Upload 12 files

added_tokens.json

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+{
+  "<|/tool_call|>": 200026,
+  "<|/tool|>": 200024,
+  "<|assistant|>": 200019,
+  "<|end|>": 200020,
+  "<|system|>": 200022,
+  "<|tag|>": 200028,
+  "<|tool_call|>": 200025,
+  "<|tool_response|>": 200027,
+  "<|tool|>": 200023,
+  "<|user|>": 200021
+}

config.json

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+{
+    "architectures": [
+      "Phi4FlashForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "auto_map": {
+      "AutoConfig": "configuration_phi4flash.Phi4FlashConfig",
+      "AutoModelForCausalLM": "modeling_phi4flash.Phi4FlashForCausalLM",
+      "AutoTokenizer": "Xenova/gpt-4o"
+    },
+    "pad_token_id": 199999,
+    "bos_token_id": 199999,
+    "embd_pdrop": 0.0,
+    "eos_token_id": 199999,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 10240,
+    "layer_norm_eps": 1e-5,
+    "max_position_embeddings": 262144,
+    "_attn_implementation": "flash_attention_2",
+    "mb_per_layer": 2,
+    "model_type": "phi4flash",
+    "num_attention_heads": 40,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 20,
+    "resid_pdrop": 0.0,
+    "sliding_window": 512,
+    "torch_dtype": "bfloat16",
+    "tie_word_embeddings": true,
+    "transformers_version": "4.46.1",
+    "use_cache": true,
+    "mlp_bias": false,
+    "lm_head_bias": false,
+    "vocab_size": 200064
+  }
+  

configuration_phi4flash.py

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+# coding=utf-8
+# Copyright 2025 Microsoft 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.
+
+""" Phi4Flash model configuration"""
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+import math
+logger = logging.get_logger(__name__)
+
+
+class Phi4FlashConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Phi4FlashModel`]. It is used to instantiate an Phi4Flash
+    model according to the specified arguments, defining the model architecture.
+
+    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 51200):
+            Vocabulary size of the Phi4Flash model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Phi4FlashModel`].
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 24):
+            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`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
+            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. Phi-1 and Phi-1.5 supports up to 2048
+            tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
+        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.
+
+    Example:
+
+    ```python
+    >>> from transformers import Phi4FlashModel, Phi4FlashConfig
+
+    >>> # Initializing a Phi4Flash style configuration
+    >>> configuration = Phi4FlashConfig.from_pretrained("microsoft/Phi4-mini-flash-reasoning")
+
+    >>> # Initializing a model from the configuration
+    >>> model = Phi4FlashModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phi4flash"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=51200,
+        hidden_size=2560,
+        intermediate_size=9216,
+        num_hidden_layers=32,
+        num_attention_heads=40,
+        num_key_value_heads=4,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        bos_token_id=1,
+        eos_token_id=2,
+        sliding_window=2047,
+        mb_per_layer= 2,
+        mamba_d_state=16,
+        mamba_d_conv=4,
+        mamba_expand=2,
+        mamba_dt_rank="auto",
+        mamba_conv_bias=True,
+        mamba_proj_bias=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.mb_per_layer = mb_per_layer
+        self.sliding_window = [
+            sliding_window if layer_idx < num_hidden_layers // 2 and layer_idx % 2 == 1 else None
+            for layer_idx in range(num_hidden_layers)
+        ]
+    
+        self.mamba_d_state = mamba_d_state
+        self.mamba_d_conv = mamba_d_conv
+        self.mamba_expand = mamba_expand
+        self.mamba_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank
+        self.mamba_conv_bias = mamba_conv_bias
+        self.mamba_proj_bias = mamba_proj_bias
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        
+
+    @property
+    def layers_block_type(self):
+        layer_block_types = []
+        for i in range(self.num_hidden_layers):
+            if i % 2 == 1:
+                layer_block_type = "attention" if i <= (self.num_hidden_layers //2 +1) else "shared_attention"
+            else:
+                layer_block_type = "mamba"
+            layer_block_types.append(layer_block_type)
+        return layer_block_types

generation_config.json

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+{
+    "_from_model_config": true,
+    "bos_token_id": 199999,
+    "eos_token_id": [
+        200020,
+        199999
+    ],
+    "pad_token_id": 199999,
+    "transformers_version": "4.45.0"
+}

model-00001-of-00002.safetensors

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+}

modeling_phi4flash.py

@@ -0,0 +1,2098 @@
+# coding=utf-8
+# Copyright 2025 Microsoft 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.
+
+""" PyTorch Phi4Flash model."""
+
+
+import inspect
+import math
+import warnings
+from typing import List, Optional, Tuple, Union, Dict, Any
+import copy
+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.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.utils import is_torchdynamo_compiling
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.generation import GenerationMixin
+from transformers.utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from einops import rearrange, repeat
+
+from .configuration_phi4flash import Phi4FlashConfig
+
+logger = logging.get_logger(__name__)
+
+from flash_attn import flash_attn_func, flash_attn_varlen_func
+from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+if not _flash_supports_window_size:
+    raise ValueError("Please update flash-attention to support window size.")
+
+from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+import causal_conv1d_cuda
+from mamba_ssm.ops.triton.selective_state_update import selective_state_update
+
+from torch.amp import custom_bwd, custom_fwd
+import selective_scan_cuda
+
+_CHECKPOINT_FOR_DOC = "microsoft/Phi-4-mini-flash-reasoning"
+_CONFIG_FOR_DOC = "Phi4FlashConfig"
+
+# monkey patch to add support for our cache
+def _prepare_cache_for_generation(
+    self,
+    generation_config,
+    model_kwargs: Dict,
+    assistant_model: "PreTrainedModel",
+    batch_size: int,
+    max_cache_length: int,
+    device: torch.device,
+) -> bool:
+    """
+    Prepares the cache for generation (if applicable), given `generate`'s parameterization. If a cache is
+    instantiated, writes it to `model_kwargs`, under the name expected by the model.
+    """
+
+    cache_name = "past_key_values" 
+
+    # Quick escape route 2: if the user specifies no cache is to be used. (conflicting arguments are handled in
+    # `generation_config.validate()`)
+    if generation_config.use_cache is False:
+        return
+
+    # Otherwise we NEED to prepare a cache, based on `generation_config.cache_implementation`
+
+    # TODO(joao): support static caches in assisted generation. assisted generation needs to roll back caches,
+    # which is only supported in dynamic caches atm
+    if assistant_model is not None:
+        logger.warning_once(
+            "An assistant model is provided, using a dynamic cache instead of a cache of type="
+            f"'{generation_config.cache_implementation}'."
+        )
+        model_kwargs[cache_name] = DynamicCache()
+        return
+
+    model_kwargs[cache_name] = self._get_cache(
+        cache_implementation="sambay",
+        batch_size=max(generation_config.num_beams, generation_config.num_return_sequences) * batch_size,
+        max_cache_len=max_cache_length,
+        device=device,
+        model_kwargs=model_kwargs,
+    )
+
+def _get_cache(
+    self, cache_implementation: str, batch_size: int, max_cache_len: int, device: torch.device, model_kwargs
+) -> Cache:
+    """
+    Sets a cache for `generate`, that will persist across calls. A new cache will only be initialized a
+    new `generate` call requires a larger cache or uses a different batch size.
+
+    Returns the resulting cache object.
+    """
+    cache_cls: Cache = SambaYCache
+    requires_cross_attention_cache = (
+        self.config.is_encoder_decoder or model_kwargs.get("encoder_outputs") is not None
+    )
+
+    if hasattr(self, "_cache"):
+        cache_to_check = self._cache.self_attention_cache if requires_cross_attention_cache else self._cache
+
+    if cache_implementation == "sliding_window":
+        max_cache_len = min(self.config.sliding_window[1], max_cache_len)
+
+    need_new_cache = (
+        not hasattr(self, "_cache")
+        or (not isinstance(cache_to_check, cache_cls))
+        or cache_to_check.batch_size != batch_size
+    )
+    if cache_implementation != "mamba":
+        need_new_cache = need_new_cache or cache_to_check.max_cache_len < max_cache_len
+
+    if requires_cross_attention_cache and hasattr(self, "_cache"):
+        need_new_cache = (
+            need_new_cache
+            or self._cache.cross_attention_cache.max_cache_len != model_kwargs["encoder_outputs"][0].shape[1]
+        )
+
+    if need_new_cache:
+        if hasattr(self.config, "_pre_quantization_dtype"):
+            cache_dtype = self.config._pre_quantization_dtype
+        else:
+            if not is_torchdynamo_compiling():
+                cache_dtype = self.dtype
+            else:
+                # NOTE: self.dtype is not compatible with torch.compile, as it calls `self.parameters()`.
+                # Workaround: trust the lm_head, whose attribute name is somewhat consistent across generative
+                # models. May cause trobles with non-text modalities.
+                cache_dtype = self.get_output_embeddings().weight.dtype
+
+        def get_layer_device_map(execution_device_map: Optional[dict] = None):
+            if execution_device_map is None:
+                return None
+            elif len(execution_device_map) == 1 and "" in execution_device_map:
+                return {idx: execution_device_map[""] for idx in range(self.config.num_hidden_layers)}
+            layer_device_map = {}
+            for layer in execution_device_map:
+                for idx in range(self.config.num_hidden_layers):
+                    if f".{idx}." in f"{layer}.":
+                        layer_device_map[idx] = execution_device_map[layer]
+                        break
+            for idx in range(self.config.num_hidden_layers):
+                if idx not in layer_device_map:
+                    raise RuntimeError(f"layer {idx} has not been mapped to a device.")
+            return layer_device_map
+
+        execution_device_map = None
+        # Taken from dispatch_model from accelerate.
+        # This is needed here if we don't want to make changes in accelerate in order to save execution_device
+        # For offloaded case, we need to get the execution device, not just the device where it is offloaded
+        if hasattr(self, "hf_device_map"):
+            main_device = [d for d in self.hf_device_map.values() if d not in ["cpu", "disk"]][0]
+            execution_device_map = {
+                name: main_device if device in ["cpu", "disk"] else device
+                for name, device in self.hf_device_map.items()
+            }
+        layer_device_map = get_layer_device_map(execution_device_map)
+
+        cache_kwargs = {
+            "config": self.config.get_text_config(),
+            "batch_size": batch_size,
+            "max_cache_len": max_cache_len,
+            "device": device,
+            "dtype": cache_dtype,
+            "layer_device_map": layer_device_map,
+        }
+        self._cache = cache_cls(**cache_kwargs)
+    else:
+        self._cache.reset()
+    return self._cache
+
+GenerationMixin._prepare_cache_for_generation = _prepare_cache_for_generation
+GenerationMixin._get_cache = _get_cache
+
+class SambaYCache(Cache):
+    """
+    A dynamic cache that can handle the sliding window attention cache, one layer of full attention cache and the mamba cache
+    (which has a constant shape regardless of seq_len).
+
+    """
+
+    def __init__(self,
+        config: Phi4FlashConfig,
+        batch_size: int = None,
+        max_cache_len: int = None,
+        device: Union[torch.device, str] = "cuda",
+        dtype: torch.dtype = torch.float16,
+        max_batch_size: Optional[int] = None,
+        layer_device_map: Optional[Dict[int, Union[str, torch.device, int]]] = None,
+    ) -> None:
+        super().__init__()
+        self.dtype = dtype
+        self.has_previous_state = False  # only used by mamba
+        intermediate_size = config.mamba_expand * config.hidden_size
+        ssm_state_size = config.mamba_d_state
+        conv_kernel_size = config.mamba_d_conv
+        self.conv_kernel_size = conv_kernel_size
+    
+        if batch_size is not None:
+            logger.warning_once(
+                f"The 'batch_size' argument of {self.__class__.__name__} is deprecated and will be removed in "
+                "v4.49. Use the more precisely named 'max_batch_size' argument instead."
+            )
+            
+        self.max_cache_len = max_cache_len
+        self.max_batch_size = batch_size or max_batch_size
+        # Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.global_attn_idx = config.num_hidden_layers//2 + 1
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        global_cache_shape = (self.max_batch_size, self.num_key_value_heads, max_cache_len, self.head_dim)
+        sliding_cache_shape = (
+            self.max_batch_size,
+            self.num_key_value_heads,
+            min(config.sliding_window[1], max_cache_len),
+            self.head_dim,
+        )
+        conv_cache_shape = (self.max_batch_size, intermediate_size, conv_kernel_size)
+        ssm_cache_shape = (self.max_batch_size, intermediate_size, ssm_state_size)
+        for i in range(config.num_hidden_layers//2 + 2):
+            if layer_device_map is not None:
+                layer_device = layer_device_map[i]
+            else:
+                layer_device = device
+            # Note: `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
+            # breaks when updating the cache.
+            if i == self.global_attn_idx:
+                key_cache_shape = value_cache_shape = global_cache_shape
+            elif i % 2 == 0:
+                key_cache_shape = conv_cache_shape
+                value_cache_shape = ssm_cache_shape
+            else:
+                key_cache_shape = value_cache_shape =  sliding_cache_shape
+            new_layer_key_cache = torch.zeros(key_cache_shape, dtype=dtype, device=layer_device)
+            new_layer_value_cache = torch.zeros(value_cache_shape, dtype=dtype, device=layer_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")
+        k_out = self.key_cache[layer_idx]
+        v_out = self.value_cache[layer_idx]
+        if layer_idx == self.global_attn_idx:
+            update_fn = self._static_update
+        elif layer_idx % 2 == 1:
+            update_fn = self._sliding_update
+
+        return update_fn(
+            cache_position,
+            layer_idx,
+            key_states,
+            value_states,
+            k_out,
+            v_out,
+            k_out.shape[2],
+        )
+    
+    def get_max_cache_shape(self) -> Optional[int]:
+        return self.max_cache_len
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0):
+        # 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[self.global_attn_idx][0, 0].any(dim=-1)).sum()
+
+    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_()
+
+    @property
+    def batch_size(self):
+        logger.warning_once(
+            f"The 'batch_size' attribute of {self.__class__.__name__} is deprecated and will be removed in "
+            "v4.49. Use the more precisely named 'self.max_batch_size' attribute instead."
+        )
+        return self.max_batch_size
+    
+
+
+
+swiglu_fwd_codestring = """
+template <typename T> T swiglu_fwd(T x, T y) {
+    return float(x) * float(y) / (1.0f + ::exp(-float(x)));
+}
+"""
+swiglu_bwd_codestring = """
+template <typename T> T swiglu_bwd(T x, T y, T g, T& dx, T& dy) {
+    float x_sigmoid = 1.0f / (1.0f + ::exp(-float(x)));
+    dx = x_sigmoid * (1 + float(x) * (1.0f - x_sigmoid)) * float(g) * float(y);
+    dy = float(x) * x_sigmoid * float(g);
+}
+"""
+swiglu_fwd = torch.cuda.jiterator._create_jit_fn(swiglu_fwd_codestring)
+swiglu_bwd = torch.cuda.jiterator._create_multi_output_jit_fn(swiglu_bwd_codestring, num_outputs=2)
+
+
+class SwiGLUFunction(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, x, y):
+        ctx.save_for_backward(x, y)
+        return swiglu_fwd(x, y)
+
+    @staticmethod
+    def backward(ctx, dout):
+        x, y = ctx.saved_tensors
+        return swiglu_bwd(x, y, dout)
+
+swiglu = SwiGLUFunction.apply
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->SambaY
+class SambaYRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-5):
+        """
+        SambaYRMSNorm 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)
+
+
+PHI_NORM_CLASS = nn.LayerNorm
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    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,
+    )
+
+
+class SambaYMLP(nn.Module):
+    """Gated Linear Unit.
+
+    Reference:
+        Language Modeling with Gated Convolutional Networks.
+        https://arxiv.org/pdf/1612.08083v3.pdf.
+
+    """
+
+    def __init__(self, config):
+        super().__init__()
+
+        self.config = config
+        self.fc1 = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+
+        self.activation_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
+        y = self.fc1(hidden_states)
+
+        # Special case for SwiGLU
+        if self.config.hidden_act == "silu" and swiglu is not None:
+            gate, y = y.chunk(2, dim=-1)
+            y = swiglu(gate, y)
+        else:
+            gate, y = y.chunk(2, dim=-1)
+            y = y * self.activation_fn(gate)
+
+        return self.fc2(y)
+
+
+class SambaYAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Phi4FlashConfig, layer_idx: Optional[int] = None, yoco_cross: bool = False):
+        super().__init__()
+        self.config = 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_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.is_causal = True
+        self.yoco_cross = yoco_cross
+        
+        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})."
+            )
+
+        op_size = self.num_heads * self.head_dim + 2 * (self.num_key_value_heads * self.head_dim)
+        self.out_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True)
+        if yoco_cross:
+            self.Wqkv =  nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
+        else:
+            self.Wqkv = nn.Linear(self.hidden_size, op_size, bias=True)
+            
+        self.inner_cross_attn = FlashDiffCustomAttention(self.head_dim, self.layer_idx,)        
+        
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    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,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        raise NotImplementedError("SambaYAttention only support flash attention")
+
+
+class SambaYFlashAttention2(SambaYAttention):
+    """
+    SambaY flash attention module. This module inherits from `SambaYAttention` 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.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    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()
+
+        
+        
+    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,
+        yoco_key_values: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # SambaYFlashAttention2 attention does not support output_attentions
+
+        output_attentions = False
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        bsz, q_len, _ = hidden_states.size()
+        if self.yoco_cross:
+            q = self.Wqkv(hidden_states)
+            q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim).transpose(1,2)
+            key_states, value_states  = yoco_key_values   
+            query_states = q
+            
+            use_sliding_windows = False
+        else:
+
+            qkv = self.Wqkv(hidden_states)
+            query_pos = self.num_heads * self.head_dim
+            query_states = qkv[..., :query_pos]
+            key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
+            value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+
+            # 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)
+
+            use_sliding_windows = self.config.sliding_window is not None and self.config.sliding_window[self.layer_idx] is not None
+
+            if past_key_value is not None:
+
+                cache_kwargs = {"cache_position": cache_position}# Specific to RoPE models
+                key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+
+        yoco_key_values = key_states, value_states
+
+        attn_dropout = 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.
+
+        if query_states.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.Wqkv.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)
+
+        # Reashape to the expected shape for Flash Attention
+        # -> b,q,h,d
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        if attention_mask is not None:
+            key_states = key_states[:, :attention_mask.shape[-1]]
+            value_states = value_states[:, :attention_mask.shape[-1]]
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=attn_dropout,
+            use_sliding_windows=use_sliding_windows,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, yoco_key_values
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+    ):
+        """
+        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_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        causal = self.is_causal
+        # 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,
+            ) = self._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
+
+            if not use_sliding_windows:
+                attn_output_unpad = self.inner_cross_attn(
+                    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,
+                )
+            else:
+                attn_output_unpad = self.inner_cross_attn(
+                    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,
+                    window_size=(
+                        self.config.sliding_window[self.layer_idx] -1,
+                        self.config.sliding_window[self.layer_idx] -1,
+                    ),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                attn_output = self.inner_cross_attn(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = self.inner_cross_attn(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(
+                        self.config.sliding_window[self.layer_idx] -1,
+                        self.config.sliding_window[self.layer_idx] -1,
+                    ),
+                )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_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),
+        )
+
+
+
+class Phi3Mamba(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        d_state=16,
+        d_conv=4,
+        expand=2,
+        dt_rank="auto",
+        conv_bias=True,
+        bias=False,
+        use_fast_path=True,  # Fused kernel options
+        layer_idx=None,
+        yoco_cross=False,
+        yoco_kv=False,
+        dtype=None,
+    ):
+        factory_kwargs = {"dtype": dtype}
+        super().__init__()
+        self.d_model = d_model
+        self.d_state = d_state
+        self.d_conv = d_conv
+        self.expand = expand
+        self.d_inner = int(self.expand * self.d_model)
+        self.dt_rank = math.ceil(self.d_model / 16) if dt_rank == "auto" else dt_rank
+        self.use_fast_path = use_fast_path
+        self.layer_idx = layer_idx
+
+        self.yoco_cross = yoco_cross
+        self.yoco_kv = yoco_kv
+        if self.yoco_cross:
+            self.in_proj = nn.Linear(self.d_model, self.d_inner, bias=bias, **factory_kwargs)
+            self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=bias, **factory_kwargs)
+        else:   
+            self.in_proj = nn.Linear(self.d_model, self.d_inner * 2, bias=bias, **factory_kwargs)
+
+            self.conv1d = nn.Conv1d(
+                in_channels=self.d_inner,
+                out_channels=self.d_inner,
+                bias=conv_bias,
+                kernel_size=d_conv,
+                groups=self.d_inner,
+                padding=d_conv - 1,
+                **factory_kwargs,
+            )
+
+            self.activation = "silu"
+            self.act = nn.SiLU()
+
+            self.x_proj = nn.Linear(
+                self.d_inner, self.dt_rank + self.d_state * 2, bias=False, **factory_kwargs
+            )
+            self.dt_proj = nn.Linear(self.dt_rank, self.d_inner, bias=True, **factory_kwargs)
+
+            # S4D real initialization
+            A = repeat(
+                torch.arange(1, self.d_state + 1, dtype=torch.float32),
+                "n -> d n",
+                d=self.d_inner,
+            ).contiguous()
+            A_log = torch.log(A)  # Keep A_log in fp32
+            self.A_log = nn.Parameter(A_log)
+
+            # D "skip" parameter
+            self.D = nn.Parameter(torch.ones(self.d_inner))  # Keep in fp32
+
+            self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=bias, **factory_kwargs)
+
+    def forward(self, hidden_states, inference_params=None, mask= None, yoco_key_values = None, cache_position = None):
+        """
+        hidden_states: (B, L, D)
+        Returns: same shape as hidden_states
+        """
+        
+        if self.yoco_cross:
+            out = self.in_proj(hidden_states)
+            out = swiglu(out, yoco_key_values)
+            out = self.out_proj(out)
+            return out, yoco_key_values 
+        
+        batch, seqlen, _ = hidden_states.shape
+        conv_state, ssm_state = None, None
+        if inference_params is not None:
+            conv_state, ssm_state = self._get_states_from_cache(inference_params)
+            if cache_position[0] > 0: #inference_params.get_seq_length(self.layer_idx) > 0:
+                # The states are updated inplace
+                out, _, _, yoco_key_values = self.step(hidden_states, conv_state, ssm_state, yoco_key_values)
+                return out, yoco_key_values
+
+        # We do matmul and transpose BLH -> HBL at the same time
+        xz = rearrange(
+            self.in_proj.weight @ rearrange(hidden_states.to(dtype = self.in_proj.weight.dtype), "b l d -> d (b l)"),
+            "d (b l) -> b d l",
+            l=seqlen,
+        )
+        if self.in_proj.bias is not None:
+            xz = xz + rearrange(self.in_proj.bias.to(dtype=xz.dtype), "d -> d 1")
+
+
+        A = -torch.exp(self.A_log.float())  # (d_inner, d_state)
+        # In the backward pass we write dx and dz next to each other to avoid torch.cat
+        if (not self.yoco_kv) and self.use_fast_path and inference_params is None:  # Doesn't support outputting the states
+            out = mamba_inner_fn(
+                xz,
+                self.conv1d.weight,
+                self.conv1d.bias,
+                self.x_proj.weight,
+                self.dt_proj.weight,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                A,
+                None,  # input-dependent B
+                None,  # input-dependent C
+                self.D.float(),
+                delta_bias=self.dt_proj.bias.float(),
+                mask=mask,
+                delta_softplus=True,
+            )
+        else:
+            x, z = xz.chunk(2, dim=1)
+            if self.yoco_kv:
+                z = z.transpose(-1,-2).contiguous()
+            if mask is not None:
+                x = x * mask.unsqueeze(1)
+            # Compute short convolution
+            if conv_state is not None:
+                # If we just take x[:, :, -self.d_conv :], it will error if seqlen < self.d_conv
+                # Instead F.pad will pad with zeros if seqlen < self.d_conv, and truncate otherwise.
+                conv_state.copy_(F.pad(x, (self.d_conv - x.shape[-1], 0)))  # Update state (B D W)
+            if causal_conv1d_fn is None:
+                x = self.act(self.conv1d(x)[..., :seqlen])
+            else:
+                assert self.activation in ["silu", "swish"]
+                x = causal_conv1d_fn(
+                    x=x,
+                    weight=rearrange(self.conv1d.weight, "d 1 w -> d w"),
+                    bias=self.conv1d.bias,
+                    activation=self.activation,
+                )
+            if mask is not None:
+                x = x * mask.unsqueeze(1)
+            # We're careful here about the layout, to avoid extra transposes.
+            # We want dt to have d as the slowest moving dimension
+            # and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.
+            x_dbl = self.x_proj(rearrange(x, "b d l -> (b l) d"))  # (bl d)
+            dt, B, C = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=-1)
+            dt = self.dt_proj.weight @ dt.t()
+            dt = rearrange(dt, "d (b l) -> b d l", l=seqlen)
+            B = rearrange(B, "(b l) dstate -> b dstate l", l=seqlen).contiguous()
+            C = rearrange(C, "(b l) dstate -> b dstate l", l=seqlen).contiguous()
+            assert self.activation in ["silu", "swish"]
+            y = selective_scan_fn(
+                x,
+                dt,
+                A,
+                B,
+                C,
+                self.D.float(),
+                z= None if self.yoco_kv else z,
+                delta_bias=self.dt_proj.bias.float(),
+                delta_softplus=True,
+                return_last_state=ssm_state is not None,
+            )
+            if ssm_state is not None:
+                y, last_state = y
+                ssm_state.copy_(last_state)
+            y = rearrange(y, "b d l -> b l d")
+            if self.yoco_kv:
+                yoco_key_values = y
+                y = swiglu(z, y)
+            out = self.out_proj(y)
+        return out, yoco_key_values
+
+    def step(self, hidden_states, conv_state, ssm_state, yoco_key_values):
+        dtype = hidden_states.dtype
+        assert hidden_states.shape[1] == 1, "Only support decoding with 1 token at a time for now"
+        xz = self.in_proj(hidden_states.to(dtype = self.in_proj.weight.dtype).squeeze(1))  # (B 2D)
+        x, z = xz.chunk(2, dim=-1)  # (B D)
+
+        # Conv step
+        if causal_conv1d_update is None:
+            conv_state.copy_(torch.roll(conv_state, shifts=-1, dims=-1))  # Update state (B D W)
+            conv_state[:, :, -1] = x
+            x = torch.sum(conv_state * rearrange(self.conv1d.weight, "d 1 w -> d w"), dim=-1)  # (B D)
+            if self.conv1d.bias is not None:
+                x = x + self.conv1d.bias
+            x = self.act(x).to(dtype=dtype)
+        else:
+            x = causal_conv1d_update(
+                x,
+                conv_state,
+                rearrange(self.conv1d.weight, "d 1 w -> d w"),
+                self.conv1d.bias,
+                self.activation,
+            )
+
+        x_db = self.x_proj(x)  # (B dt_rank+2*d_state)
+        dt, B, C = torch.split(x_db, [self.dt_rank, self.d_state, self.d_state], dim=-1)
+        # Don't add dt_bias here
+        dt = F.linear(dt, self.dt_proj.weight)  # (B d_inner)
+        A = -torch.exp(self.A_log.float())  # (d_inner, d_state)
+
+        # SSM step
+        if selective_state_update is None:
+            # Discretize A and B
+            dt = F.softplus(dt + self.dt_proj.bias.to(dtype=dt.dtype))
+            dA = torch.exp(torch.einsum("bd,dn->bdn", dt, A))
+            dB = torch.einsum("bd,bn->bdn", dt, B)
+            ssm_state.copy_(ssm_state * dA + rearrange(x, "b d -> b d 1") * dB)
+            y = torch.einsum("bdn,bn->bd", ssm_state.to(dtype), C)
+            y = y + self.D.to(dtype) * x
+            y = y * self.act(z)  # (B D)
+        else:
+            y = selective_state_update(
+                ssm_state, x, dt, A, B, C, self.D, z= None if self.yoco_kv else z, dt_bias=self.dt_proj.bias, dt_softplus=True
+            )
+        if self.yoco_kv:
+            yoco_key_values = y.unsqueeze(1)
+            y = swiglu(z, y)
+        out = self.out_proj(y)
+        return out.unsqueeze(1), conv_state, ssm_state, yoco_key_values
+
+    def _get_states_from_cache(self, inference_params):
+        conv_state, ssm_state = inference_params.key_cache[self.layer_idx], inference_params.value_cache[self.layer_idx]           
+        return conv_state, ssm_state
+
+
+
+
+class SambaYDecoderLayer(nn.Module):
+    def __init__(self, config: Phi4FlashConfig, layer_idx: int):
+        super().__init__()
+
+        self.mlp = SambaYMLP(config)
+        self.input_layernorm = PHI_NORM_CLASS(config.hidden_size, eps=config.layer_norm_eps)
+        
+        self.yoco_kv = False
+        self.yoco_cross = False
+        self.yoco_mb = False
+        self.layer_idx = layer_idx
+        assert config.num_hidden_layers % 4 == 0, 'n_layer should be divisible by 4 for SambaY '
+        if layer_idx >= config.num_hidden_layers//2:
+            self.yoco_mb = True
+            self.yoco_kv = (layer_idx >= (config.num_hidden_layers//2 +1))
+            self.yoco_cross = (layer_idx >= (config.num_hidden_layers//2 +2))
+            if (layer_idx >= (config.num_hidden_layers//2 +1)):
+                config = copy.deepcopy(config)
+                config.sliding_window = None     
+        self.config= config   
+          
+        self.use_mamba = config.mb_per_layer > 0 and layer_idx % config.mb_per_layer == 0
+        if self.use_mamba:
+            factory_kwargs = {"d_conv": config.mamba_d_conv, "d_state": config.mamba_d_state, "expand": config.mamba_expand , "dtype": None}
+            self.attn = Phi3Mamba(config.hidden_size, layer_idx=layer_idx, yoco_cross=self.yoco_cross, yoco_kv=self.yoco_mb, **factory_kwargs)
+        else:
+            self.attn = SambaYFlashAttention2(config, layer_idx=layer_idx, yoco_cross=self.yoco_cross)
+
+        self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
+        self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
+        self.post_attention_layernorm = PHI_NORM_CLASS(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        ssm_output: Optional[torch.Tensor] = None,
+        yoco_key_values: Optional[torch.Tensor] = None,
+        **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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            position_ids (`torch.LongTensor` of shape `({0})`, *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)
+            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
+        """
+
+        residual = hidden_states
+        
+        hidden_states = self.input_layernorm(hidden_states.to(dtype=self.input_layernorm.weight.dtype))
+
+        if self.use_mamba:
+            attn_outputs, ssm_output = self.attn(
+                hidden_states, inference_params=past_key_value, 
+                mask = attention_mask, yoco_key_values = ssm_output,
+                cache_position=cache_position,
+            )
+            residual = residual.to(torch.float32) 
+            self_attn_weights = None
+        else:
+            if self.config.sliding_window is not None and self.config.sliding_window[self.layer_idx] is not None and attention_mask is not None:  # efficient SDPA and no padding
+                if past_key_value is not None and cache_position[0] > 0:  # when decoding
+                    attention_mask = attention_mask[:, -self.config.sliding_window[self.layer_idx]:]
+            #hidden_states = self.input_layernorm2(hidden_states.to(dtype=self.input_layernorm2.weight.dtype))
+            # Self Attention
+            attn_outputs, self_attn_weights, yoco_key_values = 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,
+                yoco_key_values = yoco_key_values,
+            )
+
+        hidden_states = residual + self.resid_attn_dropout(attn_outputs)
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states.to(dtype=self.post_attention_layernorm.weight.dtype))
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + self.resid_mlp_dropout(hidden_states)
+
+        outputs = (hidden_states,)
+        outputs += (ssm_output,)    
+        outputs += (yoco_key_values,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+        
+        return outputs
+
+
+PHI_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 ([`Phi4FlashConfig`]):
+            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 Phi4Flash Model outputting raw hidden-states without any specific head on top.",
+    PHI_START_DOCSTRING,
+)
+class Phi4FlashPreTrainedModel(PreTrainedModel):
+    config_class = Phi4FlashConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["SambaYDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = False
+    _supports_cache_class = 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_()
+
+
+PHI_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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *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`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            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.
+"""
+
+
+@add_start_docstrings(
+    "The bare Phi4Flash Model outputting raw hidden-states without any specific head on top.",
+    PHI_START_DOCSTRING,
+)
+class Phi4FlashModel(Phi4FlashPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`SambaYDecoderLayer`]
+
+    Args:
+        config: Phi4FlashConfig
+    """
+
+    def __init__(self, config: Phi4FlashConfig):
+        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.embed_dropout = nn.Dropout(config.embd_pdrop)
+        self.layers = nn.ModuleList(
+            [SambaYDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.final_layernorm = PHI_NORM_CLASS(config.hidden_size, eps=config.layer_norm_eps)
+
+        self._attn_implementation = config._attn_implementation
+
+        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(PHI_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[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
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+
+        if self.gradient_checkpointing and self.training:
+            if 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)
+
+        if use_cache and past_key_values is None and not self.training:
+            batch_size, seq_len, _ = inputs_embeds.shape
+            past_key_values = SambaYCache(
+                self.config,
+                max_batch_size=batch_size,
+                max_cache_len=seq_len,
+                device=self.device,
+                dtype=inputs_embeds.dtype,
+            )
+
+                
+        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 attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache and not self.training:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            if is_padding_right:
+                raise ValueError(
+                    "You are attempting to perform batched generation with padding_side='right'"
+                    " this may lead to unexpected behaviour for Flash Attention version of Phi4Flash. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        ssm_output = None
+        yoco_key_values = None
+        for decoder_layer in self.layers: # TODO: only need to inference the first half of the layers during pre-fill
+            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,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    ssm_output,
+                    yoco_key_values,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position = cache_position,
+                    ssm_output = ssm_output,
+                    yoco_key_values = yoco_key_values,
+                )
+
+            hidden_states = layer_outputs[0]
+            ssm_output = layer_outputs[1]
+            yoco_key_values = layer_outputs[2] 
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[3],)
+
+        hidden_states = self.final_layernorm(hidden_states.to(dtype=self.final_layernorm.weight.dtype))
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        output = BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+        return output if return_dict else output.to_tuple()
+
+
+
+class Phi4FlashForCausalLM(Phi4FlashPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi4Flash,bias=False->bias=True
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Phi4FlashModel(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()
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(PHI_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[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,
+        num_logits_to_keep: int = 0,
+        **loss_kwargs,
+    ) -> 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]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Phi4FlashForCausalLM
+
+        >>> model = Phi4FlashForCausalLM.from_pretrained("microsoft/Phi4-mini-flash-reasoning")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi4-mini-flash-reasoning")
+
+        >>> prompt = "This is an example script ."
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
+        ```"""
+
+        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]
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
+
+        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,
+        )
+
+
+@add_start_docstrings(
+    """
+    The Phi4FlashModel with a sequence classification head on top (linear layer).
+
+    [`Phi4FlashForSequenceClassification`] 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).
+    """,
+    PHI_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->PHI,Llama->Phi4Flash with self.transformer->self.model, transformer_outputs->model_outputs
+class Phi4FlashForSequenceClassification(Phi4FlashPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = Phi4FlashModel(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(PHI_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[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
+
+        model_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 = model_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,) + model_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=model_outputs.past_key_values,
+            hidden_states=model_outputs.hidden_states,
+            attentions=model_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Phi4FlashModel 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.
+    """,
+    PHI_START_DOCSTRING,
+)
+# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with MPT->PHI,Mpt->Phi4Flash,self.transformer->self.model,transformer_outputs->model_outputs
+class Phi4FlashForTokenClassification(Phi4FlashPreTrainedModel):
+    def __init__(self, config: Phi4FlashConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.model = Phi4FlashModel(config)
+        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
+            classifier_dropout = config.classifier_dropout
+        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **deprecated_arguments,
+    ) -> Union[Tuple[torch.Tensor], 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
+
+        model_outputs = self.model(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = model_outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            batch_size, seq_length = labels.shape
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length))
+
+        if not return_dict:
+            output = (logits,) + model_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=model_outputs.hidden_states,
+            attentions=model_outputs.attentions,
+        )
+
+## support batched generation
+
+class SelectiveScanFn(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False,
+                return_last_state=False):
+        if u.stride(-1) != 1:
+            u = u.contiguous()
+        if delta.stride(-1) != 1:
+            delta = delta.contiguous()
+        if D is not None:
+            D = D.contiguous()
+        if B.stride(-1) != 1:
+            B = B.contiguous()
+        if C.stride(-1) != 1:
+            C = C.contiguous()
+        if z is not None and z.stride(-1) != 1:
+            z = z.contiguous()
+        if B.dim() == 3:
+            B = rearrange(B, "b dstate l -> b 1 dstate l")
+            ctx.squeeze_B = True
+        if C.dim() == 3:
+            C = rearrange(C, "b dstate l -> b 1 dstate l")
+            ctx.squeeze_C = True
+        out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, z, delta_bias, delta_softplus)
+        ctx.delta_softplus = delta_softplus
+        ctx.has_z = z is not None
+        last_state = x[:, :, -1, 1::2]  # (batch, dim, dstate)
+        if not ctx.has_z:
+            ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
+            return out if not return_last_state else (out, last_state)
+        else:
+            ctx.save_for_backward(u, delta, A, B, C, D, z, delta_bias, x, out)
+            out_z = rest[0]
+            return out_z if not return_last_state else (out_z, last_state)
+
+    @staticmethod
+    def backward(ctx, dout, *args):
+        if not ctx.has_z:
+            u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
+            z = None
+            out = None
+        else:
+            u, delta, A, B, C, D, z, delta_bias, x, out = ctx.saved_tensors
+        if dout.stride(-1) != 1:
+            dout = dout.contiguous()
+        # The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
+        # backward of selective_scan_cuda with the backward of chunk).
+        # Here we just pass in None and dz will be allocated in the C++ code.
+        du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
+            u, delta, A, B, C, D, z, delta_bias, dout, x, out, None, ctx.delta_softplus,
+            False  # option to recompute out_z, not used here
+        )
+        dz = rest[0] if ctx.has_z else None
+        dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
+        dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
+        return (du, ddelta, dA, dB, dC,
+                dD if D is not None else None,
+                dz,
+                ddelta_bias if delta_bias is not None else None,
+                None,
+                None)
+
+
+def selective_scan_fn(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False,
+                     return_last_state=False):
+    """if return_last_state is True, returns (out, last_state)
+    last_state has shape (batch, dim, dstate). Note that the gradient of the last state is
+    not considered in the backward pass.
+    """
+    return SelectiveScanFn.apply(u, delta, A, B, C, D, z, delta_bias, delta_softplus, return_last_state)
+
+
+class MambaInnerFn(torch.autograd.Function):
+
+    @staticmethod
+    @custom_fwd(device_type="cuda")
+    def forward(ctx, xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
+                out_proj_weight, out_proj_bias,
+                A, B=None, C=None, D=None, delta_bias=None, B_proj_bias=None,
+                C_proj_bias=None, mask=None, delta_softplus=True, checkpoint_lvl=1,):
+        """
+             xz: (batch, dim, seqlen)
+        """
+        assert causal_conv1d_cuda is not None, "causal_conv1d_cuda is not available. Please install causal-conv1d."
+        assert checkpoint_lvl in [0, 1]
+        L = xz.shape[-1]
+        delta_rank = delta_proj_weight.shape[1]
+        d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
+        if torch.is_autocast_enabled():
+            x_proj_weight = x_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
+            delta_proj_weight = delta_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
+            out_proj_weight = out_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
+            out_proj_bias = (out_proj_bias.to(dtype=torch.get_autocast_gpu_dtype())
+                             if out_proj_bias is not None else None)
+        if xz.stride(-1) != 1:
+            xz = xz.contiguous()
+        conv1d_weight = rearrange(conv1d_weight, "d 1 w -> d w")
+        x, z = xz.chunk(2, dim=1)
+        if mask is not None:
+            x = x * mask.unsqueeze(1)
+        conv1d_bias = conv1d_bias.contiguous() if conv1d_bias is not None else None
+        conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(
+            x, conv1d_weight, conv1d_bias, None, None, None, True
+        )
+        if mask is not None:
+            conv1d_out = conv1d_out * mask.unsqueeze(1)
+        # We're being very careful here about the layout, to avoid extra transposes.
+        # We want delta to have d as the slowest moving dimension
+        # and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.
+        x_dbl = F.linear(rearrange(conv1d_out, 'b d l -> (b l) d'), x_proj_weight)  # (bl d)
+        delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(), "d (b l) -> b d l", l = L)
+        ctx.is_variable_B = B is None
+        ctx.is_variable_C = C is None
+        ctx.B_proj_bias_is_None = B_proj_bias is None
+        ctx.C_proj_bias_is_None = C_proj_bias is None
+        if B is None:  # variable B
+            B = x_dbl[:, delta_rank:delta_rank + d_state]  # (bl dstate)
+            if B_proj_bias is not None:
+                B = B + B_proj_bias.to(dtype=B.dtype)
+            if not A.is_complex():
+                # B = rearrange(B, "(b l) dstate -> b dstate l", l=L).contiguous()
+                B = rearrange(B, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
+            else:
+                B = rearrange(B, "(b l) (dstate two) -> b 1 dstate (l two)", l=L, two=2).contiguous()
+        else:
+            if B.stride(-1) != 1:
+                B = B.contiguous()
+        if C is None:  # variable C
+            C = x_dbl[:, -d_state:]  # (bl dstate)
+            if C_proj_bias is not None:
+                C = C + C_proj_bias.to(dtype=C.dtype)
+            if not A.is_complex():
+                # C = rearrange(C, "(b l) dstate -> b dstate l", l=L).contiguous()
+                C = rearrange(C, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
+            else:
+                C = rearrange(C, "(b l) (dstate two) -> b 1 dstate (l two)", l=L, two=2).contiguous()
+        else:
+            if C.stride(-1) != 1:
+                C = C.contiguous()
+        if D is not None:
+            D = D.contiguous()
+        out, scan_intermediates, out_z = selective_scan_cuda.fwd(
+            conv1d_out, delta, A, B, C, D, z, delta_bias, delta_softplus
+        )
+        ctx.delta_softplus = delta_softplus
+        ctx.out_proj_bias_is_None = out_proj_bias is None
+        ctx.checkpoint_lvl = checkpoint_lvl
+        if checkpoint_lvl >= 1:  # Will recompute conv1d_out and delta in the backward pass
+            conv1d_out, delta = None, None
+        ctx.save_for_backward(xz, conv1d_weight, conv1d_bias, x_dbl, x_proj_weight,
+                              delta_proj_weight, out_proj_weight, conv1d_out, delta,
+                              A, B, C, D, delta_bias, scan_intermediates, out)
+        return F.linear(rearrange(out_z, "b d l -> b l d"), out_proj_weight, out_proj_bias)
+
+    @staticmethod
+    @custom_bwd(device_type="cuda")
+    def backward(ctx, dout):
+        # dout: (batch, seqlen, dim)
+        assert causal_conv1d_cuda is not None, "causal_conv1d_cuda is not available. Please install causal-conv1d."
+        (xz, conv1d_weight, conv1d_bias, x_dbl, x_proj_weight, delta_proj_weight, out_proj_weight,
+         conv1d_out, delta, A, B, C, D, delta_bias, scan_intermediates, out) = ctx.saved_tensors
+        L = xz.shape[-1]
+        delta_rank = delta_proj_weight.shape[1]
+        d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
+        x, z = xz.chunk(2, dim=1)
+        if dout.stride(-1) != 1:
+            dout = dout.contiguous()
+        if ctx.checkpoint_lvl == 1:
+            conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(
+                x, conv1d_weight, conv1d_bias, None, None, None, True
+            )
+            delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(),
+                              "d (b l) -> b d l", l = L)
+        # The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
+        # backward of selective_scan_cuda with the backward of chunk).
+        dxz = torch.empty_like(xz)  # (batch, dim, seqlen)
+        dx, dz = dxz.chunk(2, dim=1)
+        dout = rearrange(dout, "b l e -> e (b l)")
+        dout_y = rearrange(out_proj_weight.t() @ dout, "d (b l) -> b d l", l=L)
+        dconv1d_out, ddelta, dA, dB, dC, dD, ddelta_bias, dz, out_z = selective_scan_cuda.bwd(
+            conv1d_out, delta, A, B, C, D, z, delta_bias, dout_y, scan_intermediates, out, dz,
+            ctx.delta_softplus,
+            True  # option to recompute out_z
+        )
+        dout_proj_weight = torch.einsum("eB,dB->ed", dout, rearrange(out_z, "b d l -> d (b l)"))
+        dout_proj_bias = dout.sum(dim=(0, 1)) if not ctx.out_proj_bias_is_None else None
+        dD = dD if D is not None else None
+        dx_dbl = torch.empty_like(x_dbl)
+        dB_proj_bias = None
+        if ctx.is_variable_B:
+            if not A.is_complex():
+                dB = rearrange(dB, "b 1 dstate l -> (b l) dstate").contiguous()
+            else:
+                dB = rearrange(dB, "b 1 dstate (l two) -> (b l) (dstate two)", two=2).contiguous()
+            dB_proj_bias = dB.sum(0) if not ctx.B_proj_bias_is_None else None
+            dx_dbl[:, delta_rank:delta_rank + d_state] = dB  # (bl d)
+            dB = None
+        dC_proj_bias = None
+        if ctx.is_variable_C:
+            if not A.is_complex():
+                dC = rearrange(dC, "b 1 dstate l -> (b l) dstate").contiguous()
+            else:
+                dC = rearrange(dC, "b 1 dstate (l two) -> (b l) (dstate two)", two=2).contiguous()
+            dC_proj_bias = dC.sum(0) if not ctx.C_proj_bias_is_None else None
+            dx_dbl[:, -d_state:] = dC  # (bl d)
+            dC = None
+        ddelta = rearrange(ddelta, "b d l -> d (b l)")
+        ddelta_proj_weight = torch.einsum("dB,Br->dr", ddelta, x_dbl[:, :delta_rank])
+        dx_dbl[:, :delta_rank] = torch.einsum("dB,dr->Br", ddelta, delta_proj_weight)
+        dconv1d_out = rearrange(dconv1d_out, "b d l -> d (b l)")
+        dx_proj_weight = torch.einsum("Br,Bd->rd", dx_dbl, rearrange(conv1d_out, "b d l -> (b l) d"))
+        dconv1d_out = torch.addmm(dconv1d_out, x_proj_weight.t(), dx_dbl.t(), out=dconv1d_out)
+        dconv1d_out = rearrange(dconv1d_out, "d (b l) -> b d l", b=x.shape[0], l=x.shape[-1])
+        # The kernel supports passing in a pre-allocated dx (e.g., in case we want to fuse the
+        # backward of conv1d with the backward of chunk).
+        dx, dconv1d_weight, dconv1d_bias, *_ = causal_conv1d_cuda.causal_conv1d_bwd(
+            x, conv1d_weight, conv1d_bias, dconv1d_out, None, None, None, dx, False, True
+        )
+        dconv1d_bias = dconv1d_bias if conv1d_bias is not None else None
+        dconv1d_weight = rearrange(dconv1d_weight, "d w -> d 1 w")
+        return (dxz, dconv1d_weight, dconv1d_bias, dx_proj_weight, ddelta_proj_weight,
+                dout_proj_weight, dout_proj_bias,
+                dA, dB, dC, dD,
+                ddelta_bias if delta_bias is not None else None,
+                dB_proj_bias, dC_proj_bias, None, None)
+
+
+def mamba_inner_fn(
+    xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
+    out_proj_weight, out_proj_bias,
+    A, B=None, C=None, D=None, delta_bias=None, B_proj_bias=None,
+    C_proj_bias=None, mask=None, delta_softplus=True
+):
+    return MambaInnerFn.apply(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
+                              out_proj_weight, out_proj_bias,
+                              A, B, C, D, delta_bias, B_proj_bias, C_proj_bias, mask, delta_softplus)
+
+
+def lambda_init_fn(depth):
+    return 0.8 - 0.6 * math.exp(-0.3 * depth)
+
+
+def split_heads(x):
+    # split by num_heads, the stripe pattern is friendly to tensor parallel.
+    x = rearrange(x, "... (H two) D -> ... H two D", two=2)
+    x1 = x[..., 0, :]
+    x2 = x[..., 1, :]
+    return x1, x2
+
+class FlashDiffCustomAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        head_dim: The dimension of the heads.
+        depth: The layer id, starting from 0.
+    """
+
+    def __init__(
+        self,
+        head_dim,
+        depth,
+        fa_og = True,
+    ):
+        super().__init__()
+        assert flash_attn_varlen_func is not None, "FlashAttention is not installed"
+        assert flash_attn_func is not None, "FlashAttention is not installed"
+        self.head_dim = head_dim
+        self.fa_og = fa_og # turning it to false needs customized flash attention https://github.com/xiayuqing0622/flex_head_fa
+        self.lambda_init = lambda_init_fn(depth)
+        self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
+        self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
+        self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
+        self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
+
+        self.subln = SambaYRMSNorm(2 * self.head_dim, eps=1e-5)
+
+    def forward(
+        self,
+        q,
+        k,
+        v,
+        dropout_p = 0.0,
+        cu_seqlens_q=None,
+        max_seqlen_q=None,
+        cu_seqlens_k=None,
+        max_seqlen_k=None,
+        softmax_scale=None,
+        window_size=(-1, -1),
+        causal=None,
+    ):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            q, k, v: The tensors containing the query, key, and value.
+                If cu_seqlens is None and max_seqlen is None, then each has shape (B, S, H, D).
+                If cu_seqlens is not None and max_seqlen is not None, then each has shape
+                (total, H, D), where total is the sum of the sequence lengths in the batch.
+            causal: if passed, will override self.causal
+            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
+                of the sequences in the batch, used to index into qkv.
+            max_seqlen: int. Maximum sequence length in the batch.
+        Returns:
+        --------
+            out: (total, H, D) if cu_seqlens is not None and max_seqlen is not None,
+                else (B, S, H, D).
+        """
+        q = q.to(torch.bfloat16)
+        k = k.to(torch.bfloat16)
+        v = v.to(torch.bfloat16)
+
+        assert q.dtype in [torch.float16, torch.bfloat16]
+        assert q.is_cuda and k.is_cuda and v.is_cuda
+        #causal = self.causal if causal is None else causal
+        unpadded = cu_seqlens_q is not None
+        q1, q2 = split_heads(q)
+        k1, k2 = split_heads(k)
+        if self.fa_og:
+            v1, v2 = split_heads(v)
+        else:
+            v = rearrange(v, "... (H two) D -> ... H (two D)", two=2)
+
+        kwargs = {
+            "dropout_p": dropout_p,
+            "softmax_scale": softmax_scale,
+            "causal": causal,
+            "window_size": window_size,
+        }
+
+        if unpadded:
+            assert cu_seqlens_q.dtype == torch.int32
+            assert max_seqlen_q is not None
+            assert isinstance(max_seqlen_q, int)
+            assert cu_seqlens_k is not None
+            assert cu_seqlens_k.dtype == torch.int32
+            assert max_seqlen_k is not None
+            assert isinstance(max_seqlen_k, int)
+
+            kwargs.update({
+                "cu_seqlens_q": cu_seqlens_q,
+                "max_seqlen_q": max_seqlen_q,
+                "cu_seqlens_k": cu_seqlens_k,
+                "max_seqlen_k": max_seqlen_k,
+            })
+            attn_func = flash_attn_varlen_func
+        else:
+            attn_func = flash_attn_func
+
+        if self.fa_og:
+            attn11 = attn_func(q1, k1, v1, **kwargs)
+            attn12 = attn_func(q1, k1, v2, **kwargs)
+            attn1 = torch.cat([attn11, attn12], dim=-1)
+            attn21 = attn_func(q2, k2, v1, **kwargs)
+            attn22 = attn_func(q2, k2, v2, **kwargs)
+            attn2 = torch.cat([attn21, attn22], dim=-1)
+        else:
+            attn1 = attn_func(q1, k1, v, **kwargs)
+            attn2 = attn_func(q2, k2, v, **kwargs)
+
+        lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(q)
+        lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(q)
+        lambda_full = lambda_1 - lambda_2 + self.lambda_init
+
+        attn = attn1 - lambda_full * attn2
+        attn = self.subln(attn)
+        attn = attn * (1 - self.lambda_init)
+        # reshape back to 2 * num_head
+        attn = rearrange(attn, "... H (two D) -> ... (H two) D", two=2)
+        return attn

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,111 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "199999": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "200018": {
+      "content": "<|endofprompt|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "200019": {
+      "content": "<|assistant|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "200020": {
+      "content": "<|end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "200021": {
+      "content": "<|user|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "200022": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "200023": {
+      "content": "<|tool|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "200024": {
+      "content": "<|/tool|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "200025": {
+      "content": "<|tool_call|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "200026": {
+      "content": "<|/tool_call|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "200027": {
+      "content": "<|tool_response|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "200028": {
+      "content": "<|tag|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "chat_template": "{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|tool|>' + message['tools'] + '<|/tool|>' + '<|end|>' }}{% else %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 65536,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}