fix: modeling_deepseek.py should use `deepseek` instead of `deepseek_v2` architecture

modeling_deepseek.py

@@ -5,7 +5,7 @@
 # and OPT implementations in this library. It has been modified from its
 # original forms to accommodate minor architectural differences compared
 # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
+# 
 # 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
@@ -34,17 +34,11 @@ from transformers.modeling_attn_mask_utils import (
     AttentionMaskConverter,
     _prepare_4d_attention_mask,
     _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
 )
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-)
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
 from transformers.modeling_utils import PreTrainedModel
-from transformers.pytorch_utils import (
-    ALL_LAYERNORM_LAYERS,
-    is_torch_greater_or_equal_than_1_13,
-)
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
 from transformers.utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -54,9 +48,8 @@ from transformers.utils import (
     replace_return_docstrings,
 )
 from transformers.utils.import_utils import is_torch_fx_available
-from .configuration_deepseek import DeepseekV2Config
-import torch.distributed as dist
-import numpy as np
+from .configuration_deepseek import DeepseekConfig
+
 
 if is_flash_attn_2_available():
     from flash_attn import flash_attn_func, flash_attn_varlen_func
@@ -74,16 +67,14 @@ if is_torch_fx_available():
 
 logger = logging.get_logger(__name__)
 
-_CONFIG_FOR_DOC = "DeepseekV2Config"
+_CONFIG_FOR_DOC = "DeepseekConfig"
 
 
 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.torch.int32), (1, 0))
     return (
         indices,
         cu_seqlens,
@@ -91,10 +82,28 @@ def _get_unpad_data(attention_mask):
     )
 
 
-class DeepseekV2RMSNorm(nn.Module):
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    warnings.warn(
+        "Calling `transformers.models.Deepseek.modeling_Deepseek._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
+    )
+    return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    warnings.warn(
+        "Calling `transformers.models.Deepseek.modeling_Deepseek._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.Deepseek.modeling_Deepseek.AttentionMaskConverter._make_causal_mask"
+    )
+    return AttentionMaskConverter._make_causal_mask(
+        input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
+    )
+
+
+class DeepseekRMSNorm(nn.Module):
     def __init__(self, hidden_size, eps=1e-6):
         """
-        DeepseekV2RMSNorm is equivalent to T5LayerNorm
+        DeepseekRMSNorm is equivalent to T5LayerNorm
         """
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
@@ -108,34 +117,29 @@ class DeepseekV2RMSNorm(nn.Module):
         return self.weight * hidden_states.to(input_dtype)
 
 
-ALL_LAYERNORM_LAYERS.append(DeepseekV2RMSNorm)
+ALL_LAYERNORM_LAYERS.append(DeepseekRMSNorm)
 
 
-class DeepseekV2RotaryEmbedding(nn.Module):
+class DeepseekRotaryEmbedding(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).float().to(device) / self.dim)
-        )
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).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.get_default_dtype(),
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
         )
         self.max_seq_len_cached = None
 
+
     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 = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
 
         freqs = torch.outer(t, self.inv_freq.to(t.device))
         # Different from paper, but it uses a different permutation in order to obtain the same calculation
@@ -154,26 +158,17 @@ class DeepseekV2RotaryEmbedding(nn.Module):
         )
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV2
-class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
-    """DeepseekV2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Deepseek
+class DeepseekLinearScalingRotaryEmbedding(DeepseekRotaryEmbedding):
+    """DeepseekRotaryEmbedding 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,
-    ):
+    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 = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
         t = t / self.scaling_factor
 
         freqs = torch.outer(t, self.inv_freq)
@@ -183,18 +178,11 @@ class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV2
-class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
-    """DeepseekV2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Deepseek
+class DeepseekDynamicNTKScalingRotaryEmbedding(DeepseekRotaryEmbedding):
+    """DeepseekRotaryEmbedding 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,
-    ):
+    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)
 
@@ -203,17 +191,12 @@ class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
 
         if seq_len > self.max_position_embeddings:
             base = self.base * (
-                (self.scaling_factor * seq_len / self.max_position_embeddings)
-                - (self.scaling_factor - 1)
+                (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)
-            )
+            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)
 
-        t = torch.arange(
-            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
-        )
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
 
         freqs = torch.outer(t, self.inv_freq)
         # Different from paper, but it uses a different permutation in order to obtain the same calculation
@@ -222,111 +205,6 @@ class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
 
 
-# Inverse dim formula to find dim based on number of rotations
-def yarn_find_correction_dim(
-    num_rotations, dim, base=10000, max_position_embeddings=2048
-):
-    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
-        2 * math.log(base)
-    )
-
-
-# Find dim range bounds based on rotations
-def yarn_find_correction_range(
-    low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
-):
-    low = math.floor(
-        yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
-    )
-    high = math.ceil(
-        yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
-    )
-    return max(low, 0), min(high, dim - 1)  # Clamp values just in case
-
-
-def yarn_get_mscale(scale=1, mscale=1):
-    if scale <= 1:
-        return 1.0
-    return 0.1 * mscale * math.log(scale) + 1.0
-
-
-def yarn_linear_ramp_mask(min, max, dim):
-    if min == max:
-        max += 0.001  # Prevent singularity
-
-    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
-    ramp_func = torch.clamp(linear_func, 0, 1)
-    return ramp_func
-
-
-class DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding):
-
-    def __init__(
-        self,
-        dim,
-        max_position_embeddings=2048,
-        base=10000,
-        device=None,
-        scaling_factor=1.0,
-        original_max_position_embeddings=4096,
-        beta_fast=32,
-        beta_slow=1,
-        mscale=1,
-        mscale_all_dim=0,
-    ):
-        self.scaling_factor = scaling_factor
-        self.original_max_position_embeddings = original_max_position_embeddings
-        self.beta_fast = beta_fast
-        self.beta_slow = beta_slow
-        self.mscale = mscale
-        self.mscale_all_dim = mscale_all_dim
-        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
-        dim = self.dim
-
-        freq_extra = 1.0 / (
-            self.base
-            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
-        )
-        freq_inter = 1.0 / (
-            self.scaling_factor
-            * self.base
-            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
-        )
-
-        low, high = yarn_find_correction_range(
-            self.beta_fast,
-            self.beta_slow,
-            dim,
-            self.base,
-            self.original_max_position_embeddings,
-        )
-        inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
-            device=device, dtype=torch.float32
-        )
-        inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        t = torch.arange(seq_len, device=device, dtype=torch.float32)
-
-        freqs = torch.outer(t, inv_freq)
-
-        _mscale = float(
-            yarn_get_mscale(self.scaling_factor, self.mscale)
-            / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
-        )
-
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer(
-            "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False
-        )
-        self.register_buffer(
-            "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False
-        )
-
-
 # Copied from transformers.models.llama.modeling_llama.rotate_half
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
@@ -359,26 +237,17 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     """
     cos = cos[position_ids].unsqueeze(unsqueeze_dim)
     sin = sin[position_ids].unsqueeze(unsqueeze_dim)
-
-    b, h, s, d = q.shape
-    q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
-
-    b, h, s, d = k.shape
-    k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
-
     q_embed = (q * cos) + (rotate_half(q) * sin)
     k_embed = (k * cos) + (rotate_half(k) * sin)
     return q_embed, k_embed
 
 
-class DeepseekV2MLP(nn.Module):
-    def __init__(self, config, hidden_size=None, intermediate_size=None):
+class DeepseekMLP(nn.Module):
+    def __init__(self, config, hidden_size = None, intermediate_size = None):
         super().__init__()
         self.config = config
         self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
-        self.intermediate_size = (
-            config.intermediate_size if intermediate_size is None else intermediate_size
-        )
+        self.intermediate_size = config.intermediate_size if intermediate_size is None else 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)
@@ -386,7 +255,25 @@ class DeepseekV2MLP(nn.Module):
         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))
+        if self.config.pretraining_tp > 1:
+            slice = self.intermediate_size // self.config.pretraining_tp
+            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+            down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+            gate_proj = torch.cat(
+                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+            )
+            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+            down_proj = [
+                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
         return down_proj
 
 
@@ -396,75 +283,39 @@ class MoEGate(nn.Module):
         self.config = config
         self.top_k = config.num_experts_per_tok
         self.n_routed_experts = config.n_routed_experts
-        self.routed_scaling_factor = config.routed_scaling_factor
+
         self.scoring_func = config.scoring_func
         self.alpha = config.aux_loss_alpha
         self.seq_aux = config.seq_aux
-        self.topk_method = config.topk_method
-        self.n_group = config.n_group
-        self.topk_group = config.topk_group
 
         # topk selection algorithm
         self.norm_topk_prob = config.norm_topk_prob
         self.gating_dim = config.hidden_size
-        self.weight = nn.Parameter(
-            torch.empty((self.n_routed_experts, self.gating_dim))
-        )
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
         self.reset_parameters()
 
     def reset_parameters(self) -> None:
-        import torch.nn.init as init
-
+        import torch.nn.init  as init
         init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-
+    
     def forward(self, hidden_states):
-        bsz, seq_len, h = hidden_states.shape
+        bsz, seq_len, h = hidden_states.shape        
         ### compute gating score
         hidden_states = hidden_states.view(-1, h)
-        logits = F.linear(
-            hidden_states.type(torch.float32), self.weight.type(torch.float32), None
-        )
-        if self.scoring_func == "softmax":
-            scores = logits.softmax(dim=-1, dtype=torch.float32)
+        logits = F.linear(hidden_states, self.weight, None)
+        if self.scoring_func == 'softmax':
+            scores = logits.softmax(dim=-1)
         else:
-            raise NotImplementedError(
-                f"insupportable scoring function for MoE gating: {self.scoring_func}"
-            )
-
+            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+        
         ### select top-k experts
-        if self.topk_method == "greedy":
-            topk_weight, topk_idx = torch.topk(
-                scores, k=self.top_k, dim=-1, sorted=False
-            )
-        elif self.topk_method == "group_limited_greedy":
-            group_scores = (
-                scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values
-            )  # [n, n_group]
-            group_idx = torch.topk(
-                group_scores, k=self.topk_group, dim=-1, sorted=False
-            )[
-                1
-            ]  # [n, top_k_group]
-            group_mask = torch.zeros_like(group_scores)  # [n, n_group]
-            group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
-            score_mask = (
-                group_mask.unsqueeze(-1)
-                .expand(
-                    bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
-                )
-                .reshape(bsz * seq_len, -1)
-            )  # [n, e]
-            tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
-            topk_weight, topk_idx = torch.topk(
-                tmp_scores, k=self.top_k, dim=-1, sorted=False
-            )
-
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+        
         ### norm gate to sum 1
         if self.top_k > 1 and self.norm_topk_prob:
             denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
             topk_weight = topk_weight / denominator
-        else:
-            topk_weight = topk_weight * self.routed_scaling_factor
+
         ### expert-level computation auxiliary loss
         if self.training and self.alpha > 0.0:
             scores_for_aux = scores
@@ -473,21 +324,11 @@ class MoEGate(nn.Module):
             topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
             if self.seq_aux:
                 scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
-                ce = torch.zeros(
-                    bsz, self.n_routed_experts, device=hidden_states.device
-                )
-                ce.scatter_add_(
-                    1,
-                    topk_idx_for_aux_loss,
-                    torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
-                ).div_(seq_len * aux_topk / self.n_routed_experts)
-                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(
-                    dim=1
-                ).mean() * self.alpha
+                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+                ce.scatter_add_(1, topk_idx_for_aux_loss, torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim = 1)).sum(dim = 1).mean() * self.alpha
             else:
-                mask_ce = F.one_hot(
-                    topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
-                )
+                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
                 ce = mask_ce.float().mean(0)
                 Pi = scores_for_aux.mean(0)
                 fi = ce * self.n_routed_experts
@@ -499,10 +340,9 @@ class MoEGate(nn.Module):
 
 class AddAuxiliaryLoss(torch.autograd.Function):
     """
-    The trick function of adding auxiliary (aux) loss,
+    The trick function of adding auxiliary (aux) loss, 
     which includes the gradient of the aux loss during backpropagation.
     """
-
     @staticmethod
     def forward(ctx, x, loss):
         assert loss.numel() == 1
@@ -516,53 +356,22 @@ class AddAuxiliaryLoss(torch.autograd.Function):
         if ctx.required_aux_loss:
             grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
         return grad_output, grad_loss
-
-
-class DeepseekV2MoE(nn.Module):
+    
+    
+class DeepseekMoE(nn.Module):
     """
     A mixed expert module containing shared experts.
     """
-
     def __init__(self, config):
         super().__init__()
         self.config = config
         self.num_experts_per_tok = config.num_experts_per_tok
-
-        if hasattr(config, "ep_size") and config.ep_size > 1:
-            assert config.ep_size == dist.get_world_size()
-            self.ep_size = config.ep_size
-            self.experts_per_rank = config.n_routed_experts // config.ep_size
-            self.ep_rank = dist.get_rank()
-            self.experts = nn.ModuleList(
-                [
-                    (
-                        DeepseekV2MLP(
-                            config, intermediate_size=config.moe_intermediate_size
-                        )
-                        if i >= self.ep_rank * self.experts_per_rank
-                        and i < (self.ep_rank + 1) * self.experts_per_rank
-                        else None
-                    )
-                    for i in range(config.n_routed_experts)
-                ]
-            )
-        else:
-            self.ep_size = 1
-            self.experts_per_rank = config.n_routed_experts
-            self.ep_rank = 0
-            self.experts = nn.ModuleList(
-                [
-                    DeepseekV2MLP(config, intermediate_size=config.moe_intermediate_size)
-                    for i in range(config.n_routed_experts)
-                ]
-            )
+        self.experts = nn.ModuleList([DeepseekMLP(config, intermediate_size = config.moe_intermediate_size) for i in range(config.n_routed_experts)])
         self.gate = MoEGate(config)
         if config.n_shared_experts is not None:
             intermediate_size = config.moe_intermediate_size * config.n_shared_experts
-            self.shared_experts = DeepseekV2MLP(
-                config=config, intermediate_size=intermediate_size
-            )
-
+            self.shared_experts = DeepseekMLP(config=config, intermediate_size = intermediate_size)
+    
     def forward(self, hidden_states):
         identity = hidden_states
         orig_shape = hidden_states.shape
@@ -570,96 +379,36 @@ class DeepseekV2MoE(nn.Module):
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
         flat_topk_idx = topk_idx.view(-1)
         if self.training:
-            hidden_states = hidden_states.repeat_interleave(
-                self.num_experts_per_tok, dim=0
-            )
+            hidden_states = hidden_states.repeat_interleave(self.num_experts_per_tok, dim=0)
             y = torch.empty_like(hidden_states)
             for i, expert in enumerate(self.experts):
                 y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
             y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
-            y = y.view(*orig_shape)
+            y =  y.view(*orig_shape)
             y = AddAuxiliaryLoss.apply(y, aux_loss)
         else:
-            y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
+            y = self.moe_infer(hidden_states, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
         if self.config.n_shared_experts is not None:
             y = y + self.shared_experts(identity)
         return y
-
+    
     @torch.no_grad()
-    def moe_infer(self, x, topk_ids, topk_weight):
-        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
-        cnts.scatter_(1, topk_ids, 1)
-        tokens_per_expert = cnts.sum(dim=0)
-        idxs = topk_ids.view(-1).argsort()
-        sorted_tokens = x[idxs // topk_ids.shape[1]]
-        sorted_tokens_shape = sorted_tokens.shape
-        if self.ep_size > 1:
-            tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
-            tokens_per_expert_group = tokens_per_expert.new_empty(
-                tokens_per_expert.shape[0]
-            )
-            dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert)
-            output_splits = (
-                tokens_per_expert_group.view(self.ep_size, -1)
-                .sum(1)
-                .cpu()
-                .numpy()
-                .tolist()
-            )
-            gathered_tokens = sorted_tokens.new_empty(
-                tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1]
-            )
-            input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist()
-            dist.all_to_all(
-                list(gathered_tokens.split(output_splits)),
-                list(sorted_tokens.split(input_split_sizes)),
-            )
-            tokens_per_expert_post_gather = tokens_per_expert_group.view(
-                self.ep_size, self.experts_per_rank
-            ).sum(dim=0)
-            gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
-            s = 0
-            for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
-                gatherd_idxs[s : s + k] = i % self.experts_per_rank
-                s += k
-            gatherd_idxs = gatherd_idxs.argsort()
-            sorted_tokens = gathered_tokens[gatherd_idxs]
-            tokens_per_expert = tokens_per_expert_post_gather
-        tokens_per_expert = tokens_per_expert.cpu().numpy()
-
-        outputs = []
-        start_idx = 0
-        for i, num_tokens in enumerate(tokens_per_expert):
-            end_idx = start_idx + num_tokens
-            if num_tokens == 0:
+    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+        expert_cache = torch.zeros_like(x)
+        idxs = flat_expert_indices.argsort()
+        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+        token_idxs = idxs // self.num_experts_per_tok
+        for i, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if i == 0 else tokens_per_expert[i-1]
+            if start_idx == end_idx:
                 continue
-            expert = self.experts[i + self.ep_rank * self.experts_per_rank]
-            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
-            expert_out = expert(tokens_for_this_expert)
-            outputs.append(expert_out)
-            start_idx = end_idx
-
-        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
-        if self.ep_size > 1:
-            new_x = torch.empty_like(outs)
-            new_x[gatherd_idxs] = outs
-            gathered_tokens = new_x.new_empty(*sorted_tokens_shape)
-            dist.all_to_all(
-                list(gathered_tokens.split(input_split_sizes)),
-                list(new_x.split(output_splits)),
-            )
-            outs = gathered_tokens
-
-        new_x = torch.empty_like(outs)
-        new_x[idxs] = outs
-        final_out = (
-            new_x.view(*topk_ids.shape, -1)
-            .type(topk_weight.dtype)
-            .mul_(topk_weight.unsqueeze(dim=-1))
-            .sum(dim=1)
-            .type(new_x.dtype)
-        )
-        return final_out
+            expert = self.experts[i]
+            exp_token_idx = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idx]
+            expert_out = expert(expert_tokens)
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum')
+        return expert_cache
 
 
 # Copied from transformers.models.llama.modeling_llama.repeat_kv
@@ -671,17 +420,15 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     batch, num_key_value_heads, slen, head_dim = hidden_states.shape
     if n_rep == 1:
         return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(
-        batch, num_key_value_heads, n_rep, slen, head_dim
-    )
+    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.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2
-class DeepseekV2Attention(nn.Module):
+# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->Deepseek
+class DeepseekAttention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
-    def __init__(self, config: DeepseekV2Config, layer_idx: Optional[int] = None):
+    def __init__(self, config: DeepseekConfig, layer_idx: Optional[int] = None):
         super().__init__()
         self.config = config
         self.layer_idx = layer_idx
@@ -695,63 +442,29 @@ class DeepseekV2Attention(nn.Module):
         self.attention_dropout = config.attention_dropout
         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.q_lora_rank = config.q_lora_rank
-        self.qk_rope_head_dim = config.qk_rope_head_dim
-        self.kv_lora_rank = config.kv_lora_rank
-        self.v_head_dim = config.v_head_dim
-        self.qk_nope_head_dim = config.qk_nope_head_dim
-        self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
-
         self.is_causal = True
 
-        if self.q_lora_rank is None:
-            self.q_proj = nn.Linear(
-                self.hidden_size, self.num_heads * self.q_head_dim, bias=False
-            )
-        else:
-            self.q_a_proj = nn.Linear(
-                self.hidden_size, config.q_lora_rank, bias=config.attention_bias
-            )
-            self.q_a_layernorm = DeepseekV2RMSNorm(config.q_lora_rank)
-            self.q_b_proj = nn.Linear(
-                config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
             )
 
-        self.kv_a_proj_with_mqa = nn.Linear(
-            self.hidden_size,
-            config.kv_lora_rank + config.qk_rope_head_dim,
-            bias=config.attention_bias,
-        )
-        self.kv_a_layernorm = DeepseekV2RMSNorm(config.kv_lora_rank)
-        self.kv_b_proj = nn.Linear(
-            config.kv_lora_rank,
-            self.num_heads
-            * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
-            bias=False,
-        )
-
-        self.o_proj = nn.Linear(
-            self.num_heads * self.v_head_dim,
-            self.hidden_size,
-            bias=config.attention_bias,
-        )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
         self._init_rope()
 
-        self.softmax_scale = self.q_head_dim ** (-0.5)
-        if self.config.rope_scaling is not None:
-            mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
-            scaling_factor = self.config.rope_scaling["factor"]
-            if mscale_all_dim:
-                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
-                self.softmax_scale = self.softmax_scale * mscale * mscale
-
     def _init_rope(self):
         if self.config.rope_scaling is None:
-            self.rotary_emb = DeepseekV2RotaryEmbedding(
-                self.qk_rope_head_dim,
+            self.rotary_emb = DeepseekRotaryEmbedding(
+                self.head_dim,
                 max_position_embeddings=self.max_position_embeddings,
                 base=self.rope_theta,
             )
@@ -759,47 +472,24 @@ class DeepseekV2Attention(nn.Module):
             scaling_type = self.config.rope_scaling["type"]
             scaling_factor = self.config.rope_scaling["factor"]
             if scaling_type == "linear":
-                self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
-                    self.qk_rope_head_dim,
+                self.rotary_emb = DeepseekLinearScalingRotaryEmbedding(
+                    self.head_dim,
                     max_position_embeddings=self.max_position_embeddings,
                     scaling_factor=scaling_factor,
                     base=self.rope_theta,
                 )
             elif scaling_type == "dynamic":
-                self.rotary_emb = DeepseekV2DynamicNTKScalingRotaryEmbedding(
-                    self.qk_rope_head_dim,
+                self.rotary_emb = DeepseekDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
                     max_position_embeddings=self.max_position_embeddings,
                     scaling_factor=scaling_factor,
                     base=self.rope_theta,
                 )
-            elif scaling_type == "yarn":
-                kwargs = {
-                    key: self.config.rope_scaling[key]
-                    for key in [
-                        "original_max_position_embeddings",
-                        "beta_fast",
-                        "beta_slow",
-                        "mscale",
-                        "mscale_all_dim",
-                    ]
-                    if key in self.config.rope_scaling
-                }
-                self.rotary_emb = DeepseekV2YarnRotaryEmbedding(
-                    self.qk_rope_head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                    **kwargs,
-                )
             else:
                 raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
 
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return (
-            tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim)
-            .transpose(1, 2)
-            .contiguous()
-        )
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
 
     def forward(
         self,
@@ -815,32 +505,36 @@ class DeepseekV2Attention(nn.Module):
             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()
 
-        if self.q_lora_rank is None:
-            q = self.q_proj(hidden_states)
+        if self.config.pretraining_tp > 1:
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+            )
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+            value_states = torch.cat(value_states, dim=-1)
+
         else:
-            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
-        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
-        q_nope, q_pe = torch.split(
-            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
-        )
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
 
-        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
-        compressed_kv, k_pe = torch.split(
-            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
-        )
-        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
-        kv = (
-            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
-            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
-            .transpose(1, 2)
-        )
+        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)
 
-        k_nope, value_states = torch.split(
-            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
-        )
-        kv_seq_len = value_states.shape[-2]
+        kv_seq_len = key_states.shape[-2]
         if past_key_value is not None:
             if self.layer_idx is None:
                 raise ValueError(
@@ -850,32 +544,23 @@ class DeepseekV2Attention(nn.Module):
                 )
             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)
 
-        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
-
-        query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
-        query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
-
-        key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
-        key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
         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
-            )
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
-        attn_weights = (
-            torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale
-        )
+        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()}"
             )
-        assert attention_mask is not None
+
         if attention_mask is not None:
             if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                 raise ValueError(
@@ -884,25 +569,26 @@ class DeepseekV2Attention(nn.Module):
             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_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.v_head_dim):
+        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.v_head_dim)}, but is"
+                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.num_heads * self.v_head_dim)
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
 
-        attn_output = self.o_proj(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.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+        else:
+            attn_output = self.o_proj(attn_output)
 
         if not output_attentions:
             attn_weights = None
@@ -910,10 +596,10 @@ class DeepseekV2Attention(nn.Module):
         return attn_output, attn_weights, past_key_value
 
 
-# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV2
-class DeepseekV2FlashAttention2(DeepseekV2Attention):
+# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->Deepseek
+class DeepseekFlashAttention2(DeepseekAttention):
     """
-    DeepseekV2 flash attention module. This module inherits from `DeepseekV2Attention` as the weights of the module stays
+    Deepseek flash attention module. This module inherits from `DeepseekAttention` 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.
     """
@@ -936,7 +622,7 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
         use_cache: bool = False,
         **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # DeepseekV2FlashAttention2 attention does not support output_attentions
+        # DeepseekFlashAttention2 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.`"
@@ -949,57 +635,26 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
 
         bsz, q_len, _ = hidden_states.size()
 
-        if self.q_lora_rank is None:
-            q = self.q_proj(hidden_states)
-        else:
-            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
-        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
-        q_nope, q_pe = torch.split(
-            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
-        )
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
 
         # Flash attention requires the input to have the shape
         # batch_size x seq_length x head_dim x hidden_dim
         # therefore we just need to keep the original shape
-        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
-        compressed_kv, k_pe = torch.split(
-            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
-        )
-        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
-        kv = (
-            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
-            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
-            .transpose(1, 2)
-        )
-
-        k_nope, value_states = torch.split(
-            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
-        )
-        kv_seq_len = value_states.shape[-2]
+        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 = value_states.shape[-2]
+        kv_seq_len = key_states.shape[-2]
         if past_key_value is not None:
             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)
-        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
-
-        query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
-        query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
-
-        key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
-        key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
-
-        if self.q_head_dim != self.v_head_dim:
-            value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
+        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
-            )
+            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.
@@ -1013,7 +668,7 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
         # 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. (DeepseekV2RMSNorm handles it correctly)
+        # in fp32. (DeepseekRMSNorm handles it correctly)
 
         input_dtype = query_states.dtype
         if input_dtype == torch.float32:
@@ -1023,7 +678,7 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
             elif torch.is_autocast_enabled():
                 target_dtype = torch.get_autocast_gpu_dtype()
             else:
-                target_dtype = self.q_proj.weight.dtype if self.q_lora_rank is None else self.q_a_proj.weight.dtype
+                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"
@@ -1036,20 +691,10 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
             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,
-            softmax_scale=self.softmax_scale,
+            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
         )
-        if self.q_head_dim != self.v_head_dim:
-            attn_output = attn_output[:, :, :, : self.v_head_dim]
 
-        attn_output = attn_output.reshape(
-            bsz, q_len, self.num_heads * self.v_head_dim
-        ).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
         attn_output = self.o_proj(attn_output)
 
         if not output_attentions:
@@ -1058,14 +703,7 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
         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,
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
     ):
         """
         Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
@@ -1089,20 +727,13 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
         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 DeepseekV2FlashAttention2 __init__.
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekFlashAttention2 __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, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
                 query_states, key_states, value_states, attention_mask, query_length
             )
 
@@ -1122,39 +753,27 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
                 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)
         else:
             attn_output = flash_attn_func(
-                query_states,
-                key_states,
-                value_states,
-                dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
             )
 
         return attn_output
 
-    def _upad_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.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
-            indices_k,
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
         )
         value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
-            indices_k,
+            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.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
-                indices_k,
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
             )
             cu_seqlens_q = cu_seqlens_k
             max_seqlen_in_batch_q = max_seqlen_in_batch_k
@@ -1169,9 +788,7 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
         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(query_layer, attention_mask)
 
         return (
             query_layer,
@@ -1183,36 +800,113 @@ class DeepseekV2FlashAttention2(DeepseekV2Attention):
         )
 
 
-ATTENTION_CLASSES = {
-    "eager": DeepseekV2Attention,
-    "flash_attention_2": DeepseekV2FlashAttention2,
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Deepseek
+class DeepseekSdpaAttention(DeepseekAttention):
+    """
+    Deepseek attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `DeepseekAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from DeepseekAttention.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,
+    ) -> 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(
+                "DeepseekModel is using DeepseekSdpaAttention, 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,
+            )
+
+        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.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)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        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()}"
+                )
+
+        # 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 attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        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)
+
+        return attn_output, None, past_key_value
+
+
+Deepseek_ATTENTION_CLASSES = {
+    "eager": DeepseekAttention,
+    "flash_attention_2": DeepseekFlashAttention2,
+    "sdpa": DeepseekSdpaAttention,
 }
 
 
-class DeepseekV2DecoderLayer(nn.Module):
-    def __init__(self, config: DeepseekV2Config, layer_idx: int):
+class DeepseekDecoderLayer(nn.Module):
+    def __init__(self, config: DeepseekConfig, layer_idx: int):
         super().__init__()
         self.hidden_size = config.hidden_size
 
-        self.self_attn = ATTENTION_CLASSES[config._attn_implementation](
-            config=config, layer_idx=layer_idx
-        )
+        self.self_attn = Deepseek_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
 
-        self.mlp = (
-            DeepseekV2MoE(config)
-            if (
-                config.n_routed_experts is not None
-                and layer_idx >= config.first_k_dense_replace
-                and layer_idx % config.moe_layer_freq == 0
-            )
-            else DeepseekV2MLP(config)
-        )
-        self.input_layernorm = DeepseekV2RMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps
-        )
-        self.post_attention_layernorm = DeepseekV2RMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps
-        )
+        self.mlp = DeepseekMoE(config) if (config.n_routed_experts is not None and  \
+                                           layer_idx >= config.first_k_dense_replace and layer_idx % config.moe_layer_freq == 0) \
+                                        else DeepseekMLP(config)
+        self.input_layernorm = DeepseekRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = DeepseekRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
     def forward(
         self,
@@ -1223,9 +917,7 @@ class DeepseekV2DecoderLayer(nn.Module):
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         **kwargs,
-    ) -> Tuple[
-        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
-    ]:
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
         """
         Args:
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
@@ -1277,7 +969,7 @@ class DeepseekV2DecoderLayer(nn.Module):
         return outputs
 
 
-DeepseekV2_START_DOCSTRING = r"""
+Deepseek_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.)
@@ -1287,7 +979,7 @@ DeepseekV2_START_DOCSTRING = r"""
     and behavior.
 
     Parameters:
-        config ([`DeepseekV2Config`]):
+        config ([`DeepseekConfig`]):
             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.
@@ -1295,16 +987,17 @@ DeepseekV2_START_DOCSTRING = r"""
 
 
 @add_start_docstrings(
-    "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
-    DeepseekV2_START_DOCSTRING,
+    "The bare Deepseek Model outputting raw hidden-states without any specific head on top.",
+    Deepseek_START_DOCSTRING,
 )
-class DeepseekV2PreTrainedModel(PreTrainedModel):
-    config_class = DeepseekV2Config
+class DeepseekPreTrainedModel(PreTrainedModel):
+    config_class = DeepseekConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
-    _no_split_modules = ["DeepseekV2DecoderLayer"]
+    _no_split_modules = ["DeepseekDecoderLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn_2 = True
+    _supports_sdpa = True
     _supports_cache_class = True
 
     def _init_weights(self, module):
@@ -1319,7 +1012,7 @@ class DeepseekV2PreTrainedModel(PreTrainedModel):
                 module.weight.data[module.padding_idx].zero_()
 
 
-DeepseekV2_INPUTS_DOCSTRING = r"""
+Deepseek_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
@@ -1390,33 +1083,29 @@ DeepseekV2_INPUTS_DOCSTRING = r"""
 
 
 @add_start_docstrings(
-    "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
-    DeepseekV2_START_DOCSTRING,
+    "The bare Deepseek Model outputting raw hidden-states without any specific head on top.",
+    Deepseek_START_DOCSTRING,
 )
-class DeepseekV2Model(DeepseekV2PreTrainedModel):
+class DeepseekModel(DeepseekPreTrainedModel):
     """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekDecoderLayer`]
 
     Args:
-        config: DeepseekV2Config
+        config: DeepseekConfig
     """
 
-    def __init__(self, config: DeepseekV2Config):
+    def __init__(self, config: DeepseekConfig):
         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.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
         self.layers = nn.ModuleList(
-            [
-                DeepseekV2DecoderLayer(config, layer_idx)
-                for layer_idx in range(config.num_hidden_layers)
-            ]
+            [DeepseekDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
+        self._use_sdpa = config._attn_implementation == "sdpa"
         self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-        self.norm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.norm = DeepseekRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
         self.gradient_checkpointing = False
         # Initialize weights and apply final processing
@@ -1428,7 +1117,7 @@ class DeepseekV2Model(DeepseekV2PreTrainedModel):
     def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(Deepseek_INPUTS_DOCSTRING)
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1441,27 +1130,17 @@ class DeepseekV2Model(DeepseekV2PreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
+        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
+            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
-        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         # retrieve input_ids and inputs_embeds
         if input_ids is not None and inputs_embeds is not None:
-            raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time"
-            )
+            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:
@@ -1486,10 +1165,7 @@ class DeepseekV2Model(DeepseekV2PreTrainedModel):
         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,
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
             )
             position_ids = position_ids.unsqueeze(0)
 
@@ -1498,19 +1174,21 @@ class DeepseekV2Model(DeepseekV2PreTrainedModel):
 
         if self._use_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:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._use_sdpa and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                 attention_mask,
                 (batch_size, seq_length),
                 inputs_embeds,
                 past_key_values_length,
             )
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
 
         # embed positions
         hidden_states = inputs_embeds
@@ -1560,17 +1238,9 @@ class DeepseekV2Model(DeepseekV2PreTrainedModel):
 
         next_cache = None
         if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache()
-                if use_legacy_cache
-                else next_decoder_cache
-            )
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
         if not return_dict:
-            return tuple(
-                v
-                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
-                if v is not None
-            )
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=next_cache,
@@ -1579,12 +1249,12 @@ class DeepseekV2Model(DeepseekV2PreTrainedModel):
         )
 
 
-class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
+class DeepseekForCausalLM(DeepseekPreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
         super().__init__(config)
-        self.model = DeepseekV2Model(config)
+        self.model = DeepseekModel(config)
         self.vocab_size = config.vocab_size
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
 
@@ -1609,10 +1279,8 @@ class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
     def get_decoder(self):
         return self.model
 
-    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(
-        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-    )
+    @add_start_docstrings_to_model_forward(Deepseek_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1638,9 +1306,9 @@ class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
         Example:
 
         ```python
-        >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
+        >>> from transformers import AutoTokenizer, DeepseekForCausalLM
 
-        >>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> model = DeepseekForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
         >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
 
         >>> prompt = "Hey, are you conscious? Can you talk to me?"
@@ -1651,19 +1319,11 @@ class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
         >>> 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_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
+            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(
@@ -1679,7 +1339,12 @@ class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
         )
 
         hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
+        if self.config.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.lm_head(hidden_states)
         logits = logits.float()
 
         loss = None
@@ -1708,12 +1373,7 @@ class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
         )
 
     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, **kwargs
     ):
         if past_key_values is not None:
             if isinstance(past_key_values, Cache):
@@ -1728,10 +1388,7 @@ class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
             # 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 exclusivelly 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]
-            ):
+            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.
@@ -1776,19 +1433,16 @@ class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
         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
-                ),
+                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 DeepseekV2 Model transformer with a sequence classification head on top (linear layer).
+    The Deepseek Model transformer with a sequence classification head on top (linear layer).
 
-    [`DeepseekV2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    [`DeepseekForSequenceClassification`] 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
@@ -1797,13 +1451,13 @@ class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
     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).
     """,
-    DeepseekV2_START_DOCSTRING,
+    Deepseek_START_DOCSTRING,
 )
-class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
+class DeepseekForSequenceClassification(DeepseekPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
-        self.model = DeepseekV2Model(config)
+        self.model = DeepseekModel(config)
         self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
 
         # Initialize weights and apply final processing
@@ -1815,7 +1469,7 @@ class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
     def set_input_embeddings(self, value):
         self.model.embed_tokens = value
 
-    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(Deepseek_INPUTS_DOCSTRING)
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1835,9 +1489,7 @@ class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
             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
-        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         transformer_outputs = self.model(
             input_ids,
@@ -1859,22 +1511,18 @@ class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
             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."
-            )
+            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:
-                sequence_lengths = (
-                    torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                ).to(logits.device)
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+                    logits.device
+                )
             else:
                 sequence_lengths = -1
 
-        pooled_logits = logits[
-            torch.arange(batch_size, device=logits.device), sequence_lengths
-        ]
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
 
         loss = None
         if labels is not None:
@@ -1882,9 +1530,7 @@ class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
             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
-                ):
+                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"
@@ -1897,9 +1543,7 @@ class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
                     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)
-                )
+                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)
@@ -1913,4 +1557,4 @@ class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
             past_key_values=transformer_outputs.past_key_values,
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
-        )
+        )