update rope calculation; update modeling.py; update gate for moe

ktransformers/configs/config.yaml

@@ -54,4 +54,4 @@ long_context:
   token_step: 
 
 local_chat:
-  prompt_file: "./ktransformers/p.txt"
+  prompt_file: ""

ktransformers/local_chat.py

@@ -15,7 +15,7 @@ from ktransformers.server.args import ArgumentParser
 
 
 from ktransformers.models.modeling_deepseek import DeepseekV2ForCausalLM
-from ktransformers.models.modeling_deepseekv3 import DeepseekV3ForCausalLM
+from ktransformers.models.modeling_deepseek_v3 import DeepseekV3ForCausalLM
 from ktransformers.models.modeling_qwen2_moe import Qwen2MoeForCausalLM
 from ktransformers.models.modeling_llama import LlamaForCausalLM
 from ktransformers.models.modeling_mixtral import MixtralForCausalLM
@@ -78,7 +78,7 @@ def local_chat():
                 else:
                     content += line + "\n"
         if content == "":
-            if config.prompt_file == None or config.prompt_file == "":
+            if not config.prompt_file:
                 content = "hi"
             else:
                 content = open(config.prompt_file, "r").read()

ktransformers/models/configuration_deepseek_v3.py

@@ -14,19 +14,25 @@
 # 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.
-""" DeepSeekV3 model configuration """
+"""DeepSeekV3 model configuration"""
 
 from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
 
 
 DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
 class DeepseekV3Config(PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
     model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
     defaults will yield a similar configuration to that of the DeepSeek-V3.
+
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
+
+
     Args:
         vocab_size (`int`, *optional*, defaults to 129280):
             Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
@@ -39,8 +45,6 @@ class DeepseekV3Config(PretrainedConfig):
             Dimension of the MoE representations.
         num_hidden_layers (`int`, *optional*, defaults to 61):
             Number of hidden layers in the Transformer decoder.
-        num_nextn_predict_layers (`int`, *optional*, defaults to 1):
-            Number of nextn predict layers in the DeepSeekV3 Model.
         num_attention_heads (`int`, *optional*, defaults to 128):
             Number of attention heads for each attention layer in the Transformer decoder.
         num_key_value_heads (`int`, *optional*, defaults to 128):
@@ -52,38 +56,35 @@ class DeepseekV3Config(PretrainedConfig):
             paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
             `num_attention_heads`.
         n_shared_experts (`int`, *optional*, defaults to 1):
-            Number of shared experts, None means dense model.
+            Number of shared experts.
         n_routed_experts (`int`, *optional*, defaults to 256):
-            Number of routed experts, None means dense model.
-        ep_size (`<fill_type>`, *optional*, defaults to 1): <fill_docstring>
+            Number of routed experts.
         routed_scaling_factor (`float`, *optional*, defaults to 2.5):
             Scaling factor or routed experts.
-        kv_lora_rank (`<fill_type>`, *optional*, defaults to 512): <fill_docstring>
-        q_lora_rank (`<fill_type>`, *optional*, defaults to 1536): <fill_docstring>
-        qk_rope_head_dim (`<fill_type>`, *optional*, defaults to 64): <fill_docstring>
-        v_head_dim (`<fill_type>`, *optional*, defaults to 128): <fill_docstring>
-        qk_nope_head_dim (`<fill_type>`, *optional*, defaults to 128): <fill_docstring>
-        topk_method (`str`, *optional*, defaults to `"noaux_tc"`):
-            Topk method used in routed gate.
+        kv_lora_rank (`int`, *optional*, defaults to 512):
+            Rank of the LoRA matrices for key and value projections.
+        q_lora_rank (`int`, *optional*, defaults to 1536):
+            Rank of the LoRA matrices for query projections.
+        qk_rope_head_dim (`int`, *optional*, defaults to 64):
+            Dimension of the query/key heads that use rotary position embeddings.
+        v_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of the value heads.
+        qk_nope_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of the query/key heads that don't use rotary position embeddings.
         n_group (`int`, *optional*, defaults to 8):
             Number of groups for routed experts.
         topk_group (`int`, *optional*, defaults to 4):
             Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
         num_experts_per_tok (`int`, *optional*, defaults to 8):
             Number of selected experts, None means dense model.
-        moe_layer_freq (`int`, *optional*, defaults to 1):
-            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
         first_k_dense_replace (`int`, *optional*, defaults to 3):
             Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                             \--k dense layers--/
         norm_topk_prob (`bool`, *optional*, defaults to `True`):
             Whether to normalize the weights of the routed experts.
-        scoring_func (`str`, *optional*, defaults to `"sigmoid"`):
-            Method of computing expert weights.
         aux_loss_alpha (`float`, *optional*, defaults to 0.001):
             Auxiliary loss weight coefficient.
             Whether to compute the auxiliary loss for each individual sample.
-        seq_aux (`<fill_type>`, *optional*, defaults to `True`): <fill_docstring>
         hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
             The non-linear activation function (function or string) in the decoder.
         max_position_embeddings (`int`, *optional*, defaults to 4096):
@@ -119,46 +120,49 @@ class DeepseekV3Config(PretrainedConfig):
             Whether to use a bias in the query, key, value and output projection layers during self-attention.
         attention_dropout (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
+
     ```python
     >>> from transformers import DeepseekV3Model, DeepseekV3Config
+
     >>> # Initializing a Deepseek-V3 style configuration
     >>> configuration = DeepseekV3Config()
+
     >>> # Accessing the model configuration
     >>> configuration = model.config
     ```"""
 
     model_type = "deepseek_v3"
     keys_to_ignore_at_inference = ["past_key_values"]
+    # Default tensor parallel plan for base model `DeepseekV3Model`
+    base_model_tp_plan = {
+        "layers.*.gate_proj": "colwise",
+        "layers.*.up_proj": "colwise",
+        "layers.*.down_proj": "rowwise",
+    }
 
     def __init__(
         self,
         vocab_size=129280,
         hidden_size=7168,
         intermediate_size=18432,
-        moe_intermediate_size = 2048,
+        moe_intermediate_size=2048,
         num_hidden_layers=61,
-        num_nextn_predict_layers=1,
         num_attention_heads=128,
         num_key_value_heads=128,
-        n_shared_experts = 1,
-        n_routed_experts = 256,
-        ep_size = 1,
-        routed_scaling_factor = 2.5,
-        kv_lora_rank = 512,
-        q_lora_rank = 1536,
-        qk_rope_head_dim = 64,
-        v_head_dim = 128,
-        qk_nope_head_dim = 128,
-        topk_method = 'noaux_tc',
-        n_group = 8,
-        topk_group = 4,
-        num_experts_per_tok = 8,
-        moe_layer_freq = 1,
-        first_k_dense_replace = 3,
-        norm_topk_prob = True,
-        scoring_func = 'sigmoid',
-        aux_loss_alpha = 0.001,
-        seq_aux = True,
+        n_shared_experts=1,
+        n_routed_experts=256,
+        routed_scaling_factor=2.5,
+        kv_lora_rank=512,
+        q_lora_rank=1536,
+        qk_rope_head_dim=64,
+        v_head_dim=128,
+        qk_nope_head_dim=128,
+        n_group=8,
+        topk_group=4,
+        num_experts_per_tok=8,
+        first_k_dense_replace=3,
+        norm_topk_prob=True,
+        aux_loss_alpha=0.001,
         hidden_act="silu",
         max_position_embeddings=4096,
         initializer_range=0.02,
@@ -173,7 +177,6 @@ class DeepseekV3Config(PretrainedConfig):
         rope_scaling=None,
         attention_bias=False,
         attention_dropout=0.0,
-        mlp_bias=False,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -182,27 +185,24 @@ class DeepseekV3Config(PretrainedConfig):
         self.intermediate_size = intermediate_size
         self.moe_intermediate_size = moe_intermediate_size
         self.num_hidden_layers = num_hidden_layers
-        self.num_nextn_predict_layers = num_nextn_predict_layers
         self.num_attention_heads = num_attention_heads
         self.n_shared_experts = n_shared_experts
         self.n_routed_experts = n_routed_experts
-        self.ep_size = ep_size
         self.routed_scaling_factor = routed_scaling_factor
         self.kv_lora_rank = kv_lora_rank
         self.q_lora_rank = q_lora_rank
         self.qk_rope_head_dim = qk_rope_head_dim
         self.v_head_dim = v_head_dim
         self.qk_nope_head_dim = qk_nope_head_dim
-        self.topk_method = topk_method
+        self.q_head_dim = qk_nope_head_dim + qk_rope_head_dim
+        self.head_dim = qk_rope_head_dim
         self.n_group = n_group
         self.topk_group = topk_group
         self.num_experts_per_tok = num_experts_per_tok
-        self.moe_layer_freq = moe_layer_freq
         self.first_k_dense_replace = first_k_dense_replace
         self.norm_topk_prob = norm_topk_prob
-        self.scoring_func = scoring_func
         self.aux_loss_alpha = aux_loss_alpha
-        self.seq_aux = seq_aux
+
         # for backward compatibility
         if num_key_value_heads is None:
             num_key_value_heads = num_attention_heads
@@ -217,7 +217,11 @@ class DeepseekV3Config(PretrainedConfig):
         self.rope_scaling = rope_scaling
         self.attention_bias = attention_bias
         self.attention_dropout = attention_dropout
-        self.mlp_bias = mlp_bias
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, copy it it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
 
         super().__init__(
             pad_token_id=pad_token_id,

ktransformers/models/custom_cache.py

@@ -135,3 +135,7 @@ class StaticCache(transformers.StaticCache):
             # In-place ops prevent breaking the static address
             self.key_cache[layer_idx].zero_()
             self.value_cache[layer_idx].zero_()
+    
+    def get_max_cache_shape(self) -> Tuple[int, int, int, int]:
+        """Returns the maximum shape of the cache."""
+        return self.max_cache_len

ktransformers/models/modeling_deepseek_v3.py

@@ -1,15 +1,13 @@
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           This file was automatically generated from src/transformers/models/deepseekv3/modular_deepseekv3.py.
+#           This file was automatically generated from src/transformers/models/deepseek_v3/modular_deepseek_v3.py.
 #               Do NOT edit this file manually as any edits will be overwritten by the generation of
 #             the file from the modular. If any change should be done, please apply the change to the
-#                          modular_deepseekv3.py file directly. One of our CI enforces this.
+#                          modular_deepseek_v3.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 import math
 from typing import Callable, List, Optional, Tuple, Union
 
-import numpy as np
 import torch
-import torch.distributed as dist
 import torch.nn.functional as F
 from torch import nn
 
@@ -30,7 +28,7 @@ from transformers.utils import (
     replace_return_docstrings,
 )
 from transformers.utils.deprecation import deprecate_kwarg
-from .configuration_deepseekv3 import DeepseekV3Config
+from .configuration_deepseek_v3 import DeepseekV3Config
 
 
 logger = logging.get_logger(__name__)
@@ -119,15 +117,15 @@ class DeepseekV3RotaryEmbedding(nn.Module):
 
 
 class DeepseekV3MLP(nn.Module):
-    def __init__(self, config):
+    def __init__(self, config, hidden_size=None, intermediate_size=None):
         super().__init__()
         self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = config.moe_intermediate_size
-        # TODO rm hard coding
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)# config.mlp_bias)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)# config.mlp_bias)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)# config.mlp_bias)
+        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.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
         self.act_fn = ACT2FN[config.hidden_act]
 
     def forward(self, x):
@@ -135,70 +133,46 @@ class DeepseekV3MLP(nn.Module):
         return down_proj
 
 
-class MoEGate(nn.Module):
+class DeepseekV3TopkRouter(nn.Module):
     def __init__(self, config):
         super().__init__()
         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.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)))
-        if self.topk_method == "noaux_tc":
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
         self.e_score_correction_bias = nn.Parameter(torch.empty((self.n_routed_experts)))
-        self.reset_parameters()
-
-    def reset_parameters(self) -> None:
-        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
-        ### 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 == "sigmoid":
-            scores = logits.sigmoid()
-        else:
-            raise NotImplementedError(f"insupportable scoring function for MoE gating: {self.scoring_func}")
+        batch_size, seq_length = hidden_states.shape[:-1]
+        hidden_states = hidden_states.view(-1, self.config.hidden_size)
+        router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
 
-        ### select top-k experts
-        if self.topk_method == "noaux_tc":
-            # assert not self.training
-            scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0)
+        scores = router_logits.sigmoid()
+        scores_for_choice = scores.view(-1, self.n_routed_experts) + self.e_score_correction_bias.unsqueeze(0)
         group_scores = (
-                scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim=-1)
+            scores_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group)
+            .topk(2, dim=-1)[0]
+            .sum(dim=-1)
         )  # [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)
+            .expand(batch_size * seq_length, self.n_group, self.n_routed_experts // self.n_group)
+            .reshape(-1, self.n_routed_experts)
         )  # [n, e]
-            tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
-            _, topk_idx = torch.topk(tmp_scores, k=self.top_k, dim=-1, sorted=False)
-            topk_weight = scores.gather(1, topk_idx)
-        else:
-            raise NotImplementedError(f"insupportable TopK function for MoE gating: {self.topk_method}")
-
-        ### 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
-        topk_weight = topk_weight * self.routed_scaling_factor  # must multiply the scaling factor
-
-        return topk_idx, topk_weight
+        scores_for_choice = scores_for_choice.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
+        _, topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)
+        topk_weights = scores.gather(1, topk_indices)
+        denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
+        topk_weights /= denominator
+        topk_weights = topk_weights * self.routed_scaling_factor  # must multiply the scaling factor
+        return topk_indices, topk_weights, router_logits
 
 
 class DeepseekV3MoE(nn.Module):
@@ -209,116 +183,75 @@ class DeepseekV3MoE(nn.Module):
     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(
             [
-                    (
                 DeepseekV3MLP(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)
+                for _ 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(
-                [
-                    DeepseekV3MLP(config)
-                    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 = DeepseekV3MLP(config=config)
+        self.gate = DeepseekV3TopkRouter(config)
+        self.shared_experts = DeepseekV3MLP(config=config, intermediate_size=config.moe_intermediate_size)
 
     def forward(self, hidden_states):
-        identity = hidden_states
+        residuals = hidden_states
         orig_shape = hidden_states.shape
-        topk_idx, topk_weight = self.gate(hidden_states)
+        topk_indices, topk_weights, router_logits = self.gate(hidden_states)
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        if not self.training:
-            y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
-        if self.config.n_shared_experts is not None:
-            y = y + self.shared_experts(identity)
-        return y
+        hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape)
+        hidden_states = hidden_states + self.shared_experts(residuals)
+        return hidden_states, router_logits
 
-    @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()
+    def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor):
+        final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype)
+        expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=len(self.experts))
+        expert_mask = expert_mask.permute(2, 0, 1)
 
-        outputs = []
-        start_idx = 0
-        for i, num_tokens in enumerate(tokens_per_expert):
-            end_idx = start_idx + num_tokens
-            if num_tokens == 0:
-                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
+        for expert_idx in range(len(self.experts)):
+            expert = self.experts[expert_idx]
+            mask = expert_mask[expert_idx]
+            token_indices, weight_indices = torch.where(mask)
 
-        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
+            if token_indices.numel() > 0:
+                expert_weights = topk_weights[token_indices, weight_indices]
+                expert_input = hidden_states[token_indices]
+                expert_output = expert(expert_input)
+                weighted_output = expert_output * expert_weights.unsqueeze(-1)
+                final_hidden_states.index_add_(0, token_indices, weighted_output)
+        return final_hidden_states.type(hidden_states.dtype)
 
-        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
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
 
 
 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
@@ -359,150 +292,94 @@ def eager_attention_forward(
     return attn_output, attn_weights
 
 
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`, *optional*):
-            Deprecated and unused.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
+def yarn_get_mscale(scale=1, mscale=1):
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
 
 
 class DeepseekV3Attention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
-    def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None):
+    def __init__(self, config: DeepseekV3Config, layer_idx: int):
         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 `layer_idx` is not recommended and will "
-                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
         self.attention_dropout = config.attention_dropout
-        self.hidden_size = config.hidden_size
         self.num_heads = config.num_attention_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.q_head_dim = config.qk_rope_head_dim + config.qk_nope_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_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
         self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank)
         self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False)
 
         self.kv_a_proj_with_mqa = nn.Linear(
-            self.hidden_size,
-            config.kv_lora_rank + config.qk_rope_head_dim,
+            config.hidden_size,
+            self.kv_lora_rank + self.qk_rope_head_dim,
             bias=config.attention_bias,
         )
-        self.kv_a_layernorm = DeepseekV3RMSNorm(config.kv_lora_rank)
+        self.kv_a_layernorm = DeepseekV3RMSNorm(self.kv_lora_rank)
         self.kv_b_proj = nn.Linear(
-            config.kv_lora_rank,
+            self.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,
+            config.hidden_size,
             bias=config.attention_bias,
         )
 
-        self.rotary_emb = DeepseekV3RotaryEmbedding(
-            config=self.config,
-        )
+        self.scaling = 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.scaling = self.scaling * mscale * mscale
+        # TODO apply in DeepSeekV3Model to share accrose layers
+        self.rotary_emb = DeepseekV3RotaryEmbedding(config=config)
 
     def forward(
         self,
         hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
         attention_mask: Optional[torch.Tensor],
-        position_ids: Optional[torch.LongTensor] = None,
         past_key_value: Optional[Cache] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs# : Unpack[FlashAttentionKwargs],
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, self.num_heads, -1)
 
-        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)
+        q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))).view(hidden_shape).transpose(1, 2)
+        q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
 
         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_pass, k_rot = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
 
-        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]
-        if past_key_value is not None:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        k_pass = self.kv_b_proj(self.kv_a_layernorm(k_pass)).view(hidden_shape).transpose(1, 2)
+        k_pass, value_states = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
 
-        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
+        k_rot = k_rot.view(*input_shape, 1, self.qk_rope_head_dim).transpose(1, 2)
 
-        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
+        cos, sin = position_embeddings
+        q_rot, k_rot = apply_rotary_pos_emb(q_rot, k_rot, cos, sin)
+        k_rot = k_rot.expand(-1, self.num_heads, -1, -1)
 
-        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
+        query_states = torch.cat((q_pass, q_rot), dim=-1)
+        key_states = torch.cat((k_pass, k_rot), dim=-1)
 
-        if self.q_head_dim != self.v_head_dim:
+        if self.config._attn_implementation == "flash_attention_2" and self.q_head_dim != self.v_head_dim:
             value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
 
         if past_key_value is not None:
@@ -518,8 +395,11 @@ class DeepseekV3Attention(nn.Module):
                     'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
                 )
             else:
-                pass
-                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+                raise NotImplementedError(
+                    f"Attention implementation {self.config._attn_implementation} is not supported. "
+                    "Please use 'eager' or 'sdpa'."
+                )
+                # attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
 
         attn_output, attn_weights = attention_interface(
             self,
@@ -531,9 +411,12 @@ class DeepseekV3Attention(nn.Module):
             scaling=self.scaling,
             **kwargs,
         )
-        attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
-        attn_output = self.o_proj(attn_output)
 
+        if self.config._attn_implementation == "flash_attention_2" and self.q_head_dim != self.v_head_dim:
+            attn_output = attn_output[:, :, :, : self.v_head_dim]
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
         return attn_output, attn_weights
 
 
@@ -544,15 +427,11 @@ class DeepseekV3DecoderLayer(nn.Module):
 
         self.self_attn = DeepseekV3Attention(config=config, layer_idx=layer_idx)
 
-        self.mlp = (
-            DeepseekV3MoE(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 DeepseekV3MLP(config)
-        )
+        if layer_idx >= config.first_k_dense_replace:
+            self.mlp = DeepseekV3MoE(config)
+        else:
+            self.mlp = DeepseekV3MLP(config)
+
         self.input_layernorm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.post_attention_layernorm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
@@ -563,6 +442,7 @@ class DeepseekV3DecoderLayer(nn.Module):
         position_ids: Optional[torch.LongTensor] = None,
         past_key_value: Optional[Cache] = None,
         output_attentions: Optional[bool] = False,
+        output_router_logits: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
         position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
@@ -590,16 +470,24 @@ class DeepseekV3DecoderLayer(nn.Module):
         residual = hidden_states
         hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
+
+        if isinstance(hidden_states, tuple):
+            hidden_states, router_logits = hidden_states
+        else:
+            router_logits = (torch.zeros((1,), device=hidden_states.device, dtype=torch.int64),)
+
         hidden_states = residual + hidden_states
 
         outputs = (hidden_states,)
         if output_attentions:
             outputs += (self_attn_weights,)
+        if output_router_logits:
+            outputs += (router_logits,)
 
         return outputs
 
 
-DEEPSEEKV3_START_DOCSTRING = r"""
+DEEPSEEK_V3_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.)
@@ -618,7 +506,7 @@ DEEPSEEKV3_START_DOCSTRING = r"""
 
 @add_start_docstrings(
     "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
-    DEEPSEEKV3_START_DOCSTRING,
+    DEEPSEEK_V3_START_DOCSTRING,
 )
 class DeepseekV3PreTrainedModel(PreTrainedModel):
     config_class = DeepseekV3Config
@@ -646,7 +534,7 @@ class DeepseekV3PreTrainedModel(PreTrainedModel):
                 module.weight.data[module.padding_idx].zero_()
 
 
-DEEPSEEKV3_INPUTS_DOCSTRING = r"""
+DEEPSEEK_V3_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
@@ -723,7 +611,7 @@ DEEPSEEKV3_INPUTS_DOCSTRING = r"""
 
 @add_start_docstrings(
     "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
-    DEEPSEEKV3_START_DOCSTRING,
+    DEEPSEEK_V3_START_DOCSTRING,
 )
 class DeepseekV3Model(DeepseekV3PreTrainedModel):
     """
@@ -733,7 +621,7 @@ class DeepseekV3Model(DeepseekV3PreTrainedModel):
         config: DeepseekV3Config
     """
 
-    def __init__(self, config: DeepseekV3Config):
+    def __init__(self, config):
         super().__init__(config)
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
@@ -745,6 +633,7 @@ class DeepseekV3Model(DeepseekV3PreTrainedModel):
         self.norm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.rotary_emb = DeepseekV3RotaryEmbedding(config=config)
         self.gradient_checkpointing = False
+        self._register_load_state_dict_pre_hook(self.load_hook)
 
         # Initialize weights and apply final processing
         self.post_init()
@@ -755,7 +644,7 @@ class DeepseekV3Model(DeepseekV3PreTrainedModel):
     def set_input_embeddings(self, value):
         self.embed_tokens = value
 
-    @add_start_docstrings_to_model_forward(DEEPSEEKV3_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(DEEPSEEK_V3_INPUTS_DOCSTRING)
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -983,6 +872,49 @@ class DeepseekV3Model(DeepseekV3PreTrainedModel):
 
         return causal_mask
 
+    def load_hook(self, state_dict, prefix, *args):
+        """
+        Weights have to be permuted for correct rope formulation. We can't do this in the weights
+        as every other framework already uses the `Llama` original function (which is copyrighted btw).
+        And I am not even sure it's better.... anyways end of my rant
+        """
+
+        def permute_for_rope(input_tensor):
+            """
+            When you go from the complex ROPE formulation to sin and cos one, you need
+            to permute the query and key weights (to avoid doing it on the fly)
+            """
+            n_heads, dim1, dim2 = input_tensor.shape[0], input_tensor.shape[1], input_tensor.shape[2]
+            input_tensor = input_tensor.reshape(n_heads * dim1, dim2)
+            input_tensor = input_tensor.view(n_heads, dim1 // 2, 2, dim2)
+            input_tensor = input_tensor.transpose(1, 2).reshape(n_heads, dim1, dim2)
+            return input_tensor
+
+        def permute_layer_for_rope(key, num_heads, head_dim, rope_dim):
+            weight = state_dict[key]
+            weight = weight.view(num_heads, head_dim, -1)
+            weight_rot = weight[:, -rope_dim:]
+            weight_rot = permute_for_rope(weight_rot)
+            weight[:, -rope_dim:] = weight_rot
+            weight = weight.view(-1, weight.shape[-1])
+            state_dict[key] = weight
+
+        for k in state_dict:
+            if "q_b_proj." in k:
+                permute_layer_for_rope(
+                    k,
+                    num_heads=self.config.num_attention_heads,
+                    head_dim=self.config.q_head_dim,
+                    rope_dim=self.config.qk_rope_head_dim,
+                )
+            if "kv_a_proj_with_mqa." in k:
+                permute_layer_for_rope(
+                    k,
+                    num_heads=1,
+                    head_dim=self.config.kv_lora_rank + self.config.qk_rope_head_dim,
+                    rope_dim=self.config.qk_rope_head_dim,
+                )
+
 
 # class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
 
@@ -1019,7 +951,7 @@ class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel, GenerationMixin):
         return self.model
 
     @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-    @add_start_docstrings_to_model_forward(DEEPSEEKV3_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(DEEPSEEK_V3_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1058,8 +990,8 @@ class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel, GenerationMixin):
         ```python
         >>> from transformers import AutoTokenizer, DeepseekV3ForCausalLM
 
-        >>> model = DeepseekV3ForCausalLM.from_pretrained("meta-deepseekv3/DeepseekV3-2-7b-hf")
-        >>> tokenizer = AutoTokenizer.from_pretrained("meta-deepseekv3/DeepseekV3-2-7b-hf")
+        >>> model = DeepseekV3ForCausalLM.from_pretrained("meta-deepseek_v3/DeepseekV3-2-7b-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("meta-deepseek_v3/DeepseekV3-2-7b-hf")
 
         >>> prompt = "Hey, are you conscious? Can you talk to me?"
         >>> inputs = tokenizer(prompt, return_tensors="pt")
@@ -1125,7 +1057,7 @@ class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel, GenerationMixin):
     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).
     """,
-    DEEPSEEKV3_START_DOCSTRING,
+    DEEPSEEK_V3_START_DOCSTRING,
 )
 class DeepseekV3ForSequenceClassification(DeepseekV3PreTrainedModel):
     def __init__(self, config):
@@ -1143,7 +1075,7 @@ class DeepseekV3ForSequenceClassification(DeepseekV3PreTrainedModel):
     def set_input_embeddings(self, value):
         self.model.embed_tokens = value
 
-    @add_start_docstrings_to_model_forward(DEEPSEEKV3_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(DEEPSEEK_V3_INPUTS_DOCSTRING)
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
@@ -1214,3 +1146,11 @@ class DeepseekV3ForSequenceClassification(DeepseekV3PreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+
+__all__ = [
+    "DeepseekV3PreTrainedModel",
+    "DeepseekV3Model",
+    "DeepseekV3ForCausalLM",
+    "DeepseekV3ForSequenceClassification",
+]

ktransformers/operators/attention.py

@@ -13,7 +13,8 @@ from ktransformers.models.configuration_deepseek import DeepseekV2Config
 from ktransformers.models.configuration_llama import LlamaConfig
 from ktransformers.models.modeling_llama import LlamaRotaryEmbedding
 from ktransformers.models.modeling_deepseek import DeepseekV2Attention, apply_rotary_pos_emb
-from ktransformers.models.modeling_deepseekv3 import DeepseekV3Attention, apply_rotary_pos_emb
+from ktransformers.models.modeling_deepseek_v3 import DeepseekV3Attention
+from ktransformers.models.modeling_deepseek_v3 import apply_rotary_pos_emb as apply_rotary_pos_emb_v3
 from typing import Optional, Tuple
 from ktransformers.operators.base_operator import BaseInjectedModule
 from ktransformers.util.custom_gguf import GGUFLoader
@@ -95,7 +96,7 @@ class KDeepseekV3Attention(BaseInjectedModule, DeepseekV3Attention):
             kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
 
         cos, sin = self.rotary_emb(q_pe, position_ids)
-        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin)
+        q_pe, k_pe = apply_rotary_pos_emb_v3(q_pe, k_pe, cos, sin)
 
         if past_key_value is not None:
             cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models

ktransformers/operators/experts.py

@@ -519,7 +519,7 @@ class KTransformersExperts(BaseInjectedModule, KExpertsBase):
 
 
 from ktransformers.models.modeling_deepseek import DeepseekV2MoE
-from ktransformers.models.modeling_deepseekv3 import DeepseekV3MoE
+from ktransformers.models.modeling_deepseek_v3 import DeepseekV3MoE
 from ktransformers.models.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
 from ktransformers.models.modeling_mixtral import MixtralSparseMoeBlock
 
@@ -734,9 +734,10 @@ class KDeepseekV3MoE(BaseInjectedModule, DeepseekV3MoE):
         identity = hidden_states
         orig_shape = hidden_states.shape
         sequence_length = orig_shape[1]
-        topk_idx, topk_weight= self.gate(hidden_states)
+        topk_idx, topk_weight, router_logits= self.gate(hidden_states)
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
         
+        # only for generate phase
         if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_current_stream_capturing():
             self.experts.generate_experts.submit_for_one_decode(hidden_states[0], topk_idx[0], topk_weight[0])
             if self.config.n_shared_experts is not None:
@@ -744,7 +745,7 @@ class KDeepseekV3MoE(BaseInjectedModule, DeepseekV3MoE):
             y = self.experts.generate_experts.sync_for_one_decode().unsqueeze(0)
             y += y_
             y.resize_(*orig_shape)
-            return y
+            return y, router_logits
 
         if self.config.n_shared_experts is not None:
             y_ = self.shared_experts(identity).squeeze(0)
@@ -767,7 +768,7 @@ class KDeepseekV3MoE(BaseInjectedModule, DeepseekV3MoE):
             )
         if self.config.n_shared_experts is not None:
             y += y_
-        return y
+        return y, router_logits
 
     @torch.no_grad()
     def moe_on_cpuinfer(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor) -> torch.Tensor:

ktransformers/operators/gate.py

@@ -16,7 +16,7 @@ from cpuinfer_ext.moe import MOEConfig, MOE
 import ctypes
 from ktransformers.operators.base_operator import BaseInjectedModule
 from ktransformers.util.custom_gguf import GGUFLoader
-from ktransformers.models.modeling_deepseekv3 import MoEGate
+from ktransformers.models.modeling_deepseek_v3 import DeepseekV3TopkRouter
 from ktransformers.util.utils import InferenceState
 from ktransformers.server.config.config import Config
 from transformers.activations import ACT2FN
@@ -118,11 +118,10 @@ class KMoEGate(BaseInjectedModule, KMoEGateBase):
         else:
             raise ValueError("Invalid weight type")
         self.orig_module.weight = self.orig_module.weight.to(device)
-        if self.topk_method == "noaux_tc":
         self.orig_module.e_score_correction_bias = self.orig_module.e_score_correction_bias.to(device)
 
     def unload(self):
         if self.weight is not None:
             self.weight = None
-        if self.topk_method == "noaux_tc":
+        if self.e_score_correction_bias is not None:
             self.e_score_correction_bias = None

ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu.yaml

@@ -47,7 +47,7 @@
   
 - match:
     name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp$"
-    class: ktransformers.models.modeling_deepseekv3.DeepseekV3MoE
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
   replace:
     class: ktransformers.operators.experts.KDeepseekV3MoE     # mlp module with custom forward function
     kwargs:
@@ -55,7 +55,7 @@
       prefill_device: "cuda:0"
 - match:
     name: "^model\\.layers\\.([3456][0-9])\\.mlp$"
-    class: ktransformers.models.modeling_deepseekv3.DeepseekV3MoE
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
   replace:
     class: ktransformers.operators.experts.KDeepseekV3MoE     # mlp module with custom forward function
     kwargs:
@@ -64,7 +64,7 @@
 
 - match:
     name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.gate$"
-    class: ktransformers.models.modeling_deepseekv3.MoEGate
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3TopkRouter
   replace:
     class: ktransformers.operators.gate.KMoEGate
     kwargs:
@@ -72,7 +72,7 @@
       prefill_device: "cuda:0"
 - match:
     name: "^model\\.layers\\.([3456][0-9])\\.mlp\\.gate$"
-    class: ktransformers.models.modeling_deepseekv3.MoEGate
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3TopkRouter
   replace:
     class: ktransformers.operators.gate.KMoEGate     # mlp module with custom forward function
     kwargs:

ktransformers/server/config/config.py

@@ -102,7 +102,7 @@ class Config(metaclass=Singleton):
         self.total_context = self.model.get("total_context", 2**18)
         self.max_batch_size = self.model.get("max_batch_size", 20 if self.paged else 1)
         self.max_chunk_size = self.model.get("max_chunk_size", 2048)
-        self.max_new_tokens = self.model.get("max_new_tokens", 500)
+        self.max_new_tokens = self.model.get("max_new_tokens", 2000)
         self.json_mode = self.model.get("json_mode", False)
         self.healing = self.model.get("healing", False)
         self.ban_strings: Optional[list] = self.model.get("ban_strings", None)

ktransformers/util/modeling_rope_utils.py

@@ -58,7 +58,8 @@ def _compute_default_rope_parameters(
     elif config is not None:
         base = config.rope_theta
         partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-        dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        dim = int(head_dim * partial_rotary_factor)
 
     attention_factor = 1.0  # Unused in this type of RoPE
 
@@ -143,14 +144,15 @@ def _compute_dynamic_ntk_parameters(
     elif config is not None:
         base = config.rope_theta
         partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-        dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        dim = int(head_dim * partial_rotary_factor)
         max_position_embeddings = config.max_position_embeddings
         factor = config.rope_scaling["factor"]
 
     attention_factor = 1.0  # Unused in this type of RoPE
 
     # seq_len: default to max_position_embeddings, e.g. at init time
-    seq_len = seq_len if seq_len is not None else max_position_embeddings
+    seq_len = seq_len if seq_len is not None and seq_len > max_position_embeddings else max_position_embeddings
 
     # Compute the inverse frequencies
     base = base * ((factor * seq_len / max_position_embeddings) - (factor - 1)) ** (dim / (dim - 2))
@@ -185,15 +187,33 @@ def _compute_yarn_parameters(
 
     base = config.rope_theta
     partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-    dim = config.qk_rope_head_dim
-
-    max_position_embeddings = config.max_position_embeddings
+    head_dim = getattr(config, "qk_rope_head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
     factor = config.rope_scaling["factor"]
+    attention_factor = config.rope_scaling.get("attention_factor")
+    mscale = config.rope_scaling.get("mscale")
+    mscale_all_dim = config.rope_scaling.get("mscale_all_dim")
+
+    # NOTE: DeekSeek-V3 (and potentially other models) modify `max_position_embeddings` and have a
+    # `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
+    # values to compute the default attention scaling factor, instead of using `factor`.
+    if "original_max_position_embeddings" in config.rope_scaling:
+        original_max_position_embeddings = config.rope_scaling["original_max_position_embeddings"]
+        factor = config.max_position_embeddings / original_max_position_embeddings
+    else:
+        original_max_position_embeddings = config.max_position_embeddings
+
+    def get_mscale(scale, mscale=1):
+        if scale <= 1:
+            return 1.0
+        return 0.1 * mscale * math.log(scale) + 1.0
 
     # Sets the attention factor as suggested in the paper
-    attention_factor = config.rope_scaling.get("attention_factor")
     if attention_factor is None:
-        attention_factor = 0.1 * math.log(factor) + 1.0
+        if mscale and mscale_all_dim:
+            attention_factor = float(get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dim))
+        else:
+            attention_factor = get_mscale(factor)
 
     # Optional config options
     # beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
@@ -211,7 +231,7 @@ def _compute_yarn_parameters(
         high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings))
         return max(low, 0), min(high, dim - 1)
 
-    def linear_ramp_mask(min, max, dim):
+    def linear_ramp_factor(min, max, dim):
         if min == max:
             max += 0.001  # Prevent singularity
 
@@ -219,16 +239,20 @@ def _compute_yarn_parameters(
         ramp_func = torch.clamp(linear_func, 0, 1)
         return ramp_func
 
+    # Note on variable naming: "interpolation" comes from the original technique, where we interpolate the position IDs
+    # to expand the possible context length. In other words, interpolation = apply scaling factor.
     pos_freqs = base ** (torch.arange(0, dim, 2).float().to(device) / dim)
     inv_freq_extrapolation = 1.0 / pos_freqs
     inv_freq_interpolation = 1.0 / (factor * pos_freqs)
 
-    low, high = find_correction_range(beta_fast, beta_slow, dim, base, max_position_embeddings)
+    low, high = find_correction_range(beta_fast, beta_slow, dim, base, original_max_position_embeddings)
 
     # Get n-dimensional rotational scaling corrected for extrapolation
-    inv_freq_mask = 1 - linear_ramp_mask(low, high, dim // 2).float().to(device)
-    inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
-
+    inv_freq_extrapolation_factor = 1 - linear_ramp_factor(low, high, dim // 2).float().to(device)
+    inv_freq = (
+        inv_freq_interpolation * (1 - inv_freq_extrapolation_factor)
+        + inv_freq_extrapolation * inv_freq_extrapolation_factor
+    )
     return inv_freq, attention_factor
 
 
@@ -244,7 +268,7 @@ def _compute_longrope_parameters(
         device (`torch.device`):
             The device to use for initialization of the inverse frequencies.
         seq_len (`int`, *optional*):
-            The current sequence length. Unused for this type of RoPE.
+            The current sequence length.
         rope_kwargs (`Dict`, *optional*):
             BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
     Returns:
@@ -261,7 +285,8 @@ def _compute_longrope_parameters(
 
     base = config.rope_theta
     partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-    dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
     long_factor = config.rope_scaling["long_factor"]
     short_factor = config.rope_scaling["short_factor"]
     factor = config.rope_scaling.get("factor")
@@ -271,22 +296,20 @@ def _compute_longrope_parameters(
     # `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
     # values to compute the default attention scaling factor, instead of using `factor`.
     if hasattr(config, "original_max_position_embeddings"):
-        max_position_embeddings = config.original_max_position_embeddings
-        expanded_max_position_embeddings = config.max_position_embeddings
-        factor = expanded_max_position_embeddings / max_position_embeddings
+        original_max_position_embeddings = config.original_max_position_embeddings
+        factor = config.max_position_embeddings / config.original_max_position_embeddings
     else:
-        max_position_embeddings = config.max_position_embeddings
-        expanded_max_position_embeddings = max_position_embeddings * factor
+        original_max_position_embeddings = config.max_position_embeddings
 
     # Sets the attention factor as suggested in the paper
     if attention_factor is None:
         if factor <= 1.0:
             attention_factor = 1.0
         else:
-            attention_factor = math.sqrt(1 + math.log(factor) / math.log(max_position_embeddings))
+            attention_factor = math.sqrt(1 + math.log(factor) / math.log(original_max_position_embeddings))
 
     # Compute the inverse frequencies -- scaled based on the target sequence length
-    if expanded_max_position_embeddings > max_position_embeddings:
+    if seq_len and seq_len > original_max_position_embeddings:
         ext_factors = torch.tensor(long_factor, dtype=torch.float32, device=device)
     else:
         ext_factors = torch.tensor(short_factor, dtype=torch.float32, device=device)
@@ -325,19 +348,18 @@ def _compute_llama3_parameters(
 
     low_freq_wavelen = old_context_len / low_freq_factor
     high_freq_wavelen = old_context_len / high_freq_factor
-    new_freqs = []
-    for freq in inv_freq:
-        wavelen = 2 * math.pi / freq
-        if wavelen < high_freq_wavelen:
-            new_freqs.append(freq)
-        elif wavelen > low_freq_wavelen:
-            new_freqs.append(freq / factor)
-        else:
-            assert low_freq_wavelen != high_freq_wavelen
-            smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
-            new_freqs.append((1 - smooth) * freq / factor + smooth * freq)
-    inv_freq = torch.tensor(new_freqs, dtype=inv_freq.dtype, device=inv_freq.device)
-    return inv_freq, attention_factor
+
+    wavelen = 2 * math.pi / inv_freq
+    # wavelen < high_freq_wavelen: do nothing
+    # wavelen > low_freq_wavelen: divide by factor
+    inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
+    # otherwise: interpolate between the two, using a smooth factor
+    smooth_factor = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+    smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama / factor + smooth_factor * inv_freq_llama
+    is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen)
+    inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
+
+    return inv_freq_llama, attention_factor
 
 
 # This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
@@ -353,12 +375,22 @@ ROPE_INIT_FUNCTIONS = {
 }
 
 
-def _check_received_keys(rope_type: str, received_keys: set, required_keys: set, optional_keys: Optional[set] = None):
+def _check_received_keys(
+    rope_type: str,
+    received_keys: set,
+    required_keys: set,
+    optional_keys: Optional[set] = None,
+    ignore_keys: Optional[set] = None,
+):
     """Compare the received keys in `config.rope_scaling` against the expected and optional keys"""
-    # BC: "rope_type" was originally "type" -- let's gracefully handle it
-    if "rope_type" not in received_keys and "type" in received_keys:
+    # BC: "rope_type" was originally "type" -- let's check for "rope_type" when "type" is present
+    if "type" in received_keys:
         received_keys -= {"type"}
-        received_keys.add("rope_type")
+        required_keys.add("rope_type")
+
+    # Some models need to store model-specific keys, and we don't want to throw warning at them
+    if ignore_keys is not None:
+        received_keys -= ignore_keys
 
     missing_keys = required_keys - received_keys
     if missing_keys:
@@ -372,47 +404,54 @@ def _check_received_keys(rope_type: str, received_keys: set, required_keys: set,
         logger.warning(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
 
 
-def _validate_default_rope_parameters(config: PretrainedConfig):
+def _validate_default_rope_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
     rope_scaling = config.rope_scaling
     rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type"}
     received_keys = set(rope_scaling.keys())
-    _check_received_keys(rope_type, received_keys, required_keys)
+    _check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)
 
 
-def _validate_linear_scaling_rope_parameters(config: PretrainedConfig):
+def _validate_linear_scaling_rope_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
     rope_scaling = config.rope_scaling
     rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor"}
     received_keys = set(rope_scaling.keys())
-    _check_received_keys(rope_type, received_keys, required_keys)
+    _check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
         logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
 
-def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
+def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
     rope_scaling = config.rope_scaling
     rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor"}
     # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
     optional_keys = {"original_max_position_embeddings"}
     received_keys = set(rope_scaling.keys())
-    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys, ignore_keys=ignore_keys)
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
         logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
 
-def _validate_yarn_parameters(config: PretrainedConfig):
+def _validate_yarn_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
     rope_scaling = config.rope_scaling
     rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor"}
-    optional_keys = {"attention_factor", "beta_fast", "beta_slow"}
+    optional_keys = {
+        "attention_factor",
+        "beta_fast",
+        "beta_slow",
+        "original_max_position_embeddings",
+        "mscale",
+        "mscale_all_dim",
+    }
     received_keys = set(rope_scaling.keys())
-    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys, ignore_keys=ignore_keys)
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
@@ -437,17 +476,18 @@ def _validate_yarn_parameters(config: PretrainedConfig):
         )
 
 
-def _validate_longrope_parameters(config: PretrainedConfig):
+def _validate_longrope_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
     rope_scaling = config.rope_scaling
     rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "short_factor", "long_factor"}
     # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
     optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
     received_keys = set(rope_scaling.keys())
-    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys, ignore_keys=ignore_keys)
 
     partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-    dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
 
     short_factor = rope_scaling.get("short_factor")
     if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
@@ -479,18 +519,19 @@ def _validate_longrope_parameters(config: PretrainedConfig):
             logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
         attention_factor = rope_scaling.get("attention_factor")
-        if attention_factor is not None and not isinstance(attention_factor, float) or attention_factor < 0:
+        if attention_factor is not None:
+            if not isinstance(attention_factor, float) or attention_factor < 0.0:
                 logger.warning(
                     f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
                 )
 
 
-def _validate_llama3_parameters(config: PretrainedConfig):
+def _validate_llama3_parameters(config: PretrainedConfig, ignore_keys: Optional[set] = None):
     rope_scaling = config.rope_scaling
     rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
     received_keys = set(rope_scaling.keys())
-    _check_received_keys(rope_type, received_keys, required_keys)
+    _check_received_keys(rope_type, received_keys, required_keys, ignore_keys=ignore_keys)
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
@@ -502,7 +543,7 @@ def _validate_llama3_parameters(config: PretrainedConfig):
         logger.warning(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
     if high_freq_factor is None or not isinstance(high_freq_factor, float):
         logger.warning(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
-    if high_freq_factor < low_freq_factor:
+    if high_freq_factor <= low_freq_factor:
         logger.warning(
             "`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
             f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
@@ -532,7 +573,7 @@ ROPE_VALIDATION_FUNCTIONS = {
 }
 
 
-def rope_config_validation(config: PretrainedConfig):
+def rope_config_validation(config: PretrainedConfig, ignore_keys: Optional[set] = None):
     """
     Validate the RoPE config arguments, given a `PretrainedConfig` object
     """
@@ -544,7 +585,7 @@ def rope_config_validation(config: PretrainedConfig):
     rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
     validation_fn = ROPE_VALIDATION_FUNCTIONS.get(rope_type)
     if validation_fn is not None:
-        validation_fn(config)
+        validation_fn(config, ignore_keys=ignore_keys)
     else:
         logger.warning(
             f"Missing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='{rope_type}'"