Add gemma 2

docs/source/en/index.md

@@ -145,6 +145,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                  [Funnel Transformer](model_doc/funnel)                  |       ✅        |         ✅         |      ❌      |
 |                          [Fuyu](model_doc/fuyu)                          |       ✅        |         ❌         |      ❌      |
 |                         [Gemma](model_doc/gemma)                         |       ✅        |         ❌         |      ✅      |
+|                        [Gemma2](model_doc/gemma2)                        |       ✅        |         ❌         |      ❌      |
 |                           [GIT](model_doc/git)                           |       ✅        |         ❌         |      ❌      |
 |                          [GLPN](model_doc/glpn)                          |       ✅        |         ❌         |      ❌      |
 |                       [GPT Neo](model_doc/gpt_neo)                       |       ✅        |         ❌         |      ✅      |

docs/source/en/model_doc/gemma2.md

@@ -0,0 +1,58 @@
+
+<!--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.
+
+-->
+
+# Gemma2
+
+## Overview
+
+The Gemma2 model was proposed in [Gemma2: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/Gemma2-open-models/) by Gemma2 Team, Google.
+Gemma2 models are trained on 6T tokens, and released with 2 versions, 2b and 7b.
+
+The abstract from the paper is the following:
+
+*This work introduces Gemma2, a new family of open language models demonstrating strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma2 outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of our model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations*
+
+Tips:
+
+- The original checkpoints can be converted using the conversion script `src/transformers/models/Gemma2/convert_Gemma2_weights_to_hf.py` 
+
+This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ), [Pedro Cuenca](https://huggingface.co/pcuenq) and [Tom Arsen]().
+
+
+## Gemma2Config
+
+[[autodoc]] Gemma2Config
+
+## Gemma2Model
+
+[[autodoc]] Gemma2Model
+    - forward
+
+## Gemma2ForCausalLM
+
+[[autodoc]] Gemma2ForCausalLM
+    - forward
+
+## Gemma2ForSequenceClassification
+
+[[autodoc]] Gemma2ForSequenceClassification
+    - forward
+
+## Gemma2ForTokenClassification
+
+[[autodoc]] Gemma2ForTokenClassification
+    - forward

src/transformers/__init__.py

@@ -435,6 +435,7 @@
     ],
     "models.fuyu": ["FuyuConfig"],
     "models.gemma": ["GemmaConfig"],
+    "models.gemma2": ["Gemma2Config"],
     "models.git": [
         "GitConfig",
         "GitProcessor",
@@ -2181,6 +2182,15 @@
             "GemmaPreTrainedModel",
         ]
     )
+    _import_structure["models.gemma2"].extend(
+        [
+            "Gemma2ForCausalLM",
+            "Gemma2ForSequenceClassification",
+            "Gemma2ForTokenClassification",
+            "Gemma2Model",
+            "Gemma2PreTrainedModel",
+        ]
+    )
     _import_structure["models.git"].extend(
         [
             "GitForCausalLM",
@@ -5062,6 +5072,7 @@
     )
     from .models.fuyu import FuyuConfig
     from .models.gemma import GemmaConfig
+    from .models.gemma2 import Gemma2Config
     from .models.git import (
         GitConfig,
         GitProcessor,
@@ -6694,6 +6705,13 @@
             GemmaModel,
             GemmaPreTrainedModel,
         )
+        from .models.gemma2 import (
+            Gemma2ForCausalLM,
+            Gemma2ForSequenceClassification,
+            Gemma2ForTokenClassification,
+            Gemma2Model,
+            Gemma2PreTrainedModel,
+        )
         from .models.git import (
             GitForCausalLM,
             GitModel,

src/transformers/cache_utils.py

@@ -970,3 +970,125 @@ def get_max_length(self) -> Optional[int]:
         # in theory there is no limit because the sliding window size is fixed
         # no matter how long the sentence is
         return None
+
+
+class HybridCache(Cache):
+    def __init__(self, config: PretrainedConfig, max_batch_size, max_cache_len, device="cpu", dtype=None) -> None:
+        if not hasattr(config, "sliding_window") or config.sliding_window is None:
+            raise ValueError(
+                "Setting `cache_implementation` to 'sliding_window' requires the model config supporting "
+                "sliding window attention, please check if there is a `sliding_window` field in the model "
+                "config and it's not set to None."
+            )
+        self.max_cache_len = max_cache_len
+        self.max_batch_size = max_batch_size
+        # Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
+        self.head_dim = (
+            config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
+        )
+
+        self.dtype = dtype if dtype is not None else torch.float32
+        self.num_key_value_heads = (
+            config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
+        )
+        self.is_sliding = torch.tensor(
+            [i % 2 for i in range(config.num_hidden_layers)], dtype=torch.bool, device=device
+        )
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        global_cache_shape = (max_batch_size, self.num_key_value_heads, max_cache_len, self.head_dim)
+        sliding_cache_shape = (
+            max_batch_size,
+            self.num_key_value_heads,
+            min(config.sliding_window, max_cache_len),
+            self.head_dim,
+        )
+        for i in range(config.num_hidden_layers):
+            # Note: `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
+            # breaks when updating the cache.
+            cache_shape = global_cache_shape if not self.is_sliding[i] else sliding_cache_shape
+            new_layer_key_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
+            new_layer_value_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
+            torch._dynamo.mark_static_address(new_layer_key_cache)
+            torch._dynamo.mark_static_address(new_layer_value_cache)
+            self.key_cache.append(new_layer_key_cache)
+            self.value_cache.append(new_layer_value_cache)
+
+    def _sliding_update(self, cache_position, layer_idx, key_states, value_states, k_out, v_out, max_cache_len):
+        if cache_position.shape[0] > max_cache_len:
+            k_out = key_states[:, :, -max_cache_len:, :]
+            v_out = value_states[:, :, -max_cache_len:, :]
+            # Assumption: caches are all zeros at this point, `+=` is equivalent to `=` but compile-friendly
+            self.key_cache[layer_idx] += k_out
+            self.value_cache[layer_idx] += v_out
+            # we should return the whole states instead of k_out, v_out to take the whole prompt
+            # into consideration when building kv cache instead of just throwing away tokens outside of the window
+            return key_states, value_states
+
+        slicing = torch.ones(max_cache_len, dtype=torch.long, device=value_states.device).cumsum(0)
+        cache_position = cache_position.clamp(0, max_cache_len - 1)
+        to_shift = cache_position >= max_cache_len - 1
+        indices = (slicing + to_shift[-1].int() - 1) % max_cache_len
+        k_out = k_out[:, :, indices]
+        v_out = v_out[:, :, indices]
+
+        k_out[:, :, cache_position] = key_states
+        v_out[:, :, cache_position] = value_states
+        # `_.zero()` followed by `+=` is equivalent `=`, but compile-friendly (without graph breaks due to assignment)
+        self.key_cache[layer_idx].zero_()
+        self.value_cache[layer_idx].zero_()
+
+        self.key_cache[layer_idx] += k_out
+        self.value_cache[layer_idx] += v_out
+        return k_out, v_out
+
+    def _static_update(self, cache_position, layer_idx, key_states, value_states, k_out, v_out, max_cache_len):
+        k_out[:, :, cache_position] = key_states
+        v_out[:, :, cache_position] = value_states
+
+        self.key_cache[layer_idx] = k_out
+        self.value_cache[layer_idx] = v_out
+        return k_out, v_out
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+        sliding_window: Optional[int] = None,
+    ) -> Tuple[torch.Tensor]:
+        cache_position = cache_kwargs.get("cache_position")
+        self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device=key_states.device)
+        self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device=value_states.device)
+        k_out = self.key_cache[layer_idx]
+        v_out = self.value_cache[layer_idx]
+        if sliding_window:
+            update_fn = self._sliding_update
+        else:
+            update_fn = self._static_update
+
+        return update_fn(
+            cache_position,
+            layer_idx,
+            key_states,
+            value_states,
+            k_out,
+            v_out,
+            k_out.shape[2],
+        )
+
+    def get_max_length(self) -> Optional[int]:
+        # in theory there is no limit because the sliding window size is fixed
+        # no matter how long the sentence is
+        return self.max_cache_len
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        return None
+
+    def reset(self):
+        """Resets the cache values while preserving the objects"""
+        for layer_idx in range(len(self.key_cache)):
+            # In-place ops prevent breaking the static address
+            self.key_cache[layer_idx].zero_()
+            self.value_cache[layer_idx].zero_()

src/transformers/generation/configuration_utils.py

@@ -400,7 +400,7 @@ def __init__(self, **kwargs):
         # Cache implementation
         self.cache_implementation = kwargs.pop("cache_implementation", None)
         self.cache_config = kwargs.pop("cache_config", None)
-        if self.cache_implementation is not None:
+        if self.cache_implementation is not None and self.cache_implementation in NEEDS_CACHE_CONFIG:
             cache_config_class = NEEDS_CACHE_CONFIG[self.cache_implementation]
             if self.cache_config is None:
                 self.cache_config = cache_config_class()

src/transformers/generation/utils.py

@@ -28,6 +28,7 @@
     Cache,
     DynamicCache,
     HQQQuantizedCache,
+    HybridCache,
     QuantizedCacheConfig,
     QuantoQuantizedCache,
     SlidingWindowCache,
@@ -112,7 +113,7 @@
 if is_accelerate_available():
     from accelerate.hooks import AlignDevicesHook, add_hook_to_module
 
-NEED_SETUP_CACHE_CLASSES_MAPPING = {"static": StaticCache, "sliding_window": SlidingWindowCache}
+NEED_SETUP_CACHE_CLASSES_MAPPING = {"static": StaticCache, "sliding_window": SlidingWindowCache, "hybrid": HybridCache}
 QUANT_BACKEND_CLASSES_MAPPING = {"quanto": QuantoQuantizedCache, "HQQ": HQQQuantizedCache}
 
 
@@ -1395,10 +1396,12 @@ def _get_initial_cache_position(self, input_ids, model_kwargs):
 
         past_length = 0
         if model_kwargs.get("past_key_values") is not None:
-            if isinstance(model_kwargs["past_key_values"], Cache):
-                past_length = model_kwargs["past_key_values"].get_seq_length()
-            else:
-                past_length = model_kwargs["past_key_values"][0][0].shape[2]
+            cache = model_kwargs["past_key_values"]
+            if not isinstance(cache, Cache):
+                past_length = cache[0][0].shape[2]
+            elif hasattr(cache, "get_seq_length"):
+                past_length = cache.get_seq_length()
+
         if "inputs_embeds" in model_kwargs:
             cur_len = model_kwargs["inputs_embeds"].shape[1]
         else:
@@ -1739,7 +1742,9 @@ def generate(
                         "issue: https://github.com/huggingface/transformers/issues/28981"
                     )
                 model_kwargs["past_key_values"] = self._get_cache(
-                    generation_config.cache_implementation, batch_size, generation_config.max_length
+                    generation_config.cache_implementation,
+                    getattr(generation_config, "num_beams", 1) * batch_size,
+                    generation_config.max_length,
                 )
             elif generation_config.cache_implementation == "quantized":
                 if not self._supports_quantized_cache:

src/transformers/models/__init__.py

@@ -92,6 +92,7 @@
     funnel,
     fuyu,
     gemma,
+    gemma2,
     git,
     glpn,
     gpt2,

src/transformers/models/auto/configuration_auto.py

@@ -108,6 +108,7 @@
         ("funnel", "FunnelConfig"),
         ("fuyu", "FuyuConfig"),
         ("gemma", "GemmaConfig"),
+        ("gemma2", "Gemma2Config"),
         ("git", "GitConfig"),
         ("glpn", "GLPNConfig"),
         ("gpt-sw3", "GPT2Config"),
@@ -385,6 +386,7 @@
         ("funnel", "Funnel Transformer"),
         ("fuyu", "Fuyu"),
         ("gemma", "Gemma"),
+        ("gemma2", "Gemma2"),
         ("git", "GIT"),
         ("glpn", "GLPN"),
         ("gpt-sw3", "GPT-Sw3"),

src/transformers/models/auto/modeling_auto.py

@@ -105,6 +105,7 @@
         ("fsmt", "FSMTModel"),
         ("funnel", ("FunnelModel", "FunnelBaseModel")),
         ("gemma", "GemmaModel"),
+        ("gemma2", "Gemma2Model"),
         ("git", "GitModel"),
         ("glpn", "GLPNModel"),
         ("gpt-sw3", "GPT2Model"),
@@ -454,6 +455,7 @@
         ("falcon", "FalconForCausalLM"),
         ("fuyu", "FuyuForCausalLM"),
         ("gemma", "GemmaForCausalLM"),
+        ("gemma2", "Gemma2ForCausalLM"),
         ("git", "GitForCausalLM"),
         ("gpt-sw3", "GPT2LMHeadModel"),
         ("gpt2", "GPT2LMHeadModel"),
@@ -863,6 +865,7 @@
         ("fnet", "FNetForSequenceClassification"),
         ("funnel", "FunnelForSequenceClassification"),
         ("gemma", "GemmaForSequenceClassification"),
+        ("gemma2", "Gemma2ForSequenceClassification"),
         ("gpt-sw3", "GPT2ForSequenceClassification"),
         ("gpt2", "GPT2ForSequenceClassification"),
         ("gpt_bigcode", "GPTBigCodeForSequenceClassification"),
@@ -1044,6 +1047,7 @@
         ("fnet", "FNetForTokenClassification"),
         ("funnel", "FunnelForTokenClassification"),
         ("gemma", "GemmaForTokenClassification"),
+        ("gemma2", "Gemma2ForTokenClassification"),
         ("gpt-sw3", "GPT2ForTokenClassification"),
         ("gpt2", "GPT2ForTokenClassification"),
         ("gpt_bigcode", "GPTBigCodeForTokenClassification"),

src/transformers/models/auto/tokenization_auto.py

@@ -188,6 +188,13 @@
                     "GemmaTokenizerFast" if is_tokenizers_available() else None,
                 ),
             ),
+            (
+                "gemma2",
+                (
+                    "GemmaTokenizer" if is_sentencepiece_available() else None,
+                    "GemmaTokenizerFast" if is_tokenizers_available() else None,
+                ),
+            ),
             ("git", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
             ("gpt-sw3", ("GPTSw3Tokenizer" if is_sentencepiece_available() else None, None)),
             ("gpt2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),

src/transformers/models/gemma/diff_gemma.py

@@ -257,6 +257,7 @@ def __init__(self, config: GemmaConfig, layer_idx: Optional[int] = None):
         self.max_position_embeddings = config.max_position_embeddings
         self.rope_theta = config.rope_theta
         self.is_causal = True
+        self.scaling = 1 / math.sqrt(config.head_dim)
 
         if self.hidden_size % self.num_heads != 0:
             raise ValueError(
@@ -305,7 +306,7 @@ def forward(
         key_states = repeat_kv(key_states, self.num_key_value_groups)
         value_states = repeat_kv(value_states, self.num_key_value_groups)
 
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
 
         if attention_mask is not None:  # no matter the length, we just slice it
             causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]