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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

block_config.py

@@ -0,0 +1,118 @@
+import dataclasses
+import json
+import warnings
+from dataclasses import dataclass, MISSING
+from functools import partial
+from typing import Optional, Any
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class JsonComparable:
+    def to_json(self) -> str:
+        return json.dumps(dataclasses.asdict(self))
+
+    def __eq__(self, other: "JsonComparable") -> bool:
+        return self.to_json() == other.to_json()
+
+    def __hash__(self) -> int:
+        return hash(self.to_json())
+
+    def __lt__(self, other: "JsonComparable") -> bool:
+        return self.to_json() < other.to_json()
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class SubblockConfig(JsonComparable):
+    no_op: bool = False
+    replace_with_linear: bool = False
+    sparsify: Optional[list[str]] = None
+
+    def __post_init__(self):
+        assert not (self.no_op and self.replace_with_linear)
+
+    def _force_setattr(self, name: str, value: Any) -> None:
+        """
+        Set an attribute even in frozen dataclasses.
+        Use only inside __post_init__!
+        """
+        object.__setattr__(self, name, value)
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class AttentionConfig(SubblockConfig):
+    n_heads_in_group: Optional[int] = None
+    window_length: Optional[int] = None
+    num_sink_tokens: Optional[int] = None
+    use_prefill_window_in_sink_attention: bool = False
+    unshifted_sink: bool = False
+
+    def __post_init__(self):
+        super().__post_init__()
+        assert not (self.no_op and self.replace_with_linear)
+
+        if self.no_op or self.replace_with_linear:
+            for irrelevant_att in ["n_heads_in_group", "window_length", "num_sink_tokens"]:
+                self._force_setattr(irrelevant_att, None)
+        else:
+            assert self.n_heads_in_group is not None
+
+        if self.is_sink:
+            assert not (self.unshifted_sink and self.use_prefill_window_in_sink_attention), \
+                ("Unshifted sink uses its own kind of explicit masking, not standard window. "
+                 "Set use_prefill_window_in_sink_attention to False.")
+            assert not (self.num_sink_tokens == 0 and not self.unshifted_sink), \
+                "Fake sink attention with 0 sink tokens is only supported with unshifted_sink=True"
+
+    @property
+    def prefill_sliding_window(self) -> Optional[int]:
+        if self.window_length is not None:
+            if not self.is_sink or self.use_prefill_window_in_sink_attention:
+                return self.window_length
+        return None
+
+    @property
+    def is_sliding(self) -> bool:
+        return self.prefill_sliding_window is not None
+
+    @property
+    def is_sink(self) -> bool:
+        return (
+                (self.window_length is not None)
+                and
+                (self.num_sink_tokens is not None)
+        )
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class FFNConfig(SubblockConfig):
+    ffn_mult: Optional[float] = None
+
+    def __post_init__(self):
+        super().__post_init__()
+        if self.no_op or self.replace_with_linear:
+            self._force_setattr("ffn_mult", None)
+        else:
+            assert self.ffn_mult is not None
+            self._force_setattr("ffn_mult", round(self.ffn_mult, 6))
+
+
+@partial(dataclass, frozen=True, kw_only=True)
+class BlockConfig(JsonComparable):
+    attention: AttentionConfig = MISSING
+    ffn: FFNConfig = MISSING
+
+    def __post_init__(self):
+        """
+        Init subblock dataclasses from dicts
+        """
+        for subblock_name in dataclasses.fields(self):
+            subblock_config = getattr(self, subblock_name.name)
+            if isinstance(subblock_config, dict):
+                subblock_fields = [field.name for field in dataclasses.fields(subblock_name.type)]
+                unsupported_fields = [field_name for field_name in subblock_config.keys()
+                                      if field_name not in subblock_fields]
+                if len(unsupported_fields) > 0:
+                    warnings.warn(f"Removed unsupported fields {unsupported_fields} from {subblock_name.type.__name__}")
+                subblock_config = {k: v for k, v in subblock_config.items() if k not in unsupported_fields}
+                object.__setattr__(self, subblock_name.name,
+                                   subblock_name.type(**subblock_config))  # __setattr__ to overcome frozen=True

config.json

@@ -0,0 +1,1485 @@
+{
+  "_name_or_path": "llama_nemotron_super",
+  "architectures": [
+    "DeciLMForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_decilm.DeciLMConfig",
+    "AutoModelForCausalLM": "modeling_decilm.DeciLMForCausalLM"
+  },
+  "block_configs": [
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 2.625,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": null,
+        "no_op": true,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 2.625,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": null,
+        "no_op": true,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 2.625,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
+        "n_heads_in_group": 8,
+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
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+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
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+        "no_op": true,
+        "num_sink_tokens": null,
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
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+      }
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+      }
+    },
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+      }
+    },
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+      }
+    },
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+      }
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+      }
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+      }
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+      }
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+      }
+    },
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+        "sparsify": null
+      }
+    },
+    {
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+      }
+    },
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+      }
+    },
+    {
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+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
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+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
+        "use_prefill_window_in_sink_attention": false,
+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
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+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
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+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
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+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
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+      },
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
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+        "no_op": false,
+        "num_sink_tokens": null,
+        "replace_with_linear": false,
+        "sparsify": null,
+        "unshifted_sink": false,
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+        "window_length": null
+      },
+      "ffn": {
+        "ffn_mult": 5.25,
+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    },
+    {
+      "attention": {
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+        "no_op": false,
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+        "replace_with_linear": false,
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+        "unshifted_sink": false,
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+      },
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+        "no_op": false,
+        "replace_with_linear": false,
+        "sparsify": null
+      }
+    }
+  ],
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": null,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "nemotron-nas",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

configuration_decilm.py

@@ -0,0 +1,65 @@
+# coding=utf-8
+# Copyright 2024 Nvidia Corporation. 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.
+
+import dataclasses
+import warnings
+from typing import Dict, Any
+
+from transformers.utils import is_flash_attn_2_available
+
+from .block_config import BlockConfig
+from .transformers_4_44_2__configuration_llama import LlamaConfig
+from .transformers_4_44_2__modeling_rope_utils import \
+    rope_config_validation  # fake import to make AutoConfig infer the dependency
+
+rope_config_validation  # this line is here to make sure that auto-formatting doesn't remove the import
+
+
+class DeciLMConfig(LlamaConfig):
+    model_type = "nemotron-nas"
+
+    def __init__(
+            self,
+            block_configs: list[dict] | list[BlockConfig] = None,
+            **kwargs,
+    ):
+        attn_implementation = kwargs.pop("attn_implementation", None)
+        if attn_implementation is None and is_flash_attn_2_available():
+            attn_implementation = "flash_attention_2"
+
+        if block_configs is not None:
+            if isinstance(block_configs[0], dict):
+                block_configs = [BlockConfig(**conf) for conf in block_configs]
+
+            using_unshifted_sink = any([block_config.attention.unshifted_sink for block_config in block_configs])
+            if using_unshifted_sink and attn_implementation != "eager":
+                warnings.warn("Forcing attn_implementation='eager' since some attention layers use unshifted sink")
+                attn_implementation = "eager"
+
+        super().__init__(attn_implementation=attn_implementation, **kwargs)
+
+        self.intermediate_size = None
+        self.num_key_value_heads = None
+
+        if block_configs is not None:
+            assert len(block_configs) == self.num_hidden_layers
+
+        self.block_configs: list[BlockConfig] = block_configs
+
+    def to_dict(self) -> Dict[str, Any]:
+        self_dict = super().to_dict()
+        if self.block_configs is not None:
+            self_dict["block_configs"] = [dataclasses.asdict(conf) for conf in self.block_configs]
+        return self_dict

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modeling_decilm.py

@@ -0,0 +1,1681 @@
+# coding=utf-8
+# Copyright 2024 Nvidia Corporation, Google Inc, HuggingFace Inc, EleutherAI. All rights reserved.
+#
+# This code for Nvidia's model is based on the Llama modeling code by HuggingFace,
+# which is in turn based on EleutherAI's GPT-NeoX library and the GPT-NeoX and
+# OPT implementations in this library.
+# Sliding window code based on Gemma2 by Google.
+#
+# 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.
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers import GenerationConfig
+from transformers.generation.utils import NEED_SETUP_CACHE_CLASSES_MAPPING, GenerationMixin, GenerateOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+
+from .block_config import AttentionConfig, FFNConfig
+from .configuration_decilm import DeciLMConfig
+from .transformers_4_44_2__activations import ACT2FN
+from .transformers_4_44_2__cache_utils import Cache, StaticCache
+from .transformers_4_44_2__modeling_attn_mask_utils import AttentionMaskConverter
+from .transformers_4_44_2__modeling_flash_attention_utils_backward_compat import _flash_attention_forward
+from .transformers_4_44_2__modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from .transformers_4_44_2__modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from .transformers_4_44_2__pytorch_utils import ALL_LAYERNORM_LAYERS
+from .variable_cache import VariableCache
+
+MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[DeciLMConfig.model_type] = "DeciLMForCausalLM"
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DeciLMConfig"
+
+
+def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        min_dtype: float,
+        cache_position: torch.Tensor,
+        batch_size: int,
+):
+    """
+    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+    `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+    Args:
+        attention_mask (`torch.Tensor`):
+            A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+        sequence_length (`int`):
+            The sequence length being processed.
+        target_length (`int`):
+            The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+        dtype (`torch.dtype`):
+            The dtype to use for the 4D attention mask.
+        device (`torch.device`):
+            The device to place the 4D attention mask on.
+        min_dtype (`float`):
+            The minimum value representable with the dtype `dtype`.
+        cache_position (`torch.Tensor`):
+            Indices depicting the position of the input sequence tokens in the sequence.
+        batch_size (`torch.Tensor`):
+            Batch size.
+    """
+    if attention_mask is not None and attention_mask.dim() == 4:
+        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+        causal_mask = attention_mask
+    else:
+        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+        if attention_mask is not None:
+            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+            mask_length = attention_mask.shape[-1]
+            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+            padding_mask = padding_mask == 0
+            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                padding_mask, min_dtype
+            )
+
+    return causal_mask
+
+
+class DeciLMRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        DeciLMRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+ALL_LAYERNORM_LAYERS.append(DeciLMRMSNorm)
+
+
+class DeciLMRotaryEmbedding(nn.Module):
+    def __init__(
+            self,
+            dim=None,
+            max_position_embeddings=2048,
+            base=10000,
+            device=None,
+            scaling_factor=1.0,
+            rope_type="default",
+            config: Optional[DeciLMConfig] = None,
+    ):
+        super().__init__()
+        # TODO (joao): remove the `if` below, only used for BC
+        self.rope_kwargs = {}
+        if config is None:
+            logger.warning_once(
+                "`DeciLMRotaryEmbedding` can now be fully parameterized by passing the model config through the "
+                "`config` argument. All other arguments will be removed in v4.45"
+            )
+            self.rope_kwargs = {
+                "rope_type": rope_type,
+                "factor": scaling_factor,
+                "dim": dim,
+                "base": base,
+                "max_position_embeddings": max_position_embeddings,
+            }
+            self.rope_type = rope_type
+            self.max_seq_len_cached = max_position_embeddings
+            self.original_max_seq_len = max_position_embeddings
+        else:
+            # BC: "rope_type" was originally "type"
+            if config.rope_scaling is not None:
+                self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+            else:
+                self.rope_type = "default"
+            self.max_seq_len_cached = config.max_position_embeddings
+            self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    def _dynamic_frequency_update(self, position_ids, device):
+        """
+        dynamic RoPE layers should recompute `inv_freq` in the following situations:
+        1 - growing beyond the cached sequence length (allow scaling)
+        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+        """
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:  # growth
+            inv_freq, self.attention_scaling = self.rope_init_fn(
+                self.config, device, seq_len=seq_len, **self.rope_kwargs
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
+            self.max_seq_len_cached = seq_len
+
+        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.original_max_seq_len
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            self._dynamic_frequency_update(position_ids, device=x.device)
+
+        # Core RoPE block
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+
+        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
+        cos = cos * self.attention_scaling
+        sin = sin * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class DeciLMLinearScalingRotaryEmbedding(DeciLMRotaryEmbedding):
+    """DeciLMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, *args, **kwargs):
+        logger.warning_once(
+            "`DeciLMLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.45. Please use "
+            "`DeciLMRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)."
+        )
+        kwargs["rope_type"] = "linear"
+        super().__init__(*args, **kwargs)
+
+
+class DeciLMDynamicNTKScalingRotaryEmbedding(DeciLMRotaryEmbedding):
+    """DeciLMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, *args, **kwargs):
+        logger.warning_once(
+            "`DeciLMDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.45. Please use "
+            "`DeciLMRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to "
+            "__init__)."
+        )
+        kwargs["rope_type"] = "dynamic"
+        super().__init__(*args, **kwargs)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class DeciLMMLP(nn.Module):
+    def __init__(self,
+                 config: DeciLMConfig,
+                 ffn_config: FFNConfig,
+                 ):
+        super().__init__()
+        self.config = config
+        self.ffn_config = ffn_config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = _ffn_mult_to_intermediate_size(
+            ffn_config.ffn_mult, config.hidden_size)  # DeciLM-specific code
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+        if ffn_config.sparsify is not None:
+            self.register_full_backward_hook(sparsity_backward_hook)
+
+    def forward(self, x):
+        if self.config.pretraining_tp > 1:
+            slice = self.intermediate_size // self.config.pretraining_tp
+            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+            down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+            gate_proj = torch.cat(
+                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+            )
+            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+            down_proj = [
+                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class DeciLMAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self,
+                 config: DeciLMConfig,
+                 attention_config: AttentionConfig,
+                 layer_idx: Optional[int] = None,
+                 ):
+        super().__init__()
+        self.config = config
+        self.attention_config = attention_config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_groups = attention_config.n_heads_in_group  # DeciLM-specific code
+        self.num_key_value_heads = self.num_heads // self.num_key_value_groups  # DeciLM-specific code
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
+
+        # TODO (joao): remove in v4.45 (RoPE is computed in the model, not in the decoder layers)
+        self.rotary_emb = DeciLMRotaryEmbedding(config=self.config)
+
+        if attention_config.sparsify is not None:
+            self.register_full_backward_hook(sparsity_backward_hook)
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Cache] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+            cache_position: Optional[torch.LongTensor] = None,
+            position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.45
+            **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+        if self.config.pretraining_tp > 1:
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+            )
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.45 `position_ids` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            cos, sin = self.rotary_emb(value_states, position_ids)
+        else:
+            cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        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)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+
+        if self.config.pretraining_tp > 1:
+            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class DeciLMFlashAttention2(DeciLMAttention):
+    """
+    DeciLM flash attention module. This module inherits from `DeciLMAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+        self.sliding_window = self.attention_config.prefill_sliding_window
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Cache] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+            cache_position: Optional[torch.LongTensor] = None,
+            position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.45
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.45 `position_ids` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            cos, sin = self.rotary_emb(value_states, position_ids)
+        else:
+            cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (DeciLMRMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            position_ids=position_ids,
+            dropout=dropout_rate,
+            sliding_window=self.sliding_window,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=self.is_causal,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+DECILM_ATTENTION_CLASSES = {
+    "eager": DeciLMAttention,
+    "flash_attention_2": DeciLMFlashAttention2,
+}
+
+
+class DeciLMDecoderLayer(nn.Module):
+    # DeciLM-specific code
+    def __init__(self, config: DeciLMConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.block_config = config.block_configs[layer_idx]
+        self.attention_config = self.block_config.attention
+        self.ffn_config = self.block_config.ffn
+
+        if not self.attention_config.no_op:
+            self.input_layernorm = DeciLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            if not self.attention_config.replace_with_linear:
+                self.self_attn = DECILM_ATTENTION_CLASSES[config._attn_implementation](
+                    config=config, attention_config=self.attention_config, layer_idx=layer_idx)
+            else:
+                self.self_attn = DeciLMLinearAttention(config)
+
+        if not self.ffn_config.no_op:
+            self.post_attention_layernorm = DeciLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            if not self.ffn_config.replace_with_linear:
+                self.mlp = DeciLMMLP(config, self.ffn_config)
+            else:
+                self.mlp = DeciLMLinearMLP(config)
+
+        self.is_sliding = self.attention_config.is_sliding
+        self.sliding_window = self.attention_config.prefill_sliding_window
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Cache] = None,
+            output_attentions: Optional[bool] = False,
+            use_cache: Optional[bool] = False,
+            cache_position: Optional[torch.LongTensor] = None,
+            position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.45
+            **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+                with `head_dim` being the embedding dimension of each attention head.
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+        if self.attention_config.unshifted_sink and self.attention_config.is_sink:
+            attention_mask = self._unshifted_sink_mask(
+                attention_mask, hidden_states,
+                self.attention_config.window_length, self.attention_config.num_sink_tokens)
+        else:
+            attention_mask = self._gemma2_window_mask(attention_mask, hidden_states, past_key_value)
+
+        self_attn_weights = None
+        present_key_value = past_key_value
+        if self.attention_config.no_op:
+            pass
+        elif self.attention_config.replace_with_linear:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+            hidden_states = self.self_attn(hidden_states)
+            hidden_states = residual + hidden_states
+        else:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+            hidden_states, self_attn_weights, present_key_value = self.self_attn(
+                hidden_states=hidden_states,
+                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,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+            hidden_states = residual + hidden_states
+
+        if not self.ffn_config.no_op:
+            residual = hidden_states
+            hidden_states = self.post_attention_layernorm(hidden_states)
+            hidden_states = self.mlp(hidden_states)
+            hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+    def _gemma2_window_mask(self,
+                            attention_mask: Optional[torch.Tensor],
+                            hidden_states: torch.Tensor,
+                            past_key_value: Optional[VariableCache],
+                            ) -> Optional[torch.Tensor]:
+        if self.is_sliding and attention_mask is not None:  # efficient SDPA and no padding
+            # Flash-attn is a 2D tensor
+            if self.config._attn_implementation == "flash_attention_2":
+                if past_key_value is not None:  # when decoding
+                    attention_mask = attention_mask[:, -self.sliding_window:]
+            else:
+                min_dtype = torch.finfo(hidden_states.dtype).min
+                sliding_window_mask = torch.tril(
+                    torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window
+                )
+                attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
+                if attention_mask.shape[-1] <= 1:  # when decoding
+                    attention_mask = attention_mask[:, :, :, -self.sliding_window:]
+        return attention_mask
+
+    def _unshifted_sink_mask(self,
+                             attention_mask: torch.Tensor,
+                             hidden_states: torch.Tensor,
+                             window_length: int,
+                             num_sink_tokens: Optional[int],
+                             ) -> torch.Tensor:
+        assert self.config._attn_implementation == "eager", "Unshifted sink is only supported in 'eager' mode."
+        assert attention_mask is not None, "The attention mask seems to not be prepared"
+
+        attention_mask = attention_mask.clone()
+        min_dtype = torch.finfo(hidden_states.dtype).min
+
+        if window_length == 0:
+            attention_mask = torch.full_like(attention_mask, fill_value=min_dtype)
+        else:
+            query_length = attention_mask.shape[-2]
+            is_decode = (query_length == 1)
+            if is_decode:
+                attention_mask[:, :, :, :-window_length] = min_dtype
+            else:
+                sliding_window_mask = torch.tril(
+                    torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-window_length
+                )
+                attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
+
+        attention_mask[:, :, :, :num_sink_tokens] = 0
+        return attention_mask
+
+
+DECILM_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`DeciLMConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare DeciLM Model outputting raw hidden-states without any specific head on top.",
+    DECILM_START_DOCSTRING,
+)
+class DeciLMPreTrainedModel(PreTrainedModel):
+    config_class = DeciLMConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DeciLMDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = False
+    _supports_cache_class = True
+    _supports_quantized_cache = False
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _prepare_generation_config(
+            self, generation_config: Optional[GenerationConfig], **kwargs: dict
+    ) -> tuple[GenerationConfig, dict]:
+        # DeciLM-specific code
+        generation_config, model_kwargs = super()._prepare_generation_config(generation_config, **kwargs)
+        generation_config.cache_implementation = "variable"
+        NEED_SETUP_CACHE_CLASSES_MAPPING["variable"] = VariableCache
+        return generation_config, model_kwargs
+
+
+DECILM_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`VariableCache`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            If passed to the forward function, past_key_values must be a VariableCache object (see imports).
+            For generation purposes, this is already handled inside model.generate().
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare DeciLM Model outputting raw hidden-states without any specific head on top.",
+    DECILM_START_DOCSTRING,
+)
+class DeciLMModel(DeciLMPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeciLMDecoderLayer`]
+
+    Args:
+        config: DeciLMConfig
+    """
+
+    def __init__(self, config: DeciLMConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [DeciLMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = DeciLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = DeciLMRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        is_legacy_cache_format = (past_key_values is not None) and not isinstance(past_key_values, Cache)
+        if is_legacy_cache_format:
+            raise NotImplementedError("DeciLMModel does not support legacy cache format, please use a newer "
+                                      "transformers version or use VariableCache explicitly (see import in this file).")
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _update_causal_mask(
+            self,
+            attention_mask: torch.Tensor,
+            input_tensor: torch.Tensor,
+            cache_position: torch.Tensor,
+            past_key_values: Cache,
+            output_attentions: bool,
+    ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
+
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        assert not isinstance(past_key_values, StaticCache), "DeciLM does not support StaticCache"
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                    attention_mask,
+                    inputs_embeds=input_tensor,
+                    past_key_values_length=past_seen_tokens,
+                    is_training=self.training,
+            ) and all([not layer.is_sliding for layer in self.layers]):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            min_dtype=min_dtype,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+                self.config._attn_implementation == "sdpa"
+                and attention_mask is not None
+                and attention_mask.device.type == "cuda"
+                and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+
+class DeciLMForCausalLM(DeciLMPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = DeciLMModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Return:
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        if self.config.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+            self,
+            input_ids,
+            past_key_values=None,
+            attention_mask=None,
+            inputs_embeds=None,
+            cache_position=None,
+            position_ids=None,
+            use_cache=True,
+            **kwargs,
+    ):
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+        if past_key_values is not None:
+            if inputs_embeds is not None:  # Exception 1
+                input_ids = input_ids[:, -cache_position.shape[0]:]
+            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1]:]
+
+                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
+                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
+        else:
+            # The clone here is for the same reason as for `position_ids`.
+            model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
+
+        assert not isinstance(past_key_values, StaticCache), "DeciLM does not support StaticCache"
+        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+            if model_inputs["inputs_embeds"] is not None:
+                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
+                device = model_inputs["inputs_embeds"].device
+            else:
+                batch_size, sequence_length = model_inputs["input_ids"].shape
+                device = model_inputs["input_ids"].device
+
+            dtype = self.lm_head.weight.dtype
+            min_dtype = torch.finfo(dtype).min
+
+            attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+                attention_mask,
+                sequence_length=sequence_length,
+                target_length=past_key_values.get_max_length(),
+                dtype=dtype,
+                device=device,
+                min_dtype=min_dtype,
+                cache_position=cache_position,
+                batch_size=batch_size,
+            )
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    def _maybe_initialize_input_ids_for_generation(
+            self,
+            inputs: Optional[torch.Tensor] = None,
+            bos_token_id: Optional[torch.Tensor] = None,
+            model_kwargs: Optional[dict[str, torch.Tensor]] = None,
+    ) -> torch.LongTensor:
+        """
+        Patching hf bug that creates wrong cache length if only inputs_embeds are passed to the model
+        """
+        input_ids = super()._maybe_initialize_input_ids_for_generation(
+            inputs=inputs, bos_token_id=bos_token_id, model_kwargs=model_kwargs)
+        if (
+                "inputs_embeds" in model_kwargs
+                and input_ids is not None
+                and input_ids.shape[1] == 0
+        ):
+            batch_size, input_sequence_length = model_kwargs["inputs_embeds"].shape[:2]
+            input_ids = torch.zeros((batch_size, input_sequence_length), dtype=torch.long, device=self.device)
+        return input_ids
+
+    def generate(
+            self,
+            inputs: Optional[torch.Tensor] = None,
+            *args,
+            **kwargs,
+    ) -> Union[GenerateOutput, torch.LongTensor]:
+        """
+        Patching hf bug that creates wrong cache length if only inputs_embeds are passed to the model
+        """
+        only_passed_inputs_embeds = (
+                "inputs_embeds" in kwargs and
+                "input_ids" not in kwargs and
+                inputs is None
+        )
+        if only_passed_inputs_embeds:
+            input_sequence_length = kwargs["inputs_embeds"].shape[1]
+
+        generation_output = super().generate(inputs=inputs, *args, **kwargs)
+
+        if only_passed_inputs_embeds and isinstance(generation_output, torch.Tensor):
+            generation_output = generation_output[:, input_sequence_length:]
+
+        return generation_output
+
+
+@add_start_docstrings(
+    """
+    The DeciLM Model transformer with a sequence classification head on top (linear layer).
+
+    [`DeciLMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    DECILM_START_DOCSTRING,
+)
+class DeciLMForSequenceClassification(DeciLMPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = DeciLMModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+The DeciLM Model transformer with a span classification head on top for extractive question-answering tasks like
+SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    DECILM_START_DOCSTRING,
+)
+class DeciLMForQuestionAnswering(DeciLMPreTrainedModel):
+    base_model_prefix = "transformer"
+
+    # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->DeciLM
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = DeciLMModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.transformer.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.transformer.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            start_positions: Optional[torch.LongTensor] = None,
+            end_positions: Optional[torch.LongTensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The DeciLM Model transformer with a token classification head on top (a linear layer on top of the hidden-states
+    output) e.g. for Named-Entity-Recognition (NER) tasks.
+    """,
+    DECILM_START_DOCSTRING,
+)
+class DeciLMForTokenClassification(DeciLMPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = DeciLMModel(config)
+        if getattr(config, "classifier_dropout", None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, "hidden_dropout", None) is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        sequence_output = self.dropout(sequence_output)
+        logits = self.score(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+########################################################################
+# DeciLM-specific code
+########################################################################
+
+
+def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
+    # DeciLM-specific code
+    intermediate_size = int(2 * ffn_mult * n_embd / 3)
+    return _find_multiple(intermediate_size, 256)
+
+
+def _find_multiple(n: int, k: int) -> int:
+    # DeciLM-specific code
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+class DeciLMLinearMLP(nn.Module):
+    # DeciLM-specific code
+    def __init__(self,
+                 config: DeciLMConfig,
+                 ):
+        super().__init__()
+        self.linear_mlp = nn.Linear(in_features=config.hidden_size,
+                                    out_features=config.hidden_size,
+                                    bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.linear_mlp.forward(x)
+
+
+class DeciLMLinearAttention(nn.Module):
+    # DeciLM-specific code
+    def __init__(self,
+                 config: DeciLMConfig,
+                 ):
+        super().__init__()
+        self.linear_attn = nn.Linear(in_features=config.hidden_size,
+                                     out_features=config.hidden_size,
+                                     bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.linear_attn.forward(x)
+
+
+def sparsity_backward_hook(*args, **kwargs):
+    raise NotImplementedError("No support for sparsity when training HF DeciLM (inference is ok though)")

special_tokens_map.json

@@ -0,0 +1,16 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
+size 17209920

tokenizer_config.json

@@ -0,0 +1,2063 @@
+{
+  "added_tokens_decoder": {
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+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "<|end_of_text|>",
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+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}{%- if messages[0]['role'] == 'system' %}{%- set system_message = messages[0]['content']|trim %}{%- set messages = messages[1:] %}{%- else %}{%- set system_message = \"\" %}{%- endif %}{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}{{- system_message }}{{- \"<|eot_id|>\" }}{%- for message in messages %}{%- if message['role'] == 'assistant' and '</think>' in message['content'] %}{%- set content = message['content'].split('</think>')[-1].lstrip() %}{%- else %}{%- set content = message['content'] %}{%- endif %}{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' + content | trim + '<|eot_id|>' }}{%- endfor %}{%- if add_generation_prompt %}{{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}{%- endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

transformers_4_44_2__activations.py

@@ -0,0 +1,239 @@
+# Copyright 2020 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.
+
+import math
+from collections import OrderedDict
+
+import torch
+from packaging import version
+from torch import Tensor, nn
+
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class PytorchGELUTanh(nn.Module):
+    """
+    A fast C implementation of the tanh approximation of the GeLU activation function. See
+    https://arxiv.org/abs/1606.08415.
+
+    This implementation is equivalent to NewGELU and FastGELU but much faster. However, it is not an exact numerical
+    match due to rounding errors.
+    """
+
+    def __init__(self):
+        super().__init__()
+        if version.parse(torch.__version__) < version.parse("1.12.0"):
+            raise ImportError(
+                f"You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use "
+                "PytorchGELUTanh. Please upgrade torch."
+            )
+
+    def forward(self, input: Tensor) -> Tensor:
+        return nn.functional.gelu(input, approximate="tanh")
+
+
+class NewGELUActivation(nn.Module):
+    """
+    Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
+    the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
+    """
+
+    def forward(self, input: Tensor) -> Tensor:
+        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))
+
+
+class GELUActivation(nn.Module):
+    """
+    Original Implementation of the GELU activation function in Google BERT repo when initially created. For
+    information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 +
+    torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional
+    Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
+    """
+
+    def __init__(self, use_gelu_python: bool = False):
+        super().__init__()
+        if use_gelu_python:
+            self.act = self._gelu_python
+        else:
+            self.act = nn.functional.gelu
+
+    def _gelu_python(self, input: Tensor) -> Tensor:
+        return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))
+
+    def forward(self, input: Tensor) -> Tensor:
+        return self.act(input)
+
+
+class FastGELUActivation(nn.Module):
+    """
+    Applies GELU approximation that is slower than QuickGELU but more accurate. See: https://github.com/hendrycks/GELUs
+    """
+
+    def forward(self, input: Tensor) -> Tensor:
+        return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)))
+
+
+class QuickGELUActivation(nn.Module):
+    """
+    Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs
+    """
+
+    def forward(self, input: Tensor) -> Tensor:
+        return input * torch.sigmoid(1.702 * input)
+
+
+class ClippedGELUActivation(nn.Module):
+    """
+    Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as
+    it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer to
+    https://arxiv.org/abs/2004.09602.
+
+    Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
+    initially created.
+
+    For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 +
+    torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))). See https://arxiv.org/abs/1606.08415
+    """
+
+    def __init__(self, min: float, max: float):
+        if min > max:
+            raise ValueError(f"min should be < max (got min: {min}, max: {max})")
+
+        super().__init__()
+        self.min = min
+        self.max = max
+
+    def forward(self, x: Tensor) -> Tensor:
+        return torch.clip(gelu(x), self.min, self.max)
+
+
+class AccurateGELUActivation(nn.Module):
+    """
+    Applies GELU approximation that is faster than default and more accurate than QuickGELU. See:
+    https://github.com/hendrycks/GELUs
+
+    Implemented along with MEGA (Moving Average Equipped Gated Attention)
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.precomputed_constant = math.sqrt(2 / math.pi)
+
+    def forward(self, input: Tensor) -> Tensor:
+        return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(input, 3))))
+
+
+class MishActivation(nn.Module):
+    """
+    See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also
+    visit the official repository for the paper: https://github.com/digantamisra98/Mish
+    """
+
+    def __init__(self):
+        super().__init__()
+        if version.parse(torch.__version__) < version.parse("1.9.0"):
+            self.act = self._mish_python
+        else:
+            self.act = nn.functional.mish
+
+    def _mish_python(self, input: Tensor) -> Tensor:
+        return input * torch.tanh(nn.functional.softplus(input))
+
+    def forward(self, input: Tensor) -> Tensor:
+        return self.act(input)
+
+
+class LinearActivation(nn.Module):
+    """
+    Applies the linear activation function, i.e. forwarding input directly to output.
+    """
+
+    def forward(self, input: Tensor) -> Tensor:
+        return input
+
+
+class LaplaceActivation(nn.Module):
+    """
+    Applies elementwise activation based on Laplace function, introduced in MEGA as an attention activation. See
+    https://arxiv.org/abs/2209.10655
+
+    Inspired by squared relu, but with bounded range and gradient for better stability
+    """
+
+    def forward(self, input, mu=0.707107, sigma=0.282095):
+        input = (input - mu).div(sigma * math.sqrt(2.0))
+        return 0.5 * (1.0 + torch.erf(input))
+
+
+class ReLUSquaredActivation(nn.Module):
+    """
+    Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2
+    """
+
+    def forward(self, input):
+        relu_applied = nn.functional.relu(input)
+        squared = torch.square(relu_applied)
+        return squared
+
+
+class ClassInstantier(OrderedDict):
+    def __getitem__(self, key):
+        content = super().__getitem__(key)
+        cls, kwargs = content if isinstance(content, tuple) else (content, {})
+        return cls(**kwargs)
+
+
+ACT2CLS = {
+    "gelu": GELUActivation,
+    "gelu_10": (ClippedGELUActivation, {"min": -10, "max": 10}),
+    "gelu_fast": FastGELUActivation,
+    "gelu_new": NewGELUActivation,
+    "gelu_python": (GELUActivation, {"use_gelu_python": True}),
+    "gelu_pytorch_tanh": PytorchGELUTanh,
+    "gelu_accurate": AccurateGELUActivation,
+    "laplace": LaplaceActivation,
+    "leaky_relu": nn.LeakyReLU,
+    "linear": LinearActivation,
+    "mish": MishActivation,
+    "quick_gelu": QuickGELUActivation,
+    "relu": nn.ReLU,
+    "relu2": ReLUSquaredActivation,
+    "relu6": nn.ReLU6,
+    "sigmoid": nn.Sigmoid,
+    "silu": nn.SiLU,
+    "swish": nn.SiLU,
+    "tanh": nn.Tanh,
+}
+ACT2FN = ClassInstantier(ACT2CLS)
+
+
+def get_activation(activation_string):
+    if activation_string in ACT2FN:
+        return ACT2FN[activation_string]
+    else:
+        raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}")
+
+
+# For backwards compatibility with: from activations import gelu_python
+gelu_python = get_activation("gelu_python")
+gelu_new = get_activation("gelu_new")
+gelu = get_activation("gelu")
+gelu_fast = get_activation("gelu_fast")
+quick_gelu = get_activation("quick_gelu")
+silu = get_activation("silu")
+mish = get_activation("mish")
+linear_act = get_activation("linear")

transformers_4_44_2__cache_utils.py

@@ -0,0 +1,1347 @@
+import copy
+import importlib.metadata
+import json
+import os
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from packaging import version
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import is_torchdynamo_compiling, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Cache(torch.nn.Module):
+    """
+    Base, abstract class for all caches. The actual data structure is specific to each subclass.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. These are specific to each subclass and allow new types of
+                cache to be created.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        raise NotImplementedError("Make sure to implement `update` in a subclass.")
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        # TODO: deprecate this function in favor of `cache_position`
+        raise NotImplementedError("Make sure to implement `get_seq_length` in a subclass.")
+
+    def get_max_length(self) -> Optional[int]:
+        """Returns the maximum sequence length of the cached states, if there is any."""
+        raise NotImplementedError("Make sure to implement `get_max_length` in a subclass.")
+
+    def get_usable_length(self, new_seq_length: int, layer_idx: Optional[int] = 0) -> int:
+        """Given the sequence length of the new inputs, returns the usable length of the cache."""
+        # Cache without size limit -> all cache is usable
+        # Cache with size limit -> if the length cache plus the length of the new inputs is larger the maximum cache
+        #   length, we will need to evict part of the cache (and thus not all cache is usable)
+        max_length = self.get_max_length()
+        previous_seq_length = self.get_seq_length(layer_idx)
+        if max_length is not None and previous_seq_length + new_seq_length > max_length:
+            return max_length - new_seq_length
+        return previous_seq_length
+
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        for layer_idx in range(len(self.key_cache)):
+            device = self.key_cache[layer_idx].device
+            self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
+            device = self.value_cache[layer_idx].device
+            self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+    @property
+    def seen_tokens(self):
+        logger.warning_once(
+            "The `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` "
+            "model input instead."
+        )
+        if hasattr(self, "_seen_tokens"):
+            return self._seen_tokens
+        else:
+            return None
+
+
+@dataclass
+class CacheConfig:
+    """
+    Base class for cache configs
+    """
+
+    cache_implementation: None
+
+    @classmethod
+    def from_dict(cls, config_dict, **kwargs):
+        """
+        Constructs a CacheConfig instance from a dictionary of parameters.
+        Args:
+            config_dict (Dict[str, Any]): Dictionary containing configuration parameters.
+            **kwargs: Additional keyword arguments to override dictionary values.
+
+        Returns:
+            CacheConfig: Instance of CacheConfig constructed from the dictionary.
+        """
+        config = cls(**config_dict)
+        to_remove = []
+        for key, value in kwargs.items():
+            if hasattr(config, key):
+                setattr(config, key, value)
+                to_remove.append(key)
+        for key in to_remove:
+            kwargs.pop(key, None)
+        return config
+
+    # Copied from transformers.utils.quantization_config.QuantizationConfigMixin.to_json_file
+    def to_json_file(self, json_file_path: Union[str, os.PathLike]):
+        """
+        Save this instance to a JSON file.
+
+        Args:
+            json_file_path (`str` or `os.PathLike`):
+                Path to the JSON file in which this configuration instance's parameters will be saved.
+            use_diff (`bool`, *optional*, defaults to `True`):
+                If set to `True`, only the difference between the config instance and the default
+                `QuantizationConfig()` is serialized to JSON file.
+        """
+        with open(json_file_path, "w", encoding="utf-8") as writer:
+            config_dict = self.to_dict()
+            json_string = json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
+
+            writer.write(json_string)
+
+    # Copied from transformers.utils.quantization_config.QuantizationConfigMixin.to_dict
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Serializes this instance to a Python dictionary. Returns:
+            `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
+        """
+        return copy.deepcopy(self.__dict__)
+
+    # Copied from transformers.utils.quantization_config.QuantizationConfigMixin.__iter__
+    def __iter__(self):
+        """allows `dict(obj)` for situations where obj may be a dict or QuantizationConfigMixin"""
+        for attr, value in copy.deepcopy(self.__dict__).items():
+            yield attr, value
+
+    # Copied from transformers.utils.quantization_config.QuantizationConfigMixin.__repr__
+    def __repr__(self):
+        return f"{self.__class__.__name__} {self.to_json_string()}"
+
+    def to_json_string(self):
+        """
+        Serializes this instance to a JSON formatted string.
+        Returns:
+            str: JSON formatted string representing the configuration instance.
+        """
+        return json.dumps(self.__dict__, indent=2) + "\n"
+
+    # Copied from transformers.utils.quantization_config.QuantizationConfigMixin.update
+    def update(self, **kwargs):
+        """
+        Updates attributes of this class instance with attributes from `kwargs` if they match existing attributes,
+        returning all the unused kwargs.
+
+        Args:
+            kwargs (`Dict[str, Any]`):
+                Dictionary of attributes to tentatively update this class.
+
+        Returns:
+            `Dict[str, Any]`: Dictionary containing all the key-value pairs that were not used to update the instance.
+        """
+        to_remove = []
+        for key, value in kwargs.items():
+            if hasattr(self, key):
+                setattr(self, key, value)
+                to_remove.append(key)
+
+        # Remove all the attributes that were updated, without modifying the input dict
+        unused_kwargs = {key: value for key, value in kwargs.items() if key not in to_remove}
+        return unused_kwargs
+
+
+class DynamicCache(Cache):
+    """
+    A cache that grows dynamically as more tokens are generated. This is the default for generative models.
+
+    It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
+    `[batch_size, num_heads, seq_len, head_dim]`.
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
+
+        >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
+        >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
+
+        >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> past_key_values = DynamicCache()
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
+        ```
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        self._seen_tokens = 0  # Used in `generate` to keep tally of how many tokens the cache has seen
+
+    def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
+        """
+        Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the
+        sequence length.
+        """
+        if layer_idx < len(self):
+            return (self.key_cache[layer_idx], self.value_cache[layer_idx])
+        else:
+            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
+
+    def __iter__(self):
+        """
+        Support for backwards-compatible `past_key_value` iteration, e.g. `for x in past_key_value:` to iterate over
+        keys and values
+        """
+        for layer_idx in range(len(self)):
+            yield (self.key_cache[layer_idx], self.value_cache[layer_idx])
+
+    def __len__(self):
+        """
+        Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds
+        to the number of layers in the model.
+        """
+        return len(self.key_cache)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        # Update the number of seen tokens
+        if layer_idx == 0:
+            self._seen_tokens += key_states.shape[-2]
+
+        # Update the cache
+        if len(self.key_cache) <= layer_idx:
+            self.key_cache.append(key_states)
+            self.value_cache.append(value_states)
+        else:
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        # TODO: deprecate this function in favor of `cache_position`
+        if len(self.key_cache) <= layer_idx:
+            return 0
+        return self.key_cache[layer_idx].shape[-2]
+
+    def get_max_length(self) -> Optional[int]:
+        """Returns the maximum sequence length of the cached states. DynamicCache does not have a maximum length."""
+        return None
+
+    def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
+        """Converts the `DynamicCache` instance into the its equivalent in the legacy cache format. Used for
+        backward compatibility."""
+        legacy_cache = ()
+        for layer_idx in range(len(self)):
+            legacy_cache += ((self.key_cache[layer_idx], self.value_cache[layer_idx]),)
+        return legacy_cache
+
+    @classmethod
+    def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None) -> "DynamicCache":
+        """Converts a cache in the legacy cache format into an equivalent `DynamicCache`. Used for
+        backward compatibility."""
+        cache = cls()
+        if past_key_values is not None:
+            for layer_idx in range(len(past_key_values)):
+                key_states, value_states = past_key_values[layer_idx]
+                cache.update(key_states, value_states, layer_idx)
+        return cache
+
+    def crop(self, max_length: int):
+        """Crop the past key values up to a new `max_length` in terms of tokens. `max_length` can also be
+        negative to remove `max_length` tokens. This is used in assisted decoding and contrastive search."""
+        # In case it is negative
+        if max_length < 0:
+            max_length = self.get_seq_length() - abs(max_length)
+
+        if self.get_seq_length() <= max_length:
+            return
+
+        self._seen_tokens = max_length
+        for idx in range(len(self.key_cache)):
+            self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
+            self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
+
+    def batch_split(self, full_batch_size: int, split_size: int) -> List["DynamicCache"]:
+        """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
+        `_split_model_inputs()` in `generation.utils`"""
+        out = []
+        for i in range(0, full_batch_size, split_size):
+            current_split = DynamicCache()
+            current_split._seen_tokens = self._seen_tokens
+            current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
+            current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
+            out.append(current_split)
+        return out
+
+    @classmethod
+    def from_batch_splits(cls, splits: List["DynamicCache"]) -> "DynamicCache":
+        """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
+        `generation.utils`"""
+        cache = cls()
+        for idx in range(len(splits[0])):
+            layer_keys = torch.cat([current.key_cache[idx] for current in splits], dim=0)
+            layer_values = torch.cat([current.value_cache[idx] for current in splits], dim=0)
+            cache.update(layer_keys, layer_values, idx)
+        return cache
+
+    def batch_repeat_interleave(self, repeats: int):
+        """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0)
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...]
+            self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
+
+
+class OffloadedCache(DynamicCache):
+    """
+    A drop-in replacement for DynamicCache that conserves GPU memory at the expense of more CPU memory.
+    Useful for generating from models with very long context.
+
+    In addition to the default CUDA stream, where all forward() computations happen,
+    this class uses another stream, the prefetch stream, which it creates itself.
+    Since scheduling of operations on separate streams happens independently, this class uses
+    the prefetch stream to asynchronously prefetch the KV cache of layer k+1 when layer k is executing.
+    The movement of the layer k-1 cache to the CPU is handled by the default stream as a simple way to
+    ensure the eviction is scheduled after all computations on that cache are finished.
+    """
+
+    def __init__(self) -> None:
+        if not torch.cuda.is_available():
+            raise RuntimeError("OffloadedCache can only be used with a GPU")
+        super().__init__()
+        self.original_device = []
+        self.prefetch_stream = torch.cuda.Stream()
+        self.beam_idx = None  # used to delay beam search operations
+
+    def prefetch_layer(self, layer_idx: int):
+        "Starts prefetching the next layer cache"
+        if layer_idx < len(self):
+            with torch.cuda.stream(self.prefetch_stream):
+                # Prefetch next layer tensors to GPU
+                device = self.original_device[layer_idx]
+                self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device, non_blocking=True)
+                self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device, non_blocking=True)
+
+    def evict_previous_layer(self, layer_idx: int):
+        "Moves the previous layer cache to the CPU"
+        if len(self) > 2:
+            # We do it on the default stream so it occurs after all earlier computations on these tensors are done
+            prev_layer_idx = (layer_idx - 1) % len(self)
+            self.key_cache[prev_layer_idx] = self.key_cache[prev_layer_idx].to("cpu", non_blocking=True)
+            self.value_cache[prev_layer_idx] = self.value_cache[prev_layer_idx].to("cpu", non_blocking=True)
+
+    def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
+        "Gets the cache for this layer to the device. Prefetches the next and evicts the previous layer."
+        if layer_idx < len(self):
+            # Evict the previous layer if necessary
+            torch.cuda.current_stream().synchronize()
+            self.evict_previous_layer(layer_idx)
+            # Load current layer cache to its original device if not already there
+            original_device = self.original_device[layer_idx]
+            self.prefetch_stream.synchronize()
+            key_tensor = self.key_cache[layer_idx]
+            value_tensor = self.value_cache[layer_idx]
+            # Now deal with beam search ops which were delayed
+            if self.beam_idx is not None:
+                self.beam_idx = self.beam_idx.to(original_device)
+                key_tensor = key_tensor.index_select(0, self.beam_idx)
+                value_tensor = value_tensor.index_select(0, self.beam_idx)
+            # Prefetch the next layer
+            self.prefetch_layer((layer_idx + 1) % len(self))
+            return (key_tensor, value_tensor)
+        else:
+            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
+
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Saves the beam indices and reorders the cache when the tensor is back to its device."""
+        # We delay this operation until the tensors are back to their original
+        # device because performing torch.index_select on the CPU is very slow
+        del self.beam_idx
+        self.beam_idx = beam_idx.clone()
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. No additional arguments are used in `OffloadedCache`.
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        # Update the number of seen tokens
+        if layer_idx == 0:
+            self._seen_tokens += key_states.shape[-2]
+
+        # Update the cache
+        if len(self.key_cache) <= layer_idx:
+            self.key_cache.append(key_states)
+            self.value_cache.append(value_states)
+            self.original_device.append(key_states.device)
+            self.evict_previous_layer(layer_idx)
+        else:
+            key_tensor, value_tensor = self[layer_idx]
+            self.key_cache[layer_idx] = torch.cat([key_tensor, key_states], dim=-2)
+            self.value_cache[layer_idx] = torch.cat([value_tensor, value_states], dim=-2)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+    # According to https://docs.python.org/3/library/exceptions.html#NotImplementedError
+    # if a method is not supposed to be supported in a subclass we should set it to None
+    from_legacy_cache = None
+
+    to_legacy_cache = None
+
+
+class SinkCache(Cache):
+    """
+    A cache that as described in the [Attention Sinks paper](https://arxiv.org/abs/2309.17453). It allows the model to
+    generate beyond the length of its context window, without losing fluency in the conversation. As it discards past
+    tokens, the model will lose the ability to generate tokens that depend on the context that was discarded.
+
+    It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
+    `[batch_size, num_heads, seq_len, head_dim]`.
+
+    Parameters:
+        window_length (`int`):
+            The length of the context window.
+        num_sink_tokens (`int`):
+            The number of sink tokens. See the original paper for more information.
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModelForCausalLM, SinkCache
+
+        >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
+        >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
+
+        >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> past_key_values = SinkCache(window_length=256, num_sink_tokens=4)
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
+        ```
+    """
+
+    def __init__(self, window_length: int, num_sink_tokens: int) -> None:
+        super().__init__()
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        self.window_length = window_length
+        self.num_sink_tokens = num_sink_tokens
+        self.cos_sin_rerotation_cache = {}
+        self._cos_cache = None
+        self._sin_cache = None
+        self._seen_tokens = 0  # Used in `generate` to keep tally of how many tokens the cache has seen
+
+    @staticmethod
+    def _rotate_half(x):
+        x1 = x[..., : x.shape[-1] // 2]
+        x2 = x[..., x.shape[-1] // 2 :]
+        return torch.cat((-x2, x1), dim=-1)
+
+    def _apply_key_rotary_pos_emb(
+        self, key_states: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+    ) -> torch.Tensor:
+        rotated_key_states = (key_states * cos) + (self._rotate_half(key_states) * sin)
+        return rotated_key_states
+
+    def _get_rerotation_cos_sin(
+        self, key_states: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if key_states.shape[-2] not in self.cos_sin_rerotation_cache:
+            # Upcast to float32 temporarily for better accuracy
+            cos = cos.to(torch.float32)
+            sin = sin.to(torch.float32)
+
+            # Compute the cos and sin required for back- and forward-rotating to one position earlier in the sequence
+            original_cos = cos[self.num_sink_tokens + key_states.shape[-2] :]
+            shifted_cos = cos[self.num_sink_tokens : -key_states.shape[-2]]
+            original_sin = sin[self.num_sink_tokens + key_states.shape[-2] :]
+            shifted_sin = sin[self.num_sink_tokens : -key_states.shape[-2]]
+            rerotation_cos = original_cos * shifted_cos + original_sin * shifted_sin
+            rerotation_sin = -original_sin * shifted_cos + original_cos * shifted_sin
+
+            self.cos_sin_rerotation_cache[key_states.shape[-2]] = (
+                rerotation_cos.to(key_states.dtype).unsqueeze(0),
+                rerotation_sin.to(key_states.dtype).unsqueeze(0),
+            )
+        return self.cos_sin_rerotation_cache[key_states.shape[-2]]
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        # TODO: deprecate this function in favor of `cache_position`
+        # Workaround to make 'key_states.shape[-2] + past_key_value.get_seq_length(self.layer_idx)' <= window_length
+        if len(self.key_cache) <= layer_idx:
+            return 0
+        return self.key_cache[layer_idx].shape[-2]
+
+    def get_max_length(self) -> Optional[int]:
+        """Returns the maximum sequence length of the cached states."""
+        return self.window_length
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. The following arguments can be used in `SinkCache`: `sin`,
+                `cos` and `partial_rotation_size`. These arguments are used with models using RoPE, to recompute the
+                rotation as the tokens are shifted.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        # Optional kwargs for `SinkCache` -- needed on models using RoPE. `partial_rotation_size` is used on models
+        # with partially rotated position embeddings, like Phi or Persimmon.
+        sin = cache_kwargs.get("sin")
+        cos = cache_kwargs.get("cos")
+        partial_rotation_size = cache_kwargs.get("partial_rotation_size")
+        using_rope = cos is not None and sin is not None
+
+        # Update the number of seen tokens
+        if layer_idx == 0:
+            self._seen_tokens += key_states.shape[-2]
+
+        # Update the sin/cos cache, which holds sin/cos values for all possible positions
+        if using_rope and layer_idx == 0:
+            # BC: some models still pass `sin`/`cos` with 2 dims. In those models, they are the full sin/cos. Remove
+            # after all RoPE models have a llama-like cache utilization.
+            if cos.dim() == 2:
+                self._cos_cache = cos
+                self._sin_cache = sin
+            else:
+                if self._cos_cache is None:
+                    self._cos_cache = cos[0, ...]
+                    self._sin_cache = sin[0, ...]
+                elif self._cos_cache.shape[0] < self.window_length:
+                    self._cos_cache = torch.cat([self._cos_cache, cos[0, ...]], dim=0)
+                    self._sin_cache = torch.cat([self._sin_cache, sin[0, ...]], dim=0)
+
+        # [bsz, num_heads, seq_len, head_dim]
+        if len(self.key_cache) <= layer_idx:
+            # Empty cache
+            self.key_cache.append(key_states)
+            self.value_cache.append(value_states)
+
+        elif key_states.shape[-2] + self.get_seq_length(layer_idx) < self.window_length:
+            # Growing cache
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+
+        else:
+            # Shifting cache
+            keys_to_keep = self.key_cache[layer_idx][
+                :, :, -self.window_length + self.num_sink_tokens + key_states.shape[-2] :
+            ]
+
+            # On RoPE models, we need to recompute the Key rotation as the tokens are shifted
+            if using_rope:
+                rerotation_cos, rerotation_sin = self._get_rerotation_cos_sin(
+                    key_states, self._cos_cache[: self.window_length], self._sin_cache[: self.window_length]
+                )
+                if partial_rotation_size is not None:
+                    keys_to_keep, keys_pass = (
+                        keys_to_keep[..., :partial_rotation_size],
+                        keys_to_keep[..., partial_rotation_size:],
+                    )
+                keys_to_keep = self._apply_key_rotary_pos_emb(keys_to_keep, rerotation_cos, rerotation_sin)
+                if partial_rotation_size is not None:
+                    keys_to_keep = torch.cat((keys_to_keep, keys_pass), dim=-1)
+
+            # Concatenate sink tokens, shifted & rotated tokens (if needed), and new tokens
+            sink_keys = self.key_cache[layer_idx][:, :, : self.num_sink_tokens]
+            self.key_cache[layer_idx] = torch.cat([sink_keys, keys_to_keep, key_states], dim=-2)
+
+            sink_values = self.value_cache[layer_idx][:, :, : self.num_sink_tokens]
+            values_to_keep = self.value_cache[layer_idx][
+                :, :, -self.window_length + self.num_sink_tokens + value_states.shape[-2] :
+            ]
+            self.value_cache[layer_idx] = torch.cat([sink_values, values_to_keep, value_states], dim=-2)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+
+class StaticCache(Cache):
+    """
+    Static Cache class to be used with `torch.compile(model)` and `torch.export()`.
+
+    Parameters:
+        config (`PretrainedConfig`):
+            The configuration file defining the shape-related attributes required to initialize the static cache.
+        max_batch_size (`int`):
+            The maximum batch size with which the model will be used.
+        max_cache_len (`int`):
+            The maximum sequence length with which the model will be used.
+        device (`torch.device`):
+            The device on which the cache should be initialized. Should be the same as the layer.
+        dtype (*optional*, defaults to `torch.float32`):
+            The default `dtype` to use when initializing the layer.
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModelForCausalLM, StaticCache
+
+        >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
+        >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
+
+        >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
+        >>> max_generated_length = inputs.input_ids.shape[1] + 10
+        >>> past_key_values = StaticCache(config=model.config, max_batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
+        ```
+    """
+
+    def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len: int, device, dtype=None) -> None:
+        super().__init__()
+        self.max_batch_size = max_batch_size
+        self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
+        # 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.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        # Note: There will be significant perf decrease if switching to use 5D tensors instead.
+        cache_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, self.head_dim)
+        for idx in range(config.num_hidden_layers):
+            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)
+            # Notes:
+            # 1. `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
+            #     breaks when updating the cache. It can't be used if the cache code is being compiled (but in that case
+            #     it is not needed anyway)
+            # 2. `torch.export()` requires mutations to be registered as buffers.
+            if not is_torchdynamo_compiling():
+                self.register_buffer(f"key_cache_{idx}", torch.zeros(cache_shape, dtype=dtype, device=device))
+                self.register_buffer(f"value_cache_{idx}", torch.zeros(cache_shape, dtype=dtype, device=device))
+                new_layer_key_cache = getattr(self, f"key_cache_{idx}")
+                new_layer_value_cache = getattr(self, f"value_cache_{idx}")
+                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)
+        self._seen_tokens = 0  # Used in `generate` to keep tally of how many tokens the cache has seen
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+        It is VERY important to index using a tensor, otherwise you introduce a copy to the device.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input
+                to know how where to write in the cache.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        # Update the number of seen tokens
+        if layer_idx == 0:
+            self._seen_tokens += key_states.shape[-2]
+        
+        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 cache_position is None:
+            k_out.copy_(key_states)
+            v_out.copy_(value_states)
+        else:
+            # Note: here we use `tensor.index_copy_(dim, index, tensor)` that is equivalent to
+            # `tensor[:, :, index] = tensor`, but the first one is compile-friendly and it does explicitly an in-place
+            # operation, that avoids copies and uses less memory.
+            try:
+                k_out.index_copy_(2, cache_position, key_states)
+                v_out.index_copy_(2, cache_position, value_states)
+            except NotImplementedError:
+                # The operator 'aten::index_copy.out' is not currently implemented for the MPS device.
+                k_out[:, :, cache_position] = key_states
+                v_out[:, :, cache_position] = value_states
+
+        return k_out, v_out
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states that were seen by the model."""
+        # Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
+        # limit the check to the first batch member and head dimension.
+        # TODO: deprecate this function in favor of `cache_position`
+        # return (self.key_cache[layer_idx][0, 0].any(dim=-1)).sum()
+        return self._seen_tokens
+
+    def get_max_length(self) -> Optional[int]:
+        """Returns the maximum sequence length of the cached states."""
+        return self.max_cache_len
+
+    def reset(self):
+        self._seen_tokens = 0
+        """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_()
+
+
+class SlidingWindowCache(StaticCache):
+    """
+    Sliding Window Cache class to be used with `torch.compile` for models like Mistral that support sliding window attention.
+    Every time when we try to update the cache, we compute the `indices` based on `cache_position >= self.config.sliding_window - 1`,
+    if true(which means the cache can not hold all the old key value states and new states together because of the sliding window constraint),
+    we need to do a cycle shift based on `indices` to replace the oldest states by the new key value states passed in.
+
+    The `to_shift` is only true once we are above sliding_window. Thus with `sliding_window==64`:
+
+    indices = (slicing + to_shift[-1].int()-1) % self.config.sliding_window
+    tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+        19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
+        37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+        55, 56, 57, 58, 59, 60, 61, 62, 63,  0])
+
+    We overwrite the cache using these, then we always write at cache_position (clamped to `sliding_window`)
+
+    Parameters:
+        config (`PretrainedConfig`):
+            The configuration file defining the shape-related attributes required to initialize the static cache.
+        max_batch_size (`int`):
+            The maximum batch size with which the model will be used.
+        max_cache_len (`int`):
+            The maximum sequence length with which the model will be used.
+        device (`torch.device`):
+            The device on which the cache should be initialized. Should be the same as the layer.
+        dtype (*optional*, defaults to `torch.float32`):
+            The default `dtype` to use when initializing the layer.
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModelForCausalLM, SlidingWindowCache
+
+        >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
+        >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
+
+        >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
+        >>> max_generated_length = inputs.input_ids.shape[1] + 10
+        >>> past_key_values = SlidingWindowCache(config=model.config, max_batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
+        ```
+    """
+
+    def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len: int, device, dtype=None) -> None:
+        super().__init__(config, max_batch_size, max_cache_len, device, dtype)
+        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."
+            )
+        max_cache_len = min(config.sliding_window, max_cache_len)
+        super().__init__(
+            config=config, max_batch_size=max_batch_size, max_cache_len=max_cache_len, device=device, dtype=dtype
+        )
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor]:
+        cache_position = cache_kwargs.get("cache_position")
+        k_out = self.key_cache[layer_idx]
+        v_out = self.value_cache[layer_idx]
+
+        # assume this only happens in prefill phase when prompt length > sliding_window_size (= max_cache_len)
+        if cache_position.shape[0] > self.max_cache_len:
+            k_out = key_states[:, :, -self.max_cache_len :, :]
+            v_out = value_states[:, :, -self.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(self.max_cache_len, dtype=torch.long, device=value_states.device).cumsum(0)
+        cache_position = cache_position.clamp(0, self.max_cache_len - 1)
+        to_shift = cache_position >= self.max_cache_len - 1
+        indices = (slicing + to_shift[-1].int() - 1) % self.max_cache_len
+
+        k_out = k_out[:, :, indices]
+        v_out = v_out[:, :, indices]
+
+        try:
+            cache_position.to(device=k_out.device)
+            k_out.index_copy_(2, cache_position, key_states)
+            v_out.index_copy_(2, cache_position, value_states)
+        except NotImplementedError:
+            # The operator 'aten::index_copy.out' is not currently implemented for the MPS device.
+            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 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
+
+    def reset(self):
+        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_()
+
+
+class EncoderDecoderCache(Cache):
+    """
+    Base, abstract class for all encoder-decoder caches. Can be used to hold combinations of self-attention and
+    cross-attention caches.
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoProcessor, AutoModelForCausalLM, DynamicCache, EncoderDecoderCache
+
+        >>> model = AutoModelForCausalLM.from_pretrained("openai/whisper-small")
+        >>> processor = AutoProcessor.from_pretrained("openai/whisper-small")
+
+        >>> inputs = processor(audio=YOUR-AUDIO, return_tensors="pt")
+
+        >>> # Prepare cache classes for encoder and decoder and pass it to model's forward
+        >>> self_attention_cache = DynamicCache()
+        >>> cross_attention_cache = DynamicCache()
+        >>> past_key_values = EncoderDecoderCache(self_attention_cache, cross_attention_cache)
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
+        ```
+
+    """
+
+    def __init__(self, self_attention_cache: Cache, cross_attention_cache: Cache):
+        super().__init__()
+        self.self_attention_cache = self_attention_cache
+        self.cross_attention_cache = cross_attention_cache
+
+        self.is_updated = {}
+        for layer_idx in range(len(cross_attention_cache.key_cache)):
+            self.is_updated[layer_idx] = bool(cross_attention_cache.get_seq_length(layer_idx) > 0)
+
+    def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
+        """
+        Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the
+        sequence length.
+        """
+        if layer_idx < len(self):
+            return (
+                self.self_attention_cache.key_cache[layer_idx],
+                self.self_attention_cache.value_cache[layer_idx],
+                self.cross_attention_cache.key_cache[layer_idx],
+                self.cross_attention_cache.value_cache[layer_idx],
+            )
+        else:
+            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
+
+    def __len__(self):
+        """
+        Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds
+        to the number of layers in the model.
+        """
+        return len(self.self_attention_cache)
+
+    def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
+        """Converts the `EncoderDecoderCache` instance into  its equivalent in the legacy cache format."""
+        legacy_cache = ()
+        if len(self.cross_attention_cache) > 0:
+            for self_attn, cross_attn in zip(
+                self.self_attention_cache.to_legacy_cache(), self.cross_attention_cache.to_legacy_cache()
+            ):
+                legacy_cache += (self_attn + cross_attn,)
+        else:
+            legacy_cache = self.self_attention_cache.to_legacy_cache()
+        return legacy_cache
+
+    @classmethod
+    def from_legacy_cache(
+        cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    ) -> "EncoderDecoderCache":
+        """Converts a cache in the legacy cache format into an equivalent `EncoderDecoderCache`."""
+        cache = cls(self_attention_cache=DynamicCache(), cross_attention_cache=DynamicCache())
+        if past_key_values is not None:
+            for layer_idx in range(len(past_key_values)):
+                key_states, value_states = past_key_values[layer_idx][:2]
+                cache.self_attention_cache.update(key_states, value_states, layer_idx)
+                if len(past_key_values[layer_idx]) > 2:
+                    key_states, value_states = past_key_values[layer_idx][2:]
+                    cache.cross_attention_cache.update(key_states, value_states, layer_idx)
+                    cache.is_updated[layer_idx] = True
+        return cache
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        if len(self.self_attention_cache.key_cache) <= layer_idx:
+            return 0
+        return (self.self_attention_cache.key_cache[layer_idx][0, 0].any(dim=-1)).sum()
+
+    def reset(self):
+        if hasattr(self.self_attention_cache, "reset"):
+            self.self_attention_cache.reset()
+        if hasattr(self.cross_attention_cache, "reset"):
+            self.cross_attention_cache.reset()
+        elif not hasattr(self.self_attention_cache, "reset") and not hasattr(self.cross_attention_cache, "reset"):
+            raise ValueError(
+                "Neither self nor cross-attention cache have valid `.reset()` methods. `.reset()` should "
+                "only be called on compatible cache classes, such as `StaticCache` or `SlidingWindowCache`. "
+                f"Got {self.self_attention_cache.__str__()} for the self attention cache and "
+                f"{self.cross_attention_cache.__str__()} for the cross attention cache."
+            )
+        for layer_idx in self.is_updated:
+            self.is_updated[layer_idx] = False
+
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        self.self_attention_cache.reorder_cache(beam_idx)
+        self.cross_attention_cache.reorder_cache(beam_idx)
+
+    def check_dynamic_cache(self, method: str):
+        if not (
+            isinstance(self.self_attention_cache, DynamicCache)
+            and isinstance(self.cross_attention_cache, DynamicCache)
+        ):
+            raise ValueError(
+                f"`{method}` is only defined for dynamic cache, got {self.self_attention_cache.__str__()} for the self "
+                f"attention cache and {self.cross_attention_cache.__str__()} for the cross attention cache."
+            )
+
+    # TODO(gante, sanchit-gandhi): move following functionality into `.generate`
+    def crop(self, maximum_length: int):
+        """Crop the past key values up to a new `maximum_length` in terms of tokens. `maximum_length` can also be
+        negative to remove `maximum_length` tokens. This is used in assisted decoding and contrastive search."""
+        self.check_dynamic_cache(self.crop.__name__)
+        self.self_attention_cache.crop(maximum_length)
+
+    def batch_split(self, full_batch_size: int, split_size: int) -> "List[EncoderDecoderCache]":
+        """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
+        `_split_model_inputs()` in `generation.utils`"""
+        self.check_dynamic_cache(self.batch_split.__name__)
+        self_attention_cache = self.self_attention_cache.batch_split(full_batch_size, split_size)
+        cross_attention_cache = self.cross_attention_cache.batch_split(full_batch_size, split_size)
+
+        out = []
+        for self_attn, cross_attn in zip(self_attention_cache, cross_attention_cache):
+            out.append(EncoderDecoderCache(self_attn, cross_attn))
+        return out
+
+    @classmethod
+    def from_batch_splits(cls, splits: List["EncoderDecoderCache"]) -> "EncoderDecoderCache":
+        """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
+        `generation.utils`"""
+        self_attention_cache = DynamicCache()
+        cross_attention_cache = DynamicCache()
+        for idx in range(len(splits[0])):
+            layer_keys = torch.cat([current.self_attention_cache.key_cache[idx] for current in splits], dim=0)
+            layer_values = torch.cat([current.self_attention_cache.value_cache[idx] for current in splits], dim=0)
+            self_attention_cache.update(layer_keys, layer_values, idx)
+
+            layer_keys = torch.cat([current.cross_attention_cache.key_cache[idx] for current in splits], dim=0)
+            layer_values = torch.cat([current.cross_attention_cache.value_cache[idx] for current in splits], dim=0)
+            cross_attention_cache.update(layer_keys, layer_values, idx)
+        return cls(self_attention_cache, cross_attention_cache)
+
+    def batch_repeat_interleave(self, repeats: int):
+        """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
+        self.check_dynamic_cache(self.batch_repeat_interleave.__name__)
+        self.self_attention_cache.batch_repeat_interleave(repeats)
+        self.cross_attention_cache.batch_repeat_interleave(repeats)
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
+        self.check_dynamic_cache(self.batch_select_indices.__name__)
+        self.self_attention_cache.batch_select_indices(indices)
+        self.cross_attention_cache.batch_select_indices(indices)
+
+
+class HybridCache(Cache):
+    """
+    Hybrid Cache class to be used with `torch.compile` for Gemma2 models that alternate between a local sliding window attention
+    and global attention in every other layer. Under the hood, Hybrid Cache leverages ["SlidingWindowCache"] for sliding window attention
+    and ["StaticCache"] for global attention. For more information, see the documentation of each subcomponeent cache class.
+
+    Parameters:
+        config (`PretrainedConfig):
+            The configuration file defining the shape-related attributes required to initialize the static cache.
+        max_batch_size (`int`):
+            The maximum batch size with which the model will be used.
+        max_cache_len (`int`):
+            The maximum sequence length with which the model will be used.
+        device (`torch.device`, *optional*, defaults to `"cpu"`):
+            The device on which the cache should be initialized. Should be the same as the layer.
+        dtype (*optional*, defaults to `torch.float32`):
+            The default `dtype` to use when initializing the layer.
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModelForCausalLM, HybridCache
+
+        >>> model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b")
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
+
+        >>> inputs = tokenizer(text="My name is Gemma", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
+        >>> max_generated_length = inputs.input_ids.shape[1] + 10
+        >>> past_key_values = HybridCache(config=model.config, max_batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
+        ```
+    """
+
+    def __init__(self, config: PretrainedConfig, max_batch_size, max_cache_len, device="cpu", dtype=None) -> None:
+        super().__init__()
+        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(
+            [not bool(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,
+    ) -> Tuple[torch.Tensor]:
+        cache_position = cache_kwargs.get("cache_position")
+        sliding_window = cache_kwargs.get("sliding_window")
+        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):
+        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_()
+
+
+class MambaCache:
+    """
+    Cache for mamba model which does not have attention mechanism and key value states.
+
+    Arguments:
+        config (`PretrainedConfig):
+            The configuration file defining the shape-related attributes required to initialize the static cache.
+        max_batch_size (`int`):
+            The maximum batch size with which the model will be used.
+        dtype (*optional*, defaults to `torch.float16`):
+            The default `dtype` to use when initializing the layer.
+        device (`torch.device`, *optional*):
+            The device on which the cache should be initialized. Should be the same as the layer.
+
+    Attributes:
+        dtype: (`torch.dtype`):
+            The default `dtype` used to initializing the cache.
+        intermediate_size: (`int`):
+            Model's intermediate_size taken from config.
+        ssm_state_size: (`int`):
+            Model's state_size taken from config.
+        conv_kernel_size: (`int`):
+            Model's convolution kernel size taken from config
+        conv_states: (`torch.Tensor`):
+            A tensor of shape `[layer_idx, batch_size, intermediate_size, conv_kernel_size]` that holds convolutional states.
+        ssm_states: (`torch.Tensor`):
+            A tensor of shape `[layer_idx, batch_size, intermediate_size, ssm_state_size]` that holds ssm states
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MambaForCausalLM, MambaCache
+
+        >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
+
+        >>> inputs = tokenizer(text="My name is Mamba", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> past_key_values = MambaCache(config=model.config, max_batch_size=1, device=model.device, dtype=model.dtype)
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> past_kv = outputs.past_key_values
+        ```
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        max_batch_size: int,
+        dtype: torch.dtype = torch.float16,
+        device: Optional[str] = None,
+        **kwargs,
+    ):
+        self.dtype = dtype
+        self.max_batch_size = max_batch_size
+        self.intermediate_size = config.intermediate_size
+        self.ssm_state_size = config.state_size
+        self.conv_kernel_size = config.conv_kernel
+
+        self.conv_states: torch.Tensor = torch.zeros(
+            config.num_hidden_layers,
+            self.max_batch_size,
+            self.intermediate_size,
+            self.conv_kernel_size,
+            device=device,
+            dtype=dtype,
+        )
+        self.ssm_states: torch.Tensor = torch.zeros(
+            config.num_hidden_layers,
+            self.max_batch_size,
+            self.intermediate_size,
+            self.ssm_state_size,
+            device=device,
+            dtype=dtype,
+        )
+
+        torch._dynamo.mark_static_address(self.conv_states)
+        torch._dynamo.mark_static_address(self.ssm_states)
+
+    def update_conv_state(
+        self, layer_idx: int, new_conv_state: torch.Tensor, cache_position: torch.LongTensor
+    ) -> torch.Tensor:
+        conv_state = self.conv_states[layer_idx]
+        cache_position = cache_position.clamp(0, self.conv_kernel_size - 1)
+
+        conv_state = conv_state.roll(shifts=-1, dims=-1)
+        conv_state[:, :, cache_position] = new_conv_state.to(conv_state.device)
+        self.conv_states[layer_idx].zero_()
+        self.conv_states[layer_idx] += conv_state
+        return self.conv_states[layer_idx]
+
+    def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
+        self.ssm_states[layer_idx] = new_ssm_state.to(self.ssm_states.device)
+        return self.ssm_states[layer_idx]
+
+    def reset(self):
+        self.conv_states.zero_()
+        self.ssm_states.zero_()

transformers_4_44_2__configuration_llama.py

@@ -0,0 +1,203 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+"""LLaMA model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from .transformers_4_44_2__modeling_rope_utils import rope_config_validation
+
+
+class LlamaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    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 LLaMA-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 LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LlamaModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        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=num_attention_heads`, 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). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
+            Llama 2 up to 4096, CodeLlama up to 16384.
+        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-06):
+            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`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
+            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
+            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
+
+    ```python
+    >>> from transformers import LlamaModel, LlamaConfig
+
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = LlamaConfig()
+
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = LlamaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "llama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        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,
+        )

transformers_4_44_2__modeling_attn_mask_utils.py

@@ -0,0 +1,482 @@
+# Copyright 2023 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 dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+
+
+@dataclass
+class AttentionMaskConverter:
+    """
+    A utility attention mask class that allows one to:
+        - Create a causal 4d mask
+        - Create a causal 4d mask with slided window
+        - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
+          key_value_length) that can be multiplied with attention scores
+
+    Examples:
+
+    ```python
+    >>> import torch
+    >>> from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+
+    >>> converter = AttentionMaskConverter(True)
+    >>> converter.to_4d(torch.tensor([[0, 0, 0, 1, 1]]), 5, key_value_length=5, dtype=torch.float32)
+    tensor([[[[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
+            [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
+            [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
+            [-3.4028e+38, -3.4028e+38, -3.4028e+38,  0.0000e+00, -3.4028e+38],
+            [-3.4028e+38, -3.4028e+38, -3.4028e+38,  0.0000e+00,  0.0000e+00]]]])
+    ```
+
+    Parameters:
+        is_causal (`bool`):
+            Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
+
+        sliding_window (`int`, *optional*):
+            Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
+    """
+
+    is_causal: bool
+    sliding_window: int
+
+    def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
+        self.is_causal = is_causal
+        self.sliding_window = sliding_window
+
+        if self.sliding_window is not None and self.sliding_window <= 0:
+            raise ValueError(
+                f"Make sure that when passing `sliding_window` that its value is a strictly positive integer, not `{self.sliding_window}`"
+            )
+
+    def to_causal_4d(
+        self,
+        batch_size: int,
+        query_length: int,
+        key_value_length: int,
+        dtype: torch.dtype,
+        device: Union[torch.device, "str"] = "cpu",
+    ) -> Optional[torch.Tensor]:
+        """
+        Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
+        bias to upper right hand triangular matrix (causal mask).
+        """
+        if not self.is_causal:
+            raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")
+
+        # If shape is not cached, create a new causal mask and cache it
+        input_shape = (batch_size, query_length)
+        past_key_values_length = key_value_length - query_length
+
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        causal_4d_mask = None
+        if input_shape[-1] > 1 or self.sliding_window is not None:
+            causal_4d_mask = self._make_causal_mask(
+                input_shape,
+                dtype,
+                device=device,
+                past_key_values_length=past_key_values_length,
+                sliding_window=self.sliding_window,
+            )
+
+        return causal_4d_mask
+
+    def to_4d(
+        self,
+        attention_mask_2d: torch.Tensor,
+        query_length: int,
+        dtype: torch.dtype,
+        key_value_length: Optional[int] = None,
+    ) -> torch.Tensor:
+        """
+        Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
+        key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
+        causal, a causal mask will be added.
+        """
+        input_shape = (attention_mask_2d.shape[0], query_length)
+
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        causal_4d_mask = None
+        if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
+            if key_value_length is None:
+                raise ValueError(
+                    "This attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask."
+                )
+
+            past_key_values_length = key_value_length - query_length
+            causal_4d_mask = self._make_causal_mask(
+                input_shape,
+                dtype,
+                device=attention_mask_2d.device,
+                past_key_values_length=past_key_values_length,
+                sliding_window=self.sliding_window,
+            )
+        elif self.sliding_window is not None:
+            raise NotImplementedError("Sliding window is currently only implemented for causal masking")
+
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
+            attention_mask_2d.device
+        )
+
+        if causal_4d_mask is not None:
+            expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min)
+
+        # expanded_attn_mask + causal_4d_mask can cause some overflow
+        expanded_4d_mask = expanded_attn_mask
+
+        return expanded_4d_mask
+
+    @staticmethod
+    def _make_causal_mask(
+        input_ids_shape: torch.Size,
+        dtype: torch.dtype,
+        device: torch.device,
+        past_key_values_length: int = 0,
+        sliding_window: Optional[int] = None,
+    ):
+        """
+        Make causal mask used for bi-directional self-attention.
+        """
+        bsz, tgt_len = input_ids_shape
+        mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+        mask_cond = torch.arange(mask.size(-1), device=device)
+        mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+
+        mask = mask.to(dtype)
+
+        if past_key_values_length > 0:
+            mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+
+        # add lower triangular sliding window mask if necessary
+        if sliding_window is not None:
+            diagonal = past_key_values_length - sliding_window - 1
+
+            context_mask = torch.tril(torch.ones_like(mask, dtype=torch.bool), diagonal=diagonal)
+            mask.masked_fill_(context_mask, torch.finfo(dtype).min)
+
+        return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+    @staticmethod
+    def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+        """
+        Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+        """
+        bsz, src_len = mask.size()
+        tgt_len = tgt_len if tgt_len is not None else src_len
+
+        expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+        inverted_mask = 1.0 - expanded_mask
+
+        return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+    @staticmethod
+    def _unmask_unattended(
+        expanded_mask: torch.FloatTensor,
+        min_dtype: float,
+    ):
+        # fmt: off
+        """
+        Attend to all tokens in masked rows from the expanded attention mask, for example the relevant first rows when
+        using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+        Details: https://github.com/pytorch/pytorch/issues/110213
+
+        `expanded_mask` is [bsz, num_masks, tgt_seq_len, src_seq_len] or [bsz, tgt_seq_len, src_seq_len].
+        `attention_mask` is [bsz, src_seq_len].
+
+        The dimension num_masks of `expanded_mask` is most often 1, but it can also be the number of heads in the case of alibi attention bias.
+
+        For example, if `expanded_mask` is (e.g. here left-padding case)
+        ```
+        [[[[0, 0, 0],
+           [0, 0, 0],
+           [0, 0, 1]]],
+         [[[1, 0, 0],
+           [1, 1, 0],
+           [1, 1, 1]]],
+         [[[0, 0, 0],
+           [0, 1, 0],
+           [0, 1, 1]]]]
+        ```
+        then the modified `expanded_mask` will be
+        ```
+        [[[[1, 1, 1],   <-- modified
+           [1, 1, 1],   <-- modified
+           [0, 0, 1]]],
+         [[[1, 0, 0],
+           [1, 1, 0],
+           [1, 1, 1]]],
+         [[[1, 1, 1],   <-- modified
+           [0, 1, 0],
+           [0, 1, 1]]]]
+        ```
+        """
+        # fmt: on
+        if expanded_mask.dtype == torch.bool:
+            raise ValueError(
+                "AttentionMaskConverter._unmask_unattended expects a float `expanded_mask`, got a BoolTensor."
+            )
+
+        return expanded_mask.mul(~torch.all(expanded_mask == min_dtype, dim=-1, keepdim=True))
+
+    @staticmethod
+    def _ignore_causal_mask_sdpa(
+        attention_mask: Optional[torch.Tensor],
+        inputs_embeds: torch.Tensor,
+        past_key_values_length: int,
+        sliding_window: Optional[int] = None,
+        is_training: bool = False,
+    ) -> bool:
+        """
+        Detects whether the optional user-specified attention_mask & the automatically created causal mask can be ignored in case PyTorch's SDPA is used, rather relying on SDPA's `is_causal` argument.
+
+        In case no token is masked in the `attention_mask` argument, if `query_length == 1` or
+        `key_value_length == query_length`, we rather rely on SDPA `is_causal` argument to use causal/non-causal masks,
+        allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
+        """
+
+        _, query_length = inputs_embeds.shape[0], inputs_embeds.shape[1]
+        key_value_length = query_length + past_key_values_length
+
+        is_tracing = (
+            torch.jit.is_tracing()
+            or isinstance(inputs_embeds, torch.fx.Proxy)
+            or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
+        )
+
+        ignore_causal_mask = False
+
+        if attention_mask is None:
+            # TODO: When tracing with TorchDynamo with fullgraph=True, the model is recompiled depending on the input shape, thus SDPA's `is_causal` argument is rightfully updated (see https://gist.github.com/fxmarty/1313f39037fc1c112508989628c57363). However, when using `torch.export` or
+            # or `torch.onnx.dynamo_export`, we must pass an example input, and `is_causal` behavior is hard-coded. If a user exports a model with q_len > 1, the exported model will hard-code `is_causal=True` which is in general wrong (see https://github.com/pytorch/pytorch/issues/108108).
+            # Thus, we only set `ignore_causal_mask = True` if the model is set to training.
+            #
+            # Besides, jit.trace can not handle the `q_len > 1` condition for `is_causal` ("TypeError: scaled_dot_product_attention(): argument 'is_causal' must be bool, not Tensor").
+            if (
+                (is_training or not is_tracing)
+                and (query_length == 1 or key_value_length == query_length)
+                and (sliding_window is None or key_value_length < sliding_window)
+            ):
+                ignore_causal_mask = True
+        elif sliding_window is None or key_value_length < sliding_window:
+            if len(attention_mask.shape) == 4:
+                return False
+            elif (is_training or not is_tracing) and torch.all(attention_mask == 1):
+                if query_length == 1 or key_value_length == query_length:
+                    # For query_length == 1, causal attention and bi-directional attention are the same.
+                    ignore_causal_mask = True
+
+                # Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
+                # may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
+                # Reference: https://github.com/pytorch/pytorch/issues/108108
+                # TODO: maybe revisit this with https://github.com/pytorch/pytorch/pull/114823 in PyTorch 2.3.
+
+        return ignore_causal_mask
+
+
+def _prepare_4d_causal_attention_mask(
+    attention_mask: Optional[torch.Tensor],
+    input_shape: Union[torch.Size, Tuple, List],
+    inputs_embeds: torch.Tensor,
+    past_key_values_length: int,
+    sliding_window: Optional[int] = None,
+):
+    """
+    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+    `(batch_size, key_value_length)`
+
+    Args:
+        attention_mask (`torch.Tensor` or `None`):
+            A 2D attention mask of shape `(batch_size, key_value_length)`
+        input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
+            The input shape should be a tuple that defines `(batch_size, query_length)`.
+        inputs_embeds (`torch.Tensor`):
+            The embedded inputs as a torch Tensor.
+        past_key_values_length (`int`):
+            The length of the key value cache.
+        sliding_window (`int`, *optional*):
+            If the model uses windowed attention, a sliding window should be passed.
+    """
+    attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
+
+    key_value_length = input_shape[-1] + past_key_values_length
+
+    # 4d mask is passed through the layers
+    if attention_mask is not None and len(attention_mask.shape) == 2:
+        attention_mask = attn_mask_converter.to_4d(
+            attention_mask, input_shape[-1], key_value_length=key_value_length, dtype=inputs_embeds.dtype
+        )
+    elif attention_mask is not None and len(attention_mask.shape) == 4:
+        expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
+        if tuple(attention_mask.shape) != expected_shape:
+            raise ValueError(
+                f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
+            )
+        else:
+            # if the 4D mask has correct shape - invert it and fill with negative infinity
+            inverted_mask = 1.0 - attention_mask
+            attention_mask = inverted_mask.masked_fill(
+                inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min
+            )
+    else:
+        attention_mask = attn_mask_converter.to_causal_4d(
+            input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+        )
+
+    return attention_mask
+
+
+# Adapted from _prepare_4d_causal_attention_mask
+def _prepare_4d_causal_attention_mask_for_sdpa(
+    attention_mask: Optional[torch.Tensor],
+    input_shape: Union[torch.Size, Tuple, List],
+    inputs_embeds: torch.Tensor,
+    past_key_values_length: int,
+    sliding_window: Optional[int] = None,
+):
+    """
+    Prepares the correct `attn_mask` argument to be used by `torch.nn.functional.scaled_dot_product_attention`.
+
+    In case no token is masked in the `attention_mask` argument, we simply set it to `None` for the cases `query_length == 1` and
+    `key_value_length == query_length`, and rely instead on SDPA `is_causal` argument to use causal/non-causal masks,
+    allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
+    """
+    attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
+
+    key_value_length = input_shape[-1] + past_key_values_length
+
+    # torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
+    # used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
+    # TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
+    is_tracing = (
+        torch.jit.is_tracing()
+        or isinstance(inputs_embeds, torch.fx.Proxy)
+        or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
+    )
+
+    ignore_causal_mask = AttentionMaskConverter._ignore_causal_mask_sdpa(
+        attention_mask=attention_mask,
+        inputs_embeds=inputs_embeds,
+        past_key_values_length=past_key_values_length,
+        sliding_window=sliding_window,
+    )
+
+    if ignore_causal_mask:
+        expanded_4d_mask = None
+    elif attention_mask is None:
+        expanded_4d_mask = attn_mask_converter.to_causal_4d(
+            input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+        )
+    else:
+        if attention_mask.dim() == 4:
+            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
+            if attention_mask.max() != 0:
+                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
+            expanded_4d_mask = attention_mask
+        else:
+            expanded_4d_mask = attn_mask_converter.to_4d(
+                attention_mask,
+                input_shape[-1],
+                dtype=inputs_embeds.dtype,
+                key_value_length=key_value_length,
+            )
+
+        # Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
+        # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+        # Details: https://github.com/pytorch/pytorch/issues/110213
+        if not is_tracing and expanded_4d_mask.device.type == "cuda":
+            expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
+                expanded_4d_mask, min_dtype=torch.finfo(inputs_embeds.dtype).min
+            )
+
+    return expanded_4d_mask
+
+
+def _prepare_4d_attention_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+    `(batch_size, key_value_length)`
+
+    Args:
+        mask (`torch.Tensor`):
+            A 2D attention mask of shape `(batch_size, key_value_length)`
+        dtype (`torch.dtype`):
+            The torch dtype the created mask shall have.
+        tgt_len (`int`):
+            The target length or query length the created mask shall have.
+    """
+    return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _prepare_4d_attention_mask_for_sdpa(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+    `(batch_size, key_value_length)`
+
+    Args:
+        mask (`torch.Tensor`):
+            A 2D attention mask of shape `(batch_size, key_value_length)`
+        dtype (`torch.dtype`):
+            The torch dtype the created mask shall have.
+        tgt_len (`int`):
+            The target length or query length the created mask shall have.
+    """
+    _, key_value_length = mask.shape
+    tgt_len = tgt_len if tgt_len is not None else key_value_length
+
+    is_tracing = (
+        torch.jit.is_tracing()
+        or isinstance(mask, torch.fx.Proxy)
+        or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
+    )
+
+    # torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture data-dependent controlflows.
+    if not is_tracing and torch.all(mask == 1):
+        return None
+    else:
+        return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _create_4d_causal_attention_mask(
+    input_shape: Union[torch.Size, Tuple, List],
+    dtype: torch.dtype,
+    device: torch.device,
+    past_key_values_length: int = 0,
+    sliding_window: Optional[int] = None,
+) -> Optional[torch.Tensor]:
+    """
+    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)`
+
+    Args:
+        input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
+            The input shape should be a tuple that defines `(batch_size, query_length)`.
+        dtype (`torch.dtype`):
+            The torch dtype the created mask shall have.
+        device (`int`):
+            The torch device the created mask shall have.
+        sliding_window (`int`, *optional*):
+            If the model uses windowed attention, a sliding window should be passed.
+    """
+    attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
+
+    key_value_length = past_key_values_length + input_shape[-1]
+    attention_mask = attn_mask_converter.to_causal_4d(
+        input_shape[0], input_shape[-1], key_value_length, dtype=dtype, device=device
+    )
+
+    return attention_mask

transformers_4_44_2__modeling_flash_attention_utils_backward_compat.py

@@ -0,0 +1,348 @@
+# coding=utf-8
+# Copyright 2024 The Fairseq Authors 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.
+
+import inspect
+import os
+from typing import Optional, Tuple, Union
+
+
+import torch
+import torch.nn.functional as F
+
+from functools import lru_cache
+import importlib.metadata
+import importlib.util
+from packaging import version
+
+from transformers.utils import is_flash_attn_2_available
+
+
+if is_flash_attn_2_available():
+    try:
+        from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+        from flash_attn import flash_attn_func, flash_attn_varlen_func
+        _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+    except ImportError:
+        raise "Unable to import flash_attn"
+
+
+def _is_package_available(pkg_name: str, return_version: bool = False) -> Union[Tuple[bool, str], bool]:
+    # Check if the package spec exists and grab its version to avoid importing a local directory
+    package_exists = importlib.util.find_spec(pkg_name) is not None
+    package_version = "N/A"
+    if package_exists:
+        try:
+            # Primary method to get the package version
+            package_version = importlib.metadata.version(pkg_name)
+        except importlib.metadata.PackageNotFoundError:
+            # Fallback method: Only for "torch" and versions containing "dev"
+            if pkg_name == "torch":
+                try:
+                    package = importlib.import_module(pkg_name)
+                    temp_version = getattr(package, "__version__", "N/A")
+                    # Check if the version contains "dev"
+                    if "dev" in temp_version:
+                        package_version = temp_version
+                        package_exists = True
+                    else:
+                        package_exists = False
+                except ImportError:
+                    # If the package can't be imported, it's not available
+                    package_exists = False
+            else:
+                # For packages other than "torch", don't attempt the fallback and set as not available
+                package_exists = False
+    if return_version:
+        return package_exists, package_version
+    else:
+        return package_exists
+    
+
+@lru_cache()
+def is_flash_attn_greater_or_equal(library_version: str):
+    if not _is_package_available("flash_attn"):
+        return False
+
+    return version.parse(importlib.metadata.version("flash_attn")) >= version.parse(library_version)
+
+
+def _get_unpad_data(attention_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, int]:
+    """
+    Retrieves indexing data required to repad unpadded (ragged) tensors.
+
+    Arguments:
+        attention_mask (`torch.Tensor`):
+            Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
+
+    Return:
+        indices (`torch.Tensor`):
+            The indices of non-masked tokens from the flattened input sequence.
+        cu_seqlens (`torch.Tensor`):
+            The cumulative sequence lengths, used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
+        max_seqlen_in_batch (`int`):
+            Maximum sequence length in batch.
+    """
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+def _upad_input(
+    query_layer: torch.Tensor,
+    key_layer: torch.Tensor,
+    value_layer: torch.Tensor,
+    attention_mask: torch.Tensor,
+    query_length: int,
+):
+    """
+    Unpads query, key, and values tensors, using a single dimension for all tokens even though they belong to different batches.
+
+    This function is used instead of `flash_attn.bert_padding.unpad_input` in order to avoid the recomputation of the same intermediary
+    tensors for query, key, value tensors.
+
+    Arguments:
+        query_layer (`torch.Tensor`):
+            Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim).
+        key_layer (`torch.Tensor`):
+            Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
+        value_layer (`torch.Tensor`):
+            Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
+        attention_mask (`torch.Tensor`):
+            Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
+        query_length (`int`):
+            Target length.
+
+    Return:
+        query_layer (`torch.Tensor`):
+            Query state without padding. Shape: (total_target_length, num_heads, head_dim).
+        key_layer (`torch.Tensor`):
+            Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
+        value_layer (`torch.Tensor`):
+            Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
+        indices_q (`torch.Tensor`):
+            The indices of non-masked tokens from the flattened input target sequence.
+        (cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`):
+            The cumulative sequence lengths for the target (query) and source (key, value), used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
+        (max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`):
+            Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value).
+    """
+    indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+    batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+    key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k)
+    value_layer = index_first_axis(
+        value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+    )
+    if query_length == kv_seq_len:
+        query_layer = index_first_axis(query_layer.reshape(batch_size * kv_seq_len, -1, head_dim), indices_k)
+        cu_seqlens_q = cu_seqlens_k
+        max_seqlen_in_batch_q = max_seqlen_in_batch_k
+        indices_q = indices_k
+    elif query_length == 1:
+        max_seqlen_in_batch_q = 1
+        cu_seqlens_q = torch.arange(
+            batch_size + 1, dtype=torch.int32, device=query_layer.device
+        )  # There is a memcpy here, that is very bad.
+        indices_q = cu_seqlens_q[:-1]
+        query_layer = query_layer.squeeze(1)
+    else:
+        # The -q_len: slice assumes left padding.
+        attention_mask = attention_mask[:, -query_length:]
+        query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+    return (
+        query_layer,
+        key_layer,
+        value_layer,
+        indices_q,
+        (cu_seqlens_q, cu_seqlens_k),
+        (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+    )
+
+
+def prepare_fa2_from_position_ids(query, key, value, position_ids):
+    """
+    This function returns necessary arguments to call `flash_attn_varlen_func`.
+    All three query, key, value states will be flattened.
+    Cummulative lengths of each examples in the batch will be extracted from position_ids.
+
+    NOTE: ideally cummulative lengths should be prepared at the data collator stage
+
+    Arguments:
+        query (`torch.Tensor`):
+            Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim).
+        key (`torch.Tensor`):
+            Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
+        value (`torch.Tensor`):
+            Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
+        position_ids (`torch.Tensor`):
+            Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
+
+    Return:
+        query (`torch.Tensor`):
+            Query state without padding. Shape: (total_target_length, num_heads, head_dim).
+        key (`torch.Tensor`):
+            Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
+        value (`torch.Tensor`):
+            Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
+        indices_q (`torch.Tensor`):
+            The indices of non-masked tokens from the flattened input target sequence.
+        (cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`):
+            The cumulative sequence lengths for the target (query) and source (key, value), used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
+        (max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`):
+            Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value).
+    """
+    query = query.view(-1, query.size(-2), query.size(-1))
+    key = key.view(-1, key.size(-2), key.size(-1))
+    value = value.view(-1, value.size(-2), value.size(-1))
+    position_ids = position_ids.flatten()
+    indices_q = torch.arange(position_ids.size(0), device=position_ids.device, dtype=torch.int32)
+
+    cu_seq_lens = torch.cat(
+        (
+            indices_q[position_ids == 0],
+            torch.tensor(position_ids.size(), device=position_ids.device, dtype=torch.int32),
+        )
+    )
+
+    max_length = position_ids.max() + 1
+
+    return (query, key, value, indices_q, (cu_seq_lens, cu_seq_lens), (max_length, max_length))
+
+
+def _flash_attention_forward(
+    query_states: torch.Tensor,
+    key_states: torch.Tensor,
+    value_states: torch.Tensor,
+    attention_mask: torch.Tensor,
+    query_length: int,
+    is_causal: bool,
+    dropout: float = 0.0,
+    position_ids: Optional[torch.Tensor] = None,
+    softmax_scale: Optional[float] = None,
+    sliding_window: Optional[int] = None,
+    use_top_left_mask: bool = False,
+    softcap: Optional[float] = None,
+    deterministic: bool = None,
+):
+    """
+    Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+    first unpad the input, then computes the attention scores and pad the final attention scores.
+
+    Args:
+        query_states (`torch.Tensor`):
+            Input query states to be passed to Flash Attention API
+        key_states (`torch.Tensor`):
+            Input key states to be passed to Flash Attention API
+        value_states (`torch.Tensor`):
+            Input value states to be passed to Flash Attention API
+        attention_mask (`torch.Tensor`):
+            The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+            position of padding tokens and 1 for the position of non-padding tokens.
+        dropout (`float`):
+            Attention dropout
+        softmax_scale (`float`, *optional*):
+            The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        use_top_left_mask (`bool`, defaults to `False`):
+            flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference.
+        softcap (`float`, *optional*):
+            Softcap for the attention logits, used e.g. in gemma2.
+        deterministic (`bool`, *optional*):
+            Determines if the deterministic option introduced in flash_attn>=2.4.1 is enabled.
+    """
+    if not use_top_left_mask:
+        causal = is_causal
+    else:
+        # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__.
+        causal = is_causal and query_length != 1
+
+    # Assuming 4D tensors, key_states.shape[1] is the key/value sequence length (source length).
+    use_sliding_windows = (
+        _flash_supports_window_size and sliding_window is not None and key_states.shape[1] > sliding_window
+    )
+    flash_kwargs = {"window_size": (sliding_window, sliding_window)} if use_sliding_windows else {}
+
+    if is_flash_attn_greater_or_equal("2.4.1"):
+        if deterministic is None:
+            deterministic = os.environ.get("FLASH_ATTENTION_DETERMINISTIC", "0") == "1"
+        flash_kwargs["deterministic"] = deterministic
+
+    if softcap is not None:
+        flash_kwargs["softcap"] = softcap
+
+    # Contains at least one padding token in the sequence
+    if attention_mask is not None:
+        batch_size = query_states.shape[0]
+        query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = _upad_input(
+            query_states, key_states, value_states, attention_mask, query_length
+        )
+        cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+        max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+        attn_output_unpad = flash_attn_varlen_func(
+            query_states,
+            key_states,
+            value_states,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_in_batch_q,
+            max_seqlen_k=max_seqlen_in_batch_k,
+            dropout_p=dropout,
+            softmax_scale=softmax_scale,
+            causal=causal,
+            **flash_kwargs,
+        )
+        attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+
+    # If position_ids is provided and check all examples do not contain only 1 sequence, If tensor in increasing
+    # then we probably have one sequence, otherwise it is packed. Additionally check we are in pre-fill/training stage.
+    # Use `flash_attn_varlen_func` to prevent cross-example attention and also allow padding free approach
+    elif position_ids is not None and query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all():
+        batch_size = query_states.size(0)
+        query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = prepare_fa2_from_position_ids(
+            query_states, key_states, value_states, position_ids
+        )
+
+        cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+        max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+        attn_output = flash_attn_varlen_func(
+            query_states,
+            key_states,
+            value_states,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_in_batch_q,
+            max_seqlen_k=max_seqlen_in_batch_k,
+            dropout_p=dropout,
+            softmax_scale=softmax_scale,
+            causal=causal,
+            **flash_kwargs,
+        )
+
+        attn_output = attn_output.view(batch_size, -1, attn_output.size(-2), attn_output.size(-1))
+
+    else:
+        attn_output = flash_attn_func(
+            query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal, **flash_kwargs
+        )
+
+    return attn_output

transformers_4_44_2__modeling_rope_utils.py

@@ -0,0 +1,559 @@
+# 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.
+
+import math
+from typing import Optional, Tuple
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import is_torch_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_torch_available():
+    import torch
+
+
+def _compute_default_rope_parameters(
+    config: Optional[PretrainedConfig] = None,
+    device: Optional["torch.device"] = None,
+    seq_len: Optional[int] = None,
+    **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies according to the original RoPE implementation
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_default_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        base = rope_kwargs["base"]
+        dim = rope_kwargs["dim"]
+    elif config is not None:
+        base = config.rope_theta
+        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        dim = int(head_dim * partial_rotary_factor)
+
+    attention_factor = 1.0  # Unused in this type of RoPE
+
+    # Compute the inverse frequencies
+    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, attention_factor
+
+
+def _compute_linear_scaling_rope_parameters(
+    config: Optional[PretrainedConfig] = None,
+    device: Optional["torch.device"] = None,
+    seq_len: Optional[int] = None,
+    **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with linear scaling. Credits to the Reddit user /u/kaiokendev
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_linear_scaling_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        factor = rope_kwargs["factor"]
+    elif config is not None:
+        factor = config.rope_scaling["factor"]
+
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    # Then applies linear scaling to the frequencies.
+    # NOTE: originally, scaling was applied to the position_ids. However, we get `embs = inv_freq @ position_ids`, so
+    # applying scaling to the inverse frequencies is equivalent.
+    inv_freq /= factor
+    return inv_freq, attention_factor
+
+
+def _compute_dynamic_ntk_parameters(
+    config: Optional[PretrainedConfig] = None,
+    device: Optional["torch.device"] = None,
+    seq_len: Optional[int] = None,
+    **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length, used to update the dynamic RoPE at inference time.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_dynamic_ntk_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        base = rope_kwargs["base"]
+        dim = rope_kwargs["dim"]
+        max_position_embeddings = rope_kwargs["max_position_embeddings"]
+        factor = rope_kwargs["factor"]
+    elif config is not None:
+        base = config.rope_theta
+        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        dim = int(head_dim * partial_rotary_factor)
+        max_position_embeddings = config.max_position_embeddings
+        factor = config.rope_scaling["factor"]
+
+    attention_factor = 1.0  # Unused in this type of RoPE
+
+    # seq_len: default to max_position_embeddings, e.g. at init time
+    seq_len = seq_len if seq_len is not None and seq_len > max_position_embeddings else max_position_embeddings
+
+    # Compute the inverse frequencies
+    base = base * ((factor * seq_len / max_position_embeddings) - (factor - 1)) ** (dim / (dim - 2))
+    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, attention_factor
+
+
+def _compute_yarn_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with NTK scaling. Please refer to the
+    [original paper](https://arxiv.org/abs/2309.00071)
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # No need to keep BC with yarn, unreleased when this new pattern was created.
+    if len(rope_kwargs) > 0:
+        raise ValueError(
+            f"Unexpected arguments: `**rope_kwargs` should be unset in `_compute_yarn_parameters`, got {rope_kwargs}"
+        )
+
+    base = config.rope_theta
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
+    max_position_embeddings = config.max_position_embeddings
+    factor = config.rope_scaling["factor"]
+
+    # Sets the attention factor as suggested in the paper
+    attention_factor = config.rope_scaling.get("attention_factor")
+    if attention_factor is None:
+        attention_factor = 0.1 * math.log(factor) + 1.0
+
+    # Optional config options
+    # beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
+    beta_fast = config.rope_scaling.get("beta_fast") or 32
+    beta_slow = config.rope_scaling.get("beta_slow") or 1
+
+    # Compute the inverse frequencies
+    def find_correction_dim(num_rotations, dim, base, max_position_embeddings):
+        """Inverse dimension formula to find the dimension based on the number of rotations"""
+        return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))
+
+    def find_correction_range(low_rot, high_rot, dim, base, max_position_embeddings):
+        """Find dimension range bounds based on rotations"""
+        low = math.floor(find_correction_dim(low_rot, dim, base, max_position_embeddings))
+        high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings))
+        return max(low, 0), min(high, dim - 1)
+
+    def linear_ramp_factor(min, max, dim):
+        if min == max:
+            max += 0.001  # Prevent singularity
+
+        linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+        ramp_func = torch.clamp(linear_func, 0, 1)
+        return ramp_func
+
+    # Note on variable naming: "interpolation" comes from the original technique, where we interpolate the position IDs
+    # to expand the possible context length. In other words, interpolation = apply scaling factor.
+    pos_freqs = base ** (torch.arange(0, dim, 2).float().to(device) / dim)
+    inv_freq_extrapolation = 1.0 / pos_freqs
+    inv_freq_interpolation = 1.0 / (factor * pos_freqs)
+
+    low, high = find_correction_range(beta_fast, beta_slow, dim, base, max_position_embeddings)
+
+    # Get n-dimensional rotational scaling corrected for extrapolation
+    inv_freq_extrapolation_factor = 1 - linear_ramp_factor(low, high, dim // 2).float().to(device)
+    inv_freq = (
+        inv_freq_interpolation * (1 - inv_freq_extrapolation_factor)
+        + inv_freq_extrapolation * inv_freq_extrapolation_factor
+    )
+
+    return inv_freq, attention_factor
+
+
+def _compute_longrope_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with LongRoPE scaling. Please refer to the
+    [original implementation](https://github.com/microsoft/LongRoPE)
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
+    # No need to keep BC with longrope, unreleased when this new pattern was created.
+    if len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` should be unset in `_compute_longrope_parameters`, got "
+            f"{rope_kwargs}"
+        )
+
+    base = config.rope_theta
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
+    long_factor = config.rope_scaling["long_factor"]
+    short_factor = config.rope_scaling["short_factor"]
+    factor = config.rope_scaling.get("factor")
+    attention_factor = config.rope_scaling.get("attention_factor")
+
+    # NOTE: Phi3 (and potentially other models) modify `max_position_embeddings` and have a
+    # `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
+    # values to compute the default attention scaling factor, instead of using `factor`.
+    if hasattr(config, "original_max_position_embeddings"):
+        max_position_embeddings = config.original_max_position_embeddings
+        expanded_max_position_embeddings = config.max_position_embeddings
+        factor = expanded_max_position_embeddings / max_position_embeddings
+    else:
+        max_position_embeddings = config.max_position_embeddings
+        expanded_max_position_embeddings = max_position_embeddings * factor
+
+    # Sets the attention factor as suggested in the paper
+    if attention_factor is None:
+        if factor <= 1.0:
+            attention_factor = 1.0
+        else:
+            attention_factor = math.sqrt(1 + math.log(factor) / math.log(max_position_embeddings))
+
+    # Compute the inverse frequencies -- scaled based on the target sequence length
+    if expanded_max_position_embeddings > max_position_embeddings:
+        ext_factors = torch.tensor(long_factor, dtype=torch.float32, device=device)
+    else:
+        ext_factors = torch.tensor(short_factor, dtype=torch.float32, device=device)
+    inv_freq_shape = torch.arange(0, dim, 2, dtype=torch.int64, device=device).float() / dim
+    inv_freq = 1.0 / (ext_factors * base**inv_freq_shape)
+
+    return inv_freq, attention_factor
+
+
+def _compute_llama3_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies for llama 3.1.
+
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    factor = config.rope_scaling["factor"]  # `8` in the original implementation
+    low_freq_factor = config.rope_scaling["low_freq_factor"]  # `1` in the original implementation
+    high_freq_factor = config.rope_scaling["high_freq_factor"]  # `4` in the original implementation
+    old_context_len = config.rope_scaling["original_max_position_embeddings"]  # `8192` in the original implementation
+
+    low_freq_wavelen = old_context_len / low_freq_factor
+    high_freq_wavelen = old_context_len / high_freq_factor
+
+    wavelen = 2 * math.pi / inv_freq
+    # wavelen < high_freq_wavelen: do nothing
+    # wavelen > low_freq_wavelen: divide by factor
+    inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
+    # otherwise: interpolate between the two, using a smooth factor
+    smooth_factor = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+    smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama / factor + smooth_factor * inv_freq_llama
+    is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen)
+    inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
+
+    return inv_freq_llama, attention_factor
+
+
+# This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
+# from the model config. You can append new {'rope_type': callable} pairs to this dictionary to enable custom RoPE
+# parameterizations, as long as the callable has the same signature.
+ROPE_INIT_FUNCTIONS = {
+    "default": _compute_default_rope_parameters,
+    "linear": _compute_linear_scaling_rope_parameters,
+    "dynamic": _compute_dynamic_ntk_parameters,
+    "yarn": _compute_yarn_parameters,
+    "longrope": _compute_longrope_parameters,
+    "llama3": _compute_llama3_parameters,
+}
+
+
+def _check_received_keys(rope_type: str, received_keys: set, required_keys: set, optional_keys: Optional[set] = None):
+    """Compare the received keys in `config.rope_scaling` against the expected and optional keys"""
+    # BC: "rope_type" was originally "type" -- let's gracefully handle it
+    if "rope_type" not in received_keys and "type" in received_keys:
+        received_keys -= {"type"}
+        received_keys.add("rope_type")
+
+    missing_keys = required_keys - received_keys
+    if missing_keys:
+        raise KeyError(f"Missing required keys in `rope_scaling` for 'rope_type'='{rope_type}': {missing_keys}")
+
+    if optional_keys is not None:
+        unused_keys = received_keys - required_keys - optional_keys
+    else:
+        unused_keys = received_keys - required_keys
+    if unused_keys:
+        logger.warning(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
+
+
+def _validate_default_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+
+def _validate_linear_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
+def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
+def _validate_yarn_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    optional_keys = {"attention_factor", "beta_fast", "beta_slow"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    attention_factor = rope_scaling.get("attention_factor")
+    if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0):
+        logger.warning(
+            f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
+        )
+    beta_fast = rope_scaling.get("beta_fast")
+    if beta_fast is not None and not isinstance(beta_fast, float):
+        logger.warning(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
+    beta_slow = rope_scaling.get("beta_slow")
+    if beta_slow is not None and not isinstance(beta_slow, float):
+        logger.warning(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
+
+    if (beta_fast or 32) < (beta_slow or 1):
+        logger.warning(
+            f"`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
+            f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
+        )
+
+
+def _validate_longrope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "short_factor", "long_factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
+
+    short_factor = rope_scaling.get("short_factor")
+    if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
+        logger.warning(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
+    if not len(short_factor) == dim // 2:
+        logger.warning(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
+
+    long_factor = rope_scaling.get("long_factor")
+    if not isinstance(long_factor, list) and all(isinstance(x, (int, float)) for x in long_factor):
+        logger.warning(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
+    if not len(long_factor) == dim // 2:
+        logger.warning(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
+
+    # Handle Phi3 divergence: prefer the use of `attention_factor` and/or `factor` over
+    # `original_max_position_embeddings` to compute internal variables. The latter lives outside `rope_scaling` and is
+    # unique to longrope (= undesirable)
+    if hasattr(config, "original_max_position_embeddings"):
+        logger.warning_once(
+            "This model has set a `original_max_position_embeddings` field, to be used together with "
+            "`max_position_embeddings` to determine a scaling factor. Please set the `factor` field of `rope_scaling`"
+            "with this ratio instead -- we recommend the use of this field over `original_max_position_embeddings`, "
+            "as it is compatible with most model architectures."
+        )
+    else:
+        factor = rope_scaling.get("factor")
+        if factor is None:
+            logger.warning("Missing required keys in `rope_scaling`: 'factor'")
+        elif not isinstance(factor, float) or factor < 1.0:
+            logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+        attention_factor = rope_scaling.get("attention_factor")
+        if attention_factor is not None and not isinstance(attention_factor, float) or attention_factor < 0:
+            logger.warning(
+                f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
+            )
+
+
+def _validate_llama3_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    low_freq_factor = rope_scaling["low_freq_factor"]
+    high_freq_factor = rope_scaling["high_freq_factor"]
+    if low_freq_factor is None or not isinstance(low_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
+    if high_freq_factor is None or not isinstance(high_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
+    if high_freq_factor <= low_freq_factor:
+        logger.warning(
+            "`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
+            f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
+        )
+
+    original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
+    if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be an integer, got "
+            f"{original_max_position_embeddings}"
+        )
+    if original_max_position_embeddings >= config.max_position_embeddings:
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
+            f"{original_max_position_embeddings} and max_position_embeddings={config.max_position_embeddings}"
+        )
+
+
+# Like `ROPE_INIT_FUNCTIONS`, this validation function mapping can be dynamically updated for custom RoPE types.
+ROPE_VALIDATION_FUNCTIONS = {
+    "default": _validate_default_rope_parameters,
+    "linear": _validate_linear_scaling_rope_parameters,
+    "dynamic": _validate_dynamic_scaling_rope_parameters,
+    "yarn": _validate_yarn_parameters,
+    "longrope": _validate_longrope_parameters,
+    "llama3": _validate_llama3_parameters,
+}
+
+
+def rope_config_validation(config: PretrainedConfig):
+    """
+    Validate the RoPE config arguments, given a `PretrainedConfig` object
+    """
+    rope_scaling = getattr(config, "rope_scaling", None)  # not a default parameter in `PretrainedConfig`
+    if rope_scaling is None:
+        return
+
+    # BC: "rope_type" was originally "type"
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
+    validation_fn = ROPE_VALIDATION_FUNCTIONS.get(rope_type)
+    if validation_fn is not None:
+        validation_fn(config)
+    else:
+        logger.warning(
+            f"Missing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='{rope_type}'"
+        )

transformers_4_44_2__pytorch_utils.py

@@ -0,0 +1,17 @@
+# Copyright 2022 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 torch import nn
+
+ALL_LAYERNORM_LAYERS = [nn.LayerNorm]

variable_cache.py

@@ -0,0 +1,139 @@
+# coding=utf-8
+# Copyright 2024 Nvidia Corporation. 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 copy import deepcopy
+from typing import Optional, Dict, Any, Tuple
+
+import torch
+from transformers.cache_utils import Cache  # used to let GenerationMixin know that we use a Cache object
+
+from .configuration_decilm import DeciLMConfig
+from .transformers_4_44_2__cache_utils import Cache as Cache_4_44_2, SinkCache, StaticCache, SlidingWindowCache
+
+
+class VariableCache(Cache_4_44_2, Cache):
+    """
+    A Cache object that supports a different Cache implementation for every layer,
+    including layers without any kv-cache.
+    Implemented using a list of Cache objects, each represents a "model" with 1 layer.
+    The default implementation for the layer caches is StaticCache.
+    The cache of each layer is allocated to the same gpu as the layer itself.
+    """
+
+    def __init__(
+            self,
+            *,  # key-word only, no positional args allowed to avoid mix-ups with newer transformers versions
+            config: DeciLMConfig,
+            batch_size: int = None,
+            max_cache_len: int = None,
+            dtype: torch.dtype = torch.float32,
+            max_batch_size: Optional[int] = None,
+            **kwargs,
+    ) -> None:
+        Cache_4_44_2.__init__(self)
+
+        self.config = deepcopy(config)
+        self.max_batch_size = batch_size or max_batch_size
+        self.batch_size = self.max_batch_size
+        self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
+        self.dtype = dtype
+
+        self.layer_caches: list[Cache_4_44_2 | None] = [None] * config.num_hidden_layers
+        self.layer_devices: list[torch.device | None] = [None] * config.num_hidden_layers
+
+    def update(
+            self,
+            key_states: torch.Tensor,
+            value_states: torch.Tensor,
+            layer_idx: int,
+            cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if self.layer_caches[layer_idx] is None:
+            self.layer_devices[layer_idx] = key_states.device
+            self._init_layer_cache(layer_idx)
+
+        layer_cache = self.layer_caches[layer_idx]
+        assert layer_cache is not None, f"Trying to update the cache of a cache-less layer: {layer_idx=}"
+
+        k_out, v_out = layer_cache.update(key_states=key_states,
+                                          value_states=value_states,
+                                          layer_idx=0,
+                                          cache_kwargs=cache_kwargs)
+        seq_len = self.get_seq_length(layer_idx)
+        k_out = k_out[:, :, :seq_len, :]
+        v_out = v_out[:, :, :seq_len, :]
+        return k_out, v_out
+
+    def _init_layer_cache(self, layer_idx: int) -> None:
+        block_config = self.config.block_configs[layer_idx]
+        attention_config = block_config.attention
+
+        if attention_config.no_op or attention_config.replace_with_linear:
+            return None
+
+        device = self.layer_devices[layer_idx]
+        assert device is not None, f"Trying to init layer cache for {layer_idx=} without device"
+
+        config = deepcopy(self.config)
+        config.num_hidden_layers = 1
+        config.num_key_value_heads = self.config.num_attention_heads // attention_config.n_heads_in_group
+
+        if attention_config.window_length is not None:
+            if not attention_config.is_sink:
+                config.sliding_window = attention_config.window_length
+                self.layer_caches[layer_idx] = SlidingWindowCache(config=config,
+                                                                  max_batch_size=self.max_batch_size,
+                                                                  max_cache_len=self.max_cache_len,
+                                                                  device=device,
+                                                                  dtype=self.dtype)
+                return
+            elif not attention_config.unshifted_sink:
+                self.layer_caches[layer_idx] = SinkCache(window_length=attention_config.window_length,
+                                                         num_sink_tokens=attention_config.num_sink_tokens)
+                return
+
+        self.layer_caches[layer_idx] = StaticCache(config=config,
+                                                   max_batch_size=self.max_batch_size,
+                                                   max_cache_len=self.max_cache_len,
+                                                   device=device,
+                                                   dtype=self.dtype)
+
+    def _get_first_real_cache(self) -> Cache:
+        for layer_cache in self.layer_caches:
+            if layer_cache is not None:
+                return layer_cache
+        raise ValueError(f"No real cache found, all layer caches are None.")
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        if layer_idx == 0 and self.layer_caches[0] is None:
+            try:
+                layer_cache = self._get_first_real_cache()
+            except ValueError:
+                return 0
+        else:
+            layer_cache = self.layer_caches[layer_idx]
+        return layer_cache.get_seq_length()
+
+    def get_max_length(self) -> Optional[int]:
+        """Returns the maximum sequence length of the cached states."""
+        return self.max_cache_len
+
+    def reset(self):
+        for layer_idx in range(len(self.layer_caches)):
+            layer_cache = self.layer_caches[layer_idx]
+            if hasattr(layer_cache, "reset"):
+                layer_cache.reset()
+            else:
+                self._init_layer_cache(layer_idx)