update for new version

config.json

@@ -28,5 +28,6 @@
   "torch_dtype": "bfloat16",
   "transformers_version": "4.39.3",
   "use_cache": true,
-  "vocab_size": 92544
+  "vocab_size": 92544,
+  "pretraining_tp": 1
 }

configuration_internlm2.py

@@ -37,16 +37,16 @@ class InternLM2Config(PretrainedConfig):
 
     Args:
         vocab_size (`int`, *optional*, defaults to 32000):
-            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented
-            by the `inputs_ids` passed when calling [`InternLM2Model`]
+            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLM2Model`]
         hidden_size (`int`, *optional*, defaults to 4096):
             Dimension of the hidden representations.
         intermediate_size (`int`, *optional*, defaults to 11008):
             Dimension of the MLP representations.
         num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
+            Number of hidden layers in the Transformer decoder.
         num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
+            Number of attention heads for each attention layer in the Transformer decoder.
         num_key_value_heads (`int`, *optional*):
             This is the number of key_value heads that should be used to implement Grouped Query Attention. If
             `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
@@ -58,22 +58,42 @@ class InternLM2Config(PretrainedConfig):
         hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
             The non-linear activation function (function or string) in the decoder.
         max_position_embeddings (`int`, *optional*, defaults to 2048):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
+            The maximum sequence length that this model might ever be used with. InternLM2 supports up to 32768 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-12):
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
             The epsilon used by the rms normalization layers.
         use_cache (`bool`, *optional*, defaults to `True`):
             Whether or not the model should return the last key/values attentions (not used by all models). Only
             relevant if `config.is_decoder=True`.
-        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism)
+            to understand more about it. This value is necessary to ensure exact reproducibility
+            of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
             Whether to tie weight embeddings
-        Example:
-
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
     """
-    model_type = "internlm2"
     _auto_class = "AutoConfig"
+    model_type = "internlm2"
+    keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(  # pylint: disable=W0102
         self,
@@ -91,11 +111,12 @@ class InternLM2Config(PretrainedConfig):
         pad_token_id=0,
         bos_token_id=1,
         eos_token_id=2,
+        pretraining_tp=1,
         tie_word_embeddings=False,
         bias=True,
         rope_theta=10000,
         rope_scaling=None,
-        attn_implementation="eager",
+        attn_implementation=None,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -113,14 +134,15 @@ class InternLM2Config(PretrainedConfig):
         self.hidden_act = hidden_act
         self.initializer_range = initializer_range
         self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
         self.use_cache = use_cache
         self.rope_theta = rope_theta
         self.rope_scaling = rope_scaling
         self._rope_scaling_validation()
-
         self.attn_implementation = attn_implementation
         if self.attn_implementation is None:
             self.attn_implementation = "eager"
+
         super().__init__(
             pad_token_id=pad_token_id,
             bos_token_id=bos_token_id,

modeling_internlm2.py

@@ -13,11 +13,10 @@
 # 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 InternLM2 model."""
+"""PyTorch InternLM2 model."""
 import math
 import queue
 import threading
-import warnings
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -27,15 +26,22 @@ from einops import rearrange
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
 from transformers.modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
     SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
 )
 from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 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,
 )
@@ -47,36 +53,21 @@ except Exception:
 
 from .configuration_internlm2 import InternLM2Config
 
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "InternLM2Config"
+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
 
-flash_attn_func, flash_attn_varlen_func = None, None
-pad_input, index_first_axis, unpad_input = None, None, None
 
+logger = logging.get_logger(__name__)
 
-def _import_flash_attn():
-    global flash_attn_func, flash_attn_varlen_func
-    global pad_input, index_first_axis, unpad_input
-    try:
-        from flash_attn import flash_attn_func as _flash_attn_func
-        from flash_attn import flash_attn_varlen_func as _flash_attn_varlen_func
-        from flash_attn.bert_padding import index_first_axis as _index_first_axis
-        from flash_attn.bert_padding import pad_input as _pad_input
-        from flash_attn.bert_padding import unpad_input as _unpad_input
-
-        flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
-        pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
-    except ImportError:
-        raise ImportError("flash_attn is not installed.")
+_CONFIG_FOR_DOC = "InternLM2Config"
 
 
-# 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.torch.int32), (1, 0))
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))  # pylint: disable=E1102
     return (
         indices,
         cu_seqlens,
@@ -84,49 +75,10 @@ def _get_unpad_data(attention_mask):
     )
 
 
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
 class InternLM2RMSNorm(nn.Module):
-    """
-    InternLM2 RMSNorm implemention.
-    """
+    """InternLM2RMSNorm is equivalent to T5LayerNorm."""
 
     def __init__(self, hidden_size, eps=1e-6):
-        """
-        InternLM2RMSNorm is equivalent to T5LayerNorm
-        """
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
@@ -139,97 +91,68 @@ class InternLM2RMSNorm(nn.Module):
         return self.weight * hidden_states.to(input_dtype)
 
 
-# Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
+ALL_LAYERNORM_LAYERS.append(InternLM2RMSNorm)
+
+
 class InternLM2RotaryEmbedding(nn.Module):
-    """
-    Normal InternLM2 rotary embedding.
-    """
+    """Rotary Position Embedding for the InternLM2 model. Credits to the Reddit user /u/lucidrains."""
 
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
         super().__init__()
-
+        self.scaling_factor = scaling_factor
         self.dim = dim
         self.max_position_embeddings = max_position_embeddings
         self.base = base
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        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)
+        # For BC we register cos and sin cached
+        self.max_seq_len_cached = max_position_embeddings
 
-        # 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.get_default_dtype()
-        )
-
-    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=self.inv_freq.dtype)
-
-        freqs = torch.einsum("i,j->ij", 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):
+    @torch.no_grad()
+    def forward(self, x, position_ids):
         # 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)
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
 
-        return (
-            self.cos_cached[:seq_len].to(dtype=x.dtype),
-            self.sin_cached[:seq_len].to(dtype=x.dtype),
-        )
 
-
-# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
 class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
     """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
 
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
-        self.scaling_factor = scaling_factor
-        super().__init__(dim, max_position_embeddings, base, device)
-
-    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=self.inv_freq.dtype)
-        t = t / self.scaling_factor
-
-        freqs = torch.einsum("i,j->ij", 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, position_ids):
+        # difference to the original RoPE: a scaling factor is aplied to the position ids
+        position_ids = position_ids.float() / self.scaling_factor
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
 
 
-# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
 class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
     """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
-    Credits to the Reddit users /u/bloc97 and /u/emozilla.
-    """
-
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
-        self.scaling_factor = scaling_factor
-        super().__init__(dim, max_position_embeddings, base, device)
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
+    Credits to the Reddit users /u/bloc97 and /u/emozilla"""
 
+    def forward(self, x, position_ids):
+        # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
+        seq_len = torch.max(position_ids) + 1
         if seq_len > self.max_position_embeddings:
             base = self.base * (
                 (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
             ) ** (self.dim / (self.dim - 2))
-            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
-            self.register_buffer("inv_freq", inv_freq, persistent=False)
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: this may break with compilation
 
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
 
-        freqs = torch.einsum("i,j->ij", 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)
 
-
-# Copied from transformers.model.llama.modeling_llama.rotate_half
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -237,20 +160,35 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-# Copied from transformers.model.llama.modeling_llama.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."""
-    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
-    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):  # pylint: disable=unused-argument
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
     q_embed = (q * cos) + (rotate_half(q) * sin)
     k_embed = (k * cos) + (rotate_half(k) * sin)
     return q_embed, k_embed
 
 
 class InternLM2MLP(nn.Module):
-    """
-    InternLM2 FFN.
-    """
+    """MLP for InternLM2 model."""
 
     def __init__(self, config):
         super().__init__()
@@ -268,7 +206,6 @@ class InternLM2MLP(nn.Module):
         return down_proj
 
 
-# Copied from transformers.model.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,
@@ -281,19 +218,27 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
 
 
-# Modified from transformers.model.llama.modeling_llama.LlamaAttention
 class InternLM2Attention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
-    def __init__(self, config: InternLM2Config):
+    def __init__(self, config: InternLM2Config, 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 = 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
 
         if (self.head_dim * self.num_heads) != self.hidden_size:
@@ -307,8 +252,8 @@ class InternLM2Attention(nn.Module):
             (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
             bias=config.bias,
         )
-
         self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
+
         self._init_rope()
 
     def _init_rope(self):
@@ -316,51 +261,49 @@ class InternLM2Attention(nn.Module):
             self.rotary_emb = InternLM2RotaryEmbedding(
                 self.head_dim,
                 max_position_embeddings=self.max_position_embeddings,
-                base=self.config.rope_theta,
+                base=self.rope_theta,
             )
         else:
             scaling_type = self.config.rope_scaling["type"]
             scaling_factor = self.config.rope_scaling["factor"]
-            if scaling_type == "dynamic":
-                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
+            if scaling_type == "linear":
+                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
                     self.head_dim,
                     max_position_embeddings=self.max_position_embeddings,
-                    base=self.config.rope_theta,
                     scaling_factor=scaling_factor,
+                    base=self.rope_theta,
                 )
-            elif scaling_type == "linear":
-                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
+            elif scaling_type == "dynamic":
+                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
                     self.head_dim,
                     max_position_embeddings=self.max_position_embeddings,
-                    base=self.config.rope_theta,
                     scaling_factor=scaling_factor,
+                    base=self.rope_theta,
                 )
             else:
-                raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
-        return self.rotary_emb
-
-    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()
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
 
     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,
+        past_key_value: Optional[Cache] = None,
         output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        cache_position: Optional[torch.LongTensor] = None,
     ) -> 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()
 
-        qkv_states = self.wqkv(hidden_states)
+        if self.config.pretraining_tp > 1:
+            # split qkv_states by tp size
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+            qkv_slices = self.wqkv.weight.split(key_value_slicing, dim=0)
+            qkv_states = torch.cat(
+                [F.linear(hidden_states, qkv_slice) for qkv_slice in qkv_slices], dim=-1  # pylint: disable=E1102
+            )
+        else:
+            qkv_states = self.wqkv(hidden_states)
 
         qkv_states = rearrange(
             qkv_states,
@@ -370,44 +313,26 @@ class InternLM2Attention(nn.Module):
         )
 
         query_states = qkv_states[..., : self.num_key_value_groups, :]
-        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
-        key_states = qkv_states[..., -2, :]
-        value_states = qkv_states[..., -1, :]
+        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d").transpose(1, 2)
+        key_states = qkv_states[..., -2, :].transpose(1, 2)
+        value_states = qkv_states[..., -1, :].transpose(1, 2)
 
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.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[-2]
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        cos, sin = self.rotary_emb(value_states, position_ids)
         query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
 
         if past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-        past_key_value = (key_states, value_states) if use_cache else 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_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
         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
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
 
         # upcast attention to fp32
         attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
@@ -420,9 +345,20 @@ class InternLM2Attention(nn.Module):
             )
 
         attn_output = attn_output.transpose(1, 2).contiguous()
+
         attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
 
-        attn_output = self.wo(attn_output)
+        if self.config.pretraining_tp > 1:
+            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+            o_proj_slices = self.wo.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+            attn_output = sum(
+                [
+                    F.linear(attn_output[i], o_proj_slices[i])  # pylint: disable=E1102
+                    for i in range(self.config.pretraining_tp)
+                ]
+            )
+        else:
+            attn_output = self.wo(attn_output)
 
         if not output_attentions:
             attn_weights = None
@@ -430,7 +366,6 @@ class InternLM2Attention(nn.Module):
         return attn_output, attn_weights, past_key_value
 
 
-# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
 class InternLM2FlashAttention2(InternLM2Attention):
     """
     InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
@@ -438,26 +373,34 @@ class InternLM2FlashAttention2(InternLM2Attention):
     flash attention and deal with padding tokens in case the input contains any of them.
     """
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement,
+        #   that was made default for flash_attn>=2.1. This attribute is used to handle this difference.
+        # Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1)
+        #   produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
     def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: Optional[torch.LongTensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        past_key_value: Optional[Cache] = None,
         output_attentions: bool = False,
         use_cache: bool = False,
-        **kwargs,
+        cache_position: Optional[torch.LongTensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # InternLM2FlashAttention2 attention does not support output_attentions
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
-                "Please make sure use `attention_mask` instead.`"
+        if isinstance(past_key_value, StaticCache):
+            raise ValueError(
+                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+                "make sure to use `sdpa` in the mean time, and open an issue at "
+                "https://github.com/huggingface/transformers"
             )
 
-            # overwrite attention_mask with padding_mask
-            attention_mask = kwargs.pop("padding_mask")
-
         output_attentions = False
 
         bsz, q_len, _ = hidden_states.size()
@@ -480,33 +423,61 @@ class InternLM2FlashAttention2(InternLM2Attention):
         key_states = key_states.transpose(1, 2)
         value_states = value_states.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[-2]
-
-        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)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
         if past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-        past_key_value = (key_states, value_states) if use_cache else 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_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout
+        # [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
         query_states = query_states.transpose(1, 2)
         key_states = key_states.transpose(1, 2)
         value_states = value_states.transpose(1, 2)
 
-        attn_output = self._flash_attention_forward(query_states, key_states, value_states, attention_mask, q_len)
+        # dropout_rate = self.attention_dropout if self.training else 0.0
+        dropout_rate = 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (InternLM2RMSNorm handles it correctly)
+
+        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.wqkv.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+        )
+
         attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
         attn_output = self.wo(attn_output)
 
         if not output_attentions:
             attn_weights = None
 
-        return attn_output, attn_weights, past_key_value
+        return attn_output, attn_weights, past_key_value  # pylint: disable=E0606
 
     def _flash_attention_forward(
         self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
@@ -525,23 +496,29 @@ class InternLM2FlashAttention2(InternLM2Attention):
             attention_mask (`torch.Tensor`):
                 The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                 position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`int`, *optional*):
+            dropout (`float`):
                 Attention dropout
             softmax_scale (`float`, *optional*):
                 The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
         """
+        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 InternLM2FlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
         # Contains at least one padding token in the sequence
-        causal = self.is_causal and query_length != 1
         if attention_mask is not None:
             batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
+            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
 
-            attn_output_unpad = flash_attn_varlen_func(
+            attn_output_unpad = flash_attn_varlen_func(  # pylint: disable=E0606
                 query_states,
                 key_states,
                 value_states,
@@ -554,27 +531,26 @@ class InternLM2FlashAttention2(InternLM2Attention):
                 causal=causal,
             )
 
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)  # pylint: disable=E0606
         else:
-            attn_output = flash_attn_func(
+            attn_output = flash_attn_func(  # pylint: disable=E0606
                 query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
             )
 
         return attn_output
 
-    def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
         indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
         batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
 
-        key_layer = index_first_axis(
+        key_layer = index_first_axis(  # pylint: disable=E0606
             key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
         )
-        value_layer = index_first_axis(
+        value_layer = index_first_axis(  # pylint: disable=E0606
             value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
         )
-
         if query_length == kv_seq_len:
-            query_layer = index_first_axis(
+            query_layer = index_first_axis(  # pylint: disable=E0606
                 query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
             )
             cu_seqlens_q = cu_seqlens_k
@@ -590,35 +566,139 @@ class InternLM2FlashAttention2(InternLM2Attention):
         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)
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(  # pylint: disable=E0606
+                query_layer, attention_mask
+            )
 
         return (
             query_layer,
             key_layer,
             value_layer,
-            indices_q.to(torch.int64),
+            indices_q,
             (cu_seqlens_q, cu_seqlens_k),
             (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
         )
 
 
+# Copied from transformers.models.llama.modeling_llama.LllamaSdpaAttention with Llama->InternLM2
+class InternLM2SdpaAttention(InternLM2Attention):
+    """
+    InternLM2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `InternLM2Attention` as the weights of the module stays untouched. The only changes are on the forward pass
+    to adapt to SDPA API.
+    """
+
+    # Adapted from InternLM2Attention.forward
+    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,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"`
+            # once this is implemented.
+            logger.warning_once(
+                "InternLM2Model uses InternLM2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` "
+                "does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                "but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. "
+                'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            "b q (h gs d) -> b q h gs d",
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value 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_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:
+            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with
+        # custom attn_mask, Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and causal_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of
+        # an inline conditional assignment in SDPA to support both torch.compile's dynamic shapes and full graph
+        # options. An inline conditional prevents dynamic shapes from compiling.
+        is_causal = bool(causal_mask is None and q_len > 1)
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(  # pylint: disable=E1102
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.wo(attn_output)
+
+        return attn_output, None, past_key_value
+
+
 INTERNLM2_ATTENTION_CLASSES = {
     "eager": InternLM2Attention,
     "flash_attention_2": InternLM2FlashAttention2,
+    "sdpa": InternLM2SdpaAttention,
 }
 
 
-# Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
+# Modified from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->InternLM2
 class InternLM2DecoderLayer(nn.Module):
-    """
-    InternLM2 decoder layer.
-    """
+    """InternLM2 Decoder Layer. This module is a single layer of the InternLM2 model."""
 
-    def __init__(self, config: InternLM2Config):
+    def __init__(self, config: InternLM2Config, layer_idx: int):
         super().__init__()
         self.hidden_size = config.hidden_size
+        self.layer_idx = layer_idx
 
-        self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
+        self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config, layer_idx=layer_idx)
 
         self.feed_forward = InternLM2MLP(config)
         self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -629,10 +709,10 @@ class InternLM2DecoderLayer(nn.Module):
         hidden_states: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        past_key_value: Optional[Cache] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
-        **kwargs,
+        cache_position: Optional[torch.LongTensor] = None,
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         """
         Args:
@@ -648,12 +728,6 @@ class InternLM2DecoderLayer(nn.Module):
                 (see `past_key_values`).
             past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
         """
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
-                "Please make sure use `attention_mask` instead.`"
-            )
-
         residual = hidden_states
 
         hidden_states = self.attention_norm(hidden_states)
@@ -666,7 +740,7 @@ class InternLM2DecoderLayer(nn.Module):
             past_key_value=past_key_value,
             output_attentions=output_attentions,
             use_cache=use_cache,
-            **kwargs,
+            cache_position=cache_position,
         )
         hidden_states = residual + hidden_states
 
@@ -718,7 +792,12 @@ class InternLM2PreTrainedModel(PreTrainedModel):
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
     _no_split_modules = ["InternLM2DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
 
     def _init_weights(self, module):
         std = self.config.initializer_range
@@ -767,14 +846,19 @@ InternLM2_INPUTS_DOCSTRING = r"""
             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, decoder_sequence_length, embed_size_per_head)`.
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
 
-            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.
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
 
             If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
             have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
@@ -794,10 +878,14 @@ InternLM2_INPUTS_DOCSTRING = r"""
             more detail.
         return_dict (`bool`, *optional*):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
 """
 
 
-# Modified from transformers.model.llama.modeling_llama.LlamaModel
+# Modified from transformers.models.llama.modeling_llama.LlamaModel with Llama->InternLM2
 @add_start_docstrings(
     "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
     InternLM2_START_DOCSTRING,
@@ -820,7 +908,9 @@ class InternLM2Model(InternLM2PreTrainedModel):
 
         self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
 
-        self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.layers = nn.ModuleList(
+            [InternLM2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
         self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
         self.gradient_checkpointing = False
@@ -833,142 +923,96 @@ class InternLM2Model(InternLM2PreTrainedModel):
     def set_input_embeddings(self, value):
         self.tok_embeddings = value
 
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
-                inputs_embeds.device
-            )
-            combined_attention_mask = (
-                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-            )
-
-        return combined_attention_mask
-
     @add_start_docstrings_to_model_forward(InternLM2_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,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
     ) -> Union[Tuple, BaseModelOutputWithPast]:
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         use_cache = use_cache if use_cache is not None else self.config.use_cache
-
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
-        if self.config.attn_implementation == "flash_attention_2":
-            _import_flash_attn()
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
 
-        if 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
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
             )
-            position_ids = position_ids.unsqueeze(0)
+            use_cache = False
 
         if inputs_embeds is None:
             inputs_embeds = self.tok_embeddings(input_ids)
 
-        if self.config.attn_implementation == "flash_attention_2":
-            # 2d mask is passed through the layers
-            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-        else:
-            if attention_mask is None:
-                attention_mask = torch.ones(
-                    (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
-                )
-            attention_mask = self._prepare_decoder_attention_mask(
-                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+            return_legacy_cache = True
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+
+        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)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
 
         # embed positions
         hidden_states = inputs_embeds
 
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
         # decoder layers
         all_hidden_states = () if output_hidden_states else None
         all_self_attns = () if output_attentions else None
-        next_decoder_cache = () if use_cache else None
+        next_decoder_cache = None
 
-        for idx, decoder_layer in enumerate(self.layers):
+        for decoder_layer in 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
-
             if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, output_attentions, None)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
                     hidden_states,
-                    attention_mask,
+                    causal_mask,
                     position_ids,
-                    None,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
                 )
             else:
                 layer_outputs = decoder_layer(
                     hidden_states,
-                    attention_mask=attention_mask,
+                    attention_mask=causal_mask,
                     position_ids=position_ids,
-                    past_key_value=past_key_value,
+                    past_key_value=past_key_values,
                     output_attentions=output_attentions,
                     use_cache=use_cache,
+                    cache_position=cache_position,
                 )
 
             hidden_states = layer_outputs[0]
 
             if use_cache:
-                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
 
             if output_attentions:
                 all_self_attns += (layer_outputs[1],)
@@ -980,6 +1024,9 @@ class InternLM2Model(InternLM2PreTrainedModel):
             all_hidden_states += (hidden_states,)
 
         next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
         if not return_dict:
             return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
         return BaseModelOutputWithPast(
@@ -989,15 +1036,91 @@ class InternLM2Model(InternLM2PreTrainedModel):
             attentions=all_self_attns,
         )
 
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length
+        # even when the static KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at
+        # each decode steps due to the dynamic shapes. (`recording cudagraph tree for symint key 13`, etc.), which is
+        # VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using `fullgraph=True`.
+        # See more context in https://github.com/huggingface/transformers/pull/29114
 
-# Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
+        if self.config.attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config.attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
+            if attention_mask.max() != 0:
+                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
+            causal_mask = attention_mask
+        else:
+            causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+        if (
+            self.config.attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)  # pylint: disable=E1120
+
+        return causal_mask
+
+
+# Modified from transformers.models.llama.modeling_llama.LlamaForCausalLM
 class InternLM2ForCausalLM(InternLM2PreTrainedModel):
-    """
-    InternLM2 causal language model.
-    """
+    """Causal language model (CLM) for InternLM2."""
 
     _auto_class = "AutoModelForCausalLM"
-
     _tied_weights_keys = ["output.weight"]
 
     def __init__(self, config):
@@ -1034,13 +1157,14 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
         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,
+        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,
+        cache_position: Optional[torch.LongTensor] = None,
     ) -> Union[Tuple, CausalLMOutputWithPast]:
         r"""
         Args:
@@ -1056,8 +1180,8 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
         ```python
         >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
 
-        >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+        >>> model = InternLM2ForCausalLM.from_pretrained("meta-InternLM2/InternLM2-2-7b-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("meta-InternLM2/InternLM2-2-7b-hf")
 
         >>> prompt = "Hey, are you conscious? Can you talk to me?"
         >>> inputs = tokenizer(prompt, return_tensors="pt")
@@ -1085,10 +1209,19 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
+            cache_position=cache_position,
         )
 
         hidden_states = outputs[0]
-        logits = self.output(hidden_states)
+        if self.config.pretraining_tp > 1:
+            output_slices = self.output.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+            logits = [
+                F.linear(hidden_states, output_slices[i])  # pylint: disable=not-callable
+                for i in range(self.config.pretraining_tp)
+            ]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.output(hidden_states)
         logits = logits.float()
 
         loss = None
@@ -1117,19 +1250,48 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
         )
 
     def prepare_inputs_for_generation(
-        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        use_cache=True,
+        **kwargs,
     ):
+        past_length = 0
         if past_key_values is not None:
-            past_length = past_key_values[0][0].shape[2]
-
-            # Some generation methods already pass only the last input ID
-            if input_ids.shape[1] > past_length:
-                remove_prefix_length = past_length
+            if isinstance(past_key_values, Cache):
+                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
+                max_cache_length = (
+                    torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
+                    if past_key_values.get_max_length() is not None
+                    else None
+                )
+                cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
+            # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
             else:
-                # Default to old behavior: keep only final ID
-                remove_prefix_length = input_ids.shape[1] - 1
-
-            input_ids = input_ids[:, remove_prefix_length:]
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]  # pylint: disable=E1130
 
         position_ids = kwargs.get("position_ids", None)
         if attention_mask is not None and position_ids is None:
@@ -1143,13 +1305,24 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
         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}
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard.
+            # Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {"input_ids": input_ids.contiguous()}
+
+        input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
+        if cache_position is None:
+            cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
+        elif use_cache:
+            cache_position = cache_position[-input_length:]
 
         model_inputs.update(
             {
                 "position_ids": position_ids,
+                "cache_position": cache_position,
                 "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
+                "use_cache": use_cache,
                 "attention_mask": attention_mask,
             }
         )
@@ -1311,13 +1484,13 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
         return consumer()
 
 
-# Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
 @add_start_docstrings(
     """
     The InternLM2 Model transformer with a sequence classification head on top (linear layer).
 
-    [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
-    as other causal models (e.g. GPT-2) do.
+    [`InternLM2ForSequenceClassification`] 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
@@ -1328,9 +1501,7 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
     InternLM2_START_DOCSTRING,
 )
 class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
-    """
-    InternLM2 sequence classification model.
-    """
+    """Sequence Classification Head for InternLM2 Model."""
 
     def __init__(self, config):
         super().__init__(config)
@@ -1353,7 +1524,7 @@ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
         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,
+        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,
@@ -1394,9 +1565,10 @@ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
             sequence_lengths = -1
         else:
             if input_ids is not None:
-                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
-                    logits.device
-                )
+                # 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
 
@@ -1408,8 +1580,7 @@ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
             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 in (torch.long, torch.int):
+                elif self.num_labels > 1 and (labels.dtype in (torch.long, torch.int)):
                     self.config.problem_type = "single_label_classification"
                 else:
                     self.config.problem_type = "multi_label_classification"
@@ -1437,3 +1608,191 @@ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaForQuestionAnswering with Llama->InternLM2
+@add_start_docstrings(
+    """
+The InternLM2 Model transformer with a span classification head on top for extractive question-answering tasks like
+SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForQuestionAnswering(InternLM2PreTrainedModel):
+    """Question Answering model for InternLM2."""
+
+    base_model_prefix = "transformer"
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = InternLM2Model(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.transformer.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.transformer.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->InternLM2
+@add_start_docstrings(
+    """
+    The InternLM2 Model transformer with a token classification head on top (a linear layer on top of the hidden-states
+    output) e.g. for Named-Entity-Recognition (NER) tasks.
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForTokenClassification(InternLM2PreTrainedModel):
+    """Token classification model for InternLM2."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = InternLM2Model(config)
+        if getattr(config, "classifier_dropout", None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, "hidden_dropout", None) is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+
+        # 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(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        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,
+        )
+        sequence_output = outputs[0]
+        sequence_output = self.dropout(sequence_output)
+        logits = self.score(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )