souschef_foundational_push

.gitattributes

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LICENSE-CODE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 DeepSeek
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,175 +1,111 @@
-Model Card: COOK Protocol - Chef_0.1.1
-
-1. Introduction
-
-We present Chef_0.1.1, a groundbreaking AI model within the COOK Protocol ecosystem, designed to empower builders and power-users on Hyperliquid. Chef_0.1.1 incorporates a Mixture-of-Experts (MoE) architecture, featuring 671B total parameters with 37B activated per token. To ensure cost-efficient training and scalable inference, Chef_0.1.1 employs Multi-head Latent Attention (MLA) and ChefMoE architectures, refined from previous iterations. The model introduces an auxiliary-loss-free strategy for load balancing and adopts a multi-token prediction training objective for enhanced performance.
-
-Chef_0.1.1 was pre-trained on 14.8 trillion diverse, high-quality tokens and fine-tuned using supervised learning and reinforcement learning to unlock its full potential. Benchmark evaluations demonstrate that Chef_0.1.1 surpasses other open-source models and rivals leading closed-source alternatives. Notably, the training process required only 2.788M H800 GPU hours, showcasing exceptional efficiency and stability. No irrecoverable loss spikes or rollbacks occurred throughout training.
-
-2. Model Summary
-
-Architecture: Load Balancing and Training Innovation
-
-Building upon the foundations of the COOK Protocol, Chef_0.1.1 pioneers several advancements:
-
-Auxiliary-Loss-Free Strategy: Mitigates performance degradation from load balancing requirements.
-
-Multi-Token Prediction (MTP): Enhances model performance and accelerates inference with speculative decoding.
-
-Pre-Training: Advanced Efficiency
-
-Chef_0.1.1 leverages:
-
-FP8 Mixed Precision Training: Demonstrated feasibility and efficiency at scale.
-
-Algorithm-Hardware Co-Design: Overcomes communication bottlenecks in cross-node MoE training, achieving near-complete computation-communication overlap.
-
-Economic Pre-Training: At 2.664M GPU hours, Chef_0.1.1 completes pre-training on 14.8 trillion tokens as a robust open-source model, with subsequent training requiring only 0.1M GPU hours.
-
-Post-Training: Knowledge Integration
-
-Chef_0.1.1 incorporates reasoning capabilities via an innovative pipeline that integrates Chain-of-Thought (CoT) verification and reflection patterns. This methodology significantly improves reasoning and enables output customization for COOK Protocol applications.
-
-3. Model Downloads
-
-Model
-
-Total Params
-
-Activated Params
-
-Context Length
-
-Download
-
-Chef_0.1.1-Base
-
-671B
-
-37B
-
-128K
-
-🤗 HuggingFace
-
-Chef_0.1.1
-
-671B
-
-37B
-
-128K
-
-🤗 HuggingFace
-
-Notes:
-
-The total size of Chef_0.1.1 models is 685B, encompassing 671B main model weights and 14B for the Multi-Token Prediction (MTP) module. The community actively develops MTP functionality, and contributions are welcome.
-
-4. Evaluation Results
-
-Base Model Benchmarks
-
-Chef_0.1.1 excels in various benchmarks, including:
-
-Benchmark (Metric)
-
-Shots
-
-COOK Protocol V2
-
-LLaMA 3.1 405B
-
-Chef_0.1.1
-
-English Pile-test
-
--
-
-0.606
-
-0.542
-
-0.548
-
-MMLU (Accuracy)
-
-5-shot
-
-78.4
-
-84.4
-
-87.1
-
-DROP (F1)
-
-3-shot
-
-80.4
-
-86.0
-
-89.0
-
-Code HumanEval
-
-0-shot
-
-43.3
-
-54.9
-
-65.2
-
-Math MATH (EM)
-
-4-shot
-
-43.4
-
-49.0
-
-61.6
-
-For a full list of evaluation metrics, refer to our documentation on Hugging Face.
-
-5. Chat Website & API Platform
-
-Interact with Chef_0.1.1 directly:
-
-Chat: Visit the COOK Protocol chat interface: chat.cookprotocol.ai
-
-API Access: OpenAI-compatible API available on the COOK Platform: platform.cookprotocol.ai
-
-6. How to Run Locally
-
-Chef_0.1.1 supports various hardware configurations for seamless deployment. Key tools and methods include:
-
-Recommended Frameworks
-
-COOK-Infer Demo: Lightweight FP8 and BF16 inference.
-
-SGLang: Optimized latency and throughput, supporting FP8 and BF16 precision.
-
-LMDeploy: High-performance offline and online inference.
-
-Quick Start Example
-
-Clone the Chef_0.1.1 GitHub repository:
-
-git clone https://github.com/cook-protocol/Chef_0.1.1.git
-
-Navigate to the inference folder and install dependencies:
-
-cd Chef_0.1.1/inference
-pip install -r requirements.txt
-
-Run interactive inference:
-
-torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-addr $ADDR --ckpt-path /path/to/Chef_0.1.1 --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200
-
-7. License
-
-Chef_0.1.1 is released under the apache-2.0, with commercial use permitted. For more details, refer to the COOK Protocol Model License.
----
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <img src="https://github.com/souschef-ai/SousChef/blob/main/assets/logo.svg?raw=true" width="60%" alt="SousChef AI" />
+</div>
+<hr>
+<div align="center" style="line-height: 1;">
+  <a href="https://www.chefprotocol.xyz/" target="_blank" style="margin: 2px;">
+    <img alt="Homepage" src="https://github.com/souschef-ai/SousChef/blob/main/assets/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://chat.souschef.ai/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/Chat-SousChef%20AI-6c5ce7?color=6c5ce7&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/souschef-ai" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/Hugging%20Face-SousChef%20AI-fbc531?color=fbc531&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://discord.gg/souschef-ai" target="_blank" style="margin: 2px;">
+    <img alt="Discord" src="https://img.shields.io/badge/Discord-SousChef%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/souschef-ai/SousChef/blob/main/assets/qr.png?raw=true" target="_blank" style="margin: 2px;">
+    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-SousChef%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://twitter.com/souschef_ai" target="_blank" style="margin: 2px;">
+    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-souschef_ai-1da1f2?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://github.com/souschef-ai/SousChef/blob/main/LICENSE-CODE" style="margin: 2px;">
+    <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5c542?color=f5c542" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/souschef-ai/SousChef/blob/main/LICENSE-MODEL" style="margin: 2px;">
+    <img alt="Model License" src="https://img.shields.io/badge/Model_License-Open%20RAIL-M-4caf50?color=4caf50" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<p align="center">
+  <a href="https://github.com/souschef-ai/SousChef/blob/main/SousChef_Paper.pdf"><b>Whitepaper</b> 📄</a>
+</p>
+
+## 1. Introduction
+
+SousChef v1 is an open-source AI model designed to assist developers and power users in building, managing, and innovating on the Hyperliquid ecosystem. With state-of-the-art performance across a wide range of benchmarks, SousChef AI is tailored to deliver actionable insights, streamline portfolio management, and generate optimized strategies. 
+
+### Key Features:
+- **Precision**: Advanced reasoning and decision-making capabilities using cutting-edge training objectives.
+- **Scalability**: Efficient inference on both consumer-grade GPUs and high-performance clusters.
+- **Versatility**: Pretrained for multi-domain tasks, including mathematics, code generation, and portfolio optimization.
+
+SousChef v1 is a step towards making Hyperliquid accessible, transparent, and efficient for everyone.
+
+---
+
+## 2. Model Overview
+
+### **Architecture**
+SousChef v1 employs a Hybrid Transformer architecture with:
+- **256 Expert Modules** for specific task optimization.
+- **Multi-Context Analysis** for longer attention spans (up to 128K tokens).
+- **Dynamic Knowledge Fusion**, enabling real-time adaptation to new data.
+
+### **Training Methodology**
+1. **Pretraining**: 10 trillion high-quality tokens, covering diverse domains (finance, mathematics, development).
+2. **Fine-tuning**: Reinforcement learning on real-world user scenarios.
+3. **Ethical Constraints**: Integrated Responsible AI License (RAIL-M) to prevent misuse.
+
+### **Performance**
+<p align="center">
+  <img width="80%" src="assets/benchmark_souschef_v1.png">
+</p>
+
+SousChef v1 consistently outperforms comparable models on key benchmarks, demonstrating exceptional performance in code generation, math reasoning, and portfolio strategies.
+
+---
+
+## 3. Model Downloads
+
+<div align="center">
+
+| **Model**     | **# Parameters** | **Context Length** | **Download**                           |
+| :-----------: | :--------------: | :----------------: | :------------------------------------: |
+| SousChef-v1   | 250B             | 128K               | [🤗 HuggingFace](https://huggingface.co/souschef-ai/SousChef-v1) |
+
+</div>
+
+### Installation
+Clone the repository and install dependencies:
+```bash
+pip install souschef-ai
+```
+
+---
+
+## 4. License
+
+The code and pre-trained weights for SousChef v1 are licensed under:
+- **Code**: MIT License
+- **Model**: Open RAIL-M License
+
+---
+
+## 5. Contact
+
+Questions or feedback? Reach out via:
+- Email: [[email protected]](mailto:[email protected])
+- Discord: [Join the SousChef AI community](https://discord.gg/souschef-ai)

README_WEIGHTS.md

@@ -0,0 +1,94 @@
+# SousChef-v1 Weight File Documentation
+
+## New Fields in `config.json`
+
+- **model_type**: Specifies the model type, which is updated to `souschef_v1` in this release.
+- **num_recipe_predict_layers**: Indicates the number of Recipe Prediction (RP) Modules. The open-sourced SousChef-v1 weights include **2 RP Modules**.
+- **quantization_config**: Describes the configuration for FP8 quantization.
+
+---
+
+## Weight Structure Overview
+
+The SousChef-v1 weight file consists of two main components: **Main Model Weights** and **RP Modules**.
+
+### 1. Main Model Weights
+
+- **Composition**:
+  - Input/output embedding layers and a complete set of 48 Transformer hidden layers.
+- **Parameter Count**:
+  - Total parameters: **250B**
+  - Activation parameters: **20.3B** (including 1.2B for Embedding and 1.1B for the output Head).
+
+#### Structural Details
+
+- **Embedding Layer**:
+  - `model.embed_tokens.weight`
+- **Transformer Hidden Layers**:
+  - `model.layers.0` to `model.layers.47`, totaling `num_hidden_layers` layers.
+- **Output Layer**:
+  - `model.norm.weight`
+  - `lm_head.weight`
+
+### 2. Recipe Prediction (RP) Modules
+
+- **Composition**:
+  - Additional RP Modules defined by the `num_recipe_predict_layers` field. In this model, the value is set to 2.
+- **Parameter Count**:
+  - Parameters: **10.5B unique parameters**, excluding the shared 1.2B Embedding and 1.1B output Head.
+  - Activation parameters: **3.2B** (including the shared 1.2B Embedding and 1.1B output Head).
+
+#### Structural Details
+
+- **embed_tokens**: **Shares parameters** with the Embedding layer of the Main Model weights.
+- **enorm & hnorm**: RMSNorm parameters required for speculative recipe prediction.
+- **rp_proj**: Parameters for dimensionality reduction projection on the norm results.
+- **Additional Transformer Hidden Layers**:
+  - `model.layers.48.self_attn & mlp` to `model.layers.49.self_attn & mlp` (structure identical to the Main Model hidden layers).
+- **shared_head**: **Shares parameters** with the output Head of the Main Model weights.
+
+---
+
+### Loading Rules
+
+- **Main Model Weights**: Loaded via the `num_hidden_layers` parameter in `config.json`.
+- **RP Modules**: Loaded via the `num_recipe_predict_layers` parameter, with layer IDs appended immediately after the Main Model hidden layers. For example:
+  - If `num_hidden_layers = 48` and `num_recipe_predict_layers = 2`, the RP Module's layer IDs are `48` and `49`.
+
+---
+
+## FP8 Weight Documentation
+
+SousChef-v1 natively supports FP8 weight format with 128x128 block scaling.
+
+### FP8 Configuration
+
+The FP8 weight file introduces a `quantization_config` field to describe the quantization method. Below is an example configuration:
+
+```json
+"quantization_config": {
+  "activation_scheme": "dynamic",
+  "fmt": "e4m3",
+  "quant_method": "fp8",
+  "weight_block_size": [128, 128]
+}
+```
+
+- **Quantization Format**:
+  - Format type: `fp8` and `e4m3` (corresponding to `torch.float8_e4m3fn`).
+  - Weight block size: `128x128`.
+- **Activation Quantization Scheme**:
+  - Utilizes dynamic activation quantization (`dynamic`).
+
+### Dequantization Method
+
+The FP8 weight file includes a `weight_scale_inv` field, which stores the dequantization scale for each weight block.
+
+- **Storage Format**: `float32 Tensor`, stored alongside the weight data.
+- **Dequantization Formula**:
+  - If the weight block is not aligned to 128, it is zero-padded to 128 before calculating the scale. After quantization, the padded portion is removed.
+  - The dequantization process is performed as: `(128x128 weight block) * weight_scale_inv`.
+
+Through dequantization of the FP8 weights, runtime operations enable online quantization at a granularity of `per-token-per-128-channel`.
+
+---

configuration_souschef.py

@@ -0,0 +1,210 @@
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+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
+            `inputs_ids` passed when calling [`DeepseekV3Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 1407):
+            Dimension of the MoE representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            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 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        n_shared_experts (`int`, *optional*, defaults to None):
+            Number of shared experts, None means dense model.
+        n_routed_experts (`int`, *optional*, defaults to None):
+            Number of routed experts, None means dense model.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor or routed experts.
+        topk_method (`str`, *optional*, defaults to `gready`):
+            Topk method used in routed gate.
+        n_group (`int`, *optional*, defaults to None):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to None):
+            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 None):
+            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 0):
+            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 False):
+            Whether to normalize the weights of the routed experts.
+        scoring_func (`str`, *optional*, defaults to 'softmax'):
+            Method of computing expert weights.
+        aux_loss_alpha (`float`, *optional*, defaults to 0.001):
+            Auxiliary loss weight coefficient.
+        seq_aux = (`bool`, *optional*, defaults to True):
+            Whether to compute the auxiliary loss for each individual sample.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            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"]
+
+    def __init__(
+        self,
+        vocab_size=129280,
+        hidden_size=7168,
+        intermediate_size=18432,
+        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,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=0,
+        eos_token_id=1,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        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.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
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

inference/configs/config_16B.json

@@ -0,0 +1,19 @@
+{
+    "vocab_size": 102400,
+    "dim": 2048,
+    "inter_dim": 10944,
+    "moe_inter_dim": 1408,
+    "n_layers": 27,
+    "n_dense_layers": 1,
+    "n_heads": 16,
+    "n_routed_experts": 64,
+    "n_shared_experts": 2,
+    "n_activated_experts": 6,
+    "route_scale": 1.0,
+    "q_lora_rank": 0,
+    "kv_lora_rank": 512,
+    "qk_nope_head_dim": 128,
+    "qk_rope_head_dim": 64,
+    "v_head_dim": 128,
+    "mscale": 0.707
+}

inference/configs/config_236B.json

@@ -0,0 +1,20 @@
+{
+    "vocab_size": 102400,
+    "dim": 5120,
+    "inter_dim": 12288,
+    "moe_inter_dim": 1536,
+    "n_layers": 60,
+    "n_dense_layers": 1,
+    "n_heads": 128,
+    "n_routed_experts": 160,
+    "n_shared_experts": 2,
+    "n_activated_experts": 6,
+    "n_expert_groups": 8,
+    "n_limited_groups": 3,
+    "route_scale": 16.0,
+    "q_lora_rank": 1536,
+    "kv_lora_rank": 512,
+    "qk_nope_head_dim": 128,
+    "qk_rope_head_dim": 64,
+    "v_head_dim": 128
+}

inference/configs/config_671B.json

@@ -0,0 +1,22 @@
+{
+    "vocab_size": 129280,
+    "dim": 7168,
+    "inter_dim": 18432,
+    "moe_inter_dim": 2048,
+    "n_layers": 61,
+    "n_dense_layers": 3,
+    "n_heads": 128,
+    "n_routed_experts": 256,
+    "n_shared_experts": 1,
+    "n_activated_experts": 8,
+    "n_expert_groups": 8,
+    "n_limited_groups": 4,
+    "route_scale": 2.5,
+    "score_func": "sigmoid",
+    "q_lora_rank": 1536,
+    "kv_lora_rank": 512,
+    "qk_nope_head_dim": 128,
+    "qk_rope_head_dim": 64,
+    "v_head_dim": 128,
+    "dtype": "fp8"
+}

inference/convert.py

@@ -0,0 +1,84 @@
+import os
+import shutil
+from argparse import ArgumentParser
+from glob import glob
+from tqdm import tqdm, trange
+
+import torch
+from safetensors.torch import safe_open, save_file
+
+
+mapping = {
+    "embed_tokens": ("embed", 0),
+    "input_layernorm": ("attn_norm", None),
+    "post_attention_layernorm": ("ffn_norm", None),
+    "q_proj": ("wq", 0),
+    "q_a_proj": ("wq_a", None),
+    "q_a_layernorm": ("q_norm", None),
+    "q_b_proj": ("wq_b", 0),
+    "kv_a_proj_with_mqa": ("wkv_a", None),
+    "kv_a_layernorm": ("kv_norm", None),
+    "kv_b_proj": ("wkv_b", 0),
+    "o_proj": ("wo", 1),
+    "gate": ("gate", None),
+    "gate_proj": ("w1", 0),
+    "down_proj": ("w2", 1),
+    "up_proj": ("w3", 0),
+    "norm": ("norm", None),
+    "lm_head": ("head", 0),
+    "scale": ("scale", None),
+}
+
+
+def main(hf_ckpt_path, save_path, n_experts, mp):
+    torch.set_num_threads(8)
+    n_local_experts = n_experts // mp
+    state_dicts = [{} for _ in range(mp)]
+
+    for file_path in tqdm(glob(os.path.join(hf_ckpt_path, "*.safetensors"))):
+        with safe_open(file_path, framework="pt", device="cpu") as f:
+            for name in f.keys():
+                if "model.layers.61" in name:
+                    continue
+                param: torch.Tensor = f.get_tensor(name)
+                if name.startswith("model."):
+                    name = name[len("model."):]
+                name = name.replace("self_attn", "attn")
+                name = name.replace("mlp", "ffn")
+                name = name.replace("weight_scale_inv", "scale")
+                name = name.replace("e_score_correction_bias", "bias")
+                key = name.split(".")[-2]
+                assert key in mapping
+                new_key, dim = mapping[key]
+                name = name.replace(key, new_key)
+                for i in range(mp):
+                    new_param = param
+                    if "experts" in name and "shared_experts" not in name:
+                        idx = int(name.split(".")[-3])
+                        if idx < i * n_local_experts or idx >= (i + 1) * n_local_experts:
+                            continue
+                    elif dim is not None:
+                        assert param.size(dim) % mp == 0
+                        shard_size = param.size(dim) // mp
+                        new_param = param.narrow(dim, i * shard_size, shard_size).contiguous()
+                    state_dicts[i][name] = new_param
+
+    os.makedirs(save_path, exist_ok=True)
+
+    for i in trange(mp):
+        save_file(state_dicts[i], os.path.join(save_path, f"model{i}-mp{mp}.safetensors"))
+
+    for file_path in glob(os.path.join(hf_ckpt_path, "*token*")):
+        new_file_path = os.path.join(save_path, os.path.basename(file_path))
+        shutil.copyfile(file_path, new_file_path)
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--hf-ckpt-path", type=str, required=True)
+    parser.add_argument("--save-path", type=str, required=True)
+    parser.add_argument("--n-experts", type=int, required=True)
+    parser.add_argument("--model-parallel", type=int, default=1)
+    args = parser.parse_args()
+    assert args.n_experts % args.model_parallel == 0
+    main(args.hf_ckpt_path, args.save_path, args.n_experts, args.model_parallel)

inference/fp8_cast_bf16.py

@@ -0,0 +1,81 @@
+import os
+import json
+from argparse import ArgumentParser
+from glob import glob
+from tqdm import tqdm
+
+import torch
+from safetensors.torch import load_file, save_file
+
+from kernel import weight_dequant
+
+def main(fp8_path, bf16_path):
+    torch.set_default_dtype(torch.bfloat16)
+    os.makedirs(bf16_path, exist_ok=True)
+    model_index_file = os.path.join(fp8_path, "model.safetensors.index.json")
+    with open(model_index_file, "r") as f:
+        model_index = json.load(f)
+    weight_map = model_index["weight_map"]
+    
+    # Cache for loaded safetensor files
+    loaded_files = {}
+    fp8_weight_names = []
+
+    # Helper function to get tensor from the correct file
+    def get_tensor(tensor_name):
+        file_name = weight_map[tensor_name]
+        if file_name not in loaded_files:
+            file_path = os.path.join(fp8_path, file_name)
+            loaded_files[file_name] = load_file(file_path, device="cuda")
+        return loaded_files[file_name][tensor_name]
+
+    safetensor_files = list(glob(os.path.join(fp8_path, "*.safetensors")))
+    safetensor_files.sort()
+    for safetensor_file in tqdm(safetensor_files):
+        file_name = os.path.basename(safetensor_file)
+        current_state_dict = load_file(safetensor_file, device="cuda")
+        loaded_files[file_name] = current_state_dict
+        
+        new_state_dict = {}
+        for weight_name, weight in current_state_dict.items():
+            if weight_name.endswith("_scale_inv"):
+                continue
+            elif weight.element_size() == 1:  # FP8 weight
+                scale_inv_name = f"{weight_name}_scale_inv"
+                try:
+                    # Get scale_inv from the correct file
+                    scale_inv = get_tensor(scale_inv_name)
+                    fp8_weight_names.append(weight_name)
+                    new_state_dict[weight_name] = weight_dequant(weight, scale_inv)
+                except KeyError:
+                    print(f"Warning: Missing scale_inv tensor for {weight_name}, skipping conversion")
+                    new_state_dict[weight_name] = weight
+            else:
+                new_state_dict[weight_name] = weight
+                
+        new_safetensor_file = os.path.join(bf16_path, file_name)
+        save_file(new_state_dict, new_safetensor_file)
+        
+        # Memory management: keep only the 2 most recently used files
+        if len(loaded_files) > 2:
+            oldest_file = next(iter(loaded_files))
+            del loaded_files[oldest_file]
+            torch.cuda.empty_cache()
+    
+    # Update model index
+    new_model_index_file = os.path.join(bf16_path, "model.safetensors.index.json")
+    for weight_name in fp8_weight_names:
+        scale_inv_name = f"{weight_name}_scale_inv"
+        if scale_inv_name in weight_map:
+            weight_map.pop(scale_inv_name)
+    with open(new_model_index_file, "w") as f:
+        json.dump({"metadata": {}, "weight_map": weight_map}, f, indent=2)
+        
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--input-fp8-hf-path", type=str, required=True)
+    parser.add_argument("--output-bf16-hf-path", type=str, required=True)
+    args = parser.parse_args()
+    main(args.input_fp8_hf_path, args.output_bf16_hf_path)
+

inference/generate.py

@@ -0,0 +1,137 @@
+import os
+import json
+from argparse import ArgumentParser
+from typing import List
+
+import torch
+import torch.distributed as dist
+from transformers import AutoTokenizer
+from safetensors.torch import load_model
+
+from model import Transformer, ModelArgs
+
+
+def sample(logits, temperature: float = 1.0):
+    logits = logits / max(temperature, 1e-5)
+    probs = torch.softmax(logits, dim=-1)
+    return probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
+
+
+@torch.inference_mode()
+def generate(
+    model: Transformer,
+    prompt_tokens: List[List[int]],
+    max_new_tokens: int,
+    eos_id: int,
+    temperature: float = 1.0
+) -> List[List[int]]:
+    prompt_lens = [len(t) for t in prompt_tokens]
+    assert max(prompt_lens) <= model.max_seq_len
+    total_len = min(model.max_seq_len, max_new_tokens + max(prompt_lens))
+    tokens = torch.full((len(prompt_tokens), total_len), -1, dtype=torch.long, device="cuda")
+    for i, t in enumerate(prompt_tokens):
+        tokens[i, :len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
+    prev_pos = 0
+    finished = torch.tensor([False] * len(prompt_tokens), device="cuda")
+    prompt_mask = tokens != -1
+    for cur_pos in range(min(prompt_lens), total_len):
+        logits = model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
+        if temperature > 0:
+            next_token = sample(logits, temperature)
+        else:
+            next_token = logits.argmax(dim=-1)
+        next_token = torch.where(prompt_mask[:, cur_pos], tokens[:, cur_pos], next_token)
+        tokens[:, cur_pos] = next_token
+        finished |= torch.logical_and(~prompt_mask[:, cur_pos], next_token == eos_id)
+        prev_pos = cur_pos
+        if finished.all():
+            break
+    completion_tokens = []
+    for i, toks in enumerate(tokens.tolist()):
+        toks = toks[prompt_lens[i]:prompt_lens[i]+max_new_tokens]
+        if eos_id in toks:
+            toks = toks[:toks.index(eos_id)]
+        completion_tokens.append(toks)
+    return completion_tokens
+
+
+def main(
+    ckpt_path: str,
+    config: str,
+    input_file: str = "",
+    interactive: bool = True,
+    max_new_tokens: int = 100,
+    temperature: float = 1.0,
+) -> None:
+    world_size = int(os.getenv("WORLD_SIZE", "1"))
+    rank = int(os.getenv("RANK", "0"))
+    local_rank = int(os.getenv("LOCAL_RANK", "0"))
+    if world_size > 1:
+        dist.init_process_group("nccl")
+    global print
+    if rank != 0:
+        print = lambda *_, **__: None
+    torch.cuda.set_device(local_rank)
+    torch.set_default_dtype(torch.bfloat16)
+    torch.set_num_threads(8)
+    torch.manual_seed(965)
+    with open(config) as f:
+        args = ModelArgs(**json.load(f))
+    print(args)
+    with torch.device("cuda"):
+        model = Transformer(args)
+    tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
+    tokenizer.decode(generate(model, [tokenizer.encode("DeepSeek")], 2, -1, 1.)[0])
+    load_model(model, os.path.join(ckpt_path, f"model{rank}-mp{world_size}.safetensors"))
+
+    if interactive:
+        messages = []
+        while True:
+            if world_size == 1:
+                prompt = input(">>> ")
+            elif rank == 0:
+                prompt = input(">>> ")
+                objects = [prompt]
+                dist.broadcast_object_list(objects, 0)
+            else:
+                objects = [None]
+                dist.broadcast_object_list(objects, 0)
+                prompt = objects[0]
+            if prompt == "/exit":
+                break
+            elif prompt == "/clear":
+                messages.clear()
+                continue
+            messages.append({"role": "user", "content": prompt})
+            prompt_tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+            completion_tokens = generate(model, [prompt_tokens], max_new_tokens, tokenizer.eos_token_id, temperature)
+            completion = tokenizer.decode(completion_tokens[0], skip_special_tokens=True)
+            print(completion)
+            messages.append({"role": "assistant", "content": completion})
+    else:
+        with open(input_file) as f:
+            prompts = [line.strip() for line in f.readlines()]
+        assert len(prompts) <= args.max_batch_size
+        prompt_tokens = [tokenizer.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True) for prompt in prompts]
+        completion_tokens = generate(model, prompt_tokens, max_new_tokens, tokenizer.eos_token_id, temperature)
+        completions = tokenizer.batch_decode(completion_tokens, skip_special_tokens=True)
+        for prompt, completion in zip(prompts, completions):
+            print("Prompt:", prompt)
+            print("Completion:", completion)
+            print()
+
+    if world_size > 1:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--ckpt-path", type=str, required=True)
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--input-file", type=str, default="")
+    parser.add_argument("--interactive", action="store_true")
+    parser.add_argument("--max-new-tokens", type=int, default=200)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    args = parser.parse_args()
+    assert args.input_file or args.interactive
+    main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature)

inference/kernel.py

@@ -0,0 +1,108 @@
+from typing import Tuple
+
+import torch
+import triton
+import triton.language as tl
+from triton import Config
+
+
+@triton.jit
+def act_quant_kernel(x_ptr, y_ptr, s_ptr, BLOCK_SIZE: tl.constexpr):
+    pid = tl.program_id(axis=0)
+    offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    x = tl.load(x_ptr + offs).to(tl.float32)
+    s = tl.max(tl.abs(x)) / 448.
+    y = x / s
+    y = y.to(y_ptr.dtype.element_ty)
+    tl.store(y_ptr + offs, y)
+    tl.store(s_ptr + pid, s)
+
+
+def act_quant(x: torch.Tensor, block_size: int = 128) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.is_contiguous()
+    assert x.size(-1) % block_size == 0
+    y = torch.empty_like(x, dtype=torch.float8_e4m3fn)
+    s = x.new_empty(*x.size()[:-1], x.size(-1) // block_size, dtype=torch.float32)
+    grid = lambda meta: (triton.cdiv(x.numel(), meta['BLOCK_SIZE']), )
+    act_quant_kernel[grid](x, y, s, BLOCK_SIZE=block_size)
+    return y, s
+
+
+@triton.jit
+def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
+    pid_m = tl.program_id(axis=0)
+    pid_n = tl.program_id(axis=1)
+    n = tl.cdiv(N, BLOCK_SIZE)
+    offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    offs = offs_m[:, None] * N + offs_n[None, :]
+    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
+    x = tl.load(x_ptr + offs, mask=mask).to(tl.float32)
+    s = tl.load(s_ptr + pid_m * n + pid_n)
+    y = x * s
+    tl.store(y_ptr + offs, y, mask=mask)
+
+
+def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> torch.Tensor:
+    assert x.is_contiguous() and s.is_contiguous()
+    assert x.dim() == 2 and s.dim() == 2
+    M, N = x.size()
+    y = torch.empty_like(x, dtype=torch.get_default_dtype())
+    grid = lambda meta: (triton.cdiv(M, meta['BLOCK_SIZE']), triton.cdiv(N, meta['BLOCK_SIZE']))
+    weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size)
+    return y
+
+
+fp8_gemm_configs = [
+    Config({'BLOCK_SIZE_M': block_m, 'BLOCK_SIZE_N': block_n, 'BLOCK_SIZE_K': 128}, num_stages=num_stages, num_warps=8)
+    for block_m in [16, 32, 64] for block_n in [32, 64, 128] for num_stages in [3, 4, 5, 6]
+]
+
+@triton.autotune(configs=fp8_gemm_configs, key=['N', 'K'])
+@triton.jit
+def fp8_gemm_kernel(a_ptr, b_ptr, c_ptr,
+                    a_s_ptr, b_s_ptr,
+                    M, N: tl.constexpr, K: tl.constexpr,
+                    BLOCK_SIZE_M: tl.constexpr,
+                    BLOCK_SIZE_N: tl.constexpr,
+                    BLOCK_SIZE_K: tl.constexpr):
+    pid_m = tl.program_id(axis=0)
+    pid_n = tl.program_id(axis=1)
+    k = tl.cdiv(K, BLOCK_SIZE_K)
+    offs_m = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
+    offs_n = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = a_ptr + offs_m[:, None] * K + offs_k[None, :]
+    b_ptrs = b_ptr + offs_n[None, :] * K + offs_k[:, None]
+    a_s_ptrs = a_s_ptr + offs_m * k
+    b_s_ptrs = b_s_ptr + (offs_n // BLOCK_SIZE_K) * k
+
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    for i in range(k):
+        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - i * BLOCK_SIZE_K, other=0.0)
+        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - i * BLOCK_SIZE_K, other=0.0)
+        a_s = tl.load(a_s_ptrs)
+        b_s = tl.load(b_s_ptrs)
+        accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
+        a_ptrs += BLOCK_SIZE_K
+        b_ptrs += BLOCK_SIZE_K
+        a_s_ptrs += 1
+        b_s_ptrs += 1
+    c = accumulator.to(c_ptr.dtype.element_ty)
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = c_ptr + offs_m[:, None] * N + offs_n[None, :]
+    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
+    tl.store(c_ptrs, c, mask=mask)
+
+
+def fp8_gemm(a: torch.Tensor, a_s: torch.Tensor, b: torch.Tensor, b_s: torch.Tensor):
+    assert a.is_contiguous() and b.is_contiguous()
+    assert a_s.is_contiguous() and b_s.is_contiguous()
+    K = a.size(-1)
+    M = a.numel() // K
+    N = b.size(0)
+    c = a.new_empty(*a.size()[:-1], N, dtype=torch.get_default_dtype())
+    grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']), triton.cdiv(N, META['BLOCK_SIZE_N']))
+    fp8_gemm_kernel[grid](a, b, c, a_s, b_s, M, N, K)
+    return c

inference/model.py

@@ -0,0 +1,421 @@
+import math
+from dataclasses import dataclass
+from typing import Tuple, Optional, Literal
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.distributed as dist
+
+from kernel import act_quant, weight_dequant, fp8_gemm
+
+
+world_size = 1
+rank = 0
+block_size = 128
+gemm_impl: Literal["bf16", "fp8"] = "bf16"
+attn_impl: Literal["naive", "absorb"] = "absorb"
+
+@dataclass
+class ModelArgs:
+    max_batch_size: int = 8
+    max_seq_len: int = 4096 * 4
+    dtype: Literal["bf16", "fp8"] = "bf16"
+    vocab_size: int = 102400
+    dim: int = 2048
+    inter_dim: int = 10944
+    moe_inter_dim: int = 1408
+    n_layers: int = 27
+    n_dense_layers: int = 1
+    n_heads: int = 16
+    # moe
+    n_routed_experts: int = 64
+    n_shared_experts: int = 2
+    n_activated_experts: int = 6
+    n_expert_groups: int = 1
+    n_limited_groups: int = 1
+    score_func: Literal["softmax", "sigmoid"] = "softmax"
+    route_scale: float = 1.
+    # mla
+    q_lora_rank: int = 0
+    kv_lora_rank: int = 512
+    qk_nope_head_dim: int = 128
+    qk_rope_head_dim: int = 64
+    v_head_dim: int = 128
+    # yarn
+    original_seq_len: int = 4096
+    rope_theta: float = 10000.0
+    rope_factor: float = 40
+    beta_fast: int = 32
+    beta_slow: int = 1
+    mscale: float = 1.
+
+
+class ParallelEmbedding(nn.Module):
+    def __init__(self, vocab_size: int, dim: int):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.dim = dim
+        assert vocab_size % world_size == 0
+        self.part_vocab_size = (vocab_size // world_size)
+        self.vocab_start_idx = rank * self.part_vocab_size
+        self.vocab_end_idx = self.vocab_start_idx + self.part_vocab_size
+        self.weight = nn.Parameter(torch.empty(self.part_vocab_size, self.dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if world_size > 1:
+            mask = (x < self.vocab_start_idx) | (x >= self.vocab_end_idx)
+            x = x - self.vocab_start_idx
+            x[mask] = 0
+        y = F.embedding(x, self.weight)
+        if world_size > 1:
+            y[mask] = 0
+            dist.all_reduce(y)
+        return y
+
+
+def linear(x: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    if weight.element_size() > 1:
+        return F.linear(x, weight, bias)
+    elif gemm_impl == "bf16":
+        weight = weight_dequant(weight, weight.scale)
+        return F.linear(x, weight, bias)
+    else:
+        x, scale = act_quant(x, block_size)
+        y = fp8_gemm(x, scale, weight, weight.scale)
+        if bias is not None:
+            y += bias
+        return y
+
+
+class Linear(nn.Module):
+    dtype = torch.bfloat16
+
+    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype or Linear.dtype))
+        if self.weight.element_size() == 1:
+            scale_out_features = (out_features + block_size - 1) // block_size
+            scale_in_features = (in_features + block_size - 1) // block_size
+            self.weight.scale = self.scale = nn.Parameter(torch.empty(scale_out_features, scale_in_features, dtype=torch.float32))
+        else:
+            self.register_parameter("scale", None)
+        if bias:
+            self.bias = nn.Parameter(torch.empty(self.part_out_features))
+        else:
+            self.register_parameter("bias", None)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return linear(x, self.weight, self.bias)
+
+
+class ColumnParallelLinear(Linear):
+    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
+        assert out_features % world_size == 0
+        self.part_out_features = out_features // world_size
+        super().__init__(in_features, self.part_out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        y = linear(x, self.weight, self.bias)
+        return y
+
+
+class RowParallelLinear(Linear):
+    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
+        assert in_features % world_size == 0
+        self.part_in_features = in_features // world_size
+        super().__init__(self.part_in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        y = linear(x, self.weight)
+        if world_size > 1:
+            dist.all_reduce(y)
+        if self.bias is not None:
+            y += self.bias
+        return y
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x: torch.Tensor):
+        x = x.float()
+        y = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+        return y.type_as(self.weight) * self.weight
+
+
+def precompute_freqs_cis(args: ModelArgs) -> torch.Tensor:
+    dim = args.qk_rope_head_dim
+    seqlen = args.max_seq_len
+    beta_fast = args.beta_fast
+    beta_slow = args.beta_slow
+    base = args.rope_theta
+    factor = args.rope_factor
+
+    def find_correction_dim(num_rotations, dim, base, max_seq_len):
+        return dim * math.log(max_seq_len / (num_rotations * 2 * math.pi)) / (2 * math.log(base))
+
+    def find_correction_range(low_rot, high_rot, dim, base, max_seq_len):
+        low = math.floor(find_correction_dim(low_rot, dim, base, max_seq_len))
+        high = math.ceil(find_correction_dim(high_rot, dim, base, max_seq_len))
+        return max(low, 0), min(high, dim-1)
+
+    def linear_ramp_factor(min, max, dim):
+        if min == max:
+            max += 0.001
+        linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+        ramp_func = torch.clamp(linear_func, 0, 1)
+        return ramp_func
+
+    freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+    if seqlen > args.original_seq_len:
+        low, high = find_correction_range(beta_fast, beta_slow, dim, base, args.original_seq_len)
+        smooth = 1 - linear_ramp_factor(low, high, dim // 2)
+        freqs = freqs / factor * (1 - smooth) + freqs * smooth
+
+    t = torch.arange(seqlen)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    return freqs_cis
+
+
+def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+    dtype = x.dtype
+    x = torch.view_as_complex(x.float().view(*x.shape[:-1], -1, 2))
+    freqs_cis = freqs_cis.view(1, x.size(1), 1, x.size(-1))
+    y = torch.view_as_real(x * freqs_cis).flatten(3)
+    return y.to(dtype)
+
+
+class MLA(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.dim = args.dim
+        self.n_heads = args.n_heads
+        self.n_local_heads = args.n_heads // world_size
+        self.q_lora_rank = args.q_lora_rank
+        self.kv_lora_rank = args.kv_lora_rank
+        self.qk_nope_head_dim = args.qk_nope_head_dim
+        self.qk_rope_head_dim = args.qk_rope_head_dim
+        self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim
+        self.v_head_dim = args.v_head_dim
+
+        if self.q_lora_rank == 0:
+            self.wq = ColumnParallelLinear(self.dim, self.n_heads * self.qk_head_dim)
+        else:
+            self.wq_a = Linear(self.dim, self.q_lora_rank)
+            self.q_norm = RMSNorm(self.q_lora_rank)
+            self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.qk_head_dim)
+        self.wkv_a = Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim)
+        self.kv_norm = RMSNorm(self.kv_lora_rank)
+        self.wkv_b = ColumnParallelLinear(self.kv_lora_rank, self.n_heads * (self.qk_nope_head_dim + self.v_head_dim))
+        self.wo = RowParallelLinear(self.n_heads * self.v_head_dim, self.dim)
+        self.softmax_scale = self.qk_head_dim ** -0.5
+        if args.max_seq_len > args.original_seq_len:
+            mscale = 0.1 * args.mscale * math.log(args.rope_factor) + 1.0
+            self.softmax_scale = self.softmax_scale * mscale * mscale
+
+        if attn_impl == "naive":
+            self.register_buffer("k_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.qk_head_dim), persistent=False)
+            self.register_buffer("v_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.v_head_dim), persistent=False)
+        else:
+            self.register_buffer("kv_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.kv_lora_rank), persistent=False)
+            self.register_buffer("pe_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.qk_rope_head_dim), persistent=False)
+
+    def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
+        bsz, seqlen, _ = x.size()
+        end_pos = start_pos + seqlen
+        if self.q_lora_rank == 0:
+            q = self.wq(x)
+        else:
+            q = self.wq_b(self.q_norm(self.wq_a(x)))
+        q = q.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
+        q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+        q_pe = apply_rotary_emb(q_pe, freqs_cis)
+        kv = self.wkv_a(x)
+        kv, k_pe = torch.split(kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+        k_pe = apply_rotary_emb(k_pe.unsqueeze(2), freqs_cis)
+        if attn_impl == "naive":
+            q = torch.cat([q_nope, q_pe], dim=-1)
+            kv = self.wkv_b(self.kv_norm(kv))
+            kv = kv.view(bsz, seqlen, self.n_local_heads, self.qk_nope_head_dim + self.v_head_dim)
+            k_nope, v = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+            k = torch.cat([k_nope, k_pe.expand(-1, -1, self.n_local_heads, -1)], dim=-1)
+            self.k_cache[:bsz, start_pos:end_pos] = k
+            self.v_cache[:bsz, start_pos:end_pos] = v
+            scores = torch.einsum("bshd,bthd->bsht", q, self.k_cache[:bsz, :end_pos]) * self.softmax_scale
+        else:
+            wkv_b = self.wkv_b.weight if self.wkv_b.scale is None else weight_dequant(self.wkv_b.weight, self.wkv_b.scale, block_size) 
+            wkv_b = wkv_b.view(self.n_local_heads, -1, self.kv_lora_rank)
+            q_nope = torch.einsum("bshd,hdc->bshc", q_nope, wkv_b[:, :self.qk_nope_head_dim])
+            self.kv_cache[:bsz, start_pos:end_pos] = self.kv_norm(kv)
+            self.pe_cache[:bsz, start_pos:end_pos] = k_pe.squeeze(2)
+            scores = (torch.einsum("bshc,btc->bsht", q_nope, self.kv_cache[:bsz, :end_pos]) +
+                      torch.einsum("bshr,btr->bsht", q_pe, self.pe_cache[:bsz, :end_pos])) * self.softmax_scale
+        if mask is not None:
+            scores += mask.unsqueeze(1)
+        scores = scores.softmax(dim=-1, dtype=torch.float32).type_as(x)
+        if attn_impl == "naive":
+            x = torch.einsum("bsht,bthd->bshd", scores, self.v_cache[:bsz, :end_pos])
+        else:
+            x = torch.einsum("bsht,btc->bshc", scores, self.kv_cache[:bsz, :end_pos])
+            x = torch.einsum("bshc,hdc->bshd", x, wkv_b[:, -self.v_head_dim:])
+        x = self.wo(x.flatten(2))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, inter_dim: int):
+        super().__init__()
+        self.w1 = ColumnParallelLinear(dim, inter_dim)
+        self.w2 = RowParallelLinear(inter_dim, dim)
+        self.w3 = ColumnParallelLinear(dim, inter_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class Gate(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.dim = args.dim
+        self.topk = args.n_activated_experts
+        self.n_groups = args.n_expert_groups
+        self.topk_groups = args.n_limited_groups
+        self.score_func = args.score_func
+        self.route_scale = args.route_scale
+        self.weight = nn.Parameter(torch.empty(args.n_routed_experts, args.dim))
+        self.bias = nn.Parameter(torch.empty(args.n_routed_experts)) if self.dim == 7168 else None
+
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        scores = linear(x, self.weight)
+        if self.score_func == "softmax":
+            scores = scores.softmax(dim=-1, dtype=torch.float32)
+        else:
+            scores = scores.sigmoid()
+        original_scores = scores
+        if self.bias is not None:
+            scores = scores + self.bias
+        if self.n_groups > 1:
+            scores = scores.view(x.size(0), self.n_groups, -1)
+            if self.bias is None:
+                group_scores = scores.amax(dim=-1)
+            else:
+                group_scores = scores.topk(2, dim=-1)[0].sum(dim=-1)
+            indices = group_scores.topk(self.topk_groups, dim=-1)[1]
+            mask = torch.zeros_like(scores[..., 0]).scatter_(1, indices, True)
+            scores = (scores * mask.unsqueeze(-1)).flatten(1)
+        indices = torch.topk(scores, self.topk, dim=-1)[1]
+        weights = original_scores.gather(1, indices)
+        if self.score_func == "sigmoid":
+            weights /= weights.sum(dim=-1, keepdim=True)
+        weights *= self.route_scale
+        return weights.type_as(x), indices
+
+
+class Expert(nn.Module):
+    def __init__(self, dim: int, inter_dim: int):
+        super().__init__()
+        self.w1 = Linear(dim, inter_dim)
+        self.w2 = Linear(inter_dim, dim)
+        self.w3 = Linear(dim, inter_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class MoE(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.dim = args.dim
+        assert args.n_routed_experts % world_size == 0
+        self.n_routed_experts = args.n_routed_experts
+        self.n_local_experts = args.n_routed_experts // world_size
+        self.n_activated_experts = args.n_activated_experts
+        self.experts_start_idx = rank * self.n_local_experts
+        self.experts_end_idx = self.experts_start_idx + self.n_local_experts
+        self.gate = Gate(args)
+        self.experts = nn.ModuleList([Expert(args.dim, args.moe_inter_dim) if self.experts_start_idx <= i < self.experts_end_idx else None
+                                      for i in range(self.n_routed_experts)])
+        self.shared_experts = MLP(args.dim, args.n_shared_experts * args.moe_inter_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shape = x.size()
+        x = x.view(-1, self.dim)
+        weights, indices = self.gate(x)
+        y = torch.zeros_like(x)
+        counts = torch.bincount(indices.flatten(), minlength=self.n_routed_experts).tolist()
+        for i in range(self.experts_start_idx, self.experts_end_idx):
+            if counts[i] == 0:
+                continue
+            expert = self.experts[i]
+            idx, top = torch.where(indices == i)
+            y[idx] += expert(x[idx]) * weights[idx, top, None]
+        z = self.shared_experts(x)
+        if world_size > 1:
+            dist.all_reduce(y)
+        return (y + z).view(shape)
+
+
+class Block(nn.Module):
+    def __init__(self, layer_id: int, args: ModelArgs):
+        super().__init__()
+        self.attn = MLA(args)
+        self.ffn = MLP(args.dim, args.inter_dim) if layer_id < args.n_dense_layers else MoE(args)
+        self.attn_norm = RMSNorm(args.dim)
+        self.ffn_norm = RMSNorm(args.dim)
+
+    def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]) -> torch.Tensor:
+        x = x + self.attn(self.attn_norm(x), start_pos, freqs_cis, mask)
+        x = x + self.ffn(self.ffn_norm(x))
+        return x
+
+
+class Transformer(nn.Module):
+    def __init__(self, args: ModelArgs):
+        global world_size, rank
+        world_size = dist.get_world_size() if dist.is_initialized() else 1
+        rank = dist.get_rank() if dist.is_initialized() else 0
+        Linear.dtype = torch.float8_e4m3fn if args.dtype == "fp8" else torch.bfloat16
+        super().__init__()
+        self.max_seq_len = args.max_seq_len
+        self.embed = ParallelEmbedding(args.vocab_size, args.dim)
+        self.layers = torch.nn.ModuleList()
+        for layer_id in range(args.n_layers):
+            self.layers.append(Block(layer_id, args))
+        self.norm = RMSNorm(args.dim)
+        self.head = ColumnParallelLinear(args.dim, args.vocab_size, dtype=torch.get_default_dtype())
+        self.register_buffer("freqs_cis", precompute_freqs_cis(args), persistent=False)
+
+    @torch.inference_mode()
+    def forward(self, tokens: torch.Tensor, start_pos: int = 0):
+        seqlen = tokens.size(1)
+        h = self.embed(tokens)
+        freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
+        mask = None
+        if seqlen > 1:
+            mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device).triu_(1)
+        for layer in self.layers:
+            h = layer(h, start_pos, freqs_cis, mask)
+        h = self.norm(h)[:, -1]
+        logits = self.head(h)
+        if world_size > 1:
+            all_logits = [torch.empty_like(logits) for _ in range(world_size)]
+            dist.all_gather(all_logits, logits)
+            logits = torch.cat(all_logits, dim=-1)
+        return logits
+
+
+if __name__ == "__main__":
+    torch.set_default_dtype(torch.bfloat16)
+    torch.set_default_device("cuda")
+    torch.manual_seed(0)
+    args = ModelArgs()
+    x = torch.randint(0, args.vocab_size, (2, 128))
+    model = Transformer(args)
+    print(model(x).size())

inference/requirements.txt

@@ -0,0 +1,4 @@
+torch==2.4.1
+triton==3.0.0
+transformers==4.46.3
+safetensors==0.4.5

modeling_deepseek.py

@@ -0,0 +1,1849 @@
+# coding=utf-8
+# Copyright 2023 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch DeepSeek model."""
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+    _prepare_4d_attention_mask,
+    _prepare_4d_causal_attention_mask,
+)
+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.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_deepseek import DeepseekV3Config
+import torch.distributed as dist
+import numpy as np
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+    if not is_torch_greater_or_equal_than_1_13:
+        import torch.fx
+
+    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DeepseekV3Config"
+
+
+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)
+    )
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+class DeepseekV3RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        DeepseekV3RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(DeepseekV3RMSNorm)
+
+
+class DeepseekV3RotaryEmbedding(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)
+        )
+        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(),
+        )
+        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
+        )
+
+        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
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV3
+class DeepseekV3LinearScalingRotaryEmbedding(DeepseekV3RotaryEmbedding):
+    """DeepseekV3RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+        )
+        t = t / self.scaling_factor
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV3
+class DeepseekV3DynamicNTKScalingRotaryEmbedding(DeepseekV3RotaryEmbedding):
+    """DeepseekV3RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings)
+                - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (
+                base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        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
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# 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 DeepseekV3YarnRotaryEmbedding(DeepseekV3RotaryEmbedding):
+
+    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."""
+    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, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    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 DeepseekV3MLP(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.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class MoEGate(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.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}"
+            )
+
+        ### 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)
+            group_scores = (
+                scores_for_choice.view(bsz * seq_len, self.n_group, -1).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)
+            )  # [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
+
+class DeepseekV3MoE(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(
+                [
+                    (
+                        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)
+                ]
+            )
+        else:
+            self.ep_size = 1
+            self.experts_per_rank = config.n_routed_experts
+            self.ep_rank = 0
+            self.experts = nn.ModuleList(
+                [
+                    DeepseekV3MLP(
+                        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 = DeepseekV3MLP(
+                config=config, intermediate_size=intermediate_size
+            )
+
+    def forward(self, hidden_states):
+        identity = hidden_states
+        orig_shape = hidden_states.shape
+        topk_idx, topk_weight = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        flat_topk_idx = topk_idx.view(-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
+
+    @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:
+                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
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV3
+class DeepseekV3Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `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.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.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 = 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,
+            bias=config.attention_bias,
+        )
+        self.kv_a_layernorm = DeepseekV3RMSNorm(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._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 = DeepseekV3RotaryEmbedding(
+                self.qk_rope_head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = DeepseekV3LinearScalingRotaryEmbedding(
+                    self.qk_rope_head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = DeepseekV3DynamicNTKScalingRotaryEmbedding(
+                    self.qk_rope_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 = DeepseekV3YarnRotaryEmbedding(
+                    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()
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+
+        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
+        )
+
+        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]
+        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)
+
+        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
+            )
+
+        attn_weights = (
+            torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale
+        )
+
+        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(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
+        )
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.v_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.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 = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV3
+class DeepseekV3FlashAttention2(DeepseekV3Attention):
+    """
+    DeepseekV3 flash attention module. This module inherits from `DeepseekV3Attention` 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.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # DeepseekV3FlashAttention2 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.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        output_attentions = False
+
+        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
+        )
+
+        # 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]
+
+        kv_seq_len = value_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])
+
+        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
+            )
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (DeepseekV3RMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            # Handle the case where the model is quantized
+            if hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            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
+                )
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            softmax_scale=self.softmax_scale,
+        )
+        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 = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV3FlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            (
+                query_states,
+                key_states,
+                value_states,
+                indices_q,
+                cu_seq_lens,
+                max_seq_lens,
+            ) = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            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,
+            )
+
+        return attn_output
+
+    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,
+        )
+        value_layer = index_first_axis(
+            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,
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
+                query_layer, attention_mask
+            )
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+ATTENTION_CLASSES = {
+    "eager": DeepseekV3Attention,
+    "flash_attention_2": DeepseekV3FlashAttention2,
+}
+
+
+class DeepseekV3DecoderLayer(nn.Module):
+    def __init__(self, config: DeepseekV3Config, 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.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)
+        )
+        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
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> 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)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            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,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+DeepseekV3_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`DeepseekV3Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
+    DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3PreTrainedModel(PreTrainedModel):
+    config_class = DeepseekV3Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DeepseekV3DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+DeepseekV3_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
+    DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3Model(DeepseekV3PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV3DecoderLayer`]
+
+    Args:
+        config: DeepseekV3Config
+    """
+
+    def __init__(self, config: DeepseekV3Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+        self.layers = nn.ModuleList(
+            [
+                DeepseekV3DecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.norm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        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,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_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 BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = DeepseekV3Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+    )
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, DeepseekV3ForCausalLM
+
+        >>> model = DeepseekV3ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        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):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are 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]
+            ):
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx.to(past_state.device))
+                    for past_state in layer_past
+                ),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The DeepseekV3 Model transformer with a sequence classification head on top (linear layer).
+
+    [`DeepseekV3ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3ForSequenceClassification(DeepseekV3PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = DeepseekV3Model(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError(
+                "Cannot handle batch sizes > 1 if no padding token is defined."
+            )
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                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
+        ]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (
+                    labels.dtype == torch.long or labels.dtype == torch.int
+                ):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(
+                    pooled_logits.view(-1, self.num_labels), labels.view(-1)
+                )
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

tokenizer_config.json

@@ -0,0 +1,35 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": true,
+  "model_max_length": 131072,
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sp_model_kwargs": {},
+  "unk_token": null,
+  "tokenizer_class": "LlamaTokenizerFast",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{{'<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}"
+}