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LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 test-time-training
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,3 +1,105 @@
 ---
 license: mit
+language:
+- en
+tags:
+- Test-time Training
+pipeline_tag: text-generation
+base_model:
+  - Test-Time-Training/ttt-mlp-350m-books-2k
+library_name: transformers
 ---
+
+# Learning to (Learn at Test Time): RNNs with Expressive Hidden States
+
+[**Paper**](https://arxiv.org/abs/2407.04620)
+| [**JAX Codebase**](https://github.com/test-time-training/ttt-lm-jax)
+| [**Setup**](#environment-setup)
+| [**Quick Start**](#quick-start)
+| [**Inference Benchmark**](https://github.com/test-time-training/ttt-lm-kernels)
+
+This is the official PyTorch model implementation of [Learning to (Learn at Test Time): RNNs with Expressive Hidden States](https://arxiv.org/abs/2407.04620). 
+We **do not recommend training** with this codebase, because it is written in pure PyTorch without any systems optimization, so training will be slow, especially when the per-device batch size is small.
+
+
+For training code, or to replicate results from our paper, please view our [JAX codebase](https://github.com/test-time-training/ttt-lm-jax). For inference kernels, or to replicate speed benchmarks from our paper, please view our [kernel implementations](https://github.com/test-time-training/ttt-lm-kernels).
+
+## Abstract
+
+Self-attention performs well in long context but has quadratic complexity. Existing RNN layers
+have linear complexity, but their performance in long context is limited by the expressive power
+of their hidden state. We propose a new class of sequence modeling layers with linear complexity
+and an expressive hidden state. The key idea is to make the hidden state a machine learning
+model itself, and the update rule a step of self-supervised learning. 
+
+Since the hidden state is updated by training even on test sequences, our layers are called **Test-Time Training (TTT) layers**.
+We consider two instantiations: TTT-Linear and TTT-MLP, whose hidden state is a linear model
+and a two-layer MLP respectively. 
+
+## Environment Setup
+
+```bash
+pip install "transformers[torch]"
+```
+
+## Quick Start
+
+Our implementation is based on Huggingface Transformers. You can use the following code to load the model and generate text.
+
+### Load with AutoModel
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+model_id = "RetentionLabs/TTT-Linear-350M-Base-Books-2k"
+
+# Initializing a model from remote
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Generate
+with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+    inputs = tokenizer("The future of AI is", return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=100)
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### From scratch
+
+```python
+from transformers import AutoTokenizer
+from modeling_ttt import TTTForCausalLM, TTTConfig, TTT_STANDARD_CONFIGS
+
+# Initializing a TTT ttt-1b style configuration
+# configuration = TTTConfig(**TTT_STANDARD_CONFIGS['1b']) is equivalent to the following
+configuration = TTTConfig()
+
+# Initializing a model from the ttt-1b style configuration
+model = TTTForCausalLM(configuration)
+model.eval()
+
+# Accessing the model configuration
+configuration = model.config
+
+# Tokenizer
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+
+# Prefill
+input_ids = tokenizer("Greeting from TTT!", return_tensors="pt").input_ids
+logits = model(input_ids=input_ids)
+print(logits)
+
+# Decoding
+out_ids = model.generate(input_ids=input_ids, max_length=50)
+out_str = tokenizer.batch_decode(out_ids, skip_special_tokens=True)
+print(out_str)
+```
+
+**Note: This is a naive implementation of TTT layers for tutorial purposes.** This model can be trained using Huggingface Accelerate, or custom training loops. We have released our faster inference kernel and its speed benchmark [here](https://github.com/test-time-training/ttt-lm-kernels).

config.json

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+{
+  "architectures": [
+    "TTTForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_ttt.TTTConfig",
+    "AutoModel": "modeling_ttt.TTTModel",
+    "AutoModelForCausalLM": "modeling_ttt.TTTForCausalLM"
+  },
+  "bos_token_id": 1,
+  "conv_kernel": 4,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 2736,
+  "max_position_embeddings": 2048,
+  "mini_batch_size": 16,
+  "model_type": "ttt",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pre_conv": true,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "scan_checkpoint_group_size": 0,
+  "share_qk": true,
+  "transformers_version": "4.57.6",
+  "ttt_base_lr": 1.0,
+  "ttt_layer_type": "linear",
+  "use_cache": true,
+  "use_gate": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.57.6",
+  "do_sample": true,
+  "temperature": 0.7,
+  "top_p": 0.9,
+  "repetition_penalty": 1.1,
+  "max_new_tokens": 512
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5502a910469aaabaad74b4242ced63c4d8f3ab4891e08586b384afd2ac1296b4
+size 675438944

modeling_ttt.py

@@ -0,0 +1,1650 @@
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from torch.utils._pytree import tree_map
+
+from transformers import PretrainedConfig
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.generation import GenerationMixin
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import ModelOutput, logging
+from transformers.utils.import_utils import is_causal_conv1d_available
+
+if is_causal_conv1d_available():
+    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+else:
+    causal_conv1d_update, causal_conv1d_fn = None, None
+
+
+logger = logging.get_logger(__name__)
+
+TTT_STANDARD_CONFIGS = {
+    "125m": {
+        "hidden_size": 768,
+        "intermediate_size": 2048,
+        "num_hidden_layers": 12,
+        "num_attention_heads": 12,
+    },
+    "350m": {
+        "hidden_size": 1024,
+        "intermediate_size": 2736,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 16,
+    },
+    "760m": {
+        "hidden_size": 1536,
+        "intermediate_size": 4096,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 16,
+    },
+    "1b": {
+        "hidden_size": 2048,
+        "intermediate_size": 5504,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 32,
+    },
+}
+
+
+class TTTConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`TTTModel`]. It is used to instantiate an TTT
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the TTT-1B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LlamaModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
+            Llama 2 up to 4096, CodeLlama up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        use_gate (`bool`, *optional*, defaults to `False`): whether use gating in Mamba backbone
+        share_qk (`bool`, *optional*, defaults to `False`): whether share Q/K projection matrix
+        ttt_layer_type (`str`, *optional*, defaults to `"linear"`): ttt block type, "linear" or "mlp", stands for TTT-Linear and TTT-MLP
+        ttt_base_lr (`float`, *optional*, defaults to 1.0): base learning rate for TTT learner
+        pre_conv (`bool`, *optional*, defaults to `False`): whether use conv before TTT
+        conv_kernel (`int`, *optional*, defaults to 4): kernel size of the conv layer
+        scan_checkpoint_group_size (`int`, *optional*, defaults to 0):
+            gradient checkpoint group size on seq dimension, 0 means no checkpointing.
+            In JAX implementation, we set it 4, which means we group 4 mini-batches together in 1 gradient checkpointg to save memory.
+
+
+    ```python
+    >>> from . import TTTModel, TTTConfig
+
+    >>> # Initializing a TTT ttt-1b style configuration
+    >>> configuration = TTTConfig()
+
+    >>> # Initializing a model from the ttt-1b style configuration
+    >>> model = TTTModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "ttt"
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=2048,
+        intermediate_size=5504,
+        num_hidden_layers=24,
+        num_attention_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=False,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        use_gate=False,
+        share_qk=False,
+        ttt_layer_type="linear",
+        ttt_base_lr=1.0,
+        mini_batch_size=16,
+        pre_conv=False,
+        conv_kernel=4,
+        scan_checkpoint_group_size=0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+
+        self.use_gate = use_gate
+        self.share_qk = share_qk
+        self.ttt_layer_type = ttt_layer_type
+        self.ttt_base_lr = ttt_base_lr
+        self.mini_batch_size = mini_batch_size
+
+        self.pre_conv = pre_conv
+        self.conv_kernel = conv_kernel
+        self.scan_checkpoint_group_size = scan_checkpoint_group_size
+
+        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,
+        )
+
+
+########################
+### Backbone Modules ###
+########################
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def permute_qk(q, k):
+    # NOTE: EasyLM and transformers use different method to compute rotary emebdding
+    # we manually reorder the dim here to match our JAX implementation
+    # which may not be optimal for speed
+    # reference: https://github.com/young-geng/EasyLM/blob/981a2ed9630f44258a94b6f44dff2b7bd203ae8d/EasyLM/models/llama/convert_hf_to_easylm.py#L33
+    bsz, num_head, seq_len, head_dim = q.shape
+    q = q.reshape(bsz, num_head, seq_len, head_dim // 2, 2).transpose(3, 4).reshape(bsz, num_head, seq_len, head_dim)
+    k = k.reshape(bsz, num_head, seq_len, head_dim // 2, 2).transpose(3, 4).reshape(bsz, num_head, seq_len, head_dim)
+
+    return q, k
+
+
+def undo_permute_qk(q, k):
+    # NOTE: EasyLM and transformers use different method to compute rotary emebdding
+    # we manually undo the reorder the dim here to match our JAX implementation
+    # which may not be optimal for speed
+    # reference: https://github.com/young-geng/EasyLM/blob/981a2ed9630f44258a94b6f44dff2b7bd203ae8d/EasyLM/models/llama/convert_hf_to_easylm.py#L33
+    bsz, num_head, seq_len, head_dim = q.shape
+    q = q.reshape(bsz, num_head, seq_len, 2, head_dim // 2).transpose(3, 4).reshape(bsz, num_head, seq_len, head_dim)
+    k = k.reshape(bsz, num_head, seq_len, 2, head_dim // 2).transpose(3, 4).reshape(bsz, num_head, seq_len, head_dim)
+
+    return q, k
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class SwiGluMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        if self.config.pretraining_tp > 1:
+            slice = self.intermediate_size // self.config.pretraining_tp
+            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+            down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+            gate_proj = torch.cat(
+                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)],
+                dim=-1,
+            )
+            up_proj = torch.cat(
+                [F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)],
+                dim=-1,
+            )
+
+            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+            down_proj = [
+                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=16,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        super().__init__()
+        self.scaling_factor = scaling_factor
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Conv(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.conv = nn.Conv1d(
+            config.hidden_size,
+            config.hidden_size,
+            bias=True,
+            kernel_size=config.conv_kernel,
+            groups=config.hidden_size,
+            padding=config.conv_kernel - 1,
+        )
+
+    def __call__(self, hidden_states, cache_params=None):
+        seq_len = hidden_states.shape[1]
+        hidden_states = self.norm(hidden_states)
+        # [B, C, L]
+        hidden_states = hidden_states.transpose(1, 2)
+
+        if causal_conv1d_fn is None:
+            if cache_params is not None:
+                if cache_params.seqlen_offset > 0:
+                    conv_state = cache_params.conv_states_dic["pre_conv"][self.layer_idx]
+                    conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
+                    conv_state[:, :, -1] = hidden_states[:, :, 0]
+                    cache_params.conv_states_dic["pre_conv"][self.layer_idx].copy_(conv_state)
+                    hidden_states = torch.sum(conv_state * self.conv.weight[:, 0, :], dim=-1)
+                    hidden_states += self.conv.bias
+                    hidden_states = hidden_states.unsqueeze(-1)
+                else:
+                    conv_state = nn.functional.pad(
+                        hidden_states,
+                        (self.config.conv_kernel - hidden_states.shape[-1], 0),
+                    )
+                    cache_params.conv_states_dic["pre_conv"][self.layer_idx].copy_(conv_state)
+                    hidden_states = self.conv(hidden_states)[..., :seq_len]
+            else:
+                hidden_states = self.conv(hidden_states)[..., :seq_len]
+        else:
+            conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
+            if cache_params is not None and cache_params.seqlen_offset > 0:
+                hidden_states = causal_conv1d_update(
+                    hidden_states.squeeze(-1),
+                    cache_params.conv_states_dic["pre_conv"][self.layer_idx],
+                    conv_weights,
+                    self.conv.bias,
+                    None,
+                )
+                hidden_states = hidden_states.unsqueeze(-1)
+            else:
+                if cache_params is not None:
+                    conv_states = nn.functional.pad(
+                        hidden_states,
+                        (self.config.conv_kernel - hidden_states.shape[-1], 0),
+                    )
+                    cache_params.conv_states_dic["pre_conv"][self.layer_idx].copy_(conv_states)
+                hidden_states = causal_conv1d_fn(hidden_states, conv_weights, self.conv.bias, activation=None)
+
+        # [B, L, C]
+        hidden_states = hidden_states.transpose(1, 2)
+
+        return hidden_states
+
+
+#########################
+### TTT Layer Modules ###
+#########################
+
+
+def scan(f, init, xs, out, checkpoint_group=0):
+    """Minic jax.lax.scan function."""
+    carry = init
+    if isinstance(xs, dict):
+        num_items = len(next(iter(xs.values())))
+    else:
+        num_items = len(xs[0])
+
+    def scan_fn(carry, i_start, i_end):
+        for i in range(i_start, i_end):
+            if isinstance(xs, dict):
+                x = {key: tensor[i] for key, tensor in xs.items()}
+            else:
+                x = [x[i] for x in xs]
+            carry, y = f(carry, x)
+            out[i] = y
+        return carry
+
+    if checkpoint_group > 0:
+        ckpt_every_n = num_items // checkpoint_group
+        for k in range(0, num_items, ckpt_every_n):
+            carry = torch.utils.checkpoint.checkpoint(
+                scan_fn, carry, k, min(k + ckpt_every_n, num_items), use_reentrant=False
+            )
+    else:
+        carry = scan_fn(carry, 0, num_items)
+
+    return carry, out
+
+
+def ln_fwd(x, gamma, beta, eps=1e-6):
+    "Batch forward for LayerNorm."
+
+    # Mean and variance computation
+    mu = x.mean(dim=-1, keepdim=True)
+    var = x.var(dim=-1, keepdim=True, unbiased=False)
+
+    # Normalization
+    std = torch.sqrt(var + eps)
+    x_hat = (x - mu) / std
+
+    # Scale and shift
+    y = gamma * x_hat + beta
+
+    return y
+
+
+def ln_fused_l2_bwd(x, l2_target, gamma, beta, eps=1e-6):
+    "Batch backward for LayerNorm fused with L2 loss."
+    D = x.shape[-1]
+
+    # Mean and variance computation
+    mu = x.mean(dim=-1, keepdim=True)
+    var = x.var(dim=-1, keepdim=True, unbiased=False)
+
+    # Normalization
+    std = torch.sqrt(var + eps)
+    x_hat = (x - mu) / std
+
+    # Scale and shift
+    y = gamma * x_hat + beta
+
+    grad_output = y - l2_target
+    grad_x_hat = grad_output * gamma
+    z = (
+        (1.0 / D)
+        * (
+            D * grad_x_hat
+            - grad_x_hat.sum(dim=-1, keepdim=True)
+            - x_hat * (grad_x_hat * x_hat).sum(dim=-1, keepdim=True)
+        )
+        / std
+    )
+
+    return z
+
+
+# Modified from https://github.com/NVIDIA/Megatron-LM/blob/e33c8f78a35765d5aa37475a144da60e8a2349d1/megatron/core/fusions/fused_bias_gelu.py#L26
+def gelu_bwd(x):
+    tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
+    ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out)
+    return ff
+
+
+class TTTCache:
+    """
+    TTTCache is a data structure that holds the last hidden states and gradients for the TTT layer.
+
+    Arguments:
+        model: TTTModel
+        batch_size: int
+
+    Attributes:
+        seqlen_offset: int
+        mini_batch_size: int
+        params_dict: Dict[str, Dict[int, torch.Tensor]]  *_states, *_grad -> # layer_idx -> [batch_size, ...]
+        conv_states_dic: Dict[str, Dict[int, torch.Tensor]]  *_states -> # layer_idx -> [batch_size, ...]
+
+    """
+
+    def __init__(self, model, batch_size: int):
+        config = model.config
+        self.seqlen_offset = 0
+        self.mini_batch_size = config.mini_batch_size
+
+        self.ttt_params_dict = defaultdict(dict)
+        if "linear" in config.ttt_layer_type:
+            self.ttt_param_names = ["W1", "b1"]
+        elif "mlp" in config.ttt_layer_type:
+            self.ttt_param_names = ["W1", "b1", "W2", "b2"]
+        else:
+            raise ValueError(f"TTT Layer Type {config.ttt_layer_type} not supported yet")
+
+        self.conv_states_dic = defaultdict(dict)
+        logger.info(f"Creating cache of size: {batch_size}")
+        for layer_idx in range(config.num_hidden_layers):
+            for name in self.ttt_param_names:
+                weight = getattr(model.layers[layer_idx].seq_modeling_block, name)
+                tiled_weight = torch.tile(weight.unsqueeze(0), (batch_size,) + (1,) * weight.dim()).to(model.device)
+                self.ttt_params_dict[f"{name}_states"][layer_idx] = tiled_weight
+                # for decoding, we need to store the gradients as well
+                self.ttt_params_dict[f"{name}_grad"][layer_idx] = torch.zeros_like(tiled_weight)
+
+            if config.pre_conv:
+                self.conv_states_dic["pre_conv"][layer_idx] = torch.zeros(
+                    batch_size,
+                    config.hidden_size,
+                    config.conv_kernel,
+                    device=model.device,
+                )
+            if config.share_qk:
+                self.conv_states_dic["ttt_conv_q"][layer_idx] = torch.zeros(
+                    batch_size,
+                    config.hidden_size,
+                    config.conv_kernel,
+                    device=model.device,
+                )
+                self.conv_states_dic["ttt_conv_k"][layer_idx] = torch.zeros(
+                    batch_size,
+                    config.hidden_size,
+                    config.conv_kernel,
+                    device=model.device,
+                )
+
+    def update(self, py_tree, layer_idx, seq_len):
+        if seq_len % self.mini_batch_size == 0:
+            # copy last mini-batch states, clear gradients
+            for name in self.ttt_param_names:
+                self.ttt_params_dict[f"{name}_states"][layer_idx].copy_(py_tree[f"{name}_states"])
+                self.ttt_params_dict[f"{name}_grad"][layer_idx].zero_()
+        elif seq_len < self.mini_batch_size:
+            if seq_len != 1 and self.seqlen_offset > 0 and self.seqlen_offset % self.mini_batch_size != 0:
+                raise ValueError("fractional update not supported yet.")
+            if (seq_len + self.seqlen_offset) % self.mini_batch_size == 0:
+                # copy last mini-batch states, clear gradients
+                for name in self.ttt_param_names:
+                    self.ttt_params_dict[f"{name}_states"][layer_idx].copy_(py_tree[f"{name}_states"])
+                    self.ttt_params_dict[f"{name}_grad"][layer_idx].zero_()
+            else:
+                # copy gradients for the next update
+                for name in self.ttt_param_names:
+                    self.ttt_params_dict[f"{name}_grad"][layer_idx].copy_(py_tree[f"{name}_grad"])
+        else:
+            raise ValueError(f"seq_len {seq_len} is a partial update not supported yet")
+
+    def ttt_params_to_dict(self, layer_idx):
+        return {name: self.ttt_params_dict[name][layer_idx] for name in self.ttt_params_dict}
+
+
+class TTTBase(nn.Module):
+    def __init__(self, config: TTTConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.width = config.hidden_size
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.width // self.num_heads
+        self.mini_batch_size = config.mini_batch_size
+
+        # token_idx is a scale factor that scale the summation in Eqn. 4
+        token_idx = 1.0 / torch.arange(1, self.mini_batch_size + 1)
+        self.register_buffer("token_idx", token_idx, persistent=False)
+        # make the scale factor learnable
+        self.learnable_token_idx = nn.Parameter(torch.zeros((self.mini_batch_size,)))
+
+        self.share_qk = config.share_qk
+        self.conv_kernel = config.conv_kernel
+        self._init_qkvo_proj()
+        self._init_rope()
+        # Learnable eta in Sec. 2.7
+        self._init_ttt_lr_gate()
+        self._init_ttt_ln()
+
+        # use gating as in Mamba backbone
+        self.use_gate = config.use_gate
+        if self.use_gate:
+            self.g_proj = nn.Linear(self.width, self.width, bias=False)
+
+        self.post_norm = nn.LayerNorm(self.width, eps=1e-6)
+
+    def _init_qkvo_proj(self):
+        self.q_proj = nn.Linear(self.width, self.num_heads * self.head_dim, bias=False)
+        # we share Q/K projection when using Mamba backbone
+        if not self.share_qk:
+            self.k_proj = nn.Linear(self.width, self.num_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.width, self.num_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.width, self.num_heads * self.head_dim, bias=False)
+
+        # after share Q/K projection, we use different conv layers for Q and K
+        if self.share_qk:
+            self.conv_q = nn.Conv1d(
+                self.hidden_size,
+                self.hidden_size,
+                bias=True,
+                kernel_size=self.conv_kernel,
+                groups=self.hidden_size,
+                padding=self.conv_kernel - 1,
+            )
+            self.conv_k = nn.Conv1d(
+                self.hidden_size,
+                self.hidden_size,
+                bias=True,
+                kernel_size=self.conv_kernel,
+                groups=self.hidden_size,
+                padding=self.conv_kernel - 1,
+            )
+
+    def _init_rope(self):
+        self.rope_theta = self.config.rope_theta
+        self.rotary_emb = RotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.mini_batch_size,
+            base=self.rope_theta,
+        )
+
+    def _init_ttt_lr_gate(self):
+        # [width, 1]
+        linear_weight_data = nn.Linear(self.width, 1, bias=True).weight.data
+        # prepending head dim -> [num_heads, width, 1]
+        self.learnable_ttt_lr_weight = nn.Parameter(
+            torch.stack(
+                [torch.normal(0, 0.02, size=linear_weight_data.shape) for _ in range(self.num_heads)],
+                dim=0,
+            )
+        )
+        linear_bias_data = nn.Linear(self.width, 1, bias=True).bias.data
+        # init bias to 0 following original JAX impl.
+        # [num_heads, 1]
+        self.learnable_ttt_lr_bias = nn.Parameter(
+            torch.stack(
+                [torch.zeros_like(linear_bias_data) for _ in range(self.num_heads)],
+                dim=0,
+            )
+        )
+
+    def _init_ttt_ln(self):
+        ln_weight_data = nn.LayerNorm(self.head_dim).weight.data
+        # prepending head dim -> [num_heads, width]
+        self.ttt_norm_weight = nn.Parameter(torch.tile(ln_weight_data.unsqueeze(0), (self.num_heads, 1)))
+        ln_bias_data = nn.LayerNorm(self.head_dim).bias.data
+        self.ttt_norm_bias = nn.Parameter(torch.tile(ln_bias_data.unsqueeze(0), (self.num_heads, 1)))
+
+    def get_qkv_projections(self, hidden_states, cache_params: Optional[TTTCache] = None):
+        if self.share_qk:
+            xq, XV = self.q_proj(hidden_states), self.v_proj(hidden_states)
+            seq_len = xq.shape[1]
+            xq = xq.transpose(1, 2)
+            if causal_conv1d_fn is None:
+                if cache_params is not None:
+                    if cache_params.seqlen_offset > 0:
+                        conv_q_state = cache_params.conv_states_dic["ttt_conv_q"][self.layer_idx]
+                        conv_q_state = torch.roll(conv_q_state, shifts=-1, dims=-1)
+                        conv_q_state[:, :, -1] = xq[:, :, 0]
+                        cache_params.conv_states_dic["ttt_conv_q"][self.layer_idx].copy_(conv_q_state)
+                        XQ = torch.sum(conv_q_state * self.conv_q.weight[:, 0, :], dim=-1)
+                        XQ += self.conv_q.bias
+                        XQ = XQ.unsqueeze(-1)
+
+                        conv_k_state = cache_params.conv_states_dic["ttt_conv_k"][self.layer_idx]
+                        conv_k_state = torch.roll(conv_k_state, shifts=-1, dims=-1)
+                        conv_k_state[:, :, -1] = xq[:, :, 0]
+                        cache_params.conv_states_dic["ttt_conv_k"][self.layer_idx].copy_(conv_k_state)
+                        XK = torch.sum(conv_k_state * self.conv_k.weight[:, 0, :], dim=-1)
+                        XK += self.conv_k.bias
+                        XK = XK.unsqueeze(-1)
+                    else:
+                        conv_q_state = nn.functional.pad(xq, (self.config.conv_kernel - xq.shape[-1], 0))
+                        cache_params.conv_states_dic["ttt_conv_q"][self.layer_idx].copy_(conv_q_state)
+                        XQ = self.conv_q(xq)[..., :seq_len]
+                        conv_k_state = nn.functional.pad(xq, (self.config.conv_kernel - xq.shape[-1], 0))
+                        cache_params.conv_states_dic["ttt_conv_k"][self.layer_idx].copy_(conv_k_state)
+                        XK = self.conv_k(xq)[..., :seq_len]
+                else:
+                    XQ = self.conv_q(xq)[..., :seq_len]
+                    XK = self.conv_k(xq)[..., :seq_len]
+            else:
+                conv_q_weights = self.conv_q.weight.view(self.conv_q.weight.size(0), self.conv_q.weight.size(2))
+                conv_k_weights = self.conv_k.weight.view(self.conv_k.weight.size(0), self.conv_k.weight.size(2))
+                if cache_params is not None and cache_params.seqlen_offset > 0:
+                    XQ = causal_conv1d_update(
+                        xq.squeeze(-1),
+                        cache_params.conv_states_dic["ttt_conv_q"][self.layer_idx],
+                        conv_q_weights,
+                        self.conv_q.bias,
+                        None,
+                    )
+                    XQ = XQ.unsqueeze(-1)
+                    XK = causal_conv1d_update(
+                        xq.squeeze(-1),
+                        cache_params.conv_states_dic["ttt_conv_k"][self.layer_idx],
+                        conv_k_weights,
+                        self.conv_k.bias,
+                        None,
+                    )
+                    XK = XK.unsqueeze(-1)
+                else:
+                    if cache_params is not None:
+                        conv_q_states = nn.functional.pad(xq, (self.config.conv_kernel - xq.shape[-1], 0))
+                        cache_params.conv_states_dic["ttt_conv_q"][self.layer_idx].copy_(conv_q_states)
+                        conv_k_states = nn.functional.pad(xq, (self.config.conv_kernel - xq.shape[-1], 0))
+                        cache_params.conv_states_dic["ttt_conv_k"][self.layer_idx].copy_(conv_k_states)
+                    XQ = causal_conv1d_fn(xq, conv_q_weights, self.conv_q.bias, activation=None)
+                    XK = causal_conv1d_fn(xq, conv_k_weights, self.conv_k.bias, activation=None)
+
+            XQ = XQ.transpose(1, 2)
+            XK = XK.transpose(1, 2)
+        else:
+            XQ, XK, XV = (
+                self.q_proj(hidden_states),
+                self.k_proj(hidden_states),
+                self.v_proj(hidden_states),
+            )
+        return XQ, XK, XV
+
+    def _split_heads(self, hidden_states):
+        return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
+
+    def get_eta(self, X, mini_batch_step_offset, mini_batch_size):
+        # [B, num_heads, num_mini_batch, mini_batch_size, 1]
+        ttt_lr = torch.einsum("bnkc,hdc->bhnkd", X, self.learnable_ttt_lr_weight) + self.learnable_ttt_lr_bias.reshape(
+            1, -1, 1, 1, 1
+        )
+        ttt_lr = F.sigmoid(ttt_lr)
+
+        # [B, num_heads, num_mini_batch, 1, mini_batch_size]
+        ttt_lr = ttt_lr.permute(0, 1, 2, 4, 3)
+        ttt_lr_eta = self.config.ttt_base_lr * ttt_lr / self.head_dim
+
+        # [B, L]
+        token_idx = self.token_idx + self.learnable_token_idx
+        token_idx = token_idx[mini_batch_step_offset : mini_batch_step_offset + mini_batch_size]
+
+        # token idx should be greast than 0
+        token_idx = torch.clamp_min(token_idx, 0.0)
+
+        # NOTE: token_eta is a scale factor that applies to each token in the mini-batch
+        # [B, num_heads, num_mini_batch, mini_batch_size, 1]
+        token_eta = torch.broadcast_to(
+            token_idx.reshape(1, 1, 1, mini_batch_size, 1),
+            (X.shape[0], self.num_heads, X.shape[1], mini_batch_size, 1),
+        )
+
+        return token_eta, ttt_lr_eta
+
+    def apply_gate(self, hidden_states, ttt_output):
+        y = self.g_proj(hidden_states)
+        # use 'tanh' approximation for matching JAX impl.
+        y = F.gelu(y, approximate="tanh")
+        output = y * ttt_output
+        return output
+
+    def get_ttt_inputs(self, inputs, mini_batch_size, cache_params):
+        XQ = inputs["XQ"]
+        XK = inputs["XK"]
+        XV = inputs["XV"]
+        X = inputs["X"]
+        B, L, C = X.shape
+        num_mini_batch = L // mini_batch_size
+        # [B ,num_mini_batch, mini_batch_size, C]
+        X = X.reshape(B, num_mini_batch, mini_batch_size, self.width)
+
+        XQ = XQ.reshape(B, self.num_heads, L // mini_batch_size, mini_batch_size, self.head_dim)
+        XK = XK.reshape(B, self.num_heads, L // mini_batch_size, mini_batch_size, self.head_dim)
+        XV = XV.reshape(B, self.num_heads, L // mini_batch_size, mini_batch_size, self.head_dim)
+
+        if cache_params is not None:
+            mini_batch_step_offset = cache_params.seqlen_offset % self.mini_batch_size
+        else:
+            mini_batch_step_offset = 0
+        token_eta, ttt_lr_eta = self.get_eta(X, mini_batch_step_offset, mini_batch_size)
+        eta = token_eta * ttt_lr_eta
+        # decouple token_coeff and ilr_coeff for decoding
+        inputs = {
+            "XQ": XQ,
+            "XK": XK,
+            "XV": XV,
+            "eta": eta,
+            "token_eta": token_eta,
+            "ttt_lr_eta": ttt_lr_eta,
+        }
+        return inputs
+
+    def ttt(
+        self,
+        inputs,
+        mini_batch_size,
+        last_mini_batch_params_dict,
+        cache_params: Optional[TTTCache] = None,
+    ):
+        raise NotImplementedError("ttt method must be implemented in TTTBase subclasses.")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        cache_params: Optional[TTTCache] = None,
+    ):
+        B, L = hidden_states.shape[:2]
+        reminder_len = L % self.mini_batch_size
+        num_mini_batch = L // self.mini_batch_size
+        last_mini_batch_params_dict = None
+
+        XQ, XK, XV = self.get_qkv_projections(hidden_states, cache_params=cache_params)
+
+        # [B, L, C] -> [B, L, num_heads, head_dim] -> [B, num_heads, L, head_dim]
+        XQ = XQ.reshape(B, L, self.num_heads, self.head_dim).transpose(1, 2)
+        XK = XK.reshape(B, L, self.num_heads, self.head_dim).transpose(1, 2)
+        XV = XV.reshape(B, L, self.num_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(XV, position_ids % self.mini_batch_size)
+
+        # permute_qk and undo_permute_qk is just for aligning pytorch with jax pre-training
+        XQ, XK = permute_qk(XQ, XK)
+        XQ, XK = apply_rotary_pos_emb(XQ, XK, cos, sin)
+        XQ, XK = undo_permute_qk(XQ, XK)
+
+        output_hidden_states = []
+        # when input sequence length is not a multiple of mini_batch_size
+        # we need to compute them seperately, when computing the reminder,
+        # we will need the last_mini_batch_params_dict to continue TTT learning
+        if num_mini_batch > 0:
+            inputs = {
+                "XQ": XQ[:, :, : num_mini_batch * self.mini_batch_size],
+                "XK": XK[:, :, : num_mini_batch * self.mini_batch_size],
+                "XV": XV[:, :, : num_mini_batch * self.mini_batch_size],
+                "X": hidden_states[:, : num_mini_batch * self.mini_batch_size],
+            }
+            output_mod, last_mini_batch_params_dict = self.ttt(
+                self.get_ttt_inputs(inputs, self.mini_batch_size, cache_params),
+                mini_batch_size=self.mini_batch_size,
+                last_mini_batch_params_dict=last_mini_batch_params_dict,
+                cache_params=cache_params,
+            )
+            output_hidden_states.append(output_mod)
+        if reminder_len > 0:
+            inputs = {
+                "XQ": XQ[:, :, -reminder_len:],
+                "XK": XK[:, :, -reminder_len:],
+                "XV": XV[:, :, -reminder_len:],
+                "X": hidden_states[:, -reminder_len:],
+            }
+            output_reminder, _ = self.ttt(
+                self.get_ttt_inputs(inputs, reminder_len, cache_params),
+                mini_batch_size=reminder_len,
+                last_mini_batch_params_dict=last_mini_batch_params_dict,
+                cache_params=cache_params,
+            )
+            output_hidden_states.append(output_reminder)
+
+        output_hidden_states = torch.cat(output_hidden_states, dim=1)
+        output_hidden_states = self.post_norm(output_hidden_states)
+        if self.use_gate:
+            output_hidden_states = self.apply_gate(hidden_states, output_hidden_states)
+        output_hidden_states = self.o_proj(output_hidden_states)
+
+        return output_hidden_states
+
+
+class TTTLinear(TTTBase):
+    def __init__(self, config: TTTConfig, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        # TTT model initialization for TTT-Linear
+        self.W1 = nn.Parameter(torch.normal(0, 0.02, size=(self.num_heads, self.head_dim, self.head_dim)))
+        self.b1 = nn.Parameter(torch.zeros(self.num_heads, 1, self.head_dim))
+
+    def ttt(
+        self,
+        inputs,
+        mini_batch_size,
+        last_mini_batch_params_dict,
+        cache_params: Optional[TTTCache] = None,
+    ):
+        if mini_batch_size is None:
+            mini_batch_size = self.mini_batch_size
+
+        # in this case, we are decoding
+        if last_mini_batch_params_dict is None and cache_params is not None:
+            last_mini_batch_params_dict = cache_params.ttt_params_to_dict(self.layer_idx)
+
+        # [B, num_heads, num_mini_batch, mini_batch_size, head_dim]
+        B = inputs["XV"].shape[0]
+        num_mini_batch = inputs["XV"].shape[2]
+        L = inputs["XV"].shape[2] * inputs["XV"].shape[3]
+        device = inputs["XV"].device
+        dtype = inputs["XV"].dtype
+
+        # NOTE:
+        # for prefilling, we will always use dual form for faster computation
+        # we need to use primal form if mini_batch_size is not a multiple of self.mini_batch_size
+        # since we need store the gradient for the next mini-batch computation
+        use_dual_form = cache_params is None or mini_batch_size % self.mini_batch_size == 0
+
+        def compute_mini_batch(params_dict, inputs):
+            # [B, nh, f, f], nh=num_heads, f=head_dim
+            W1_init = params_dict["W1_states"]
+            # [B, nh, 1, f]
+            b1_init = params_dict["b1_states"]
+
+            # [B,nh,K,f], K=mini_batch_size
+            XQ_mini_batch = inputs["XQ"]
+            XV_mini_batch = inputs["XV"]
+            XK_mini_batch = inputs["XK"]
+            # [B, nh, K, 1]
+            eta_mini_batch = inputs["eta"]
+            token_eta_mini_batch = inputs["token_eta"]
+            ttt_lr_eta_mini_batch = inputs["ttt_lr_eta"]
+
+            X1 = XK_mini_batch
+            # [B,nh,K,f] @ [B,nh,f,f] -> [B,nh,K,f]
+            Z1 = X1 @ W1_init + b1_init
+            reconstruction_target = XV_mini_batch - XK_mini_batch
+
+            ln_weight = self.ttt_norm_weight.reshape(self.num_heads, 1, self.head_dim)
+            ln_bias = self.ttt_norm_bias.reshape(self.num_heads, 1, self.head_dim)
+            # [B,nh,K,f]
+            grad_l_wrt_Z1 = ln_fused_l2_bwd(Z1, reconstruction_target, ln_weight, ln_bias)
+
+            if use_dual_form:
+                # [B,nh,K,K]
+                Attn1 = torch.tril(XQ_mini_batch @ X1.transpose(-2, -1))
+                # [B,nh,1,f] - [B,nh,K,K] @ [B,nh,K,f] -> [B,nh,K,f]
+                b1_bar = b1_init - torch.tril(eta_mini_batch) @ grad_l_wrt_Z1
+                # [B,nh,K,f] @ [B,nh,f,f] - ([B,nh,K,1] * [B,nh,K,K]) @ [B,nh,K,f] + [B,nh,K,f]
+                Z1_bar = XQ_mini_batch @ W1_init - (eta_mini_batch * Attn1) @ grad_l_wrt_Z1 + b1_bar
+
+                last_eta_mini_batch = eta_mini_batch[:, :, -1, :, None]
+                # [B,nh,f,f] - [B,nh,f,K] @ [B,nh,K,f]
+                W1_last = W1_init - (last_eta_mini_batch * X1).transpose(-1, -2) @ grad_l_wrt_Z1
+                # [B,nh,1,f]
+                b1_last = b1_init - torch.sum(last_eta_mini_batch * grad_l_wrt_Z1, dim=-2, keepdim=True)
+                grad_W1_last = torch.zeros_like(W1_last)
+                grad_b1_last = torch.zeros_like(b1_last)
+            else:
+                ttt_lr_eta_mini_batch = torch.broadcast_to(
+                    ttt_lr_eta_mini_batch,
+                    (
+                        *ttt_lr_eta_mini_batch.shape[:2],
+                        mini_batch_size,
+                        mini_batch_size,
+                    ),
+                )
+
+                # [B, nh, K, f, f]
+                grad_W1 = torch.einsum("bhki,bhkj->bhkij", X1, grad_l_wrt_Z1)
+                grad_W1 = torch.einsum("bhnk,bhkij->bhnij", torch.tril(ttt_lr_eta_mini_batch), grad_W1)
+                grad_W1 = grad_W1 + params_dict["W1_grad"].unsqueeze(2)
+                # [B, nh, K, f]
+                grad_b1 = torch.einsum("bhnk,bhki->bhni", torch.tril(ttt_lr_eta_mini_batch), grad_l_wrt_Z1)
+                grad_b1 = grad_b1 + params_dict["b1_grad"]
+
+                W1_bar = W1_init.unsqueeze(2) - grad_W1 * token_eta_mini_batch.unsqueeze(-1)
+                b1_bar = b1_init - grad_b1 * token_eta_mini_batch
+
+                # [B, nh, K, 1, f] @ [B, nh, K, f, f]
+                Z1_bar = (XQ_mini_batch.unsqueeze(3) @ W1_bar).squeeze(3) + b1_bar
+
+                W1_last = W1_bar[:, :, -1]
+                b1_last = b1_bar[:, :, -1:]
+                grad_W1_last = grad_W1[:, :, -1]
+                grad_b1_last = grad_b1[:, :, -1:]
+
+            Z1_bar = ln_fwd(Z1_bar, ln_weight, ln_bias)
+
+            XQW_mini_batch = XQ_mini_batch + Z1_bar
+
+            last_param_dict = {
+                "W1_states": W1_last,
+                "b1_states": b1_last,
+                "W1_grad": grad_W1_last,
+                "b1_grad": grad_b1_last,
+            }
+            return last_param_dict, XQW_mini_batch
+
+        if last_mini_batch_params_dict is not None:
+            init_params_dict = last_mini_batch_params_dict
+        else:
+            init_params_dict = {
+                "W1_states": torch.tile(self.W1.unsqueeze(0), dims=(B, 1, 1, 1)),
+                "b1_states": torch.tile(self.b1.unsqueeze(0), dims=(B, 1, 1, 1)),
+            }
+            init_params_dict.update(W1_grad=torch.zeros_like(init_params_dict["W1_states"]))
+            init_params_dict.update(b1_grad=torch.zeros_like(init_params_dict["b1_states"]))
+
+        # [B,num_heads, num_mini_batch, mini_batch_size, f] -> [num_mini_batch, B, num_heads, mini_batch_size, f]
+        inputs = tree_map(lambda x: x.permute(2, 0, 1, 3, 4), inputs)
+
+        # allocate output tensor
+        XQW_batch = torch.empty(
+            (num_mini_batch, B, self.num_heads, mini_batch_size, self.head_dim),
+            device=device,
+            dtype=dtype,
+        )
+        # XQW_batch: [num_mini_batch, B, num_heads, mini_batch_size, head_dim]
+        batch_params_dict, XQW_batch = scan(
+            compute_mini_batch,
+            init_params_dict,
+            inputs,
+            XQW_batch,
+            self.config.scan_checkpoint_group_size if self.training else 0,
+        )
+
+        # [B, num_heads, L, C]
+        if cache_params is not None:
+            cache_params.update(batch_params_dict, self.layer_idx, L)
+
+        # [num_mini_batch, B, num_heads, mini_batch_size, head_dim] -> [B, num_mini_batch, mini_batch_size, num_heads, head_dim]
+        XQW_batch = XQW_batch.permute(1, 0, 3, 2, 4)
+        # [B, L, C]
+        XQW_batch = XQW_batch.reshape(B, L, self.width)
+        return XQW_batch, batch_params_dict
+
+
+class TTTMLP(TTTBase):
+    def __init__(self, config: TTTConfig, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        # TTT model initialization for TTT-MLP
+        self.W1 = nn.Parameter(torch.normal(0, 0.02, size=(self.num_heads, self.head_dim, 4 * self.head_dim)))
+        self.b1 = nn.Parameter(torch.zeros(self.num_heads, 1, 4 * self.head_dim))
+        self.W2 = nn.Parameter(torch.normal(0, 0.02, size=(self.num_heads, 4 * self.head_dim, self.head_dim)))
+        self.b2 = nn.Parameter(torch.zeros(self.num_heads, 1, self.head_dim))
+
+    def ttt(
+        self,
+        inputs,
+        mini_batch_size,
+        last_mini_batch_params_dict,
+        cache_params: Optional[TTTCache] = None,
+    ):
+        if mini_batch_size is None:
+            mini_batch_size = self.mini_batch_size
+
+        # in this case, we are decoding
+        if last_mini_batch_params_dict is None and cache_params is not None:
+            last_mini_batch_params_dict = cache_params.ttt_params_to_dict(self.layer_idx)
+
+        # [B, num_heads, num_mini_batch, mini_batch_size, head_dim]
+        B = inputs["XV"].shape[0]
+        num_mini_batch = inputs["XV"].shape[2]
+        L = inputs["XV"].shape[2] * inputs["XV"].shape[3]
+        device = inputs["XV"].device
+        dtype = inputs["XV"].dtype
+        # NOTE:
+        # for prefilling, we will always use dual form for faster computation
+        # we need to use primal form if mini_batch_size is not a multiple of self.mini_batch_size
+        # since we need store the gradient for the next mini-batch computation
+        use_dual_form = cache_params is None or mini_batch_size % self.mini_batch_size == 0
+
+        def compute_mini_batch(params_dict, inputs):
+            # [B, nh, f, 4f]
+            W1_init = params_dict["W1_states"]
+            # [B, nh, 1, 4f]
+            b1_init = params_dict["b1_states"]
+            # [B, nh, 4f, f]
+            W2_init = params_dict["W2_states"]
+            # [B, nh, 1, f]
+            b2_init = params_dict["b2_states"]
+
+            # [B,nh,K,f]
+            XQ_mini_batch = inputs["XQ"]
+            XV_mini_batch = inputs["XV"]
+            XK_mini_batch = inputs["XK"]
+            # [B,nh,K,1]
+            eta_mini_batch = inputs["eta"]
+            token_eta_mini_batch = inputs["token_eta"]
+            ttt_lr_eta_mini_batch = inputs["ttt_lr_eta"]
+
+            X1 = XK_mini_batch
+            # [B,nh,K,f] @ [B,nh,f,4f] -> [B,nh,K,4f]
+            Z1 = X1 @ W1_init + b1_init
+            X2 = F.gelu(Z1, approximate="tanh")
+            # [B,nh,K,4f] @ [B,nh,4f,f] -> [B,nh,K,f]
+            Z2 = X2 @ W2_init + b2_init
+            reconstruction_target = XV_mini_batch - XK_mini_batch
+
+            ln_weight = self.ttt_norm_weight.reshape(self.num_heads, 1, self.head_dim)
+            ln_bias = self.ttt_norm_bias.reshape(self.num_heads, 1, self.head_dim)
+            # [B, nh, K, f]
+            grad_l_wrt_Z2 = ln_fused_l2_bwd(Z2, reconstruction_target, ln_weight, ln_bias)
+            # [B, nh, K, 4f]
+            grad_l_wrt_Z1 = grad_l_wrt_Z2 @ W2_init.transpose(-2, -1) * gelu_bwd(Z1)
+
+            if use_dual_form:
+                Attn1 = torch.tril(XQ_mini_batch @ X1.transpose(-2, -1))  # [B,nh,K,K]
+                # [B,nh,1,f] - [B,nh,K,K] @ [B,nh,K,4f] -> [B,nh,K,4f]
+                b1_bar = b1_init - torch.tril(eta_mini_batch) @ grad_l_wrt_Z1
+                # [B,nh,K,f] @ [B,nh,f,4f] - ([B,nh,K,1] * [B,nh,K,K]) @ [B,nh,K,4f] + [B,nh,K,4f]
+                Z1_bar = XQ_mini_batch @ W1_init - (eta_mini_batch * Attn1) @ grad_l_wrt_Z1 + b1_bar
+                X2_bar = F.gelu(Z1_bar, approximate="tanh")
+
+                # [B,nh,K,K]
+                Attn2 = torch.tril(X2_bar @ X2.transpose(-2, -1))
+                # [B,nh,1,f] - [B,nh,K,1] * [B,nh,K,f] -> [B,nh,K,f]
+                b2_bar = b2_init - torch.tril(eta_mini_batch) @ grad_l_wrt_Z2
+                # [B,nh,K,f] @ [1,nh,4f,f] - ([B,nh,K,1] * [B,nh,K,K]) @ [B,nh,K,f] + [B,nh,K,f]
+                Z2_bar = X2_bar @ W2_init - (eta_mini_batch * Attn2) @ grad_l_wrt_Z2 + b2_bar
+
+                last_eta_mini_batch = eta_mini_batch[:, :, -1, :, None]
+                # [B,nh,f,4f] - [B,nh,f,K] @ [B,nh,K,4f]
+                W1_last = W1_init - (last_eta_mini_batch * X1).transpose(-1, -2) @ grad_l_wrt_Z1
+                # [B,nh,1,4f]
+                b1_last = b1_init - torch.sum(last_eta_mini_batch * grad_l_wrt_Z1, dim=-2, keepdim=True)
+                # [B,nh,4f,f] - [B,nh,4f,K] @ [B,nh,K,f]
+                W2_last = W2_init - (last_eta_mini_batch * X2).transpose(-1, -2) @ grad_l_wrt_Z2
+                # [B,nh,1,f]
+                b2_last = b2_init - torch.sum(last_eta_mini_batch * grad_l_wrt_Z2, dim=-2, keepdim=True)
+                grad_W1_last = torch.zeros_like(W1_last)
+                grad_b1_last = torch.zeros_like(b1_last)
+                grad_W2_last = torch.zeros_like(W2_last)
+                grad_b2_last = torch.zeros_like(b2_last)
+
+            else:
+                ttt_lr_eta_mini_batch = torch.broadcast_to(
+                    ttt_lr_eta_mini_batch,
+                    (
+                        *ttt_lr_eta_mini_batch.shape[:2],
+                        mini_batch_size,
+                        mini_batch_size,
+                    ),
+                )
+
+                # [B, nh, K, 4f, f]
+                grad_W2 = torch.einsum("bhki,bhkj->bhkij", X2, grad_l_wrt_Z2)
+                grad_W2 = torch.einsum("bhnk,bhkij->bhnij", torch.tril(ttt_lr_eta_mini_batch), grad_W2)
+                grad_W2 = grad_W2 + params_dict["W2_grad"].unsqueeze(2)
+                # [B, nh, K, f]
+                grad_b2 = torch.einsum("bhnk,bhki->bhni", torch.tril(ttt_lr_eta_mini_batch), grad_l_wrt_Z2)
+                grad_b2 = grad_b2 + params_dict["b2_grad"]
+
+                # [B, nh, K, f, 4f]
+                grad_W1 = torch.einsum("bhki,bhkj->bhkij", X1, grad_l_wrt_Z1)
+                grad_W1 = torch.einsum("bhnk,bhkij->bhnij", torch.tril(ttt_lr_eta_mini_batch), grad_W1)
+                grad_W1 = grad_W1 + params_dict["W1_grad"].unsqueeze(2)
+                # [B, nh, K, 4f]
+                grad_b1 = torch.einsum("bhnk,bhki->bhni", torch.tril(ttt_lr_eta_mini_batch), grad_l_wrt_Z1)
+                grad_b1 = grad_b1 + params_dict["b1_grad"]
+
+                W1_bar = W1_init.unsqueeze(2) - grad_W1 * token_eta_mini_batch.unsqueeze(-1)
+                b1_bar = b1_init - grad_b1 * token_eta_mini_batch
+                W2_bar = W2_init.unsqueeze(2) - grad_W2 * token_eta_mini_batch.unsqueeze(-1)
+                b2_bar = b2_init - grad_b2 * token_eta_mini_batch
+
+                # [B, nh, K, 1, f] @ [B, nh, K, f, 4f] -> [B, nh, K, 4f]
+                Z1_bar = (XQ_mini_batch.unsqueeze(3) @ W1_bar).squeeze(3) + b1_bar
+                X2_bar = F.gelu(Z1_bar, approximate="tanh")
+                Z2_bar = (X2_bar.unsqueeze(3) @ W2_bar).squeeze(3) + b2_bar
+
+                W1_last = W1_bar[:, :, -1]
+                b1_last = b1_bar[:, :, -1:]
+                W2_last = W2_bar[:, :, -1]
+                b2_last = b2_bar[:, :, -1:]
+                grad_W1_last = grad_W1[:, :, -1]
+                grad_b1_last = grad_b1[:, :, -1:]
+                grad_W2_last = grad_W2[:, :, -1]
+                grad_b2_last = grad_b2[:, :, -1:]
+
+            Z2_bar = ln_fwd(Z2_bar, ln_weight, ln_bias)
+
+            XQW_mini_batch = XQ_mini_batch + Z2_bar
+
+            last_param_dict = {
+                "W1_states": W1_last,
+                "b1_states": b1_last,
+                "W2_states": W2_last,
+                "b2_states": b2_last,
+                "W1_grad": grad_W1_last,
+                "b1_grad": grad_b1_last,
+                "W2_grad": grad_W2_last,
+                "b2_grad": grad_b2_last,
+            }
+            return last_param_dict, XQW_mini_batch
+
+        if last_mini_batch_params_dict is not None:
+            init_params_dict = last_mini_batch_params_dict
+        else:
+            init_params_dict = {
+                "W1_states": torch.tile(self.W1.unsqueeze(0), dims=(B, 1, 1, 1)),
+                "b1_states": torch.tile(self.b1.unsqueeze(0), dims=(B, 1, 1, 1)),
+                "W2_states": torch.tile(self.W2.unsqueeze(0), dims=(B, 1, 1, 1)),
+                "b2_states": torch.tile(self.b2.unsqueeze(0), dims=(B, 1, 1, 1)),
+            }
+            init_params_dict.update(W1_grad=torch.zeros_like(init_params_dict["W1_states"]))
+            init_params_dict.update(b1_grad=torch.zeros_like(init_params_dict["b1_states"]))
+            init_params_dict.update(W2_grad=torch.zeros_like(init_params_dict["W2_states"]))
+            init_params_dict.update(b2_grad=torch.zeros_like(init_params_dict["b2_states"]))
+        inputs = tree_map(lambda x: x.permute(2, 0, 1, 3, 4), inputs)  # [B,nh,NC,CS,f] -> [NC,B,nh,CS,f]
+        # allocate output tensor
+        XQW_batch = torch.empty(
+            (num_mini_batch, B, self.num_heads, mini_batch_size, self.head_dim),
+            device=device,
+            dtype=dtype,
+        )
+        # XQW_batch: [num_mini_batch, B, num_heads, mini_batch_size, head_dim]
+        batch_params_dict, XQW_batch = scan(
+            compute_mini_batch,
+            init_params_dict,
+            inputs,
+            XQW_batch,
+            self.config.scan_checkpoint_group_size if self.training else 0,
+        )
+
+        # [B, num_heads, L, C]
+        if cache_params is not None:
+            cache_params.update(batch_params_dict, self.layer_idx, L)
+
+        # [num_mini_batch, B, num_heads, mini_batch_size, head_dim] -> [B, num_mini_batch, mini_batch_size, num_heads, head_dim]
+        XQW_batch = XQW_batch.permute(1, 0, 3, 2, 4)
+        # [B, L, C]
+        XQW_batch = XQW_batch.reshape(B, L, self.width)
+        return XQW_batch, batch_params_dict
+
+
+################################
+### E2E Architecture Modules ###
+################################
+
+
+class Block(nn.Module):
+    def __init__(self, config: TTTConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.pre_conv = config.pre_conv
+
+        if config.ttt_layer_type == "linear":
+            ttt_layer = TTTLinear
+        elif config.ttt_layer_type == "mlp":
+            ttt_layer = TTTMLP
+        else:
+            raise ValueError(f"Invalid ttt_layer_type: {config.ttt_layer_type}")
+
+        self.seq_modeling_block = ttt_layer(config=config, layer_idx=layer_idx)
+
+        self.mlp = SwiGluMLP(config)
+        if self.pre_conv:
+            self.conv = Conv(config, layer_idx)
+
+        self.seq_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.ffn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.layer_idx = layer_idx
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        cache_params: Optional[TTTCache] = None,
+    ):
+        if self.pre_conv:
+            residual = hidden_states
+            hidden_states = self.conv(hidden_states, cache_params=cache_params)
+            hidden_states = residual + hidden_states
+
+        residual = hidden_states
+
+        hidden_states = self.seq_norm(hidden_states)
+
+        # TTT Layer
+        hidden_states = self.seq_modeling_block(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            cache_params=cache_params,
+        )
+        hidden_states = residual + hidden_states
+
+        # Feed-Forward-Network
+        residual = hidden_states
+        hidden_states = self.ffn_norm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class TTTPreTrainedModel(PreTrainedModel):
+    config_class = TTTConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Block"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+@dataclass
+class TTTOutput(ModelOutput):
+    """
+    Class for the TTT model outputs.
+
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        cache_params (`TTTCache`):
+            The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
+            avoid providing the old `input_ids`.
+    """
+
+    last_hidden_state: Optional[torch.FloatTensor] = None
+    cache_params: Optional[TTTCache] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+class TTTCausalLMOutput(ModelOutput):
+    """
+    Base class for causal language model (or autoregressive) outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        cache_params (`TTTCache`):
+            The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
+            avoid providing the old `input_ids`.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: Optional[torch.FloatTensor] = None
+    cache_params: Optional[TTTCache] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class TTTModel(TTTPreTrainedModel):
+    """
+    Decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Block`]
+
+    Args:
+        config: TTTConfig
+    """
+
+    def __init__(self, config: TTTConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([Block(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        cache_params: Optional[TTTCache] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        use_cache: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if cache_params is None and use_cache:
+            cache_params = TTTCache(self, inputs_embeds.size(0))
+
+        seqlen_offset = 0
+        if cache_params is not None:
+            seqlen_offset = cache_params.seqlen_offset
+        position_ids = torch.arange(
+            seqlen_offset,
+            seqlen_offset + inputs_embeds.shape[1],
+            dtype=torch.long,
+            device=inputs_embeds.device,
+        ).unsqueeze(0)
+
+        hidden_states = inputs_embeds
+
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+
+        for decoder_layer in self.layers:
+            if self.gradient_checkpointing and self.training:
+                hidden_states = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    cache_params,
+                )
+            else:
+                hidden_states = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    cache_params=cache_params,
+                )
+
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if use_cache:
+            cache_params.seqlen_offset += inputs_embeds.shape[1]
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, cache_params, all_hidden_states] if v is not None)
+
+        return TTTOutput(
+            last_hidden_state=hidden_states,
+            cache_params=cache_params if use_cache else None,
+            hidden_states=all_hidden_states,
+        )
+
+
+class TTTForCausalLM(TTTPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = TTTModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def _update_model_kwargs_for_generation(
+        self, outputs: ModelOutput, model_kwargs: Dict[str, Any], **kwargs
+    ) -> Dict[str, Any]:
+        model_kwargs["cache_params"] = outputs.get("cache_params", None)
+        # update attention mask
+        if "attention_mask" in model_kwargs:
+            attention_mask = model_kwargs["attention_mask"]
+            model_kwargs["attention_mask"] = torch.cat(
+                [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))],
+                dim=-1,
+            )
+        return model_kwargs
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        attention_mask=None,
+        cache_params: Optional[TTTCache] = None,
+        inputs_embeds=None,
+        **kwargs,
+    ):
+        # only last token for inputs_ids if the state is passed along.
+        if cache_params is not None:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            attention_mask = attention_mask[:, -1].unsqueeze(-1) if attention_mask is not None else None
+
+        if inputs_embeds is not None and cache_params is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "cache_params": cache_params,
+                "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,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        cache_params: Optional[TTTCache] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        use_cache: Optional[bool] = None,
+        *,
+        output_attentions: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        """
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        """
+        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
+        assert not output_attentions, "output_attentions is not available in TTTForCausalLM"
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            cache_params=cache_params,
+            inputs_embeds=inputs_embeds,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            use_cache=use_cache,
+        )
+
+        hidden_states = outputs[0]
+        if self.config.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return TTTCausalLMOutput(
+            loss=loss,
+            logits=logits,
+            cache_params=outputs.cache_params,
+            hidden_states=outputs.hidden_states,
+        )

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,43 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}