Add Zamba2 (#34517)

* First commit

* Finish model implementation

* First commit

* Finish model implementation

* Register zamba2

* generated modeling and configuration

* generated modeling and configuration

* added hybrid cache

* fix attention_mask in mamba

* dropped unused loras

* fix flash2

* config docstrings

* fix config and fwd pass

* make fixup fixes

* text_modeling_zamba2

* small fixes

* make fixup fixes

* Fix modular model converter

* added inheritances in modular, renamed zamba cache

* modular rebase

* new modular conversion

* fix generated modeling file

* fixed import for Zamba2RMSNormGated

* modular file cleanup

* make fixup and model tests

* dropped inheritance for Zamba2PreTrainedModel

* make fixup and unit tests

* Add inheritance of rope from GemmaRotaryEmbedding

* moved rope to model init

* drop del self.self_attn and del self.feed_forward

* fix tests

* renamed lora -> adapter

* rewrote adapter implementation

* fixed tests

* Fix torch_forward in mamba2 layer

* Fix torch_forward in mamba2 layer

* Fix torch_forward in mamba2 layer

* Dropped adapter in-place sum

* removed rope from attention init

* updated rope

* created get_layers method

* make fixup fix

* make fixup fixes

* make fixup fixes

* update to new attention standard

* update to new attention standard

* make fixup fixes

* minor fixes

* cache_position

* removed cache_position postion_ids use_cache

* remove config from modular

* removed config from modular (2)

* import apply_rotary_pos_emb from llama

* fixed rope_kwargs

* Instantiate cache in Zamba2Model

* fix cache

* fix @slow decorator

* small fix in modular file

* Update docs/source/en/model_doc/zamba2.md

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

* several minor fixes

* inherit mamba2decoder fwd and drop position_ids in mamba

* removed docstrings from modular

* reinstate zamba2 attention decoder fwd

* use regex for tied keys

* Revert "use regex for tied keys"

This reverts commit 9007a522b1.

* use regex for tied keys

* add cpu to slow forward tests

* dropped config.use_shared_mlp_adapter

* Update docs/source/en/model_doc/zamba2.md

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

* re-convert from modular

---------

Co-authored-by: root <root@node-2.us-southcentral1-a.compute.internal>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

docs/source/en/index.md

@@ -385,6 +385,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                         [YOLOS](model_doc/yolos)                         |       ✅        |         ❌         |      ❌      |
 |                          [YOSO](model_doc/yoso)                          |       ✅        |         ❌         |      ❌      |
 |                         [Zamba](model_doc/zamba)                         |       ✅        |         ❌         |      ❌      |
+|                        [Zamba2](model_doc/zamba2)                        |       ✅        |         ❌         |      ❌      |
 |                      [ZoeDepth](model_doc/zoedepth)                      |       ✅        |         ❌         |      ❌      |
 
 <!-- End table-->

docs/source/en/model_doc/zamba2.md

@@ -0,0 +1,91 @@
+<!--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.
+
+-->
+# Zamba2
+
+Zamba2 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
+
+Zamba2-1.2B, Zamba2-2.7B and Zamba2-7B are hybrid models combining state-space models (Specifically [Mamba](https://github.com/state-spaces/mamba)) and transformer, and were trained using next-token prediction. Zamba2 uses shared transformer layers 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. Zamba2-1.2B, Zamba2-2.7B and Zamba2-7B were pre-trained on 2T and 3T tokens, respectively.
+
+<img src=https://github.com/user-attachments/assets/c2cff209-b901-483c-87aa-774b82a0769f width=30% height=40% />
+
+## Quick start
+
+
+### Presequities
+
+Zamba2 requires you use `transformers` version 4.48.0 or higher:
+```bash
+pip install transformers>=4.48.0
+## Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-7B")
+model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-7B", device_map="cuda", torch_dtype=torch.bfloat16)
+
+input_text = "What factors contributed to the fall of the Roman Empire?"
+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:
+* [Zamba2-1.2B](https://huggingface.co/Zyphra/Zamba2-1.2B)
+* [Zamba2-2.7B](https://huggingface.co/Zyphra/Zamba2-2.7B)
+* [Zamba2-7B](https://huggingface.co/Zyphra/Zamba2-7B)
+
+
+## Issues
+For issues with model output, or community discussion, please use the Hugging Face community [forum](https://huggingface.co/Zyphra/Zamba2-7B/discussions)
+
+
+## License
+
+The model weights are open-sourced via an Apache 2.0 license.
+
+
+## Zamba2Config
+
+[[autodoc]] Zamba2Config
+
+
+## Zamba2Model
+
+[[autodoc]] Zamba2Model
+    - forward
+
+
+## Zamba2ForCausalLM
+
+[[autodoc]] Zamba2ForCausalLM
+    - forward
+
+
+## Zamba2ForSequenceClassification
+
+[[autodoc]] transformers.Zamba2ForSequenceClassification
+    - forward

docs/source/en/perf_infer_gpu_one.md

@@ -111,6 +111,7 @@ FlashAttention-2 is currently supported for the following architectures:
 * [UniSpeech](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech#transformers.UniSpeechModel)
 * [unispeech_sat](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel)
 * [helium](https://huggingface.co/docs/transformers/main/en/model_doc/heliumtransformers.HeliumModel)
+* [Zamba2](https://huggingface.co/docs/transformers/model_doc/zamba2)
 
 You can request to add FlashAttention-2 support for another model by opening a GitHub Issue or Pull Request.
 
@@ -328,6 +329,7 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel)
 * [YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos#transformers.YolosModel)
 * [helium](https://huggingface.co/docs/transformers/main/en/model_doc/heliumtransformers.HeliumModel)
+* [Zamba2](https://huggingface.co/docs/transformers/model_doc/zamba2)
 
 <Tip>
 

src/transformers/__init__.py

@@ -889,6 +889,7 @@ _import_structure = {
     "models.yolos": ["YolosConfig"],
     "models.yoso": ["YosoConfig"],
     "models.zamba": ["ZambaConfig"],
+    "models.zamba2": ["Zamba2Config"],
     "models.zoedepth": ["ZoeDepthConfig"],
     "onnx": [],
     "pipelines": [
@@ -3989,6 +3990,14 @@ else:
             "ZambaPreTrainedModel",
         ]
     )
+    _import_structure["models.zamba2"].extend(
+        [
+            "Zamba2ForCausalLM",
+            "Zamba2ForSequenceClassification",
+            "Zamba2Model",
+            "Zamba2PreTrainedModel",
+        ]
+    )
     _import_structure["models.zoedepth"].extend(
         [
             "ZoeDepthForDepthEstimation",
@@ -6004,6 +6013,7 @@ if TYPE_CHECKING:
     from .models.yolos import YolosConfig
     from .models.yoso import YosoConfig
     from .models.zamba import ZambaConfig
+    from .models.zamba2 import Zamba2Config
     from .models.zoedepth import ZoeDepthConfig
 
     # Pipelines
@@ -8542,6 +8552,12 @@ if TYPE_CHECKING:
             ZambaModel,
             ZambaPreTrainedModel,
         )
+        from .models.zamba2 import (
+            Zamba2ForCausalLM,
+            Zamba2ForSequenceClassification,
+            Zamba2Model,
+            Zamba2PreTrainedModel,
+        )
         from .models.zoedepth import (
             ZoeDepthForDepthEstimation,
             ZoeDepthPreTrainedModel,

src/transformers/models/__init__.py

@@ -303,5 +303,6 @@ from . import (
     yolos,
     yoso,
     zamba,
+    zamba2,
     zoedepth,
 )

src/transformers/models/auto/configuration_auto.py

@@ -335,6 +335,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
         ("yolos", "YolosConfig"),
         ("yoso", "YosoConfig"),
         ("zamba", "ZambaConfig"),
+        ("zamba2", "Zamba2Config"),
         ("zoedepth", "ZoeDepthConfig"),
     ]
 )
@@ -680,6 +681,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("yolos", "YOLOS"),
         ("yoso", "YOSO"),
         ("zamba", "Zamba"),
+        ("zamba2", "Zamba2"),
         ("zoedepth", "ZoeDepth"),
     ]
 )

src/transformers/models/auto/modeling_auto.py

@@ -303,6 +303,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("yolos", "YolosModel"),
         ("yoso", "YosoModel"),
         ("zamba", "ZambaModel"),
+        ("zamba2", "Zamba2Model"),
     ]
 )
 
@@ -577,6 +578,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
         ("xlnet", "XLNetLMHeadModel"),
         ("xmod", "XmodForCausalLM"),
         ("zamba", "ZambaForCausalLM"),
+        ("zamba2", "Zamba2ForCausalLM"),
     ]
 )
 
@@ -1055,6 +1057,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("xmod", "XmodForSequenceClassification"),
         ("yoso", "YosoForSequenceClassification"),
         ("zamba", "ZambaForSequenceClassification"),
+        ("zamba2", "Zamba2ForSequenceClassification"),
     ]
 )
 

src/transformers/models/auto/tokenization_auto.py

@@ -583,6 +583,13 @@ else:
                     "LlamaTokenizerFast" if is_tokenizers_available() else None,
                 ),
             ),
+            (
+                "zamba2",
+                (
+                    "LlamaTokenizer" if is_sentencepiece_available() else None,
+                    "LlamaTokenizerFast" if is_tokenizers_available() else None,
+                ),
+            ),
         ]
     )
 

src/transformers/models/zamba/modeling_zamba.py

@@ -272,7 +272,6 @@ class ZambaAttention(nn.Module):
         layer_idx: int,
         attention_mask: Optional[torch.Tensor],
         past_key_value: Optional[ZambaHybridDynamicCache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         input_shape = hidden_states.shape[:-1]
@@ -621,11 +620,9 @@ class ZambaAttentionDecoderLayer(nn.Module):
         original_hidden_states: torch.Tensor,
         layer_idx: int,
         attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
         past_key_value: Optional[ZambaHybridDynamicCache] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         """
@@ -638,7 +635,6 @@ class ZambaAttentionDecoderLayer(nn.Module):
             layer_idx (`int`): layer_idx in the forward pass. Used to distinguish Zamba's tied transformer layers.
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            position_ids (`torch.LongTensor`, *optional*): token positions of shape `(batch, seq_len)`. Used for positional encodings.
             past_key_value (`ZambaHybridDynamicCache`, *optional*): cached past key and value projection states
             output_attentions (`bool`, *optional*):
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
@@ -655,11 +651,9 @@ class ZambaAttentionDecoderLayer(nn.Module):
             hidden_states=hidden_states,
             layer_idx=layer_idx,
             attention_mask=attention_mask,
-            position_ids=position_ids,
             past_key_value=past_key_value,
             output_attentions=output_attentions,
             use_cache=use_cache,
-            cache_position=cache_position,
             **kwargs,
         )
         # feed-forward (MLP)
@@ -688,12 +682,12 @@ class ZambaMambaDecoderLayer(nn.Module):
         layer_idx: int = None,
         attention_mask: Optional[torch.Tensor] = None,
         causal_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
         past_key_value: Optional[ZambaHybridDynamicCache] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
         transformer_hidden_states: Optional[torch.Tensor] = None,
+        **kwargs,
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         """
         Args:
@@ -756,7 +750,6 @@ class ZambaHybridLayer(nn.Module):
         layer_idx: int = None,
         attention_mask: Optional[torch.Tensor] = None,
         causal_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
         past_key_value: Optional[ZambaHybridDynamicCache] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
@@ -786,7 +779,6 @@ class ZambaHybridLayer(nn.Module):
             original_hidden_states=original_hidden_states,
             layer_idx=layer_idx,
             attention_mask=causal_mask,
-            position_ids=position_ids,
             past_key_value=past_key_value,
             output_attentions=output_attentions,
             use_cache=use_cache,
@@ -804,7 +796,6 @@ class ZambaHybridLayer(nn.Module):
             hidden_states,
             transformer_hidden_states=transformer_hidden_states,
             attention_mask=attention_mask,
-            position_ids=position_ids,
             past_key_value=past_key_value,
             output_attentions=output_attentions,
             use_cache=use_cache,
@@ -1108,7 +1099,6 @@ class ZambaModel(ZambaPreTrainedModel):
                     layer_idx,
                     attention_mask,
                     causal_mask,
-                    position_ids,
                     past_key_values,
                     output_attentions,
                     use_cache,
@@ -1121,7 +1111,6 @@ class ZambaModel(ZambaPreTrainedModel):
                     layer_idx=layer_idx,
                     attention_mask=attention_mask,
                     causal_mask=causal_mask,
-                    position_ids=position_ids,
                     past_key_value=past_key_values,
                     output_attentions=output_attentions,
                     use_cache=use_cache,

src/transformers/models/zamba2/__init__.py

@@ -0,0 +1,27 @@
+# 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 _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_zamba2 import *
+    from .modeling_zamba2 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/zamba2/configuration_zamba2.py

@@ -0,0 +1,236 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/zamba2/modular_zamba2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_zamba2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# 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.
+
+from ...configuration_utils import PretrainedConfig
+
+
+class Zamba2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Zamba2Model`]. It is used to instantiate a
+    Zamba2 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 Zamba2 model.
+
+    [Zyphra/Zamba2-2.7B](https://huggingface.co/Zyphra/Zamba2-2.7B)
+
+    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 Zamba2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Zamba2Model`]
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        hidden_size (`int`, *optional*, defaults to 2560):
+            Dimension of the hidden representations.
+        num_hidden_layers (`int`, *optional*, defaults to 54):
+            Number of hidden layers in the model.
+        layers_block_type (`list`, *optional*):
+            List of layer types, which can be either "mamba" or "hybrid".
+        mamba_d_state (`int`, *optional*, defaults to 64): shape of the state space latents.
+        mamba_d_conv (`int`, *optional*, defaults to 4): Size of the convolution kernel.
+        mamba_expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
+        mamba_ngroups (`int`, *optional*, defaults to 1):
+            Number of groups for the evolution matrices of mamba 2.
+        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.
+        time_step_limit (`tuple`, *optional*):
+            Accepted range of time step values.
+        n_mamba_heads (`int`, *optional*, defaults to 8):
+            Number of heads for the evolution matrices of mamba 2.
+        use_conv_bias (`bool`, *optional*, defaults to `True`):
+            Whether or not to use bias in the convolution layer of the mixer block.
+        chunk_size (`int`, *optional*, defaults to 256):
+            Size of the chunks that will comprise the sequence.
+        add_bias_linear (`bool`, *optional*, defaults to `False`):
+            Flag indicating whether or not to use bias in various layers
+        intermediate_size (`int`, *optional*, defaults to 4 * hidden_size):
+            Dimension of the MLP representations.
+        hidden_act (`str`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the MLP.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            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).
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_mem_blocks (`int`, *optional*, defaults to 1):
+            Number of unshared transformer blocks.
+        use_shared_attention_adapter (`bool`, *optional*, defaults to `False`):
+            If True, unshared adapters (formally the same as LoRA but used in the base model) will be added to the q, k, v projectors in the shared attention layers.
+        adapter_rank (`int`, *optional*, defaults to 128):
+            Rank of the adapter in the shared MLP and shared attention layers.
+        use_mem_rope (`bool`, *optional*, defaults to `False`):
+            If True, includes RoPE in the shared attention layers.
+        rope_theta (`float`, *optional*, defaults to `10000.0`):
+            The base period of the RoPE embeddings.
+        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.
+        use_long_context (`bool`, *optional*, defaults to `False`):
+            Activates the context-extended version of Zamba by modifying RoPE.
+    ```python
+    >>> from transformers import Zamba2Model, Zamba2Config
+    >>> # Initializing a Zamba2-2.7B style configuration
+    >>> configuration = Zamba2Config()
+    >>> # Initializing a model from the Zamba2-2.7B style configuration
+    >>> model = Zamba2Model(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    """
+
+    model_type = "zamba2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        max_position_embeddings=4096,
+        hidden_size=2560,
+        num_hidden_layers=54,
+        layers_block_type=None,
+        mamba_d_state=64,
+        mamba_d_conv=4,
+        mamba_expand=2,
+        mamba_ngroups=1,
+        time_step_min=0.001,
+        time_step_max=0.1,
+        time_step_floor=1e-4,
+        time_step_limit=None,
+        n_mamba_heads=8,
+        use_conv_bias=True,
+        chunk_size=256,
+        add_bias_linear=False,
+        intermediate_size=None,
+        hidden_act="gelu",
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        attention_dropout=0.0,
+        num_mem_blocks=1,
+        use_shared_attention_adapter=False,
+        adapter_rank=128,
+        use_mem_rope=False,
+        rope_theta=10000,
+        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,
+        use_long_context=False,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        if intermediate_size is None:
+            self.intermediate_size = 4 * hidden_size
+        else:
+            self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_mem_blocks = num_mem_blocks
+        self.attention_hidden_size = 2 * hidden_size
+        self.attention_head_dim = 2 * self.hidden_size // self.num_attention_heads
+        self.attention_dropout = attention_dropout
+        self.use_mem_rope = use_mem_rope
+        self.use_long_context = use_long_context
+        if use_mem_rope and use_long_context:
+            a = 8
+            rope_theta = rope_theta * a ** (self.attention_head_dim / (self.attention_head_dim - 2))
+        self.rope_theta = rope_theta
+        self.mamba_d_state = mamba_d_state
+        self.mamba_d_conv = mamba_d_conv
+        self.mamba_expand = mamba_expand
+        self.add_bias_linear = add_bias_linear
+        self.mamba_ngroups = mamba_ngroups
+        self.n_mamba_heads = n_mamba_heads
+        self.mamba_headdim = int(mamba_expand * hidden_size) // n_mamba_heads
+        self.use_conv_bias = use_conv_bias
+        self.chunk_size = chunk_size
+        self.time_step_limit = time_step_limit
+        self.use_shared_attention_adapter = use_shared_attention_adapter
+        self.adapter_rank = adapter_rank
+        self.time_step_min = time_step_min
+        self.time_step_max = time_step_max
+        self.time_step_floor = time_step_floor
+        if use_long_context:
+            self.max_position_embeddings = 16384
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.num_attention_heads = num_attention_heads
+        self.kv_channels = self.hidden_size // self.num_attention_heads
+        self.num_query_groups = self.num_attention_heads
+        # Below, "mamba" stands for mamba layer, "hybrid" stands for hybrid layer (composed by a shared transformer followed by mamba layer)
+        if layers_block_type is None:
+            self.layers_block_type = (
+                ["mamba"]
+                + (["mamba"] * 5 + ["hybrid"]) * 7
+                + ["mamba"] * 4
+                + ["hybrid"]
+                + ["mamba"] * 3
+                + ["hybrid"]
+                + ["mamba"] * 2
+            )
+        else:
+            self.layers_block_type = layers_block_type
+        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.hybrid_layer_ids = [index for index, type in enumerate(self.layers_block_type) if type == "hybrid"]
+
+
+__all__ = ["Zamba2Config"]

src/transformers/testing_utils.py

@@ -1435,6 +1435,7 @@ def set_model_tester_for_less_flaky_test(test_case):
     # TODO (if possible): Avoid exceptional cases
     exceptional_classes = [
         "ZambaModelTester",
+        "Zamba2ModelTester",
         "RwkvModelTester",
         "AriaVisionText2TextModelTester",
         "GPTNeoModelTester",

src/transformers/utils/dummy_pt_objects.py

@@ -10576,6 +10576,34 @@ class ZambaPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class Zamba2ForCausalLM(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class Zamba2ForSequenceClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class Zamba2Model(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class Zamba2PreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class ZoeDepthForDepthEstimation(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/generation/test_utils.py

@@ -2279,6 +2279,7 @@ class GenerationTesterMixin:
             "mamba",
             "xlnet",
             "zamba",
+            "zamba2",
         )
         has_standard_cache = not any(
             model_name in config.__class__.__name__.lower() for model_name in models_without_standard_cache

tests/models/zamba2/test_modeling_zamba2.py

@@ -0,0 +1,666 @@
+# 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, Zamba2Config, 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 (
+        Zamba2ForCausalLM,
+        Zamba2ForSequenceClassification,
+        Zamba2Model,
+    )
+    from transformers.models.zamba2.modeling_zamba2 import (
+        Zamba2HybridDynamicCache,
+    )
+
+
+class Zamba2ModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=14,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=16,
+        mamba_d_state=2,
+        chunk_size=8,
+        mamba_dt_rank="auto",
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        n_mamba_heads=8,
+        mamba_ngroups=8,
+        intermediate_size=4,
+        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,
+        layers_block_type=["mamba", "hybrid"],
+        num_mem_blocks=1,
+        use_mem_rope=True,
+    ):
+        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.mamba_d_state = mamba_d_state
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_mamba_heads = n_mamba_heads
+        self.mamba_ngroups = mamba_ngroups
+        self.chunk_size = chunk_size
+        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
+        self.layers_block_type = layers_block_type
+        self.num_mem_blocks = num_mem_blocks
+        self.use_mem_rope = use_mem_rope
+
+    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 Zamba2Config(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            mamba_dt_rank=self.mamba_dt_rank,
+            mamba_d_state=self.mamba_d_state,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            n_mamba_heads=self.n_mamba_heads,
+            intermediate_size=self.intermediate_size,
+            chunk_size=self.chunk_size,
+            hidden_act=self.hidden_act,
+            mamba_ngroups=self.mamba_ngroups,
+            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,
+            layers_block_type=self.layers_block_type,
+            num_mem_blocks=self.num_mem_blocks,
+            use_mem_rope=self.use_mem_rope,
+        )
+
+    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 = Zamba2Model(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 = Zamba2ForCausalLM(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 = False
+        model = Zamba2ForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        # first forward pass
+        # Attention: Zamba2 needs the cache to be initialized to return a cache!
+        past_key_values = Zamba2HybridDynamicCache(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, 1), config.vocab_size)
+        next_mask = ids_tensor((self.batch_size, 1), 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[:, -1:, 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 = Zamba2ForSequenceClassification(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 Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    test_torchscript = False
+    all_model_classes = (
+        (
+            Zamba2Model,
+            Zamba2ForCausalLM,
+            Zamba2ForSequenceClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = (Zamba2ForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Zamba2Model,
+            "text-classification": Zamba2ForSequenceClassification,
+            "text-generation": Zamba2ForCausalLM,
+            "zero-shot": Zamba2ForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+
+    def setUp(self):
+        self.model_tester = Zamba2ModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Zamba2Config, hidden_size=37)
+
+    @unittest.skip("position_ids cannot be used to pad due to Mamba2 layers")
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip("Zamba2 has a hybrid cache")
+    def test_past_key_values_format(self):
+        r"""
+        Zamba2's cache shape depends on whether a given layer is mamba or attention.
+        For mamba layers, the KV cache has shape is empty and has shape [batch_size, 0].
+        The shape checks of this test assume instead that every layer has an attention cache, so we skip it.
+        """
+        pass
+
+    @unittest.skip(reason="A large mamba2 would be necessary (and costly) for that")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    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_causal_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.n_mamba_heads + 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 "dt_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",
+                        )
+
+    @unittest.skip(reason="Cumbersome and redundant for Zamba2")
+    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
+        """
+        pass
+
+    def test_attention_outputs(self):
+        r"""
+        Overriding the test_attention_outputs test as the Zamba2 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)
+
+        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
+
+            # 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.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.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.
+        # Zamba2 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 Zamba2 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: Zamba2 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_inference_equivalence_right_padding(self):
+        r"""
+        Overriding the test_flash_attn_2_inference_padding_right test as the Zamba2 model, like Mixtral, doesn't support
+        right padding + use cache with FA2
+        """
+        self.skipTest(reason="Zamba2 flash attention does not support right padding")
+
+    @unittest.skip(reason="Zamba2 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 Zamba2ModelIntegrationTest(unittest.TestCase):
+    model = None
+    tokenizer = None
+
+    @classmethod
+    @slow
+    def setUpClass(cls):
+        model_id = "Zyphra/Zamba2-1.2B"
+        cls.model = Zamba2ForCausalLM.from_pretrained(
+            model_id, torch_dtype=torch.float32, low_cpu_mem_usage=True, revision="PR"
+        )
+        cls.tokenizer = AutoTokenizer.from_pretrained(model_id, revision="PR")
+
+    @parameterized.expand([(torch_device,), ("cpu",)])
+    @slow
+    def test_simple_generate(self, torch_device):
+        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?\n\nI'm doing well, thanks for",
+        )
+
+        with torch.no_grad():
+            logits = self.model(input_ids=input_ids).logits.to(dtype=torch.float32)
+
+        EXPECTED_LOGITS_NO_GRAD = torch.tensor(
+            [
+               -5.9587, 10.5152,  7.0382, -2.8728, -4.8143, -4.8142, -4.8142, -4.8144,
+               -4.8143, -4.8143, -4.8142, -4.8142,  6.0185, 18.0037, -4.8142, -4.8144,
+               -4.8143, -4.8142, -4.8143, -4.8143, -4.8143, -4.8143, -4.8142, -4.8143,
+               -4.8144, -4.8143, -4.8143, -4.8141, -4.8142, -4.8142, -4.8142, -4.8144,
+               -4.8143, -4.8143, -4.8143, -4.8142, -4.8144, -4.8144, -4.8142, -4.8142
+            ]
+            , dtype=torch.float32)  # fmt: skip
+        torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
+
+    @parameterized.expand([(torch_device,), ("cpu",)])
+    @slow
+    def test_simple_batched_generate_with_padding(self, torch_device):
+        self.model.to(torch_device)
+
+        inputs = self.tokenizer(
+            ["Hey how are you doing on this lovely evening?", "When did the Roman empire "],
+            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?\n\nI'm doing well, thanks for",
+        )
+
+        self.assertEqual(
+            output_sentences[1],
+            "[PAD][PAD][PAD][PAD]<s> When did the Roman empire 1st fall?\nThe Roman Empire fell in",
+        )
+
+        with torch.no_grad():
+            logits = self.model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]).logits.to(
+                dtype=torch.float32
+            )
+
+        EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
+            [
+                -5.9611, 10.5208,  7.0411, -2.8743, -4.8167, -4.8167, -4.8167, -4.8168,
+                -4.8167, -4.8167, -4.8167, -4.8166,  6.0218, 18.0062, -4.8167, -4.8168,
+                -4.8167, -4.8167, -4.8167, -4.8168, -4.8168, -4.8168, -4.8167, -4.8167,
+                -4.8168, -4.8167, -4.8167, -4.8165, -4.8167, -4.8167, -4.8167, -4.8169,
+                -4.8168, -4.8168, -4.8168, -4.8166, -4.8169, -4.8168, -4.8167, -4.8167
+            ]
+            , dtype=torch.float32)  # fmt: skip
+
+        EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
+            [
+               0.1966,  6.3449,  3.8350, -5.7291, -6.5106, -6.5104, -6.5103, -6.5104,
+               -6.5103, -6.5104, -6.5106, -6.5105,  7.8700, 13.5434, -6.5104, -6.5096,
+               -6.5106, -6.5102, -6.5106, -6.5106, -6.5105, -6.5106, -6.5104, -6.5106,
+               -6.5105, -6.5106, -6.5106, -6.5113, -6.5102, -6.5105, -6.5108, -6.5105,
+               -6.5104, -6.5106, -6.5106, -6.5104, -6.5106, -6.5107, -6.5103, -6.5105          ]
+            , 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=6e-3 if torch_device == "cpu" else 1e-3,
+        )