx": [],
     "pipelines": [
@@ -3759,6 +3760,14 @@ else:
             "YosoPreTrainedModel",
         ]
     )
+    _import_structure["models.zamba"].extend(
+        [
+            "ZambaForCausalLM",
+            "ZambaForSequenceClassification",
+            "ZambaModel",
+            "ZambaPreTrainedModel",
+        ]
+    )
     _import_structure["models.zoedepth"].extend(
         [
             "ZoeDepthForDepthEstimation",
@@ -5729,6 +5738,7 @@ if TYPE_CHECKING:
     from .models.xmod import XmodConfig
     from .models.yolos import YolosConfig
     from .models.yoso import YosoConfig
+    from .models.zamba import ZambaConfig
     from .models.zoedepth import ZoeDepthConfig
 
     # Pipelines
@@ -8126,6 +8136,12 @@ if TYPE_CHECKING:
             YosoModel,
             YosoPreTrainedModel,
         )
+        from .models.zamba import (
+            ZambaForCausalLM,
+            ZambaForSequenceClassification,
+            ZambaModel,
+            ZambaPreTrainedModel,
+        )
         from .models.zoedepth import (
             ZoeDepthForDepthEstimation,
             ZoeDepthPreTrainedModel,

src/transformers/generation/utils.py

@@ -1581,7 +1581,11 @@ class GenerationMixin:
         order to save memory (because no back and forth `to_legacy_cache` and `from_legacy_cache` will be performed
         for `HybridMambaAttentionDynamicCache`).
         """
-        return self._supports_cache_class and "jamba" not in self.__class__.__name__.lower()
+        return (
+            self._supports_cache_class
+            and "jamba" not in self.__class__.__name__.lower()
+            and "zamba" not in self.__class__.__name__.lower()
+        )
 
     def _prepare_cache_for_generation(
         self,

src/transformers/models/__init__.py

@@ -281,5 +281,6 @@ from . import (
     xmod,
     yolos,
     yoso,
+    zamba,
     zoedepth,
 )

src/transformers/models/auto/configuration_auto.py

@@ -311,6 +311,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
         ("xmod", "XmodConfig"),
         ("yolos", "YolosConfig"),
         ("yoso", "YosoConfig"),
+        ("zamba", "ZambaConfig"),
         ("zoedepth", "ZoeDepthConfig"),
     ]
 )
@@ -630,6 +631,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("xmod", "X-MOD"),
         ("yolos", "YOLOS"),
         ("yoso", "YOSO"),
+        ("zamba", "Zamba"),
         ("zoedepth", "ZoeDepth"),
     ]
 )

src/transformers/models/auto/modeling_auto.py

@@ -284,6 +284,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("xmod", "XmodModel"),
         ("yolos", "YolosModel"),
         ("yoso", "YosoModel"),
+        ("zamba", "ZambaModel"),
     ]
 )
 
@@ -546,6 +547,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
         ("xlm-roberta-xl", "XLMRobertaXLForCausalLM"),
         ("xlnet", "XLNetLMHeadModel"),
         ("xmod", "XmodForCausalLM"),
+        ("zamba", "ZambaForCausalLM"),
     ]
 )
 
@@ -974,6 +976,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("xlnet", "XLNetForSequenceClassification"),
         ("xmod", "XmodForSequenceClassification"),
         ("yoso", "YosoForSequenceClassification"),
+        ("zamba", "ZambaForSequenceClassification"),
     ]
 )
 

src/transformers/models/auto/tokenization_auto.py

@@ -555,6 +555,13 @@ else:
                     "AlbertTokenizerFast" if is_tokenizers_available() else None,
                 ),
             ),
+            (
+                "zamba",
+                (
+                    "LlamaTokenizer" if is_sentencepiece_available() else None,
+                    "LlamaTokenizerFast" if is_tokenizers_available() else None,
+                ),
+            ),
         ]
     )
 

src/transformers/models/zamba/__init__.py

@@ -0,0 +1,57 @@
+# 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.
+from typing import TYPE_CHECKING
+
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_zamba": ["ZambaConfig"],
+}
+
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_zamba"] = [
+        "ZambaForCausalLM",
+        "ZambaForSequenceClassification",
+        "ZambaModel",
+        "ZambaPreTrainedModel",
+    ]
+
+if TYPE_CHECKING:
+    from .configuration_zamba import ZambaConfig
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_zamba import (
+            ZambaForCausalLM,
+            ZambaForSequenceClassification,
+            ZambaModel,
+            ZambaPreTrainedModel,
+        )
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

src/transformers/models/zamba/configuration_zamba.py

@@ -0,0 +1,224 @@
+# coding=utf-8
+# Copyright 2024 Zyphra Technologies 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.
+"""Zamba model configuration"""
+
+import math
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class ZambaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ZambaModel`]. It is used to instantiate a
+    Zamba model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Zamba-v0.1 model.
+
+    [Zyphra/Zamba-7B-v1](https://huggingface.co/Zyphra/Zamba-7B-v1)
+
+    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 Zamba model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ZambaModel`]
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+            Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
+            model has a output word embedding layer.
+        hidden_size (`int`, *optional*, defaults to 3712):
+            Dimension of the hidden representations.
+        attention_hidden_size (`int`, *optional*):
+            Dimension of the hidden representations of the inputs to the Attention layer.
+        intermediate_size (`int`, *optional*, defaults to 14848):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 76):
+            Number of hidden layers in the model.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        attention_head_dim (`int`, *optional*):
+            Dimension of the attention head in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*, defaults to 16):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=None`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf).
+        n_mamba_heads (`int`, *optional*, defaults to 2):
+            Number of mamba heads for each mamba layer.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the decoder.
+        hidden_mamba_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the mamba layer.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        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`.
+        num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
+            Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an
+            integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the
+            logits of the last prompt token are needed for generation. For long sequences, the logits for the entire
+            sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint
+            significantly.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            The id of the padding token.
+        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.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            This value doesn't have any real effect. The maximum sequence length that this model is intended to be
+            used with. It can be used with longer sequences, but performance may degrade.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        attn_layer_period (`int`, *optional*, defaults to 6):
+            Once in this many layers, we will have a shared attention layer
+        attn_layer_offset (`int`, *optional*, defaults to 4):
+            Offset of the shared attention layer
+        use_mamba_kernels (`bool`, *optional*, defaults to `True`):
+            Flag indicating whether or not to use the fast mamba kernels. These are available only if `mamba-ssm` and
+            `causal-conv1d` are installed, and the mamba modules are running on a CUDA device. Raises ValueError if
+            `True` and kernels are not available
+        mamba_d_state (`int`, *optional*, defaults to 16):
+            The dimension the mamba state space latents
+        mamba_d_conv (`int`, *optional*, defaults to 4):
+            The size of the mamba convolution kernel
+        mamba_expand (`int`, *optional*, defaults to 2):
+            Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
+        mamba_dt_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
+            Rank of the the mamba discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
+        time_step_min (`float`, *optional*, defaults to 0.001):
+            Minimum `time_step` used to bound `dt_proj_bias`.
+        time_step_max (`float`, *optional*, defaults to 0.1):
+            Maximum `time_step` used to bound `dt_proj_bias`.
+        time_step_floor (`float`, *optional*, defaults to 0.0001):
+            Minimum clamping value of the `dt_proj.bias` layer initialization.
+        mamba_conv_bias (`bool`, *optional*, defaults to `True`):
+            Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
+        mamba_proj_bias (`bool`, *optional*, defaults to `False`):
+            Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block
+
+    """
+
+    model_type = "zamba"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        tie_word_embeddings=True,
+        hidden_size=3712,
+        attention_hidden_size=None,
+        intermediate_size=14848,
+        num_hidden_layers=76,
+        num_attention_heads=16,
+        attention_head_dim=None,
+        num_key_value_heads=16,
+        n_mamba_heads=2,
+        hidden_act="gelu",
+        hidden_mamba_act="silu",
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        num_logits_to_keep=1,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        max_position_embeddings=4096,
+        attention_dropout=0.0,
+        attn_layer_period=6,
+        attn_layer_offset=4,
+        use_mamba_kernels=True,
+        mamba_d_state=16,
+        mamba_d_conv=4,
+        mamba_expand=2,
+        mamba_dt_rank="auto",
+        time_step_min=0.001,
+        time_step_max=0.1,
+        time_step_floor=1e-4,
+        mamba_conv_bias=True,
+        mamba_proj_bias=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.tie_word_embeddings = tie_word_embeddings
+        self.hidden_size = hidden_size
+        if attention_hidden_size is None:
+            self.attention_hidden_size = 2 * hidden_size
+        else:
+            self.attention_hidden_size = attention_hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        if attention_head_dim is None:
+            self.attention_head_dim = 2 * self.hidden_size // self.num_attention_heads
+        else:
+            self.attention_head_dim = attention_head_dim
+        self.max_position_embeddings = max_position_embeddings
+        self.attention_dropout = attention_dropout
+
+        self.num_key_value_heads = num_key_value_heads
+        self.n_mamba_heads = n_mamba_heads
+        self.hidden_act = hidden_act
+        self.hidden_mamba_act = hidden_mamba_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+
+        self.use_cache = use_cache
+        self.num_logits_to_keep = num_logits_to_keep
+
+        self.attn_layer_period = attn_layer_period
+        self.attn_layer_offset = attn_layer_offset
+
+        self.use_mamba_kernels = use_mamba_kernels
+        self.mamba_d_state = mamba_d_state
+        self.mamba_d_conv = mamba_d_conv
+        self.mamba_expand = mamba_expand
+        self.mamba_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank
+        self.time_step_min = time_step_min
+        self.time_step_max = time_step_max
+        self.time_step_floor = time_step_floor
+        self.mamba_conv_bias = mamba_conv_bias
+        self.mamba_proj_bias = mamba_proj_bias
+
+        self.layers_block_type = self._layers_block_type(num_hidden_layers, attn_layer_period, attn_layer_offset)
+
+        assert (
+            self.mamba_expand * self.hidden_size
+        ) % self.n_mamba_heads == 0, "`intermediate_size` should be divisible by `n_mamba_heads`."
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _layers_block_type(self, num_hidden_layers, attn_layer_period, attn_layer_offset):
+        layers = [
+            "mamba",
+            "mamba",
+            "hybrid",
+        ] + ["hybrid" if i % attn_layer_period == attn_layer_offset else "mamba" for i in range(num_hidden_layers - 3)]
+        return layers

src/transformers/utils/dummy_pt_objects.py

@@ -9975,6 +9975,34 @@ class YosoPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class ZambaForCausalLM(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class ZambaForSequenceClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class ZambaModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class ZambaPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class ZoeDepthForDepthEstimation(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/generation/test_utils.py

@@ -2100,7 +2100,17 @@ class GenerationTesterMixin:
         # 1. Its inner sequence length is with respect to the inputs of the latest forward pass, hence the "-1"
         # 2. We ignore models that have unique cache structures (e.g. mamba) or are in need of refatoring to match the
         #    standard cache format (e.g.gptbigcode )
-        models_without_standard_cache = ("ctrl", "fsmt", "gptbigcode", "mega", "reformer", "jamba", "mamba", "xlnet")
+        models_without_standard_cache = (
+            "ctrl",
+            "fsmt",
+            "gptbigcode",
+            "mega",
+            "reformer",
+            "jamba",
+            "mamba",
+            "xlnet",
+            "zamba",
+        )
         has_standard_cache = not any(
             model_name in config.__class__.__name__.lower() for model_name in models_without_standard_cache
         )

tests/models/zamba/test_modeling_zamba.py

@@ -0,0 +1,736 @@
+# coding=utf-8
+# Copyright 2024 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.
+"""Testing suite for the PyTorch Zamba model."""
+
+import math
+import tempfile
+import unittest
+
+import pytest
+from parameterized import parameterized
+
+from transformers import AutoTokenizer, ZambaConfig, is_torch_available
+from transformers.testing_utils import (
+    require_bitsandbytes,
+    require_flash_attn,
+    require_torch,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        ZambaForCausalLM,
+        ZambaForSequenceClassification,
+        ZambaModel,
+    )
+    from transformers.models.zamba.modeling_zamba import (
+        HybridMambaAttentionDynamicCache,
+    )
+
+
+class ZambaModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=64,
+        mamba_dt_rank=32,
+        num_hidden_layers=5,
+        attn_layer_offset=1,
+        attn_layer_period=8,
+        num_attention_heads=4,
+        num_key_value_heads=4,
+        n_mamba_heads=2,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_mamba_act="silu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.mamba_dt_rank = mamba_dt_rank
+        self.num_hidden_layers = num_hidden_layers
+        self.attn_layer_offset = attn_layer_offset
+        self.attn_layer_period = attn_layer_period
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.n_mamba_heads = n_mamba_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_mamba_act = hidden_mamba_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return ZambaConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            mamba_dt_rank=self.mamba_dt_rank,
+            num_hidden_layers=self.num_hidden_layers,
+            attn_layer_offset=self.attn_layer_offset,
+            attn_layer_period=self.attn_layer_period,
+            num_attention_heads=self.num_attention_heads,
+            num_key_value_heads=self.num_key_value_heads,
+            n_mamba_heads=self.n_mamba_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_mamba_act=self.hidden_mamba_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=True,
+            initializer_range=self.initializer_range,
+            use_mamba_kernels=False,
+        )
+
+    def prepare_config_and_inputs_for_decoder(self):
+        (
+            config,
+            input_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = self.prepare_config_and_inputs()
+
+        config.is_decoder = True
+
+        return (
+            config,
+            input_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
+        model = ZambaModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_causal_lm(
+        self,
+        config,
+        input_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+    ):
+        model = ZambaForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
+        result = model(input_ids, attention_mask=input_mask)
+        result = model(input_ids, labels=token_labels)
+        result = model(input_ids)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_decoder_model_past_large_inputs(
+        self,
+        config,
+        input_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+    ):
+        config.is_decoder = True
+        config.add_cross_attention = True
+        model = ZambaForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        # first forward pass
+        # Attention: Zamba needs the cache to be initialized to return a cache!
+        past_key_values = HybridMambaAttentionDynamicCache(
+            config, input_ids.shape[0], model.dtype, device=model.device
+        )
+        outputs = model(
+            input_ids,
+            attention_mask=input_mask,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+        past_key_values = outputs.past_key_values
+
+        # create hypothetical multiple next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
+        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
+
+        # append to next input_ids and
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
+
+        output_from_no_past = model(
+            next_input_ids,
+            attention_mask=next_attention_mask,
+            output_hidden_states=True,
+        )["hidden_states"][0]
+        output_from_past = model(
+            next_tokens,
+            attention_mask=next_attention_mask,
+            past_key_values=past_key_values,
+            output_hidden_states=True,
+            cache_position=torch.arange(
+                input_ids.shape[1], input_ids.shape[1] + next_tokens.shape[1], device=model.device
+            ),
+        )["hidden_states"][0]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
+        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
+
+        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = ZambaForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class ZambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            ZambaModel,
+            ZambaForCausalLM,
+            ZambaForSequenceClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = (ZambaForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ZambaModel,
+            "text-classification": ZambaForSequenceClassification,
+            "text-generation": ZambaForCausalLM,
+            "zero-shot": ZambaForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+
+    def setUp(self):
+        self.model_tester = ZambaModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ZambaConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_casual_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
+    def test_decoder_model_past_with_large_inputs(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
+
+    def test_initialization(self):
+        r"""
+        Overriding the test_initialization test as the A_log and D params of the Mamba block are initialized differently
+        """
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            for name, param in model.named_parameters():
+                if param.requires_grad:
+                    if "A_log" in name:
+                        A = torch.arange(1, config.mamba_d_state + 1, dtype=torch.float32)[None, :]
+                        self.assertTrue(torch.allclose(param.data, torch.log(A), atol=1e-5, rtol=1e-5))
+                    elif "D" in name:
+                        # check if it's a ones like
+                        self.assertTrue(torch.allclose(param.data, torch.ones_like(param.data), atol=1e-5, rtol=1e-5))
+                    elif "x_proj" in name or "dt_proj_weight" in name:
+                        self.assertIn(
+                            ((param.data.mean() * 1e2).round() / 1e2).item(),
+                            [0.0, 1.0],
+                            msg=f"Parameter {name} of model {model_class} seems not properly initialized (raw value {param.data.mean()})",
+                        )
+                    elif "dt_proj_bias" in name:
+                        dt = torch.exp(
+                            torch.tensor([0, 1]) * (math.log(config.time_step_max) - math.log(config.time_step_min))
+                            + math.log(config.time_step_min)
+                        ).clamp(min=config.time_step_floor)
+                        inv_dt = dt + torch.log(-torch.expm1(-dt))
+                        if param.requires_grad:
+                            self.assertTrue(param.data.max().item() <= inv_dt[1])
+                            self.assertTrue(param.data.min().item() >= inv_dt[0])
+                    else:
+                        self.assertIn(
+                            ((param.data.mean() * 1e9).round() / 1e9).item(),
+                            [0.0, 1.0],
+                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                        )
+
+    def test_mismatched_shapes_have_properly_initialized_weights(self):
+        r"""
+        Overriding the test_mismatched_shapes_have_properly_initialized_weights test because A_log and D params of the
+        Mamba block are initialized differently and we tested that in test_initialization
+        """
+        self.skipTest("Cumbersome and redundant for Zamba")
+
+    def test_attention_outputs(self):
+        r"""
+        Overriding the test_attention_outputs test as the Zamba model outputs attention only for its attention layers
+        """
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        seq_len = getattr(self.model_tester, "seq_length", None)
+        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
+        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
+
+        expected_num_attentions = (
+            math.ceil(
+                (self.model_tester.num_hidden_layers - self.model_tester.attn_layer_offset)
+                / self.model_tester.attn_layer_period
+            )
+            + 1
+        )
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), expected_num_attentions)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), expected_num_attentions)
+
+            self.assertListEqual(
+                list(attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+            )
+            out_len = len(outputs)
+
+            # Check attention is always last and order is fine
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            added_hidden_states = 1
+            self.assertEqual(out_len + added_hidden_states, len(outputs))
+
+            self_attentions = outputs.attentions
+
+            self.assertEqual(len(self_attentions), expected_num_attentions)
+            self.assertListEqual(
+                list(self_attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+            )
+
+    def _get_input_ids_and_config(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        return config, input_ids, input_mask
+
+    def test_left_padding_compatibility(self):
+        r"""
+        Overriding the test_left_padding_compatibility test as the mamba layers accentuate the numerical differences
+        effect of the left padding discussed in the issue in the note. Using a more permissive tolerance value.
+        """
+        import inspect
+        # NOTE: left-padding results in small numerical differences. This is expected.
+        # See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535
+
+        # First, filter out models that don't support left padding - generative and decoder-only.
+        # Zamba is a decoder-only architecture
+        decoder_only_classes = self.all_generative_model_classes
+
+        # Then, test left-padding
+        def _prepare_model_kwargs(input_ids, attention_mask, signature):
+            model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask}
+            if "position_ids" in signature:
+                position_ids = torch.cumsum(attention_mask, dim=-1) - 1
+                position_ids.masked_fill_(attention_mask == 0, 1)
+                model_kwargs["position_ids"] = position_ids
+            if "cache_position" in signature:
+                cache_position = torch.arange(input_ids.shape[-1], device=torch_device)
+                model_kwargs["cache_position"] = cache_position
+            return model_kwargs
+
+        for model_class in decoder_only_classes:
+            config, input_ids, attention_mask = self._get_input_ids_and_config()
+            model = model_class(config).to(torch_device).eval()
+            signature = inspect.signature(model.forward).parameters.keys()
+
+            # Without padding
+            model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, signature)
+            next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :]
+
+            # With left-padding (length 32)
+            pad_size = (input_ids.shape[0], 32)
+            padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * config.pad_token_id
+            padded_input_ids = torch.cat((padding, input_ids), dim=1)
+            padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
+            model_kwargs = _prepare_model_kwargs(padded_input_ids, padded_attention_mask, signature)
+            next_logits_with_padding = model(**model_kwargs).logits[:, -1, :]
+
+            # They should result in very similar logits
+            self.assertTrue(torch.allclose(next_logits_wo_padding, next_logits_with_padding, atol=3e-3))
+
+    @require_flash_attn
+    @require_torch_gpu
+    @require_bitsandbytes
+    @pytest.mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_fp32_ln(self):
+        r"""
+        Overriding the test_flash_attn_2_fp32_ln test as the Zamba model, like Mixtral, doesn't support
+        right padding + use cache with FA2
+        """
+        for model_class in self.all_generative_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                dummy_input = inputs_dict[model.main_input_name]
+                dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input))
+                # NOTE: Zamba does not support right padding + use_cache with FA2.
+                dummy_attention_mask[:, -1] = 1
+
+                model = model_class.from_pretrained(
+                    tmpdirname,
+                    torch_dtype=torch.float16,
+                    attn_implementation="flash_attention_2",
+                    low_cpu_mem_usage=True,
+                    load_in_4bit=True,
+                )
+
+                for _, param in model.named_parameters():
+                    # upcast only layer norms
+                    if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
+                        param.data = param.data.to(torch.float32)
+
+                _ = model(dummy_input)
+                # with attention mask
+                _ = model(dummy_input, attention_mask=dummy_attention_mask)
+
+    @require_flash_attn
+    @require_torch_gpu
+    @pytest.mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_generate_padding_right(self):
+        r"""
+        Overriding the test_flash_attn_2_generate_padding_right test as the Zamba model, like Mixtral, doesn't support
+        right padding + use cache with FA2
+        """
+        import torch
+
+        for model_class in self.all_generative_model_classes:
+            config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+                model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to(
+                    torch_device
+                )
+
+                dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device)
+                dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device)
+
+                model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False)
+
+                model = model_class.from_pretrained(
+                    tmpdirname,
+                    torch_dtype=torch.float16,
+                    attn_implementation="flash_attention_2",
+                    low_cpu_mem_usage=True,
+                ).to(torch_device)
+
+                with self.assertRaises(ValueError):
+                    _ = model.generate(
+                        dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False
+                    )
+
+    @require_flash_attn
+    @require_torch_gpu
+    @pytest.mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_generate_use_cache(self):
+        r"""
+        Overriding the test_flash_attn_2_generate_use_cache test as the Zamba model, like Mixtral, doesn't support
+        right padding + use cache with FA2
+        """
+        import torch
+
+        max_new_tokens = 30
+
+        for model_class in self.all_generative_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            dummy_input = inputs_dict[model_class.main_input_name]
+            if dummy_input.dtype in [torch.float32, torch.bfloat16]:
+                dummy_input = dummy_input.to(torch.float16)
+
+            # make sure that all models have enough positions for generation
+            if hasattr(config, "max_position_embeddings"):
+                config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1
+
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input))
+                # NOTE: Zamba does not support right padding + use_cache with FA2.
+                dummy_attention_mask[:, -1] = 1
+
+                model = model_class.from_pretrained(
+                    tmpdirname,
+                    torch_dtype=torch.float16,
+                    attn_implementation="flash_attention_2",
+                    low_cpu_mem_usage=True,
+                ).to(torch_device)
+
+                # Just test that a large cache works as expected
+                _ = model.generate(
+                    dummy_input,
+                    attention_mask=dummy_attention_mask,
+                    max_new_tokens=max_new_tokens,
+                    do_sample=False,
+                    use_cache=True,
+                )
+
+    @require_flash_attn
+    @require_torch_gpu
+    @pytest.mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        r"""
+        Overriding the test_flash_attn_2_inference_padding_right test as the Zamba model, like Mixtral, doesn't support
+        right padding + use cache with FA2
+        """
+        self.skipTest(reason="Zamba flash attention does not support right padding")
+
+    @unittest.skip(reason="Zamba has its own special cache type")
+    @parameterized.expand([(1, False), (1, True), (4, False)])
+    def test_new_cache_format(self, num_beams, do_sample):
+        pass
+
+
+@require_torch
+class ZambaModelIntegrationTest(unittest.TestCase):
+    model = None
+    tokenizer = None
+
+    @classmethod
+    @slow
+    def setUpClass(cls):
+        model_id = "Zyphra/Zamba-7B-v1"
+        cls.model = ZambaForCausalLM.from_pretrained(
+            model_id, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_mamba_kernels=False
+        )
+        cls.tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+    @slow
+    def test_simple_generate(self):
+        self.model.to(torch_device)
+
+        input_ids = self.tokenizer("Hey how are you doing on this lovely evening?", return_tensors="pt")[
+            "input_ids"
+        ].to(torch_device)
+        out = self.model.generate(input_ids, do_sample=False, max_new_tokens=10)
+        output_sentence = self.tokenizer.decode(out[0, :])
+        self.assertEqual(
+            output_sentence,
+            "<s> Hey how are you doing on this lovely evening? I hope you are all doing well. I am",
+        )
+
+        with torch.no_grad():
+            logits = self.model(input_ids=input_ids).logits
+
+        EXPECTED_LOGITS_NO_GRAD = torch.tensor(
+            [
+                -7.9375,  8.1875,  1.3984, -6.0000, -7.9375, -7.9375, -7.9375, -7.9375,
+                -7.9375, -7.9375, -7.9375, -7.9375,  2.7500, 13.0625, -7.9375, -7.9375,
+                -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375,
+                -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375,
+                -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375
+            ]
+            , dtype=torch.float32)  # fmt: skip
+
+        torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
+
+    @slow
+    def test_simple_batched_generate_with_padding(self):
+        self.model.to(torch_device)
+        self.tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+        self.model.resize_token_embeddings(len(self.tokenizer))
+
+        inputs = self.tokenizer(
+            ["Hey how are you doing on this lovely evening?", "Tell me a story"], padding=True, return_tensors="pt"
+        ).to(torch_device)
+        out = self.model.generate(**inputs, do_sample=False, max_new_tokens=10)
+        output_sentences = self.tokenizer.batch_decode(out)
+        self.assertEqual(
+            output_sentences[0],
+            "<s> Hey how are you doing on this lovely evening? I hope you are all doing well. I am",
+        )
+        self.assertEqual(
+            output_sentences[1],
+            "[PAD][PAD][PAD][PAD][PAD][PAD]<s> Tell me a story about a time when you were in a difficult situation",
+        )
+
+        with torch.no_grad():
+            logits = self.model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]).logits
+
+        EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
+            [
+                -7.9375,  8.1250,  1.3594, -6.0000, -7.9375, -7.9375, -7.9375, -7.9375,
+                -7.9375, -7.9375, -7.9375, -7.9375,  2.7344, 13.0625, -7.9375, -7.9375,
+                -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375,
+                -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375,
+                -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375, -7.9375
+            ]
+            , dtype=torch.float32)  # fmt: skip
+
+        EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
+            [
+               -6.3750,  3.4219,  0.6719, -5.0312, -8.5000, -8.5000, -8.5000, -8.5000,
+               -8.5000, -8.5000, -8.5000, -8.5000,  2.0625, 10.3750, -8.5000, -8.5000,
+               -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000,
+               -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000,
+               -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000, -8.5000
+            ]
+            , dtype=torch.float32)  # fmt: skip
+
+        torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3)
+        torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1e-3)

utils/check_config_attributes.py

@@ -132,6 +132,11 @@ SPECIAL_CASES_TO_ALLOW = {
         "t2u_variance_predictor_hidden_dim",
         "t2u_variance_predictor_kernel_size",
     ],
+    "ZambaConfig": [
+        "tie_word_embeddings",
+        "attn_layer_offset",
+        "attn_layer_period",
+    ],
     "MllamaTextConfig": [
         "initializer_range",
     ],

utils/not_doctested.txt

@@ -907,6 +907,8 @@ src/transformers/models/xmod/convert_xmod_original_pytorch_checkpoint_to_pytorch
 src/transformers/models/yolos/convert_yolos_to_pytorch.py
 src/transformers/models/yoso/convert_yoso_pytorch_to_pytorch.py
 src/transformers/models/yoso/modeling_yoso.py
+src/transformers/models/zamba/configuration_zamba.py
+src/transformers/models/zamba/modeling_zamba.py
 src/transformers/onnx/__main__.py
 src/transformers/onnx/config.py
 src/transformers/onnx/convert.py