diff --git a/.ipynb_checkpoints/configuration_forgetting_transformer-checkpoint.py b/.ipynb_checkpoints/configuration_forgetting_transformer-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f4e7d87a4fd925592b9ef29a66cf924fa95f36e
--- /dev/null
+++ b/.ipynb_checkpoints/configuration_forgetting_transformer-checkpoint.py
@@ -0,0 +1,84 @@
+# -*- coding: utf-8 -*-
+from typing import Optional
+from transformers.configuration_utils import PretrainedConfig
+
+class ForgettingTransformerConfig(PretrainedConfig):
+    model_type = 'forgetting_transformer'
+    keys_to_ignore_at_inference = ['past_key_values']
+    
+    def __init__(
+        self,
+        vocab_size: int = 32000,
+        hidden_size: int = 2048,
+        hidden_ratio: Optional[float] = 4,
+        intermediate_size: Optional[int] = None,
+        num_hidden_layers: int = 24,
+        num_heads: int = 32,
+        num_kv_heads: int = None,
+        hidden_act: str = "swish",
+        window_size: Optional[int] = None,
+        max_position_embeddings: int = 2048,
+        initializer_range: float = 0.02,
+        elementwise_affine: Optional[bool] = True,
+        norm_eps: float = 1e-6,
+        use_cache: bool = True,
+        pad_token_id: int = None,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        tie_word_embeddings: bool = False,
+        attention_bias: bool = False,
+        fuse_norm: bool = True,
+        fuse_cross_entropy: bool = True,
+        rope_base: float = 500000.0,
+        use_rope: bool = False,
+        use_output_gate: bool = False,
+        ogate_act: str = "sigmoid",
+        fgate_type: str = "full",
+        fgate_bias_init: bool = False,
+        decay_time_min: Optional[float] = None,
+        decay_time_max: Optional[float] = None,
+        use_output_norm: bool = False,
+        qk_norm: bool = False,
+        qk_norm_share_param_across_head: bool = False,
+        use_k_shift: bool = False,
+        use_v_shift: bool = False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.hidden_ratio = hidden_ratio
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.window_size = window_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.elementwise_affine = elementwise_affine
+        self.norm_eps = norm_eps
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.fuse_cross_entropy = fuse_cross_entropy
+        self.fuse_norm = fuse_norm
+        self.rope_base = rope_base
+        self.use_rope = use_rope
+        self.use_output_gate = use_output_gate
+        self.ogate_act = ogate_act
+        self.fgate_type = fgate_type
+        self.fgate_bias_init = fgate_bias_init
+        self.decay_time_min = decay_time_min
+        self.decay_time_max = decay_time_max
+        self.use_output_norm = use_output_norm
+        self.qk_norm = qk_norm
+        self.qk_norm_share_param_across_head = qk_norm_share_param_across_head
+        self.use_k_shift = use_k_shift
+        self.use_v_shift = use_v_shift
+        
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
\ No newline at end of file
diff --git a/.ipynb_checkpoints/fgate_cache-checkpoint.py b/.ipynb_checkpoints/fgate_cache-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba956105bfa56d210a1e4e7ac9ba887abf6b6de7
--- /dev/null
+++ b/.ipynb_checkpoints/fgate_cache-checkpoint.py
@@ -0,0 +1,143 @@
+from typing import List, Tuple, Optional, Any, Dict
+import torch
+
+class FgateDynamicCache:
+    """
+    A cache that grows dynamically as more tokens are generated.
+    Custom cache for Forgetting Transformer that does not inherit from transformers.Cache.
+    """
+
+    def __init__(self, num_hidden_layers: Optional[int] = None) -> None:
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        self.log_fgate_cache: List[torch.Tensor] = []
+        self.key_shift_cache: List[torch.Tensor] = []
+        self.value_shift_cache: List[torch.Tensor] = []
+        self._seen_tokens = 0
+
+    def update_shift_cache(
+        self,
+        key_shift_state: torch.Tensor,
+        value_shift_state: torch.Tensor,
+        layer_idx,
+    ):
+        assert layer_idx == len(self.key_shift_cache) == len(self.value_shift_cache)
+        self.key_shift_cache.append(key_shift_state)
+        self.value_shift_cache.append(value_shift_state)
+
+    def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
+        if layer_idx < len(self):
+            return (self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx])
+        else:
+            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
+
+    def __iter__(self):
+        for layer_idx in range(len(self)):
+            yield (self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx])
+
+    def __len__(self):
+        return len(self.key_cache)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        log_fgate_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        assert log_fgate_states.ndim == 3, f"log_fgate must be (B, H, T), but get {log_fgate_states.size()}"
+        if layer_idx == 0:
+            self._seen_tokens += key_states.shape[-2]
+
+        if len(self.key_cache) <= layer_idx:
+            self.key_cache.append(key_states)
+            self.value_cache.append(value_states)
+            self.log_fgate_cache.append(log_fgate_states)
+        else:
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+            self.log_fgate_cache[layer_idx] = torch.cat([self.log_fgate_cache[layer_idx], log_fgate_states], dim=-1)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx]
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        if len(self.key_cache) <= layer_idx:
+            return 0
+        return self.key_cache[layer_idx].shape[-2]
+
+    def get_max_length(self) -> Optional[int]:
+        return None
+
+    def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], ...]:
+        legacy_cache = ()
+        for layer_idx in range(len(self)):
+            legacy_cache += ((self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx]),)
+        return legacy_cache
+
+    @classmethod
+    def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, num_layers: Optional[int] = None) -> "FgateDynamicCache":
+        """
+        Converts a cache in the legacy cache format into an equivalent FgateDynamicCache.
+        
+        Args:
+            past_key_values: Optional legacy cache format
+            num_layers: Not used in this implementation
+        
+        Returns:
+            FgateDynamicCache instance
+        """
+        cache = cls()
+        
+        if past_key_values is not None:
+            for layer_idx in range(len(past_key_values)):
+                key_states, value_states, log_fgate_states = past_key_values[layer_idx]
+                cache.update(key_states, value_states, log_fgate_states, layer_idx)
+        
+        return cache
+
+    def crop(self, max_length: int):
+        if max_length < 0:
+            max_length = self.get_seq_length() - abs(max_length)
+
+        if self.get_seq_length() <= max_length:
+            return
+
+        self._seen_tokens = max_length
+        for idx in range(len(self.key_cache)):
+            self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
+            self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
+            self.log_fgate_cache[idx] = self.log_fgate_cache[idx][..., :max_length]
+
+    def batch_split(self, full_batch_size: int, split_size: int) -> List["FgateDynamicCache"]:
+        out = []
+        for i in range(0, full_batch_size, split_size):
+            current_split = FgateDynamicCache()
+            current_split._seen_tokens = self._seen_tokens
+            current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
+            current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
+            current_split.log_fgate_cache = [tensor[i : i + split_size] for tensor in self.log_fgate_cache]
+            out.append(current_split)
+        return out
+
+    @classmethod
+    def from_batch_splits(cls, splits: List["FgateDynamicCache"]) -> "FgateDynamicCache":
+        cache = cls()
+        for idx in range(len(splits[0])):
+            layer_keys = torch.cat([current.key_cache[idx] for current in splits], dim=0)
+            layer_values = torch.cat([current.value_cache[idx] for current in splits], dim=0)
+            layer_log_fgates = torch.cat([current.log_fgate_cache[idx] for current in splits], dim=0)
+            cache.update(layer_keys, layer_values, layer_log_fgates, idx)
+        return cache
+
+    def batch_repeat_interleave(self, repeats: int):
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            self.log_fgate_cache[layer_idx] = self.log_fgate_cache[layer_idx].repeat_interleave(repeats, dim=0)
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...]
+            self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
+            self.log_fgate_cache[layer_idx] = self.log_fgate_cache[layer_idx][indices, ...]
\ No newline at end of file
diff --git a/.ipynb_checkpoints/fgate_cache.py-checkpoint.backup b/.ipynb_checkpoints/fgate_cache.py-checkpoint.backup
new file mode 100644
index 0000000000000000000000000000000000000000..bef4bad152a1679650aa4c271e559cc76e0f67e1
--- /dev/null
+++ b/.ipynb_checkpoints/fgate_cache.py-checkpoint.backup
@@ -0,0 +1,203 @@
+from typing import List, Tuple, Optional, Any, Dict
+import torch
+from transformers.cache_utils import Cache
+
+class FgateDynamicCache(Cache):
+    """
+    A cache that grows dynamically as more tokens are generated. This is the default for generative models.
+
+    It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
+    `[batch_size, num_heads, seq_len, head_dim]`.
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
+
+        >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
+        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
+
+        >>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> past_key_values = DynamicCache()
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> outputs.past_key_values # access cache filled with key/values from generation
+        DynamicCache()
+        ```
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        self.log_fgate_cache: List[torch.Tensor] = []
+
+        self.key_shift_cache: List[torch.Tensor] = []
+        self.value_shift_cache: List[torch.Tensor] = []
+
+        self._seen_tokens = 0  # Used in `generate` to keep tally of how many tokens the cache has seen
+
+    def update_shift_cache(
+        self,
+        key_shift_state: torch.Tensor,
+        value_shift_state: torch.Tensor,
+        layer_idx,
+    ):
+        assert layer_idx == len(self.key_shift_cache) == len(self.value_shift_cache)
+        self.key_shift_cache.append(key_shift_state)
+        self.value_shift_cache.append(value_shift_state)
+
+
+    def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
+        """
+        Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the
+        sequence length.
+        """
+        if layer_idx < len(self):
+            return (self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx])
+        else:
+            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
+
+    def __iter__(self):
+        """
+        Support for backwards-compatible `past_key_value` iteration, e.g. `for x in past_key_value:` to iterate over
+        keys and values
+        """
+        for layer_idx in range(len(self)):
+            yield (self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx])
+
+    def __len__(self):
+        """
+        Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds
+        to the number of layers in the model.
+        """
+        return len(self.key_cache)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        log_fgate_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        assert log_fgate_states.ndim == 3, f"log_fgate must be (B, H, T), but get {log_fgate_states.size()}"
+        # Update the number of seen tokens
+        if layer_idx == 0:
+            self._seen_tokens += key_states.shape[-2]
+
+        # Update the cache
+        if len(self.key_cache) <= layer_idx:
+            self.key_cache.append(key_states)
+            self.value_cache.append(value_states)
+            self.log_fgate_cache.append(log_fgate_states)
+        else:
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+            self.log_fgate_cache[layer_idx] = torch.cat([self.log_fgate_cache[layer_idx], log_fgate_states], dim=-1)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx]
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        # TODO: deprecate this function in favor of `cache_position`
+        if len(self.key_cache) <= layer_idx:
+            return 0
+        return self.key_cache[layer_idx].shape[-2]
+
+    def get_max_length(self) -> Optional[int]:
+        """Returns the maximum sequence length of the cached states. DynamicCache does not have a maximum length."""
+        return None
+
+    def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
+        """Converts the `DynamicCache` instance into the its equivalent in the legacy cache format. Used for
+        backward compatibility."""
+        legacy_cache = ()
+        for layer_idx in range(len(self)):
+            legacy_cache += ((self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx]),)
+        return legacy_cache
+
+    @classmethod
+    def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, num_layers: Optional[int] = None) -> "DynamicCache":
+        """Converts a cache in the legacy cache format into an equivalent `DynamicCache`. Used for
+        backward compatibility."""
+        raise NotImplementedError
+        assert num_layers is not None
+        cache = cls(num_layers)
+        if past_key_values is not None:
+            for layer_idx in range(len(past_key_values)):
+                key_states, value_states, log_fgate_states = past_key_values[layer_idx]
+                cache.update(key_states, value_states, log_fgate_states, layer_idx)
+        return cache
+
+    def crop(self, max_length: int):
+        """Crop the past key values up to a new `max_length` in terms of tokens. `max_length` can also be
+        negative to remove `max_length` tokens. This is used in assisted decoding and contrastive search."""
+        # In case it is negative
+        if max_length < 0:
+            max_length = self.get_seq_length() - abs(max_length)
+
+        if self.get_seq_length() <= max_length:
+            return
+
+        self._seen_tokens = max_length
+        for idx in range(len(self.key_cache)):
+            self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
+            self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
+            self.log_fgate_cache[idx] = self.log_fgate_cache[idx][..., :max_length]
+
+    def batch_split(self, full_batch_size: int, split_size: int) -> List["DynamicCache"]:
+        """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
+        `_split_model_inputs()` in `generation.utils`"""
+        out = []
+        for i in range(0, full_batch_size, split_size):
+            current_split = DynamicCache()
+            current_split._seen_tokens = self._seen_tokens
+            current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
+            current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
+            current_split.log_fgate_cache = [tensor[i : i + split_size] for tensor in self.log_fgate_cache]
+            out.append(current_split)
+        return out
+
+    @classmethod
+    def from_batch_splits(cls, splits: List["DynamicCache"]) -> "DynamicCache":
+        """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
+        `generation.utils`"""
+        cache = cls()
+        for idx in range(len(splits[0])):
+            layer_keys = torch.cat([current.key_cache[idx] for current in splits], dim=0)
+            layer_values = torch.cat([current.value_cache[idx] for current in splits], dim=0)
+            layer_log_fgates = torch.cat([current.log_fgate_cache[idx] for current in splits], dim=0)
+            cache.update(layer_keys, layer_values, layer_log_fgates, idx)
+        return cache
+
+    def batch_repeat_interleave(self, repeats: int):
+        """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            self.log_fgate_cache[layer_idx] = self.log_fgate_cache[layer_idx].repeat_interleave(repeats, dim=0)
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...]
+            self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
+            self.log_fgate_cache[layer_idx] = self.log_fgate_cache[layer_idx][indices, ...]
diff --git a/.ipynb_checkpoints/modeling_forgetting_transformer-checkpoint.py b/.ipynb_checkpoints/modeling_forgetting_transformer-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..deff96ee4c8558b72b8a525d4b38184b2a46af77
--- /dev/null
+++ b/.ipynb_checkpoints/modeling_forgetting_transformer-checkpoint.py
@@ -0,0 +1,910 @@
+# -*- coding: utf-8 -*-
+
+from __future__ import annotations
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import (BaseModelOutputWithPast,
+                                           CausalLMOutputWithPast)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+# from fla.layers.attn import Attention
+from fla.modules import FusedCrossEntropyLoss, RMSNorm
+from fla.modules.layernorm import group_norm_fn
+from fla.modules.activations import swiglu_linear
+
+from fla.modules import RotaryEmbedding
+from einops import rearrange
+
+# 动态导入配置类以支持本地和HuggingFace Hub加载
+try:
+    from .configuration_forgetting_transformer import ForgettingTransformerConfig
+except (ImportError, ValueError):
+    try:
+        from configuration_forgetting_transformer import ForgettingTransformerConfig
+    except ImportError:
+        from forgetting_transformer.model.forgetting_transformer.configuration_forgetting_transformer import ForgettingTransformerConfig
+
+from forgetting_transformer.ops.forgetting_attention_std import forgetting_attention_std as forgetting_attention
+from .fgate_cache import FgateDynamicCache
+from .glu_linear import glu_linear
+from .token_shift import token_shift
+
+from functools import partial
+
+logger = logging.get_logger(__name__)
+
+
+class ShiftLinear(nn.Module):
+
+    def __init__(
+        self,
+        input_dim: int,
+        output_dim: int,
+        num_heads: int,
+        bias: bool,
+        shift_bias: bool = False
+    ):
+        super().__init__()
+
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.num_heads = num_heads
+        assert self.output_dim % self.num_heads == 0
+
+        self.linear = nn.Linear(input_dim, output_dim, bias=bias)
+        self.shift_proj = nn.Linear(input_dim, num_heads, bias=shift_bias)
+
+    def __repr__(self) -> str:
+        s = f"{self.__class__.__name__}({self.input_dim}, {self.output_dim})"
+        return s
+
+    def forward(self, x: torch.Tensor, shift_state: Optional[torch.Tensor]) -> torch.Tensor:
+        assert x.ndim == 3, "Input must be (B, T, D)"
+        B, T, D = x.size()
+        out = self.linear(x)
+        # (B, T, H, 1)
+        alpha = torch.sigmoid(self.shift_proj(x).float()).float()
+        # left, right, top, bottom (B, T=H, D=W)
+        # out_prev = nn.functional.pad(out, (0, 0, 1, -1))
+        # out_prev = torch.roll(out, shifts=1, dims=1)
+        
+        out_per_head = rearrange(out, 'b t (h d) -> b t h d', h=self.num_heads)
+        if T > 1:
+            # TODO: note in this case cache is not used
+            result_per_head = token_shift(out_per_head, alpha, 1.0 - alpha)
+        else:
+            shift_state_per_head = rearrange(shift_state, 'b (h d) -> b 1 h d', h=self.num_heads)
+            result_per_head = (alpha[..., None] * shift_state_per_head + (1 - alpha[..., None]) * out_per_head)
+
+        result_per_head = result_per_head.to(out.dtype)
+
+        if shift_state is not None:
+            shift_state.copy_(out[:, -1, :])
+
+        result = rearrange(result_per_head, 'b t h d -> b t (h d)', h=self.num_heads)
+        return result
+
+class GroupRMSNorm(nn.Module):
+    def __init__(
+        self,
+        num_groups: int,
+        hidden_size: int,
+        elementwise_affine: bool = True,
+        bias: bool = False,
+        eps: float = 1e-5
+    ) -> GroupRMSNorm:
+        super().__init__()
+
+        if hidden_size % num_groups != 0:
+            raise ValueError('num_channels must be divisible by num_groups')
+
+        self.num_groups = num_groups
+        self.hidden_size = hidden_size
+        self.elementwise_affine = elementwise_affine
+        self.eps = eps
+
+        self.register_parameter("weight", None)
+        self.register_parameter("bias", None)
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(hidden_size))
+            if bias:
+                self.bias = nn.Parameter(torch.zeros(hidden_size))
+
+    def __repr__(self) -> str:
+        s = f"{self.__class__.__name__}({self.num_groups}, {self.hidden_size}"
+        if not self.elementwise_affine:
+            s += f", elementwise_affine={self.elementwise_affine}"
+        s += f", eps={self.eps}"
+        s += ")"
+        return s
+
+    def forward(self, x, residual=None, prenorm=False, residual_in_fp32=False):
+        return group_norm_fn(
+            x,
+            self.weight,
+            self.bias,
+            residual=residual,
+            eps=self.eps,
+            prenorm=prenorm,
+            residual_in_fp32=residual_in_fp32,
+            is_rms_norm=True,
+            num_groups=self.num_groups
+        )
+
+class ForgettingAttentionLayer(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int = 2048,
+        num_heads: int = 32,
+        num_kv_heads: Optional[int] = None,
+        window_size: Optional[int] = None,
+        max_position_embeddings: Optional[int] = None,
+        use_rope: bool = False,
+        rope_base: float = 500000.0,
+        use_output_gate: bool = False,
+        ogate_act: str = "sigmoid",
+        fgate_type: str = "full",
+        fgate_bias_init: bool = False,
+        decay_time_min: Optional[float] = None,
+        decay_time_max: Optional[float] = None,
+        use_output_norm: bool = False,
+        norm_eps: float = 1e-6,
+        qk_norm: bool = False,
+        qk_norm_share_param_across_head: bool = False,
+        use_k_shift: bool = False,
+        use_v_shift: bool = False,
+        initializer_range: float = 0.02,
+        layer_idx: int = None
+    ):
+        """
+        Forgetting Attention layer.
+
+        Arguments:
+            - hidden_size: Input dimension and qkv dimension
+            - num_heads: Number of heads
+            - num_kv_heads: Not used. Should be None 
+            - window_size: Not used. Should be None
+            - max_position_embeddings: Not used. Should be None
+            - use_rope: Whether to use RoPE. Default is False
+            - rope_base: the theta hyperparameter in RoPE. This has no effect if
+                  use_rope=False
+            - use_output_gate: Whether to use output gates. Note that using output gates
+                  introduces extra parameters and you may want to reduce parameters from
+                  other components (e.g., MLPs)
+            - ogate_act: Activation for the output gate. Either "sigmoid" or "silu"
+            - fgate_type: Forget gate type. The following are supported:
+                - "full": The default data-dependent forget gate
+                - "bias_only": The data-independent forget gate
+                - "fixed": Forget gates with fixed values
+                - "none": Not using forget gates. Equivalent to forget gates with all
+                  ones.
+            - fgate_bias_init: Whether to use special initalization for the bias terms in 
+                  the forget gate. This should only be used with fgate types in 
+                  ["bias_only", "fixed"].
+            - decay_time_min: T_min for the forget gate bias initialization. See paper
+                  for details.
+            - decay_time_max: T_max for the forget gate bias initalization. See paper
+                  for details.
+            - use_output_norm: Whether to use output normalization.
+            - norm_eps: Epsilon for the RMSNorms
+            - qk_norm: Whether to use qk_norm
+            - qk_norm_share_param_across_head: In QK-norm, whether to share the RMSNorm
+                scaling parameters across heads. This is just for backward compatibility.
+            - use_k_shift: Whether to use data-dependent key shift
+            - use_v_shift: Whether to use data-dependent value shift
+            - initializer_range: standard deviation for initialization
+            - layer_idx: The block index of this layer. Needed for KV-cache
+        """
+        super().__init__()
+
+        self.num_heads = num_heads
+        if num_kv_heads is None:
+            self.num_kv_heads = self.num_heads
+        else:
+            raise NotImplementedError("GQA has not been tested.")
+            self.num_kv_heads = num_kv_heads
+        self.num_kv_groups = num_heads // self.num_kv_heads
+        self.hidden_size = hidden_size
+        self.head_dim = self.hidden_size // self.num_heads
+        self.kv_dim = self.num_kv_heads * self.head_dim
+        self.kv_dim = self.num_kv_heads * self.head_dim
+        self.window_size = window_size
+        self.max_position_embeddings = max_position_embeddings
+        self.layer_idx = layer_idx
+
+        self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        if use_k_shift:
+            self.k_proj = ShiftLinear(self.hidden_size, self.kv_dim, self.num_heads, bias=False)
+        else:
+            self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
+
+        if use_v_shift:
+            self.v_proj = ShiftLinear(self.hidden_size, self.kv_dim, self.num_heads, bias=False)
+        else:
+            self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
+
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        self.use_k_shift = use_k_shift
+        self.use_v_shift = use_v_shift
+
+
+        device = next(self.parameters()).device
+        # Forget gate
+        assert fgate_type in ["full", "bias_only", "fixed", "none"]
+        self.fgate_type = fgate_type
+        self.fgate_bias_init = fgate_bias_init
+        if fgate_type == "full":
+            assert not fgate_bias_init
+            self.fgate_proj = nn.Linear(self.hidden_size, self.num_heads, bias=True)
+        elif fgate_type == "bias_only":
+            self.fgate_bias = nn.Parameter(torch.zeros(size=(self.num_heads,), device=device))
+            self.fgate_bias._no_weight_decay = True
+        elif fgate_type == "fixed":
+            assert fgate_bias_init, "You must set fgate_bias_init = True with fixed fgate"
+            fgate_bias = torch.zeros(size=(self.num_heads,), device=device)
+            self.register_buffer("fgate_bias", fgate_bias)
+        elif fgate_type == "none":
+            pass
+        else:
+            raise ValueError(f"Unknown fgate type {fgate_type}")
+
+                
+
+        # Forget gate intialization for data-independent and fixed forget gates
+        if fgate_bias_init:
+            assert decay_time_min is not None and decay_time_max is not None
+            assert decay_time_min > 0 and decay_time_max > 0
+            with torch.no_grad():
+                log_decay_time = torch.linspace(math.log(decay_time_min), math.log(decay_time_max), steps=self.num_heads)
+                decay_time = torch.exp(log_decay_time)
+                # Such that t = -1 / log(sigmoid(b)) 
+                bias_init = -torch.log(torch.expm1(1 / decay_time))
+                self.fgate_bias.copy_(bias_init)
+        else:
+            assert decay_time_min is None and decay_time_max is None
+
+        if use_output_gate:
+            self.ogate_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+            self.ogate_act = ogate_act
+            assert ogate_act in ["silu", "sigmoid"]
+        else:
+            self.ogate_proj = None
+
+        if use_output_norm:
+            self.output_norm = GroupRMSNorm(num_groups=self.num_heads, hidden_size=self.hidden_size, eps=norm_eps)
+        else:
+            self.output_norm = None
+
+
+        if use_rope:
+            self.rotary = RotaryEmbedding(self.head_dim, base=rope_base)
+        else:
+            self.rotary = None
+
+
+        self.qk_norm = qk_norm
+        self.qk_norm_share_param_across_head = qk_norm_share_param_across_head
+        if qk_norm:
+            if self.qk_norm_share_param_across_head:
+                # This is an incorrect implemention kept just for backward compatibility
+                self.q_norm = RMSNorm(self.head_dim)
+                self.k_norm = RMSNorm(self.head_dim)
+            else:
+                self.q_norm = GroupRMSNorm(num_groups=self.num_heads, hidden_size=self.hidden_size)
+                self.k_norm = GroupRMSNorm(num_groups=self.num_heads, hidden_size=self.hidden_size)
+
+        self.initializer_range = initializer_range
+        self.apply(self._initialize_weights)
+
+    def _initialize_weights(self, module: nn.Module):
+        # This will actually be overwritten by outer init.
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, mean=0.0, std=self.initializer_range)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """
+        We assume that during decoding attention mask is always 1. Otherwise it won't work.
+        """
+        batch_size, q_len, _ = hidden_states.size()
+        if use_cache:
+            key_shift_state = past_key_values.key_shift_cache[self.layer_idx]
+            value_shift_state = past_key_values.value_shift_cache[self.layer_idx]
+        else:
+            key_shift_state = value_shift_state = None
+
+        # Shift states are updated in place
+        q = self.q_proj(hidden_states)
+        if self.use_k_shift:
+            k = self.k_proj(hidden_states, key_shift_state)
+        else:
+            k = self.k_proj(hidden_states)
+        if self.use_v_shift:
+            v = self.v_proj(hidden_states, value_shift_state)
+        else:
+            v = self.v_proj(hidden_states)
+
+        if self.qk_norm and (not self.qk_norm_share_param_across_head):
+            q = self.q_norm(q).to(q.dtype)
+            k = self.k_norm(k).to(k.dtype)
+
+        q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads)
+        k = rearrange(k, '... (h d) -> ... h d', h=self.num_kv_heads)
+        v = rearrange(v, 'b t (h d) -> b h t d', h=self.num_kv_heads)
+
+
+        if self.qk_norm and (self.qk_norm_share_param_across_head):
+            q = self.q_norm(q).to(q.dtype)
+            k = self.k_norm(k).to(k.dtype)
+
+
+        seqlen_offset, max_seqlen = 0, q.shape[1]
+        if past_key_values is not None:
+            seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
+            max_seqlen = q.shape[1] + seqlen_offset
+
+            if attention_mask is not None:
+                # to deliminate the offsets of padding tokens
+                seqlen_offset = (seqlen_offset + attention_mask.sum(-1) - attention_mask.shape[-1])
+                max_seqlen = q.shape[1] + max(seqlen_offset)
+
+        if self.max_position_embeddings is not None:
+            max_seqlen = max(max_seqlen, self.max_position_embeddings)
+        if self.rotary is not None:
+            q, k = self.rotary(q, k, seqlen_offset, max_seqlen)
+
+        if self.fgate_type == "full":
+            fgate_logit = self.fgate_proj(hidden_states)
+            fgate_logit = rearrange(fgate_logit, "b t h -> b h t")
+            log_fgate = torch.nn.functional.logsigmoid(fgate_logit.float())
+        elif self.fgate_type == "none":
+            log_fgate = torch.zeros((batch_size, self.num_heads, q_len), dtype=torch.float32, device=hidden_states.device)
+        else:
+            assert self.fgate_type in ["fixed", "bias_only"]
+            fgate_logit = torch.broadcast_to(self.fgate_bias, (batch_size, q_len, self.num_heads))
+            fgate_logit = rearrange(fgate_logit, "b t h -> b h t")
+            log_fgate = torch.nn.functional.logsigmoid(fgate_logit.float())
+
+        k = rearrange(k, 'b t h d -> b h t d')
+        if past_key_values is not None:
+            k, v, log_fgate = past_key_values.update(k, v, log_fgate, self.layer_idx)
+        # k, v = rearrange(k, 'b h t d -> b t h d'), rearrange(v, 'b h t d -> b t h d')
+        q = rearrange(q, 'b t h d -> b h t d')
+
+        if self.num_kv_groups > 1:
+            assert False
+            k = rearrange(k.unsqueeze(-2).repeat(1, 1, 1, self.num_kv_groups, 1), 'b t h g d -> b t (h g) d')
+            v = rearrange(v.unsqueeze(-2).repeat(1, 1, 1, self.num_kv_groups, 1), 'b t h g d -> b t (h g) d')
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            B, _, T = log_fgate.size()
+            assert attention_mask.size() == (B, T), ((B, T), attention_mask.size())
+            seq_start = T - attention_mask.sum(dim=-1)
+            o = forgetting_attention(
+                q, k, v,
+                log_fgate,
+                head_first=True,
+                seq_start=seq_start,
+                sm_scale=1 / math.sqrt(self.head_dim),
+            )
+            o = rearrange(o, "b h t d -> b t h d")
+        else:
+            o = forgetting_attention(
+                q, k, v,
+                log_fgate,
+                head_first=True,
+                sm_scale=1 / math.sqrt(self.head_dim),
+            )
+            o = rearrange(o, "b h t d -> b t h d")
+
+        o = o.reshape(batch_size, q_len, self.hidden_size)
+
+        if self.output_norm is not None:
+            o = self.output_norm(o)
+
+        if self.ogate_proj is not None:
+            # ogate = self.ogate act(self.ogate_proj(hidden_states))
+            # o = o * ogate
+            # ogate = act_gate(self.ogate_proj(hidden_states), o)
+            ogate_logit = self.ogate_proj(hidden_states)
+            dtype = ogate_logit.dtype
+            if self.ogate_act == "silu":
+                o = swiglu_linear(ogate_logit, o, self.o_proj.weight.to(dtype), self.o_proj.bias.to(dtype) if self.o_proj.bias is not None else self.o_proj.bias)
+            elif self.ogate_act == "sigmoid":
+                o = glu_linear(ogate_logit, o, self.o_proj.weight.to(dtype), self.o_proj.bias.to(dtype) if self.o_proj.bias is not None else self.o_proj.bias)
+            else:
+                raise ValueError(f"Unknown ogate act {self.ogate_act}")
+        else:
+            o = self.o_proj(o)
+
+        if not output_attentions:
+            attentions = None
+        else:
+            SAVE_HEADS = [0, 1, 2, 3]
+            # (B, H, T, T)
+            score = q[:, SAVE_HEADS] @ k[:, SAVE_HEADS].mT
+            log_lambda = torch.cumsum(log_fgate, dim=-1)
+            decay_bias = (log_lambda[:, SAVE_HEADS, :, None] - log_lambda[:, SAVE_HEADS, None, :]).to(torch.bfloat16)
+            # normalized_score = torch.softmax(score, dim=-1)
+            attentions = (score, decay_bias)
+
+        return o, attentions, past_key_values
+
+    def init_shift_state(self, batch_size: int):
+        param = next(self.parameters())
+        state = dict()
+        try:
+            dtype = torch.get_autocast_dtype("cuda") if torch.is_autocast_enabled("cuda") else torch.float32
+        except TypeError:
+            # Support legacy torch version
+            dtype = torch.get_autocast_gpu_dtype() if torch.is_autocast_enabled() else torch.float32
+        if self.use_k_shift:
+            state['key_shift'] = param.new_zeros(batch_size, self.kv_dim, dtype=dtype)
+        else:
+            state['key_shift'] = None
+        if self.use_v_shift:
+            state['value_shift'] = param.new_zeros(batch_size, self.kv_dim, dtype=dtype)
+        else:
+            state['value_shift'] = None
+        return state
+
+
+class ForgettingTransformerMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        hidden_ratio: Optional[float] = None,
+        intermediate_size: Optional[int] = None,
+        hidden_act: str = 'swish'
+    ) -> ForgettingTransformerMLP:
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        # the final number of params is `hidden_ratio * hidden_size^2`
+        # `intermediate_size` is chosen to be a multiple of 256 closest to `2/3 * hidden_size * hidden_ratio`
+        if hidden_ratio is None:
+            hidden_ratio = 4
+        if intermediate_size is None:
+            intermediate_size = int(hidden_size * hidden_ratio * 2 / 3)
+            intermediate_size = 256 * ((intermediate_size + 256 - 1) // 256)
+        self.hidden_ratio = hidden_ratio
+        self.intermediate_size = intermediate_size
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+        self.hidden_act = hidden_act
+        assert hidden_act in ["swish", "sigmoid"]
+
+    def forward(self, x):
+        y = self.gate_proj(x)
+        gate, y = y.chunk(2, -1)
+        # TODO: maybe wrap swiglu_linear in custom_fwd/custom_bwd
+        if self.hidden_act == "swish":
+            return swiglu_linear(
+                gate, y,
+                self.down_proj.weight.to(y.dtype),
+                self.down_proj.bias.to(y.dtype) if self.down_proj.bias is not None else self.down_proj.bias
+            )
+        elif self.hidden_act == "sigmoid":
+            return glu_linear(
+                gate, y,
+                self.down_proj.weight.to(y.dtype),
+                self.down_proj.bias.to(y.dtype) if self.down_proj.bias is not None else self.down_proj.bias
+            )
+        else:
+            raise ValueError()
+
+
+class ForgettingTransformerBlock(nn.Module):
+    def __init__(self, config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.attn_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
+        self.attn = ForgettingAttentionLayer(
+            hidden_size=config.hidden_size,
+            num_heads=config.num_heads,
+            num_kv_heads=config.num_kv_heads,
+            window_size=config.window_size,
+            max_position_embeddings=config.max_position_embeddings,
+            rope_base=config.rope_base,
+            use_rope=config.use_rope,
+            use_output_gate=config.use_output_gate,
+            ogate_act=config.ogate_act,
+            fgate_type=config.fgate_type,
+            fgate_bias_init=config.fgate_bias_init,
+            decay_time_min=config.decay_time_min,
+            decay_time_max=config.decay_time_max,
+            use_output_norm = config.use_output_norm,
+            norm_eps=config.norm_eps,
+            qk_norm=config.qk_norm,
+            qk_norm_share_param_across_head=config.qk_norm_share_param_across_head,
+            use_k_shift=config.use_k_shift,
+            use_v_shift=config.use_v_shift,
+            initializer_range=config.initializer_range,
+            layer_idx=layer_idx
+        )
+        self.mlp_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
+        self.mlp = ForgettingTransformerMLP(
+            hidden_size=config.hidden_size,
+            hidden_ratio=config.hidden_ratio,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act
+        )
+
+    def forward_attn(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ):
+        # residual handled outside of this
+        # residual = hidden_states
+        hidden_states = self.attn_norm(hidden_states)
+        hidden_states, attentions, past_key_values = self.attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions
+        )
+        return hidden_states, attentions, past_key_values
+
+    def forward_mlp(
+        self,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor,
+    ):
+        hidden_states, residual = self.mlp_norm(hidden_states, residual, True)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        gradient_checkpointing: bool = False
+        # **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+
+        residual = hidden_states
+
+
+        if gradient_checkpointing:
+            forward_attn = partial(torch.utils.checkpoint.checkpoint, self.forward_attn, use_reentrant=False)
+            forward_mlp = partial(torch.utils.checkpoint.checkpoint, self.forward_mlp, use_reentrant=False)
+        else:
+            forward_attn = self.forward_attn
+            forward_mlp = self.forward_mlp
+
+        hidden_states, attentions, past_key_values = forward_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions
+        )
+
+        hidden_states = forward_mlp(
+            hidden_states,
+            residual,
+        )
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attentions,)
+
+        if use_cache:
+            outputs += (past_key_values,)
+
+        return outputs
+
+
+
+class ForgettingTransformerPreTrainedModel(PreTrainedModel):
+
+    config_class = ForgettingTransformerConfig
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['ForgettingTransformerBlock']
+
+    def __init__(self, config, *inputs, **kwargs):
+        # 动态修复 config_class 以支持远程代码加载
+        if hasattr(config, '__class__'):
+            config_module = config.__class__.__module__
+            if 'transformers_modules' in config_module or config_module == 'configuration_forgetting_transformer':
+                self.__class__.config_class = config.__class__
+        super().__init__(config, *inputs, **kwargs)
+
+    def _init_weights(
+        self,
+        module: nn.Module,
+    ):
+        # if isinstance(module, (nn.Linear, nn.Conv1d)):
+        if isinstance(module, (nn.Linear)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class ForgettingTransformerModel(ForgettingTransformerPreTrainedModel):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([ForgettingTransformerBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
+
+        self.gradient_checkpointing = False
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = 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
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        # if output_attentions:
+            # warnings.warn(
+                # "`ForgettingTransformerModel` does not support output attention weights now, so `output_attentions` is set to `False`."
+            # )
+        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 if not self.training else False)
+        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 None and inputs_embeds is None:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if use_cache:
+            # use_legacy_cache = not isinstance(past_key_values, Cache)
+            # if use_legacy_cache:
+                # past_key_values = FgateDynamicCache.from_legacy_cache(past_key_values)
+            if past_key_values is None:
+                past_key_values = FgateDynamicCache()
+                for layer_idx, layer in enumerate(self.layers):
+                    shift_state = layer.attn.init_shift_state(
+                        batch_size=input_ids.size(0),
+                    )
+                    past_key_values.update_shift_cache(
+                        key_shift_state=shift_state["key_shift"],
+                        value_shift_state=shift_state["value_shift"],
+                        layer_idx=layer_idx
+                    )
+            else:
+                assert isinstance(past_key_values, FgateDynamicCache)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embeddings(input_ids)
+
+        # embed positions
+        hidden_states = 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
+
+        all_hidden_states = () if output_hidden_states else None
+        all_attns = {} if output_attentions else None
+        next_decoder_cache = None
+
+        for layer_id, layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                gradient_checkpointing=self.gradient_checkpointing and self.training
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                OUTPUT_ATTN_LAYERS = [0, 7, 15, 23]
+                if layer_id in OUTPUT_ATTN_LAYERS:
+                    # all_attns += (layer_outputs[1],)
+                    all_attns[layer_id] = layer_outputs[1]
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            # next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+            next_cache = next_decoder_cache
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attns] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_attns
+        )
+
+
+class ForgettingTransformerForCausalLM(ForgettingTransformerPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ForgettingTransformerModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.embeddings = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor = None,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs
+    ):
+        # only last token for `inputs_ids` if the `past_key_values` is passed along.
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard.
+            # Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {'input_ids': input_ids.contiguous()}
+
+        model_inputs.update({
+            'past_key_values': past_key_values,
+            'use_cache': kwargs.get('use_cache'),
+            'attention_mask': attention_mask,
+        })
+        return model_inputs
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = 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, CausalLMOutputWithPast]:
+        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
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            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 = outputs[0]
+
+        loss = None
+        if labels is not None:
+            if self.config.fuse_cross_entropy:
+                loss_fct = FusedCrossEntropyLoss(inplace_backward=True, reduction='none')
+            else:
+                loss_fct = nn.CrossEntropyLoss(reduction='none')
+            logits = self.lm_head(hidden_states)
+            # Enable model parallelism
+            labels = labels.to(logits.device)
+            # labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], loss_fct.ignore_index)), 1)
+            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+            loss = loss.view(*labels.size())
+            del logits
+            logits = None
+        else:
+            logits = self.lm_head(hidden_states)
+
+        if not return_dict:
+            raise NotImplementedError
+            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,
+        )
\ No newline at end of file
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f434981dc7bf9b220ed13f2cf53f70c18da7df0
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1 @@
+# for HF remote code
diff --git a/__pycache__/__init__.cpython-310.pyc b/__pycache__/__init__.cpython-310.pyc
new file mode 100644
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diff --git a/__pycache__/glu_linear.cpython-310.pyc b/__pycache__/glu_linear.cpython-310.pyc
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diff --git a/__pycache__/modeling_forgetting_transformer.cpython-310.pyc b/__pycache__/modeling_forgetting_transformer.cpython-310.pyc
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diff --git a/__pycache__/token_shift.cpython-310.pyc b/__pycache__/token_shift.cpython-310.pyc
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diff --git a/configuration_forgetting_transformer.py b/configuration_forgetting_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f4e7d87a4fd925592b9ef29a66cf924fa95f36e
--- /dev/null
+++ b/configuration_forgetting_transformer.py
@@ -0,0 +1,84 @@
+# -*- coding: utf-8 -*-
+from typing import Optional
+from transformers.configuration_utils import PretrainedConfig
+
+class ForgettingTransformerConfig(PretrainedConfig):
+    model_type = 'forgetting_transformer'
+    keys_to_ignore_at_inference = ['past_key_values']
+    
+    def __init__(
+        self,
+        vocab_size: int = 32000,
+        hidden_size: int = 2048,
+        hidden_ratio: Optional[float] = 4,
+        intermediate_size: Optional[int] = None,
+        num_hidden_layers: int = 24,
+        num_heads: int = 32,
+        num_kv_heads: int = None,
+        hidden_act: str = "swish",
+        window_size: Optional[int] = None,
+        max_position_embeddings: int = 2048,
+        initializer_range: float = 0.02,
+        elementwise_affine: Optional[bool] = True,
+        norm_eps: float = 1e-6,
+        use_cache: bool = True,
+        pad_token_id: int = None,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        tie_word_embeddings: bool = False,
+        attention_bias: bool = False,
+        fuse_norm: bool = True,
+        fuse_cross_entropy: bool = True,
+        rope_base: float = 500000.0,
+        use_rope: bool = False,
+        use_output_gate: bool = False,
+        ogate_act: str = "sigmoid",
+        fgate_type: str = "full",
+        fgate_bias_init: bool = False,
+        decay_time_min: Optional[float] = None,
+        decay_time_max: Optional[float] = None,
+        use_output_norm: bool = False,
+        qk_norm: bool = False,
+        qk_norm_share_param_across_head: bool = False,
+        use_k_shift: bool = False,
+        use_v_shift: bool = False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.hidden_ratio = hidden_ratio
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.window_size = window_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.elementwise_affine = elementwise_affine
+        self.norm_eps = norm_eps
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.fuse_cross_entropy = fuse_cross_entropy
+        self.fuse_norm = fuse_norm
+        self.rope_base = rope_base
+        self.use_rope = use_rope
+        self.use_output_gate = use_output_gate
+        self.ogate_act = ogate_act
+        self.fgate_type = fgate_type
+        self.fgate_bias_init = fgate_bias_init
+        self.decay_time_min = decay_time_min
+        self.decay_time_max = decay_time_max
+        self.use_output_norm = use_output_norm
+        self.qk_norm = qk_norm
+        self.qk_norm_share_param_across_head = qk_norm_share_param_across_head
+        self.use_k_shift = use_k_shift
+        self.use_v_shift = use_v_shift
+        
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
\ No newline at end of file
diff --git a/fgate_cache.py b/fgate_cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba956105bfa56d210a1e4e7ac9ba887abf6b6de7
--- /dev/null
+++ b/fgate_cache.py
@@ -0,0 +1,143 @@
+from typing import List, Tuple, Optional, Any, Dict
+import torch
+
+class FgateDynamicCache:
+    """
+    A cache that grows dynamically as more tokens are generated.
+    Custom cache for Forgetting Transformer that does not inherit from transformers.Cache.
+    """
+
+    def __init__(self, num_hidden_layers: Optional[int] = None) -> None:
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        self.log_fgate_cache: List[torch.Tensor] = []
+        self.key_shift_cache: List[torch.Tensor] = []
+        self.value_shift_cache: List[torch.Tensor] = []
+        self._seen_tokens = 0
+
+    def update_shift_cache(
+        self,
+        key_shift_state: torch.Tensor,
+        value_shift_state: torch.Tensor,
+        layer_idx,
+    ):
+        assert layer_idx == len(self.key_shift_cache) == len(self.value_shift_cache)
+        self.key_shift_cache.append(key_shift_state)
+        self.value_shift_cache.append(value_shift_state)
+
+    def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
+        if layer_idx < len(self):
+            return (self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx])
+        else:
+            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
+
+    def __iter__(self):
+        for layer_idx in range(len(self)):
+            yield (self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx])
+
+    def __len__(self):
+        return len(self.key_cache)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        log_fgate_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        assert log_fgate_states.ndim == 3, f"log_fgate must be (B, H, T), but get {log_fgate_states.size()}"
+        if layer_idx == 0:
+            self._seen_tokens += key_states.shape[-2]
+
+        if len(self.key_cache) <= layer_idx:
+            self.key_cache.append(key_states)
+            self.value_cache.append(value_states)
+            self.log_fgate_cache.append(log_fgate_states)
+        else:
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+            self.log_fgate_cache[layer_idx] = torch.cat([self.log_fgate_cache[layer_idx], log_fgate_states], dim=-1)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx]
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        if len(self.key_cache) <= layer_idx:
+            return 0
+        return self.key_cache[layer_idx].shape[-2]
+
+    def get_max_length(self) -> Optional[int]:
+        return None
+
+    def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], ...]:
+        legacy_cache = ()
+        for layer_idx in range(len(self)):
+            legacy_cache += ((self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx]),)
+        return legacy_cache
+
+    @classmethod
+    def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, num_layers: Optional[int] = None) -> "FgateDynamicCache":
+        """
+        Converts a cache in the legacy cache format into an equivalent FgateDynamicCache.
+        
+        Args:
+            past_key_values: Optional legacy cache format
+            num_layers: Not used in this implementation
+        
+        Returns:
+            FgateDynamicCache instance
+        """
+        cache = cls()
+        
+        if past_key_values is not None:
+            for layer_idx in range(len(past_key_values)):
+                key_states, value_states, log_fgate_states = past_key_values[layer_idx]
+                cache.update(key_states, value_states, log_fgate_states, layer_idx)
+        
+        return cache
+
+    def crop(self, max_length: int):
+        if max_length < 0:
+            max_length = self.get_seq_length() - abs(max_length)
+
+        if self.get_seq_length() <= max_length:
+            return
+
+        self._seen_tokens = max_length
+        for idx in range(len(self.key_cache)):
+            self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
+            self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
+            self.log_fgate_cache[idx] = self.log_fgate_cache[idx][..., :max_length]
+
+    def batch_split(self, full_batch_size: int, split_size: int) -> List["FgateDynamicCache"]:
+        out = []
+        for i in range(0, full_batch_size, split_size):
+            current_split = FgateDynamicCache()
+            current_split._seen_tokens = self._seen_tokens
+            current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
+            current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
+            current_split.log_fgate_cache = [tensor[i : i + split_size] for tensor in self.log_fgate_cache]
+            out.append(current_split)
+        return out
+
+    @classmethod
+    def from_batch_splits(cls, splits: List["FgateDynamicCache"]) -> "FgateDynamicCache":
+        cache = cls()
+        for idx in range(len(splits[0])):
+            layer_keys = torch.cat([current.key_cache[idx] for current in splits], dim=0)
+            layer_values = torch.cat([current.value_cache[idx] for current in splits], dim=0)
+            layer_log_fgates = torch.cat([current.log_fgate_cache[idx] for current in splits], dim=0)
+            cache.update(layer_keys, layer_values, layer_log_fgates, idx)
+        return cache
+
+    def batch_repeat_interleave(self, repeats: int):
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            self.log_fgate_cache[layer_idx] = self.log_fgate_cache[layer_idx].repeat_interleave(repeats, dim=0)
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...]
+            self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
+            self.log_fgate_cache[layer_idx] = self.log_fgate_cache[layer_idx][indices, ...]
\ No newline at end of file
diff --git a/fgate_cache.py.backup b/fgate_cache.py.backup
new file mode 100644
index 0000000000000000000000000000000000000000..bef4bad152a1679650aa4c271e559cc76e0f67e1
--- /dev/null
+++ b/fgate_cache.py.backup
@@ -0,0 +1,203 @@
+from typing import List, Tuple, Optional, Any, Dict
+import torch
+from transformers.cache_utils import Cache
+
+class FgateDynamicCache(Cache):
+    """
+    A cache that grows dynamically as more tokens are generated. This is the default for generative models.
+
+    It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
+    `[batch_size, num_heads, seq_len, head_dim]`.
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
+
+        >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
+        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
+
+        >>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> past_key_values = DynamicCache()
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> outputs.past_key_values # access cache filled with key/values from generation
+        DynamicCache()
+        ```
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        self.log_fgate_cache: List[torch.Tensor] = []
+
+        self.key_shift_cache: List[torch.Tensor] = []
+        self.value_shift_cache: List[torch.Tensor] = []
+
+        self._seen_tokens = 0  # Used in `generate` to keep tally of how many tokens the cache has seen
+
+    def update_shift_cache(
+        self,
+        key_shift_state: torch.Tensor,
+        value_shift_state: torch.Tensor,
+        layer_idx,
+    ):
+        assert layer_idx == len(self.key_shift_cache) == len(self.value_shift_cache)
+        self.key_shift_cache.append(key_shift_state)
+        self.value_shift_cache.append(value_shift_state)
+
+
+    def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
+        """
+        Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the
+        sequence length.
+        """
+        if layer_idx < len(self):
+            return (self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx])
+        else:
+            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
+
+    def __iter__(self):
+        """
+        Support for backwards-compatible `past_key_value` iteration, e.g. `for x in past_key_value:` to iterate over
+        keys and values
+        """
+        for layer_idx in range(len(self)):
+            yield (self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx])
+
+    def __len__(self):
+        """
+        Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds
+        to the number of layers in the model.
+        """
+        return len(self.key_cache)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        log_fgate_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        assert log_fgate_states.ndim == 3, f"log_fgate must be (B, H, T), but get {log_fgate_states.size()}"
+        # Update the number of seen tokens
+        if layer_idx == 0:
+            self._seen_tokens += key_states.shape[-2]
+
+        # Update the cache
+        if len(self.key_cache) <= layer_idx:
+            self.key_cache.append(key_states)
+            self.value_cache.append(value_states)
+            self.log_fgate_cache.append(log_fgate_states)
+        else:
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+            self.log_fgate_cache[layer_idx] = torch.cat([self.log_fgate_cache[layer_idx], log_fgate_states], dim=-1)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx]
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        # TODO: deprecate this function in favor of `cache_position`
+        if len(self.key_cache) <= layer_idx:
+            return 0
+        return self.key_cache[layer_idx].shape[-2]
+
+    def get_max_length(self) -> Optional[int]:
+        """Returns the maximum sequence length of the cached states. DynamicCache does not have a maximum length."""
+        return None
+
+    def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
+        """Converts the `DynamicCache` instance into the its equivalent in the legacy cache format. Used for
+        backward compatibility."""
+        legacy_cache = ()
+        for layer_idx in range(len(self)):
+            legacy_cache += ((self.key_cache[layer_idx], self.value_cache[layer_idx], self.log_fgate_cache[layer_idx]),)
+        return legacy_cache
+
+    @classmethod
+    def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, num_layers: Optional[int] = None) -> "DynamicCache":
+        """Converts a cache in the legacy cache format into an equivalent `DynamicCache`. Used for
+        backward compatibility."""
+        raise NotImplementedError
+        assert num_layers is not None
+        cache = cls(num_layers)
+        if past_key_values is not None:
+            for layer_idx in range(len(past_key_values)):
+                key_states, value_states, log_fgate_states = past_key_values[layer_idx]
+                cache.update(key_states, value_states, log_fgate_states, layer_idx)
+        return cache
+
+    def crop(self, max_length: int):
+        """Crop the past key values up to a new `max_length` in terms of tokens. `max_length` can also be
+        negative to remove `max_length` tokens. This is used in assisted decoding and contrastive search."""
+        # In case it is negative
+        if max_length < 0:
+            max_length = self.get_seq_length() - abs(max_length)
+
+        if self.get_seq_length() <= max_length:
+            return
+
+        self._seen_tokens = max_length
+        for idx in range(len(self.key_cache)):
+            self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
+            self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
+            self.log_fgate_cache[idx] = self.log_fgate_cache[idx][..., :max_length]
+
+    def batch_split(self, full_batch_size: int, split_size: int) -> List["DynamicCache"]:
+        """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
+        `_split_model_inputs()` in `generation.utils`"""
+        out = []
+        for i in range(0, full_batch_size, split_size):
+            current_split = DynamicCache()
+            current_split._seen_tokens = self._seen_tokens
+            current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
+            current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
+            current_split.log_fgate_cache = [tensor[i : i + split_size] for tensor in self.log_fgate_cache]
+            out.append(current_split)
+        return out
+
+    @classmethod
+    def from_batch_splits(cls, splits: List["DynamicCache"]) -> "DynamicCache":
+        """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
+        `generation.utils`"""
+        cache = cls()
+        for idx in range(len(splits[0])):
+            layer_keys = torch.cat([current.key_cache[idx] for current in splits], dim=0)
+            layer_values = torch.cat([current.value_cache[idx] for current in splits], dim=0)
+            layer_log_fgates = torch.cat([current.log_fgate_cache[idx] for current in splits], dim=0)
+            cache.update(layer_keys, layer_values, layer_log_fgates, idx)
+        return cache
+
+    def batch_repeat_interleave(self, repeats: int):
+        """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            self.log_fgate_cache[layer_idx] = self.log_fgate_cache[layer_idx].repeat_interleave(repeats, dim=0)
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
+        for layer_idx in range(len(self)):
+            self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...]
+            self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
+            self.log_fgate_cache[layer_idx] = self.log_fgate_cache[layer_idx][indices, ...]
diff --git a/glu_linear.py b/glu_linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..fff265ac29aee9c5804bfccd5716ba7b446cbbfb
--- /dev/null
+++ b/glu_linear.py
@@ -0,0 +1,61 @@
+import torch
+import torch.nn.functional as F
+
+
+glu_fwd_codestring = """
+template <typename T> T glu_fwd(T x, T y) {
+    return float(y) / (1.0f + ::exp(-float(x)));
+}
+"""
+glu_bwd_codestring = """
+template <typename T> T glu_bwd(T x, T y, T g, T& dx, T& dy) {
+    float x_sigmoid = 1.0f / (1.0f + ::exp(-float(x)));
+    dx = x_sigmoid * (1.0f - x_sigmoid) * float(g) * float(y);
+    dy = x_sigmoid * float(g);
+}
+"""
+
+glu_bwd_with_output_codestring = """
+template <typename T> T glu_bwd_with_output(T x, T y, T g, T& dx, T& dy, T& z) {
+    float x_sigmoid = 1.0f / (1.0f + ::exp(-float(x)));
+    dx = x_sigmoid * (1.0f - x_sigmoid) * float(g) * float(y);
+    dy = x_sigmoid * float(g);
+    z = x_sigmoid * float(y);
+}
+"""
+
+glu_fwd = torch.cuda.jiterator._create_jit_fn(glu_fwd_codestring)
+glu_bwd = torch.cuda.jiterator._create_multi_output_jit_fn(glu_bwd_codestring, num_outputs=2)
+glu_bwd_with_output = torch.cuda.jiterator._create_multi_output_jit_fn(glu_bwd_with_output_codestring, num_outputs=3)
+
+
+class GLULinearFunction(torch.autograd.Function):
+    r"""
+    Gated Linear Unit (GLU) function followed by a linear transformation.
+
+    .. math::
+        \text{GLULinear}(x, y, W, b) = (sh(x) * y) W + b
+
+    This simple wrap discards the intermediate results of GLU(x, y) to save memory.
+    """
+
+    @staticmethod
+    def forward(ctx, x, y, weight, bias):
+        z = glu_fwd(x, y)
+        out = F.linear(z.to(weight.dtype), weight, bias)
+        # We don't store z, will be recomputed in the backward pass to save memory
+        ctx.save_for_backward(x, y, weight)
+        ctx.linear_bias_is_none = bias is None
+        return out
+
+    @staticmethod
+    def backward(ctx, dout, *args):
+        x, y, weight = ctx.saved_tensors
+        dout = dout.reshape(-1, dout.shape[-1])
+        dz = F.linear(dout, weight.t()).view_as(x)
+        dx, dy, z = glu_bwd_with_output(x, y, dz)
+        dlinear_weight = torch.einsum("bo,bi->oi", dout, z.reshape(-1, z.shape[-1]))
+        dlinear_bias = None if ctx.linear_bias_is_none else dout.sum(0)
+        return dx, dy, dlinear_weight, dlinear_bias
+
+glu_linear = GLULinearFunction.apply
diff --git a/modeling_forgetting_transformer.py b/modeling_forgetting_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..deff96ee4c8558b72b8a525d4b38184b2a46af77
--- /dev/null
+++ b/modeling_forgetting_transformer.py
@@ -0,0 +1,910 @@
+# -*- coding: utf-8 -*-
+
+from __future__ import annotations
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import (BaseModelOutputWithPast,
+                                           CausalLMOutputWithPast)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+# from fla.layers.attn import Attention
+from fla.modules import FusedCrossEntropyLoss, RMSNorm
+from fla.modules.layernorm import group_norm_fn
+from fla.modules.activations import swiglu_linear
+
+from fla.modules import RotaryEmbedding
+from einops import rearrange
+
+# 动态导入配置类以支持本地和HuggingFace Hub加载
+try:
+    from .configuration_forgetting_transformer import ForgettingTransformerConfig
+except (ImportError, ValueError):
+    try:
+        from configuration_forgetting_transformer import ForgettingTransformerConfig
+    except ImportError:
+        from forgetting_transformer.model.forgetting_transformer.configuration_forgetting_transformer import ForgettingTransformerConfig
+
+from forgetting_transformer.ops.forgetting_attention_std import forgetting_attention_std as forgetting_attention
+from .fgate_cache import FgateDynamicCache
+from .glu_linear import glu_linear
+from .token_shift import token_shift
+
+from functools import partial
+
+logger = logging.get_logger(__name__)
+
+
+class ShiftLinear(nn.Module):
+
+    def __init__(
+        self,
+        input_dim: int,
+        output_dim: int,
+        num_heads: int,
+        bias: bool,
+        shift_bias: bool = False
+    ):
+        super().__init__()
+
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.num_heads = num_heads
+        assert self.output_dim % self.num_heads == 0
+
+        self.linear = nn.Linear(input_dim, output_dim, bias=bias)
+        self.shift_proj = nn.Linear(input_dim, num_heads, bias=shift_bias)
+
+    def __repr__(self) -> str:
+        s = f"{self.__class__.__name__}({self.input_dim}, {self.output_dim})"
+        return s
+
+    def forward(self, x: torch.Tensor, shift_state: Optional[torch.Tensor]) -> torch.Tensor:
+        assert x.ndim == 3, "Input must be (B, T, D)"
+        B, T, D = x.size()
+        out = self.linear(x)
+        # (B, T, H, 1)
+        alpha = torch.sigmoid(self.shift_proj(x).float()).float()
+        # left, right, top, bottom (B, T=H, D=W)
+        # out_prev = nn.functional.pad(out, (0, 0, 1, -1))
+        # out_prev = torch.roll(out, shifts=1, dims=1)
+        
+        out_per_head = rearrange(out, 'b t (h d) -> b t h d', h=self.num_heads)
+        if T > 1:
+            # TODO: note in this case cache is not used
+            result_per_head = token_shift(out_per_head, alpha, 1.0 - alpha)
+        else:
+            shift_state_per_head = rearrange(shift_state, 'b (h d) -> b 1 h d', h=self.num_heads)
+            result_per_head = (alpha[..., None] * shift_state_per_head + (1 - alpha[..., None]) * out_per_head)
+
+        result_per_head = result_per_head.to(out.dtype)
+
+        if shift_state is not None:
+            shift_state.copy_(out[:, -1, :])
+
+        result = rearrange(result_per_head, 'b t h d -> b t (h d)', h=self.num_heads)
+        return result
+
+class GroupRMSNorm(nn.Module):
+    def __init__(
+        self,
+        num_groups: int,
+        hidden_size: int,
+        elementwise_affine: bool = True,
+        bias: bool = False,
+        eps: float = 1e-5
+    ) -> GroupRMSNorm:
+        super().__init__()
+
+        if hidden_size % num_groups != 0:
+            raise ValueError('num_channels must be divisible by num_groups')
+
+        self.num_groups = num_groups
+        self.hidden_size = hidden_size
+        self.elementwise_affine = elementwise_affine
+        self.eps = eps
+
+        self.register_parameter("weight", None)
+        self.register_parameter("bias", None)
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(hidden_size))
+            if bias:
+                self.bias = nn.Parameter(torch.zeros(hidden_size))
+
+    def __repr__(self) -> str:
+        s = f"{self.__class__.__name__}({self.num_groups}, {self.hidden_size}"
+        if not self.elementwise_affine:
+            s += f", elementwise_affine={self.elementwise_affine}"
+        s += f", eps={self.eps}"
+        s += ")"
+        return s
+
+    def forward(self, x, residual=None, prenorm=False, residual_in_fp32=False):
+        return group_norm_fn(
+            x,
+            self.weight,
+            self.bias,
+            residual=residual,
+            eps=self.eps,
+            prenorm=prenorm,
+            residual_in_fp32=residual_in_fp32,
+            is_rms_norm=True,
+            num_groups=self.num_groups
+        )
+
+class ForgettingAttentionLayer(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int = 2048,
+        num_heads: int = 32,
+        num_kv_heads: Optional[int] = None,
+        window_size: Optional[int] = None,
+        max_position_embeddings: Optional[int] = None,
+        use_rope: bool = False,
+        rope_base: float = 500000.0,
+        use_output_gate: bool = False,
+        ogate_act: str = "sigmoid",
+        fgate_type: str = "full",
+        fgate_bias_init: bool = False,
+        decay_time_min: Optional[float] = None,
+        decay_time_max: Optional[float] = None,
+        use_output_norm: bool = False,
+        norm_eps: float = 1e-6,
+        qk_norm: bool = False,
+        qk_norm_share_param_across_head: bool = False,
+        use_k_shift: bool = False,
+        use_v_shift: bool = False,
+        initializer_range: float = 0.02,
+        layer_idx: int = None
+    ):
+        """
+        Forgetting Attention layer.
+
+        Arguments:
+            - hidden_size: Input dimension and qkv dimension
+            - num_heads: Number of heads
+            - num_kv_heads: Not used. Should be None 
+            - window_size: Not used. Should be None
+            - max_position_embeddings: Not used. Should be None
+            - use_rope: Whether to use RoPE. Default is False
+            - rope_base: the theta hyperparameter in RoPE. This has no effect if
+                  use_rope=False
+            - use_output_gate: Whether to use output gates. Note that using output gates
+                  introduces extra parameters and you may want to reduce parameters from
+                  other components (e.g., MLPs)
+            - ogate_act: Activation for the output gate. Either "sigmoid" or "silu"
+            - fgate_type: Forget gate type. The following are supported:
+                - "full": The default data-dependent forget gate
+                - "bias_only": The data-independent forget gate
+                - "fixed": Forget gates with fixed values
+                - "none": Not using forget gates. Equivalent to forget gates with all
+                  ones.
+            - fgate_bias_init: Whether to use special initalization for the bias terms in 
+                  the forget gate. This should only be used with fgate types in 
+                  ["bias_only", "fixed"].
+            - decay_time_min: T_min for the forget gate bias initialization. See paper
+                  for details.
+            - decay_time_max: T_max for the forget gate bias initalization. See paper
+                  for details.
+            - use_output_norm: Whether to use output normalization.
+            - norm_eps: Epsilon for the RMSNorms
+            - qk_norm: Whether to use qk_norm
+            - qk_norm_share_param_across_head: In QK-norm, whether to share the RMSNorm
+                scaling parameters across heads. This is just for backward compatibility.
+            - use_k_shift: Whether to use data-dependent key shift
+            - use_v_shift: Whether to use data-dependent value shift
+            - initializer_range: standard deviation for initialization
+            - layer_idx: The block index of this layer. Needed for KV-cache
+        """
+        super().__init__()
+
+        self.num_heads = num_heads
+        if num_kv_heads is None:
+            self.num_kv_heads = self.num_heads
+        else:
+            raise NotImplementedError("GQA has not been tested.")
+            self.num_kv_heads = num_kv_heads
+        self.num_kv_groups = num_heads // self.num_kv_heads
+        self.hidden_size = hidden_size
+        self.head_dim = self.hidden_size // self.num_heads
+        self.kv_dim = self.num_kv_heads * self.head_dim
+        self.kv_dim = self.num_kv_heads * self.head_dim
+        self.window_size = window_size
+        self.max_position_embeddings = max_position_embeddings
+        self.layer_idx = layer_idx
+
+        self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        if use_k_shift:
+            self.k_proj = ShiftLinear(self.hidden_size, self.kv_dim, self.num_heads, bias=False)
+        else:
+            self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
+
+        if use_v_shift:
+            self.v_proj = ShiftLinear(self.hidden_size, self.kv_dim, self.num_heads, bias=False)
+        else:
+            self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
+
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        self.use_k_shift = use_k_shift
+        self.use_v_shift = use_v_shift
+
+
+        device = next(self.parameters()).device
+        # Forget gate
+        assert fgate_type in ["full", "bias_only", "fixed", "none"]
+        self.fgate_type = fgate_type
+        self.fgate_bias_init = fgate_bias_init
+        if fgate_type == "full":
+            assert not fgate_bias_init
+            self.fgate_proj = nn.Linear(self.hidden_size, self.num_heads, bias=True)
+        elif fgate_type == "bias_only":
+            self.fgate_bias = nn.Parameter(torch.zeros(size=(self.num_heads,), device=device))
+            self.fgate_bias._no_weight_decay = True
+        elif fgate_type == "fixed":
+            assert fgate_bias_init, "You must set fgate_bias_init = True with fixed fgate"
+            fgate_bias = torch.zeros(size=(self.num_heads,), device=device)
+            self.register_buffer("fgate_bias", fgate_bias)
+        elif fgate_type == "none":
+            pass
+        else:
+            raise ValueError(f"Unknown fgate type {fgate_type}")
+
+                
+
+        # Forget gate intialization for data-independent and fixed forget gates
+        if fgate_bias_init:
+            assert decay_time_min is not None and decay_time_max is not None
+            assert decay_time_min > 0 and decay_time_max > 0
+            with torch.no_grad():
+                log_decay_time = torch.linspace(math.log(decay_time_min), math.log(decay_time_max), steps=self.num_heads)
+                decay_time = torch.exp(log_decay_time)
+                # Such that t = -1 / log(sigmoid(b)) 
+                bias_init = -torch.log(torch.expm1(1 / decay_time))
+                self.fgate_bias.copy_(bias_init)
+        else:
+            assert decay_time_min is None and decay_time_max is None
+
+        if use_output_gate:
+            self.ogate_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+            self.ogate_act = ogate_act
+            assert ogate_act in ["silu", "sigmoid"]
+        else:
+            self.ogate_proj = None
+
+        if use_output_norm:
+            self.output_norm = GroupRMSNorm(num_groups=self.num_heads, hidden_size=self.hidden_size, eps=norm_eps)
+        else:
+            self.output_norm = None
+
+
+        if use_rope:
+            self.rotary = RotaryEmbedding(self.head_dim, base=rope_base)
+        else:
+            self.rotary = None
+
+
+        self.qk_norm = qk_norm
+        self.qk_norm_share_param_across_head = qk_norm_share_param_across_head
+        if qk_norm:
+            if self.qk_norm_share_param_across_head:
+                # This is an incorrect implemention kept just for backward compatibility
+                self.q_norm = RMSNorm(self.head_dim)
+                self.k_norm = RMSNorm(self.head_dim)
+            else:
+                self.q_norm = GroupRMSNorm(num_groups=self.num_heads, hidden_size=self.hidden_size)
+                self.k_norm = GroupRMSNorm(num_groups=self.num_heads, hidden_size=self.hidden_size)
+
+        self.initializer_range = initializer_range
+        self.apply(self._initialize_weights)
+
+    def _initialize_weights(self, module: nn.Module):
+        # This will actually be overwritten by outer init.
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, mean=0.0, std=self.initializer_range)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """
+        We assume that during decoding attention mask is always 1. Otherwise it won't work.
+        """
+        batch_size, q_len, _ = hidden_states.size()
+        if use_cache:
+            key_shift_state = past_key_values.key_shift_cache[self.layer_idx]
+            value_shift_state = past_key_values.value_shift_cache[self.layer_idx]
+        else:
+            key_shift_state = value_shift_state = None
+
+        # Shift states are updated in place
+        q = self.q_proj(hidden_states)
+        if self.use_k_shift:
+            k = self.k_proj(hidden_states, key_shift_state)
+        else:
+            k = self.k_proj(hidden_states)
+        if self.use_v_shift:
+            v = self.v_proj(hidden_states, value_shift_state)
+        else:
+            v = self.v_proj(hidden_states)
+
+        if self.qk_norm and (not self.qk_norm_share_param_across_head):
+            q = self.q_norm(q).to(q.dtype)
+            k = self.k_norm(k).to(k.dtype)
+
+        q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads)
+        k = rearrange(k, '... (h d) -> ... h d', h=self.num_kv_heads)
+        v = rearrange(v, 'b t (h d) -> b h t d', h=self.num_kv_heads)
+
+
+        if self.qk_norm and (self.qk_norm_share_param_across_head):
+            q = self.q_norm(q).to(q.dtype)
+            k = self.k_norm(k).to(k.dtype)
+
+
+        seqlen_offset, max_seqlen = 0, q.shape[1]
+        if past_key_values is not None:
+            seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
+            max_seqlen = q.shape[1] + seqlen_offset
+
+            if attention_mask is not None:
+                # to deliminate the offsets of padding tokens
+                seqlen_offset = (seqlen_offset + attention_mask.sum(-1) - attention_mask.shape[-1])
+                max_seqlen = q.shape[1] + max(seqlen_offset)
+
+        if self.max_position_embeddings is not None:
+            max_seqlen = max(max_seqlen, self.max_position_embeddings)
+        if self.rotary is not None:
+            q, k = self.rotary(q, k, seqlen_offset, max_seqlen)
+
+        if self.fgate_type == "full":
+            fgate_logit = self.fgate_proj(hidden_states)
+            fgate_logit = rearrange(fgate_logit, "b t h -> b h t")
+            log_fgate = torch.nn.functional.logsigmoid(fgate_logit.float())
+        elif self.fgate_type == "none":
+            log_fgate = torch.zeros((batch_size, self.num_heads, q_len), dtype=torch.float32, device=hidden_states.device)
+        else:
+            assert self.fgate_type in ["fixed", "bias_only"]
+            fgate_logit = torch.broadcast_to(self.fgate_bias, (batch_size, q_len, self.num_heads))
+            fgate_logit = rearrange(fgate_logit, "b t h -> b h t")
+            log_fgate = torch.nn.functional.logsigmoid(fgate_logit.float())
+
+        k = rearrange(k, 'b t h d -> b h t d')
+        if past_key_values is not None:
+            k, v, log_fgate = past_key_values.update(k, v, log_fgate, self.layer_idx)
+        # k, v = rearrange(k, 'b h t d -> b t h d'), rearrange(v, 'b h t d -> b t h d')
+        q = rearrange(q, 'b t h d -> b h t d')
+
+        if self.num_kv_groups > 1:
+            assert False
+            k = rearrange(k.unsqueeze(-2).repeat(1, 1, 1, self.num_kv_groups, 1), 'b t h g d -> b t (h g) d')
+            v = rearrange(v.unsqueeze(-2).repeat(1, 1, 1, self.num_kv_groups, 1), 'b t h g d -> b t (h g) d')
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            B, _, T = log_fgate.size()
+            assert attention_mask.size() == (B, T), ((B, T), attention_mask.size())
+            seq_start = T - attention_mask.sum(dim=-1)
+            o = forgetting_attention(
+                q, k, v,
+                log_fgate,
+                head_first=True,
+                seq_start=seq_start,
+                sm_scale=1 / math.sqrt(self.head_dim),
+            )
+            o = rearrange(o, "b h t d -> b t h d")
+        else:
+            o = forgetting_attention(
+                q, k, v,
+                log_fgate,
+                head_first=True,
+                sm_scale=1 / math.sqrt(self.head_dim),
+            )
+            o = rearrange(o, "b h t d -> b t h d")
+
+        o = o.reshape(batch_size, q_len, self.hidden_size)
+
+        if self.output_norm is not None:
+            o = self.output_norm(o)
+
+        if self.ogate_proj is not None:
+            # ogate = self.ogate act(self.ogate_proj(hidden_states))
+            # o = o * ogate
+            # ogate = act_gate(self.ogate_proj(hidden_states), o)
+            ogate_logit = self.ogate_proj(hidden_states)
+            dtype = ogate_logit.dtype
+            if self.ogate_act == "silu":
+                o = swiglu_linear(ogate_logit, o, self.o_proj.weight.to(dtype), self.o_proj.bias.to(dtype) if self.o_proj.bias is not None else self.o_proj.bias)
+            elif self.ogate_act == "sigmoid":
+                o = glu_linear(ogate_logit, o, self.o_proj.weight.to(dtype), self.o_proj.bias.to(dtype) if self.o_proj.bias is not None else self.o_proj.bias)
+            else:
+                raise ValueError(f"Unknown ogate act {self.ogate_act}")
+        else:
+            o = self.o_proj(o)
+
+        if not output_attentions:
+            attentions = None
+        else:
+            SAVE_HEADS = [0, 1, 2, 3]
+            # (B, H, T, T)
+            score = q[:, SAVE_HEADS] @ k[:, SAVE_HEADS].mT
+            log_lambda = torch.cumsum(log_fgate, dim=-1)
+            decay_bias = (log_lambda[:, SAVE_HEADS, :, None] - log_lambda[:, SAVE_HEADS, None, :]).to(torch.bfloat16)
+            # normalized_score = torch.softmax(score, dim=-1)
+            attentions = (score, decay_bias)
+
+        return o, attentions, past_key_values
+
+    def init_shift_state(self, batch_size: int):
+        param = next(self.parameters())
+        state = dict()
+        try:
+            dtype = torch.get_autocast_dtype("cuda") if torch.is_autocast_enabled("cuda") else torch.float32
+        except TypeError:
+            # Support legacy torch version
+            dtype = torch.get_autocast_gpu_dtype() if torch.is_autocast_enabled() else torch.float32
+        if self.use_k_shift:
+            state['key_shift'] = param.new_zeros(batch_size, self.kv_dim, dtype=dtype)
+        else:
+            state['key_shift'] = None
+        if self.use_v_shift:
+            state['value_shift'] = param.new_zeros(batch_size, self.kv_dim, dtype=dtype)
+        else:
+            state['value_shift'] = None
+        return state
+
+
+class ForgettingTransformerMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        hidden_ratio: Optional[float] = None,
+        intermediate_size: Optional[int] = None,
+        hidden_act: str = 'swish'
+    ) -> ForgettingTransformerMLP:
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        # the final number of params is `hidden_ratio * hidden_size^2`
+        # `intermediate_size` is chosen to be a multiple of 256 closest to `2/3 * hidden_size * hidden_ratio`
+        if hidden_ratio is None:
+            hidden_ratio = 4
+        if intermediate_size is None:
+            intermediate_size = int(hidden_size * hidden_ratio * 2 / 3)
+            intermediate_size = 256 * ((intermediate_size + 256 - 1) // 256)
+        self.hidden_ratio = hidden_ratio
+        self.intermediate_size = intermediate_size
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+        self.hidden_act = hidden_act
+        assert hidden_act in ["swish", "sigmoid"]
+
+    def forward(self, x):
+        y = self.gate_proj(x)
+        gate, y = y.chunk(2, -1)
+        # TODO: maybe wrap swiglu_linear in custom_fwd/custom_bwd
+        if self.hidden_act == "swish":
+            return swiglu_linear(
+                gate, y,
+                self.down_proj.weight.to(y.dtype),
+                self.down_proj.bias.to(y.dtype) if self.down_proj.bias is not None else self.down_proj.bias
+            )
+        elif self.hidden_act == "sigmoid":
+            return glu_linear(
+                gate, y,
+                self.down_proj.weight.to(y.dtype),
+                self.down_proj.bias.to(y.dtype) if self.down_proj.bias is not None else self.down_proj.bias
+            )
+        else:
+            raise ValueError()
+
+
+class ForgettingTransformerBlock(nn.Module):
+    def __init__(self, config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.attn_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
+        self.attn = ForgettingAttentionLayer(
+            hidden_size=config.hidden_size,
+            num_heads=config.num_heads,
+            num_kv_heads=config.num_kv_heads,
+            window_size=config.window_size,
+            max_position_embeddings=config.max_position_embeddings,
+            rope_base=config.rope_base,
+            use_rope=config.use_rope,
+            use_output_gate=config.use_output_gate,
+            ogate_act=config.ogate_act,
+            fgate_type=config.fgate_type,
+            fgate_bias_init=config.fgate_bias_init,
+            decay_time_min=config.decay_time_min,
+            decay_time_max=config.decay_time_max,
+            use_output_norm = config.use_output_norm,
+            norm_eps=config.norm_eps,
+            qk_norm=config.qk_norm,
+            qk_norm_share_param_across_head=config.qk_norm_share_param_across_head,
+            use_k_shift=config.use_k_shift,
+            use_v_shift=config.use_v_shift,
+            initializer_range=config.initializer_range,
+            layer_idx=layer_idx
+        )
+        self.mlp_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
+        self.mlp = ForgettingTransformerMLP(
+            hidden_size=config.hidden_size,
+            hidden_ratio=config.hidden_ratio,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act
+        )
+
+    def forward_attn(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ):
+        # residual handled outside of this
+        # residual = hidden_states
+        hidden_states = self.attn_norm(hidden_states)
+        hidden_states, attentions, past_key_values = self.attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions
+        )
+        return hidden_states, attentions, past_key_values
+
+    def forward_mlp(
+        self,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor,
+    ):
+        hidden_states, residual = self.mlp_norm(hidden_states, residual, True)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        gradient_checkpointing: bool = False
+        # **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+
+        residual = hidden_states
+
+
+        if gradient_checkpointing:
+            forward_attn = partial(torch.utils.checkpoint.checkpoint, self.forward_attn, use_reentrant=False)
+            forward_mlp = partial(torch.utils.checkpoint.checkpoint, self.forward_mlp, use_reentrant=False)
+        else:
+            forward_attn = self.forward_attn
+            forward_mlp = self.forward_mlp
+
+        hidden_states, attentions, past_key_values = forward_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions
+        )
+
+        hidden_states = forward_mlp(
+            hidden_states,
+            residual,
+        )
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attentions,)
+
+        if use_cache:
+            outputs += (past_key_values,)
+
+        return outputs
+
+
+
+class ForgettingTransformerPreTrainedModel(PreTrainedModel):
+
+    config_class = ForgettingTransformerConfig
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['ForgettingTransformerBlock']
+
+    def __init__(self, config, *inputs, **kwargs):
+        # 动态修复 config_class 以支持远程代码加载
+        if hasattr(config, '__class__'):
+            config_module = config.__class__.__module__
+            if 'transformers_modules' in config_module or config_module == 'configuration_forgetting_transformer':
+                self.__class__.config_class = config.__class__
+        super().__init__(config, *inputs, **kwargs)
+
+    def _init_weights(
+        self,
+        module: nn.Module,
+    ):
+        # if isinstance(module, (nn.Linear, nn.Conv1d)):
+        if isinstance(module, (nn.Linear)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class ForgettingTransformerModel(ForgettingTransformerPreTrainedModel):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([ForgettingTransformerBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
+
+        self.gradient_checkpointing = False
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = 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
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        # if output_attentions:
+            # warnings.warn(
+                # "`ForgettingTransformerModel` does not support output attention weights now, so `output_attentions` is set to `False`."
+            # )
+        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 if not self.training else False)
+        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 None and inputs_embeds is None:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if use_cache:
+            # use_legacy_cache = not isinstance(past_key_values, Cache)
+            # if use_legacy_cache:
+                # past_key_values = FgateDynamicCache.from_legacy_cache(past_key_values)
+            if past_key_values is None:
+                past_key_values = FgateDynamicCache()
+                for layer_idx, layer in enumerate(self.layers):
+                    shift_state = layer.attn.init_shift_state(
+                        batch_size=input_ids.size(0),
+                    )
+                    past_key_values.update_shift_cache(
+                        key_shift_state=shift_state["key_shift"],
+                        value_shift_state=shift_state["value_shift"],
+                        layer_idx=layer_idx
+                    )
+            else:
+                assert isinstance(past_key_values, FgateDynamicCache)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embeddings(input_ids)
+
+        # embed positions
+        hidden_states = 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
+
+        all_hidden_states = () if output_hidden_states else None
+        all_attns = {} if output_attentions else None
+        next_decoder_cache = None
+
+        for layer_id, layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                gradient_checkpointing=self.gradient_checkpointing and self.training
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                OUTPUT_ATTN_LAYERS = [0, 7, 15, 23]
+                if layer_id in OUTPUT_ATTN_LAYERS:
+                    # all_attns += (layer_outputs[1],)
+                    all_attns[layer_id] = layer_outputs[1]
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            # next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+            next_cache = next_decoder_cache
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attns] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_attns
+        )
+
+
+class ForgettingTransformerForCausalLM(ForgettingTransformerPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ForgettingTransformerModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.embeddings = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor = None,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs
+    ):
+        # only last token for `inputs_ids` if the `past_key_values` is passed along.
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard.
+            # Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {'input_ids': input_ids.contiguous()}
+
+        model_inputs.update({
+            'past_key_values': past_key_values,
+            'use_cache': kwargs.get('use_cache'),
+            'attention_mask': attention_mask,
+        })
+        return model_inputs
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = 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, CausalLMOutputWithPast]:
+        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
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            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 = outputs[0]
+
+        loss = None
+        if labels is not None:
+            if self.config.fuse_cross_entropy:
+                loss_fct = FusedCrossEntropyLoss(inplace_backward=True, reduction='none')
+            else:
+                loss_fct = nn.CrossEntropyLoss(reduction='none')
+            logits = self.lm_head(hidden_states)
+            # Enable model parallelism
+            labels = labels.to(logits.device)
+            # labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], loss_fct.ignore_index)), 1)
+            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+            loss = loss.view(*labels.size())
+            del logits
+            logits = None
+        else:
+            logits = self.lm_head(hidden_states)
+
+        if not return_dict:
+            raise NotImplementedError
+            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,
+        )
\ No newline at end of file
diff --git a/ops/.ipynb_checkpoints/forgetting_attention-checkpoint.py b/ops/.ipynb_checkpoints/forgetting_attention-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6cba53a9a3ae49a448247e739f7f3d644caeca5
--- /dev/null
+++ b/ops/.ipynb_checkpoints/forgetting_attention-checkpoint.py
@@ -0,0 +1,1138 @@
+"""
+Implementation of Forgetting Attention.
+
+Our code is adapted from https://github.com/FlagOpen/FlagAttention/blob/ee91638dec6da8c00c4113d179f469e0ffcd5852/src/flag_attn/flash.py. The code is modified to implement Forgetting Attention.
+
+The original license info from FlagAttention:
+
+Copyright 2023 BAAI
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import pytest
+import math
+import torch
+import triton
+import triton.language as tl
+from einops import rearrange
+from typing import Optional
+
+
+__all__ = ["forgetting_attention"]
+
+
+# File flash.py
+def maybe_contiguous(x):
+    # only when the inner most dimension is contiguous can LDGSTS be used
+    # so inner-dimension contiguity is enforced.
+    return x.contiguous() if x.stride(-1) != 1 else x
+
+def rounded_multiple(a, b):
+    return (a + b - 1) // b * b
+
+# --------------------------- public API ---------------------------
+class ForgettingAttention(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, q, k, v, log_fgate, seq_start, causal, sm_scale, return_log_normalizer):
+        assert causal, "Only causal attention is supported"
+        Dq, Dk, Dv = q.shape[-1], k.shape[-1], v.shape[-1]
+        assert Dq == Dk == Dv, "feature size of q, k, v should be equal"
+        assert Dk in {16, 32, 64, 128}, "We only support head dims in {16, 32, 64, 128}"
+
+        B, H, M, D = q.shape
+        if seq_start is not None:
+            has_seq_start = True
+            assert seq_start.shape == (B,)
+        else:
+            has_seq_start = False
+            seq_start = torch.zeros((B,), device=q.device, dtype=torch.long)
+        N = k.shape[2]
+        assert log_fgate.shape == (B, H, N)
+        log_fgate = log_fgate.float()
+        if has_seq_start:
+            log_fgate = log_fgate.clone()
+            # We absolutely don't want masked value to affect result. If we
+            # don't do this then it could via affecting numerical precision of
+            # cumsum
+            mask_index = (torch.arange(N, device=q.device)[None, None, :] < seq_start[:, None, None])
+            mask_index = torch.broadcast_to(mask_index, log_fgate.size())
+            log_fgate[mask_index] = 0.0
+
+        log_lambda = torch.cumsum(log_fgate, dim=-1, dtype=log_fgate.dtype).float()
+
+        Hk, Hv = k.shape[1], v.shape[1]
+        assert Hk == Hv, "num of heads in k and v should be equal"
+        assert H == Hk, "groupped query attention has not been tested. You can uncomment this if you know what you are doing."
+        assert H % Hk == 0, "number of heads in q must be a multiple of that in k & v"
+        num_groups = H // Hk
+
+        P_SEQ = N - M
+        larger_m = M > N
+        assert (not larger_m), "The key/value tensors must be longer than the query tensor"
+
+        if sm_scale is None:
+            sm_scale = 1. / math.sqrt(D)
+
+        # contiguity
+        q, k, v = maybe_contiguous(q), maybe_contiguous(k), maybe_contiguous(v)
+
+        # to work around https://github.com/openai/triton/issues/2441
+        device = torch.cuda.device_of(q)
+
+        with torch.cuda.device(device):
+
+            config = get_fwd_config(B, H, M, N, D, causal)
+            BLOCK_M, BLOCK_N, num_stages, num_warps = config
+
+            divisible_m = M % BLOCK_M == 0
+            divisible_n = N % BLOCK_N == 0
+            # consider using 3d grid to avoid div & rem
+            grid = (triton.cdiv(M, BLOCK_M), H, B)
+            o = torch.empty_like(q)
+            L = torch.empty((B, H, M), device=q.device, dtype=torch.float32)
+            _fwd_kernel[grid](
+                q, k, v, log_lambda, seq_start, sm_scale,
+                L, o,
+                q.stride(0), q.stride(1), q.stride(2), q.stride(3),
+                k.stride(0), k.stride(1), k.stride(2), k.stride(3),
+                v.stride(0), v.stride(1), v.stride(2), v.stride(3),
+                log_lambda.stride(0), log_lambda.stride(1), log_lambda.stride(2),
+                o.stride(0), o.stride(1), o.stride(2), o.stride(3),
+                B, H, M, N, P_SEQ, num_groups,
+                BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_DMODEL=D,
+                IS_CAUSAL=causal, LARGER_M=larger_m, HAS_SEQ_START=has_seq_start,
+                DIVISIBLE_M=divisible_m, DIVISIBLE_N=divisible_n,
+                num_warps=num_warps, num_stages=num_stages,
+            )
+
+        # autograd context maintenance
+        ctx.save_for_backward(q, k, v, o, L, log_lambda, seq_start)
+        ctx.sm_scale = sm_scale
+        ctx.causal = causal
+        ctx.has_seq_start = has_seq_start
+
+        has_extra_return = return_log_normalizer
+        if has_extra_return:
+            outs = (
+                o,
+                L if return_log_normalizer else None,
+            )
+            return outs
+        return o
+
+    @staticmethod
+    def backward(ctx, do, *ignored):
+        q, k, v, o, L, log_lambda, seq_start = ctx.saved_tensors
+        sm_scale = ctx.sm_scale
+        causal = ctx.causal
+        has_seq_start = ctx.has_seq_start
+
+        B, H, M, D = q.shape
+        N = k.shape[2]
+        Hk = k.shape[1]
+        num_groups = H // Hk
+        P_SEQ = N - M
+        larger_m = M > N
+
+        if sm_scale is None:
+            sm_scale = 1. / math.sqrt(D)
+
+        # to work around https://github.com/openai/triton/issues/2441
+        device = torch.cuda.device_of(q)
+        with torch.cuda.device(device):
+            config = get_bwd_config(B, H, M, N, D, causal)
+            BLOCK_M, BLOCK_N, num_stages, num_warps = config
+
+            divisible_m = M % BLOCK_M == 0
+            divisible_n = N % BLOCK_N == 0
+
+            delta = torch.empty_like(L)
+            grid = (triton.cdiv(M, BLOCK_M), H, B)
+            _bwd_preprocess[grid](
+                o, do,
+                delta,
+                o.stride(0), o.stride(1), o.stride(2), o.stride(3),
+                do.stride(0), do.stride(1), do.stride(2), do.stride(3),
+                delta.stride(0), delta.stride(1), delta.stride(2),
+                M,
+                BLOCK_M=BLOCK_M, D_HEAD=D,
+                DIVISIBLE_M=divisible_m,
+            )
+
+            # NOTE that dk & dv always have the same number of heads as q, instead of q.
+            BLOCK_M, BLOCK_N, num_stages, num_warps = get_bwd_kv_config(B, H, M, N, D, causal)
+            divisible_m = M % BLOCK_M == 0
+            divisible_n = N % BLOCK_N == 0
+
+            dk = torch.empty((B, H, N, D), dtype=k.dtype, device=q.device)
+            dv = torch.empty((B, H, N, D), dtype=v.dtype, device=q.device)
+            dlog_lambda = torch.empty((B, H, N), dtype=log_lambda.dtype, device=q.device)
+            grid = (triton.cdiv(N, BLOCK_N), H, B)
+            _bwd_kv_kernel[grid](
+                q, k, v, log_lambda, seq_start, sm_scale, do,
+                dk, dv, dlog_lambda,
+                L, delta,
+                q.stride(0), q.stride(1), q.stride(2), q.stride(3),
+                k.stride(0), k.stride(1), k.stride(2), k.stride(3),
+                v.stride(0), v.stride(1), v.stride(2), v.stride(3),
+                log_lambda.stride(0), log_lambda.stride(1), log_lambda.stride(2),
+                do.stride(0), do.stride(1), do.stride(2), do.stride(3),
+                dk.stride(0), dk.stride(1), dk.stride(2), dk.stride(3),
+                dv.stride(0), dv.stride(1), dv.stride(2), dv.stride(3),
+                dlog_lambda.stride(0), dlog_lambda.stride(1), dlog_lambda.stride(2),
+                B, H, M, N, P_SEQ,
+                num_groups,
+                BLOCK_M=BLOCK_M, BLOCK_DMODEL=D, BLOCK_N=BLOCK_N, CAUSAL=causal,
+                DIVISIBLE_M=divisible_m, DIVISIBLE_N=divisible_n, HAS_SEQ_START=has_seq_start,
+                num_stages=num_stages, num_warps=num_warps,
+            )
+
+            BLOCK_M, BLOCK_N, num_stages, num_warps = get_bwd_q_config(B, H, M, N, D, causal)
+            divisible_m = M % BLOCK_M == 0
+            divisible_n = N % BLOCK_N == 0
+            dq = torch.zeros_like(q)
+            grid = (triton.cdiv(M, BLOCK_M), H, B)
+            _bwd_q_kernel[grid](
+                q, k, v, log_lambda, seq_start, sm_scale, do,
+                dq, dlog_lambda,
+                L, delta,
+                q.stride(0), q.stride(1), q.stride(2), q.stride(3),
+                k.stride(0), k.stride(1), k.stride(2), k.stride(3),
+                v.stride(0), v.stride(1), v.stride(2), v.stride(3),
+                log_lambda.stride(0), log_lambda.stride(1), log_lambda.stride(2),
+                do.stride(0), do.stride(1), do.stride(2), do.stride(3),
+                dq.stride(0), dq.stride(1), dq.stride(2), dq.stride(3),
+                dlog_lambda.stride(0), dlog_lambda.stride(1), dlog_lambda.stride(2),
+                B, H, M, N, P_SEQ,
+                num_groups,
+                BLOCK_M=BLOCK_M, BLOCK_DMODEL=D, BLOCK_N=BLOCK_N,
+                CAUSAL=causal, LARGER_M=larger_m, HAS_SEQ_START=has_seq_start,
+                DIVISIBLE_M=divisible_m, DIVISIBLE_N=divisible_n,
+                num_stages=num_stages, num_warps = num_warps,
+            )
+            dk = dk.reshape((B, Hk, num_groups, N, D)).sum(2)
+            dv = dv.reshape((B, Hk, num_groups, N, D)).sum(2)
+        dcumsum = torch.cumsum(dlog_lambda, dim=-1, dtype=log_lambda.dtype)
+        dlog_fgate = dlog_lambda + dcumsum[..., -1:] - dcumsum
+        dlog_fgate = dlog_fgate.float()
+        return dq, dk, dv, dlog_fgate, None, None, None, None, None, None, None
+
+
+def forgetting_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    log_fgate: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+):
+    """
+    A FlashAttention-based implementation of Forgetting Attention. 
+
+    Note:
+    - We recommand bfloat16/float16 for q, k, v and float32 for log_fgate. float32 for 
+      q, k, v is also supported, but the kernel will not use tensor cores if q, k, v are
+      in float32 (which would be slow).
+    - We only support seqlen_q <= seqlen_k
+    - We only support causal attention
+    - Head dimension must be in one of {16, 32, 64, 128}
+
+    Arguments:
+        - q: (batch_size, seqlen_q, num_heads, head_dim) unless head_first=True.
+        - k: (batch_size, seqlen_k, num_heads, head_dim) unless head_first=True.
+        - v: (batch_size, seqlen_k, num_heads, head_dim) unless head_first=True.
+        - log_fgate: (batch_size, seqlen_k, num_heads) unless head_first=True. 
+              This should be the **log** of the forget gates. This is typically the 
+              output of torch.nn.functional.logsigmoid.
+        - head_first: if True, the order the num_heads and seqlen_* axis of the all 
+              FloatTensor inputs and outputs should be (num_heads, seq_len_*) instead of
+              (seq_len_*, num_heads)
+        - seq_start: If not None, should be LongTensor with shape (batch_size,) 
+              and range in [0, seq_len_k). For each batch index batch_id, no attention 
+              will be allocated to tokens before the token index seq_start[batch_id]. 
+              This is useful for left-padded inputs.
+        - sm_scale: The scaling of attention scores before applying softmax. If
+              None, it defaults to (1.0 / math.sqrt(head_dim))
+
+    Returns:
+        out (torch.Tensor): (batch_size, seqlen_q, num_heads, head_dim) unless head_first=True.
+    """
+    if not head_first:
+        q, k, v = [rearrange(item, "b t h d -> b h t d") for item in (q, k, v)]
+        log_fgate = rearrange(log_fgate, "b t h -> b h t")
+    out = ForgettingAttention.apply(q, k, v, log_fgate, seq_start, True, sm_scale, False)
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    return out
+
+
+# --------------------------- Forward ---------------------------
+# NOTE: this function can be overwritten at runtime to use your custom config
+def get_fwd_config(B, H, M, N, D, causal):
+    assert causal
+    if torch.cuda.get_device_capability() == (8, 0):
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 32, 3, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 4, 4
+    elif torch.cuda.get_device_capability() == (9, 0):
+        # H100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 3, 8
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 128, 2, 8
+    elif torch.cuda.get_device_capability() == (8, 6):
+        if not causal:
+            if D <= 64:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 3, 4
+            else:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 2, 4
+        else: # causal
+            if D <= 64:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 3, 4
+            else:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 2, 4
+    elif torch.cuda.get_device_capability() == (8, 9):
+        # L40S
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 2, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 2, 4
+    else:
+        BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+    return (BLOCK_M, BLOCK_N, num_stages, num_warps)
+
+
+@triton.jit
+def _fwd_kernel(
+    Q, K, V, LOG_LAMBDA, SEQ_START, sm_scale,
+    L, O,
+    stride_qz, stride_qh, stride_qm, stride_qk,
+    stride_kz, stride_kh, stride_kn, stride_kk,
+    stride_vz, stride_vh, stride_vn, stride_vk,
+    stride_log_lambda_z, stride_log_lambda_h, stride_log_lambda_n,
+    stride_oz, stride_oh, stride_om, stride_ok,
+    Z, H, M, N, P_SEQ,
+    num_groups,
+    BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr,
+    IS_CAUSAL: tl.constexpr, LARGER_M: tl.constexpr, HAS_SEQ_START: tl.constexpr,
+    DIVISIBLE_M: tl.constexpr, DIVISIBLE_N: tl.constexpr,
+):
+    input_dtype = Q.dtype.element_ty
+    # -- grid id --
+    start_m = tl.program_id(0)
+    off_h = tl.program_id(1)
+    off_z = tl.program_id(2)
+
+    # scale sm_scale by log_2(e) and use
+    # 2^x instead of exp in the loop because CSE and LICM
+    # don't work as expected with `exp` in the loop
+    log2e: tl.constexpr = 1.4426950408889634
+    loge2: tl.constexpr = 0.6931471805599453
+    qk_scale = sm_scale * log2e
+
+    # offset pointers for (batch, head)
+    off_hk = off_h // num_groups
+    Q += off_z * stride_qz + off_h * stride_qh
+    K += off_z * stride_kz + off_hk * stride_kh
+    V += off_z * stride_vz + off_hk * stride_vh
+    LOG_LAMBDA += off_z * stride_log_lambda_z + off_h * stride_log_lambda_h
+    O += off_z * stride_oz + off_h * stride_oh
+    L += (off_z * H + off_h) * M # l's shape is (B, H, M)
+
+    offs_m_base = tl.arange(0, BLOCK_M)
+    offs_m = start_m * BLOCK_M + offs_m_base
+    offs_n_base = tl.arange(0, BLOCK_N)
+    offs_k = tl.arange(0, BLOCK_DMODEL)
+
+
+    # initialize pointers to value-like data
+    q_ptrs = Q + (offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk) # (BLOCK_M, BLOCK_DMODEL)
+    log_lambda_out_ptrs = LOG_LAMBDA + (P_SEQ + offs_m) * stride_log_lambda_n
+    o_ptrs = O + (offs_m[:, None] * stride_om + offs_k[None, :] * stride_ok) # (BLOCK_M, BLOCK_DMODEL)
+    l_ptrs = L + offs_m
+
+    # initialize pointer to m and l, fp32 for accumulators
+    m_i = tl.full([BLOCK_M], value=-float("inf"), dtype=tl.float32)
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
+
+    # load q
+    if DIVISIBLE_M:
+        q = tl.load(q_ptrs, cache_modifier=".cg")
+        log_lambda_out = tl.load(log_lambda_out_ptrs, cache_modifier=".cg")
+    else:
+        mask_m = offs_m < M
+        q = tl.load(q_ptrs, mask=mask_m[:, None], cache_modifier=".cg")
+        log_lambda_out = tl.load(log_lambda_out_ptrs, mask=mask_m, cache_modifier=".cg")
+
+    #Dot I trick: to place q in registers, it saves shared memory
+    # if BLOCK_DMODEL < 128:
+    #     I = tl.where(offs_k[:, None] == offs_k,
+    #                  tl.full((BLOCK_DMODEL, BLOCK_DMODEL), 1.0, dtype=input_dtype),
+    #                  tl.full((BLOCK_DMODEL, BLOCK_DMODEL), 0.0, dtype=input_dtype))
+    #     q = tl.dot(q, I, input_precision="ieee").to(input_dtype)
+    # else:
+    #     I = tl.where(offs_m_base[:, None] == offs_m_base,
+    #                  tl.full((BLOCK_M, BLOCK_M), 1.0, dtype=input_dtype),
+    #                  tl.full((BLOCK_M, BLOCK_M), 0.0, dtype=input_dtype))
+    #     q = tl.dot(I, q, input_precision="ieee").to(input_dtype)
+
+    # NOTE: Loop-Bound-For-N
+    # The indices in m-dimension that this block may access is in `[start_m * BLOCK_M, (start_m + 1) * BLOCK_M)`.
+    # According to the rule of causal masking, then max index in n-dimension that this block may access
+    # is `P_SEQ + (start_m + 1) * BLOCK_M`.
+    # However, the upper bound of index in n-dimension should never exceed the sequence length of k/v(`P_SEQ + N_CTX`).
+    # `P_SEQ + (start_m + 1) * BLOCK_M` may be larger than `N`.
+    # At this case, there would be illegal memory access when loading k & v tiles
+    # if mask_n is not applied for loading(only when `DIVISIBLE_N`` is true).
+    # See also https://github.com/FlagOpen/FlagAttention/pull/8
+    if IS_CAUSAL:
+        hi = tl.minimum(N, P_SEQ + (start_m + 1) * BLOCK_M)
+        if LARGER_M:
+            hi = tl.maximum(0, hi)
+    else:
+        hi = N
+
+    offs_n_init = offs_n_base
+    if HAS_SEQ_START:
+        SEQ_START += off_z
+        seq_start = tl.load(SEQ_START)
+        lo = tl.minimum(seq_start, hi)
+        lo = (lo // BLOCK_N) * BLOCK_N
+        offs_n_init += lo
+    else:
+        lo = 0
+        seq_start = 0
+
+    # loop over k, v and update accumulators
+    k_ptrs = K + (offs_k[:, None] * stride_kk + offs_n_init[None, :] * stride_kn) # (BLOCK_DMODEL, BLOCK_N)
+    v_ptrs = V + (offs_n_init[:, None] * stride_vn + offs_k[None, :] * stride_vk) # (BLOCK_N, BLOCK_DMODEL)
+    log_lambda_in_ptrs = LOG_LAMBDA + (offs_n_init * stride_log_lambda_n) # (BLOCK_N, BLOCK_DMODEL)
+    for start_n in range(lo, hi, BLOCK_N):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+        offs_n = start_n + offs_n_base
+
+        # -- load k, v --
+        if DIVISIBLE_N:
+            k = tl.load(k_ptrs, cache_modifier=".cg")
+            v = tl.load(v_ptrs, cache_modifier=".cg")
+            log_lambda_in = tl.load(log_lambda_in_ptrs, cache_modifier=".cg")
+        else:
+            mask_n = offs_n < N
+            k = tl.load(k_ptrs, mask=mask_n[None, :], cache_modifier=".cg")
+            v = tl.load(v_ptrs, mask=mask_n[:, None], cache_modifier=".cg")
+            log_lambda_in = tl.load(log_lambda_in_ptrs, mask=mask_n, cache_modifier=".cg")
+
+        # -- compute qk ---
+        # s = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        s = tl.dot(q, k, input_precision="ieee") * qk_scale
+        decay_bias = log_lambda_out[:, None] - log_lambda_in[None, :]
+        s += decay_bias * log2e
+
+        if not DIVISIBLE_N:
+            s = tl.where(mask_n[None, :], s, float("-inf"))
+        if IS_CAUSAL:
+            causal_mask = (P_SEQ + offs_m[:, None]) >= offs_n[None, :]
+            s = tl.where(causal_mask, s, float("-inf"))
+        if HAS_SEQ_START:
+            s = tl.where(offs_n[None, :] >= seq_start, s, float("-inf"))
+
+
+        # -- compute scaling constant ---
+        m_i_new = tl.maximum(m_i, tl.max(s, 1))
+        alpha = tl.math.exp2((m_i - m_i_new))
+        p = tl.math.exp2(s - m_i_new[:, None])
+
+        # -- compute partial sumexpn before applying dropout
+        p_sum = tl.sum(p, 1)
+
+
+        # -- scale and update acc: acc *= alpha[:, None]--
+        acc *= alpha[:, None]
+        acc += tl.dot(p.to(input_dtype), v, input_precision="ieee")
+
+        # -- update m_i and l_i --
+        l_i = l_i * alpha + p_sum
+        m_i = m_i_new
+        # update pointers
+        k_ptrs += BLOCK_N * stride_kn
+        v_ptrs += BLOCK_N * stride_vn
+        log_lambda_in_ptrs += BLOCK_N * stride_log_lambda_n
+
+    # write back l & o
+    if IS_CAUSAL and (LARGER_M or HAS_SEQ_START):
+        is_empty_line = (offs_m + P_SEQ) < seq_start
+        acc = tl.where(is_empty_line[:, None], 0.0, acc * (1.0 / l_i[:, None]))
+        l = tl.where(is_empty_line, float("-inf"), m_i * loge2 + tl.log(l_i))
+    else:
+        acc = acc * (1.0 / l_i[:, None])
+        l = m_i * loge2 + tl.log(l_i) # log(normalizer)
+
+
+    if DIVISIBLE_M:
+        tl.store(l_ptrs, l, cache_modifier=".cg")
+        tl.store(o_ptrs, acc.to(input_dtype), cache_modifier=".cg")
+    else:
+        tl.store(l_ptrs, l, mask=mask_m, cache_modifier=".cg")
+        tl.store(o_ptrs, acc.to(input_dtype), mask=mask_m[:, None], cache_modifier=".cg")
+
+
+# --------------------------- Backward ---------------------------
+# NOTE: this function can be overwritten at runtime to use your custom config
+def get_bwd_config(B, H, M, N, D, causal):
+    if torch.cuda.get_device_capability() == (9, 0):
+        if not causal:
+            BLOCK_M = 128 if D <= 64 else 64
+            BLOCK_N = 64
+            num_stages = 2
+            num_warps = 4
+        else:
+            BLOCK_M = 64
+            BLOCK_N = 64
+            num_stages = 3 if D <= 64 else 2
+            num_warps = 4
+    elif torch.cuda.get_device_capability() == (8, 0):
+        if not causal:
+            BLOCK_M = 128 if D <= 64 else 64
+            BLOCK_N = 64
+            num_stages = 2
+            num_warps = 4
+        else:
+            BLOCK_M = 64
+            BLOCK_N = 64
+            num_stages = 3 if D <= 64 else 2
+            num_warps = 4
+    elif torch.cuda.get_device_capability() == (8, 6): # tune for RTX-3090, device_capability(8, 6)
+        if not causal:
+            if D <= 64:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+            else:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 8
+        else:
+            if D <= 64:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+            else:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 32, 2, 4
+    else:
+        BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 32, 1, 4
+    return (BLOCK_M, BLOCK_N, num_stages, num_warps)
+
+def get_bwd_kv_config(B, H, M, N, D, causal):
+    assert causal
+    if torch.cuda.get_device_capability() == (8, 0): # A100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 4, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 128, 4, 8
+    elif torch.cuda.get_device_capability() == (8, 6): # tune for RTX-3090, device_capability(8, 6)
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 32, 2, 4
+    elif torch.cuda.get_device_capability() == (8, 9): # L40S
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 128, 4, 8
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 128, 2, 8
+    elif torch.cuda.get_device_capability() == (9, 0): # H100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 3, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+    else:
+        BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+    return (BLOCK_M, BLOCK_N, num_stages, num_warps)
+
+def get_bwd_q_config(B, H, M, N, D, causal):
+    assert causal
+    if torch.cuda.get_device_capability() == (8, 0): # A100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 3, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 4, 8
+    elif torch.cuda.get_device_capability() == (8, 6): # tune for RTX-3090, device_capability(8, 6)
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 32, 2, 4
+    elif torch.cuda.get_device_capability() == (8, 9): # L40S
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 4, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 3, 4
+    elif torch.cuda.get_device_capability() == (9, 0): # H100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 128, 4, 8
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 128, 2, 8
+    else:
+        BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+    return (BLOCK_M, BLOCK_N, num_stages, num_warps)
+
+
+@triton.jit
+def _bwd_preprocess(
+    Out, DO,
+    Delta,
+    stride_oz, stride_oh, stride_om, stride_ok,
+    stride_doz, stride_doh, stride_dom, stride_dok,
+    stride_dz, stride_dh, stride_dm,
+    M,
+    BLOCK_M: tl.constexpr, D_HEAD: tl.constexpr,
+    DIVISIBLE_M: tl.constexpr,
+):
+    off_h = tl.program_id(1)
+    off_z = tl.program_id(2)
+    Out += off_z * stride_oz + off_h * stride_oh
+    DO += off_z * stride_doz + off_h * stride_doh
+    Delta += off_z * stride_dz + off_h * stride_dh
+
+    # compute (Out * Dout).sum() for vector interpretation
+    off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
+    off_n = tl.arange(0, D_HEAD)
+
+    # load
+    o_ptrs = Out + off_m[:, None] * stride_om + off_n[None, :] * stride_ok
+    do_ptrs = DO + off_m[:, None] * stride_dom + off_n[None, :] * stride_dok
+
+    if DIVISIBLE_M:
+        o = tl.load(o_ptrs).to(tl.float32)
+        do = tl.load(do_ptrs).to(tl.float32)
+    else:
+        mask_m = off_m < M
+        o = tl.load(o_ptrs, mask=mask_m[:, None]).to(tl.float32)
+        do = tl.load(do_ptrs, mask=mask_m[:, None]).to(tl.float32)
+
+    # compute
+    delta = tl.sum(o * do, axis=1)
+
+    # write-back
+    d_ptrs = Delta + off_m * stride_dm
+    if DIVISIBLE_M:
+        tl.store(d_ptrs, delta)
+    else:
+        tl.store(d_ptrs, delta, mask=mask_m)
+
+
+@triton.jit
+def _bwd_kv_kernel(
+    Q, K, V, LOG_LAMBDA, SEQ_START, sm_scale, DO,
+    DK, DV, DLOG_LAMBDA,
+    L,
+    D,
+    stride_qz, stride_qh, stride_qm, stride_qk,
+    stride_kz, stride_kh, stride_kn, stride_kk,
+    stride_vz, stride_vh, stride_vn, stride_vk,
+    stride_log_lambda_z, stride_log_lambda_h, stride_log_lambda_n,
+    stride_doz, stride_doh, stride_dom, stride_dok,
+    stride_dkz, stride_dkh, stride_dkn, stride_dkk,
+    stride_dvz, stride_dvh, stride_dvn, stride_dvk,
+    stride_dlog_lambda_z, stride_dlog_lambda_h, stride_dlog_lambda_n,
+    Z, H, M, N, P_SEQ,
+    num_groups,
+    BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr,
+    CAUSAL: tl.constexpr,
+    DIVISIBLE_M: tl.constexpr, DIVISIBLE_N: tl.constexpr, HAS_SEQ_START: tl.constexpr,
+):
+    input_dtype = Q.dtype.element_ty
+    # -- grid id --
+    start_n = tl.program_id(0)
+    off_h = tl.program_id(1)
+    off_z = tl.program_id(2)
+    log2e: tl.constexpr = 1.4426950408889634
+    qk_scale = sm_scale * log2e
+
+    # offset pointers for (batch, head)
+    off_hk = off_h // num_groups
+    Q += off_z * stride_qz + off_h * stride_qh
+    K += off_z * stride_kz + off_hk * stride_kh
+    V += off_z * stride_vz + off_hk * stride_vh
+    LOG_LAMBDA += off_z * stride_log_lambda_z + off_h * stride_log_lambda_h
+    DO += off_z * stride_doz + off_h * stride_doh
+
+    # offset pointers for batch/head
+    DK += off_z * stride_dkz + off_h * stride_dkh
+    DV += off_z * stride_dvz + off_h * stride_dvh
+    DLOG_LAMBDA += off_z * stride_dlog_lambda_z + off_h * stride_dlog_lambda_h
+
+    # offset pointers for batch/head
+    D += (off_z * H + off_h) * M
+    L += (off_z * H + off_h) * M
+
+    if CAUSAL:
+        lo = tl.maximum(start_n * BLOCK_N - P_SEQ, 0)
+        lo = (lo // BLOCK_M) * BLOCK_M
+    else:
+        lo = 0
+
+    offs_m_init = lo + tl.arange(0, BLOCK_M)
+    offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    offs_m_base = tl.arange(0, BLOCK_M)
+    offs_k = tl.arange(0, BLOCK_DMODEL)
+
+    # initialize pointers to value-like data
+    q_ptrs = Q + (offs_m_init[:, None] * stride_qm + offs_k[None, :] * stride_qk) # (BLOCK_M, BLOCK_DMODEL)
+    log_lambda_out_ptrs = LOG_LAMBDA + (P_SEQ + offs_m_init) * stride_log_lambda_n # (BLOCK_N, BLOCK_DMODEL)
+    k_ptrs = K + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk) # (BLOCK_N, BLOCK_DMODEL)
+    v_ptrs = V + (offs_n[:, None] * stride_vn + offs_k[None, :] * stride_vk) # (BLOCK_N, BLOCK_DMODEL)
+    log_lambda_in_ptrs = LOG_LAMBDA + (offs_n * stride_log_lambda_n) # (BLOCK_N, BLOCK_DMODEL)
+    do_ptrs = DO + (offs_m_init[:, None] * stride_dom + offs_k[None, :] * stride_dok) # (BLOCK_M, BLOCK_DMODEL)
+
+    dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_k[None, :] * stride_dvk) # (BLOCK_N, BLOCK_DMODEL)
+    dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_k[None, :] * stride_dkk) # (BLOCK_N, BLOCK_DMODEL)
+    dlog_lambda_in_ptrs = DLOG_LAMBDA + (offs_n * stride_dlog_lambda_n) # (BLOCK_N, BLOCK_DMODEL)
+
+    # k and v stay in SRAM throughout
+    if DIVISIBLE_N:
+        v = tl.load(v_ptrs)
+        k = tl.load(k_ptrs)
+        log_lambda_in = tl.load(log_lambda_in_ptrs)
+    else:
+        mask_n = offs_n < N
+        v = tl.load(v_ptrs, mask=mask_n[:, None])
+        k = tl.load(k_ptrs, mask=mask_n[:, None])
+        log_lambda_in = tl.load(log_lambda_in_ptrs, mask=mask_n)
+
+    # If the N block doesn't contain seq_start, no need to loop
+    if HAS_SEQ_START:
+        SEQ_START += off_z
+        seq_start = tl.load(SEQ_START)
+        hi = tl.where(start_n * BLOCK_N + BLOCK_N >= seq_start - 1, M, lo)
+    else:
+        hi = M
+
+    # initialize dk amd dv
+    dk = tl.zeros([BLOCK_N, BLOCK_DMODEL], dtype=tl.float32)
+    dv = tl.zeros([BLOCK_N, BLOCK_DMODEL], dtype=tl.float32)
+    dlog_lambda_in = tl.zeros([BLOCK_N], dtype=tl.float32)
+
+    # loop over a col
+    for start_m in range(lo, hi, BLOCK_M):
+        start_m = tl.multiple_of(start_m, BLOCK_M)
+        offs_m = start_m + offs_m_base
+        causal_mask = (P_SEQ + offs_m[None, :]) >= (offs_n[:, None]) # (BLOCK_M, BLOCK_N)
+
+        # load q1, k1, q2, k2, v, do on-chip
+        if DIVISIBLE_M:
+            q = tl.load(q_ptrs)
+            log_lambda_out = tl.load(log_lambda_out_ptrs)
+        else:
+            mask_m = offs_m < M
+            valid_mask = mask_m[None, :] # & mask_n
+            q = tl.load(q_ptrs, mask=mask_m[:, None])
+            log_lambda_out = tl.load(log_lambda_out_ptrs, mask=mask_m)
+        # recompute p = softmax(qk * sm_scale, dim=-1)
+        # s = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        sT = tl.dot(k, tl.trans(q), input_precision="ieee") * qk_scale
+        decay_bias = log_lambda_out[None, :] - log_lambda_in[:, None]
+        sT += decay_bias * log2e
+        # NOTE: since softmax in backward is pointwise, the normalizer has been saved in fwd)
+        # So masking on s is not needed.
+        # s = tl.where(valid_mask, s , float("-inf"))
+        # if CAUSAL:
+        #     s = tl.where(causal_mask, s, float("-inf"))
+
+        # -- recompute p ---
+        if DIVISIBLE_M:
+            l = tl.load(L + offs_m)
+        else:
+            l = tl.load(L + offs_m, mask=mask_m)
+        pT = tl.math.exp2(sT - l[None, :] * log2e) # (BLOCK_M, BLOCK_N)
+
+        if not DIVISIBLE_M:
+            pT = tl.where(valid_mask, pT, 0.0)
+        if CAUSAL:
+            pT = tl.where(causal_mask, pT, 0.0)
+
+        # compute dv = dot(p, do)
+        if DIVISIBLE_M:
+            do = tl.load(do_ptrs)
+        else:
+            do = tl.load(do_ptrs, mask=mask_m[:, None]) # (BLOCK_M, BLOCK_DMODEL)
+
+
+        dv += tl.dot(pT.to(input_dtype), do, input_precision="ieee") # (BLOCK_N, BLOCK_DMODEL)  # still correct
+
+        # compute dp = dot(v, do)
+        if DIVISIBLE_M:
+            delta = tl.load(D + offs_m)
+        else:
+            delta = tl.load(D + offs_m, mask=mask_m)
+        # dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        dpT = tl.dot(v, tl.trans(do), input_precision="ieee")
+
+
+        # compute ds = p * (dp - delta[:, None])
+        dsT = pT * (dpT - delta[None, :]) # (BLOCK_M, BLOCK_N)
+
+        if not DIVISIBLE_M:
+            dsT = tl.where(valid_mask, dsT, 0.0)
+        if CAUSAL:
+            dsT = tl.where(causal_mask, dsT, 0.0)
+
+        # compute dk = dot(ds.T, q) masking
+        dk += tl.dot(dsT.to(input_dtype), q, input_precision="ieee")
+        dlog_lambda_in += -tl.sum(dsT, axis=1)
+
+        # increment pointers
+        q_ptrs += BLOCK_M * stride_qm
+        log_lambda_out_ptrs += BLOCK_M * stride_log_lambda_n
+        do_ptrs += BLOCK_M * stride_dom
+
+    dk *= sm_scale
+    if HAS_SEQ_START:
+        # Mask out 
+        seq_mask = (offs_n >= seq_start)
+        dk = tl.where(seq_mask[:, None], dk, 0.0)
+        dv = tl.where(seq_mask[:, None], dv, 0.0)
+        dlog_lambda_in = tl.where(seq_mask, dlog_lambda_in, 0.0)
+    if DIVISIBLE_N:
+        tl.store(dk_ptrs, dk.to(input_dtype)) # (BLOCK_N, BLOCK_DMODEL)
+        tl.store(dv_ptrs, dv.to(input_dtype)) # (BLOCK_N, BLOCK_DMODEL,)
+        tl.store(dlog_lambda_in_ptrs, dlog_lambda_in.to(tl.float32)) # (BLOCK_N, BLOCK_DMODEL,)
+    else:
+        tl.store(dk_ptrs, dk.to(input_dtype), mask=mask_n[:, None]) # (BLOCK_N, BLOCK_DMODEL)
+        tl.store(dv_ptrs, dv.to(input_dtype), mask=mask_n[:, None]) # (BLOCK_N, BLOCK_DMODEL)
+        tl.store(dlog_lambda_in_ptrs, dlog_lambda_in.to(tl.float32), mask=mask_n) # (BLOCK_N, BLOCK_DMODEL,)
+
+
+@triton.jit
+def _bwd_q_kernel(
+    Q, K, V, LOG_LAMBDA, SEQ_START, sm_scale, DO,
+    DQ, DLOG_LAMBDA,
+    L,
+    D,
+    stride_qz, stride_qh, stride_qm, stride_qk,
+    stride_kz, stride_kh, stride_kn, stride_kk,
+    stride_vz, stride_vh, stride_vn, stride_vk,
+    stride_log_lambda_z, stride_log_lambda_h, stride_log_lambda_n,
+    stride_doz, stride_doh, stride_dom, stride_dok,
+    stride_dqz, stride_dqh, stride_dqm, stride_dqk,
+    stride_dlog_lambda_z, stride_dlog_lambda_h, stride_dlog_lambda_n,
+    Z, H, M, N, P_SEQ,
+    num_groups,
+    BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr,
+    CAUSAL: tl.constexpr, LARGER_M: tl.constexpr, HAS_SEQ_START: tl.constexpr,
+    DIVISIBLE_M: tl.constexpr, DIVISIBLE_N: tl.constexpr,
+):
+    input_dtype = Q.dtype.element_ty
+    # -- grid id --
+    start_m = tl.program_id(0)
+    off_h = tl.program_id(1)
+    off_z = tl.program_id(2)
+
+    # scale sm_scale by log_2(e) and use
+    # 2^x instead of exp in the loop because CSE and LICM
+    # don't work as expected with `exp` in the loop
+    log2e: tl.constexpr = 1.4426950408889634
+    qk_scale = sm_scale * log2e
+
+    # offset pointers for (batch, head)
+    off_hk = off_h // num_groups
+    Q += off_z * stride_qz + off_h * stride_qh
+    K += off_z * stride_kz + off_hk * stride_kh
+    V += off_z * stride_vz + off_hk * stride_vh
+    LOG_LAMBDA += off_z * stride_log_lambda_z + off_h * stride_log_lambda_h
+    DO += off_z * stride_doz + off_h * stride_doh
+    D += (off_z * H + off_h) * M
+    L += (off_z * H + off_h) * M
+
+    # offset pointers for batch/head
+    DQ += off_z * stride_dqz + off_h * stride_dqh
+    DLOG_LAMBDA += off_z * stride_dlog_lambda_z + off_h * stride_dlog_lambda_h
+
+    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_k = tl.arange(0, BLOCK_DMODEL)
+
+    # initialize pointers to value-like data
+    q_ptrs = Q + (offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk) # (BLOCK_M, BLOCK_DMODEL)
+    log_lambda_out_ptrs = LOG_LAMBDA + (P_SEQ + offs_m) * stride_log_lambda_n
+
+    dq_ptrs = DQ + (offs_m[:, None] * stride_dqm + offs_k[None, :] * stride_dqk) # (BLOCK_M, BLOCK_DMODEL)
+    dlog_lambda_out_ptrs = DLOG_LAMBDA + (P_SEQ + offs_m) * stride_dlog_lambda_n
+    do_ptrs = DO + (offs_m[:, None] * stride_dom + offs_k[None, :] * stride_dok) # (BLOCK_M, BLOCK_DMODEL)
+
+    # pointer to row-wise quantities in value-like data
+    d_ptrs = D + offs_m
+    l_ptrs = L + offs_m
+
+    # load q: it will stay in SRAM throughout
+    if DIVISIBLE_M:
+        q = tl.load(q_ptrs)
+        do = tl.load(do_ptrs)
+        delta = tl.load(d_ptrs)
+        l = tl.load(l_ptrs)
+        log_lambda_out = tl.load(log_lambda_out_ptrs)
+    else:
+        mask_m = offs_m < M
+        q = tl.load(q_ptrs, mask=mask_m[:, None])
+        do = tl.load(do_ptrs, mask=mask_m[:, None])
+        delta = tl.load(d_ptrs, mask=mask_m)
+        l = tl.load(l_ptrs, mask=mask_m)
+        log_lambda_out = tl.load(log_lambda_out_ptrs, mask=mask_m)
+
+    # initialize dq
+    dq = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
+    dlog_lambda_out = tl.zeros([BLOCK_M], dtype=tl.float32)
+
+    # loop over k, v and update accumulator
+    # see note "Loop-Bound-For-N"
+    if CAUSAL:
+        hi = tl.minimum(N, P_SEQ + (start_m + 1) * BLOCK_M)
+        if LARGER_M:
+            hi = tl.maximum(0, hi)
+    else:
+        hi = N
+
+    offs_n_base = tl.arange(0, BLOCK_N)
+    offs_n_init = offs_n_base
+    if HAS_SEQ_START:
+        SEQ_START += off_z
+        seq_start = tl.load(SEQ_START)
+        lo = tl.minimum(seq_start, hi)
+        lo = (lo // BLOCK_N) * BLOCK_N
+        offs_n_init += lo
+    else:
+        lo = 0
+    k_ptrs = K + (offs_n_init[:, None] * stride_kn + offs_k[None, :] * stride_kk) # (BLOCK_N, BLOCK_DMODEL)
+    v_ptrs = V + (offs_n_init[:, None] * stride_vn + offs_k[None, :] * stride_vk) # (BLOCK_N, BLOCK_DMODEL)
+    log_lambda_in_ptrs = LOG_LAMBDA + (offs_n_init * stride_log_lambda_n)
+
+    # loop over a row
+    for start_n in range(lo, hi, BLOCK_N):
+        offs_n = start_n + offs_n_base
+
+        # load k1, k2, v on chip
+        if DIVISIBLE_N:
+            v = tl.load(v_ptrs)
+            k = tl.load(k_ptrs)
+            log_lambda_in = tl.load(log_lambda_in_ptrs)
+        else:
+            mask_n = offs_n < N
+            v = tl.load(v_ptrs, mask=mask_n[:, None])
+            k = tl.load(k_ptrs, mask=mask_n[:, None])
+            log_lambda_in = tl.load(log_lambda_in_ptrs, mask=mask_n)
+
+
+        # recompute p = softmax(qk * sm_scale, dim=-1)
+        if not DIVISIBLE_N:
+            valid_mask = mask_n[None, :] # & mask_m[:, None]
+        if CAUSAL:
+            causal_mask = (P_SEQ + offs_m[:, None]) >= (offs_n[None, :]) # (BLOCK_M, BLOCK_N)
+        # s = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        s = tl.dot(q, tl.trans(k), input_precision="ieee") * qk_scale
+        decay_bias = log_lambda_out[:, None] - log_lambda_in[None, :]
+        s += decay_bias * log2e
+
+        # NOTE: since softmax in backward is pointwise, the normalizer has been saved in fwd)
+        # So masking on s is not needed.
+        # if CAUSAL:
+        #     s = tl.where(causal_mask & valid_mask, s, float("-inf"))
+        # else:
+        #     s = tl.where(valid_mask, s, float("-inf"))
+        p = tl.math.exp2(s - l[:, None] * log2e) # (BLOCK_M, BLOCK_N)
+
+        # compute dp = dot(v, do)
+        # dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        dp = tl.dot(do.to(input_dtype), tl.trans(v), input_precision="ieee")
+
+
+        # no need to mask dp
+        # if CAUSAL:
+        #     dp = tl.where(causal_mask & valid_mask, dp, 0.0)
+        # else:
+        #     dp = tl.where(valid_mask, dp, 0.0)
+
+        # compute ds = p * (dp - delta[:, None])
+        # move scale out to dq at last
+        ds = p * (dp - delta[:, None]) # (BLOCK_M, BLOCK_N)
+
+        # mask ds to ensure no small values
+        if not DIVISIBLE_N:
+            ds = tl.where(valid_mask, ds, 0.0)
+        if CAUSAL:
+            ds = tl.where(causal_mask, ds, 0.0)
+        if HAS_SEQ_START:
+            ds = tl.where(offs_n[None, :] >= seq_start, ds, 0.0)
+
+        dq += tl.dot(ds.to(input_dtype), k, input_precision="ieee")
+        dlog_lambda_out += tl.sum(ds, axis=1)
+
+        # increment pointers
+        k_ptrs += BLOCK_N * stride_kn
+        v_ptrs += BLOCK_N * stride_vn
+        log_lambda_in_ptrs += BLOCK_N * stride_log_lambda_n
+
+    dq *= sm_scale
+    if DIVISIBLE_M:
+        tmp = tl.load(dlog_lambda_out_ptrs)
+    else:
+        tmp = tl.load(dlog_lambda_out_ptrs, mask=mask_m)
+    dlog_lambda_out += tmp
+    if DIVISIBLE_M:
+        tl.store(dq_ptrs, dq.to(input_dtype))
+        tl.store(dlog_lambda_out_ptrs, dlog_lambda_out)
+    else:
+        tl.store(dq_ptrs, dq.to(input_dtype), mask=mask_m[:, None])
+        tl.store(dlog_lambda_out_ptrs, dlog_lambda_out, mask=mask_m)
+
+
+
+@pytest.mark.parametrize("Z, H, M, N, HEAD_DIM", [(4, 2, 1020, 2098, 64), (4, 2, 1024, 2048, 64)])
+@pytest.mark.parametrize("causal", [True])
+def test_op(Z, H, M, N, HEAD_DIM, causal, dtype=torch.bfloat16):
+    torch.manual_seed(24)
+    q = (torch.empty((Z, H, M, HEAD_DIM), dtype=dtype, device="cuda").normal_(mean=0.0, std=0.5).requires_grad_())
+    k = (torch.empty((Z, H, N, HEAD_DIM), dtype=dtype, device="cuda").normal_(mean=0.0, std=0.5).requires_grad_())
+    v = (torch.empty((Z, H, N, HEAD_DIM), dtype=dtype, device="cuda").normal_(mean=0.0, std=0.5).requires_grad_())
+    fgate_logit = torch.empty((Z, H, N), dtype=torch.float32, device="cuda").uniform_(5, 10)
+    log_fgate = torch.nn.functional.logsigmoid(fgate_logit).requires_grad_()
+    seq_start = torch.randint(low=0, high=N, size=(Z,), dtype=torch.long, device="cuda")
+    # seq_start = torch.randint(low=0, high=10, size=(Z,), dtype=torch.long, device="cuda")
+    # seq_start = torch.full(fill_value=0, size=(Z,), dtype=torch.long, device="cuda")
+    sm_scale = 0.5
+    dout = torch.randn_like(q)
+    # reference implementation
+    P_SEQ = N - M
+    mask = torch.tril(torch.ones((M, N), device="cuda"), diagonal=P_SEQ)
+    p = torch.matmul(q, k.transpose(2, 3)) * sm_scale
+    p = p.float()
+
+    log_lambda = torch.cumsum(log_fgate, dim=-1)
+    decay_bias = log_lambda[..., -M:, None] - log_lambda[..., None, :]
+    p = p + decay_bias
+    if causal:
+        p[:, :, mask == 0] = float("-inf")
+
+    attention_mask = torch.arange(N, device="cuda") < seq_start[:, None, None, None]
+    p = torch.where(attention_mask, float("-inf"), p)
+    p = torch.softmax(p.float(), dim=-1).to(dtype)
+    p = p.clone()
+    p[torch.isnan(p)] = 0.0
+    # p = torch.exp(p)
+    ref_out = torch.matmul(p, v)
+    ref_out.backward(dout)
+    ref_dv, v.grad = v.grad.clone(), None
+    ref_dk, k.grad = k.grad.clone(), None
+    ref_dq, q.grad = q.grad.clone(), None
+    ref_dlog_fgate, log_fgate.grad = log_fgate.grad.clone(), None
+    # triton implementation
+    tri_out = forgetting_attention(q, k, v, log_fgate, head_first=True, seq_start=seq_start, sm_scale=sm_scale)
+    tri_out = tri_out.to(dtype)
+
+    tri_out.backward(dout)
+    tri_dv, v.grad = v.grad.clone(), None
+    tri_dk, k.grad = k.grad.clone(), None
+    tri_dq, q.grad = q.grad.clone(), None
+    tri_dlog_fgate, log_fgate.grad = log_fgate.grad.clone(), None
+    # compare
+    # assert torch.allclose(tri_log_normalizer[~torch.isnan(tri_log_normalizer)], ref_log_normalizer[~torch.isnan(ref_log_normalizer)], atol=1e-2, rtol=0)
+    assert torch.allclose(ref_out, tri_out, atol=1e-2, rtol=0), (ref_out - tri_out).abs().max()
+    rtol = 0
+    # Relative tolerance workaround for known hardware limitation of MI200 GPU.
+    # For details see https://pytorch.org/docs/stable/notes/numerical_accuracy.html#reduced-precision-fp16-and-bf16-gemms-and-convolutions-on-amd-instinct-mi200-devices
+    # if torch.version.hip is not None and triton.runtime.driver.active.get_current_target().arch == "gfx90a":
+        # rtol = 1e-2
+    assert torch.allclose(ref_dv, tri_dv, atol=1e-2, rtol=rtol), (ref_dv - tri_dv).abs().max()
+    assert torch.allclose(ref_dk, tri_dk, atol=1e-2, rtol=rtol), (ref_dk - tri_dk).abs().max()
+    assert torch.allclose(ref_dq, tri_dq, atol=1e-2, rtol=rtol), (ref_dq - tri_dq).abs().max()
+    assert torch.allclose(ref_dlog_fgate, tri_dlog_fgate, atol=1e-2, rtol=rtol), (ref_dlog_fgate - tri_dlog_fgate).abs().max()
+
+try:
+    from flash_attn.flash_attn_interface import \
+        flash_attn_qkvpacked_func as flash_attn_func
+    HAS_FLASH = True
+except BaseException:
+    HAS_FLASH = False
+
+TORCH_HAS_FP8 = hasattr(torch, 'float8_e5m2')
+BATCH, N_HEADS, HEAD_DIM = 4, 32, 128
+# vary seq length for fixed head and batch=4
+configs = []
+for mode in ["fwd", "bwd"]:
+# for mode in ["bwd"]:
+    # for causal in [True, False]:
+    for causal in [True]:
+        if mode == "bwd" and not causal:
+            continue
+        configs.append(
+            triton.testing.Benchmark(
+                x_names=["N_CTX"],
+                # x_vals=[2**i for i in range(10, 15)],
+                x_vals=[2**i for i in range(14, 15)],
+                line_arg="provider",
+                # line_vals=["triton-fp16", "flag"] + (["flash"] if HAS_FLASH else []),
+                # line_names=["Triton [FP16]", "Flag"] + (["Flash-2"] if HAS_FLASH else []),
+                line_vals=["flag"] + (["flash"] if HAS_FLASH else []),
+                line_names=["Flag"] + (["Flash-2"] if HAS_FLASH else []),
+                styles=[("red", "-"), ("blue", "-"), ("green", "-")],
+                ylabel="ms",
+                plot_name=f"fused-attention-batch{BATCH}-head{N_HEADS}-d{HEAD_DIM}-{mode}-causal={causal}",
+                args={
+                    "H": N_HEADS,
+                    "BATCH": BATCH,
+                    "HEAD_DIM": HEAD_DIM,
+                    "mode": mode,
+                    "causal": causal,
+                },
+            ))
+
+
+@triton.testing.perf_report(configs)
+def bench_flash_attention(BATCH, H, N_CTX, HEAD_DIM, causal, mode, provider, device="cuda"):
+    assert mode in ["fwd", "bwd"]
+    warmup = 25
+    rep = 100
+    dtype = torch.bfloat16
+    if "flag" in provider:
+        q = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        k = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        v = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        fgate_logit = torch.empty((BATCH, H, N_CTX), dtype=torch.float32, device="cuda").uniform_(5, 10)
+        log_fgate = torch.nn.functional.logsigmoid(fgate_logit).requires_grad_()
+        # if mode == "fwd" and "fp8" in provider:
+        #     q = q.to(torch.float8_e5m2)
+        #     k = k.to(torch.float8_e5m2)
+        #     v = v.permute(0, 1, 3, 2).contiguous()
+        #     v = v.permute(0, 1, 3, 2)
+        #     v = v.to(torch.float8_e5m2)
+        sm_scale = 1.3
+        fn = lambda: forgetting_attention(q, k, v, log_fgate, head_first=True, sm_scale=sm_scale)
+        if mode == "bwd":
+            o = fn()
+            do = torch.randn_like(o)
+            fn = lambda: o.backward(do, retain_graph=True)
+        ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
+    if provider == "flash":
+        qkv = torch.randn((BATCH, N_CTX, 3, H, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        fn = lambda: flash_attn_func(qkv, causal=causal)
+        if mode == "bwd":
+            o = fn()
+            do = torch.randn_like(o)
+            fn = lambda: o.backward(do, retain_graph=True)
+        ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
+    flops_per_matmul = 2.0 * BATCH * H * N_CTX * N_CTX * HEAD_DIM
+    total_flops = 2 * flops_per_matmul
+    if causal:
+        total_flops *= 0.5
+    if mode == "bwd":
+        total_flops *= 2.5  # 2.0(bwd) + 0.5(recompute)
+    return total_flops / ms * 1e-9
+
+
+if __name__ == "__main__":
+    # only works on post-Ampere GPUs right now
+    bench_flash_attention.run(save_path=".", print_data=True)
diff --git a/ops/.ipynb_checkpoints/forgetting_attention_std-checkpoint.py b/ops/.ipynb_checkpoints/forgetting_attention_std-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..7763fbae480987a777057c67076272cfd8345af6
--- /dev/null
+++ b/ops/.ipynb_checkpoints/forgetting_attention_std-checkpoint.py
@@ -0,0 +1,72 @@
+"""
+Forgetting Attention - 标准 Softmax 版本
+在 forgetting_attention.py 最后添加这个函数
+"""
+
+import math
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Optional
+
+
+def forgetting_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    log_fgate: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+) -> torch.Tensor:
+    """标准 Softmax 版本的 Forgetting Attention"""
+    
+    if not head_first:
+        q = rearrange(q, "b t h d -> b h t d")
+        k = rearrange(k, "b t h d -> b h t d")
+        v = rearrange(v, "b t h d -> b h t d")
+        log_fgate = rearrange(log_fgate, "b t h -> b h t")
+    
+    B, H, T_q, D = q.shape
+    T_k = k.shape[2]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # 计算 QK 分数
+    scores = torch.matmul(q.float(), k.float().transpose(-2, -1)) * sm_scale
+    
+    # 处理 seq_start
+    log_fgate_masked = log_fgate.float()
+    if seq_start is not None:
+        log_fgate_masked = log_fgate_masked.clone()
+        mask_idx = torch.arange(T_k, device=q.device)[None, None, :] < seq_start[:, None, None]
+        log_fgate_masked[mask_idx] = 0.0
+    
+    # 计算累积衰减
+    log_lambda = torch.cumsum(log_fgate_masked, dim=-1)
+    decay_bias = log_lambda[:, :, :T_q, None] - log_lambda[:, :, None, :]
+    scores = scores + decay_bias
+    
+    # Causal mask
+    P_SEQ = T_k - T_q
+    causal_mask = torch.triu(torch.ones((T_q, T_k), dtype=torch.bool, device=q.device), diagonal=P_SEQ + 1)
+    scores = scores.masked_fill(causal_mask[None, None, :, :], float('-inf'))
+    
+    # seq_start mask
+    if seq_start is not None:
+        seq_mask = torch.arange(T_k, device=q.device)[None, None, None, :] < seq_start[None, :, None, None]
+        scores = scores.masked_fill(seq_mask, float('-inf'))
+    
+    # Softmax
+    attn = F.softmax(scores, dim=-1)
+    attn = torch.nan_to_num(attn, 0.0)
+    
+    # 计算输出
+    out = torch.matmul(attn.to(v.dtype), v)
+    
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    
+    return out
diff --git a/ops/.ipynb_checkpoints/geometric_attention_std-checkpoint.py b/ops/.ipynb_checkpoints/geometric_attention_std-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..071fadffcc62a02dd7528c6b793b00e86dbcb8f2
--- /dev/null
+++ b/ops/.ipynb_checkpoints/geometric_attention_std-checkpoint.py
@@ -0,0 +1,179 @@
+"""
+Geometric Attention - 标准 Softmax 版本
+基于论文 "The Neural Data Router" (Csordás et al., 2022)
+"""
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Optional
+
+
+def geometric_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+    normalize: bool = True,
+) -> torch.Tensor:
+    """
+    标准 Softmax 版本的 Geometric Attention
+    
+    Args:
+        q: Query tensor [B, T, H, D] or [B, H, T, D] if head_first
+        k: Key tensor [B, T, H, D] or [B, H, T, D] if head_first
+        v: Value tensor [B, T, H, D] or [B, H, T, D] if head_first
+        head_first: 是否head维度在前
+        seq_start: 序列起始位置 [B]
+        sm_scale: scaling factor，默认 1/sqrt(D)
+        normalize: 是否归一化attention weights
+    
+    Returns:
+        output: [B, T, H, D] or [B, H, T, D] if head_first
+    """
+    
+    # Rearrange to head_first format
+    if not head_first:
+        q = rearrange(q, "b t h d -> b h t d")
+        k = rearrange(k, "b t h d -> b h t d")
+        v = rearrange(v, "b t h d -> b h t d")
+    
+    B, H, T_q, D = q.shape
+    T_k = k.shape[2]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # Step 1: 计算 content-based logits
+    logits = torch.matmul(q.float(), k.float().transpose(-2, -1)) * sm_scale
+    # logits: [B, H, T_q, T_k]
+    
+    # Step 2: Mask diagonal (不允许attend到自己)
+    if T_q == T_k:
+        diag_mask = torch.eye(T_q, dtype=torch.bool, device=q.device)
+        logits = logits.masked_fill(diag_mask[None, None, :, :], float('-inf'))
+    
+    # Step 3: 处理 seq_start mask
+    if seq_start is not None:
+        seq_mask = torch.arange(T_k, device=q.device)[None, None, None, :] < seq_start[None, :, None, None]
+        logits = logits.masked_fill(seq_mask, float('-inf'))
+    
+    # Step 4: Causal mask (如果需要)
+    # 注意：geometric attention论文中没有causal，如果你的任务需要可以取消注释
+    # P_SEQ = T_k - T_q
+    # causal_mask = torch.triu(torch.ones((T_q, T_k), dtype=torch.bool, device=q.device), diagonal=P_SEQ + 1)
+    # logits = logits.masked_fill(causal_mask[None, None, :, :], float('-inf'))
+    
+    # Step 5: Geometric weighting (核心算法)
+    attn_weights = geometric_weighting(logits, normalize=normalize)
+    
+    # Step 6: 应用attention到values
+    out = torch.matmul(attn_weights.to(v.dtype), v)
+    
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    
+    return out
+
+
+def geometric_weighting(
+    logits: torch.Tensor,
+    normalize: bool = True,
+) -> torch.Tensor:
+    """
+    计算geometric attention weights
+    
+    实现论文中的 Equation 7:
+    A[i,j] = P[i,j] * ∏(1 - P[i,k]) for k closer to i than j
+    
+    Args:
+        logits: [B, H, T_q, T_k] attention logits
+        normalize: 是否归一化
+    
+    Returns:
+        weights: [B, H, T_q, T_k] attention weights
+    """
+    B, H, T_q, T_k = logits.shape
+    
+    # Step 1: Sigmoid to get matching probabilities
+    P = torch.sigmoid(logits)  # [B, H, T_q, T_k]
+    
+    # Step 2: 使用 log-space 计算（数值稳定）
+    log_P = torch.log(P + 1e-10)
+    log_one_minus_P = torch.log(1.0 - P + 1e-10)
+    
+    # Step 3: 简化版本 - 使用cumsum实现几何分布
+    # 这是一个高效的近似，避免了显式的循环
+    
+    # 对于每个位置i，计算其左侧所有位置的log(1-P)累积和
+    log_decay_left = log_one_minus_P.cumsum(dim=-1)
+    
+    # 计算weights（简化版）
+    # 完整版本需要根据距离动态选择区间，这里用一个高效近似
+    weights = torch.exp(log_P + log_decay_left.roll(1, dims=-1))
+    
+    # 第一个位置特殊处理（没有左侧元素）
+    # 避免inplace操作
+    weights_first = P[:, :, :, :1]  # 获取第一列
+    weights = torch.cat([weights_first, weights[:, :, :, 1:]], dim=-1)
+    
+    # Step 4: 归一化（可选）
+    if normalize:
+        weights = F.normalize(weights, p=1, dim=-1)
+    
+    # 处理NaN（如果所有位置都是-inf）
+    weights = torch.nan_to_num(weights, 0.0)
+    
+    return weights
+
+
+def geometric_weighting_full(
+    logits: torch.Tensor,
+    normalize: bool = True,
+) -> torch.Tensor:
+    """
+    完整版geometric weighting（更慢但更准确）
+    
+    仅在需要最高精度时使用，训练时建议用上面的简化版
+    """
+    B, H, T_q, T_k = logits.shape
+    device = logits.device
+    
+    P = torch.sigmoid(logits)
+    log_P = torch.log(P + 1e-10)
+    log_one_minus_P = torch.log(1.0 - P + 1e-10)
+    
+    # 初始化weights
+    weights = torch.zeros_like(P)
+    
+    # 对每个(i,j)计算geometric weight
+    for i in range(T_q):
+        for j in range(T_k):
+            # 找出比j更接近i的所有位置k
+            if i < j:
+                # 向右看：closer positions are [i+1, ..., j-1]
+                closer_positions = range(i + 1, j)
+            elif i > j:
+                # 向左看：closer positions are [j+1, ..., i-1]
+                closer_positions = range(j + 1, i)
+            else:
+                # i == j (对角线)，已经在外面mask掉了
+                continue
+            
+            # 计算 ∏(1 - P[i,k]) in log-space
+            log_prod = sum(log_one_minus_P[:, :, i, k] for k in closer_positions) if closer_positions else 0.0
+            
+            # weights[i,j] = P[i,j] * ∏(1 - P[i,k])
+            weights[:, :, i, j] = torch.exp(log_P[:, :, i, j] + log_prod)
+    
+    if normalize:
+        weights = F.normalize(weights, p=1, dim=-1)
+    
+    weights = torch.nan_to_num(weights, 0.0)
+    
+    return weights 
\ No newline at end of file
diff --git a/ops/.ipynb_checkpoints/sliding_window_attention_std-checkpoint.py b/ops/.ipynb_checkpoints/sliding_window_attention_std-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..16c1551290ef8844ee8e3ca9793017ec90f3f896
--- /dev/null
+++ b/ops/.ipynb_checkpoints/sliding_window_attention_std-checkpoint.py
@@ -0,0 +1,88 @@
+"""
+Sliding Window / Hard Attention
+Based on "Context Limitations Make Neural Language Models More Human-Like"
+(Kuribayashi et al., 2022)
+"""
+
+import math
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Optional
+
+
+def sliding_window_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+    window_size: int = 2,  # 默认2-gram（看前1个token）
+) -> torch.Tensor:
+    """
+    Sliding Window Attention
+    
+    硬截断：只能attend到最近window_size个token
+    """
+    
+    if not head_first:
+        q = rearrange(q, "b t h d -> b h t d")
+        k = rearrange(k, "b t h d -> b h t d")
+        v = rearrange(v, "b t h d -> b h t d")
+    
+    B, H, T_q, D = q.shape
+    T_k = k.shape[2]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # Compute logits
+    logits = torch.matmul(q.float(), k.float().transpose(-2, -1)) * sm_scale
+    
+    # Create sliding window mask
+    mask = create_sliding_window_mask(T_q, T_k, window_size, device=q.device)
+    logits = logits.masked_fill(~mask, float('-inf'))
+    
+    # Seq start mask
+    if seq_start is not None:
+        seq_mask = torch.arange(T_k, device=q.device)[None, None, None, :] < seq_start[None, :, None, None]
+        logits = logits.masked_fill(seq_mask, float('-inf'))
+    
+    # Standard softmax
+    weights = F.softmax(logits, dim=-1)
+    
+    # Apply to values
+    out = torch.matmul(weights, v)
+    
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    
+    return out
+
+
+def create_sliding_window_mask(
+    T_q: int,
+    T_k: int,
+    window_size: int,
+    device: torch.device
+) -> torch.Tensor:
+    """
+    创建sliding window mask
+    
+    window_size=1: 只看前1个token (2-gram)
+    window_size=2: 只看前2个token (3-gram)
+    """
+    # 基础causal mask
+    mask = torch.tril(torch.ones(T_q, T_k, dtype=torch.bool, device=device))
+    
+    # 应用window限制
+    if window_size > 0 and window_size < T_k:
+        for i in range(T_q):
+            # 只保留 [i-window_size+1, i] 范围
+            start = max(0, i - window_size + 1)
+            if start > 0:
+                mask[i, :start] = False
+    
+    return mask[None, None, :, :]  # [1, 1, T_q, T_k]
\ No newline at end of file
diff --git a/ops/.ipynb_checkpoints/stickbreaking_attention_std-checkpoint.py b/ops/.ipynb_checkpoints/stickbreaking_attention_std-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..b193d12b63bcfd41ef851f082a9048ce734359c7
--- /dev/null
+++ b/ops/.ipynb_checkpoints/stickbreaking_attention_std-checkpoint.py
@@ -0,0 +1,117 @@
+"""
+Stick-breaking Attention - ICLR 2025
+基于论文 "Scaling Stick-Breaking Attention" (Tan et al., 2025)
+简化的PyTorch实现（不使用Triton）
+"""
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Optional
+
+
+def stickbreaking_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+    normalize: bool = True,
+    attend_current: bool = False,
+) -> torch.Tensor:
+    """
+    Stick-breaking attention
+    
+    Based on ICLR 2025 paper, simplified PyTorch implementation
+    A_{i,j} = exp(z_{i,j} - ∑_{k=i}^{j-1} softplus(z_{k,j}))
+    
+    Args:
+        q: query [B, T, H, D] or [B, H, T, D] if head_first
+        k: key [B, T, H, D] or [B, H, T, D] if head_first
+        v: value [B, T, H, D] or [B, H, T, D] if head_first
+        attend_current: whether to attend to current position
+        normalize: whether to normalize attention weights
+    """
+    
+    if not head_first:
+        q = rearrange(q, "b t h d -> b h t d")
+        k = rearrange(k, "b t h d -> b h t d")
+        v = rearrange(v, "b t h d -> b h t d")
+    
+    B, H, T_q, D = q.shape
+    T_k = k.shape[2]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # Compute logits: QK^T / sqrt(d)
+    logits = torch.matmul(q.float(), k.float().transpose(-2, -1)) * sm_scale
+    # [B, H, T_q, T_k]
+    
+    # Causal mask (optional: mask diagonal if not attend_current)
+    if T_q == T_k and not attend_current:
+        diag_mask = torch.eye(T_q, dtype=torch.bool, device=q.device)
+        logits = logits.masked_fill(diag_mask[None, None, :, :], float('-inf'))
+    
+    # Seq start mask
+    if seq_start is not None:
+        seq_mask = torch.arange(T_k, device=q.device)[None, None, None, :] < seq_start[None, :, None, None]
+        logits = logits.masked_fill(seq_mask, float('-inf'))
+    
+    # Stick-breaking weighting
+    attn_weights = stickbreaking_weighting(logits, normalize=normalize)
+    
+    # Apply attention to values
+    out = torch.matmul(attn_weights.to(v.dtype), v)
+    
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    
+    return out
+
+
+def stickbreaking_weighting(
+    logits: torch.Tensor,
+    normalize: bool = True,
+) -> torch.Tensor:
+    """
+    Compute stick-breaking attention weights
+    
+    From paper Equation 4:
+    A_{i,j} = exp(z_{i,j} - ∑_{k=i}^{j-1} log(1 + exp(z_{k,j})))
+    
+    Where log(1 + exp(x)) is softplus(x)
+    """
+    B, H, T_q, T_k = logits.shape
+    device = logits.device
+    
+    # Softplus: log(1 + exp(x))
+    # Numerically stable version from paper (Equation 5)
+    def softplus_stable(x):
+        # softplus(x) = log(1 + exp(x))
+        # When x > 15, exp(x) is huge, just return x
+        return torch.where(
+            x > 15.0,
+            x,
+            torch.log1p(torch.exp(torch.clamp(x, max=15.0)))
+        )
+    
+    # Compute softplus for all logits
+    logits_sp = softplus_stable(logits)  # [B, H, T_q, T_k]
+    
+    # For each query position, compute cumulative sum
+    # We need to accumulate from left to right (position i to j-1)
+    log_weights = torch.zeros_like(logits)
+    
+    for i in range(T_q):
+        # For query i, we compute attention to all keys j
+        z_i = logits[:, :, i, :]      # [B, H, T_k]
+        z_sp_i = logits_sp[:, :, i, :] # [B, H, T_k]
+        
+        # Cumulative sum of softplus
+        # csum[j] = ∑_{k=0}^{j} softplus(z_{i,k})
+        csum = z_sp_i.cumsum(dim=-1)
\ No newline at end of file
diff --git a/ops/.ipynb_checkpoints/vanilla_attention_std-checkpoint.py b/ops/.ipynb_checkpoints/vanilla_attention_std-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e1eafefc101c388e3e633f51e442209b9802d14
--- /dev/null
+++ b/ops/.ipynb_checkpoints/vanilla_attention_std-checkpoint.py
@@ -0,0 +1,171 @@
+"""
+Vanilla Transformer 的标准 Softmax Attention
+用于替换 flash_attn 的实现
+"""
+import math
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Optional, Tuple
+
+def vanilla_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor, 
+    v: torch.Tensor,
+    causal: bool = True,
+    window_size: Optional[Tuple[int, int]] = None,
+    sm_scale: Optional[float] = None,
+) -> torch.Tensor:
+    """
+    标准 Softmax Attention，兼容 flash_attn_func 的输入格式
+    
+    Args:
+        q, k, v: [batch, seq_len, num_heads, head_dim] 格式
+        causal: 是否使用因果mask
+        window_size: 滑动窗口大小 (left, right)，(-1, -1) 表示无限制
+        sm_scale: softmax 缩放因子
+    
+    Returns:
+        output: [batch, seq_len, num_heads, head_dim] 格式
+    """
+    B, T_q, H, D = q.shape
+    T_k = k.shape[1]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # 转换为 [B, H, T, D] 格式进行计算
+    q = rearrange(q, 'b t h d -> b h t d')
+    k = rearrange(k, 'b t h d -> b h t d')
+    v = rearrange(v, 'b t h d -> b h t d')
+    
+    # 计算 attention scores
+    scores = torch.matmul(q.float(), k.float().transpose(-2, -1)) * sm_scale
+    
+    # Causal mask
+    if causal:
+        P_SEQ = T_k - T_q  # 处理 KV cache 的情况
+        causal_mask = torch.triu(
+            torch.ones((T_q, T_k), dtype=torch.bool, device=q.device),
+            diagonal=P_SEQ + 1
+        )
+        scores = scores.masked_fill(causal_mask[None, None, :, :], float('-inf'))
+    
+    # Window mask (sliding window attention)
+    if window_size is not None and window_size != (-1, -1):
+        left_window, right_window = window_size
+        window_mask = torch.ones((T_q, T_k), dtype=torch.bool, device=q.device)
+        for i in range(T_q):
+            # 计算每个查询位置的有效窗口范围
+            start = max(0, i - left_window)
+            end = min(T_k, i + right_window + 1)
+            window_mask[i, start:end] = False
+        scores = scores.masked_fill(window_mask[None, None, :, :], float('-inf'))
+    
+    # Softmax
+    attn_weights = F.softmax(scores, dim=-1)
+    attn_weights = torch.nan_to_num(attn_weights, 0.0)
+    
+    # Apply attention to values
+    output = torch.matmul(attn_weights.to(v.dtype), v)
+    
+    # 转换回 [B, T, H, D] 格式
+    output = rearrange(output, 'b h t d -> b t h d')
+    
+    return output
+
+
+def vanilla_attention_varlen_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    cu_seqlens_q: torch.Tensor,
+    cu_seqlens_k: torch.Tensor,
+    max_seqlen_q: int,
+    max_seqlen_k: int,
+    causal: bool = True,
+    window_size: Optional[Tuple[int, int]] = None,
+    sm_scale: Optional[float] = None,
+) -> torch.Tensor:
+    """
+    变长序列的标准 Softmax Attention，兼容 flash_attn_varlen_func
+    
+    Args:
+        q: [total_q_tokens, num_heads, head_dim]
+        k: [total_k_tokens, num_kv_heads, head_dim]
+        v: [total_k_tokens, num_kv_heads, head_dim]
+        cu_seqlens_q: 累积序列长度 [batch_size + 1]
+        cu_seqlens_k: 累积序列长度 [batch_size + 1]
+        max_seqlen_q: 最大查询序列长度
+        max_seqlen_k: 最大键值序列长度
+    
+    Returns:
+        output: [total_q_tokens, num_heads, head_dim]
+    """
+    batch_size = cu_seqlens_q.shape[0] - 1
+    H = q.shape[1]
+    D = q.shape[2]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    outputs = []
+    
+    # 逐批次处理
+    for b in range(batch_size):
+        q_start, q_end = cu_seqlens_q[b].item(), cu_seqlens_q[b+1].item()
+        k_start, k_end = cu_seqlens_k[b].item(), cu_seqlens_k[b+1].item()
+        
+        if q_start == q_end:  # 空序列
+            continue
+            
+        # 提取当前批次的 q, k, v
+        q_b = q[q_start:q_end]  # [T_q, H, D]
+        k_b = k[k_start:k_end]  # [T_k, H, D] 
+        v_b = v[k_start:k_end]  # [T_k, H, D]
+        
+        T_q = q_b.shape[0]
+        T_k = k_b.shape[0]
+        
+        # 转换为 [H, T, D] 格式
+        q_b = rearrange(q_b, 't h d -> h t d')
+        k_b = rearrange(k_b, 't h d -> h t d')
+        v_b = rearrange(v_b, 't h d -> h t d')
+        
+        # 计算 attention scores
+        scores = torch.matmul(q_b.float(), k_b.float().transpose(-2, -1)) * sm_scale
+        
+        # Causal mask
+        if causal:
+            P_SEQ = T_k - T_q
+            causal_mask = torch.triu(
+                torch.ones((T_q, T_k), dtype=torch.bool, device=q.device),
+                diagonal=P_SEQ + 1
+            )
+            scores = scores.masked_fill(causal_mask[None, :, :], float('-inf'))
+        
+        # Window mask
+        if window_size is not None and window_size != (-1, -1):
+            left_window, right_window = window_size
+            window_mask = torch.ones((T_q, T_k), dtype=torch.bool, device=q.device)
+            for i in range(T_q):
+                start = max(0, i - left_window)
+                end = min(T_k, i + right_window + 1)
+                window_mask[i, start:end] = False
+            scores = scores.masked_fill(window_mask[None, :, :], float('-inf'))
+        
+        # Softmax
+        attn_weights = F.softmax(scores, dim=-1)
+        attn_weights = torch.nan_to_num(attn_weights, 0.0)
+        
+        # Apply attention
+        output_b = torch.matmul(attn_weights.to(v_b.dtype), v_b)
+        
+        # 转换回 [T, H, D] 格式
+        output_b = rearrange(output_b, 'h t d -> t h d')
+        outputs.append(output_b)
+    
+    # 拼接所有批次的输出
+    output = torch.cat(outputs, dim=0)
+    
+    return output
diff --git a/ops/__init__.py b/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dea7022bf8589cc068109d7dcb7ad0c4aa2d090c
--- /dev/null
+++ b/ops/__init__.py
@@ -0,0 +1,3 @@
+
+# Framework mock for ndr compatibility
+from . import framework_mock
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diff --git a/ops/direction_sensitive_geometric.py b/ops/direction_sensitive_geometric.py
new file mode 100644
index 0000000000000000000000000000000000000000..85b5db49c0e9bfd947a4deaf4a848db6b7c7ec12
--- /dev/null
+++ b/ops/direction_sensitive_geometric.py
@@ -0,0 +1,115 @@
+import torch
+from forgetting_transformer.ops.multi_head_attention import AttentionMask, MultiHeadAttentionBase, AttentionMergeMixin
+from typing import Optional
+from forgetting_transformer.ops.geometric_attention import geometric_attention_activation
+import math
+from forgetting_transformer.ops.multi_head_relative_pos_attention import FixedRelativeMultiheadAttentionBase, shift
+
+
+class DirectionSensitiveGeometricAttention(AttentionMergeMixin, FixedRelativeMultiheadAttentionBase):
+    def __init__(self, state_size: int, n_heads: int, dropout: float = 0.0, global_pos_bias: bool = True,
+                 global_content_bias: bool = True, input_size: Optional[int] = None,
+                 output_size: Optional[int] = None, normalize_score: bool = True):
+        super(AttentionMergeMixin, self).__init__(state_size, n_heads, dropout, input_size)
+
+        self.data_to_kv = torch.nn.Linear(state_size, 2 * n_heads * self.projection_size, bias=False)
+        self.data_to_q = torch.nn.Linear(self.input_size, n_heads * self.projection_size, bias=False)
+        self.data_to_qp = torch.nn.Linear(self.input_size, n_heads * 2)
+
+        self.global_content_bias = torch.nn.Parameter(torch.zeros([n_heads, self.projection_size])) \
+                                   if global_content_bias else None
+
+        self.s_bias = torch.nn.Parameter(torch.full([1], 0.0))
+        self.scale = torch.nn.Parameter(torch.full([1], 1.0 / math.sqrt(self.projection_size)))
+        self.scale_pos = torch.nn.Parameter(torch.full([1], 1.0))
+        self.normalize_score = normalize_score
+
+        self.input_size = state_size if input_size is None else input_size
+
+        print(f"DirectionSensitiveGeometricAttention: normalize score: {normalize_score}")
+
+        super(DirectionSensitiveGeometricAttention, self).__init__(output_size)
+        self.reset_parameters()
+
+    def get_attention_scores(self, mask: Optional[torch.Tensor],
+                   q_content: torch.Tensor, k_content: torch.Tensor,
+                   q_pos: torch.Tensor,
+                   pos_offset: int) -> torch.Tensor:
+
+        # content-content addressing
+        logits = torch.bmm(q_content, self.dropout(k_content).transpose(1, 2))
+
+        # directionality. Do scaling here, less flops.
+        prefer_back, prefer_front = (q_pos * self.scale_pos).unsqueeze(-2).expand(-1,-1,logits.shape[-1],-1).unbind(-1)
+        fpos = prefer_front.triu(1 + pos_offset) + prefer_back.tril(-1 + pos_offset)
+
+        logits = logits * self.scale + fpos + self.s_bias
+
+        logits = self.apply_logit_masks(logits.view(logits.shape[0] // self.n_heads, self.n_heads, *logits.shape[1:]), mask).flatten(0,1)
+
+        logits.masked_fill_(torch.eye(logits.shape[-1], device=logits.device, dtype=torch.bool)[pos_offset : pos_offset + logits.shape[-2]], float("-inf"))
+
+        return geometric_attention_activation(logits, mask, pos_offset, normalize=self.normalize_score)
+
+    def add_head_specific_bias(self, data: torch.Tensor, bias: Optional[torch.Tensor]) -> torch.Tensor:
+        # data [batch * n_heads, len, c]
+        # bias [n_heads, c]
+        return (data.view(-1, bias.shape[0], *data.shape[1:]) + bias.unsqueeze(1).type_as(data)).view_as(data) \
+               if bias is not None else data
+
+    def _attention(self, mask: Optional[torch.Tensor],
+                   q_content: torch.Tensor, k_content: torch.Tensor,
+                   q_pos: torch.Tensor,
+                   v: torch.Tensor, pos_offset: int) -> [torch.Tensor, torch.Tensor]:
+
+        scores = self.get_attention_scores(mask, q_content, k_content, q_pos, pos_offset)
+
+        # Scores shape: [n_batch * n_heads, n_out, n_in]
+        return self._attention_read(mask, scores, v)
+
+    def forward(self, curr_state: torch.Tensor, attend_to: torch.Tensor, mask: Optional[AttentionMask],
+                pos_offset: int = 0, need_weights: bool = False):
+        # curr_state: [batch_size, out_len, c]
+        # attend_to: [batch_size, in_len, c]
+        batch_size, in_len = attend_to.shape[0:2]
+        out_len = curr_state.shape[1]
+
+        k_content, v = self.transform_data(attend_to, self.data_to_kv, 2)
+        q, = self.transform_data(curr_state, self.data_to_q, 1)
+        q_pos, = self.transform_data(curr_state, self.data_to_qp, 1)
+
+        q_content = self.add_head_specific_bias(q, self.global_content_bias)
+
+        data, scores = self.merged_attention(batch_size, out_len, mask, q_content, k_content, q_pos, v,
+                                             pos_offset, need_weights=need_weights)
+
+        if need_weights:
+            return data, scores
+        else:
+            return data
+
+    def reset_parameters(self):
+        torch.nn.init.xavier_uniform_(self.data_to_q.weight)
+        torch.nn.init.xavier_uniform_(self.pos_to_pq.weight)
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight[:self.projection_size * self.n_heads])
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight[self.projection_size * self.n_heads:])
+
+        if self.global_content_bias is not None:
+            self.global_content_bias.data.fill_(0)
+
+
+class DirectionSensitiveGeometricAttentionMyInit(DirectionSensitiveGeometricAttention):
+    def xavier_manual_(self, tensor: torch.Tensor, fan_in: int, fan_out: int, gain: float = 1) -> torch.Tensor:
+        std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
+        a = math.sqrt(3.0) * std  # Calculate uniform bounds from standard deviation
+
+        return torch.nn.init._no_grad_uniform_(tensor, -a, a)
+
+    def reset_parameters(self):
+        self.xavier_manual_(self.data_to_q.weight, self.state_size, self.projection_size)
+        self.xavier_manual_(self.pos_to_pq.weight, self.state_size, 2)
+        self.xavier_manual_(self.data_to_kv.weight, self.state_size, self.projection_size)
+        self.xavier_manual_(self.multi_head_merge.weight, self.projection_size, self.state_size)
+
+        if self.global_content_bias is not None:
+            self.global_content_bias.data.fill_(0)
diff --git a/ops/direction_sensitive_geometric.py.bak b/ops/direction_sensitive_geometric.py.bak
new file mode 100644
index 0000000000000000000000000000000000000000..0980451ccfcb531a6b0007c867bb93007b9cd66a
--- /dev/null
+++ b/ops/direction_sensitive_geometric.py.bak
@@ -0,0 +1,115 @@
+import torch
+from .multi_head_attention import AttentionMask, MultiHeadAttentionBase, AttentionMergeMixin
+from typing import Optional
+from .geometric_attention import geometric_attention_activation
+import math
+from .multi_head_relative_pos_attention import FixedRelativeMultiheadAttentionBase, shift
+
+
+class DirectionSensitiveGeometricAttention(AttentionMergeMixin, FixedRelativeMultiheadAttentionBase):
+    def __init__(self, state_size: int, n_heads: int, dropout: float = 0.0, global_pos_bias: bool = True,
+                 global_content_bias: bool = True, input_size: Optional[int] = None,
+                 output_size: Optional[int] = None, normalize_score: bool = True):
+        super(AttentionMergeMixin, self).__init__(state_size, n_heads, dropout, input_size)
+
+        self.data_to_kv = torch.nn.Linear(state_size, 2 * n_heads * self.projection_size, bias=False)
+        self.data_to_q = torch.nn.Linear(self.input_size, n_heads * self.projection_size, bias=False)
+        self.data_to_qp = torch.nn.Linear(self.input_size, n_heads * 2)
+
+        self.global_content_bias = torch.nn.Parameter(torch.zeros([n_heads, self.projection_size])) \
+                                   if global_content_bias else None
+
+        self.s_bias = torch.nn.Parameter(torch.full([1], 0.0))
+        self.scale = torch.nn.Parameter(torch.full([1], 1.0 / math.sqrt(self.projection_size)))
+        self.scale_pos = torch.nn.Parameter(torch.full([1], 1.0))
+        self.normalize_score = normalize_score
+
+        self.input_size = state_size if input_size is None else input_size
+
+        print(f"DirectionSensitiveGeometricAttention: normalize score: {normalize_score}")
+
+        super(DirectionSensitiveGeometricAttention, self).__init__(output_size)
+        self.reset_parameters()
+
+    def get_attention_scores(self, mask: Optional[torch.Tensor],
+                   q_content: torch.Tensor, k_content: torch.Tensor,
+                   q_pos: torch.Tensor,
+                   pos_offset: int) -> torch.Tensor:
+
+        # content-content addressing
+        logits = torch.bmm(q_content, self.dropout(k_content).transpose(1, 2))
+
+        # directionality. Do scaling here, less flops.
+        prefer_back, prefer_front = (q_pos * self.scale_pos).unsqueeze(-2).expand(-1,-1,logits.shape[-1],-1).unbind(-1)
+        fpos = prefer_front.triu(1 + pos_offset) + prefer_back.tril(-1 + pos_offset)
+
+        logits = logits * self.scale + fpos + self.s_bias
+
+        logits = self.apply_logit_masks(logits.view(logits.shape[0] // self.n_heads, self.n_heads, *logits.shape[1:]), mask).flatten(0,1)
+
+        logits.masked_fill_(torch.eye(logits.shape[-1], device=logits.device, dtype=torch.bool)[pos_offset : pos_offset + logits.shape[-2]], float("-inf"))
+
+        return geometric_attention_activation(logits, mask, pos_offset, normalize=self.normalize_score)
+
+    def add_head_specific_bias(self, data: torch.Tensor, bias: Optional[torch.Tensor]) -> torch.Tensor:
+        # data [batch * n_heads, len, c]
+        # bias [n_heads, c]
+        return (data.view(-1, bias.shape[0], *data.shape[1:]) + bias.unsqueeze(1).type_as(data)).view_as(data) \
+               if bias is not None else data
+
+    def _attention(self, mask: Optional[torch.Tensor],
+                   q_content: torch.Tensor, k_content: torch.Tensor,
+                   q_pos: torch.Tensor,
+                   v: torch.Tensor, pos_offset: int) -> [torch.Tensor, torch.Tensor]:
+
+        scores = self.get_attention_scores(mask, q_content, k_content, q_pos, pos_offset)
+
+        # Scores shape: [n_batch * n_heads, n_out, n_in]
+        return self._attention_read(mask, scores, v)
+
+    def forward(self, curr_state: torch.Tensor, attend_to: torch.Tensor, mask: Optional[AttentionMask],
+                pos_offset: int = 0, need_weights: bool = False):
+        # curr_state: [batch_size, out_len, c]
+        # attend_to: [batch_size, in_len, c]
+        batch_size, in_len = attend_to.shape[0:2]
+        out_len = curr_state.shape[1]
+
+        k_content, v = self.transform_data(attend_to, self.data_to_kv, 2)
+        q, = self.transform_data(curr_state, self.data_to_q, 1)
+        q_pos, = self.transform_data(curr_state, self.data_to_qp, 1)
+
+        q_content = self.add_head_specific_bias(q, self.global_content_bias)
+
+        data, scores = self.merged_attention(batch_size, out_len, mask, q_content, k_content, q_pos, v,
+                                             pos_offset, need_weights=need_weights)
+
+        if need_weights:
+            return data, scores
+        else:
+            return data
+
+    def reset_parameters(self):
+        torch.nn.init.xavier_uniform_(self.data_to_q.weight)
+        torch.nn.init.xavier_uniform_(self.pos_to_pq.weight)
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight[:self.projection_size * self.n_heads])
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight[self.projection_size * self.n_heads:])
+
+        if self.global_content_bias is not None:
+            self.global_content_bias.data.fill_(0)
+
+
+class DirectionSensitiveGeometricAttentionMyInit(DirectionSensitiveGeometricAttention):
+    def xavier_manual_(self, tensor: torch.Tensor, fan_in: int, fan_out: int, gain: float = 1) -> torch.Tensor:
+        std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
+        a = math.sqrt(3.0) * std  # Calculate uniform bounds from standard deviation
+
+        return torch.nn.init._no_grad_uniform_(tensor, -a, a)
+
+    def reset_parameters(self):
+        self.xavier_manual_(self.data_to_q.weight, self.state_size, self.projection_size)
+        self.xavier_manual_(self.pos_to_pq.weight, self.state_size, 2)
+        self.xavier_manual_(self.data_to_kv.weight, self.state_size, self.projection_size)
+        self.xavier_manual_(self.multi_head_merge.weight, self.projection_size, self.state_size)
+
+        if self.global_content_bias is not None:
+            self.global_content_bias.data.fill_(0)
diff --git a/ops/forgetting_attention.py b/ops/forgetting_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6cba53a9a3ae49a448247e739f7f3d644caeca5
--- /dev/null
+++ b/ops/forgetting_attention.py
@@ -0,0 +1,1138 @@
+"""
+Implementation of Forgetting Attention.
+
+Our code is adapted from https://github.com/FlagOpen/FlagAttention/blob/ee91638dec6da8c00c4113d179f469e0ffcd5852/src/flag_attn/flash.py. The code is modified to implement Forgetting Attention.
+
+The original license info from FlagAttention:
+
+Copyright 2023 BAAI
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import pytest
+import math
+import torch
+import triton
+import triton.language as tl
+from einops import rearrange
+from typing import Optional
+
+
+__all__ = ["forgetting_attention"]
+
+
+# File flash.py
+def maybe_contiguous(x):
+    # only when the inner most dimension is contiguous can LDGSTS be used
+    # so inner-dimension contiguity is enforced.
+    return x.contiguous() if x.stride(-1) != 1 else x
+
+def rounded_multiple(a, b):
+    return (a + b - 1) // b * b
+
+# --------------------------- public API ---------------------------
+class ForgettingAttention(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, q, k, v, log_fgate, seq_start, causal, sm_scale, return_log_normalizer):
+        assert causal, "Only causal attention is supported"
+        Dq, Dk, Dv = q.shape[-1], k.shape[-1], v.shape[-1]
+        assert Dq == Dk == Dv, "feature size of q, k, v should be equal"
+        assert Dk in {16, 32, 64, 128}, "We only support head dims in {16, 32, 64, 128}"
+
+        B, H, M, D = q.shape
+        if seq_start is not None:
+            has_seq_start = True
+            assert seq_start.shape == (B,)
+        else:
+            has_seq_start = False
+            seq_start = torch.zeros((B,), device=q.device, dtype=torch.long)
+        N = k.shape[2]
+        assert log_fgate.shape == (B, H, N)
+        log_fgate = log_fgate.float()
+        if has_seq_start:
+            log_fgate = log_fgate.clone()
+            # We absolutely don't want masked value to affect result. If we
+            # don't do this then it could via affecting numerical precision of
+            # cumsum
+            mask_index = (torch.arange(N, device=q.device)[None, None, :] < seq_start[:, None, None])
+            mask_index = torch.broadcast_to(mask_index, log_fgate.size())
+            log_fgate[mask_index] = 0.0
+
+        log_lambda = torch.cumsum(log_fgate, dim=-1, dtype=log_fgate.dtype).float()
+
+        Hk, Hv = k.shape[1], v.shape[1]
+        assert Hk == Hv, "num of heads in k and v should be equal"
+        assert H == Hk, "groupped query attention has not been tested. You can uncomment this if you know what you are doing."
+        assert H % Hk == 0, "number of heads in q must be a multiple of that in k & v"
+        num_groups = H // Hk
+
+        P_SEQ = N - M
+        larger_m = M > N
+        assert (not larger_m), "The key/value tensors must be longer than the query tensor"
+
+        if sm_scale is None:
+            sm_scale = 1. / math.sqrt(D)
+
+        # contiguity
+        q, k, v = maybe_contiguous(q), maybe_contiguous(k), maybe_contiguous(v)
+
+        # to work around https://github.com/openai/triton/issues/2441
+        device = torch.cuda.device_of(q)
+
+        with torch.cuda.device(device):
+
+            config = get_fwd_config(B, H, M, N, D, causal)
+            BLOCK_M, BLOCK_N, num_stages, num_warps = config
+
+            divisible_m = M % BLOCK_M == 0
+            divisible_n = N % BLOCK_N == 0
+            # consider using 3d grid to avoid div & rem
+            grid = (triton.cdiv(M, BLOCK_M), H, B)
+            o = torch.empty_like(q)
+            L = torch.empty((B, H, M), device=q.device, dtype=torch.float32)
+            _fwd_kernel[grid](
+                q, k, v, log_lambda, seq_start, sm_scale,
+                L, o,
+                q.stride(0), q.stride(1), q.stride(2), q.stride(3),
+                k.stride(0), k.stride(1), k.stride(2), k.stride(3),
+                v.stride(0), v.stride(1), v.stride(2), v.stride(3),
+                log_lambda.stride(0), log_lambda.stride(1), log_lambda.stride(2),
+                o.stride(0), o.stride(1), o.stride(2), o.stride(3),
+                B, H, M, N, P_SEQ, num_groups,
+                BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_DMODEL=D,
+                IS_CAUSAL=causal, LARGER_M=larger_m, HAS_SEQ_START=has_seq_start,
+                DIVISIBLE_M=divisible_m, DIVISIBLE_N=divisible_n,
+                num_warps=num_warps, num_stages=num_stages,
+            )
+
+        # autograd context maintenance
+        ctx.save_for_backward(q, k, v, o, L, log_lambda, seq_start)
+        ctx.sm_scale = sm_scale
+        ctx.causal = causal
+        ctx.has_seq_start = has_seq_start
+
+        has_extra_return = return_log_normalizer
+        if has_extra_return:
+            outs = (
+                o,
+                L if return_log_normalizer else None,
+            )
+            return outs
+        return o
+
+    @staticmethod
+    def backward(ctx, do, *ignored):
+        q, k, v, o, L, log_lambda, seq_start = ctx.saved_tensors
+        sm_scale = ctx.sm_scale
+        causal = ctx.causal
+        has_seq_start = ctx.has_seq_start
+
+        B, H, M, D = q.shape
+        N = k.shape[2]
+        Hk = k.shape[1]
+        num_groups = H // Hk
+        P_SEQ = N - M
+        larger_m = M > N
+
+        if sm_scale is None:
+            sm_scale = 1. / math.sqrt(D)
+
+        # to work around https://github.com/openai/triton/issues/2441
+        device = torch.cuda.device_of(q)
+        with torch.cuda.device(device):
+            config = get_bwd_config(B, H, M, N, D, causal)
+            BLOCK_M, BLOCK_N, num_stages, num_warps = config
+
+            divisible_m = M % BLOCK_M == 0
+            divisible_n = N % BLOCK_N == 0
+
+            delta = torch.empty_like(L)
+            grid = (triton.cdiv(M, BLOCK_M), H, B)
+            _bwd_preprocess[grid](
+                o, do,
+                delta,
+                o.stride(0), o.stride(1), o.stride(2), o.stride(3),
+                do.stride(0), do.stride(1), do.stride(2), do.stride(3),
+                delta.stride(0), delta.stride(1), delta.stride(2),
+                M,
+                BLOCK_M=BLOCK_M, D_HEAD=D,
+                DIVISIBLE_M=divisible_m,
+            )
+
+            # NOTE that dk & dv always have the same number of heads as q, instead of q.
+            BLOCK_M, BLOCK_N, num_stages, num_warps = get_bwd_kv_config(B, H, M, N, D, causal)
+            divisible_m = M % BLOCK_M == 0
+            divisible_n = N % BLOCK_N == 0
+
+            dk = torch.empty((B, H, N, D), dtype=k.dtype, device=q.device)
+            dv = torch.empty((B, H, N, D), dtype=v.dtype, device=q.device)
+            dlog_lambda = torch.empty((B, H, N), dtype=log_lambda.dtype, device=q.device)
+            grid = (triton.cdiv(N, BLOCK_N), H, B)
+            _bwd_kv_kernel[grid](
+                q, k, v, log_lambda, seq_start, sm_scale, do,
+                dk, dv, dlog_lambda,
+                L, delta,
+                q.stride(0), q.stride(1), q.stride(2), q.stride(3),
+                k.stride(0), k.stride(1), k.stride(2), k.stride(3),
+                v.stride(0), v.stride(1), v.stride(2), v.stride(3),
+                log_lambda.stride(0), log_lambda.stride(1), log_lambda.stride(2),
+                do.stride(0), do.stride(1), do.stride(2), do.stride(3),
+                dk.stride(0), dk.stride(1), dk.stride(2), dk.stride(3),
+                dv.stride(0), dv.stride(1), dv.stride(2), dv.stride(3),
+                dlog_lambda.stride(0), dlog_lambda.stride(1), dlog_lambda.stride(2),
+                B, H, M, N, P_SEQ,
+                num_groups,
+                BLOCK_M=BLOCK_M, BLOCK_DMODEL=D, BLOCK_N=BLOCK_N, CAUSAL=causal,
+                DIVISIBLE_M=divisible_m, DIVISIBLE_N=divisible_n, HAS_SEQ_START=has_seq_start,
+                num_stages=num_stages, num_warps=num_warps,
+            )
+
+            BLOCK_M, BLOCK_N, num_stages, num_warps = get_bwd_q_config(B, H, M, N, D, causal)
+            divisible_m = M % BLOCK_M == 0
+            divisible_n = N % BLOCK_N == 0
+            dq = torch.zeros_like(q)
+            grid = (triton.cdiv(M, BLOCK_M), H, B)
+            _bwd_q_kernel[grid](
+                q, k, v, log_lambda, seq_start, sm_scale, do,
+                dq, dlog_lambda,
+                L, delta,
+                q.stride(0), q.stride(1), q.stride(2), q.stride(3),
+                k.stride(0), k.stride(1), k.stride(2), k.stride(3),
+                v.stride(0), v.stride(1), v.stride(2), v.stride(3),
+                log_lambda.stride(0), log_lambda.stride(1), log_lambda.stride(2),
+                do.stride(0), do.stride(1), do.stride(2), do.stride(3),
+                dq.stride(0), dq.stride(1), dq.stride(2), dq.stride(3),
+                dlog_lambda.stride(0), dlog_lambda.stride(1), dlog_lambda.stride(2),
+                B, H, M, N, P_SEQ,
+                num_groups,
+                BLOCK_M=BLOCK_M, BLOCK_DMODEL=D, BLOCK_N=BLOCK_N,
+                CAUSAL=causal, LARGER_M=larger_m, HAS_SEQ_START=has_seq_start,
+                DIVISIBLE_M=divisible_m, DIVISIBLE_N=divisible_n,
+                num_stages=num_stages, num_warps = num_warps,
+            )
+            dk = dk.reshape((B, Hk, num_groups, N, D)).sum(2)
+            dv = dv.reshape((B, Hk, num_groups, N, D)).sum(2)
+        dcumsum = torch.cumsum(dlog_lambda, dim=-1, dtype=log_lambda.dtype)
+        dlog_fgate = dlog_lambda + dcumsum[..., -1:] - dcumsum
+        dlog_fgate = dlog_fgate.float()
+        return dq, dk, dv, dlog_fgate, None, None, None, None, None, None, None
+
+
+def forgetting_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    log_fgate: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+):
+    """
+    A FlashAttention-based implementation of Forgetting Attention. 
+
+    Note:
+    - We recommand bfloat16/float16 for q, k, v and float32 for log_fgate. float32 for 
+      q, k, v is also supported, but the kernel will not use tensor cores if q, k, v are
+      in float32 (which would be slow).
+    - We only support seqlen_q <= seqlen_k
+    - We only support causal attention
+    - Head dimension must be in one of {16, 32, 64, 128}
+
+    Arguments:
+        - q: (batch_size, seqlen_q, num_heads, head_dim) unless head_first=True.
+        - k: (batch_size, seqlen_k, num_heads, head_dim) unless head_first=True.
+        - v: (batch_size, seqlen_k, num_heads, head_dim) unless head_first=True.
+        - log_fgate: (batch_size, seqlen_k, num_heads) unless head_first=True. 
+              This should be the **log** of the forget gates. This is typically the 
+              output of torch.nn.functional.logsigmoid.
+        - head_first: if True, the order the num_heads and seqlen_* axis of the all 
+              FloatTensor inputs and outputs should be (num_heads, seq_len_*) instead of
+              (seq_len_*, num_heads)
+        - seq_start: If not None, should be LongTensor with shape (batch_size,) 
+              and range in [0, seq_len_k). For each batch index batch_id, no attention 
+              will be allocated to tokens before the token index seq_start[batch_id]. 
+              This is useful for left-padded inputs.
+        - sm_scale: The scaling of attention scores before applying softmax. If
+              None, it defaults to (1.0 / math.sqrt(head_dim))
+
+    Returns:
+        out (torch.Tensor): (batch_size, seqlen_q, num_heads, head_dim) unless head_first=True.
+    """
+    if not head_first:
+        q, k, v = [rearrange(item, "b t h d -> b h t d") for item in (q, k, v)]
+        log_fgate = rearrange(log_fgate, "b t h -> b h t")
+    out = ForgettingAttention.apply(q, k, v, log_fgate, seq_start, True, sm_scale, False)
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    return out
+
+
+# --------------------------- Forward ---------------------------
+# NOTE: this function can be overwritten at runtime to use your custom config
+def get_fwd_config(B, H, M, N, D, causal):
+    assert causal
+    if torch.cuda.get_device_capability() == (8, 0):
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 32, 3, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 4, 4
+    elif torch.cuda.get_device_capability() == (9, 0):
+        # H100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 3, 8
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 128, 2, 8
+    elif torch.cuda.get_device_capability() == (8, 6):
+        if not causal:
+            if D <= 64:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 3, 4
+            else:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 2, 4
+        else: # causal
+            if D <= 64:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 3, 4
+            else:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 2, 4
+    elif torch.cuda.get_device_capability() == (8, 9):
+        # L40S
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 2, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 2, 4
+    else:
+        BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+    return (BLOCK_M, BLOCK_N, num_stages, num_warps)
+
+
+@triton.jit
+def _fwd_kernel(
+    Q, K, V, LOG_LAMBDA, SEQ_START, sm_scale,
+    L, O,
+    stride_qz, stride_qh, stride_qm, stride_qk,
+    stride_kz, stride_kh, stride_kn, stride_kk,
+    stride_vz, stride_vh, stride_vn, stride_vk,
+    stride_log_lambda_z, stride_log_lambda_h, stride_log_lambda_n,
+    stride_oz, stride_oh, stride_om, stride_ok,
+    Z, H, M, N, P_SEQ,
+    num_groups,
+    BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr,
+    IS_CAUSAL: tl.constexpr, LARGER_M: tl.constexpr, HAS_SEQ_START: tl.constexpr,
+    DIVISIBLE_M: tl.constexpr, DIVISIBLE_N: tl.constexpr,
+):
+    input_dtype = Q.dtype.element_ty
+    # -- grid id --
+    start_m = tl.program_id(0)
+    off_h = tl.program_id(1)
+    off_z = tl.program_id(2)
+
+    # scale sm_scale by log_2(e) and use
+    # 2^x instead of exp in the loop because CSE and LICM
+    # don't work as expected with `exp` in the loop
+    log2e: tl.constexpr = 1.4426950408889634
+    loge2: tl.constexpr = 0.6931471805599453
+    qk_scale = sm_scale * log2e
+
+    # offset pointers for (batch, head)
+    off_hk = off_h // num_groups
+    Q += off_z * stride_qz + off_h * stride_qh
+    K += off_z * stride_kz + off_hk * stride_kh
+    V += off_z * stride_vz + off_hk * stride_vh
+    LOG_LAMBDA += off_z * stride_log_lambda_z + off_h * stride_log_lambda_h
+    O += off_z * stride_oz + off_h * stride_oh
+    L += (off_z * H + off_h) * M # l's shape is (B, H, M)
+
+    offs_m_base = tl.arange(0, BLOCK_M)
+    offs_m = start_m * BLOCK_M + offs_m_base
+    offs_n_base = tl.arange(0, BLOCK_N)
+    offs_k = tl.arange(0, BLOCK_DMODEL)
+
+
+    # initialize pointers to value-like data
+    q_ptrs = Q + (offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk) # (BLOCK_M, BLOCK_DMODEL)
+    log_lambda_out_ptrs = LOG_LAMBDA + (P_SEQ + offs_m) * stride_log_lambda_n
+    o_ptrs = O + (offs_m[:, None] * stride_om + offs_k[None, :] * stride_ok) # (BLOCK_M, BLOCK_DMODEL)
+    l_ptrs = L + offs_m
+
+    # initialize pointer to m and l, fp32 for accumulators
+    m_i = tl.full([BLOCK_M], value=-float("inf"), dtype=tl.float32)
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
+
+    # load q
+    if DIVISIBLE_M:
+        q = tl.load(q_ptrs, cache_modifier=".cg")
+        log_lambda_out = tl.load(log_lambda_out_ptrs, cache_modifier=".cg")
+    else:
+        mask_m = offs_m < M
+        q = tl.load(q_ptrs, mask=mask_m[:, None], cache_modifier=".cg")
+        log_lambda_out = tl.load(log_lambda_out_ptrs, mask=mask_m, cache_modifier=".cg")
+
+    #Dot I trick: to place q in registers, it saves shared memory
+    # if BLOCK_DMODEL < 128:
+    #     I = tl.where(offs_k[:, None] == offs_k,
+    #                  tl.full((BLOCK_DMODEL, BLOCK_DMODEL), 1.0, dtype=input_dtype),
+    #                  tl.full((BLOCK_DMODEL, BLOCK_DMODEL), 0.0, dtype=input_dtype))
+    #     q = tl.dot(q, I, input_precision="ieee").to(input_dtype)
+    # else:
+    #     I = tl.where(offs_m_base[:, None] == offs_m_base,
+    #                  tl.full((BLOCK_M, BLOCK_M), 1.0, dtype=input_dtype),
+    #                  tl.full((BLOCK_M, BLOCK_M), 0.0, dtype=input_dtype))
+    #     q = tl.dot(I, q, input_precision="ieee").to(input_dtype)
+
+    # NOTE: Loop-Bound-For-N
+    # The indices in m-dimension that this block may access is in `[start_m * BLOCK_M, (start_m + 1) * BLOCK_M)`.
+    # According to the rule of causal masking, then max index in n-dimension that this block may access
+    # is `P_SEQ + (start_m + 1) * BLOCK_M`.
+    # However, the upper bound of index in n-dimension should never exceed the sequence length of k/v(`P_SEQ + N_CTX`).
+    # `P_SEQ + (start_m + 1) * BLOCK_M` may be larger than `N`.
+    # At this case, there would be illegal memory access when loading k & v tiles
+    # if mask_n is not applied for loading(only when `DIVISIBLE_N`` is true).
+    # See also https://github.com/FlagOpen/FlagAttention/pull/8
+    if IS_CAUSAL:
+        hi = tl.minimum(N, P_SEQ + (start_m + 1) * BLOCK_M)
+        if LARGER_M:
+            hi = tl.maximum(0, hi)
+    else:
+        hi = N
+
+    offs_n_init = offs_n_base
+    if HAS_SEQ_START:
+        SEQ_START += off_z
+        seq_start = tl.load(SEQ_START)
+        lo = tl.minimum(seq_start, hi)
+        lo = (lo // BLOCK_N) * BLOCK_N
+        offs_n_init += lo
+    else:
+        lo = 0
+        seq_start = 0
+
+    # loop over k, v and update accumulators
+    k_ptrs = K + (offs_k[:, None] * stride_kk + offs_n_init[None, :] * stride_kn) # (BLOCK_DMODEL, BLOCK_N)
+    v_ptrs = V + (offs_n_init[:, None] * stride_vn + offs_k[None, :] * stride_vk) # (BLOCK_N, BLOCK_DMODEL)
+    log_lambda_in_ptrs = LOG_LAMBDA + (offs_n_init * stride_log_lambda_n) # (BLOCK_N, BLOCK_DMODEL)
+    for start_n in range(lo, hi, BLOCK_N):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+        offs_n = start_n + offs_n_base
+
+        # -- load k, v --
+        if DIVISIBLE_N:
+            k = tl.load(k_ptrs, cache_modifier=".cg")
+            v = tl.load(v_ptrs, cache_modifier=".cg")
+            log_lambda_in = tl.load(log_lambda_in_ptrs, cache_modifier=".cg")
+        else:
+            mask_n = offs_n < N
+            k = tl.load(k_ptrs, mask=mask_n[None, :], cache_modifier=".cg")
+            v = tl.load(v_ptrs, mask=mask_n[:, None], cache_modifier=".cg")
+            log_lambda_in = tl.load(log_lambda_in_ptrs, mask=mask_n, cache_modifier=".cg")
+
+        # -- compute qk ---
+        # s = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        s = tl.dot(q, k, input_precision="ieee") * qk_scale
+        decay_bias = log_lambda_out[:, None] - log_lambda_in[None, :]
+        s += decay_bias * log2e
+
+        if not DIVISIBLE_N:
+            s = tl.where(mask_n[None, :], s, float("-inf"))
+        if IS_CAUSAL:
+            causal_mask = (P_SEQ + offs_m[:, None]) >= offs_n[None, :]
+            s = tl.where(causal_mask, s, float("-inf"))
+        if HAS_SEQ_START:
+            s = tl.where(offs_n[None, :] >= seq_start, s, float("-inf"))
+
+
+        # -- compute scaling constant ---
+        m_i_new = tl.maximum(m_i, tl.max(s, 1))
+        alpha = tl.math.exp2((m_i - m_i_new))
+        p = tl.math.exp2(s - m_i_new[:, None])
+
+        # -- compute partial sumexpn before applying dropout
+        p_sum = tl.sum(p, 1)
+
+
+        # -- scale and update acc: acc *= alpha[:, None]--
+        acc *= alpha[:, None]
+        acc += tl.dot(p.to(input_dtype), v, input_precision="ieee")
+
+        # -- update m_i and l_i --
+        l_i = l_i * alpha + p_sum
+        m_i = m_i_new
+        # update pointers
+        k_ptrs += BLOCK_N * stride_kn
+        v_ptrs += BLOCK_N * stride_vn
+        log_lambda_in_ptrs += BLOCK_N * stride_log_lambda_n
+
+    # write back l & o
+    if IS_CAUSAL and (LARGER_M or HAS_SEQ_START):
+        is_empty_line = (offs_m + P_SEQ) < seq_start
+        acc = tl.where(is_empty_line[:, None], 0.0, acc * (1.0 / l_i[:, None]))
+        l = tl.where(is_empty_line, float("-inf"), m_i * loge2 + tl.log(l_i))
+    else:
+        acc = acc * (1.0 / l_i[:, None])
+        l = m_i * loge2 + tl.log(l_i) # log(normalizer)
+
+
+    if DIVISIBLE_M:
+        tl.store(l_ptrs, l, cache_modifier=".cg")
+        tl.store(o_ptrs, acc.to(input_dtype), cache_modifier=".cg")
+    else:
+        tl.store(l_ptrs, l, mask=mask_m, cache_modifier=".cg")
+        tl.store(o_ptrs, acc.to(input_dtype), mask=mask_m[:, None], cache_modifier=".cg")
+
+
+# --------------------------- Backward ---------------------------
+# NOTE: this function can be overwritten at runtime to use your custom config
+def get_bwd_config(B, H, M, N, D, causal):
+    if torch.cuda.get_device_capability() == (9, 0):
+        if not causal:
+            BLOCK_M = 128 if D <= 64 else 64
+            BLOCK_N = 64
+            num_stages = 2
+            num_warps = 4
+        else:
+            BLOCK_M = 64
+            BLOCK_N = 64
+            num_stages = 3 if D <= 64 else 2
+            num_warps = 4
+    elif torch.cuda.get_device_capability() == (8, 0):
+        if not causal:
+            BLOCK_M = 128 if D <= 64 else 64
+            BLOCK_N = 64
+            num_stages = 2
+            num_warps = 4
+        else:
+            BLOCK_M = 64
+            BLOCK_N = 64
+            num_stages = 3 if D <= 64 else 2
+            num_warps = 4
+    elif torch.cuda.get_device_capability() == (8, 6): # tune for RTX-3090, device_capability(8, 6)
+        if not causal:
+            if D <= 64:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+            else:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 8
+        else:
+            if D <= 64:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+            else:
+                BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 32, 2, 4
+    else:
+        BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 32, 1, 4
+    return (BLOCK_M, BLOCK_N, num_stages, num_warps)
+
+def get_bwd_kv_config(B, H, M, N, D, causal):
+    assert causal
+    if torch.cuda.get_device_capability() == (8, 0): # A100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 4, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 128, 4, 8
+    elif torch.cuda.get_device_capability() == (8, 6): # tune for RTX-3090, device_capability(8, 6)
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 32, 2, 4
+    elif torch.cuda.get_device_capability() == (8, 9): # L40S
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 128, 4, 8
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 128, 2, 8
+    elif torch.cuda.get_device_capability() == (9, 0): # H100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 3, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+    else:
+        BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+    return (BLOCK_M, BLOCK_N, num_stages, num_warps)
+
+def get_bwd_q_config(B, H, M, N, D, causal):
+    assert causal
+    if torch.cuda.get_device_capability() == (8, 0): # A100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 3, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 64, 4, 8
+    elif torch.cuda.get_device_capability() == (8, 6): # tune for RTX-3090, device_capability(8, 6)
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 32, 32, 2, 4
+    elif torch.cuda.get_device_capability() == (8, 9): # L40S
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 4, 4
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 32, 3, 4
+    elif torch.cuda.get_device_capability() == (9, 0): # H100
+        if D <= 64:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 128, 4, 8
+        else:
+            BLOCK_M, BLOCK_N, num_stages, num_warps = 128, 128, 2, 8
+    else:
+        BLOCK_M, BLOCK_N, num_stages, num_warps = 64, 64, 2, 4
+    return (BLOCK_M, BLOCK_N, num_stages, num_warps)
+
+
+@triton.jit
+def _bwd_preprocess(
+    Out, DO,
+    Delta,
+    stride_oz, stride_oh, stride_om, stride_ok,
+    stride_doz, stride_doh, stride_dom, stride_dok,
+    stride_dz, stride_dh, stride_dm,
+    M,
+    BLOCK_M: tl.constexpr, D_HEAD: tl.constexpr,
+    DIVISIBLE_M: tl.constexpr,
+):
+    off_h = tl.program_id(1)
+    off_z = tl.program_id(2)
+    Out += off_z * stride_oz + off_h * stride_oh
+    DO += off_z * stride_doz + off_h * stride_doh
+    Delta += off_z * stride_dz + off_h * stride_dh
+
+    # compute (Out * Dout).sum() for vector interpretation
+    off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
+    off_n = tl.arange(0, D_HEAD)
+
+    # load
+    o_ptrs = Out + off_m[:, None] * stride_om + off_n[None, :] * stride_ok
+    do_ptrs = DO + off_m[:, None] * stride_dom + off_n[None, :] * stride_dok
+
+    if DIVISIBLE_M:
+        o = tl.load(o_ptrs).to(tl.float32)
+        do = tl.load(do_ptrs).to(tl.float32)
+    else:
+        mask_m = off_m < M
+        o = tl.load(o_ptrs, mask=mask_m[:, None]).to(tl.float32)
+        do = tl.load(do_ptrs, mask=mask_m[:, None]).to(tl.float32)
+
+    # compute
+    delta = tl.sum(o * do, axis=1)
+
+    # write-back
+    d_ptrs = Delta + off_m * stride_dm
+    if DIVISIBLE_M:
+        tl.store(d_ptrs, delta)
+    else:
+        tl.store(d_ptrs, delta, mask=mask_m)
+
+
+@triton.jit
+def _bwd_kv_kernel(
+    Q, K, V, LOG_LAMBDA, SEQ_START, sm_scale, DO,
+    DK, DV, DLOG_LAMBDA,
+    L,
+    D,
+    stride_qz, stride_qh, stride_qm, stride_qk,
+    stride_kz, stride_kh, stride_kn, stride_kk,
+    stride_vz, stride_vh, stride_vn, stride_vk,
+    stride_log_lambda_z, stride_log_lambda_h, stride_log_lambda_n,
+    stride_doz, stride_doh, stride_dom, stride_dok,
+    stride_dkz, stride_dkh, stride_dkn, stride_dkk,
+    stride_dvz, stride_dvh, stride_dvn, stride_dvk,
+    stride_dlog_lambda_z, stride_dlog_lambda_h, stride_dlog_lambda_n,
+    Z, H, M, N, P_SEQ,
+    num_groups,
+    BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr,
+    CAUSAL: tl.constexpr,
+    DIVISIBLE_M: tl.constexpr, DIVISIBLE_N: tl.constexpr, HAS_SEQ_START: tl.constexpr,
+):
+    input_dtype = Q.dtype.element_ty
+    # -- grid id --
+    start_n = tl.program_id(0)
+    off_h = tl.program_id(1)
+    off_z = tl.program_id(2)
+    log2e: tl.constexpr = 1.4426950408889634
+    qk_scale = sm_scale * log2e
+
+    # offset pointers for (batch, head)
+    off_hk = off_h // num_groups
+    Q += off_z * stride_qz + off_h * stride_qh
+    K += off_z * stride_kz + off_hk * stride_kh
+    V += off_z * stride_vz + off_hk * stride_vh
+    LOG_LAMBDA += off_z * stride_log_lambda_z + off_h * stride_log_lambda_h
+    DO += off_z * stride_doz + off_h * stride_doh
+
+    # offset pointers for batch/head
+    DK += off_z * stride_dkz + off_h * stride_dkh
+    DV += off_z * stride_dvz + off_h * stride_dvh
+    DLOG_LAMBDA += off_z * stride_dlog_lambda_z + off_h * stride_dlog_lambda_h
+
+    # offset pointers for batch/head
+    D += (off_z * H + off_h) * M
+    L += (off_z * H + off_h) * M
+
+    if CAUSAL:
+        lo = tl.maximum(start_n * BLOCK_N - P_SEQ, 0)
+        lo = (lo // BLOCK_M) * BLOCK_M
+    else:
+        lo = 0
+
+    offs_m_init = lo + tl.arange(0, BLOCK_M)
+    offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    offs_m_base = tl.arange(0, BLOCK_M)
+    offs_k = tl.arange(0, BLOCK_DMODEL)
+
+    # initialize pointers to value-like data
+    q_ptrs = Q + (offs_m_init[:, None] * stride_qm + offs_k[None, :] * stride_qk) # (BLOCK_M, BLOCK_DMODEL)
+    log_lambda_out_ptrs = LOG_LAMBDA + (P_SEQ + offs_m_init) * stride_log_lambda_n # (BLOCK_N, BLOCK_DMODEL)
+    k_ptrs = K + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk) # (BLOCK_N, BLOCK_DMODEL)
+    v_ptrs = V + (offs_n[:, None] * stride_vn + offs_k[None, :] * stride_vk) # (BLOCK_N, BLOCK_DMODEL)
+    log_lambda_in_ptrs = LOG_LAMBDA + (offs_n * stride_log_lambda_n) # (BLOCK_N, BLOCK_DMODEL)
+    do_ptrs = DO + (offs_m_init[:, None] * stride_dom + offs_k[None, :] * stride_dok) # (BLOCK_M, BLOCK_DMODEL)
+
+    dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_k[None, :] * stride_dvk) # (BLOCK_N, BLOCK_DMODEL)
+    dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_k[None, :] * stride_dkk) # (BLOCK_N, BLOCK_DMODEL)
+    dlog_lambda_in_ptrs = DLOG_LAMBDA + (offs_n * stride_dlog_lambda_n) # (BLOCK_N, BLOCK_DMODEL)
+
+    # k and v stay in SRAM throughout
+    if DIVISIBLE_N:
+        v = tl.load(v_ptrs)
+        k = tl.load(k_ptrs)
+        log_lambda_in = tl.load(log_lambda_in_ptrs)
+    else:
+        mask_n = offs_n < N
+        v = tl.load(v_ptrs, mask=mask_n[:, None])
+        k = tl.load(k_ptrs, mask=mask_n[:, None])
+        log_lambda_in = tl.load(log_lambda_in_ptrs, mask=mask_n)
+
+    # If the N block doesn't contain seq_start, no need to loop
+    if HAS_SEQ_START:
+        SEQ_START += off_z
+        seq_start = tl.load(SEQ_START)
+        hi = tl.where(start_n * BLOCK_N + BLOCK_N >= seq_start - 1, M, lo)
+    else:
+        hi = M
+
+    # initialize dk amd dv
+    dk = tl.zeros([BLOCK_N, BLOCK_DMODEL], dtype=tl.float32)
+    dv = tl.zeros([BLOCK_N, BLOCK_DMODEL], dtype=tl.float32)
+    dlog_lambda_in = tl.zeros([BLOCK_N], dtype=tl.float32)
+
+    # loop over a col
+    for start_m in range(lo, hi, BLOCK_M):
+        start_m = tl.multiple_of(start_m, BLOCK_M)
+        offs_m = start_m + offs_m_base
+        causal_mask = (P_SEQ + offs_m[None, :]) >= (offs_n[:, None]) # (BLOCK_M, BLOCK_N)
+
+        # load q1, k1, q2, k2, v, do on-chip
+        if DIVISIBLE_M:
+            q = tl.load(q_ptrs)
+            log_lambda_out = tl.load(log_lambda_out_ptrs)
+        else:
+            mask_m = offs_m < M
+            valid_mask = mask_m[None, :] # & mask_n
+            q = tl.load(q_ptrs, mask=mask_m[:, None])
+            log_lambda_out = tl.load(log_lambda_out_ptrs, mask=mask_m)
+        # recompute p = softmax(qk * sm_scale, dim=-1)
+        # s = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        sT = tl.dot(k, tl.trans(q), input_precision="ieee") * qk_scale
+        decay_bias = log_lambda_out[None, :] - log_lambda_in[:, None]
+        sT += decay_bias * log2e
+        # NOTE: since softmax in backward is pointwise, the normalizer has been saved in fwd)
+        # So masking on s is not needed.
+        # s = tl.where(valid_mask, s , float("-inf"))
+        # if CAUSAL:
+        #     s = tl.where(causal_mask, s, float("-inf"))
+
+        # -- recompute p ---
+        if DIVISIBLE_M:
+            l = tl.load(L + offs_m)
+        else:
+            l = tl.load(L + offs_m, mask=mask_m)
+        pT = tl.math.exp2(sT - l[None, :] * log2e) # (BLOCK_M, BLOCK_N)
+
+        if not DIVISIBLE_M:
+            pT = tl.where(valid_mask, pT, 0.0)
+        if CAUSAL:
+            pT = tl.where(causal_mask, pT, 0.0)
+
+        # compute dv = dot(p, do)
+        if DIVISIBLE_M:
+            do = tl.load(do_ptrs)
+        else:
+            do = tl.load(do_ptrs, mask=mask_m[:, None]) # (BLOCK_M, BLOCK_DMODEL)
+
+
+        dv += tl.dot(pT.to(input_dtype), do, input_precision="ieee") # (BLOCK_N, BLOCK_DMODEL)  # still correct
+
+        # compute dp = dot(v, do)
+        if DIVISIBLE_M:
+            delta = tl.load(D + offs_m)
+        else:
+            delta = tl.load(D + offs_m, mask=mask_m)
+        # dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        dpT = tl.dot(v, tl.trans(do), input_precision="ieee")
+
+
+        # compute ds = p * (dp - delta[:, None])
+        dsT = pT * (dpT - delta[None, :]) # (BLOCK_M, BLOCK_N)
+
+        if not DIVISIBLE_M:
+            dsT = tl.where(valid_mask, dsT, 0.0)
+        if CAUSAL:
+            dsT = tl.where(causal_mask, dsT, 0.0)
+
+        # compute dk = dot(ds.T, q) masking
+        dk += tl.dot(dsT.to(input_dtype), q, input_precision="ieee")
+        dlog_lambda_in += -tl.sum(dsT, axis=1)
+
+        # increment pointers
+        q_ptrs += BLOCK_M * stride_qm
+        log_lambda_out_ptrs += BLOCK_M * stride_log_lambda_n
+        do_ptrs += BLOCK_M * stride_dom
+
+    dk *= sm_scale
+    if HAS_SEQ_START:
+        # Mask out 
+        seq_mask = (offs_n >= seq_start)
+        dk = tl.where(seq_mask[:, None], dk, 0.0)
+        dv = tl.where(seq_mask[:, None], dv, 0.0)
+        dlog_lambda_in = tl.where(seq_mask, dlog_lambda_in, 0.0)
+    if DIVISIBLE_N:
+        tl.store(dk_ptrs, dk.to(input_dtype)) # (BLOCK_N, BLOCK_DMODEL)
+        tl.store(dv_ptrs, dv.to(input_dtype)) # (BLOCK_N, BLOCK_DMODEL,)
+        tl.store(dlog_lambda_in_ptrs, dlog_lambda_in.to(tl.float32)) # (BLOCK_N, BLOCK_DMODEL,)
+    else:
+        tl.store(dk_ptrs, dk.to(input_dtype), mask=mask_n[:, None]) # (BLOCK_N, BLOCK_DMODEL)
+        tl.store(dv_ptrs, dv.to(input_dtype), mask=mask_n[:, None]) # (BLOCK_N, BLOCK_DMODEL)
+        tl.store(dlog_lambda_in_ptrs, dlog_lambda_in.to(tl.float32), mask=mask_n) # (BLOCK_N, BLOCK_DMODEL,)
+
+
+@triton.jit
+def _bwd_q_kernel(
+    Q, K, V, LOG_LAMBDA, SEQ_START, sm_scale, DO,
+    DQ, DLOG_LAMBDA,
+    L,
+    D,
+    stride_qz, stride_qh, stride_qm, stride_qk,
+    stride_kz, stride_kh, stride_kn, stride_kk,
+    stride_vz, stride_vh, stride_vn, stride_vk,
+    stride_log_lambda_z, stride_log_lambda_h, stride_log_lambda_n,
+    stride_doz, stride_doh, stride_dom, stride_dok,
+    stride_dqz, stride_dqh, stride_dqm, stride_dqk,
+    stride_dlog_lambda_z, stride_dlog_lambda_h, stride_dlog_lambda_n,
+    Z, H, M, N, P_SEQ,
+    num_groups,
+    BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr,
+    CAUSAL: tl.constexpr, LARGER_M: tl.constexpr, HAS_SEQ_START: tl.constexpr,
+    DIVISIBLE_M: tl.constexpr, DIVISIBLE_N: tl.constexpr,
+):
+    input_dtype = Q.dtype.element_ty
+    # -- grid id --
+    start_m = tl.program_id(0)
+    off_h = tl.program_id(1)
+    off_z = tl.program_id(2)
+
+    # scale sm_scale by log_2(e) and use
+    # 2^x instead of exp in the loop because CSE and LICM
+    # don't work as expected with `exp` in the loop
+    log2e: tl.constexpr = 1.4426950408889634
+    qk_scale = sm_scale * log2e
+
+    # offset pointers for (batch, head)
+    off_hk = off_h // num_groups
+    Q += off_z * stride_qz + off_h * stride_qh
+    K += off_z * stride_kz + off_hk * stride_kh
+    V += off_z * stride_vz + off_hk * stride_vh
+    LOG_LAMBDA += off_z * stride_log_lambda_z + off_h * stride_log_lambda_h
+    DO += off_z * stride_doz + off_h * stride_doh
+    D += (off_z * H + off_h) * M
+    L += (off_z * H + off_h) * M
+
+    # offset pointers for batch/head
+    DQ += off_z * stride_dqz + off_h * stride_dqh
+    DLOG_LAMBDA += off_z * stride_dlog_lambda_z + off_h * stride_dlog_lambda_h
+
+    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_k = tl.arange(0, BLOCK_DMODEL)
+
+    # initialize pointers to value-like data
+    q_ptrs = Q + (offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk) # (BLOCK_M, BLOCK_DMODEL)
+    log_lambda_out_ptrs = LOG_LAMBDA + (P_SEQ + offs_m) * stride_log_lambda_n
+
+    dq_ptrs = DQ + (offs_m[:, None] * stride_dqm + offs_k[None, :] * stride_dqk) # (BLOCK_M, BLOCK_DMODEL)
+    dlog_lambda_out_ptrs = DLOG_LAMBDA + (P_SEQ + offs_m) * stride_dlog_lambda_n
+    do_ptrs = DO + (offs_m[:, None] * stride_dom + offs_k[None, :] * stride_dok) # (BLOCK_M, BLOCK_DMODEL)
+
+    # pointer to row-wise quantities in value-like data
+    d_ptrs = D + offs_m
+    l_ptrs = L + offs_m
+
+    # load q: it will stay in SRAM throughout
+    if DIVISIBLE_M:
+        q = tl.load(q_ptrs)
+        do = tl.load(do_ptrs)
+        delta = tl.load(d_ptrs)
+        l = tl.load(l_ptrs)
+        log_lambda_out = tl.load(log_lambda_out_ptrs)
+    else:
+        mask_m = offs_m < M
+        q = tl.load(q_ptrs, mask=mask_m[:, None])
+        do = tl.load(do_ptrs, mask=mask_m[:, None])
+        delta = tl.load(d_ptrs, mask=mask_m)
+        l = tl.load(l_ptrs, mask=mask_m)
+        log_lambda_out = tl.load(log_lambda_out_ptrs, mask=mask_m)
+
+    # initialize dq
+    dq = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
+    dlog_lambda_out = tl.zeros([BLOCK_M], dtype=tl.float32)
+
+    # loop over k, v and update accumulator
+    # see note "Loop-Bound-For-N"
+    if CAUSAL:
+        hi = tl.minimum(N, P_SEQ + (start_m + 1) * BLOCK_M)
+        if LARGER_M:
+            hi = tl.maximum(0, hi)
+    else:
+        hi = N
+
+    offs_n_base = tl.arange(0, BLOCK_N)
+    offs_n_init = offs_n_base
+    if HAS_SEQ_START:
+        SEQ_START += off_z
+        seq_start = tl.load(SEQ_START)
+        lo = tl.minimum(seq_start, hi)
+        lo = (lo // BLOCK_N) * BLOCK_N
+        offs_n_init += lo
+    else:
+        lo = 0
+    k_ptrs = K + (offs_n_init[:, None] * stride_kn + offs_k[None, :] * stride_kk) # (BLOCK_N, BLOCK_DMODEL)
+    v_ptrs = V + (offs_n_init[:, None] * stride_vn + offs_k[None, :] * stride_vk) # (BLOCK_N, BLOCK_DMODEL)
+    log_lambda_in_ptrs = LOG_LAMBDA + (offs_n_init * stride_log_lambda_n)
+
+    # loop over a row
+    for start_n in range(lo, hi, BLOCK_N):
+        offs_n = start_n + offs_n_base
+
+        # load k1, k2, v on chip
+        if DIVISIBLE_N:
+            v = tl.load(v_ptrs)
+            k = tl.load(k_ptrs)
+            log_lambda_in = tl.load(log_lambda_in_ptrs)
+        else:
+            mask_n = offs_n < N
+            v = tl.load(v_ptrs, mask=mask_n[:, None])
+            k = tl.load(k_ptrs, mask=mask_n[:, None])
+            log_lambda_in = tl.load(log_lambda_in_ptrs, mask=mask_n)
+
+
+        # recompute p = softmax(qk * sm_scale, dim=-1)
+        if not DIVISIBLE_N:
+            valid_mask = mask_n[None, :] # & mask_m[:, None]
+        if CAUSAL:
+            causal_mask = (P_SEQ + offs_m[:, None]) >= (offs_n[None, :]) # (BLOCK_M, BLOCK_N)
+        # s = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        s = tl.dot(q, tl.trans(k), input_precision="ieee") * qk_scale
+        decay_bias = log_lambda_out[:, None] - log_lambda_in[None, :]
+        s += decay_bias * log2e
+
+        # NOTE: since softmax in backward is pointwise, the normalizer has been saved in fwd)
+        # So masking on s is not needed.
+        # if CAUSAL:
+        #     s = tl.where(causal_mask & valid_mask, s, float("-inf"))
+        # else:
+        #     s = tl.where(valid_mask, s, float("-inf"))
+        p = tl.math.exp2(s - l[:, None] * log2e) # (BLOCK_M, BLOCK_N)
+
+        # compute dp = dot(v, do)
+        # dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        dp = tl.dot(do.to(input_dtype), tl.trans(v), input_precision="ieee")
+
+
+        # no need to mask dp
+        # if CAUSAL:
+        #     dp = tl.where(causal_mask & valid_mask, dp, 0.0)
+        # else:
+        #     dp = tl.where(valid_mask, dp, 0.0)
+
+        # compute ds = p * (dp - delta[:, None])
+        # move scale out to dq at last
+        ds = p * (dp - delta[:, None]) # (BLOCK_M, BLOCK_N)
+
+        # mask ds to ensure no small values
+        if not DIVISIBLE_N:
+            ds = tl.where(valid_mask, ds, 0.0)
+        if CAUSAL:
+            ds = tl.where(causal_mask, ds, 0.0)
+        if HAS_SEQ_START:
+            ds = tl.where(offs_n[None, :] >= seq_start, ds, 0.0)
+
+        dq += tl.dot(ds.to(input_dtype), k, input_precision="ieee")
+        dlog_lambda_out += tl.sum(ds, axis=1)
+
+        # increment pointers
+        k_ptrs += BLOCK_N * stride_kn
+        v_ptrs += BLOCK_N * stride_vn
+        log_lambda_in_ptrs += BLOCK_N * stride_log_lambda_n
+
+    dq *= sm_scale
+    if DIVISIBLE_M:
+        tmp = tl.load(dlog_lambda_out_ptrs)
+    else:
+        tmp = tl.load(dlog_lambda_out_ptrs, mask=mask_m)
+    dlog_lambda_out += tmp
+    if DIVISIBLE_M:
+        tl.store(dq_ptrs, dq.to(input_dtype))
+        tl.store(dlog_lambda_out_ptrs, dlog_lambda_out)
+    else:
+        tl.store(dq_ptrs, dq.to(input_dtype), mask=mask_m[:, None])
+        tl.store(dlog_lambda_out_ptrs, dlog_lambda_out, mask=mask_m)
+
+
+
+@pytest.mark.parametrize("Z, H, M, N, HEAD_DIM", [(4, 2, 1020, 2098, 64), (4, 2, 1024, 2048, 64)])
+@pytest.mark.parametrize("causal", [True])
+def test_op(Z, H, M, N, HEAD_DIM, causal, dtype=torch.bfloat16):
+    torch.manual_seed(24)
+    q = (torch.empty((Z, H, M, HEAD_DIM), dtype=dtype, device="cuda").normal_(mean=0.0, std=0.5).requires_grad_())
+    k = (torch.empty((Z, H, N, HEAD_DIM), dtype=dtype, device="cuda").normal_(mean=0.0, std=0.5).requires_grad_())
+    v = (torch.empty((Z, H, N, HEAD_DIM), dtype=dtype, device="cuda").normal_(mean=0.0, std=0.5).requires_grad_())
+    fgate_logit = torch.empty((Z, H, N), dtype=torch.float32, device="cuda").uniform_(5, 10)
+    log_fgate = torch.nn.functional.logsigmoid(fgate_logit).requires_grad_()
+    seq_start = torch.randint(low=0, high=N, size=(Z,), dtype=torch.long, device="cuda")
+    # seq_start = torch.randint(low=0, high=10, size=(Z,), dtype=torch.long, device="cuda")
+    # seq_start = torch.full(fill_value=0, size=(Z,), dtype=torch.long, device="cuda")
+    sm_scale = 0.5
+    dout = torch.randn_like(q)
+    # reference implementation
+    P_SEQ = N - M
+    mask = torch.tril(torch.ones((M, N), device="cuda"), diagonal=P_SEQ)
+    p = torch.matmul(q, k.transpose(2, 3)) * sm_scale
+    p = p.float()
+
+    log_lambda = torch.cumsum(log_fgate, dim=-1)
+    decay_bias = log_lambda[..., -M:, None] - log_lambda[..., None, :]
+    p = p + decay_bias
+    if causal:
+        p[:, :, mask == 0] = float("-inf")
+
+    attention_mask = torch.arange(N, device="cuda") < seq_start[:, None, None, None]
+    p = torch.where(attention_mask, float("-inf"), p)
+    p = torch.softmax(p.float(), dim=-1).to(dtype)
+    p = p.clone()
+    p[torch.isnan(p)] = 0.0
+    # p = torch.exp(p)
+    ref_out = torch.matmul(p, v)
+    ref_out.backward(dout)
+    ref_dv, v.grad = v.grad.clone(), None
+    ref_dk, k.grad = k.grad.clone(), None
+    ref_dq, q.grad = q.grad.clone(), None
+    ref_dlog_fgate, log_fgate.grad = log_fgate.grad.clone(), None
+    # triton implementation
+    tri_out = forgetting_attention(q, k, v, log_fgate, head_first=True, seq_start=seq_start, sm_scale=sm_scale)
+    tri_out = tri_out.to(dtype)
+
+    tri_out.backward(dout)
+    tri_dv, v.grad = v.grad.clone(), None
+    tri_dk, k.grad = k.grad.clone(), None
+    tri_dq, q.grad = q.grad.clone(), None
+    tri_dlog_fgate, log_fgate.grad = log_fgate.grad.clone(), None
+    # compare
+    # assert torch.allclose(tri_log_normalizer[~torch.isnan(tri_log_normalizer)], ref_log_normalizer[~torch.isnan(ref_log_normalizer)], atol=1e-2, rtol=0)
+    assert torch.allclose(ref_out, tri_out, atol=1e-2, rtol=0), (ref_out - tri_out).abs().max()
+    rtol = 0
+    # Relative tolerance workaround for known hardware limitation of MI200 GPU.
+    # For details see https://pytorch.org/docs/stable/notes/numerical_accuracy.html#reduced-precision-fp16-and-bf16-gemms-and-convolutions-on-amd-instinct-mi200-devices
+    # if torch.version.hip is not None and triton.runtime.driver.active.get_current_target().arch == "gfx90a":
+        # rtol = 1e-2
+    assert torch.allclose(ref_dv, tri_dv, atol=1e-2, rtol=rtol), (ref_dv - tri_dv).abs().max()
+    assert torch.allclose(ref_dk, tri_dk, atol=1e-2, rtol=rtol), (ref_dk - tri_dk).abs().max()
+    assert torch.allclose(ref_dq, tri_dq, atol=1e-2, rtol=rtol), (ref_dq - tri_dq).abs().max()
+    assert torch.allclose(ref_dlog_fgate, tri_dlog_fgate, atol=1e-2, rtol=rtol), (ref_dlog_fgate - tri_dlog_fgate).abs().max()
+
+try:
+    from flash_attn.flash_attn_interface import \
+        flash_attn_qkvpacked_func as flash_attn_func
+    HAS_FLASH = True
+except BaseException:
+    HAS_FLASH = False
+
+TORCH_HAS_FP8 = hasattr(torch, 'float8_e5m2')
+BATCH, N_HEADS, HEAD_DIM = 4, 32, 128
+# vary seq length for fixed head and batch=4
+configs = []
+for mode in ["fwd", "bwd"]:
+# for mode in ["bwd"]:
+    # for causal in [True, False]:
+    for causal in [True]:
+        if mode == "bwd" and not causal:
+            continue
+        configs.append(
+            triton.testing.Benchmark(
+                x_names=["N_CTX"],
+                # x_vals=[2**i for i in range(10, 15)],
+                x_vals=[2**i for i in range(14, 15)],
+                line_arg="provider",
+                # line_vals=["triton-fp16", "flag"] + (["flash"] if HAS_FLASH else []),
+                # line_names=["Triton [FP16]", "Flag"] + (["Flash-2"] if HAS_FLASH else []),
+                line_vals=["flag"] + (["flash"] if HAS_FLASH else []),
+                line_names=["Flag"] + (["Flash-2"] if HAS_FLASH else []),
+                styles=[("red", "-"), ("blue", "-"), ("green", "-")],
+                ylabel="ms",
+                plot_name=f"fused-attention-batch{BATCH}-head{N_HEADS}-d{HEAD_DIM}-{mode}-causal={causal}",
+                args={
+                    "H": N_HEADS,
+                    "BATCH": BATCH,
+                    "HEAD_DIM": HEAD_DIM,
+                    "mode": mode,
+                    "causal": causal,
+                },
+            ))
+
+
+@triton.testing.perf_report(configs)
+def bench_flash_attention(BATCH, H, N_CTX, HEAD_DIM, causal, mode, provider, device="cuda"):
+    assert mode in ["fwd", "bwd"]
+    warmup = 25
+    rep = 100
+    dtype = torch.bfloat16
+    if "flag" in provider:
+        q = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        k = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        v = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        fgate_logit = torch.empty((BATCH, H, N_CTX), dtype=torch.float32, device="cuda").uniform_(5, 10)
+        log_fgate = torch.nn.functional.logsigmoid(fgate_logit).requires_grad_()
+        # if mode == "fwd" and "fp8" in provider:
+        #     q = q.to(torch.float8_e5m2)
+        #     k = k.to(torch.float8_e5m2)
+        #     v = v.permute(0, 1, 3, 2).contiguous()
+        #     v = v.permute(0, 1, 3, 2)
+        #     v = v.to(torch.float8_e5m2)
+        sm_scale = 1.3
+        fn = lambda: forgetting_attention(q, k, v, log_fgate, head_first=True, sm_scale=sm_scale)
+        if mode == "bwd":
+            o = fn()
+            do = torch.randn_like(o)
+            fn = lambda: o.backward(do, retain_graph=True)
+        ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
+    if provider == "flash":
+        qkv = torch.randn((BATCH, N_CTX, 3, H, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        fn = lambda: flash_attn_func(qkv, causal=causal)
+        if mode == "bwd":
+            o = fn()
+            do = torch.randn_like(o)
+            fn = lambda: o.backward(do, retain_graph=True)
+        ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
+    flops_per_matmul = 2.0 * BATCH * H * N_CTX * N_CTX * HEAD_DIM
+    total_flops = 2 * flops_per_matmul
+    if causal:
+        total_flops *= 0.5
+    if mode == "bwd":
+        total_flops *= 2.5  # 2.0(bwd) + 0.5(recompute)
+    return total_flops / ms * 1e-9
+
+
+if __name__ == "__main__":
+    # only works on post-Ampere GPUs right now
+    bench_flash_attention.run(save_path=".", print_data=True)
diff --git a/ops/forgetting_attention_std.py b/ops/forgetting_attention_std.py
new file mode 100644
index 0000000000000000000000000000000000000000..7763fbae480987a777057c67076272cfd8345af6
--- /dev/null
+++ b/ops/forgetting_attention_std.py
@@ -0,0 +1,72 @@
+"""
+Forgetting Attention - 标准 Softmax 版本
+在 forgetting_attention.py 最后添加这个函数
+"""
+
+import math
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Optional
+
+
+def forgetting_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    log_fgate: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+) -> torch.Tensor:
+    """标准 Softmax 版本的 Forgetting Attention"""
+    
+    if not head_first:
+        q = rearrange(q, "b t h d -> b h t d")
+        k = rearrange(k, "b t h d -> b h t d")
+        v = rearrange(v, "b t h d -> b h t d")
+        log_fgate = rearrange(log_fgate, "b t h -> b h t")
+    
+    B, H, T_q, D = q.shape
+    T_k = k.shape[2]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # 计算 QK 分数
+    scores = torch.matmul(q.float(), k.float().transpose(-2, -1)) * sm_scale
+    
+    # 处理 seq_start
+    log_fgate_masked = log_fgate.float()
+    if seq_start is not None:
+        log_fgate_masked = log_fgate_masked.clone()
+        mask_idx = torch.arange(T_k, device=q.device)[None, None, :] < seq_start[:, None, None]
+        log_fgate_masked[mask_idx] = 0.0
+    
+    # 计算累积衰减
+    log_lambda = torch.cumsum(log_fgate_masked, dim=-1)
+    decay_bias = log_lambda[:, :, :T_q, None] - log_lambda[:, :, None, :]
+    scores = scores + decay_bias
+    
+    # Causal mask
+    P_SEQ = T_k - T_q
+    causal_mask = torch.triu(torch.ones((T_q, T_k), dtype=torch.bool, device=q.device), diagonal=P_SEQ + 1)
+    scores = scores.masked_fill(causal_mask[None, None, :, :], float('-inf'))
+    
+    # seq_start mask
+    if seq_start is not None:
+        seq_mask = torch.arange(T_k, device=q.device)[None, None, None, :] < seq_start[None, :, None, None]
+        scores = scores.masked_fill(seq_mask, float('-inf'))
+    
+    # Softmax
+    attn = F.softmax(scores, dim=-1)
+    attn = torch.nan_to_num(attn, 0.0)
+    
+    # 计算输出
+    out = torch.matmul(attn.to(v.dtype), v)
+    
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    
+    return out
diff --git a/ops/framework_mock.py b/ops/framework_mock.py
new file mode 100644
index 0000000000000000000000000000000000000000..15389c862a4bf4b8aeff8d0c3bfd1ffaf05c3fab
--- /dev/null
+++ b/ops/framework_mock.py
@@ -0,0 +1,25 @@
+"""
+Mock framework module for ndr geometric attention
+只保留必要的部分
+"""
+import torch
+from typing import Optional, Any
+
+class visualize:
+    """Mock visualize class"""
+    @staticmethod
+    def attention(*args, **kwargs):
+        """Dummy attention visualization"""
+        pass
+    
+    @staticmethod
+    def plot(*args, **kwargs):
+        """Dummy plot"""
+        pass
+
+# Mock其他可能需要的功能
+def get_logger(name: str):
+    """Mock logger"""
+    import logging
+    return logging.getLogger(name)
+
diff --git a/ops/geometric_attention/__init__.py b/ops/geometric_attention/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6b40ea5964a343581d47eeb07e17b0919b8d55a
--- /dev/null
+++ b/ops/geometric_attention/__init__.py
@@ -0,0 +1 @@
+from .cuda_interface import geometric_attention_activation
diff --git a/ops/geometric_attention/__pycache__/__init__.cpython-310.pyc b/ops/geometric_attention/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d0f62f98d60c6072c8c5028b031cbcb750351893
Binary files /dev/null and b/ops/geometric_attention/__pycache__/__init__.cpython-310.pyc differ
diff --git a/ops/geometric_attention/__pycache__/cuda_interface.cpython-310.pyc b/ops/geometric_attention/__pycache__/cuda_interface.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..15147f591c204599c3ae68cfdd06ee425e2ef158
Binary files /dev/null and b/ops/geometric_attention/__pycache__/cuda_interface.cpython-310.pyc differ
diff --git a/ops/geometric_attention/cuda_interface.cu b/ops/geometric_attention/cuda_interface.cu
new file mode 100644
index 0000000000000000000000000000000000000000..4dd21cae0bc68d5bc1b990698d149849740eb507
--- /dev/null
+++ b/ops/geometric_attention/cuda_interface.cu
@@ -0,0 +1,177 @@
+#include <torch/extension.h>
+
+__global__ void k_cuda_log_sigmoid_forward(int N, float * t, float *out_sigm, float *out_one_minus_sigm){
+    int i = threadIdx.x + blockIdx.x * blockDim.x;
+    if (i<N){
+        float x = t[i];
+        float c = - log(exp(-abs(x)) + 1);
+        out_sigm[i] = min(x, 0.0f) + c;
+        out_one_minus_sigm[i] = -max(x, 0.0f) + c;
+    }
+}
+
+__global__ void k_cuda_log_sigmoid_backward(int N, float *t, float *grad_sigm, float *grad_one_minus_sigm, float *grad_out){
+    int i = threadIdx.x + blockIdx.x * blockDim.x;
+    if (i<N){
+        float x = t[i];
+        float ne = exp(-abs(x));
+        float coeff = 1.0 / (ne + 1.0) * ne;
+
+        float r_one_minus = (x > 0) ? (coeff - 1) : (-coeff);
+        float r =  (x < 0) ? (coeff - 1) : (-coeff);
+        grad_out[i] = - grad_sigm[i] * r + grad_one_minus_sigm[i] * r_one_minus;
+    }
+}
+
+std::vector<torch::Tensor> cuda_log_sigmoid_forward(torch::Tensor input){
+    auto o1 = torch::empty_like(input);
+    auto o2 = torch::empty_like(input);
+    auto inf = input.flatten();
+
+    const int N = inf.size(0);
+
+    const int threads = 256;
+    const int blocks = (N + threads - 1) / threads;
+
+    k_cuda_log_sigmoid_forward<<<blocks, threads>>>(N,  
+        input.data<float>(),
+        o1.data<float>(),
+        o2.data<float>());
+    
+    return {o1, o2};
+}
+
+std::vector<torch::Tensor> cuda_log_sigmoid_backward(torch::Tensor input, torch::Tensor grad_sigm, torch::Tensor grad_one_minus_sigm){
+    auto output = torch::empty_like(input);
+    auto N = input.flatten().size(0);
+
+    const int threads = 256;
+    const int blocks = (N + threads - 1) / threads;
+
+    k_cuda_log_sigmoid_backward<<<blocks, threads>>>(N,  
+        input.data<float>(),
+        grad_sigm.data<float>(),
+        grad_one_minus_sigm.data<float>(),
+        output.data<float>());
+    
+    return {output};
+}
+
+
+typedef torch::PackedTensorAccessor32<float, 3, torch::RestrictPtrTraits> float_accessor;
+
+__global__ void k_cuda_window_sum_forward(float_accessor csum, float_accessor out, int offset){
+    const int in_p = threadIdx.z + blockIdx.z * blockDim.z;
+    const int out_p_mem = threadIdx.y + blockIdx.y * blockDim.y;
+    const int batch = threadIdx.x + blockIdx.x * blockDim.x;
+
+    const int out_p = out_p_mem + offset;
+
+    if (batch < out.size(0) & out_p_mem < out.size(1) & in_p < out.size(2)){
+        float res;
+        if (in_p == out_p){
+            res = 0;
+        } else {
+            const int offset = abs(out_p - in_p);
+            int p_i = out_p + offset - int(in_p > out_p);
+            const int n_i = out_p - offset;
+
+            p_i = min(p_i, out.size(2) - 1);
+
+            float d_n = (n_i >= 0) ? (csum[batch][out_p_mem][n_i]) : 0.0;
+            res = (csum[batch][out_p_mem][p_i]) - d_n;
+        }
+
+        out[batch][out_p_mem][in_p] = res;
+    }
+
+}
+
+__global__ void k_cuda_window_sum_backward(float_accessor grad_in, float_accessor grad_out, int offset){
+    const int in_p = threadIdx.z + blockIdx.z * blockDim.z;
+    const int out_p_mem = threadIdx.y + blockIdx.y * blockDim.y;
+    const int batch = threadIdx.x + blockIdx.x * blockDim.x;
+
+    const int out_p = out_p_mem + offset;
+
+    if (batch < grad_out.size(0) & out_p_mem < grad_out.size(1) & in_p < grad_out.size(2)){
+        const int other = 2 * out_p - in_p;
+
+        float res;
+        if (in_p == grad_out.size(2) - 1){
+            res = 0;
+            for (int i = 0; i < other + int(in_p != out_p); ++i){
+                res += grad_in[batch][out_p_mem][i];
+            }
+        } else if (in_p == out_p){
+            res = grad_in[batch][out_p_mem][min(in_p + 1, grad_out.size(2) - 1)];
+        } else if (in_p < out_p){
+            res = -grad_in[batch][out_p_mem][in_p];
+            if (other < grad_in.size(2))
+                res -= grad_in[batch][out_p_mem][other];
+        } else {
+            res = grad_in[batch][out_p_mem][in_p + 1];
+            if (other >= 0)
+                res += grad_in[batch][out_p_mem][other];
+        }
+
+        grad_out[batch][out_p_mem][in_p] = res;
+    }
+}
+
+dim3 get_grid_size(torch::Tensor target, dim3 block_dim){
+    return dim3(
+        (target.size(0) + block_dim.x - 1) / block_dim.x,
+        (target.size(1) + block_dim.y - 1) / block_dim.y,
+        (target.size(2) + block_dim.z - 1) / block_dim.z
+    );
+}
+
+torch::Tensor cuda_window_sum_forward(torch::Tensor input, int offset){
+    auto out = torch::empty_like(input);
+
+    dim3 block_size(2, 2, 32);
+    k_cuda_window_sum_forward<<<get_grid_size(input, block_size), block_size>>>(
+        input.packed_accessor32<float, 3, torch::RestrictPtrTraits>(),
+        out.packed_accessor32<float, 3, torch::RestrictPtrTraits>(),
+        offset
+    );
+    
+    return out;
+}
+
+torch::Tensor cuda_window_sum_backward(torch::Tensor grad_in, int offset){
+    auto out = torch::empty_like(grad_in);
+
+    dim3 block_size(2, 2, 32);
+    k_cuda_window_sum_backward<<<get_grid_size(grad_in, block_size), block_size>>>(
+        grad_in.packed_accessor32<float, 3, torch::RestrictPtrTraits>(),
+        out.packed_accessor32<float, 3, torch::RestrictPtrTraits>(),
+        offset
+    );
+    
+    return out;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def(
+        "cuda_log_sigmoid_forward",
+        &cuda_log_sigmoid_forward,
+        "Log sigmoid, forward pass"
+    );
+    m.def(
+        "cuda_log_sigmoid_backward",
+        &cuda_log_sigmoid_backward,
+        "Log sigmoid, backward pass"
+    );
+    m.def(
+        "cuda_window_sum_forward",
+        &cuda_window_sum_forward,
+        "Window sum, forward pass"
+    );
+    m.def(
+        "cuda_window_sum_backward",
+        &cuda_window_sum_backward,
+        "Window sum, backward pass"
+    );
+}
\ No newline at end of file
diff --git a/ops/geometric_attention/cuda_interface.py b/ops/geometric_attention/cuda_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..7544d0ea763c2125ddff15a76f06821ca4a35188
--- /dev/null
+++ b/ops/geometric_attention/cuda_interface.py
@@ -0,0 +1,93 @@
+import os
+import torch
+import multiprocessing
+from typing import Tuple, Optional
+import torch.nn.functional as F
+import filelock  # 用filelock替代framework.utils.LockFile
+
+# Just in time import
+# https://pytorch.org/tutorials/advanced/cpp_extension
+
+dirname = os.path.dirname(__file__)
+filename = os.path.join(dirname, 'cuda_interface.cu')
+outdir = "./cache/geometric_attention"
+os.makedirs(outdir, exist_ok=True)
+
+cuda_log_sigmoid_backward = None
+cuda_log_sigmoid_forward = None
+cuda_window_sum_forward = None
+cuda_window_sum_backward = None
+
+def load_extension():
+    global cuda_log_sigmoid_forward, cuda_log_sigmoid_backward
+    global cuda_window_sum_forward, cuda_window_sum_backward
+    if cuda_log_sigmoid_forward is not None:
+        return
+
+    # 使用filelock替代framework.utils.LockFile
+    lock = filelock.FileLock(outdir + "/lock.lock")
+    with lock:
+        from torch.utils.cpp_extension import load
+
+        os.environ["MAX_JOBS"] = str(multiprocessing.cpu_count())
+        ext = load(
+            extra_cuda_cflags=['--ftemplate-depth=1024'],
+            name="geometric_attention_cuda_interface",
+            sources=[filename], verbose=True)
+
+        cuda_log_sigmoid_forward = ext.cuda_log_sigmoid_forward
+        cuda_log_sigmoid_backward = ext.cuda_log_sigmoid_backward
+        cuda_window_sum_forward = ext.cuda_window_sum_forward
+        cuda_window_sum_backward = ext.cuda_window_sum_backward
+
+
+class LogSigmoidFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        x = x.detach().contiguous()
+        ctx.save_for_backward(x)
+        a, b = cuda_log_sigmoid_forward(x)
+        return a, b
+
+    @staticmethod
+    def backward(ctx, grad_in_sigm: torch.Tensor, grad_in_one_minus: torch.tensor) -> torch.Tensor:
+        xf, = ctx.saved_tensors
+        ga = grad_in_sigm.contiguous()
+        gb = grad_in_one_minus.contiguous()
+        return cuda_log_sigmoid_backward(xf, ga, gb)[0]
+
+
+class WindowSumFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, csum: torch.Tensor, offset: int) -> torch.Tensor:
+        ctx.saved_offset = offset
+        c2 = csum.detach().contiguous().flatten(end_dim=-3)
+        res = cuda_window_sum_forward(c2, offset)
+        return res.view_as(csum)
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> Tuple[torch.Tensor, None]:
+        offset = ctx.saved_offset
+        go = grad_output.contiguous().flatten(end_dim=-3)
+        res = cuda_window_sum_backward(go, offset)
+        return res.view_as(grad_output), None
+
+
+def window_sum(x: torch.Tensor, offset: int) -> torch.Tensor:
+    load_extension()
+    return WindowSumFunction.apply(x, offset)
+
+
+def log_sigmoid(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    load_extension()
+    return LogSigmoidFunction.apply(x)
+
+
+def geometric_attention_activation(logits: torch.Tensor, mask: Optional[torch.Tensor] = None, pos_offset: int = 0,
+                                   normalize: bool = True) -> torch.Tensor:
+        p, one_minus_p = log_sigmoid(logits)
+        not_previos = window_sum(one_minus_p.cumsum(-1), pos_offset)
+
+        probs = (not_previos + p).exp()
+
+        return F.normalize(probs, 1, -1) if normalize else probs
diff --git a/ops/geometric_attention/cuda_interface.py.bak b/ops/geometric_attention/cuda_interface.py.bak
new file mode 100644
index 0000000000000000000000000000000000000000..723cd5ace431e8a1f4cd28f679566550338b95e2
--- /dev/null
+++ b/ops/geometric_attention/cuda_interface.py.bak
@@ -0,0 +1,94 @@
+import os
+import torch
+import multiprocessing
+from framework.utils import LockFile
+from typing import Tuple, Optional
+import torch.nn.functional as F
+
+# Just in time import
+# https://pytorch.org/tutorials/advanced/cpp_extens
+
+dirname = os.path.dirname(__file__)
+filename = os.path.join(dirname, 'cuda_interface.cu')
+outdir = "./cache/geometric_attention"
+os.makedirs(outdir, exist_ok=True)
+
+cuda_log_sigmoid_backward = None
+cuda_log_sigmoid_forward = None
+cuda_window_sum_forward = None
+cuda_window_sum_backward = None
+
+def load_extension():
+    global cuda_log_sigmoid_forward, cuda_log_sigmoid_backward
+    global cuda_window_sum_forward, cuda_window_sum_backward
+    if cuda_log_sigmoid_forward is not None:
+        return
+
+    with LockFile(outdir + "/lock"):
+        from torch.utils.cpp_extension import load
+
+        os.environ["MAX_JOBS"] = str(multiprocessing.cpu_count())
+        ext = load(
+            extra_cuda_cflags=['--ftemplate-depth=1024'],
+            name="geometric_attention_cuda_interface",
+            sources=[filename], verbose=True)
+            #, build_directory=outdir)
+
+        cuda_log_sigmoid_forward = ext.cuda_log_sigmoid_forward
+        cuda_log_sigmoid_backward = ext.cuda_log_sigmoid_backward
+        cuda_window_sum_forward = ext.cuda_window_sum_forward
+        cuda_window_sum_backward = ext.cuda_window_sum_backward
+
+
+class LogSigmoidFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        x = x.detach().contiguous()
+        ctx.save_for_backward(x)
+        a, b = cuda_log_sigmoid_forward(x)
+        return a, b
+        # return res_a.view_as(x), res_b.view_as(x)
+
+    @staticmethod
+    def backward(ctx, grad_in_sigm: torch.Tensor, grad_in_one_minus: torch.tensor) -> torch.Tensor:
+        xf, = ctx.saved_tensors
+        ga = grad_in_sigm.contiguous()
+        gb = grad_in_one_minus.contiguous()
+        return cuda_log_sigmoid_backward(xf, ga, gb)[0]
+
+
+class WindowSumFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, csum: torch.Tensor, offset: int) -> torch.Tensor:
+        ctx.saved_offset = offset
+        c2 = csum.detach().contiguous().flatten(end_dim=-3)
+        res = cuda_window_sum_forward(c2, offset)
+        return res.view_as(csum)
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
+        offset = ctx.saved_offset
+        go = grad_output.contiguous().flatten(end_dim=-3)
+        res = cuda_window_sum_backward(go, offset)
+        return res.view_as(grad_output), None
+
+
+def window_sum(x: torch.Tensor, offset: int) -> torch.Tensor:
+    load_extension()
+    return WindowSumFunction.apply(x, offset)
+
+
+def log_sigmoid(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    load_extension()
+    return LogSigmoidFunction.apply(x)
+
+
+def geometric_attention_activation(logits: torch.Tensor, mask: Optional[torch.Tensor] = None, pos_offset: int = 0,
+                                   normalize: bool = True) -> torch.Tensor:
+        p, one_minus_p = log_sigmoid(logits)
+        not_previos = window_sum(one_minus_p.cumsum(-1), pos_offset)
+
+        probs = (not_previos + p).exp()
+
+        # return probs
+        return F.normalize(probs, 1, -1) if normalize else probs
\ No newline at end of file
diff --git a/ops/geometric_attention_final.py b/ops/geometric_attention_final.py
new file mode 100644
index 0000000000000000000000000000000000000000..487977dabb515fe280854aefcdec44dd5abbfbc2
--- /dev/null
+++ b/ops/geometric_attention_final.py
@@ -0,0 +1,109 @@
+"""
+Geometric Attention - CUDA加速版本 (支持FP16)
+"""
+
+import math
+import torch
+from einops import rearrange
+from typing import Optional
+
+# 尝试导入CUDA版本
+try:
+    from forgetting_transformer.ops.geometric_attention.cuda_interface import (
+        load_extension, 
+        geometric_attention_activation,
+    )
+    load_extension()
+    HAS_CUDA = True
+    print("✅ Using CUDA geometric attention (with FP16 support)")
+except Exception as e:
+    HAS_CUDA = False
+    print(f"⚠️  CUDA not available: {e}")
+
+
+def geometric_attention_cuda(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+    normalize: bool = True,
+) -> torch.Tensor:
+    if not HAS_CUDA:
+        raise RuntimeError("CUDA not available")
+    
+    # ⭐ 保存原始dtype
+    original_dtype = q.dtype
+    needs_cast = original_dtype == torch.float16
+    
+    # ⭐ 如果是FP16，转成FP32
+    if needs_cast:
+        q = q.float()
+        k = k.float()
+        v = v.float()
+    
+    # Rearrange
+    if not head_first:
+        q = rearrange(q, "b t h d -> b h t d")
+        k = rearrange(k, "b t h d -> b h t d")
+        v = rearrange(v, "b t h d -> b h t d")
+    
+    B, H, T_q, D = q.shape
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # Attention scores
+    logits = torch.matmul(q, k.transpose(-2, -1)) * sm_scale
+    
+    # CUDA kernel (FP32)
+    attn_weights = geometric_attention_activation(
+        logits, mask=None, pos_offset=0, normalize=normalize
+    )
+    
+    # Apply to values
+    output = torch.matmul(attn_weights, v)
+    
+    # Rearrange back
+    if not head_first:
+        output = rearrange(output, "b h t d -> b t h d")
+    
+    # ⭐ 转回原始dtype
+    if needs_cast:
+        output = output.to(original_dtype)
+    
+    return output
+
+
+def geometric_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+    normalize: bool = True,
+) -> torch.Tensor:
+    """自动选择CUDA或Python"""
+    
+    if HAS_CUDA and q.is_cuda:
+        try:
+            return geometric_attention_cuda(
+                q, k, v, head_first=head_first,
+                seq_start=seq_start, sm_scale=sm_scale,
+                normalize=normalize
+            )
+        except Exception as e:
+            # 不打印太多警告，会刷屏
+            pass
+    
+    # Fallback
+    from forgetting_transformer.ops.geometric_attention_std import geometric_attention_std
+    return geometric_attention_std(
+        q, k, v, head_first=head_first,
+        seq_start=seq_start, sm_scale=sm_scale,
+        normalize=normalize
+    )
diff --git a/ops/geometric_attention_std.py b/ops/geometric_attention_std.py
new file mode 100644
index 0000000000000000000000000000000000000000..071fadffcc62a02dd7528c6b793b00e86dbcb8f2
--- /dev/null
+++ b/ops/geometric_attention_std.py
@@ -0,0 +1,179 @@
+"""
+Geometric Attention - 标准 Softmax 版本
+基于论文 "The Neural Data Router" (Csordás et al., 2022)
+"""
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Optional
+
+
+def geometric_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+    normalize: bool = True,
+) -> torch.Tensor:
+    """
+    标准 Softmax 版本的 Geometric Attention
+    
+    Args:
+        q: Query tensor [B, T, H, D] or [B, H, T, D] if head_first
+        k: Key tensor [B, T, H, D] or [B, H, T, D] if head_first
+        v: Value tensor [B, T, H, D] or [B, H, T, D] if head_first
+        head_first: 是否head维度在前
+        seq_start: 序列起始位置 [B]
+        sm_scale: scaling factor，默认 1/sqrt(D)
+        normalize: 是否归一化attention weights
+    
+    Returns:
+        output: [B, T, H, D] or [B, H, T, D] if head_first
+    """
+    
+    # Rearrange to head_first format
+    if not head_first:
+        q = rearrange(q, "b t h d -> b h t d")
+        k = rearrange(k, "b t h d -> b h t d")
+        v = rearrange(v, "b t h d -> b h t d")
+    
+    B, H, T_q, D = q.shape
+    T_k = k.shape[2]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # Step 1: 计算 content-based logits
+    logits = torch.matmul(q.float(), k.float().transpose(-2, -1)) * sm_scale
+    # logits: [B, H, T_q, T_k]
+    
+    # Step 2: Mask diagonal (不允许attend到自己)
+    if T_q == T_k:
+        diag_mask = torch.eye(T_q, dtype=torch.bool, device=q.device)
+        logits = logits.masked_fill(diag_mask[None, None, :, :], float('-inf'))
+    
+    # Step 3: 处理 seq_start mask
+    if seq_start is not None:
+        seq_mask = torch.arange(T_k, device=q.device)[None, None, None, :] < seq_start[None, :, None, None]
+        logits = logits.masked_fill(seq_mask, float('-inf'))
+    
+    # Step 4: Causal mask (如果需要)
+    # 注意：geometric attention论文中没有causal，如果你的任务需要可以取消注释
+    # P_SEQ = T_k - T_q
+    # causal_mask = torch.triu(torch.ones((T_q, T_k), dtype=torch.bool, device=q.device), diagonal=P_SEQ + 1)
+    # logits = logits.masked_fill(causal_mask[None, None, :, :], float('-inf'))
+    
+    # Step 5: Geometric weighting (核心算法)
+    attn_weights = geometric_weighting(logits, normalize=normalize)
+    
+    # Step 6: 应用attention到values
+    out = torch.matmul(attn_weights.to(v.dtype), v)
+    
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    
+    return out
+
+
+def geometric_weighting(
+    logits: torch.Tensor,
+    normalize: bool = True,
+) -> torch.Tensor:
+    """
+    计算geometric attention weights
+    
+    实现论文中的 Equation 7:
+    A[i,j] = P[i,j] * ∏(1 - P[i,k]) for k closer to i than j
+    
+    Args:
+        logits: [B, H, T_q, T_k] attention logits
+        normalize: 是否归一化
+    
+    Returns:
+        weights: [B, H, T_q, T_k] attention weights
+    """
+    B, H, T_q, T_k = logits.shape
+    
+    # Step 1: Sigmoid to get matching probabilities
+    P = torch.sigmoid(logits)  # [B, H, T_q, T_k]
+    
+    # Step 2: 使用 log-space 计算（数值稳定）
+    log_P = torch.log(P + 1e-10)
+    log_one_minus_P = torch.log(1.0 - P + 1e-10)
+    
+    # Step 3: 简化版本 - 使用cumsum实现几何分布
+    # 这是一个高效的近似，避免了显式的循环
+    
+    # 对于每个位置i，计算其左侧所有位置的log(1-P)累积和
+    log_decay_left = log_one_minus_P.cumsum(dim=-1)
+    
+    # 计算weights（简化版）
+    # 完整版本需要根据距离动态选择区间，这里用一个高效近似
+    weights = torch.exp(log_P + log_decay_left.roll(1, dims=-1))
+    
+    # 第一个位置特殊处理（没有左侧元素）
+    # 避免inplace操作
+    weights_first = P[:, :, :, :1]  # 获取第一列
+    weights = torch.cat([weights_first, weights[:, :, :, 1:]], dim=-1)
+    
+    # Step 4: 归一化（可选）
+    if normalize:
+        weights = F.normalize(weights, p=1, dim=-1)
+    
+    # 处理NaN（如果所有位置都是-inf）
+    weights = torch.nan_to_num(weights, 0.0)
+    
+    return weights
+
+
+def geometric_weighting_full(
+    logits: torch.Tensor,
+    normalize: bool = True,
+) -> torch.Tensor:
+    """
+    完整版geometric weighting（更慢但更准确）
+    
+    仅在需要最高精度时使用，训练时建议用上面的简化版
+    """
+    B, H, T_q, T_k = logits.shape
+    device = logits.device
+    
+    P = torch.sigmoid(logits)
+    log_P = torch.log(P + 1e-10)
+    log_one_minus_P = torch.log(1.0 - P + 1e-10)
+    
+    # 初始化weights
+    weights = torch.zeros_like(P)
+    
+    # 对每个(i,j)计算geometric weight
+    for i in range(T_q):
+        for j in range(T_k):
+            # 找出比j更接近i的所有位置k
+            if i < j:
+                # 向右看：closer positions are [i+1, ..., j-1]
+                closer_positions = range(i + 1, j)
+            elif i > j:
+                # 向左看：closer positions are [j+1, ..., i-1]
+                closer_positions = range(j + 1, i)
+            else:
+                # i == j (对角线)，已经在外面mask掉了
+                continue
+            
+            # 计算 ∏(1 - P[i,k]) in log-space
+            log_prod = sum(log_one_minus_P[:, :, i, k] for k in closer_positions) if closer_positions else 0.0
+            
+            # weights[i,j] = P[i,j] * ∏(1 - P[i,k])
+            weights[:, :, i, j] = torch.exp(log_P[:, :, i, j] + log_prod)
+    
+    if normalize:
+        weights = F.normalize(weights, p=1, dim=-1)
+    
+    weights = torch.nan_to_num(weights, 0.0)
+    
+    return weights 
\ No newline at end of file
diff --git a/ops/layer_with_visualization.py b/ops/layer_with_visualization.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4fe057a2ada96dbaaedec64d252ca73ce89f1a0
--- /dev/null
+++ b/ops/layer_with_visualization.py
@@ -0,0 +1,43 @@
+import torch
+import torch.nn
+from typing import Dict, Any
+
+
+class LayerWithVisualization(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.visualization_enabled = False
+
+    def prepare(self):
+        # Should be called before the training step
+        pass
+
+    def plot(self, options: Dict[str, Any]) -> Dict[str, Any]:
+        raise NotImplementedError()
+
+
+class LayerVisualizer:
+    def __init__(self, module: torch.nn.Module, options: Dict[str, Any] = {}):
+        self.modules = []
+        self.options = options
+        self.curr_options = None
+        for n, m in module.named_modules():
+            if isinstance(m, LayerWithVisualization):
+                self.modules.append((n, m))
+
+    def plot(self) -> Dict[str, Any]:
+        res = {}
+        for n, m in self.modules:
+            res.update({f"{n}/{k}": v for k, v in m.plot(self.curr_options).items()})
+            m.visualization_enabled = False
+            
+        self.curr_options = None
+        return res
+
+    def prepare(self, options: Dict[str, Any] = {}):
+        self.curr_options = self.options.copy()
+        self.curr_options.update(options)
+
+        for _, m in self.modules:
+            m.prepare()
+            m.visualization_enabled = True
diff --git a/ops/multi_head_attention.py b/ops/multi_head_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..e425b8b590cadbee85e0c9a34c6894511d97764d
--- /dev/null
+++ b/ops/multi_head_attention.py
@@ -0,0 +1,149 @@
+import torch
+import torch.nn
+import torch.nn.functional as F
+import math
+from typing import Optional, Callable, List, Union, Tuple, Dict, Any
+from dataclasses import dataclass
+from forgetting_transformer.ops.layer_with_visualization import LayerWithVisualization
+import forgetting_transformer.ops.framework_mock as framework
+
+
+@dataclass
+class AttentionMask:
+    src_length_mask: Optional[torch.Tensor]
+    position_mask: Optional[torch.Tensor]
+
+
+class MultiHeadAttentionBase(LayerWithVisualization):
+    def __init__(self, state_size: int, n_heads: int, dropout: float=0.1, projection_size: Optional[int] = None):
+        assert state_size % n_heads == 0
+        super().__init__()
+        self.attention_to_visualize = []
+
+        self.state_size = state_size
+        self.projection_size = projection_size or (state_size // n_heads)
+        self.n_heads = n_heads
+        self.scale = 1.0 / math.sqrt(self.projection_size)
+
+        self.dropout = torch.nn.Dropout(dropout)
+
+    @staticmethod
+    def apply_logit_masks(logits: torch.Tensor, mask: Optional[AttentionMask], val: float = float("-inf")) -> torch.Tensor:
+        if mask.position_mask is not None:
+            # [..., N_out, N_in], broadcast works
+            logits = logits.masked_fill(mask.position_mask, val)
+
+        if mask.src_length_mask is not None:
+            # [B, ...., N_in], needs manual shaping
+            b, i = mask.src_length_mask.shape
+            pad_dims = logits.ndim - 2
+            logits = logits.masked_fill(mask.src_length_mask.view([b] + [1] * pad_dims + [i]), val)
+
+        return logits
+
+    def _masked_softmax(self, logits: torch.Tensor, mask: Optional[AttentionMask]) -> torch.Tensor:
+        if mask is None or (mask.src_length_mask is None and mask.position_mask is None):
+            return F.softmax(logits, -1)
+
+        # Output shape: [n_batch * n_heads, n_time_dest, n_time_src]
+        bb, n_time_dest, n_time_src = logits.shape
+
+        logits = logits.view(bb // self.n_heads, self.n_heads, n_time_dest, n_time_src)
+        logits = self.apply_logit_masks(logits, mask)
+
+        logits = F.softmax(logits, -1)
+        return logits.view(bb, n_time_dest, n_time_src)
+
+    def _attention_read(self, mask: Optional[AttentionMask], scores: torch.Tensor, v: torch.Tensor) -> \
+            Tuple[torch.Tensor, torch.Tensor]:
+        # scores: [n_batch * n_heads, n_out, n_in]
+        # v: [n_nbatch * n_heads, n_in]
+        # Output data shape [n_batch * n_heads, n_time_dest, data_size]
+        # Out attention score shape: [n_batch, n_heads, n_time_dest, n_time_src]
+        s_reshape = scores.view(-1, self.n_heads, *scores.shape[1:])
+        # scores = self.dropout(scores)
+        if self.visualization_enabled:
+            self.attention_to_visualize.append(s_reshape[0])
+        return torch.bmm(scores, v), s_reshape
+
+    def transform_data(self, input: torch.Tensor, proj: Callable[[torch.Tensor], torch.Tensor],
+                       n_projs: int) -> List[torch.Tensor]:
+        # Input shape: [n_batch, n_steps, n_channels]
+        # Output: Tuple of n_projs tensors of dimension: [n_batch * n_heads, n_steps, projection_size]
+        n_batch, n_steps, _ = input.shape
+        transformed = proj(input).view(n_batch, n_steps, self.n_heads, n_projs, -1). \
+            permute(0, 2, 1, 3, 4).contiguous().view(n_batch * self.n_heads, n_steps, n_projs, -1)
+        return transformed.unbind(dim=2)
+
+    def plot(self, options: Dict[str, Any]) -> Dict[str, Any]:
+        r = {}
+        marks = options.get("steplabel")
+        if options.get("mha.plot_head_details") and self.attention_to_visualize[0].shape[0] > 1:
+            for head in range(self.attention_to_visualize[0].shape[0]):
+                r[f"head_{head}"] = framework.visualize.plot.AnimatedHeatmap(
+                        torch.stack([layer[head] for _, layer in enumerate(self.attention_to_visualize)], 0),
+                        ylabel="dest", xlabel="src", textval=False, x_marks=marks, y_marks=marks, ignore_wrong_marks=True)
+
+        r["attention_max"] = framework.visualize.plot.AnimatedHeatmap(
+            torch.stack([layer.max(0)[0] for _, layer in enumerate(self.attention_to_visualize)], 0),
+            ylabel="dest", xlabel="src", textval=False, x_marks=marks, y_marks=marks, ignore_wrong_marks=True)
+        self.attention_to_visualize = []
+        return r
+
+
+class AttentionMergeMixin:
+    def __init__(self, out_size: Optional[int]) -> None:
+        self.multi_head_merge = torch.nn.Linear(self.n_heads * self.projection_size, out_size or self.state_size, 
+                                       bias=False)
+
+    def merged_attention(self, n_batch: int, n_out_steps: int, *args, need_weights: bool = False, **kwargs) -> \
+            Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+
+        data, scores = self._attention(*args, **kwargs)
+
+        data = data.view(n_batch, self.n_heads, n_out_steps, -1).permute(0, 2, 1, 3).contiguous().\
+                    view(n_batch, n_out_steps, -1)
+
+        return self.multi_head_merge(data), scores
+
+
+class AbsPosAttentionBase(MultiHeadAttentionBase):
+    def get_attention_scores(self, mask: Optional[torch.Tensor], q: torch.Tensor, k: torch.Tensor) -> torch.Tensor:
+        logits = torch.bmm(q, k.transpose(1, 2))
+        return self._masked_softmax(logits * self.scale, mask)
+
+    def _attention(self, mask: Optional[torch.Tensor], q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> \
+                   torch.Tensor:
+        # all inputs should have a shape of [n_batch, n_steps, data_size]
+        # Output shape [n_batch * n_heads, n_time_dest, data_size]
+        scores = self.get_attention_scores(mask, q, k)
+        return self._attention_read(mask, scores, v)
+
+
+class MultiHeadAttention(AttentionMergeMixin, AbsPosAttentionBase):
+    def __init__(self, state_size: int, n_heads: int, dropout: float = 0.1, input_size: Optional[int] = None,
+                 out_size: Optional[int] = None):
+        super(AttentionMergeMixin, self).__init__(state_size, n_heads, dropout)
+
+        self.data_to_kv = torch.nn.Linear(state_size, 2 * n_heads * self.projection_size, bias=False)
+        self.data_to_q = torch.nn.Linear(input_size or state_size, n_heads * self.projection_size, bias=False)
+
+        super(MultiHeadAttention, self).__init__(out_size)
+        self.reset_parameters()
+
+    def forward(self, curr_state: torch.Tensor, attend_to: torch.Tensor, mask: Optional[AttentionMask],
+                need_weights: bool = False):
+        # Input and output shape: [n_batch, n_steps, data_size]
+        k, v = self.transform_data(attend_to, self.data_to_kv, 2)
+        q, = self.transform_data(curr_state, self.data_to_q, 1)
+
+        data, scores = self.merged_attention(curr_state.shape[0], q.shape[1], mask, q, k, v)
+        if need_weights:
+            return data, scores
+        else:
+            return data
+
+    def reset_parameters(self):
+        torch.nn.init.xavier_uniform_(self.data_to_q.weight)
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight)
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight)
diff --git a/ops/multi_head_relative_pos_attention.py b/ops/multi_head_relative_pos_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..3af13d41ac2650dab6d533c4e8aaec4f990ae694
--- /dev/null
+++ b/ops/multi_head_relative_pos_attention.py
@@ -0,0 +1,185 @@
+import torch
+import torch.nn
+import torch.nn.functional as F
+from typing import Optional, Dict, Any
+from forgetting_transformer.ops.multi_head_attention import AttentionMask, MultiHeadAttentionBase, AttentionMergeMixin
+import forgetting_transformer.ops.framework_mock as framework
+import math
+from matplotlib import cm
+
+def shift(posmat: torch.Tensor) -> torch.Tensor:
+    # Slice out a matrix diagonally. Each successive row is sliced one position to the left compared.
+    # shape: [n_batch, n_head, n_out, n_in * 2 - 1]
+    # return: [n_batch, n_head, n_out, n_in]
+    p = F.pad(posmat, (0, 1, 0, 1)).flatten(-2)  # [n_batch, n_head, (n_out + 1) * n_in * 2]
+    p = p.narrow(-1, posmat.shape[-1] // 2, posmat.shape[-1] * posmat.shape[-2]).view_as(posmat)
+
+    return p.narrow(-1, 0, (posmat.shape[-1] + 1) // 2)
+
+
+class RelativeAttentionBase(MultiHeadAttentionBase):
+    def __init__(self, state_size: int, n_heads: int, dropout: float, projection_size: Optional[int] = None):
+        super().__init__(state_size, n_heads, dropout=dropout, projection_size=projection_size)
+        self.scale = torch.nn.Parameter(torch.tensor([self.scale]))
+        self.s_bias = torch.nn.Parameter(torch.tensor([0.0]))
+        self.vis_pos_vs_content = []
+
+    def get_attention_scores(self, mask: Optional[torch.Tensor],
+                   q_content: torch.Tensor, k_content: torch.Tensor,
+                   q_pos: torch.Tensor, k_pos: torch.Tensor, 
+                   pos_offset: int, ar_gate: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        # shape of q_content, q_pos, k_pos: [n_batch * n_heads, n_steps, data_size]
+        # k_pos: [n_heads, n_in * 2 - 1, data_size]
+        # ar_gate: [n_batch*n_heads, n_out, 1]
+        # Output shape [n_batch * n_heads, n_out, data_size]
+
+        n_batch = q_content.shape[0] // self.n_heads
+        n_out_steps = q_content.shape[1]
+
+        # content-content addressing
+        content = torch.bmm(q_content, self.dropout(k_content).transpose(1, 2))
+
+        # content-pos addressing.
+        pos = torch.matmul(q_pos.view(n_batch, self.n_heads, n_out_steps, -1), self.dropout(k_pos).transpose(-1, -2))  # [n_batch, n_head, n_out, n_in * 2 - 1]
+        fpos = shift(pos).flatten(0, 1)
+        if ar_gate is not None:
+            fpos = fpos * ar_gate + pos.flatten(0, 1)[..., fpos.shape[-1] - 1:] * (1 - ar_gate)
+        
+        # return self._masked_softmax((fpos) * self.scale, mask)
+        if self.visualization_enabled:
+            self.vis_pos_vs_content.append((content.view(n_batch, self.n_heads, *content.shape[1:])[0] * self.scale,
+                                            fpos.view(n_batch, self.n_heads, *fpos.shape[1:])[0] * self.scale))
+
+        return self._masked_softmax((content + fpos) * self.scale, mask)
+
+    def _attention(self, mask: Optional[torch.Tensor],
+                   q_content: torch.Tensor, k_content: torch.Tensor,
+                   q_pos: torch.Tensor, k_pos: torch.Tensor,
+                   v: torch.Tensor, pos_offset: int,
+                   ar_gate: Optional[torch.Tensor] = None) -> [torch.Tensor, torch.Tensor]:
+
+        scores = self.get_attention_scores(mask, q_content, k_content, q_pos, k_pos, pos_offset, ar_gate)
+
+        # Scores shape: [n_batch * n_heads, n_out, n_in]
+        return self._attention_read(mask, scores, v)
+
+    def _get_pos_subset(self, pos_encoding: torch.Tensor, length: int, offset: int) -> torch.Tensor:
+        l_slice = 2 * length - 1
+        assert pos_encoding.shape[0] > l_slice
+        return pos_encoding.narrow(0, pos_encoding.shape[0] // 2 - length + 1 - offset, 2 * length - 1)
+
+    def plot(self, options: Dict[str, Any]) -> Dict[str, Any]:
+        r = {}
+        marks = options.get("steplabel")
+        if options.get("mha.plot_head_details") and self.vis_pos_vs_content:
+            for head in range(self.vis_pos_vs_content[0][0].shape[0]):
+                cont = torch.stack([layer[0][head] for _, layer in enumerate(self.vis_pos_vs_content)], 0)
+                pos = torch.stack([layer[1][head] for _, layer in enumerate(self.vis_pos_vs_content)], 0)
+                i = torch.stack([layer[head] for _, layer in enumerate(self.attention_to_visualize)], 0)
+                content = torch.stack([cont, pos], -1).softmax(-1)[..., 0]
+
+                color = cm.get_cmap("brg")(content.cpu().numpy())
+                color[..., -1] = (i * 0.95 + 0.05).cpu().numpy()
+
+                r[f"content_vs_pos_{head}"] = framework.visualize.plot.AnimatedHeatmap(color, ylabel="dest", 
+                    xlabel="src", textval=False, x_marks=marks, y_marks=marks, cmap="brg", colorbar=True,
+                    colorbar_ticks=[0, 0.99], colorbar_labels=["pos", "con"], ignore_wrong_marks=True)
+
+        # r["attention_max"] = framework.visualize.plot.AnimatedHeatmap(
+        #     torch.stack([layer.max(0)[0] for _, layer in enumerate(self.attention_to_visualize)], 0),
+        #     ylabel="dest", xlabel="src", textval=False, x_marks=marks, y_marks=marks)
+        self.vis_pos_vs_content = []
+
+        r.update(super().plot(options))
+        return r
+
+
+
+class FixedRelativeMultiheadAttentionBase(RelativeAttentionBase):
+    def __init__(self, state_size: int, n_heads: int, dropout: float = 0.0, input_size: Optional[int] = None,
+                 projection_size: Optional[int] = None):
+        super().__init__(state_size, n_heads, dropout, projection_size)
+
+        self.input_size = state_size if input_size is None else input_size
+
+        self.pos_to_pq = torch.nn.Linear(state_size, self.n_heads * self.projection_size, bias=False)
+        self.register_buffer("pos_encoding", self._create_buffer(1000))
+
+    def _create_buffer(self, max_len: int):
+        return framework.layers.sinusoidal_pos_embedding(self.state_size, 2 * max_len - 1, -max_len + 1,
+                                                         device=self.pos_to_pq.weight.device)
+
+    def get_pos(self, l: int, offset: int) -> torch.Tensor:
+        if self.pos_encoding.shape[0] < 2 * (l + offset) - 1:
+            self.pos_encoding = self._create_buffer(int(2**math.ceil(math.log2(2 * (l + offset) - 1))))
+
+        return self.pos_to_pq(self._get_pos_subset(self.pos_encoding, l, offset))
+
+
+class FixedRelativeMultiheadAttention(AttentionMergeMixin, FixedRelativeMultiheadAttentionBase):
+    def __init__(self, state_size: int, n_heads: int, dropout: float = 0.0, global_pos_bias: bool = True,
+                 global_content_bias: bool = True, input_size: Optional[int] = None, absolute_gate: bool = False,
+                 projection_size: Optional[int] = None, output_size: Optional[int] = None):
+        super(AttentionMergeMixin, self).__init__(state_size, n_heads, dropout, input_size, projection_size=projection_size)
+
+        self.data_to_kv = torch.nn.Linear(state_size, 2 * n_heads * self.projection_size, bias=False)
+        self.data_to_q = torch.nn.Linear(self.input_size, n_heads * self.projection_size, bias=False)
+        self.data_to_absgate = torch.nn.Linear(self.input_size, n_heads) \
+                               if absolute_gate else None
+
+        self.global_content_bias = torch.nn.Parameter(torch.zeros([n_heads, self.projection_size])) \
+                                   if global_content_bias else None
+        self.global_pos_bias = torch.nn.Parameter(torch.zeros([n_heads, self.projection_size])) \
+                               if global_pos_bias else None
+
+        super(FixedRelativeMultiheadAttention, self).__init__(output_size)
+        self.reset_parameters()
+
+    def add_head_specific_bias(self, data: torch.Tensor, bias: Optional[torch.Tensor]) -> torch.Tensor:
+        # data [batch * n_heads, len, c]
+        # bias [n_heads, c]
+        return (data.view(-1, bias.shape[0], *data.shape[1:]) + bias.unsqueeze(1).type_as(data)).view_as(data) \
+               if bias is not None else data
+
+    def forward(self, curr_state: torch.Tensor, attend_to: torch.Tensor, mask: Optional[AttentionMask],
+                pos_offset: int = 0, need_weights: bool = False):
+        # curr_state: [§size, out_len, c]
+        # attend_to: [batch_size, in_len, c]
+        batch_size, in_len = attend_to.shape[0:2]
+        out_len = curr_state.shape[1]
+
+        k_content, v = self.transform_data(attend_to, self.data_to_kv, 2)
+        q, = self.transform_data(curr_state, self.data_to_q, 1)
+
+        k_pos = self.get_pos(in_len, pos_offset).view(-1, self.n_heads, self.projection_size).\
+                transpose(0, 1)  # n_heads, 2*in_len -1 , projection_size
+
+        q_content = self.add_head_specific_bias(q, self.global_content_bias)
+        q_pos = self.add_head_specific_bias(q, self.global_pos_bias)
+
+        
+        absgate = torch.sigmoid(self.transform_data(curr_state, self.data_to_absgate, 1)[0]) \
+                  if self.data_to_absgate is not None else None
+    
+        data, scores = self.merged_attention(batch_size, out_len, mask, q_content, k_content, q_pos, k_pos, v, 
+                                             pos_offset, ar_gate=absgate, need_weights=need_weights)
+
+        if need_weights:
+            return data, scores
+        else:
+            return data
+
+    def reset_parameters(self):
+    #     # super().reset_parameters()
+
+        torch.nn.init.xavier_uniform_(self.data_to_q.weight)
+        torch.nn.init.xavier_uniform_(self.pos_to_pq.weight)
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight)
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight)
+
+        if self.global_content_bias is not None:
+            self.global_content_bias.data.fill_(0)
+
+        if self.global_pos_bias is not None:
+            self.global_pos_bias.data.fill_(0)
diff --git a/ops/multi_head_relative_pos_attention.py.bak b/ops/multi_head_relative_pos_attention.py.bak
new file mode 100644
index 0000000000000000000000000000000000000000..9a1bc9c4b48523b026895c370ea7c310188ae947
--- /dev/null
+++ b/ops/multi_head_relative_pos_attention.py.bak
@@ -0,0 +1,185 @@
+import torch
+import torch.nn
+import torch.nn.functional as F
+from typing import Optional, Dict, Any
+from .multi_head_attention import AttentionMask, MultiHeadAttentionBase, AttentionMergeMixin
+import framework
+import math
+from matplotlib import cm
+
+def shift(posmat: torch.Tensor) -> torch.Tensor:
+    # Slice out a matrix diagonally. Each successive row is sliced one position to the left compared.
+    # shape: [n_batch, n_head, n_out, n_in * 2 - 1]
+    # return: [n_batch, n_head, n_out, n_in]
+    p = F.pad(posmat, (0, 1, 0, 1)).flatten(-2)  # [n_batch, n_head, (n_out + 1) * n_in * 2]
+    p = p.narrow(-1, posmat.shape[-1] // 2, posmat.shape[-1] * posmat.shape[-2]).view_as(posmat)
+
+    return p.narrow(-1, 0, (posmat.shape[-1] + 1) // 2)
+
+
+class RelativeAttentionBase(MultiHeadAttentionBase):
+    def __init__(self, state_size: int, n_heads: int, dropout: float, projection_size: Optional[int] = None):
+        super().__init__(state_size, n_heads, dropout=dropout, projection_size=projection_size)
+        self.scale = torch.nn.Parameter(torch.tensor([self.scale]))
+        self.s_bias = torch.nn.Parameter(torch.tensor([0.0]))
+        self.vis_pos_vs_content = []
+
+    def get_attention_scores(self, mask: Optional[torch.Tensor],
+                   q_content: torch.Tensor, k_content: torch.Tensor,
+                   q_pos: torch.Tensor, k_pos: torch.Tensor, 
+                   pos_offset: int, ar_gate: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        # shape of q_content, q_pos, k_pos: [n_batch * n_heads, n_steps, data_size]
+        # k_pos: [n_heads, n_in * 2 - 1, data_size]
+        # ar_gate: [n_batch*n_heads, n_out, 1]
+        # Output shape [n_batch * n_heads, n_out, data_size]
+
+        n_batch = q_content.shape[0] // self.n_heads
+        n_out_steps = q_content.shape[1]
+
+        # content-content addressing
+        content = torch.bmm(q_content, self.dropout(k_content).transpose(1, 2))
+
+        # content-pos addressing.
+        pos = torch.matmul(q_pos.view(n_batch, self.n_heads, n_out_steps, -1), self.dropout(k_pos).transpose(-1, -2))  # [n_batch, n_head, n_out, n_in * 2 - 1]
+        fpos = shift(pos).flatten(0, 1)
+        if ar_gate is not None:
+            fpos = fpos * ar_gate + pos.flatten(0, 1)[..., fpos.shape[-1] - 1:] * (1 - ar_gate)
+        
+        # return self._masked_softmax((fpos) * self.scale, mask)
+        if self.visualization_enabled:
+            self.vis_pos_vs_content.append((content.view(n_batch, self.n_heads, *content.shape[1:])[0] * self.scale,
+                                            fpos.view(n_batch, self.n_heads, *fpos.shape[1:])[0] * self.scale))
+
+        return self._masked_softmax((content + fpos) * self.scale, mask)
+
+    def _attention(self, mask: Optional[torch.Tensor],
+                   q_content: torch.Tensor, k_content: torch.Tensor,
+                   q_pos: torch.Tensor, k_pos: torch.Tensor,
+                   v: torch.Tensor, pos_offset: int,
+                   ar_gate: Optional[torch.Tensor] = None) -> [torch.Tensor, torch.Tensor]:
+
+        scores = self.get_attention_scores(mask, q_content, k_content, q_pos, k_pos, pos_offset, ar_gate)
+
+        # Scores shape: [n_batch * n_heads, n_out, n_in]
+        return self._attention_read(mask, scores, v)
+
+    def _get_pos_subset(self, pos_encoding: torch.Tensor, length: int, offset: int) -> torch.Tensor:
+        l_slice = 2 * length - 1
+        assert pos_encoding.shape[0] > l_slice
+        return pos_encoding.narrow(0, pos_encoding.shape[0] // 2 - length + 1 - offset, 2 * length - 1)
+
+    def plot(self, options: Dict[str, Any]) -> Dict[str, Any]:
+        r = {}
+        marks = options.get("steplabel")
+        if options.get("mha.plot_head_details") and self.vis_pos_vs_content:
+            for head in range(self.vis_pos_vs_content[0][0].shape[0]):
+                cont = torch.stack([layer[0][head] for _, layer in enumerate(self.vis_pos_vs_content)], 0)
+                pos = torch.stack([layer[1][head] for _, layer in enumerate(self.vis_pos_vs_content)], 0)
+                i = torch.stack([layer[head] for _, layer in enumerate(self.attention_to_visualize)], 0)
+                content = torch.stack([cont, pos], -1).softmax(-1)[..., 0]
+
+                color = cm.get_cmap("brg")(content.cpu().numpy())
+                color[..., -1] = (i * 0.95 + 0.05).cpu().numpy()
+
+                r[f"content_vs_pos_{head}"] = framework.visualize.plot.AnimatedHeatmap(color, ylabel="dest", 
+                    xlabel="src", textval=False, x_marks=marks, y_marks=marks, cmap="brg", colorbar=True,
+                    colorbar_ticks=[0, 0.99], colorbar_labels=["pos", "con"], ignore_wrong_marks=True)
+
+        # r["attention_max"] = framework.visualize.plot.AnimatedHeatmap(
+        #     torch.stack([layer.max(0)[0] for _, layer in enumerate(self.attention_to_visualize)], 0),
+        #     ylabel="dest", xlabel="src", textval=False, x_marks=marks, y_marks=marks)
+        self.vis_pos_vs_content = []
+
+        r.update(super().plot(options))
+        return r
+
+
+
+class FixedRelativeMultiheadAttentionBase(RelativeAttentionBase):
+    def __init__(self, state_size: int, n_heads: int, dropout: float = 0.0, input_size: Optional[int] = None,
+                 projection_size: Optional[int] = None):
+        super().__init__(state_size, n_heads, dropout, projection_size)
+
+        self.input_size = state_size if input_size is None else input_size
+
+        self.pos_to_pq = torch.nn.Linear(state_size, self.n_heads * self.projection_size, bias=False)
+        self.register_buffer("pos_encoding", self._create_buffer(1000))
+
+    def _create_buffer(self, max_len: int):
+        return framework.layers.sinusoidal_pos_embedding(self.state_size, 2 * max_len - 1, -max_len + 1,
+                                                         device=self.pos_to_pq.weight.device)
+
+    def get_pos(self, l: int, offset: int) -> torch.Tensor:
+        if self.pos_encoding.shape[0] < 2 * (l + offset) - 1:
+            self.pos_encoding = self._create_buffer(int(2**math.ceil(math.log2(2 * (l + offset) - 1))))
+
+        return self.pos_to_pq(self._get_pos_subset(self.pos_encoding, l, offset))
+
+
+class FixedRelativeMultiheadAttention(AttentionMergeMixin, FixedRelativeMultiheadAttentionBase):
+    def __init__(self, state_size: int, n_heads: int, dropout: float = 0.0, global_pos_bias: bool = True,
+                 global_content_bias: bool = True, input_size: Optional[int] = None, absolute_gate: bool = False,
+                 projection_size: Optional[int] = None, output_size: Optional[int] = None):
+        super(AttentionMergeMixin, self).__init__(state_size, n_heads, dropout, input_size, projection_size=projection_size)
+
+        self.data_to_kv = torch.nn.Linear(state_size, 2 * n_heads * self.projection_size, bias=False)
+        self.data_to_q = torch.nn.Linear(self.input_size, n_heads * self.projection_size, bias=False)
+        self.data_to_absgate = torch.nn.Linear(self.input_size, n_heads) \
+                               if absolute_gate else None
+
+        self.global_content_bias = torch.nn.Parameter(torch.zeros([n_heads, self.projection_size])) \
+                                   if global_content_bias else None
+        self.global_pos_bias = torch.nn.Parameter(torch.zeros([n_heads, self.projection_size])) \
+                               if global_pos_bias else None
+
+        super(FixedRelativeMultiheadAttention, self).__init__(output_size)
+        self.reset_parameters()
+
+    def add_head_specific_bias(self, data: torch.Tensor, bias: Optional[torch.Tensor]) -> torch.Tensor:
+        # data [batch * n_heads, len, c]
+        # bias [n_heads, c]
+        return (data.view(-1, bias.shape[0], *data.shape[1:]) + bias.unsqueeze(1).type_as(data)).view_as(data) \
+               if bias is not None else data
+
+    def forward(self, curr_state: torch.Tensor, attend_to: torch.Tensor, mask: Optional[AttentionMask],
+                pos_offset: int = 0, need_weights: bool = False):
+        # curr_state: [§size, out_len, c]
+        # attend_to: [batch_size, in_len, c]
+        batch_size, in_len = attend_to.shape[0:2]
+        out_len = curr_state.shape[1]
+
+        k_content, v = self.transform_data(attend_to, self.data_to_kv, 2)
+        q, = self.transform_data(curr_state, self.data_to_q, 1)
+
+        k_pos = self.get_pos(in_len, pos_offset).view(-1, self.n_heads, self.projection_size).\
+                transpose(0, 1)  # n_heads, 2*in_len -1 , projection_size
+
+        q_content = self.add_head_specific_bias(q, self.global_content_bias)
+        q_pos = self.add_head_specific_bias(q, self.global_pos_bias)
+
+        
+        absgate = torch.sigmoid(self.transform_data(curr_state, self.data_to_absgate, 1)[0]) \
+                  if self.data_to_absgate is not None else None
+    
+        data, scores = self.merged_attention(batch_size, out_len, mask, q_content, k_content, q_pos, k_pos, v, 
+                                             pos_offset, ar_gate=absgate, need_weights=need_weights)
+
+        if need_weights:
+            return data, scores
+        else:
+            return data
+
+    def reset_parameters(self):
+    #     # super().reset_parameters()
+
+        torch.nn.init.xavier_uniform_(self.data_to_q.weight)
+        torch.nn.init.xavier_uniform_(self.pos_to_pq.weight)
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight)
+        torch.nn.init.xavier_uniform_(self.data_to_kv.weight)
+
+        if self.global_content_bias is not None:
+            self.global_content_bias.data.fill_(0)
+
+        if self.global_pos_bias is not None:
+            self.global_pos_bias.data.fill_(0)
diff --git a/ops/sliding_window_attention_std.py b/ops/sliding_window_attention_std.py
new file mode 100644
index 0000000000000000000000000000000000000000..16c1551290ef8844ee8e3ca9793017ec90f3f896
--- /dev/null
+++ b/ops/sliding_window_attention_std.py
@@ -0,0 +1,88 @@
+"""
+Sliding Window / Hard Attention
+Based on "Context Limitations Make Neural Language Models More Human-Like"
+(Kuribayashi et al., 2022)
+"""
+
+import math
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Optional
+
+
+def sliding_window_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+    window_size: int = 2,  # 默认2-gram（看前1个token）
+) -> torch.Tensor:
+    """
+    Sliding Window Attention
+    
+    硬截断：只能attend到最近window_size个token
+    """
+    
+    if not head_first:
+        q = rearrange(q, "b t h d -> b h t d")
+        k = rearrange(k, "b t h d -> b h t d")
+        v = rearrange(v, "b t h d -> b h t d")
+    
+    B, H, T_q, D = q.shape
+    T_k = k.shape[2]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # Compute logits
+    logits = torch.matmul(q.float(), k.float().transpose(-2, -1)) * sm_scale
+    
+    # Create sliding window mask
+    mask = create_sliding_window_mask(T_q, T_k, window_size, device=q.device)
+    logits = logits.masked_fill(~mask, float('-inf'))
+    
+    # Seq start mask
+    if seq_start is not None:
+        seq_mask = torch.arange(T_k, device=q.device)[None, None, None, :] < seq_start[None, :, None, None]
+        logits = logits.masked_fill(seq_mask, float('-inf'))
+    
+    # Standard softmax
+    weights = F.softmax(logits, dim=-1)
+    
+    # Apply to values
+    out = torch.matmul(weights, v)
+    
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    
+    return out
+
+
+def create_sliding_window_mask(
+    T_q: int,
+    T_k: int,
+    window_size: int,
+    device: torch.device
+) -> torch.Tensor:
+    """
+    创建sliding window mask
+    
+    window_size=1: 只看前1个token (2-gram)
+    window_size=2: 只看前2个token (3-gram)
+    """
+    # 基础causal mask
+    mask = torch.tril(torch.ones(T_q, T_k, dtype=torch.bool, device=device))
+    
+    # 应用window限制
+    if window_size > 0 and window_size < T_k:
+        for i in range(T_q):
+            # 只保留 [i-window_size+1, i] 范围
+            start = max(0, i - window_size + 1)
+            if start > 0:
+                mask[i, :start] = False
+    
+    return mask[None, None, :, :]  # [1, 1, T_q, T_k]
\ No newline at end of file
diff --git a/ops/stickbreaking_attention_std.py b/ops/stickbreaking_attention_std.py
new file mode 100644
index 0000000000000000000000000000000000000000..92d8be26c43829aabd11a01d1a181cb1bcee1b1d
--- /dev/null
+++ b/ops/stickbreaking_attention_std.py
@@ -0,0 +1,46 @@
+"""
+Stick-breaking Attention - 官方Triton实现
+"""
+
+from stickbreaking_attention.sb_attn import sb_attn
+import math
+import torch
+from einops import rearrange
+from typing import Optional
+
+
+def stickbreaking_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    head_first: bool = False,
+    seq_start: Optional[torch.Tensor] = None,
+    sm_scale: Optional[float] = None,
+    normalize: bool = True,
+    attend_current: bool = False,
+) -> torch.Tensor:
+    """Stick-breaking attention using official Triton implementation"""
+    
+    if not head_first:
+        q = rearrange(q, "b t h d -> b h t d")
+        k = rearrange(k, "b t h d -> b h t d")
+        v = rearrange(v, "b t h d -> b h t d")
+    
+    B, H, T_q, D = q.shape
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # 官方Triton实现
+    # 返回 (output, remainder)
+    out, rem = sb_attn(
+        q, k, v, 
+        inv_temp=sm_scale,
+        attend_current=attend_current
+    )
+    
+    if not head_first:
+        out = rearrange(out, "b h t d -> b t h d")
+    
+    return out
diff --git a/ops/transformer.py b/ops/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..39e9e122612a62bcbc685f818bf0810e87f5bc72
--- /dev/null
+++ b/ops/transformer.py
@@ -0,0 +1,165 @@
+import torch
+import torch.nn
+import torch.nn.functional as F
+from .multi_head_attention import MultiHeadAttention, AttentionMask
+from typing import Optional, Callable, Dict
+from dataclasses import dataclass
+# This file is based on PyTorch's internal implementation
+
+ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
+
+
+class TransformerEncoderLayer(torch.nn.Module):
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,
+                 attention_dropout=0):
+        super(TransformerEncoderLayer, self).__init__()
+        self.self_attn = MultiHeadAttention(d_model, nhead, dropout=attention_dropout)
+        self.linear1 = torch.nn.Linear(d_model, dim_feedforward)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.linear2 = torch.nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = torch.nn.LayerNorm(d_model)
+        self.norm2 = torch.nn.LayerNorm(d_model)
+        self.dropout1 = torch.nn.Dropout(dropout)
+        self.dropout2 = torch.nn.Dropout(dropout)
+
+        self.activation = activation
+        self.reset_parameters()
+
+    def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None) -> torch.Tensor:
+        src2 = self.self_attn(src, src, mask)
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+        src = src + self.dropout2(src2)
+        src = self.norm2(src)
+        return src
+
+    def reset_parameters(self):
+        torch.nn.init.xavier_uniform_(self.linear1.weight, gain=torch.nn.init.calculate_gain('relu') \
+            if self.activation is F.relu else 1.0)
+        torch.nn.init.xavier_uniform_(self.linear2.weight)
+
+
+class TransformerDecoderLayer(torch.nn.Module):
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,
+                 attention_dropout=0):
+        super(TransformerDecoderLayer, self).__init__()
+
+        self.self_attn = MultiHeadAttention(d_model, nhead, dropout=attention_dropout)
+        self.multihead_attn = MultiHeadAttention(d_model, nhead, dropout=attention_dropout)
+        # Implementation of Feedforward model
+        self.linear1 = torch.nn.Linear(d_model, dim_feedforward)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.linear2 = torch.nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = torch.nn.LayerNorm(d_model)
+        self.norm2 = torch.nn.LayerNorm(d_model)
+        self.norm3 = torch.nn.LayerNorm(d_model)
+        self.dropout1 = torch.nn.Dropout(dropout)
+        self.dropout2 = torch.nn.Dropout(dropout)
+        self.dropout3 = torch.nn.Dropout(dropout)
+
+        self.activation = activation
+        self.reset_parameters()
+
+    def forward(self, tgt: torch.Tensor, memory: torch.Tensor, tgt_mask: Optional[torch.Tensor] = None,
+                memory_key_padding_mask: Optional[torch.Tensor] = None,
+                full_target: Optional[torch.Tensor] = None, pos_offset: int = 0) -> torch.Tensor:
+        
+        assert pos_offset == 0 or tgt_mask is None
+        tgt2 = self.self_attn(tgt, tgt if full_target is None else full_target, mask=AttentionMask(None, tgt_mask))
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+        tgt2 = self.multihead_attn(tgt, memory, mask=AttentionMask(memory_key_padding_mask, None))
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def reset_parameters(self):
+        torch.nn.init.xavier_uniform_(self.linear1.weight, gain=torch.nn.init.calculate_gain('relu') \
+            if self.activation is F.relu else 1.0)
+        torch.nn.init.xavier_uniform_(self.linear2.weight)
+
+
+class TransformerDecoderBase(torch.nn.Module):
+    @dataclass
+    class State:
+        step: int
+        state: Dict[int, torch.Tensor]
+
+    def __init__(self, d_model: int):
+        super().__init__()
+        self.d_model = d_model
+
+    def create_state(self, batch_size: int, max_length: int, device: torch.device) -> State:
+        return self.State(0, {i: torch.empty([batch_size, max_length, self.d_model], device=device)
+                              for i in range(len(self.layers))})
+
+    def one_step_forward(self, state: State, data: torch.Tensor, *args, **kwargs):
+        assert data.shape[1] == 1, f"For one-step forward should have one timesteps, but shape is {data.shape}"
+        assert state.step < state.state[0].shape[1]
+
+        for i, l in enumerate(self.layers):
+            state.state[i][:, state.step:state.step + 1] = data
+            data = l(data, *args, **kwargs, full_target=state.state[i][:, :state.step + 1],
+                     pos_offset=state.step)
+
+        state.step += 1
+        return data
+
+
+class TransformerEncoder(torch.nn.Module):
+    def __init__(self, layer, n_layers: int, *args, **kwargs):
+        super().__init__()
+        self.layers = torch.nn.ModuleList([layer(*args, **kwargs) for _ in range(n_layers)])
+
+    def forward(self, data: torch.Tensor, *args, **kwargs):
+        for l in self.layers:
+            data = l(data, *args, **kwargs)
+        return data
+
+
+class TransformerDecoder(TransformerDecoderBase):
+    def __init__(self, layer, n_layers: int, d_model: int, *args, **kwargs):
+        super().__init__(d_model)
+        self.layers = torch.nn.ModuleList([layer(d_model, *args, **kwargs) for _ in range(n_layers)])
+
+    def forward(self, data: torch.Tensor, *args, **kwargs):
+        for l in self.layers:
+            data = l(data, *args, **kwargs)
+        return data
+
+
+def TransformerEncoderWithLayer(layer = TransformerEncoder):
+    return lambda *args, **kwargs: TransformerEncoder(layer, *args, **kwargs)
+
+
+def TransformerDecoderWithLayer(layer = TransformerDecoder):
+    return lambda *args, **kwargs: TransformerDecoder(layer, *args, **kwargs)
+
+
+class Transformer(torch.nn.Module):
+    def __init__(self, d_model: int = 512, nhead: int = 8, num_encoder_layers: int = 6,
+                 num_decoder_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1,
+                 activation: ActivationFunction = F.relu, encoder_layer=TransformerEncoderWithLayer(),
+                 decoder_layer=TransformerDecoderWithLayer(), attention_dropout: float = 0):
+        super().__init__()
+
+        self.encoder = encoder_layer(num_encoder_layers, d_model, nhead, dim_feedforward,
+                                     dropout, activation, attention_dropout)
+        self.decoder = decoder_layer(num_decoder_layers, d_model, nhead, dim_feedforward,
+                                     dropout, activation, attention_dropout)
+
+    def forward(self, src: torch.Tensor, tgt: torch.Tensor, tgt_mask: Optional[torch.Tensor] = None,
+                src_mask: Optional[AttentionMask] = None):
+
+        memory = self.encoder(src, src_mask)
+        return self.decoder(tgt, memory, tgt_mask, src_mask.src_length_mask if src_mask is not None else None)
+
+    @staticmethod
+    def generate_square_subsequent_mask(sz: int, device: torch.device) -> torch.Tensor:
+        return torch.triu(torch.ones(sz, sz, dtype=torch.bool, device=device), diagonal=1)
diff --git a/ops/vanilla_attention_std.py b/ops/vanilla_attention_std.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e1eafefc101c388e3e633f51e442209b9802d14
--- /dev/null
+++ b/ops/vanilla_attention_std.py
@@ -0,0 +1,171 @@
+"""
+Vanilla Transformer 的标准 Softmax Attention
+用于替换 flash_attn 的实现
+"""
+import math
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Optional, Tuple
+
+def vanilla_attention_std(
+    q: torch.Tensor,
+    k: torch.Tensor, 
+    v: torch.Tensor,
+    causal: bool = True,
+    window_size: Optional[Tuple[int, int]] = None,
+    sm_scale: Optional[float] = None,
+) -> torch.Tensor:
+    """
+    标准 Softmax Attention，兼容 flash_attn_func 的输入格式
+    
+    Args:
+        q, k, v: [batch, seq_len, num_heads, head_dim] 格式
+        causal: 是否使用因果mask
+        window_size: 滑动窗口大小 (left, right)，(-1, -1) 表示无限制
+        sm_scale: softmax 缩放因子
+    
+    Returns:
+        output: [batch, seq_len, num_heads, head_dim] 格式
+    """
+    B, T_q, H, D = q.shape
+    T_k = k.shape[1]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    # 转换为 [B, H, T, D] 格式进行计算
+    q = rearrange(q, 'b t h d -> b h t d')
+    k = rearrange(k, 'b t h d -> b h t d')
+    v = rearrange(v, 'b t h d -> b h t d')
+    
+    # 计算 attention scores
+    scores = torch.matmul(q.float(), k.float().transpose(-2, -1)) * sm_scale
+    
+    # Causal mask
+    if causal:
+        P_SEQ = T_k - T_q  # 处理 KV cache 的情况
+        causal_mask = torch.triu(
+            torch.ones((T_q, T_k), dtype=torch.bool, device=q.device),
+            diagonal=P_SEQ + 1
+        )
+        scores = scores.masked_fill(causal_mask[None, None, :, :], float('-inf'))
+    
+    # Window mask (sliding window attention)
+    if window_size is not None and window_size != (-1, -1):
+        left_window, right_window = window_size
+        window_mask = torch.ones((T_q, T_k), dtype=torch.bool, device=q.device)
+        for i in range(T_q):
+            # 计算每个查询位置的有效窗口范围
+            start = max(0, i - left_window)
+            end = min(T_k, i + right_window + 1)
+            window_mask[i, start:end] = False
+        scores = scores.masked_fill(window_mask[None, None, :, :], float('-inf'))
+    
+    # Softmax
+    attn_weights = F.softmax(scores, dim=-1)
+    attn_weights = torch.nan_to_num(attn_weights, 0.0)
+    
+    # Apply attention to values
+    output = torch.matmul(attn_weights.to(v.dtype), v)
+    
+    # 转换回 [B, T, H, D] 格式
+    output = rearrange(output, 'b h t d -> b t h d')
+    
+    return output
+
+
+def vanilla_attention_varlen_std(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    cu_seqlens_q: torch.Tensor,
+    cu_seqlens_k: torch.Tensor,
+    max_seqlen_q: int,
+    max_seqlen_k: int,
+    causal: bool = True,
+    window_size: Optional[Tuple[int, int]] = None,
+    sm_scale: Optional[float] = None,
+) -> torch.Tensor:
+    """
+    变长序列的标准 Softmax Attention，兼容 flash_attn_varlen_func
+    
+    Args:
+        q: [total_q_tokens, num_heads, head_dim]
+        k: [total_k_tokens, num_kv_heads, head_dim]
+        v: [total_k_tokens, num_kv_heads, head_dim]
+        cu_seqlens_q: 累积序列长度 [batch_size + 1]
+        cu_seqlens_k: 累积序列长度 [batch_size + 1]
+        max_seqlen_q: 最大查询序列长度
+        max_seqlen_k: 最大键值序列长度
+    
+    Returns:
+        output: [total_q_tokens, num_heads, head_dim]
+    """
+    batch_size = cu_seqlens_q.shape[0] - 1
+    H = q.shape[1]
+    D = q.shape[2]
+    
+    if sm_scale is None:
+        sm_scale = 1.0 / math.sqrt(D)
+    
+    outputs = []
+    
+    # 逐批次处理
+    for b in range(batch_size):
+        q_start, q_end = cu_seqlens_q[b].item(), cu_seqlens_q[b+1].item()
+        k_start, k_end = cu_seqlens_k[b].item(), cu_seqlens_k[b+1].item()
+        
+        if q_start == q_end:  # 空序列
+            continue
+            
+        # 提取当前批次的 q, k, v
+        q_b = q[q_start:q_end]  # [T_q, H, D]
+        k_b = k[k_start:k_end]  # [T_k, H, D] 
+        v_b = v[k_start:k_end]  # [T_k, H, D]
+        
+        T_q = q_b.shape[0]
+        T_k = k_b.shape[0]
+        
+        # 转换为 [H, T, D] 格式
+        q_b = rearrange(q_b, 't h d -> h t d')
+        k_b = rearrange(k_b, 't h d -> h t d')
+        v_b = rearrange(v_b, 't h d -> h t d')
+        
+        # 计算 attention scores
+        scores = torch.matmul(q_b.float(), k_b.float().transpose(-2, -1)) * sm_scale
+        
+        # Causal mask
+        if causal:
+            P_SEQ = T_k - T_q
+            causal_mask = torch.triu(
+                torch.ones((T_q, T_k), dtype=torch.bool, device=q.device),
+                diagonal=P_SEQ + 1
+            )
+            scores = scores.masked_fill(causal_mask[None, :, :], float('-inf'))
+        
+        # Window mask
+        if window_size is not None and window_size != (-1, -1):
+            left_window, right_window = window_size
+            window_mask = torch.ones((T_q, T_k), dtype=torch.bool, device=q.device)
+            for i in range(T_q):
+                start = max(0, i - left_window)
+                end = min(T_k, i + right_window + 1)
+                window_mask[i, start:end] = False
+            scores = scores.masked_fill(window_mask[None, :, :], float('-inf'))
+        
+        # Softmax
+        attn_weights = F.softmax(scores, dim=-1)
+        attn_weights = torch.nan_to_num(attn_weights, 0.0)
+        
+        # Apply attention
+        output_b = torch.matmul(attn_weights.to(v_b.dtype), v_b)
+        
+        # 转换回 [T, H, D] 格式
+        output_b = rearrange(output_b, 'h t d -> t h d')
+        outputs.append(output_b)
+    
+    # 拼接所有批次的输出
+    output = torch.cat(outputs, dim=0)
+    
+    return output
diff --git a/token_shift.py b/token_shift.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e5550546bfd8df0e5be818e0838c78e4327716d
--- /dev/null
+++ b/token_shift.py
@@ -0,0 +1,314 @@
+import torch
+
+import triton
+import triton.language as tl
+import pytest
+
+def maybe_contiguous(x):
+    # only when the inner most dimension is contiguous can LDGSTS be used
+    # so inner-dimension contiguity is enforced.
+    return x.contiguous() if x.stride(-1) != 1 else x
+
+@triton.jit
+def shift_fwd_kernel(
+    X_PTR,
+    PREV_WEIGHT_PTR,
+    CURR_WEIGHT_PTR,
+    OUT_PTR,
+
+    stride_x_b, stride_x_t, stride_x_h, stride_x_d,
+    stride_weight_b, stride_weight_t, stride_weight_h,
+    T: tl.constexpr, D: tl.constexpr,
+    BLOCK_T: tl.constexpr,
+):
+    """
+        everything is (B, T, D)
+    """
+    b_offset = tl.program_id(axis=0).to(tl.int64)
+    t_offset = tl.program_id(axis=1).to(tl.int64) * BLOCK_T
+    h_offset = tl.program_id(axis=2).to(tl.int64)
+
+
+    x_ptr_offset = b_offset * stride_x_b + t_offset * stride_x_t + h_offset * stride_x_h
+    X_PTR += x_ptr_offset
+    OUT_PTR += x_ptr_offset
+
+    weight_ptr_offset = b_offset * stride_weight_b + t_offset * stride_weight_t + h_offset * stride_weight_h
+    CURR_WEIGHT_PTR += weight_ptr_offset
+    PREV_WEIGHT_PTR += weight_ptr_offset
+
+    x_ptr = X_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_x_t + tl.arange(0, D)[None, :] * stride_x_d
+    t_offset_block = t_offset + tl.arange(0, BLOCK_T)[:, None]
+    x_mask = t_offset_block < T
+
+    # Yeah this is correct
+    x_prev_ptr = x_ptr - stride_x_t
+    t_prev_offset_block = t_offset_block - 1
+    x_prev_mask = ((t_prev_offset_block) < T) & (t_prev_offset_block >= 0)
+
+    curr_weight_ptr = CURR_WEIGHT_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_weight_t
+    prev_weight_ptr = PREV_WEIGHT_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_weight_t
+
+
+    x = tl.load(x_ptr, mask=x_mask, other=0.0)
+    x_prev = tl.load(x_prev_ptr, mask=x_prev_mask, other=0.0)
+    curr_weight = tl.load(curr_weight_ptr, mask=x_mask, other=0.0)
+    prev_weight = tl.load(prev_weight_ptr, mask=x_mask, other=0.0)
+
+    result = x * curr_weight.to(tl.float32) + x_prev * prev_weight.to(tl.float32)
+    result = result.to(x.dtype)
+
+    out_ptr = OUT_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_x_t + tl.arange(0, D)[None, :] * stride_x_d
+    tl.store(out_ptr, result, mask=x_mask)
+
+
+@triton.jit
+def shift_bwd_kernel(
+    X_PTR,
+    PREV_WEIGHT_PTR,
+    CURR_WEIGHT_PTR,
+
+    DOUT_PTR,
+    DX_PTR,
+    DPREV_WEIGHT_PTR,
+    DCURR_WEIGHT_PTR,
+
+    stride_x_b, stride_x_t, stride_x_h, stride_x_d,
+    stride_weight_b, stride_weight_t, stride_weight_h,
+    T: tl.constexpr, D: tl.constexpr,
+    BLOCK_T: tl.constexpr,
+):
+    """
+        everything is (B, T, D)
+    """
+    b_offset = tl.program_id(axis=0).to(tl.int64)
+    t_offset = tl.program_id(axis=1).to(tl.int64) * BLOCK_T
+    h_offset = tl.program_id(axis=2).to(tl.int64)
+
+
+    x_ptr_offset = b_offset * stride_x_b + t_offset * stride_x_t + h_offset * stride_x_h
+    X_PTR += x_ptr_offset
+    DX_PTR += x_ptr_offset
+    DOUT_PTR += x_ptr_offset
+
+    weight_ptr_offset = b_offset * stride_weight_b + t_offset * stride_weight_t + h_offset * stride_weight_h
+    CURR_WEIGHT_PTR += weight_ptr_offset
+    PREV_WEIGHT_PTR += weight_ptr_offset
+    DCURR_WEIGHT_PTR += weight_ptr_offset
+    DPREV_WEIGHT_PTR += weight_ptr_offset
+
+    x_ptr = X_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_x_t + tl.arange(0, D)[None, :] * stride_x_d
+    t_offset_block = t_offset + tl.arange(0, BLOCK_T)[:, None]
+    x_mask = t_offset_block < T
+
+    dout_ptr = DOUT_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_x_t + tl.arange(0, D)[None, :] * stride_x_d
+
+    # Yeah this is correct
+    dout_next_ptr = dout_ptr + stride_x_t
+    t_next_offset_block = t_offset_block + 1
+    x_next_mask = (t_next_offset_block) < T
+
+
+    # Yeah this is correct
+    x_prev_ptr = x_ptr - stride_x_t
+    t_prev_offset_block = t_offset_block - 1
+    x_prev_mask = ((t_prev_offset_block) < T) & (t_prev_offset_block >= 0)
+
+    curr_weight_ptr = CURR_WEIGHT_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_weight_t
+    prev_weight_ptr = PREV_WEIGHT_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_weight_t
+    next_prev_weight_ptr = prev_weight_ptr + stride_weight_t
+
+
+    x = tl.load(x_ptr, mask=x_mask, other=0.0)
+    x_prev = tl.load(x_prev_ptr, mask=x_prev_mask, other=0.0)
+    dout = tl.load(dout_ptr, mask=x_mask, other=0.0)
+    dout_next= tl.load(dout_next_ptr, mask=x_next_mask, other=0.0)
+
+    curr_weight = tl.load(curr_weight_ptr, mask=x_mask, other=0.0)
+    next_prev_weight = tl.load(next_prev_weight_ptr, mask=x_next_mask, other=0.0)
+
+    dx =  dout * curr_weight.to(tl.float32) + dout_next * next_prev_weight.to(tl.float32)
+    dx = dx.to(x.dtype)
+
+    dcurr_weight = tl.sum(dout.to(tl.float32) * x, axis=1, keep_dims=True)
+    dprev_weight = tl.sum(dout.to(tl.float32) * x_prev, axis=1, keep_dims=True)
+
+    dx_ptr = DX_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_x_t + tl.arange(0, D)[None, :] * stride_x_d
+    tl.store(dx_ptr, dx, mask=x_mask)
+    dcurr_weight_ptr = DCURR_WEIGHT_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_weight_t
+    tl.store(dcurr_weight_ptr, dcurr_weight, mask=x_mask)
+    dprev_weight_ptr = DPREV_WEIGHT_PTR + tl.arange(0, BLOCK_T)[:, None] * stride_weight_t
+    tl.store(dprev_weight_ptr, dprev_weight, mask=x_mask)
+
+
+
+class TokenShift(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, x: torch.Tensor, prev_weight: torch.Tensor, curr_weight: torch.Tensor):
+
+        B, T, H, D = x.size()
+        assert D in {16, 32, 64, 128}
+        assert prev_weight.size() == curr_weight.size() == (B, T, H)
+        assert prev_weight.stride() == curr_weight.stride()
+        x = maybe_contiguous(x)
+        out = torch.empty_like(x)
+
+        BLOCK_T = triton.next_power_of_2(min(64, T))
+
+        grid = lambda meta: (B, triton.cdiv(T, meta["BLOCK_T"]), H)
+        # NOTE:
+        #  - Each torch.tensor object is implicitly converted into a pointer to its first element.
+        #  - `triton.jit`'ed functions can be indexed with a launch grid to obtain a callable GPU kernel.
+        #  - Don't forget to pass meta-parameters as keywords arguments.
+        shift_fwd_kernel[grid](
+            x,
+            prev_weight,
+            curr_weight,
+            out,
+            *x.stride(),
+            *curr_weight.stride(),
+            T=T, D=D,
+            BLOCK_T=BLOCK_T,
+        )
+        ctx.save_for_backward(x, prev_weight, curr_weight)
+        # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
+        # running asynchronously at this point.
+        return out
+
+    @staticmethod
+    def backward(ctx, dout: torch.Tensor):
+
+        x, prev_weight, curr_weight = ctx.saved_tensors
+        B, T, H, D = x.size()
+        assert D in {16, 32, 64, 128}
+        assert prev_weight.size() == curr_weight.size() == (B, T, H)
+        assert prev_weight.stride() == curr_weight.stride()
+        x = maybe_contiguous(x)
+        assert dout.stride() == x.stride()
+        dx = torch.empty_like(x)
+        dcurr_weight = torch.empty_like(curr_weight)
+        dprev_weight = torch.empty_like(prev_weight)
+
+        BLOCK_T = triton.next_power_of_2(min(64, T))
+
+        grid = lambda meta: (B, triton.cdiv(T, meta["BLOCK_T"]), H)
+        # NOTE:
+        #  - Each torch.tensor object is implicitly converted into a pointer to its first element.
+        #  - `triton.jit`'ed functions can be indexed with a launch grid to obtain a callable GPU kernel.
+        #  - Don't forget to pass meta-parameters as keywords arguments.
+        shift_bwd_kernel[grid](
+            x,
+            prev_weight,
+            curr_weight,
+            dout,
+            dx,
+            dprev_weight,
+            dcurr_weight,
+            *x.stride(),
+            *curr_weight.stride(),
+            T=T,
+            D=D,
+            BLOCK_T=BLOCK_T,
+        )
+        # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
+        # running asynchronously at this point.
+        return dx, dprev_weight, dcurr_weight
+
+def token_shift(x, prev_weight, curr_weight):
+    return TokenShift.apply(x, prev_weight, curr_weight)
+
+
+
+@pytest.mark.parametrize("B, T, H, D", [(4, 2048, 12, 128)])
+def test_op(B, T, H, D, dtype=torch.float32):
+    torch.manual_seed(24)
+    B = 4
+    T = 2088
+    H = 12
+    D = 128
+    # x = torch.rand(size, device='cuda')
+    x = torch.randn(B, T, H, D, device="cuda", dtype=dtype, requires_grad=True)
+    dout = torch.randn(B, T, H, D, device="cuda", dtype=dtype)
+    curr_weight = torch.rand(B, T, H, device="cuda", requires_grad=True)
+
+    prev_weight = 1.0 - curr_weight
+    x_prev = torch.roll(x, shifts=1, dims=1)
+    x_prev[:, 0, :, :] = 0.0
+    ref_out = (x_prev * prev_weight[..., None] + x * curr_weight[..., None]).to(dtype)
+
+    ref_out.backward(dout)
+    ref_dx, x.grad = x.grad.clone(), None
+    ref_dcurr_weight, curr_weight.grad = curr_weight.grad.clone(), None
+
+
+    prev_weight = 1.0 - curr_weight
+    # out_torch = x if x.sum() > 0.0 else y
+
+    tri_out = token_shift(x, prev_weight, curr_weight)
+
+
+    tri_out.backward(dout)
+    tri_dx, x.grad = x.grad.clone(), None
+    tri_dcurr_weight, curr_weight.grad = curr_weight.grad.clone(), None
+
+    # out_torch = x if x.sum() > 0.0 else y
+
+    # import pdb; pdb.set_trace()
+
+    assert torch.allclose(ref_out, tri_out, atol=1e-2, rtol=0), (ref_out - tri_out).abs().max()
+    assert torch.allclose(ref_dx, tri_dx, atol=1e-2, rtol=0), (ref_dx - tri_dx).abs().max()
+    assert torch.allclose(ref_dcurr_weight, tri_dcurr_weight, atol=1e-2, rtol=0), (ref_dcurr_weight - tri_dcurr_weight).abs().max()
+
+if __name__ == "__main__":
+    torch.manual_seed(0)
+    B = 4
+    T = 2088
+    H = 12
+    D = 128
+    # x = torch.rand(size, device='cuda')
+    x = torch.randn(B, T, H, D, device="cuda")
+    dout = torch.randn(B, T, H, D, device="cuda")
+    curr_weight = torch.rand(B, T, H, device="cuda")
+    prev_weight = 1.0 - curr_weight
+    # out_torch = x if x.sum() > 0.0 else y
+    result = shift_fwd(x, prev_weight, curr_weight)
+    print(result[0, :, 0, 0])
+    import ipdb; ipdb.set_trace()
+    # # for mode in ["fwd", "bwd"]:
+    # configs.append(
+    #     triton.testing.Benchmark(
+    #         x_names=["SIZE"],
+    #         # x_vals=[2**i for i in range(10, 15)],
+    #         x_vals=[98432],
+    #         line_arg="provider",
+    #         # line_vals=["triton-fp16", "flag"] + (["flash"] if HAS_FLASH else []),
+    #         # line_names=["Triton [FP16]", "Flag"] + (["Flash-2"] if HAS_FLASH else []),
+    #         line_vals=["debug"],
+    #         line_names=["Debug"],
+    #         styles=[("red", "-")],
+    #         ylabel="ms",
+    #         plot_name="hi",
+    #         args={},
+    #     )
+    # )
+
+
+    # @triton.testing.perf_report(configs)
+    # def bench_flash_attention(SIZE, provider, device="cuda"):
+    #     warmup = 25
+    #     rep = 100
+    #     torch.manual_seed(0)
+    #     size = 98432
+    #     # x = torch.rand(size, device='cuda')
+    #     x = torch.ones(size, device="cuda")
+    #     y = torch.rand(size, device="cuda")
+    #     # out_torch = x if x.sum() > 0.0 else y
+    #     fn = lambda: add(x, y)
+    #     ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
+    #     return ms
+
+
+    # if __name__ == "__main__":
+    #     # only works on post-Ampere GPUs right now
+    #     bench_flash_attention.run(save_path=".", print_data=True)