Merge branch 'main' into refactor/codebase

README.md

@@ -44,6 +44,18 @@
   <a href="DeepSeek_V3.pdf"><b>Paper Link</b>👁️</a>
 </p>
 
+## Table of Contents
+
+1. [Introduction](#1-introduction)
+2. [Model Summary](#2-model-summary)
+3. [Model Downloads](#3-model-downloads)
+4. [Evaluation Results](#4-evaluation-results)
+5. [Chat Website & API Platform](#5-chat-website--api-platform)
+6. [How to Run Locally](#6-how-to-run-locally)
+7. [License](#7-license)
+8. [Citation](#8-citation)
+9. [Contact](#9-contact)
+
 
 ## 1. Introduction
 
@@ -99,7 +111,7 @@ Throughout the entire training process, we did not experience any irrecoverable
 </div>
 
 > [!NOTE]
-> The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.**
+> The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.
 
 To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: [How_to Run_Locally](#6-how-to-run-locally).
 
@@ -130,7 +142,7 @@ For developers looking to dive deeper, we recommend exploring [README_WEIGHTS.md
 | | WinoGrande (Acc.) | 5-shot | **86.3** | 82.3 | 85.2 | 84.9 |
 | | RACE-Middle (Acc.) | 5-shot | 73.1 | 68.1 | **74.2** | 67.1 |
 | | RACE-High (Acc.) | 5-shot | 52.6 | 50.3 | **56.8** | 51.3 |
-| | TriviaQA (EM) | 5-shot | 80.0 | 71.9 | **82.7** | **82.9** |
+| | TriviaQA (EM) | 5-shot | 80.0 | 71.9 | 82.7 | **82.9** |
 | | NaturalQuestions (EM) | 5-shot | 38.6 | 33.2 | **41.5** | 40.0 |
 | | AGIEval (Acc.) | 0-shot | 57.5 | 75.8 | 60.6 | **79.6** |
 | Code | HumanEval (Pass@1) | 0-shot | 43.3 | 53.0 | 54.9 | **65.2** |
@@ -249,7 +261,7 @@ python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-h
 ```
 
 > [!NOTE]
-> Hugging Face's Transformers has not been directly supported yet.**
+> Hugging Face's Transformers has not been directly supported yet.
 
 ### 6.1 Inference with DeepSeek-Infer Demo (example only)
 
@@ -259,7 +271,7 @@ python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-h
 > Linux with Python 3.10 only. Mac and Windows are not supported.
 
 Dependencies:
-```
+```pip-requirements
 torch==2.4.1
 triton==3.0.0
 transformers==4.46.3

inference/generate.py

@@ -59,7 +59,8 @@ class TextGenerator:
             List[List[int]]: Generated tokens for each sequence.
         """
         prompt_lens = [len(t) for t in prompt_tokens]
-        assert max(prompt_lens) <= self.model.max_seq_len
+        if max(prompt_lens) > self.model.max_seq_len:
+            raise ValueError(f"Prompt length exceeds model maximum sequence length (max_seq_len={self.model.max_seq_len})")
         
         total_len = min(self.model.max_seq_len, config.max_new_tokens + max(prompt_lens))
         tokens = self._initialize_tokens(prompt_tokens, total_len)
@@ -193,7 +194,9 @@ class ChatSession:
     def run_batch(self, input_file: str):
         with open(input_file) as f:
             prompts = [line.strip() for line in f.readlines()]
-        assert len(prompts) <= self.generator.model.args.max_batch_size
+        
+        if len(prompts) > self.generator.model.args.max_batch_size:
+            raise ValueError(f"Number of prompts exceeds maximum batch size ({self.generator.model.args.max_batch_size})")
 
         completions = self._process_batch(prompts)
         for prompt, completion in zip(prompts, completions):
@@ -302,7 +305,9 @@ if __name__ == "__main__":
     parser.add_argument("--temperature", type=float, default=0.2)
     args = parser.parse_args()
     
-    assert args.input_file or args.interactive
+    if not args.input_file and not args.interactive:
+        raise ValueError("Either input-file or interactive mode must be specified")
+        
     main(
         args.ckpt_path,
         args.config,

inference/model.py

@@ -96,7 +96,7 @@ class ParallelEmbedding(nn.Module):
         super().__init__()
         self.vocab_size = vocab_size
         self.dim = dim
-        assert vocab_size % world_size == 0
+        assert vocab_size % world_size == 0, f"Vocabulary size must be divisible by world size (world_size={world_size})"
         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
@@ -143,7 +143,7 @@ def linear(x: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] =
         quantization-aware computations depending on the input parameters.
 
     Notes:
-        - If `weight` is quantized (e.g., `element_size() > 1`), a dequantized version 
+        - If `weight` is quantized (e.g., `element_size() == 1`), a dequantized version 
           is used for computation.
         - If `gemm_impl == "bf16"`, dequantization and a `bf16` GEMM operation are applied.
         - For other cases, the function applies quantization to `x` and uses `fp8_gemm` for computation.
@@ -185,7 +185,7 @@ class Linear(nn.Module):
         else:
             self.register_parameter("scale", None)
         if bias:
-            self.bias = nn.Parameter(torch.empty(self.part_out_features))
+            self.bias = nn.Parameter(torch.empty(out_features))
         else:
             self.register_parameter("bias", None)
 
@@ -213,7 +213,7 @@ class ColumnParallelLinear(Linear):
         dtype (optional): Data type for the layer. Defaults to `torch.bfloat16`.
     """
     def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
-        assert out_features % world_size == 0
+        assert out_features % world_size == 0, f"Output features must be divisible by world size (world_size={world_size})"
         self.part_out_features = out_features // world_size
         super().__init__(in_features, self.part_out_features, bias, dtype)
 
@@ -242,7 +242,7 @@ class RowParallelLinear(Linear):
         dtype (optional): Data type for the layer. Defaults to `torch.bfloat16`.
     """
     def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
-        assert in_features % world_size == 0
+        assert in_features % world_size == 0, f"Input features must be divisible by world size (world_size={world_size})"
         self.part_in_features = in_features // world_size
         super().__init__(self.part_in_features, out_features, bias, dtype)
 
@@ -652,7 +652,7 @@ class MoE(nn.Module):
         """
         super().__init__()
         self.dim = args.dim
-        assert args.n_routed_experts % world_size == 0
+        assert args.n_routed_experts % world_size == 0, f"Number of experts must be divisible by world size (world_size={world_size})"
         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