Upload NemotronFlashForCausalLM

config.json

@@ -1,14 +1,14 @@
 {
   "architectures": [
-    "FastSLMForCausalLM"
+    "NemotronFlashForCausalLM"
   ],
   "attention_dropout": 0.0,
   "attn_hidden_size": -1,
-  "attn_implementation": "flash_attention_2",
-  "attn_implementation_new": "flash_attention_2",
+  "attn_implementation": "fused_mha",
+  "attn_implementation_new": "fused_mha",
   "auto_map": {
-    "AutoConfig": "configuration_fast_slm.FastSLMConfig",
-    "AutoModelForCausalLM": "modeling_fast_slm.FastSLMForCausalLM"
+    "AutoConfig": "configuration_nemotron_flash.NemotronFlashConfig",
+    "AutoModelForCausalLM": "modeling_nemotron_flash.NemotronFlashForCausalLM"
   },
   "bos_token_id": 1,
   "calc_logits_for_entire_prompt": false,

configuration_nemotron_flash.py

@@ -0,0 +1,246 @@
+# coding=utf-8
+# Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Nemotron Flash model configuration"""
+import math
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class NemotronFlashConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`JambaModel`]. It is used to instantiate a
+    Jamba 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 jamba-small architecture.
+
+    [ai21labs/jamba-small](https://huggingface.co/ai21labs/Jamba-v0.1)
+
+    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 65536):
+            Vocabulary size of the Jamba model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`JambaModel`]
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
+            model has a output word embedding layer.
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            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 `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        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`.
+        calc_logits_for_entire_prompt (`bool`, *optional*, defaults to `False`):
+            Whether or not to calculate logits for entire prompt during generation. If `False`, only the logits of the
+            last prompt token will be calculated, which are the only logits needed for generation. For long sequences,
+            the logits for the entire sequence may use a lot of memory so setting `calc_logits_for_entire_prompt=False`
+            will reduce memory footprint significantly.
+            Note: some generation features may not be available if this is set to `False`.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabling this will also
+            allow the model to output the auxiliary loss. See [here]() for more details
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `None`.
+        n_ctx (`int`, *optional*, defaults to 262144):
+            This value doesn't have any real effect. The maximum sequence length that this model is intended to be
+            used with. It can be used with longer sequences, but performance may degrade.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to root per-token, can be also interpreted as the `top-p` routing
+            parameter
+        num_experts (`int`, *optional*, defaults to 16):
+            Number of experts per Sparse MLP layer.
+        use_mamba_kernels (`bool`, *optional*, defaults to `True`):
+            Flag indicating whether or not to use the fast mamba kernels. These are available only if `mamba-ssm` and
+            `causal-conv1d` are installed, and the mamba modules are running on a CUDA device. Raises ValueError if
+            `True` and kernels are not available
+        mamba_d_state (`int`, *optional*, defaults to 16):
+            The dimension the mamba state space latents
+        mamba_d_conv (`int`, *optional*, defaults to 4):
+            The size of the mamba convolution kernel
+        mamba_expand (`int`, *optional*, defaults to 2):
+            Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
+        mamba_dt_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
+            Rank of the the mamba discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
+        mamba_conv_bias (`bool`, *optional*, defaults to `True`):
+            Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
+        mamba_proj_bias (`bool`, *optional*, defaults to `False`):
+            Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block
+        mamba_inner_layernorms (`bool`, *optional*, defaults to `True`):
+            Flag indicating whether or not to apply layernorms to internal mamba activations
+
+    """
+
+    model_type = "jamba"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+            self,
+            vocab_size=65536,
+            tie_word_embeddings=False,
+            hidden_size=4096,
+            intermediate_size=14336,
+            num_hidden_layers=32,
+            num_attention_heads=32,
+            num_key_value_heads=8,
+            hidden_act="silu",
+            initializer_range=0.02,
+            rms_norm_eps=1e-6,
+            use_cache=True,
+            calc_logits_for_entire_prompt=False,
+            output_router_logits=False,
+            router_aux_loss_coef=0.001,
+            pad_token_id=0,
+            bos_token_id=1,
+            eos_token_id=2,
+            sliding_window=None,
+            max_position_embeddings=262144,
+            orig_max_position_embeddings=None,
+            attention_dropout=0.0,
+            num_experts_per_tok=2,
+            num_experts=16,
+            use_mamba_kernels=True,
+            mamba_d_state=16,
+            mamba_d_conv=4,
+            mamba_expand=2,
+            mamba_dt_rank="auto",
+            mamba_conv_bias=True,
+            mamba_proj_bias=False,
+            mamba_inner_layernorms=True,
+
+            hybrid_decoder_layer='mamba',
+
+            global_attn_idx=None,
+
+            attn_implementation_new='flash_attention_2',
+
+            mamba2_headdim=64,
+
+            rope_type=None,
+            
+            layer_types=None,
+            
+            ffn_expand_ratio=None,
+            
+            d_conv=4,
+
+            **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.tie_word_embeddings = tie_word_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.sliding_window = sliding_window
+        self.max_position_embeddings = max_position_embeddings
+        self.orig_max_position_embeddings = orig_max_position_embeddings
+        self.attention_dropout = attention_dropout
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+
+        self.use_cache = use_cache
+        self.calc_logits_for_entire_prompt = calc_logits_for_entire_prompt
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+
+        self.use_mamba_kernels = use_mamba_kernels
+        self.mamba_d_state = mamba_d_state
+        self.mamba_d_conv = mamba_d_conv
+        self.mamba_expand = mamba_expand
+        self.mamba_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank
+        self.mamba_conv_bias = mamba_conv_bias
+        self.mamba_proj_bias = mamba_proj_bias
+        self.mamba_inner_layernorms = mamba_inner_layernorms
+
+        # added by Xin 
+        self.kq_norm = kwargs.pop("kq_norm", None)
+        self.rope = kwargs.pop("rope", False)
+        self.rope_theta = kwargs.pop("rope_theta", 10000.0)
+        self.num_memory_tokens = kwargs.pop("num_memory_tokens", 0)
+        self.attn_hidden_size = kwargs.pop("attn_hidden_size", -1)
+        self.kq_head_dim = kwargs.pop("kq_head_dim", -1)
+        self.v_head_dim = kwargs.pop("v_head_dim", -1)
+
+        #! adhoc change
+        self.new_seq_length = 2048
+
+        self.hybrid_decoder_layer = hybrid_decoder_layer
+
+        self.global_attn_idx = global_attn_idx
+
+        self.attn_implementation_new = attn_implementation_new
+
+        self.mamba2_headdim = mamba2_headdim
+
+        self.rope_type = rope_type
+        
+        self.layer_types = layer_types
+        
+        self.ffn_expand_ratio = ffn_expand_ratio
+        
+        self.d_conv = d_conv
+
+        self.mlp_hidden_act = kwargs.pop("mlp_hidden_act", "silu")
+        
+        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,
+        )

generation_config.json

@@ -0,0 +1,8 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.56.2",
+  "use_cache": false
+}

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4fdb6a7f8e726e8e00f5f63dde79d18b499722f6ce7199027679cd01c9f71a87
-size 1930804728
+oid sha256:b15235dc83bb411387d9a15694e02d3697051e303b067336e17c302a17b6125d
+size 1930804368

modeling_nemotron_flash.py

@@ -0,0 +1,2023 @@
+# coding=utf-8
+# Copyright 2025 NVIDIA Corporation. All rights reserved.
+
+""" PyTorch Nemotron-Flash model."""
+import inspect
+import math
+import copy
+import warnings
+from typing import Any, Dict, List, Optional, Tuple, Union
+import time
+import numpy as np
+import os
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+torch._inductor.config.max_autotune_gemm_backends = ["aten"]
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.generation import GenerationMixin
+
+try:
+    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+    from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+except ImportError:
+    pass
+
+from transformers.utils import (
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_nemotron_flash import NemotronFlashConfig
+
+import math
+
+from flash_attn import flash_attn_func, flash_attn_varlen_func
+from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+from einops import rearrange, repeat, reduce, pack, unpack
+
+from .fused_mha_with_cache import fused_mha_interface
+
+from .mamba2 import Mamba2
+from mamba_ssm.utils.generation import InferenceParams
+from .delta_net import Cache as fla_cache
+from .delta_net import DeltaNet
+import torch._dynamo
+torch._dynamo.config.suppress_errors = True
+
+from torch.cuda import CUDAGraph
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "NemotronFlashConfig"
+
+
+class NemotronFlashRMSNorm(nn.Module):
+
+    def __init__(self, hidden_size, learnable_weight=True, eps=1e-6):
+        super().__init__()
+        if learnable_weight:
+            self.weight = nn.Parameter(torch.ones(hidden_size))
+        else:
+            self.weight = None
+        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)
+        
+        if self.weight is not None:
+            return self.weight * hidden_states.to(input_dtype)
+        else:
+            return hidden_states.to(input_dtype)
+
+class LlamaRotaryEmbedding(nn.Module):
+    def __init__(self, config, dim, base=10000, device=None, scaling_factor=1.0):
+        super().__init__()
+        self.scaling_factor = scaling_factor
+        self.dim = dim
+        self.base = base
+        self.config = config
+        
+        self.rope_type = config.rope_type
+        
+        self.factor = 2
+        
+        max_position_embeddings = self.config.max_position_embeddings
+
+        if config.rope_type is None or config.rope_type == "default":
+            inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+            self.max_seq_len_cached = max_position_embeddings
+
+        elif config.rope_type == 'ntk':
+            assert self.config.orig_max_position_embeddings is not None
+            orig_max_position_embeddings = self.config.orig_max_position_embeddings
+            
+            base = base * ((self.factor * max_position_embeddings / orig_max_position_embeddings) - (self.factor - 1)) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+            
+            self.max_seq_len_cached = orig_max_position_embeddings
+            
+        elif config.rope_type == 'dynamic_ntk':
+            inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+            self.original_inv_freq = inv_freq
+            self.max_seq_len_cached = self.config.orig_max_position_embeddings
+                
+        else:
+            raise ValueError(f"Not support rope_type: {config.rope_type}")
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        
+
+    def _dynamic_frequency_update(self, position_ids, device):
+        """
+        dynamic RoPE layers should recompute `inv_freq` in the following situations:
+        1 - growing beyond the cached sequence length (allow scaling)
+        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+        """
+        
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:  # growth
+            base = self.base * ((self.factor * seq_len / self.config.orig_max_position_embeddings) - (self.factor - 1)) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+            
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+            self.max_seq_len_cached = seq_len
+
+        if seq_len < self.config.orig_max_position_embeddings and self.max_seq_len_cached > self.config.orig_max_position_embeddings:  # reset
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.config.orig_max_position_embeddings
+        
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        if self.rope_type == 'dynamic_ntk':
+            self._dynamic_frequency_update(position_ids, device=x.device)
+            
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        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)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors."""
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    if q is not None:
+        q_embed = (q * cos) + (rotate_half(q) * sin)
+
+    else:
+        q_embed = None
+    
+    if k is not None:
+        k_embed = (k * cos) + (rotate_half(k) * sin)
+    else:
+        k_embed = None
+    return q_embed, k_embed    
+
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+
+class AttentionDynamicCache(DynamicCache):
+
+    def __init__(self, config, batch_size, dtype=torch.float16, device=None, layer_type=None):
+        self.dtype = dtype
+
+        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
+        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+        if self.key_cache[layer_idx].shape[-1] == 0:
+            self.key_cache[layer_idx] = key_states
+            self.value_cache[layer_idx] = value_states
+        else:
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+    def get_seq_length(self, layer_idx=None) -> int:
+        if layer_idx is None:
+            max_key_len = max(cache.shape[-2] for cache in self.key_cache)
+            return max_key_len
+
+        if self.key_cache[layer_idx].shape[-1] == 0:
+            return 0
+
+        return self.key_cache[layer_idx].shape[-2]
+
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralAttention
+class NemotronFlashAttention(nn.Module):
+
+    def __init__(self, config: NemotronFlashConfig, layer_idx: Optional[int] = None, input_hidden_size=None, output_hidden_size=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.hidden_size = config.attn_hidden_size if config.attn_hidden_size > 0 else config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+
+        self.kq_head_dim = config.kq_head_dim if config.kq_head_dim > 0 else self.head_dim
+        self.v_head_dim = config.v_head_dim if config.v_head_dim > 0 else self.head_dim
+
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+        
+        if (self.head_dim * self.num_heads) != self.hidden_size and self.kq_head_dim == self.head_dim:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(self.hidden_size if input_hidden_size is None else input_hidden_size, self.num_heads * self.kq_head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size if input_hidden_size is None else input_hidden_size, self.num_key_value_heads * self.kq_head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size if input_hidden_size is None else input_hidden_size, self.num_key_value_heads * self.v_head_dim, bias=False)
+
+        if output_hidden_size is None:
+            output_hidden_size = self.hidden_size
+
+        self.o_proj = nn.Linear(self.num_heads * self.v_head_dim, output_hidden_size, bias=False)
+
+        if self.config.kq_norm == "rms":
+            self.k_norm = NemotronFlashRMSNorm(self.kq_head_dim)
+            self.q_norm = NemotronFlashRMSNorm(self.kq_head_dim)
+        elif self.config.kq_norm == "none":
+            self.k_norm = None
+            self.q_norm = None
+        else:
+            raise NotImplementedError(f"Unknown kq_norm: {self.config.kq_norm}")
+
+        if self.config.rope:
+            self._init_rope()
+            
+    def _init_rope(self):
+        self.rotary_emb = LlamaRotaryEmbedding(
+            config=self.config,
+            dim=self.kq_head_dim,
+            base=self.rope_theta,
+            device=torch.device("cuda"),
+            ) 
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Cache] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+            use_swa=False,
+            query_states = None,
+            key_states=None,
+            value_states=None,
+            **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        raise NotImplementedError("NemotronFlashAttention is an abstract class. Use one of the subclasses.")
+
+
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2
+class NemotronFlashFlashAttention2(NemotronFlashAttention):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Cache] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+            use_swa=False,
+            query_states = None,
+            key_states=None,
+            value_states=None,
+            **kwargs,
+    ):  
+            
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.kq_head_dim).transpose(1, 2).contiguous()
+
+        if self.q_norm is not None:
+            query_states = self.q_norm(query_states)
+            
+        if self.config.rope:
+            cos, sin = self.rotary_emb(hidden_states, position_ids)
+            query_states, _ = apply_rotary_pos_emb(query_states, None, cos, sin)
+
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+            
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.kq_head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.v_head_dim).transpose(1, 2)
+
+        if self.k_norm is not None:
+            key_states = self.k_norm(key_states)
+        
+        if self.config.rope:
+            _, key_states = apply_rotary_pos_emb(None, key_states, cos, sin)
+
+        
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_seq_length(self.layer_idx)
+
+        use_sliding_windows = (
+                _flash_supports_window_size
+                and getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and use_swa
+        )
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
+                " make sure to upgrade flash-attn library."
+            )
+
+        swa_processed_flag = False
+        if past_key_value is not None and use_cache:
+            kv_layer_idx = self.layer_idx
+
+            cache_has_contents = past_key_value.get_seq_length(kv_layer_idx) > 0
+            
+            if (
+                    getattr(self.config, "sliding_window", None) is not None
+                    and kv_seq_len > self.config.sliding_window
+                    and cache_has_contents
+                    and use_swa
+            ):              
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[kv_layer_idx][0]
+                past_value = past_key_value[kv_layer_idx][1]
+                
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                past_key_value.key_cache[kv_layer_idx] = past_key
+                past_key_value.value_cache[kv_layer_idx] = past_value
+                                                        
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+                
+                swa_processed_flag = True
+                            
+            key_states, value_states = past_key_value.update(key_states, value_states, kv_layer_idx)
+
+        key_states_no_repeat = key_states
+        value_states_no_repeat = value_states
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)  # (batch, slen, num_heads, head_dim)
+        key_states = key_states.transpose(1, 2)  # (batch, slen, num_heads, head_dim)
+        value_states = value_states.transpose(1, 2)  # (batch, slen, num_heads, head_dim)
+                
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_sliding_windows=use_sliding_windows and not swa_processed_flag,
+        )
+
+        v_dim = value_states.shape[-2] * value_states.shape[-1]
+        attn_output = attn_output.reshape(-1, q_len, v_dim).contiguous()
+            
+        attn_output = self.o_proj(attn_output)
+        
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value, (key_states_no_repeat, value_states_no_repeat)
+
+    def _flash_attention_forward(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            query_length,
+            dropout=0.0,
+            softmax_scale=None,
+            use_sliding_windows=False,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+                    
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+                
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        if not self.training and not type(key_layer) == torch.Tensor:  ## this is for handling Mamba2 with output type <class 'mamba_ssm.ops.triton.layernorm_gated.tTensor'>
+            key_layer = torch.tensor(key_layer.clone())
+            value_layer = torch.tensor(value_layer.clone())
+            query_layer = torch.tensor(query_layer.clone())
+        
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+
+class NemotronFlashSDPAAttention(nn.Module):
+
+    def __init__(self, config, layer_idx: int, reuse_kv=False):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=False
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=False
+        )
+
+        self.sliding_window = self.config.sliding_window if self.layer_idx not in self.config.global_attn_idx else None
+
+        self.rotary_emb = NemotronFlashRotaryEmbedding(config=config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(hidden_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            past_seen_tokens = past_key_value.get_seq_length()
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
+            )
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+    
+        attention_interface = ALL_ATTENTION_FUNCTIONS['flash_attention_2']
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, attn_weights, past_key_value, (key_states, value_states)
+
+
+class NemotronFlashRotaryEmbedding(nn.Module):
+    def __init__(self, config, device=None):
+        super().__init__()
+
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+## Interface to use TRTLLM AutoDeploy attention kernel, which enables CUDA Graph capture
+class NemotronFlashFusedMHA(NemotronFlashAttention):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        
+        self.fused_mha_interface = fused_mha_interface
+        
+    def init_kv_cache(self, max_batch_size, max_seq_len, page_size=-1):
+        if hasattr(self, 'k_cache'):
+            del self.k_cache
+            del self.v_cache
+            
+            if hasattr(self, 'page_table') and self.page_table is not None:
+                del self.page_table
+            
+            import gc
+            gc.collect()
+                
+            torch.cuda.empty_cache()
+        
+        if page_size is not None and page_size > 0:
+            batch_max_pages = (max_seq_len + page_size - 1) // page_size
+            cache_max_pages = (max_batch_size * max_seq_len + page_size - 1) // page_size
+            self.k_cache = torch.zeros(cache_max_pages, page_size, self.num_key_value_heads, self.kq_head_dim).to(self.q_proj.weight)
+            self.v_cache = torch.zeros(cache_max_pages, page_size, self.num_key_value_heads, self.v_head_dim).to(self.q_proj.weight)
+
+            self.page_table = torch.zeros(max_batch_size, batch_max_pages, device=self.q_proj.weight.device, dtype=torch.int32)
+        else:
+            self.k_cache = torch.zeros(max_batch_size, max_seq_len, self.num_key_value_heads, self.kq_head_dim).to(self.q_proj.weight)
+            self.v_cache = torch.zeros(max_batch_size, max_seq_len, self.num_key_value_heads, self.v_head_dim).to(self.q_proj.weight)
+            
+            self.page_table = None
+        
+        self.max_seq_len = max_seq_len
+    
+
+    def reset_kv_cache(self):
+        self.k_cache = self.k_cache.zero_()
+        self.v_cache = self.v_cache.zero_()
+        
+        if self.page_table is not None:
+            self.page_table = self.page_table.zero_()
+        
+        
+    def forward(
+            self,
+            hidden_states: torch.Tensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Cache] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+            use_swa=False,
+            query_states = None,
+            key_states=None,
+            value_states=None,
+            **kwargs,
+    ):     
+
+        if not hasattr(self, 'k_cache'):
+            self.init_kv_cache(max_batch_size=1, max_seq_len=8000)
+        
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.kq_head_dim).transpose(1, 2).contiguous()
+
+        if self.q_norm is not None:
+            query_states = self.q_norm(query_states)
+            
+        if self.config.rope:
+            cos, sin = self.rotary_emb(hidden_states, position_ids)
+            query_states, _ = apply_rotary_pos_emb(query_states, None, cos, sin)
+         
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.kq_head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.v_head_dim).transpose(1, 2)
+
+        if self.k_norm is not None:
+            key_states = self.k_norm(key_states)
+        
+        if self.config.rope:
+            _, key_states = apply_rotary_pos_emb(None, key_states, cos, sin)
+
+        key_states_no_repeat = key_states
+        value_states_no_repeat = value_states
+        
+        query_states = query_states.transpose(1, 2)  # (batch, slen, num_heads, head_dim)
+        key_states = key_states.transpose(1, 2)  # (batch, slen, num_kv_heads, head_dim)
+        value_states = value_states.transpose(1, 2)  # (batch, slen, num_kv_heads, head_dim)
+        
+        if self.k_cache.device != query_states.device:
+            self.k_cache = self.k_cache.to(query_states)
+            self.v_cache = self.v_cache.to(query_states)
+
+        attn_output = self.fused_mha_interface(
+            query_states,
+            key_states,
+            value_states,
+            k_cache=self.k_cache,
+            v_cache=self.v_cache,
+            page_table=self.page_table,
+            max_seq_len=self.max_seq_len,
+            position_ids=position_ids,
+        )
+
+        v_dim = query_states.shape[-2] * value_states.shape[-1]
+        attn_output = attn_output.reshape(bsz, q_len, v_dim).contiguous()
+        
+        attn_output = self.o_proj(attn_output)
+                    
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value, (key_states_no_repeat, value_states_no_repeat)
+        
+
+JAMBA_ATTENTION_CLASSES = {
+    "flash_attention_2": NemotronFlashFlashAttention2,
+    "fused_mha": NemotronFlashFusedMHA,
+    "sdpa": NemotronFlashSDPAAttention,
+}
+
+class NemotronFlashMLP(nn.Module):
+    def __init__(self, config: NemotronFlashConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.act_fn_name = config.mlp_hidden_act
+        self.act_fn = ACT2FN[self.act_fn_name]
+        
+        if config.ffn_expand_ratio is not None:
+            self.ffn_dim = int(config.ffn_expand_ratio * config.hidden_size) // 128 * 128
+        else:
+            self.ffn_dim = config.intermediate_size
+        
+        self.hidden_dim = config.hidden_size
+        
+        self.layer_idx = layer_idx
+
+        if self.act_fn_name == "silu":
+            self.gate_proj = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.down_proj = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+        self.up_proj = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        
+
+    def forward(self, x):
+        if self.act_fn_name == "silu":
+            output = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        elif self.act_fn_name == "relu2":
+            output  = self.down_proj(self.act_fn(self.up_proj(x)))
+        else:
+            raise NotImplementedError(f"No such hidden_act: {self.act_fn_name}")
+
+        return output
+
+    
+class NemotronFlashAttentionDecoderLayer(nn.Module):
+    def __init__(self, config: NemotronFlashConfig, layer_idx: int,):
+        super().__init__()
+
+        self.config = config
+        
+        self.layer_idx = layer_idx
+
+        self.self_attn = JAMBA_ATTENTION_CLASSES[config.attn_implementation](config, layer_idx)
+
+        if self.config.intermediate_size > 0:
+            self.ffn = NemotronFlashMLP(config, layer_idx=layer_idx)
+        else:
+            self.ffn = None
+
+        self.input_layernorm = NemotronFlashRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.pre_ffn_layernorm = NemotronFlashRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: Optional[bool] = False,
+            use_cache: Optional[bool] = False,
+            use_swa=False,
+            **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        if position_ids is not None and position_ids.shape[1] != hidden_states.shape[1]:
+            position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
+                
+        residual = hidden_states
+
+        if self.input_layernorm is not None:
+            hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states, self_attn_weights, present_key_value, current_kv = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            use_swa=use_swa,
+        )
+        
+        hidden_states = residual + hidden_states
+        
+        if self.ffn is not None:
+            residual = hidden_states
+            if self.pre_ffn_layernorm is not None:
+                hidden_states = self.pre_ffn_layernorm(hidden_states)
+            hidden_states = self.ffn(hidden_states)
+
+            hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        outputs += (current_kv,)
+
+        return outputs
+
+
+
+class FFNDecoderLayer(nn.Module):
+    def __init__(self, config: NemotronFlashConfig, layer_idx: int):
+        super().__init__()
+
+        self.config = config
+        self.layer_idx = layer_idx
+        self.ffn = NemotronFlashMLP(config, layer_idx=layer_idx)
+        self.pre_ffn_layernorm = NemotronFlashRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: Optional[bool] = False,
+            use_cache: Optional[bool] = False,
+            use_swa=False,
+            **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        residual = hidden_states
+        if self.pre_ffn_layernorm is not None:
+            hidden_states = self.pre_ffn_layernorm(hidden_states)
+        hidden_states = self.ffn(hidden_states)
+
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (None,)
+
+        if use_cache:
+            outputs += (None,)
+
+        return outputs
+
+
+class NemotronFlashMambaDecoderLayer(nn.Module):
+    def __init__(self, config: NemotronFlashConfig, layer_idx: int):
+        super().__init__()
+
+        self.config = config
+        self.layer_idx = layer_idx
+
+        self.mamba = Mamba2(config=config, layer_idx=layer_idx)
+        
+        self.intermediate_size = config.intermediate_size
+        if self.intermediate_size > 0:
+            self.ffn = NemotronFlashMLP(config, layer_idx=layer_idx)
+
+        self.input_layernorm = NemotronFlashRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        
+        if self.intermediate_size > 0:
+            self.pre_ffn_layernorm = NemotronFlashRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.pre_ffn_layernorm = None
+                
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[AttentionDynamicCache] = None,
+            output_attentions: Optional[bool] = False,
+            use_cache: Optional[bool] = False,
+            use_swa=False,
+            mamba_inference_params=None,
+            **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        
+        if position_ids is not None and position_ids.shape[1] != hidden_states.shape[1]:
+            position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
+
+        residual = hidden_states
+        
+        if self.input_layernorm is not None:
+            hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states, present_key_value = self.mamba(
+            hidden_states=hidden_states,
+            past_key_value=past_key_value,
+            attention_mask=attention_mask,
+            inference_params=mamba_inference_params,
+        )
+
+        attn_key_value = None
+
+        hidden_states = residual + hidden_states
+                
+        if self.intermediate_size > 0:
+            residual = hidden_states
+            
+            if self.pre_ffn_layernorm is not None:
+                hidden_states = self.pre_ffn_layernorm(hidden_states)
+                
+            hidden_states = self.ffn(hidden_states)
+
+            hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        outputs += (attn_key_value,)
+
+        return outputs
+
+    def _get_past_seqlen(self, past_key_value, seqlen):
+        if past_key_value is None:
+            return seqlen
+        past_seqlen = past_key_value.get_seq_length(self.layer_idx)
+
+        if past_seqlen == 0:
+            return seqlen
+
+        return past_seqlen
+
+
+    
+class NemotronFlashHybridDecoderLayer(nn.Module):
+    def __init__(self, config: NemotronFlashConfig, layer_idx: int):
+        super().__init__()
+
+        self.config = config
+        
+        self.layer_idx = layer_idx
+                
+        if config.hybrid_decoder_layer == 'mamba':
+            self.mamba = Mamba2(config=config, layer_idx=layer_idx)
+        if config.hybrid_decoder_layer == 'deltanet':
+            if config.layer_types is not None:
+                deltanet_idx = sum(1 for i in range(layer_idx) if config.layer_types[i] == 'deltanet')
+            else:
+                deltanet_idx = layer_idx
+            
+            self.gla = DeltaNet(hidden_size=config.hidden_size, num_heads=config.num_attention_heads, layer_idx=deltanet_idx, config=self.config)
+        else:
+            raise ValueError(f"Not supported: {config.hybrid_decoder_layer}")
+    
+        self.config = config
+
+        if self.config.intermediate_size > 0:
+            self.ffn = NemotronFlashMLP(config, layer_idx=layer_idx)
+            self.pre_ffn_layernorm = NemotronFlashRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.ffn = None
+            self.pre_ffn_layernorm = None
+
+        self.input_layernorm = NemotronFlashRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+            
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[AttentionDynamicCache] = None,
+            output_attentions: Optional[bool] = False,
+            use_cache: Optional[bool] = False,
+            fla_past_key_values = None,
+            mamba_inference_params = None,
+            use_swa=False,
+            **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        if self.config.hybrid_decoder_layer == 'mamba':
+            hybrid_op_hidden_states, mamba_present_key_value = self.mamba(
+                hidden_states=hidden_states,
+                past_key_value=past_key_value,
+                attention_mask=attention_mask,
+                inference_params=mamba_inference_params,
+            )
+            
+        else:
+            hybrid_op_hidden_states, _, fla_past_key_values = self.gla(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                past_key_values=fla_past_key_values,
+                use_cache=use_cache,
+            )
+        
+        self_attn_weights = self_attn_present_key_value = current_kv = None
+
+        hidden_states = residual + hybrid_op_hidden_states
+            
+        if self.ffn is not None:
+            residual = hidden_states
+            hidden_states = self.pre_ffn_layernorm(hidden_states)
+
+            hidden_states = self.ffn(hidden_states)
+
+            hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (self_attn_present_key_value,)
+
+        outputs += (current_kv,)
+        
+
+        return outputs        
+
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralPreTrainedModel
+class NemotronFlashPreTrainedModel(PreTrainedModel):
+    config_class = NemotronFlashConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["NemotronFlashAttentionDecoderLayer", "NemotronFlashMambaDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, (nn.Linear, nn.Conv1d)):
+            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_()
+
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralModel
+class NemotronFlashModel(NemotronFlashPreTrainedModel):
+
+    def __init__(self, config: NemotronFlashConfig):
+        super().__init__(config)
+
+        config.attn_implementation = config.attn_implementation_new
+        config._attn_implementation = config.attn_implementation_new
+
+        self.config = 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)
+
+        decoder_layers = []
+                        
+        layer_type = []
+        for i in range(config.num_hidden_layers):                        
+            if config.layer_types[i] in ['deltanet']:
+                layer_type.append('m')
+                config_new = copy.deepcopy(config)
+                config_new.hybrid_decoder_layer = 'deltanet'
+                decoder_layer = NemotronFlashHybridDecoderLayer(config_new, layer_idx=i)
+            elif config.layer_types[i] in ['m', 'm2']:
+                layer_type.append('m')
+                decoder_layer = NemotronFlashMambaDecoderLayer(config, layer_idx=i)
+            elif config.layer_types[i] == 'a':
+                layer_type.append('a')
+                decoder_layer = NemotronFlashAttentionDecoderLayer(config, layer_idx=i)
+            elif config.layer_types[i] == 'f':
+                layer_type.append('a')
+                decoder_layer = FFNDecoderLayer(config, layer_idx=i)
+            else:
+                raise ValueError(f"Unsupported layer type {config.layer_types[i]}")
+            
+            decoder_layers.append(decoder_layer)
+            
+        config.layer_type = layer_type
+
+        if config.sliding_window is not None:
+            self.sliding_window = config.sliding_window
+            self.global_attn_idx = config.global_attn_idx
+        else:
+            self.sliding_window = None
+            self.global_attn_idx = None
+
+        self.layers = nn.ModuleList(decoder_layers)
+
+        self._attn_implementation = config.attn_implementation
+        
+        self.final_layernorm = NemotronFlashRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        if self.config.num_memory_tokens > 0:
+            self.memory_tokens = nn.Parameter(torch.randn(self.config.num_memory_tokens, self.config.hidden_size))
+            
+        self.gradient_checkpointing = False
+
+        self.post_init()
+
+        self.has_previous_state = False
+
+
+    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,
+            past_key_values: Optional[Union[List[torch.FloatTensor], AttentionDynamicCache]] = 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,
+            fla_past_key_values = None,
+            mamba_inference_params = None,
+    ) -> Union[Tuple, MoeModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            if self.config.num_memory_tokens > 0 and past_key_values is not None and not self.has_previous_state:
+                position_ids = position_ids.view(-1, seq_length + self.config.num_memory_tokens).long()
+            else:
+                position_ids = position_ids.view(-1, seq_length).long()
+                
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+            
+        ori_b, ori_n = inputs_embeds.shape[0], inputs_embeds.shape[1]
+
+        if self.config.num_memory_tokens > 0 and (past_key_values is None or not self.has_previous_state):
+            mem = repeat(self.memory_tokens, 'n d -> b n d', b = inputs_embeds.shape[0]) # prepend the memory to every segment of m by repeating the memory tokens
+            inputs_embeds, mem_packed_shape = pack((mem, inputs_embeds), 'b * d')      
+
+            if position_ids is not None and position_ids.shape[1] != inputs_embeds.shape[1]:
+                position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0)
+
+            if attention_mask is not None and attention_mask.shape[1] < inputs_embeds.shape[1]:
+                assert attention_mask.shape[1] + self.config.num_memory_tokens == inputs_embeds.shape[1]
+                attention_mask = torch.cat([torch.ones(inputs_embeds.shape[0], self.config.num_memory_tokens, device=attention_mask.device), attention_mask], dim=1)
+            
+   
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            if is_padding_right:
+                raise ValueError(
+                    "You are attempting to perform batched generation with padding_side='right'"
+                    " this may lead to unexpected behaviour for Flash Attention version of NemotronFlash. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+                
+        attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+
+        hidden_states = inputs_embeds
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for i, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+            
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    use_swa=self.sliding_window is not None and i not in self.global_attn_idx,
+                    fla_past_key_values=fla_past_key_values,
+                    mamba_inference_params=mamba_inference_params,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        if self.final_layernorm is not None:
+            hidden_states = self.final_layernorm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if self.config.num_memory_tokens > 0 and (past_key_values is None or not self.has_previous_state):
+            mem, hidden_states = unpack(hidden_states, mem_packed_shape, 'b * d')
+            hidden_states = hidden_states[:, :ori_n, :]
+
+        if past_key_values is not None and not self.has_previous_state:
+            self.has_previous_state = True
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if (fla_past_key_values is None and mamba_inference_params is None) else (past_key_values, fla_past_key_values, mamba_inference_params),
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+# Adapted from transformers.models.mixtral.modeling_mixtral.MixtralForCausalLM with MIXTRAL->JAMBA, Mixtral->NemotronFlash
+class NemotronFlashForCausalLM(NemotronFlashPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: NemotronFlashConfig):
+        super().__init__(config)
+        self.config = config
+        self.model = NemotronFlashModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        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
+        
+    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            calc_logits_for_entire_prompt: Optional[bool] = True,
+            fla_past_key_values = None,
+            mamba_inference_params = None,
+    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            calc_logits_for_entire_prompt (`bool`, *optional*):
+                Whether or not to calculate the logits for the entire prompt, or just the last token. Only last token
+                logits are needed for generation, and calculating them only for that token can save memory,
+                which becomes pretty significant for long sequences.
+
+        Returns:
+        ```"""
+
+        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,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            fla_past_key_values=fla_past_key_values,
+            mamba_inference_params=mamba_inference_params,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        if calc_logits_for_entire_prompt:
+            logits = self.lm_head(hidden_states)
+        else:
+            logits = self.lm_head(hidden_states[..., -1:, :])
+
+        logits = logits / self.lm_head.weight.norm(p=2, dim=1)
+                        
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            
+            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 MoeCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def get_init_cache(self, max_seqlen, batch_size=1):
+        past_key_values = AttentionDynamicCache(
+            self.config, batch_size, self.dtype, device=self.device, layer_type=self.config.layer_type
+        )
+
+        mamba_inference_params = InferenceParams(max_seqlen=max_seqlen, max_batch_size=batch_size)
+
+        fla_past_key_values = fla_cache.from_legacy_cache(None)    
+
+        return past_key_values, fla_past_key_values, mamba_inference_params
+    
+    
+    def init_cuda_graph_generation(
+        self,
+        max_new_tokens=128,
+        batch_size=1,
+        device=None,
+    ):
+        """
+        Initialize CUDA graph for generation with proper cache handling and warmup.
+        This function should be called once before generation to set up the graph.
+        
+        Args:
+            max_new_tokens: Maximum number of new tokens to generate
+            batch_size: Batch size for generation
+            device: Device to use (defaults to model device)
+            
+        Returns:
+            generation_state: Dictionary containing all necessary state for generation
+        """
+        if device is None:
+            device = next(self.parameters()).device
+            
+        self.eval()
+        
+        # Initialize caches
+        max_seqlen = max_new_tokens + 2048 + self.config.num_memory_tokens  # Add buffer for input
+        past_key_values, fla_past_key_values, mamba_inference_params = self.get_init_cache(
+            max_seqlen=max_seqlen, batch_size=batch_size
+        )
+        
+        # Initialize KV caches for all modules
+        for module in self.modules():
+            if hasattr(module, 'init_kv_cache'):
+                module.init_kv_cache(max_batch_size=batch_size, max_seq_len=max_seqlen)
+        
+        with torch.no_grad():
+            # Warmup runs
+            dummy_input = torch.ones((batch_size, 10), dtype=torch.long, device=device)
+            for _ in range(10):
+                self(dummy_input)
+            
+            # Prepare static tensors for CUDA graph
+            static_current_input = torch.zeros((batch_size, 1), dtype=torch.long, device=device)
+            static_position_ids = torch.zeros((batch_size, 1), dtype=torch.long, device=device)
+            static_logits = torch.zeros((batch_size, self.config.vocab_size), device=device)
+            
+            # Set up for graph capture
+            self.model.has_previous_state = True
+            if mamba_inference_params is not None:
+                mamba_inference_params.seqlen_offset = 1
+            
+            # Warmup runs for graph capture
+            for _ in range(10):
+                model_kwargs_warmup = {
+                    'input_ids': static_current_input,
+                    'fla_past_key_values': fla_past_key_values,
+                    'mamba_inference_params': mamba_inference_params,
+                    'past_key_values': past_key_values,
+                    'use_cache': True,
+                    'position_ids': static_position_ids,
+                }
+                warmup_outputs = self(**model_kwargs_warmup)
+            
+            # Capture CUDA graph
+            generation_graph = CUDAGraph()
+            with torch.cuda.graph(generation_graph):
+                model_kwargs_graph = {
+                    'input_ids': static_current_input,
+                    'fla_past_key_values': fla_past_key_values,
+                    'mamba_inference_params': mamba_inference_params,
+                    'past_key_values': past_key_values,
+                    'use_cache': True,
+                    'position_ids': static_position_ids,
+                }
+                graph_outputs = self(**model_kwargs_graph)
+                static_logits.copy_(graph_outputs.logits[:, -1, :])
+
+        if fla_past_key_values is not None:
+            fla_past_key_values.reset()
+        
+        if mamba_inference_params is not None:
+            mamba_inference_params.reset(mamba_inference_params.max_seqlen, mamba_inference_params.max_batch_size)
+            for key in mamba_inference_params.key_value_memory_dict:
+                conv_state, ssm_state = mamba_inference_params.key_value_memory_dict[key]
+                conv_state.zero_()
+                ssm_state.zero_()
+        
+        for module in self.modules():
+            if hasattr(module, 'reset_kv_cache'):
+                module.reset_kv_cache()
+        
+        self.model.has_previous_state = False
+
+        # Return generation state
+        generation_state = {
+            'generation_graph': generation_graph,
+            'static_current_input': static_current_input,
+            'static_position_ids': static_position_ids,
+            'static_logits': static_logits,
+            'past_key_values': past_key_values,
+            'fla_past_key_values': fla_past_key_values,
+            'mamba_inference_params': mamba_inference_params,
+            'max_seqlen': max_seqlen,
+            'batch_size': batch_size,
+            'device': device,
+        }
+        
+        return generation_state
+    
+    def generate_with_cuda_graph(
+        self,
+        input_ids,
+        generation_state,
+        max_new_tokens=128,
+        temperature=1.0,
+        top_k=0,
+        top_p=0.9,
+        eos_token_id=None,
+        verbose=False,
+        profiling=False,
+    ):
+        """
+        Generate text using pre-initialized CUDA graph state.
+        
+        Args:
+            input_ids: Input token IDs tensor of shape (batch_size, seq_len)
+            generation_state: State dictionary returned by init_cuda_graph_generation
+            max_new_tokens: Maximum number of new tokens to generate
+            temperature: Sampling temperature (0 for greedy)
+            top_k: Top-k filtering (0 to disable)
+            top_p: Top-p filtering (1.0 to disable)
+            eos_token_id: End-of-sequence token ID
+            pad_token_id: Padding token ID
+            verbose: Whether to print generated tokens
+            profiling: Whether to return timing information
+            
+        Returns:
+            generated_ids: Tensor of shape (batch_size, input_len + generated_len)
+            or decode_latency if profiling=True
+        """
+        self.eval()
+        batch_size = input_ids.shape[0]
+        device = input_ids.device
+        
+        # Extract state
+        generation_graph = generation_state['generation_graph']
+        static_current_input = generation_state['static_current_input']
+        static_position_ids = generation_state['static_position_ids']
+        static_logits = generation_state['static_logits']
+        past_key_values = generation_state['past_key_values']
+        fla_past_key_values = generation_state['fla_past_key_values']
+        mamba_inference_params = generation_state['mamba_inference_params']
+        
+        with torch.no_grad():
+            if mamba_inference_params.seqlen_offset == 0:
+                if fla_past_key_values is not None:
+                    fla_past_key_values.reset()
+                
+                if mamba_inference_params is not None:
+                    mamba_inference_params.reset(mamba_inference_params.max_seqlen, mamba_inference_params.max_batch_size)
+                    for key in mamba_inference_params.key_value_memory_dict:
+                        conv_state, ssm_state = mamba_inference_params.key_value_memory_dict[key]
+                        conv_state.zero_()
+                        ssm_state.zero_()
+                
+                for module in self.modules():
+                    if hasattr(module, 'reset_kv_cache'):
+                        module.reset_kv_cache()
+                
+                self.model.has_previous_state = False
+            
+                # Prefill phase - process input sequence
+                position_ids = torch.arange(
+                    self.config.num_memory_tokens + input_ids.shape[1], dtype=torch.long, device=device
+                ).unsqueeze(0).expand(batch_size, -1)
+
+            else:
+                # Prefill phase - process input sequence
+                position_ids = torch.arange(
+                    mamba_inference_params.seqlen_offset, mamba_inference_params.seqlen_offset + input_ids.shape[1], dtype=torch.long, device=device
+                ).unsqueeze(0).expand(batch_size, -1)
+            
+            current_input = input_ids
+            
+            model_kwargs = {
+                'input_ids': current_input,
+                'past_key_values': past_key_values,
+                'fla_past_key_values': fla_past_key_values,
+                'mamba_inference_params': mamba_inference_params,
+                'use_cache': True,
+                'position_ids': position_ids,
+            }
+            
+            if profiling:
+                torch.cuda.synchronize()
+                t1 = time.time()
+            
+            # Forward pass for prefill
+            outputs = self(**model_kwargs)
+            
+            if mamba_inference_params is not None:
+                if mamba_inference_params.seqlen_offset == 0:
+                    mamba_inference_params.seqlen_offset = current_input.shape[1] + self.config.num_memory_tokens
+                else:
+                    mamba_inference_params.seqlen_offset += current_input.shape[1]
+
+            static_position_ids.fill_(position_ids[0, -1])
+            
+            logits = outputs.logits[:, -1, :]  # (batch_size, vocab_size)
+            generated_tokens = []
+            
+            # Generation loop using CUDA graph replay
+            for step in range(max_new_tokens):
+                # Sample next token using current logits
+                if temperature == 0:
+                    next_token = torch.argmax(logits, dim=-1, keepdim=True)
+                else:
+                    next_token = sample_token(logits, temperature=temperature, top_k=top_k, top_p=top_p)
+                
+                generated_tokens.append(next_token)
+                
+                # Check for EOS
+                if not profiling and eos_token_id is not None and (next_token == eos_token_id).all():
+                    if verbose:
+                        print("\nEOS reached")
+                    break
+                
+                # Update static tensors for graph replay
+                static_current_input.copy_(next_token)
+                static_position_ids.add_(1)
+                
+                # Replay the captured graph
+                generation_graph.replay()
+                
+                if mamba_inference_params is not None:
+                    mamba_inference_params.seqlen_offset += 1
+                
+                logits = static_logits.clone()
+            
+            generated_ids = torch.cat([input_ids] + generated_tokens, dim=1)
+            
+            if profiling:
+                torch.cuda.synchronize()
+                t2 = time.time()
+                decode_latency = t2 - t1
+                return generated_ids, decode_latency
+            
+            return generated_ids
+
+
+    def generate_with_cache(
+        self,
+        input_ids,
+        max_new_tokens=128,
+        temperature=1.0,
+        top_k=0,
+        top_p=0.9,
+        eos_token_id=None,
+        verbose=False,
+    ):
+        """
+        Generate text using the hybrid model with proper cache handling using pre-initialized CUDA graph state.
+        
+        Args:
+            input_ids: Input token IDs tensor of shape (batch_size, seq_len)
+            max_new_tokens: Maximum number of new tokens to generate
+            temperature: Sampling temperature (0 for greedy)
+            top_k: Top-k filtering (0 to disable)
+            top_p: Top-p filtering (1.0 to disable)
+            eos_token_id: End-of-sequence token ID
+            verbose: Whether to print generated tokens
+            
+        Returns:
+            generated_ids: Tensor of shape (batch_size, input_len + generated_len)
+        """
+        self.eval()
+        batch_size = input_ids.shape[0]
+        device = input_ids.device
+
+        with torch.no_grad():
+            max_seqlen = input_ids.shape[1] + max_new_tokens + self.config.num_memory_tokens
+            past_key_values, fla_past_key_values, mamba_inference_params = self.get_init_cache(max_seqlen=max_seqlen, batch_size=batch_size)
+
+            for module in self.model.modules():
+                if hasattr(module, 'init_kv_cache'):
+                    module.init_kv_cache(max_batch_size=batch_size, max_seq_len=max_seqlen)
+            
+            # Prefill phase - process input sequence
+            current_input = input_ids
+            position_ids = torch.arange(
+                self.model.config.num_memory_tokens + current_input.shape[1], dtype=torch.long, device=device
+            ).unsqueeze(0).expand(batch_size, -1)
+            
+            model_kwargs = {
+                'input_ids': current_input,
+                'past_key_values': past_key_values,
+                'fla_past_key_values': fla_past_key_values,
+                'mamba_inference_params': mamba_inference_params,
+                'use_cache': True,
+                'position_ids': position_ids,
+            }
+            
+            outputs = self(**model_kwargs)
+            
+            # past_key_values, fla_past_key_values, mamba_inference_params = outputs.past_key_values
+            mamba_inference_params.seqlen_offset = current_input.shape[1] + self.model.config.num_memory_tokens
+            
+            logits = outputs.logits[:, -1, :]  # (batch_size, vocab_size)
+            
+            generated_tokens = []
+
+            # Generation loop
+            for step in range(max_new_tokens):
+                # Sample next token
+                if temperature == 0:
+                    next_token = torch.argmax(logits, dim=-1, keepdim=True)
+                else:
+                    next_token = sample_token(logits, temperature=temperature, top_k=top_k, top_p=top_p)
+                
+                generated_tokens.append(next_token)
+                
+                # Check for EOS
+                if eos_token_id is not None and (next_token == eos_token_id).all():
+                    if verbose:
+                        print("\nEOS reached")
+                    break
+                
+                current_input = next_token  # Shape: (batch_size, 1)
+                
+                # Update position_ids for decoding
+                if position_ids is not None:
+                    position_ids = torch.full(
+                        (batch_size, 1),
+                        position_ids[0, -1] + 1,
+                        dtype=torch.long,
+                        device=device
+                    )
+                
+                # Forward pass for next token
+                model_kwargs = {
+                    'input_ids': current_input,
+                    'fla_past_key_values': fla_past_key_values,
+                    'mamba_inference_params': mamba_inference_params,
+                    'past_key_values': past_key_values,
+                    'use_cache': True,
+                    'position_ids': position_ids,
+                }
+
+                outputs = self(**model_kwargs)
+                
+                mamba_inference_params.seqlen_offset += 1
+                
+                logits = outputs.logits[:, -1, :]
+            
+            generated_ids = torch.cat([input_ids] + generated_tokens, dim=1)
+
+            return generated_ids
+
+
+    def prepare_inputs_for_generation(
+            self,
+            input_ids,
+            past_key_values=None,
+            attention_mask=None,
+            inputs_embeds=None,
+            **kwargs,
+    ):         
+        if self.config.num_memory_tokens > 0:
+            attention_mask = torch.cat([torch.ones(input_ids.shape[0], self.config.num_memory_tokens, device=attention_mask.device), attention_mask], dim=1)
+
+        ### Note that KV cache is disable when using model.generate; Please use model.generate_with_cuda_graph or model.generate_with_cache instead.
+        past_key_values = None
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            position_ids = position_ids[:, -input_ids.shape[1]:]
+                
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None:
+            if input_ids.shape[1] == 0:
+                model_inputs = {"inputs_embeds": inputs_embeds}
+            else:
+                inputs_embeds_new = self.model.embed_tokens(input_ids)
+                model_inputs = {"inputs_embeds": torch.cat([inputs_embeds, inputs_embeds_new], dim=1)}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+
+def sample_token(logits, temperature=1.0, top_k=0, top_p=0.9):
+    """
+    Sample a token from logits with temperature, top-k, and top-p filtering.
+    
+    Args:
+        logits: Tensor of shape (batch_size, vocab_size)
+        temperature: Sampling temperature
+        top_k: Top-k filtering (0 to disable)
+        top_p: Top-p filtering (1.0 to disable)
+        
+    Returns:
+        next_token: Tensor of shape (batch_size, 1)
+    """
+    if temperature == 0:
+        return torch.argmax(logits, dim=-1, keepdim=True)
+    
+    logits = logits / temperature
+    
+    if top_k > 0:
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits.masked_fill_(indices_to_remove, float('-inf'))
+    
+    if top_p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+        
+        # Remove tokens with cumulative probability above the threshold
+        sorted_indices_to_remove = cumulative_probs > top_p
+        # Shift the indices to the right to keep also the first token above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+        
+        indices_to_remove = sorted_indices_to_remove.scatter(-1, sorted_indices, sorted_indices_to_remove)
+        logits.masked_fill_(indices_to_remove, float('-inf'))
+    
+    probs = F.softmax(logits, dim=-1)
+    return torch.multinomial(probs, num_samples=1)
+