[WIP] add deepseek-v3

docs/source/en/index.md

@@ -118,6 +118,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                       [DeBERTa](model_doc/deberta)                       |       ✅        |         ✅         |      ❌      |
 |                    [DeBERTa-v2](model_doc/deberta-v2)                    |       ✅        |         ✅         |      ❌      |
 |          [Decision Transformer](model_doc/decision_transformer)          |       ✅        |         ❌         |      ❌      |
+|                   [DeepSeek-V3](model_doc/deepseek_v3)                   |       ✅        |         ❌         |      ❌      |
 |               [Deformable DETR](model_doc/deformable_detr)               |       ✅        |         ❌         |      ❌      |
 |                          [DeiT](model_doc/deit)                          |       ✅        |         ✅         |      ❌      |
 |                        [DePlot](model_doc/deplot)                        |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/deepseek_v3.md

@@ -0,0 +1,46 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# DeepSeek-V3
+
+## Overview
+
+The DeepSeek-V3 model was proposed in [DeepSeek-V3 Technical Report](https://arxiv.org/abs/2412.19437) by DeepSeek-AI Team.
+
+The abstract from the paper is the following:
+We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.
+
+### Usage tips
+The model uses Multi-head Latent Attention (MLA) and DeepSeekMoE architectures for efficient inference and cost-effective training. It employs an auxiliary-loss-free strategy for load balancing and multi-token prediction training objective. The model can be used for various language tasks after being pre-trained on 14.8 trillion tokens and going through Supervised Fine-Tuning and Reinforcement Learning stages.
+
+## DeepseekV3Config
+
+[[autodoc]] DeepseekV3Config
+
+## DeepseekV3Model
+
+[[autodoc]] DeepseekV3Model
+    - forward
+
+## DeepseekV3ForCausalLM
+
+[[autodoc]] DeepseekV3ForCausalLM
+    - forward
+
+## DeepseekV3ForSequenceClassification
+
+[[autodoc]] DeepseekV3ForSequenceClassification
+    - forward

src/transformers/__init__.py

@@ -341,6 +341,7 @@
     ],
     "models.deberta_v2": ["DebertaV2Config"],
     "models.decision_transformer": ["DecisionTransformerConfig"],
+    "models.deepseek_v3": ["DeepseekV3Config"],
     "models.deformable_detr": ["DeformableDetrConfig"],
     "models.deit": ["DeiTConfig"],
     "models.deprecated": [],
@@ -1956,6 +1957,14 @@
             "DecisionTransformerPreTrainedModel",
         ]
     )
+    _import_structure["models.deepseek_v3"].extend(
+        [
+            "DeepseekV3ForCausalLM",
+            "DeepseekV3ForSequenceClassification",
+            "DeepseekV3Model",
+            "DeepseekV3PreTrainedModel",
+        ]
+    )
     _import_structure["models.deformable_detr"].extend(
         [
             "DeformableDetrForObjectDetection",
@@ -5395,6 +5404,9 @@
     from .models.decision_transformer import (
         DecisionTransformerConfig,
     )
+    from .models.deepseek_v3 import (
+        DeepseekV3Config,
+    )
     from .models.deformable_detr import (
         DeformableDetrConfig,
     )
@@ -6966,6 +6978,12 @@
             DecisionTransformerModel,
             DecisionTransformerPreTrainedModel,
         )
+        from .models.deepseek_v3 import (
+            DeepseekV3ForCausalLM,
+            DeepseekV3ForSequenceClassification,
+            DeepseekV3Model,
+            DeepseekV3PreTrainedModel,
+        )
         from .models.deformable_detr import (
             DeformableDetrForObjectDetection,
             DeformableDetrModel,

src/transformers/modeling_rope_utils.py

@@ -189,13 +189,31 @@ def _compute_yarn_parameters(
     partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
     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 = 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)
@@ -227,15 +245,14 @@ def linear_ramp_factor(min, max, 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_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
 
 
@@ -425,7 +442,14 @@ def _validate_yarn_parameters(config: PretrainedConfig, ignore_keys: Optional[se
     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, ignore_keys=ignore_keys)
 

src/transformers/models/__init__.py

@@ -69,6 +69,7 @@
     deberta,
     deberta_v2,
     decision_transformer,
+    deepseek_v3,
     deformable_detr,
     deit,
     deprecated,

src/transformers/models/auto/configuration_auto.py

@@ -87,6 +87,7 @@
         ("deberta", "DebertaConfig"),
         ("deberta-v2", "DebertaV2Config"),
         ("decision_transformer", "DecisionTransformerConfig"),
+        ("deepseek_v3", "DeepseekV3Config"),
         ("deformable_detr", "DeformableDetrConfig"),
         ("deit", "DeiTConfig"),
         ("depth_anything", "DepthAnythingConfig"),
@@ -406,6 +407,7 @@
         ("deberta", "DeBERTa"),
         ("deberta-v2", "DeBERTa-v2"),
         ("decision_transformer", "Decision Transformer"),
+        ("deepseek_v3", "DeepSeek-V3"),
         ("deformable_detr", "Deformable DETR"),
         ("deit", "DeiT"),
         ("deplot", "DePlot"),

src/transformers/models/auto/modeling_auto.py

@@ -86,6 +86,7 @@
         ("deberta", "DebertaModel"),
         ("deberta-v2", "DebertaV2Model"),
         ("decision_transformer", "DecisionTransformerModel"),
+        ("deepseek_v3", "DeepseekV3Model"),
         ("deformable_detr", "DeformableDetrModel"),
         ("deit", "DeiTModel"),
         ("deta", "DetaModel"),
@@ -501,6 +502,7 @@
         ("ctrl", "CTRLLMHeadModel"),
         ("data2vec-text", "Data2VecTextForCausalLM"),
         ("dbrx", "DbrxForCausalLM"),
+        ("deepseek_v3", "DeepseekV3ForCausalLM"),
         ("diffllama", "DiffLlamaForCausalLM"),
         ("electra", "ElectraForCausalLM"),
         ("emu3", "Emu3ForCausalLM"),

src/transformers/models/auto/tokenization_auto.py

@@ -170,6 +170,13 @@
                     "DebertaV2TokenizerFast" if is_tokenizers_available() else None,
                 ),
             ),
+            (
+                "deepseek_v3",
+                (
+                    "LlamaTokenizer" if is_sentencepiece_available() else None,
+                    "LlamaTokenizerFast" if is_tokenizers_available() else None,
+                ),
+            ),
             (
                 "diffllama",
                 (

src/transformers/models/deepseek_v3/__init__.py

@@ -0,0 +1,27 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_deepseek_v3 import *
+    from .modeling_deepseek_v3 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)