updates

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "python-envs.defaultEnvManager": "ms-python.python:conda",
+    "python-envs.defaultPackageManager": "ms-python.python:conda",
+    "python-envs.pythonProjects": []
+}

config.json

@@ -3,15 +3,15 @@
     "MiniMaxText01ForCausalLM"
   ],
   "attention_dropout": 0.0,
-  "attn_type_list": [
-    0,
-    1,
-    0,
-    1
+  "layer_types": [
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "full_attention"
   ],
   "auto_map": {
-    "AutoConfig": "configuration_minimax_text_01.MiniMaxText01Config",
-    "AutoModelForCausalLM": "modeling_minimax_text_01.MiniMaxText01ForCausalLM"
+    "AutoConfig": "configuration_minimax.MiniMaxConfig",
+    "AutoModelForCausalLM": "modeling_minimax.MiniMaxForCausalLM"
   },
   "bos_token_id": null,
   "eos_token_id": 200020,
@@ -27,7 +27,7 @@
   "layernorm_mlp_alpha": 3.5565588200778455,
   "layernorm_mlp_beta": 1.0,
   "max_position_embeddings": 1024,
-  "model_type": "minimax_text_01",
+  "model_type": "minimax",
   "num_attention_heads": 4,
   "num_experts_per_tok": 1,
   "num_hidden_layers": 4,

configuration_minimax_text_01.py → configuration_minimax.py

@@ -1,26 +1,48 @@
-""" MiniMaxText01 model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class MiniMaxText01Config(PretrainedConfig):
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/minimax/modular_minimax.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_minimax.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from transformers import PretrainedConfig
+try:
+    from transformers.configuration_utils import layer_type_validation
+except ImportError:
+    # fallback for new versions where layer_type_validation moved or was removed
+    def layer_type_validation(x):
+        return x
+
+class MiniMaxConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a [`MiniMaxText01Model`]. It is used to instantiate an
-    MiniMaxText01 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 MiniMaxText01.
+    This is the configuration class to store the configuration of a [`MiniMaxModel`]. It is used to instantiate an
+    MiniMax 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 MiniMax.
 
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
+    [MiniMaxAI/MiniMax-Text-01-hf](https://huggingface.co/MiniMaxAI/MiniMax-Text-01-hf)
+
+    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PreTrainedConfig`] for more information.
 
 
     Args:
         vocab_size (`int`, *optional*, defaults to 32000):
-            Vocabulary size of the MiniMaxText01 model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`MiniMaxText01Model`]
+            Vocabulary size of the MiniMax model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MiniMaxModel`]
         hidden_size (`int`, *optional*, defaults to 4096):
             Dimension of the hidden representations.
         intermediate_size (`int`, *optional*, defaults to 14336):
@@ -32,14 +54,16 @@ class MiniMaxText01Config(PretrainedConfig):
         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
+            `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`.
+            by meanpooling all the original heads within that group. For more details, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
+        head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+            The attention head dimension.
         hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
             The non-linear activation function (function or string) in the decoder.
         max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
-            The maximum sequence length that this model might ever be used with. MiniMaxText01's sliding window attention
+            The maximum sequence length that this model might ever be used with. MiniMax's sliding window attention
             allows sequence of up to 4096*32 tokens.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
@@ -68,28 +92,63 @@ class MiniMaxText01Config(PretrainedConfig):
         num_local_experts (`int`, *optional*, defaults to 8):
             Number of experts per Sparse MLP layer.
         output_router_logits (`bool`, *optional*, defaults to `False`):
-            Whether or not the router logits should be returned by the model. Enabeling this will also
+            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.
         router_jitter_noise (`float`, *optional*, defaults to 0.0):
             Amount of noise to add to the router.
+        layer_types (`list`, *optional*):
+            Attention pattern for each layer.
+        block_size (`int`, *optional*, defaults to 256):
+            The length of each attention block, determining how queries, keys, and values
+            are grouped and processed for intra- and inter-block attention.
+        full_attn_alpha_factor (`float`, *optional*, defaults to 1):
+            Weight for residual value in residual connection after normal attention.
+        full_attn_beta_factor (`float`, *optional*, defaults to 1):
+            Weight for hidden state value in residual connection after normal attention.
+        linear_attn_alpha_factor (`float`, *optional*, defaults to 1):
+            Weight for residual value in residual connection after lightning attention.
+        linear_attn_beta_factor (`float`, *optional*, defaults to 1):
+            Weight for hidden state value in residual connection after lightning attention.
+        mlp_alpha_factor (`float`, *optional*, defaults to 1):
+            Weight for residual value in residual connection after MLP.
+        mlp_beta_factor (`float`, *optional*, defaults to 1):
+            Weight for hidden state value in residual connection after MLP.
 
     ```python
-    >>> from transformers import MiniMaxText01Model, MiniMaxText01Config
+    >>> from transformers import MiniMaxModel, MiniMaxConfig
 
-    >>> # Initializing a MiniMaxText01 style configuration
-    >>> configuration = MiniMaxText01Config()
+    >>> # Initializing a MiniMax style configuration
+    >>> configuration = MiniMaxConfig()
 
-    >>> # Initializing a model from the MiniMaxText01 style configuration
-    >>> model = MiniMaxText01Model(configuration)
+    >>> # Initializing a model from the MiniMax style configuration
+    >>> model = MiniMaxModel(configuration)
 
     >>> # Accessing the model configuration
     >>> configuration = model.config
     ```"""
 
-    model_type = "MiniMaxText01"
+    model_type = "minimax"
     keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.block_sparse_moe.gate": "colwise_rep",  # we need to replicate here to correctly route experts
+        "layers.*.block_sparse_moe.experts.*.w1": "colwise",
+        "layers.*.block_sparse_moe.experts.*.w2": "rowwise",
+        "layers.*.block_sparse_moe.experts.*.w3": "colwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+    attribute_map = {
+        "num_experts": "num_local_experts",
+    }
 
     def __init__(
         self,
@@ -99,14 +158,15 @@ class MiniMaxText01Config(PretrainedConfig):
         num_hidden_layers=32,
         num_attention_heads=32,
         num_key_value_heads=8,
+        head_dim=None,
         hidden_act="silu",
         max_position_embeddings=4096 * 32,
         initializer_range=0.02,
         rms_norm_eps=1e-5,
         use_cache=True,
         pad_token_id=None,
-        bos_token_id=None,
-        eos_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
         tie_word_embeddings=False,
         rope_theta=1e6,
         sliding_window=None,
@@ -116,8 +176,23 @@ class MiniMaxText01Config(PretrainedConfig):
         output_router_logits=False,
         router_aux_loss_coef=0.001,
         router_jitter_noise=0.0,
+        layer_types=None,
+        block_size=256,
+        full_attn_alpha_factor=1,
+        full_attn_beta_factor=1,
+        linear_attn_alpha_factor=1,
+        linear_attn_beta_factor=1,
+        mlp_alpha_factor=1,
+        mlp_beta_factor=1,
         **kwargs,
     ):
+        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,
+        )
         self.vocab_size = vocab_size
         self.max_position_embeddings = max_position_embeddings
         self.hidden_size = hidden_size
@@ -137,16 +212,27 @@ class MiniMaxText01Config(PretrainedConfig):
         self.use_cache = use_cache
         self.rope_theta = rope_theta
         self.attention_dropout = attention_dropout
+        self.head_dim = head_dim
 
         self.num_experts_per_tok = num_experts_per_tok
         self.num_local_experts = num_local_experts
         self.output_router_logits = output_router_logits
         self.router_aux_loss_coef = router_aux_loss_coef
         self.router_jitter_noise = router_jitter_noise
-        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,
-        )
+        self.layer_types = layer_types
+        self.block_size = block_size
+        self.full_attn_alpha_factor = full_attn_alpha_factor
+        self.full_attn_beta_factor = full_attn_beta_factor
+        self.linear_attn_alpha_factor = linear_attn_alpha_factor
+        self.linear_attn_beta_factor = linear_attn_beta_factor
+        self.mlp_alpha_factor = mlp_alpha_factor
+        self.mlp_beta_factor = mlp_beta_factor
+
+        if self.layer_types is None:
+            self.layer_types = [
+                "full_attention" if bool((i + 1) % 2) else "linear_attention" for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types, self.num_hidden_layers)
+
+
+__all__ = ["MiniMaxConfig"]

create.py

@@ -4,23 +4,23 @@ from safetensors.torch import save_file
 import json
 
 # Add the directory containing your modeling.py and configuration.py to the Python path
-model_dir = "/Users/gokdenizgulmez/Desktop/mlx-lm/mlx_lm/MiniMiniMax01Text"
+model_dir = "/Users/Goekdeniz.Guelmez@computacenter.com/Library/CloudStorage/OneDrive-COMPUTACENTER/Desktop/MiniMax01Text-Dev"
 sys.path.append(model_dir)
 
 # Import your custom model and configuration classes
-from modeling_minimax_text_01 import MiniMaxText01ForCausalLM
-from configuration_minimax_text_01 import MiniMaxText01Config
+from modular_minimax import MiniMaxForCausalLM
+from configuration_minimax import MiniMaxConfig
 
 # Load the configuration
-config_path = os.path.join(model_dir, "onfig.json")
+config_path = os.path.join(model_dir, "config.json")
 with open(config_path, 'r') as f:
     config_dict = json.load(f)
 
 # Create the configuration object
-config = MiniMaxText01Config(**config_dict)
+config = MiniMaxConfig(**config_dict)
 
 # Create the model
-small_model = MiniMaxText01ForCausalLM(config)
+small_model = MiniMaxForCausalLM(config)
 
 # Print parameter count to verify
 param_count = sum(p.numel() for p in small_model.parameters())

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c67af0b37ff2ae7e3fee3c1e8bf04781f7068593773213c2cf9d7856b48d2e7a
-size 424436000
+oid sha256:9c7797546b0e092d6b3236d1f9826af42bfe293592590e2032aafe77ba8592a4
+size 423910680

modeling_minimax.py

@@ -0,0 +1,887 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/minimax/modular_minimax.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_minimax.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.integrations import use_kernel_forward_from_hub
+from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import (
+    GenericForQuestionAnswering,
+    GenericForSequenceClassification,
+    GenericForTokenClassification,
+    GradientCheckpointingLayer,
+)
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple
+from transformers.utils.generic import OutputRecorder, check_model_inputs
+from .configuration_minimax import MiniMaxConfig
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class MiniMaxRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MiniMaxRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class MiniMaxCache(DynamicCache):
+    def __init__(self):
+        super().__init__()
+        self.linear_cache: list[torch.Tensor] = []
+
+    def set_linear_cache(self, layer_idx, linear_cache):
+        # There may be skipped layers, fill them with empty lists
+        for _ in range(len(self.linear_cache), layer_idx + 1):
+            self.linear_cache.append([])
+        self.linear_cache[layer_idx] = linear_cache
+
+    def get_linear_cache(self, layer_idx: int):
+        if layer_idx < len(self):
+            return self.linear_cache[layer_idx]
+        return None
+
+    def __len__(self):
+        return max(super().__len__(), len(self.linear_cache))
+
+    def __getitem__(self, layer_idx: int):
+        if layer_idx < len(self.linear_cache) and self.linear_cache[layer_idx] != []:
+            return (self.linear_cache[layer_idx],)
+        return super().__getitem__(layer_idx)
+
+    def __iter__(self):
+        for layer_idx in range(len(self)):
+            yield self[layer_idx]
+
+    def batch_repeat_interleave(self, repeats: int):
+        for layer_idx in range(len(self)):
+            if self.linear_cache[layer_idx] != []:
+                self.linear_cache[layer_idx] = self.linear_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            else:
+                self.layers[layer_idx].batch_repeat_interleave(repeats)
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        for layer_idx in range(len(self)):
+            if self.linear_cache[layer_idx] != []:
+                self.linear_cache[layer_idx] = self.linear_cache[layer_idx][indices, ...]
+            else:
+                self.layers[layer_idx].batch_select_indices(indices)
+
+    def crop(self, max_length: int):
+        raise RuntimeError("MiniMaxCache doesnot support `crop` method")
+
+
+class MiniMaxLightningAttention(nn.Module):
+    def __init__(self, config: MiniMaxConfig, layer_idx: int):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+        self.num_attention_heads = config.num_attention_heads
+        self.num_hidden_layers = config.num_hidden_layers
+        self.block_size = config.block_size
+
+        self.act_fn = ACT2FN[config.hidden_act]
+        self.norm = MiniMaxRMSNorm(self.head_dim * self.num_attention_heads)
+        self.qkv_proj = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim * 3, bias=False)
+        self.out_proj = nn.Linear(self.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+        self.output_gate = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
+
+        slope_rate = self.get_slope_rate()
+        query_decay, key_decay, diagonal_decay = self.decay_factors(slope_rate)
+
+        self.register_buffer("slope_rate", slope_rate)
+        self.register_buffer("query_decay", query_decay)
+        self.register_buffer("key_decay", key_decay)
+        self.register_buffer("diagonal_decay", diagonal_decay)
+
+    def get_slope_rate(self):
+        base = 1 / (2 ** (8 / self.num_attention_heads))
+        exponent = torch.arange(self.num_attention_heads) + 1
+        factor = 1 - self.layer_idx / (self.num_hidden_layers - 1 + 1e-5) + 1e-5
+
+        rate = base**exponent
+        rate = rate * factor
+        rate = rate[:, None, None]
+
+        return rate
+
+    def decay_factors(self, slope_rate):
+        block_size_range = torch.arange(self.block_size) + 1
+
+        query_decay = torch.exp(-slope_rate * block_size_range[:, None])
+        key_decay = torch.exp(-slope_rate * (self.block_size - block_size_range[:, None]))
+
+        diagonal_decay = block_size_range[:, None] - block_size_range[None, :]
+        diagonal_decay = diagonal_decay[None, None, :, :]
+        diagonal_decay = slope_rate * diagonal_decay
+        diagonal_decay = torch.where(diagonal_decay >= 0, -diagonal_decay, float("-inf"))
+        diagonal_decay = torch.exp(diagonal_decay)
+
+        return query_decay, key_decay, diagonal_decay
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        batch_size, seq_len, hidden_size = hidden_states.shape
+        num_blocks = (seq_len + self.block_size - 1) // self.block_size
+
+        qkv_states = self.act_fn(self.qkv_proj(hidden_states))
+        qkv_states = qkv_states.reshape(batch_size, seq_len, self.num_attention_heads, 3 * self.head_dim)
+
+        query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=3)
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        # calculated (K.T @ V) and saved as cache
+        attn_weights_inter = None
+        if past_key_values is not None:
+            attn_weights_inter = past_key_values.get_linear_cache(self.layer_idx)
+
+        if attn_weights_inter is None:
+            attn_weights_inter = torch.zeros(batch_size, self.num_attention_heads, self.head_dim, self.head_dim).to(
+                value_states
+            )
+
+            # apply attention_mask
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(dtype=torch.bool)  # Ensure it's a boolean tensor
+                value_states = value_states.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(-1), 0)
+
+            attn_output = []
+            for i in range(num_blocks):
+                start_idx = i * self.block_size
+                end_idx = min(start_idx + self.block_size, seq_len)
+                current_block_size = end_idx - start_idx
+
+                current_query_states = query_states[:, :, start_idx:end_idx]
+                current_key_states = key_states[:, :, start_idx:end_idx]
+                current_value_states = value_states[:, :, start_idx:end_idx]
+
+                current_query_decay = self.query_decay[:, :current_block_size]
+                current_key_decay = self.key_decay[:, -current_block_size:]
+                current_diagonal_decay = self.diagonal_decay[:, :, :current_block_size, :current_block_size]
+                block_decay = torch.exp(-self.slope_rate * current_block_size)
+
+                # intra: ( Q @ K.T ) @ V -> QK * V
+                attn_weights_intra = torch.matmul(current_query_states, current_key_states.transpose(-1, -2))
+                attn_output_intra = torch.matmul(attn_weights_intra * current_diagonal_decay, current_value_states)
+
+                # inter: Q @ ( K.T @ V ) -> Q * KV
+                attn_output_inter = torch.matmul(current_query_states * current_query_decay, attn_weights_inter)
+
+                # final attention output
+                current_attn_output = attn_output_inter + attn_output_intra
+                attn_output.append(current_attn_output)
+
+                # calculate attn_weights_inter for next block or cache
+                next_attn_weights_inter = torch.matmul(
+                    (current_key_states * current_key_decay).transpose(-1, -2), current_value_states
+                )
+                attn_weights_inter = attn_weights_inter * block_decay + next_attn_weights_inter
+
+        else:
+            ratio = torch.exp(-self.slope_rate)
+            attn_output = []
+            for i in range(seq_len):
+                current_query_states = query_states[:, :, i : i + 1]
+                current_key_states = key_states[:, :, i : i + 1]
+                current_value_states = value_states[:, :, i : i + 1]
+
+                current_attn_weights_inter = torch.matmul(current_key_states.transpose(-1, -2), current_value_states)
+                attn_weights_inter = ratio * attn_weights_inter + current_attn_weights_inter
+                current_attn_output = torch.matmul(current_query_states, attn_weights_inter)
+
+                attn_output.append(current_attn_output)
+
+        # concatenate attention outputs over all blocks
+        attn_output = torch.cat(attn_output, dim=-2)
+
+        # final output projection
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(batch_size, seq_len, self.num_attention_heads * self.head_dim)
+        attn_output = self.norm(attn_output)
+        attn_output = F.sigmoid(self.output_gate(hidden_states)) * attn_output
+        attn_output = self.out_proj(attn_output)
+
+        # update cache
+        if past_key_values is not None:
+            past_key_values.set_linear_cache(self.layer_idx, attn_weights_inter)
+
+        return attn_output, attn_weights_inter
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class MiniMaxAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: MiniMaxConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", None) or 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)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> 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 = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        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=getattr(self.config, "sliding_window", None),  # main diff with Llama
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class MiniMaxMLP(nn.Module):
+    def __init__(self, config: MiniMaxConfig):
+        super().__init__()
+        self.ffn_dim = config.intermediate_size
+        self.hidden_dim = config.hidden_size
+
+        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+        self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states):
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+        current_hidden_states = self.w2(current_hidden_states)
+        return current_hidden_states
+
+
+class MiniMaxExperts(nn.ModuleList):
+    """
+    ModuleList of experts.
+    """
+
+    def __init__(self, config: MiniMaxConfig):
+        super().__init__()
+        self.top_k = config.num_experts_per_tok
+        self.num_experts = config.num_local_experts
+        for _ in range(self.num_experts):
+            self.append(MiniMaxMLP(config))
+
+    def forward(
+        self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: (batch_size * sequence_length, hidden_dim)
+            selected_experts: (batch_size * sequence_length, top_k)
+            routing_weights: (batch_size * sequence_length, top_k)
+        Returns:
+            (batch_size * sequence_length, hidden_dim)
+        """
+        final_hidden_states = torch.zeros_like(hidden_states)
+        expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts).permute(2, 1, 0)
+
+        expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+        for expert_idx in expert_hit:
+            idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
+            current_state = hidden_states[None, top_x].reshape(-1, hidden_states.shape[-1])
+            current_hidden_states = self[expert_idx](current_state) * top_k_weights[top_x, idx, None]
+            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+        return final_hidden_states
+
+
+class MiniMaxSparseMoeBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.top_k = config.num_experts_per_tok
+        self.jitter_noise = config.router_jitter_noise
+        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
+        self.experts = MiniMaxExperts(config)
+
+    def route_tokens_to_experts(self, router_logits):
+        routing_weights = torch.nn.functional.softmax(router_logits.float(), dim=-1)
+        top_k_weights, top_k_index = torch.topk(routing_weights, self.top_k, dim=-1)
+        top_k_weights /= top_k_weights.sum(dim=-1, keepdim=True)
+        return top_k_index, top_k_weights.to(router_logits.dtype)
+
+    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        if self.training and self.jitter_noise > 0:
+            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        router_logits = self.gate(hidden_states)
+        top_k_index, top_k_weights = self.route_tokens_to_experts(router_logits)
+        hidden_states = self.experts(hidden_states, top_k_index, top_k_weights.to(hidden_states.dtype))
+        hidden_states = hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return hidden_states
+
+
+class MiniMaxDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: MiniMaxConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MiniMaxAttention(config, layer_idx)
+
+        self.block_sparse_moe = MiniMaxSparseMoeBlock(config)
+        self.input_layernorm = MiniMaxRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MiniMaxRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.layer_idx = layer_idx
+        self.layer_type = config.layer_types[layer_idx]
+        self.mlp_alpha_factor = config.mlp_alpha_factor
+        self.mlp_beta_factor = config.mlp_beta_factor
+
+        if self.layer_type == "linear_attention":
+            self.self_attn = MiniMaxLightningAttention(config, layer_idx)
+            self.attn_alpha_factor = config.linear_attn_alpha_factor
+            self.attn_beta_factor = config.linear_attn_beta_factor
+        else:
+            self.self_attn = MiniMaxAttention(config, layer_idx)
+            self.attn_alpha_factor = config.full_attn_alpha_factor
+            self.attn_beta_factor = config.full_attn_beta_factor
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        hidden_states = self.input_layernorm(hidden_states)
+        residual = hidden_states
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual * self.attn_alpha_factor + hidden_states * self.attn_beta_factor
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        residual = hidden_states
+        hidden_states = self.block_sparse_moe(hidden_states)
+        hidden_states = residual * self.mlp_alpha_factor + hidden_states * self.mlp_beta_factor
+
+        return hidden_states
+
+
+@auto_docstring
+class MiniMaxPreTrainedModel(PreTrainedModel):
+    config: MiniMaxConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MiniMaxDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _can_compile_fullgraph = False
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "router_logits": OutputRecorder(nn.Linear, layer_name="block_sparse_moe.gate", index=0),
+        "hidden_states": MiniMaxDecoderLayer,
+        "attentions": [MiniMaxAttention, MiniMaxLightningAttention],
+    }
+
+
+class MiniMaxRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: MiniMaxConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            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  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    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)
+
+
+@auto_docstring
+class MiniMaxModel(MiniMaxPreTrainedModel):
+    def __init__(self, config: MiniMaxConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MiniMaxDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = MiniMaxRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = MiniMaxRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @check_model_inputs()
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[MiniMaxCache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if use_cache and past_key_values is None:
+            past_key_values = MiniMaxCache()
+        elif use_cache and not isinstance(past_key_values, MiniMaxCache):
+            raise ValueError(
+                f"MiniMax uses cache of its own and is not compatible with `past_key_values` of type {type(past_key_values)}."
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+        causal_mask = mask_function(
+            config=self.config,
+            input_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for decoder_layer in self.layers:
+            if decoder_layer.layer_type == "full_attention":
+                input_attention_mask = causal_mask
+            else:
+                # lightning attention uses original attention_mask, and uses it only for the first step
+                input_attention_mask = attention_mask
+
+            hidden_states = decoder_layer(
+                hidden_states,
+                position_embeddings=position_embeddings,
+                attention_mask=input_attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+def load_balancing_loss_func(
+    gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
+    num_experts: Optional[int] = None,
+    top_k=2,
+    attention_mask: Optional[torch.Tensor] = None,
+) -> Union[torch.Tensor, int]:
+    r"""
+    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+    See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
+    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+    experts is too unbalanced.
+
+    Args:
+        gate_logits:
+            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+            shape [batch_size X sequence_length, num_experts].
+        num_experts:
+            Number of experts
+        top_k:
+            The number of experts to route per-token, can be also interpreted as the `top-k` routing
+            parameter.
+        attention_mask (`torch.Tensor`, *optional*):
+            The attention_mask used in forward function
+            shape [batch_size X sequence_length] if not None.
+
+    Returns:
+        The auxiliary loss.
+    """
+    if gate_logits is None or not isinstance(gate_logits, tuple):
+        return 0
+
+    if isinstance(gate_logits, tuple):
+        compute_device = gate_logits[0].device
+        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+    if attention_mask is None:
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.mean(routing_weights, dim=0)
+    else:
+        batch_size, sequence_length = attention_mask.shape
+        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+        expert_attention_mask = (
+            attention_mask[None, :, :, None, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+            .reshape(-1, top_k, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+            expert_attention_mask, dim=0
+        )
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+        router_per_expert_attention_mask = (
+            attention_mask[None, :, :, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+            .reshape(-1, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+            router_per_expert_attention_mask, dim=0
+        )
+
+    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+    return overall_loss * num_experts
+
+
+@auto_docstring
+class MiniMaxForCausalLM(MiniMaxPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MiniMaxModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = config.num_local_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeCausalLMOutputWithPast:
+        r"""
+        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]`.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MiniMaxForCausalLM
+
+        >>> model = MiniMaxForCausalLM.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: MoeModelOutputWithPast = 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_router_logits=output_router_logits,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+        aux_loss = None
+        if output_router_logits:
+            aux_loss = load_balancing_loss_func(
+                outputs.router_logits,
+                self.num_experts,
+                self.num_experts_per_tok,
+                attention_mask,
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+
+
+class MiniMaxForSequenceClassification(GenericForSequenceClassification, MiniMaxPreTrainedModel):
+    pass
+
+
+class MiniMaxForTokenClassification(GenericForTokenClassification, MiniMaxPreTrainedModel):
+    pass
+
+
+class MiniMaxForQuestionAnswering(GenericForQuestionAnswering, MiniMaxPreTrainedModel):
+    pass
+
+
+__all__ = [
+    "MiniMaxPreTrainedModel",
+    "MiniMaxModel",
+    "MiniMaxForCausalLM",
+    "MiniMaxForSequenceClassification",
+    "MiniMaxForTokenClassification",
+    "MiniMaxForQuestionAnswering",
+]

modeling_minimax_text_01.py

@@ -1,1701 +0,0 @@
-""" PyTorch MiniMaxText01 model."""
-import inspect
-import math
-import warnings
-from typing import List, Optional, Tuple, Union
-import os
-import copy
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from einops import rearrange, repeat
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import (
-    _prepare_4d_causal_attention_mask,
-)
-from transformers.modeling_outputs import (
-    MoeCausalLMOutputWithPast,
-    MoeModelOutputWithPast,
-    SequenceClassifierOutputWithPast,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-from transformers.utils.import_utils import is_torch_fx_available
-from .configuration_minimax_text_01 import MiniMaxText01Config
-
-if is_flash_attn_2_available():
-    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)
-
-# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
-# It means that the function will not be traced through and simply appear as a node in the graph.
-if is_torch_fx_available():
-    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
-    
-use_triton = eval(os.environ.get("use_triton", default="False"))
-debug = eval(os.environ.get("debug", default="False"))
-do_eval = eval(os.environ.get("do_eval", default="False"))
-eval_and_not_generate = eval(os.environ.get("eval_and_not_generate", default="False"))
-BLOCK = 256
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "MiniMaxText01Config"
-
-
-def get_activation_fn(activation):
-    if debug:
-        logger.info(f"activation: {activation}")
-    if activation == "gelu":
-        return F.gelu
-    elif activation == "relu":
-        return F.relu
-    elif activation == "elu":
-        return F.elu
-    elif activation == "sigmoid":
-        return F.sigmoid
-    elif activation == "exp":
-
-        def f(x):
-            with torch.no_grad():
-                x_max = torch.max(x, dim=-1, keepdims=True).values
-            y = torch.exp(x - x_max)
-
-            return y
-
-        return f
-    elif activation == "leak":
-        return F.leaky_relu
-    elif activation == "1+elu":
-
-        def f(x):
-            return 1 + F.elu(x)
-
-        return f
-    elif activation == "2+elu":
-
-        def f(x):
-            return 2 + F.elu(x)
-
-        return f
-    elif activation == "silu" or activation == "swish":
-        return F.silu
-    elif activation == "sine":
-        return torch.sin
-    else:
-        logger.info(
-            f"activation: does not support {activation}, use Identity!!!")
-        return lambda x: x
-
-
-def load_balancing_loss_func(
-        gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2,
-        attention_mask: Optional[torch.Tensor] = None
-) -> float:
-    r"""
-    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
-
-    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
-    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
-    experts is too unbalanced.
-
-    Args:
-        gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
-            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
-            shape [batch_size X sequence_length, num_experts].
-        attention_mask (`torch.Tensor`, None):
-            The attention_mask used in forward function
-            shape [batch_size X sequence_length] if not None.
-        num_experts (`int`, *optional*):
-            Number of experts
-
-    Returns:
-        The auxiliary loss.
-    """
-    if gate_logits is None or not isinstance(gate_logits, tuple):
-        return 0
-
-    if isinstance(gate_logits, tuple):
-        compute_device = gate_logits[0].device
-        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
-
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
-
-    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
-
-    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
-
-    if attention_mask is None:
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.mean(routing_weights, dim=0)
-    else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
-        expert_attention_mask = (
-            attention_mask[None, :, :, None, None]
-            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
-            .reshape(-1, top_k, num_experts)
-            .to(compute_device)
-        )
-
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
-        )
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
-        router_per_expert_attention_mask = (
-            attention_mask[None, :, :, None]
-            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
-            .reshape(-1, num_experts)
-            .to(compute_device)
-        )
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
-            router_per_expert_attention_mask, dim=0
-        )
-
-    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
-    return overall_loss * num_experts
-
-
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-class GLU(nn.Module):
-
-    def __init__(self, d1, d2, bias=False):
-        super().__init__()
-
-        self.l1 = nn.Linear(d1, d2, bias=bias)
-        self.l2 = nn.Linear(d1, d2, bias=bias)
-        self.l3 = nn.Linear(d2, d1, bias=bias)
-
-    def forward(self, x):
-        o1 = self.l1(x)
-        o2 = self.l2(x)
-        output = o1 * o2
-        output = self.l3(output)
-        return output
-
-
-class MiniMaxText01LightningAttention(nn.Module):
-    def __init__(self, config: MiniMaxText01Config, layer_idx: Optional[int] = None):
-        super().__init__()
-        bias = False
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = getattr(config, 'head_dim', self.hidden_size // self.num_heads)
-
-        self.out_proj = nn.Linear(self.head_dim * self.num_heads, self.hidden_size, bias=bias)
-        self.act = get_activation_fn(config.hidden_act)
-        self.norm = MiniMaxText01RMSNorm(self.head_dim * self.num_heads)
-
-        self.qkv_proj = nn.Linear(self.hidden_size, 3 * self.head_dim * self.num_heads, bias=bias)
-        self.output_gate = nn.Linear(self.hidden_size, self.head_dim * self.num_heads, bias=bias)
-
-        # for inference only
-        self.offset = 0
-        self.layer_idx = layer_idx
-
-    def forward(
-            self,
-            hidden_states,
-            attn_mask: Optional[torch.Tensor] = None,  # (b, h, n, m)
-            output_attentions: bool = False,
-            past_key_value: Optional[Tuple[torch.Tensor]] = None,
-            use_cache: bool = False,
-            slope_rate: Optional[torch.Tensor] = None,
-            **kwargs
-    ):
-        if (not self.training) and (not do_eval):
-            return self.inference(
-                hidden_states,
-                attn_mask,
-                output_attentions,
-                past_key_value,
-                use_cache,
-                slope_rate,
-            )
-
-    def inference(
-            self,
-            x,
-            attn_mask: Optional[torch.Tensor] = None,  # (b, n)
-            output_attentions: bool = False,
-            past_key_value: Optional[Tuple[torch.Tensor]] = None,
-            use_cache: bool = False,
-            slope_rate: Optional[torch.Tensor] = None,  # (h, 1, 1)
-    ):
-        # x: b n d
-        b, n, d = x.shape
-        # linear map
-        qkv = self.act(self.qkv_proj(x))
-        new_shape = qkv.size()[:-1] + (self.num_heads, -1)
-        qkv = qkv.view(*new_shape)
-        q, k, v = torch.split(qkv, [self.head_dim] * 3, dim=3)
-        q = q.transpose(1, 2)
-        k = k.transpose(1, 2)
-        v = v.transpose(1, 2)
-
-        if past_key_value is None:
-            self.offset = q.shape[-2]
-        else:
-            self.offset += 1
-
-        # for align with metaseq
-        ratio = torch.exp(-slope_rate)
-
-        # only use for the first time
-        if past_key_value is None:
-            slope_rate = slope_rate.to(torch.float32)
-            if attn_mask is not None:
-                v = v.masked_fill((1 - attn_mask).unsqueeze(1).unsqueeze(-1).to(torch.bool), 0)
-            NUM_BLOCK = (n + BLOCK - 1) // BLOCK
-            b, h, n, d = q.shape
-            e = v.shape[-1]
-            # other
-            array = torch.arange(BLOCK).to(q) + 1
-            q_decay = torch.exp(-slope_rate * array.reshape(-1, 1))
-            k_decay = torch.exp(-slope_rate * (BLOCK - array.reshape(-1, 1)))
-            index = array[:, None] - array[None, :]
-            s_index = slope_rate * index[
-                None,
-                None,
-            ]
-            s_index = torch.where(index >= 0, -s_index, float("-inf"))
-            diag_decay = torch.exp(s_index)
-
-            kv = torch.zeros(b, h, d, e).to(torch.float32).to(q.device)
-            output = torch.empty((b, h, n, e), dtype=q.dtype, device=q.device)
-            for i in range(NUM_BLOCK):
-                si = i * BLOCK
-                ei = min(si + BLOCK, n)
-                m = ei - si
-                qi = q[:, :, si:ei].contiguous()
-                ki = k[:, :, si:ei].contiguous()
-                vi = v[:, :, si:ei].contiguous()
-                qkv_none_diag = torch.matmul(qi * q_decay[:, :m], kv).to(torch.float32)
-
-                # diag
-                qk = torch.matmul(qi, ki.transpose(-1, -2)).to(torch.float32) * diag_decay[:, :, :m, :m]
-                qkv_diag = torch.matmul(qk, vi.to(torch.float32))
-                block_decay = torch.exp(-slope_rate * m)
-                output[:, :, si:ei] = qkv_none_diag + qkv_diag
-                kv = block_decay * kv + torch.matmul((ki * k_decay[:, -m:]).transpose(-1, -2).to(vi.dtype), vi)
-
-        else:
-            kv = past_key_value
-            output = []
-            for i in range(n):
-                kv = ratio * kv + torch.einsum(
-                    "... n d, ... n e -> ... d e",
-                    k[:, :, i:i + 1],
-                    v[:, :, i:i + 1],
-                )
-                qkv = torch.einsum("... n e, ... e d -> ... n d", q[:, :, i:i + 1], kv.to(q.dtype))
-                output.append(qkv)
-            output = torch.concat(output, dim=-2)
-        # reshape
-        output = rearrange(output, "b h n d -> b n (h d)")
-        # normalize
-        output = self.norm(output)
-        # gate
-        output = F.sigmoid(self.output_gate(x)) * output
-        # outproj
-        output = self.out_proj(output)
-
-        attn_weights = None
-
-        return output, attn_weights, kv
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->MiniMaxText01
-class MiniMaxText01RMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        MiniMaxText01RMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-
-# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->MiniMaxText01
-class MiniMaxText01RotaryEmbedding(nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
-        super().__init__()
-
-        self.dim = dim
-        self.max_position_embeddings = max_position_embeddings
-        self.base = base
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        # Build here to make `torch.jit.trace` work.
-        self._set_cos_sin_cache(
-            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
-        )
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
-
-        freqs = torch.outer(t, self.inv_freq)
-        # Different from paper, but it uses a different permutation in order to obtain the same calculation
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.max_seq_len_cached:
-            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
-
-        return (
-            self.cos_cached[:seq_len].to(dtype=torch.float32),
-            self.sin_cached[:seq_len].to(dtype=torch.float32),
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-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)
-
-
-# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    dtype = q.dtype
-    rot_dim = cos.shape[-1]
-    q_, q_pass = q[..., :rot_dim], q[..., rot_dim:]
-    k_, k_pass = k[..., :rot_dim], k[..., rot_dim:]
-    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
-    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
-    q_embed = (q_ * cos) + (rotate_half(q_) * sin)
-    k_embed = (k_ * cos) + (rotate_half(k_) * sin)
-    return torch.cat((q_embed, q_pass), dim=-1).to(dtype), torch.cat((k_embed, k_pass), dim=-1).to(dtype)
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-# Copied from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->MiniMaxText01
-class MiniMaxText01Attention(nn.Module):
-    """
-    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
-    and "Generating Long Sequences with Sparse Transformers".
-    """
-
-    def __init__(self, config: MiniMaxText01Config, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        if layer_idx is None:
-            logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
-                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = getattr(config, 'head_dim', self.hidden_size // self.num_heads)
-        self.num_key_value_heads = config.num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.max_position_embeddings = config.max_position_embeddings
-        self.rope_theta = config.rope_theta
-        self.is_causal = True
-        self.attention_dropout = config.attention_dropout
-
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-        self.rotary_dim = getattr(config, 'rotary_dim', self.head_dim)
-
-        self.rotary_emb = MiniMaxText01RotaryEmbedding(
-            self.rotary_dim,
-            max_position_embeddings=self.max_position_embeddings,
-            base=self.rope_theta,
-        )
-
-    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,
-            **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        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.`"
-            )
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-
-        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_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # repeat k/v heads if n_kv_heads < n_heads
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                )
-
-            attn_weights = attn_weights + attention_mask
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-# Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->MiniMaxText01
-class MiniMaxText01FlashAttention2(MiniMaxText01Attention):
-    """
-    MiniMaxText01 flash attention module. This module inherits from `MiniMaxText01Attention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
-    flash attention and deal with padding tokens in case the input contains any of them.
-    """
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-            self,
-            hidden_states: torch.Tensor,
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            past_key_value: Optional[Union[Cache, Tuple[torch.Tensor]]] = None,
-            output_attentions: bool = False,
-            use_cache: bool = False,
-            **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)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-3]
-
-        # Because the input can be padded, the absolute sequence length depends on the max position id.
-        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
-        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
-
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        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
-        )
-
-        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."
-            )
-
-        dropout_rate = 0.0 if not self.training else self.attention_dropout
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in float16 just to be sure everything works as expected.
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.q_proj.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        # Reshape to the expected shape for Flash Attention
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        if past_key_value is not None:
-            # reuse k, v, for evaluation only
-            key_states = torch.cat([past_key_value[0], key_states], dim=-3)
-            value_states = torch.cat([past_key_value[1], value_states], dim=-3)
-        
-        past_key_value = (key_states, value_states) if use_cache else None
-
-        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,
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-    def _flash_attention_forward(
-            self,
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            query_length,
-            dropout=0.0,
-            softmax_scale=None,
-            use_sliding_windows=False,
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`float`):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-            use_sliding_windows (`bool`, *optional*):
-                Whether to activate sliding window attention.
-        """
-        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
-
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            if not use_sliding_windows:
-                attn_output_unpad = 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)
-
-        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 MiniMaxText01MLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, x):
-        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-        return down_proj
-
-
-class MiniMaxText01BlockSparseTop2MLP(nn.Module):
-    def __init__(self, config: MiniMaxText01Config):
-        super().__init__()
-        self.ffn_dim = config.intermediate_size
-        self.hidden_dim = config.hidden_size
-
-        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
-        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
-        self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
-
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, hidden_states):
-        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
-        current_hidden_states = self.w2(current_hidden_states)
-        return current_hidden_states
-
-
-class MiniMaxText01BLockSparseTop2MLP(MiniMaxText01BlockSparseTop2MLP):
-    def __init__(self, *args, **kwargs):
-        logger.warning_once(
-            "MiniMaxText01BLockSparseTop2MLP is deprecated by MiniMaxText01BlockSparseTop2MLP and will be removed in v4.40."
-        )
-        super().__init__(*args, **kwargs)
-
-
-class MiniMaxText01SparseMoeBlock(nn.Module):
-    """
-    This implementation is
-    strictly equivalent to standard MoE with full capacity (no
-    dropped tokens). It's faster since it formulates MoE operations
-    in terms of block-sparse operations to accomodate imbalanced
-    assignments of tokens to experts, whereas standard MoE either
-    (1) drop tokens at the cost of reduced performance or (2) set
-    capacity factor to number of experts and thus waste computation
-    and memory on padding.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.hidden_dim = config.hidden_size
-        self.ffn_dim = config.intermediate_size
-        self.num_experts = config.num_local_experts
-        self.top_k = config.num_experts_per_tok
-
-        # gating
-        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
-
-        self.experts = nn.ModuleList([MiniMaxText01BlockSparseTop2MLP(config) for _ in range(self.num_experts)])
-
-        # Jitter parameters
-        self.jitter_noise = config.router_jitter_noise
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        """ """
-        batch_size, sequence_length, hidden_dim = hidden_states.shape
-        if self.training and self.jitter_noise > 0:
-            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
-        hidden_states = hidden_states.view(-1, hidden_dim)
-        # router_logits: (batch * sequence_length, n_experts)
-        router_logits = self.gate(hidden_states)
-
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
-        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
-
-        final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
-        )
-
-        # One hot encode the selected experts to create an expert mask
-        # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
-
-        # Loop over all available experts in the model and perform the computation on each expert
-        for expert_idx in range(self.num_experts):
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx])
-
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
-        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
-        return final_hidden_states, router_logits
-
-
-class MiniMaxText01DecoderLayer(nn.Module):
-    def __init__(self, config: MiniMaxText01Config, layer_idx: int):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-
-        self.self_attn = self.build_attn(config, layer_idx)
-
-        self.layer_idx = layer_idx
-
-        self.block_sparse_moe = MiniMaxText01SparseMoeBlock(config)
-        self.input_layernorm = MiniMaxText01RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = MiniMaxText01RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.postnorm = getattr(config, 'postnorm', False)
-        self.layernorm_attention_alpha = getattr(config, 'layernorm_linear_attention_alpha', 1) \
-            if config.attention_type == 0 else getattr(config, 'layernorm_full_attention_alpha', 1)
-        self.layernorm_attention_beta = getattr(config, 'layernorm_linear_attention_beta', 1) \
-            if config.attention_type == 0 else getattr(config, 'layernorm_full_attention_beta', 1)
-        self.layernorm_mlp_alpha = getattr(config, 'layernorm_mlp_alpha', 1)
-        self.layernorm_mlp_beta = getattr(config, 'layernorm_mlp_beta', 1)
-
-        shared_intermediate = getattr(config, 'shared_intermediate_size', 0)
-        self.shared_moe = False
-        if shared_intermediate > 0:
-            self.shared_moe = True
-            self.shared_mlp = MiniMaxText01MLP(config)
-            self.coefficient = torch.nn.Linear(self.hidden_size, 1, bias=False)
-
-    def build_attn(self, config, layer_idx):
-        if config.attention_type == 0:
-            Attention_module = MiniMaxText01LightningAttention
-        else:
-            Attention_module = MiniMaxText01FlashAttention2
-
-        return Attention_module(
-            config,
-            layer_idx
-        )
-
-    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,
-            output_router_logits: Optional[bool] = False,
-            use_cache: Optional[bool] = False,
-            slope_rate: Optional[float] = 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.`"
-            )
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_router_logits (`bool`, *optional*):
-                Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
-                should not be returned during inference.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-        """
-
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-        if self.postnorm:
-            residual = hidden_states
-
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            position_ids=position_ids,
-            attn_mask=attention_mask,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            slope_rate=slope_rate,
-        )
-
-        hidden_states = residual * self.layernorm_attention_alpha \
-                        + hidden_states * self.layernorm_attention_beta
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        if self.postnorm:
-            residual = hidden_states
-
-        moe_hidden_states, router_logits = self.block_sparse_moe(hidden_states)
-        if self.shared_moe:
-            output_mlp = self.shared_mlp(hidden_states)
-            weight_fp32 = self.coefficient.weight.float()
-            coef = hidden_states.to(torch.float32) @ weight_fp32.T
-            coef = torch.nn.functional.sigmoid(coef).to(hidden_states.dtype)
-            hidden_states = moe_hidden_states * (1 - coef) + output_mlp * coef
-        else:
-            hidden_states = moe_hidden_states
-
-        hidden_states = residual * self.layernorm_mlp_alpha \
-                        + hidden_states * self.layernorm_mlp_beta
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        if output_router_logits:
-            outputs += (router_logits,)
-
-        return outputs
-
-
-MIXTRAL_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`MiniMaxText01Config`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare MiniMaxText01 Model outputting raw hidden-states without any specific head on top.",
-    MIXTRAL_START_DOCSTRING,
-)
-# Copied from transformers.models.mistral.modeling_mistral.MistralPreTrainedModel with Mistral->MiniMaxText01
-class MiniMaxText01PreTrainedModel(PreTrainedModel):
-    config_class = MiniMaxText01Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["MiniMaxText01DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-
-MIXTRAL_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-
-            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
-            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-
-            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        output_router_logits (`bool`, *optional*):
-            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
-            should not be returned during inference.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare MiniMaxText01 Model outputting raw hidden-states without any specific head on top.",
-    MIXTRAL_START_DOCSTRING,
-)
-# Copied from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->MIXTRAL,Mistral->MiniMaxText01
-class MiniMaxText01Model(MiniMaxText01PreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniMaxText01DecoderLayer`]
-
-    Args:
-        config: MiniMaxText01Config
-    """
-
-    def __init__(self, config: MiniMaxText01Config):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.attn_type_list = config.attn_type_list
-        config_copy = copy.deepcopy(config)
-
-        self.layers = nn.ModuleList([])
-        for i in range(config.num_hidden_layers):
-            _config = copy.deepcopy(config)
-            if self.attn_type_list[i] == 0:
-                _config._attn_implementation = 'linear_attention'
-                _config.attention_type = 0
-            else:
-                _config._attn_implementation = config_copy._attn_implementation
-                _config.attention_type = 1
-            self.layers.append(MiniMaxText01DecoderLayer(_config, i))
-
-        self._attn_implementation = config_copy._attn_implementation
-        self.norm = MiniMaxText01RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.gradient_checkpointing = False
-        self.slopes = self._build_slope_tensor(config.num_attention_heads)
-        # mask
-        self._linear_attn_mask = torch.empty(0)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    @staticmethod
-    def _build_slope_tensor(n_attention_heads: int):
-
-        def get_slopes(n):
-
-            def get_slopes_power_of_2(n):
-                start = 2 ** (-(2 ** -(math.log2(n) - 3)))
-                ratio = start
-                return [start * ratio ** i for i in range(n)]
-
-            if math.log2(n).is_integer():
-                return get_slopes_power_of_2(
-                    n)  # In the paper, we only train models that have 2^a heads for some a. This function has
-            else:  # some good properties that only occur when the input is a power of 2. To maintain that even
-                closest_power_of_2 = 2 ** math.floor(
-                    math.log2(n))  # when the number of heads is not a power of 2, we use this workaround.
-                return (get_slopes_power_of_2(closest_power_of_2)
-                        + get_slopes(2 * closest_power_of_2)[0::2][:n - closest_power_of_2])
-
-        # h, 1, 1
-        slopes = torch.tensor(get_slopes(n_attention_heads), dtype=torch.float32).reshape(n_attention_heads, 1, 1)
-
-        return slopes
-
-    # Ignore copy
-    @add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING)
-    def forward(
-            self,
-            input_ids: torch.LongTensor = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            past_key_values: Optional[List[torch.FloatTensor]] = None,
-            inputs_embeds: Optional[torch.FloatTensor] = None,
-            use_cache: Optional[bool] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            output_router_logits: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, MoeModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
-        )
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape
-            default_device = input_ids.device
-        elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
-            default_device = inputs_embeds.device
-        else:
-            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-
-        past_key_values_length = 0
-
-        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
-
-        seq_length_with_past = seq_length
-        if past_key_values is not None:
-            for idx in range(len(past_key_values)):
-                if self.attn_type_list[idx] == 1:
-                    past_key_values_length = past_key_values[idx][0].shape[-3]
-                    seq_length_with_past = seq_length_with_past + past_key_values_length
-                    break
-
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        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 MiniMaxText01. Make sure to "
-                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
-                )
-        slope_rates = [self.slopes.to(default_device) for _ in range(len(self.layers))]
-        hidden_states = inputs_embeds
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        all_router_logits = () if output_router_logits else None
-        next_decoder_cache = () if use_cache else None
-
-        for idx, decoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            past_key_value = (past_key_values[idx] if past_key_values is not None else None)
-            attn_mask = attention_mask
-            slope_rate = slope_rates[idx]
-            slope_rate = slope_rate * (1 - idx / (len(self.layers) - 1) + 1e-5)
-            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,
-                    output_router_logits,
-                    use_cache,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attn_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    output_router_logits=output_router_logits,
-                    use_cache=use_cache,
-                    slope_rate=slope_rate
-                )
-
-            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 output_router_logits:
-                all_router_logits += (layer_outputs[-1],)
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-        next_cache = next_decoder_cache if use_cache else None
-        if not return_dict:
-            return tuple(
-                v
-                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
-                if v is not None
-            )
-        return MoeModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-            router_logits=all_router_logits,
-        )
-
-
-class MiniMaxText01ForCausalLM(MiniMaxText01PreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = MiniMaxText01Model(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.router_aux_loss_coef = config.router_aux_loss_coef
-        self.num_experts = config.num_local_experts
-        self.num_experts_per_tok = config.num_experts_per_tok
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    @add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    # Ignore copy
-    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,
-            output_router_logits: Optional[bool] = None,
-            return_dict: Optional[bool] = 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]`.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, MiniMaxText01ForCausalLM
-
-        >>> model = MiniMaxText01ForCausalLM.from_pretrained(PATH_TO_WEIGHTS)
-        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_WEIGHTS)
-
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
-        )
-
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            output_router_logits=output_router_logits,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-        logits = logits.float()
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        aux_loss = None
-        if output_router_logits:
-            aux_loss = load_balancing_loss_func(
-                outputs.router_logits if return_dict else outputs[-1],
-                self.num_experts,
-                self.num_experts_per_tok,
-                attention_mask,
-            )
-            if labels is not None:
-                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            if output_router_logits:
-                output = (aux_loss,) + output
-            return (loss,) + output if loss is not None else output
-
-        torch.cuda.empty_cache()
-        return MoeCausalLMOutputWithPast(
-            loss=loss,
-            aux_loss=aux_loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            router_logits=outputs.router_logits,
-        )
-
-    def prepare_inputs_for_generation(
-            self,
-            input_ids,
-            past_key_values=None,
-            attention_mask=None,
-            inputs_embeds=None,
-            **kwargs,
-    ):
-        if past_key_values:
-            input_ids = input_ids[:, -1:]
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update({
-            "past_key_values": past_key_values,
-            "use_cache": kwargs.get("use_cache"),
-            "attention_mask": attention_mask,
-        })
-        return model_inputs
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (
-                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
-            )
-        return reordered_past
-
-
-@add_start_docstrings(
-    """
-    The MiniMaxText01 Model transformer with a sequence classification head on top (linear layer).
-
-    [`MiniMaxText01ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    MIXTRAL_START_DOCSTRING,
-)
-# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->MiniMaxText01, LLAMA->MIXTRAL
-class MiniMaxText01ForSequenceClassification(MiniMaxText01PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = MiniMaxText01Model(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING)
-    def forward(
-            self,
-            input_ids: torch.LongTensor = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-            inputs_embeds: Optional[torch.FloatTensor] = None,
-            labels: Optional[torch.LongTensor] = None,
-            use_cache: Optional[bool] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
-            sequence_lengths = -1
-        else:
-            if input_ids is not None:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(logits.device)
-            else:
-                sequence_lengths = -1
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(pooled_logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(pooled_logits, labels)
-        if not return_dict:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )

modular_minimax.py

@@ -0,0 +1,544 @@
+# coding=utf-8
+# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MiniMax model."""
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.configuration_utils import layer_type_validation
+from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import MoeModelOutputWithPast
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, logging
+from transformers.utils.generic import OutputRecorder, check_model_inputs
+from transformers.models.mixtral.configuration_mixtral import MixtralConfig
+from transformers.models.mixtral.modeling_mixtral import (
+    MixtralAttention,
+    MixtralDecoderLayer,
+    MixtralForCausalLM,
+    MixtralModel,
+    MixtralPreTrainedModel,
+    MixtralRMSNorm,
+    MixtralSparseMoeBlock,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+class MiniMaxConfig(MixtralConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MiniMaxModel`]. It is used to instantiate an
+    MiniMax 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 MiniMax.
+
+    [MiniMaxAI/MiniMax-Text-01-hf](https://huggingface.co/MiniMaxAI/MiniMax-Text-01-hf)
+
+    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PreTrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the MiniMax model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MiniMaxModel`]
+        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, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
+        head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+            The attention head dimension.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. MiniMax's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        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-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            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.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        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 route per-token, can be also interpreted as the `top-k` routing
+            parameter
+        num_local_experts (`int`, *optional*, defaults to 8):
+            Number of experts per Sparse MLP layer.
+        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.
+        router_jitter_noise (`float`, *optional*, defaults to 0.0):
+            Amount of noise to add to the router.
+        layer_types (`list`, *optional*):
+            Attention pattern for each layer.
+        block_size (`int`, *optional*, defaults to 256):
+            The length of each attention block, determining how queries, keys, and values
+            are grouped and processed for intra- and inter-block attention.
+        full_attn_alpha_factor (`float`, *optional*, defaults to 1):
+            Weight for residual value in residual connection after normal attention.
+        full_attn_beta_factor (`float`, *optional*, defaults to 1):
+            Weight for hidden state value in residual connection after normal attention.
+        linear_attn_alpha_factor (`float`, *optional*, defaults to 1):
+            Weight for residual value in residual connection after lightning attention.
+        linear_attn_beta_factor (`float`, *optional*, defaults to 1):
+            Weight for hidden state value in residual connection after lightning attention.
+        mlp_alpha_factor (`float`, *optional*, defaults to 1):
+            Weight for residual value in residual connection after MLP.
+        mlp_beta_factor (`float`, *optional*, defaults to 1):
+            Weight for hidden state value in residual connection after MLP.
+
+    ```python
+    >>> from transformers import MiniMaxModel, MiniMaxConfig
+
+    >>> # Initializing a MiniMax style configuration
+    >>> configuration = MiniMaxConfig()
+
+    >>> # Initializing a model from the MiniMax style configuration
+    >>> model = MiniMaxModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    def __init__(
+        self,
+        layer_types=None,
+        block_size=256,
+        full_attn_alpha_factor=1,
+        full_attn_beta_factor=1,
+        linear_attn_alpha_factor=1,
+        linear_attn_beta_factor=1,
+        mlp_alpha_factor=1,
+        mlp_beta_factor=1,
+        **super_kwargs,
+    ):
+        super().__init__(**super_kwargs)
+        self.layer_types = layer_types
+        self.block_size = block_size
+        self.full_attn_alpha_factor = full_attn_alpha_factor
+        self.full_attn_beta_factor = full_attn_beta_factor
+        self.linear_attn_alpha_factor = linear_attn_alpha_factor
+        self.linear_attn_beta_factor = linear_attn_beta_factor
+        self.mlp_alpha_factor = mlp_alpha_factor
+        self.mlp_beta_factor = mlp_beta_factor
+
+        if self.layer_types is None:
+            self.layer_types = [
+                "full_attention" if bool((i + 1) % 2) else "linear_attention" for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types, self.num_hidden_layers)
+
+
+class MiniMaxRMSNorm(MixtralRMSNorm):
+    pass
+
+
+class MiniMaxCache(DynamicCache):
+    def __init__(self):
+        super().__init__()
+        self.linear_cache: list[torch.Tensor] = []
+
+    def set_linear_cache(self, layer_idx, linear_cache):
+        # There may be skipped layers, fill them with empty lists
+        for _ in range(len(self.linear_cache), layer_idx + 1):
+            self.linear_cache.append([])
+        self.linear_cache[layer_idx] = linear_cache
+
+    def get_linear_cache(self, layer_idx: int):
+        if layer_idx < len(self):
+            return self.linear_cache[layer_idx]
+        return None
+
+    def __len__(self):
+        return max(super().__len__(), len(self.linear_cache))
+
+    def __getitem__(self, layer_idx: int):
+        if layer_idx < len(self.linear_cache) and self.linear_cache[layer_idx] != []:
+            return (self.linear_cache[layer_idx],)
+        return super().__getitem__(layer_idx)
+
+    def __iter__(self):
+        for layer_idx in range(len(self)):
+            yield self[layer_idx]
+
+    def batch_repeat_interleave(self, repeats: int):
+        for layer_idx in range(len(self)):
+            if self.linear_cache[layer_idx] != []:
+                self.linear_cache[layer_idx] = self.linear_cache[layer_idx].repeat_interleave(repeats, dim=0)
+            else:
+                self.layers[layer_idx].batch_repeat_interleave(repeats)
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        for layer_idx in range(len(self)):
+            if self.linear_cache[layer_idx] != []:
+                self.linear_cache[layer_idx] = self.linear_cache[layer_idx][indices, ...]
+            else:
+                self.layers[layer_idx].batch_select_indices(indices)
+
+    def crop(self, max_length: int):
+        raise RuntimeError("MiniMaxCache doesnot support `crop` method")
+
+
+class MiniMaxLightningAttention(nn.Module):
+    def __init__(self, config: MiniMaxConfig, layer_idx: int):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+        self.num_attention_heads = config.num_attention_heads
+        self.num_hidden_layers = config.num_hidden_layers
+        self.block_size = config.block_size
+
+        self.act_fn = ACT2FN[config.hidden_act]
+        self.norm = MiniMaxRMSNorm(self.head_dim * self.num_attention_heads)
+        self.qkv_proj = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim * 3, bias=False)
+        self.out_proj = nn.Linear(self.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+        self.output_gate = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
+
+        slope_rate = self.get_slope_rate()
+        query_decay, key_decay, diagonal_decay = self.decay_factors(slope_rate)
+
+        self.register_buffer("slope_rate", slope_rate)
+        self.register_buffer("query_decay", query_decay)
+        self.register_buffer("key_decay", key_decay)
+        self.register_buffer("diagonal_decay", diagonal_decay)
+
+    def get_slope_rate(self):
+        base = 1 / (2 ** (8 / self.num_attention_heads))
+        exponent = torch.arange(self.num_attention_heads) + 1
+        factor = 1 - self.layer_idx / (self.num_hidden_layers - 1 + 1e-5) + 1e-5
+
+        rate = base**exponent
+        rate = rate * factor
+        rate = rate[:, None, None]
+
+        return rate
+
+    def decay_factors(self, slope_rate):
+        block_size_range = torch.arange(self.block_size) + 1
+
+        query_decay = torch.exp(-slope_rate * block_size_range[:, None])
+        key_decay = torch.exp(-slope_rate * (self.block_size - block_size_range[:, None]))
+
+        diagonal_decay = block_size_range[:, None] - block_size_range[None, :]
+        diagonal_decay = diagonal_decay[None, None, :, :]
+        diagonal_decay = slope_rate * diagonal_decay
+        diagonal_decay = torch.where(diagonal_decay >= 0, -diagonal_decay, float("-inf"))
+        diagonal_decay = torch.exp(diagonal_decay)
+
+        return query_decay, key_decay, diagonal_decay
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        batch_size, seq_len, hidden_size = hidden_states.shape
+        num_blocks = (seq_len + self.block_size - 1) // self.block_size
+
+        qkv_states = self.act_fn(self.qkv_proj(hidden_states))
+        qkv_states = qkv_states.reshape(batch_size, seq_len, self.num_attention_heads, 3 * self.head_dim)
+
+        query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=3)
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        # calculated (K.T @ V) and saved as cache
+        attn_weights_inter = None
+        if past_key_values is not None:
+            attn_weights_inter = past_key_values.get_linear_cache(self.layer_idx)
+
+        if attn_weights_inter is None:
+            attn_weights_inter = torch.zeros(batch_size, self.num_attention_heads, self.head_dim, self.head_dim).to(
+                value_states
+            )
+
+            # apply attention_mask
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(dtype=torch.bool)  # Ensure it's a boolean tensor
+                value_states = value_states.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(-1), 0)
+
+            attn_output = []
+            for i in range(num_blocks):
+                start_idx = i * self.block_size
+                end_idx = min(start_idx + self.block_size, seq_len)
+                current_block_size = end_idx - start_idx
+
+                current_query_states = query_states[:, :, start_idx:end_idx]
+                current_key_states = key_states[:, :, start_idx:end_idx]
+                current_value_states = value_states[:, :, start_idx:end_idx]
+
+                current_query_decay = self.query_decay[:, :current_block_size]
+                current_key_decay = self.key_decay[:, -current_block_size:]
+                current_diagonal_decay = self.diagonal_decay[:, :, :current_block_size, :current_block_size]
+                block_decay = torch.exp(-self.slope_rate * current_block_size)
+
+                # intra: ( Q @ K.T ) @ V -> QK * V
+                attn_weights_intra = torch.matmul(current_query_states, current_key_states.transpose(-1, -2))
+                attn_output_intra = torch.matmul(attn_weights_intra * current_diagonal_decay, current_value_states)
+
+                # inter: Q @ ( K.T @ V ) -> Q * KV
+                attn_output_inter = torch.matmul(current_query_states * current_query_decay, attn_weights_inter)
+
+                # final attention output
+                current_attn_output = attn_output_inter + attn_output_intra
+                attn_output.append(current_attn_output)
+
+                # calculate attn_weights_inter for next block or cache
+                next_attn_weights_inter = torch.matmul(
+                    (current_key_states * current_key_decay).transpose(-1, -2), current_value_states
+                )
+                attn_weights_inter = attn_weights_inter * block_decay + next_attn_weights_inter
+
+        else:
+            ratio = torch.exp(-self.slope_rate)
+            attn_output = []
+            for i in range(seq_len):
+                current_query_states = query_states[:, :, i : i + 1]
+                current_key_states = key_states[:, :, i : i + 1]
+                current_value_states = value_states[:, :, i : i + 1]
+
+                current_attn_weights_inter = torch.matmul(current_key_states.transpose(-1, -2), current_value_states)
+                attn_weights_inter = ratio * attn_weights_inter + current_attn_weights_inter
+                current_attn_output = torch.matmul(current_query_states, attn_weights_inter)
+
+                attn_output.append(current_attn_output)
+
+        # concatenate attention outputs over all blocks
+        attn_output = torch.cat(attn_output, dim=-2)
+
+        # final output projection
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(batch_size, seq_len, self.num_attention_heads * self.head_dim)
+        attn_output = self.norm(attn_output)
+        attn_output = F.sigmoid(self.output_gate(hidden_states)) * attn_output
+        attn_output = self.out_proj(attn_output)
+
+        # update cache
+        if past_key_values is not None:
+            past_key_values.set_linear_cache(self.layer_idx, attn_weights_inter)
+
+        return attn_output, attn_weights_inter
+
+
+class MiniMaxAttention(MixtralAttention):
+    pass
+
+
+class MiniMaxSparseMoeBlock(MixtralSparseMoeBlock):
+    pass
+
+
+class MiniMaxDecoderLayer(MixtralDecoderLayer, GradientCheckpointingLayer):
+    def __init__(self, config: MiniMaxConfig, layer_idx: int):
+        super().__init__(config, layer_idx)
+
+        self.layer_idx = layer_idx
+        self.layer_type = config.layer_types[layer_idx]
+        self.mlp_alpha_factor = config.mlp_alpha_factor
+        self.mlp_beta_factor = config.mlp_beta_factor
+
+        if self.layer_type == "linear_attention":
+            self.self_attn = MiniMaxLightningAttention(config, layer_idx)
+            self.attn_alpha_factor = config.linear_attn_alpha_factor
+            self.attn_beta_factor = config.linear_attn_beta_factor
+        else:
+            self.self_attn = MiniMaxAttention(config, layer_idx)
+            self.attn_alpha_factor = config.full_attn_alpha_factor
+            self.attn_beta_factor = config.full_attn_beta_factor
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        hidden_states = self.input_layernorm(hidden_states)
+        residual = hidden_states
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual * self.attn_alpha_factor + hidden_states * self.attn_beta_factor
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        residual = hidden_states
+        hidden_states = self.block_sparse_moe(hidden_states)
+        hidden_states = residual * self.mlp_alpha_factor + hidden_states * self.mlp_beta_factor
+
+        return hidden_states
+
+
+class MiniMaxPreTrainedModel(MixtralPreTrainedModel):
+    _can_compile_fullgraph = False
+    _can_record_outputs = {
+        "router_logits": OutputRecorder(nn.Linear, layer_name="block_sparse_moe.gate", index=0),
+        "hidden_states": MiniMaxDecoderLayer,
+        "attentions": [MiniMaxAttention, MiniMaxLightningAttention],
+    }
+
+
+class MiniMaxModel(MixtralModel):
+    @check_model_inputs
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[MiniMaxCache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if use_cache and past_key_values is None:
+            past_key_values = MiniMaxCache()
+        elif use_cache and not isinstance(past_key_values, MiniMaxCache):
+            raise ValueError(
+                f"MiniMax uses cache of its own and is not compatible with `past_key_values` of type {type(past_key_values)}."
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+        causal_mask = mask_function(
+            config=self.config,
+            input_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for decoder_layer in self.layers:
+            if decoder_layer.layer_type == "full_attention":
+                input_attention_mask = causal_mask
+            else:
+                # lightning attention uses original attention_mask, and uses it only for the first step
+                input_attention_mask = attention_mask
+
+            hidden_states = decoder_layer(
+                hidden_states,
+                position_embeddings=position_embeddings,
+                attention_mask=input_attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+class MiniMaxForCausalLM(MixtralForCausalLM):
+    def forward(self, **super_kwargs):
+        r"""
+        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]`.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MiniMaxForCausalLM
+
+        >>> model = MiniMaxForCausalLM.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        return super().forward(**super_kwargs)
+