Merge branch 'main' into develop-0.2.3

.devcontainer/Dockerfile

-FROM pytorch/pytorch:2.3.1-cuda12.1-cudnn8-devel as compile_server
+FROM pytorch/pytorch:2.5.1-cuda12.1-cudnn9-devel as compile_server
 WORKDIR /workspace
 ENV CUDA_HOME /usr/local/cuda
 RUN <<EOF
@@ -10,6 +10,7 @@ apt update -y &&  apt install -y  --no-install-recommends \
     g++ \
     cmake && 
 rm -rf /var/lib/apt/lists/* &&
+pip install --upgrade pip &&
 pip install ninja pyproject numpy cpufeature &&
 pip install flash-attn &&
 cp /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /opt/conda/lib/

.github/ISSUE_TEMPLATE/-bug-.yaml

+name: 🐞 Bug report
+description: Create a report to help us reproduce and fix the bug
+title: "[Bug] "
+labels: ['Bug']
+
+body:
+- type: checkboxes
+  attributes:
+    label: Checklist
+    options:
+    - label: 1. I have searched related issues but cannot get the expected help.
+    - label: 2. The bug has not been fixed in the latest version.
+    - label: 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.
+    - label: 4. If the issue you raised is not a bug but a question, please raise a discussion at https://github.com/kvcache-ai/ktransformers/discussions. Otherwise, it will be closed.
+    - label: 5. To help the community, I will use Chinese/English or attach an Chinese/English translation if using another language. Non-Chinese/English content without translation may be closed.
+
+- type: textarea
+  attributes:
+    label: Describe the bug
+    description: A clear and concise description of what the bug is.
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Reproduction
+    description: |
+      What command or script did you run? Which **model** are you using?
+    placeholder: |
+      A placeholder for the command.
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Environment
+    description: |
+      Please provide necessary environment information here (e.g. OS/GPU/CPU). Otherwise the issue will be close.
+    placeholder: Environment here.
+  validations:
+    required: true
\ No newline at end of file

.github/ISSUE_TEMPLATE/-bug2-.yaml

+name: 🐞 BUG报告
+description: 创建报告以帮助我们复现并修复BUG
+title: "[Bug] "
+labels: ['Bug']
+
+body:
+- type: checkboxes
+  attributes:
+    label: 检查清单
+    options:
+    - label: 1. 我已经搜索过相关问题，但未能获得预期的帮助
+    - label: 2. 该问题在最新版本中尚未修复
+    - label: 3. 请注意，如果您提交的BUG相关 issue 缺少对应环境信息和最小可复现示例，我们将难以复现和定位问题，降低获得反馈的可能性
+    - label: 4. 如果您提出的不是bug而是问题，请在讨论区发起讨论 https://github.com/kvcache-ai/ktransformers/discussions。否则该 issue 将被关闭
+    - label: 5. 为方便社区交流，我将使用中文/英文或附上中文/英文翻译（如使用其他语言）。未附带翻译的非中文/英语内容可能会被关闭
+
+- type: textarea
+  attributes:
+    label: 问题描述
+    description: 清晰简洁地描述BUG是什么
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: 复现步骤
+    description: |
+      你运行了什么命令或脚本？使用的是哪个**模型**？
+    placeholder: |
+      在此处填写命令
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: 环境信息
+    description: |
+      请提供必要的环境信息（如操作系统/GPU/CPU），否则该 issue 将被关闭
+    placeholder: 在此处填写环境信息
+  validations:
+    required: true
\ No newline at end of file

.github/ISSUE_TEMPLATE/-feature-.yaml

+name: 🚀 Feature request
+description: Suggest an idea for this project
+title: "[Feature] "
+
+body:
+- type: checkboxes
+  attributes:
+    label: Checklist
+    options:
+    - label: 1. If the issue you raised is not a feature but a question, please raise a discussion at https://github.com/kvcache-ai/ktransformers/discussions. Otherwise, it will be closed.
+    - label: 2. To help the community, I will use Chinese/English or attach an Chinese/English translation if using another language. Non-English/Chinese content without translation may be closed.
+- type: textarea
+  attributes:
+    label: Motivation
+    description: |
+      A clear and concise description of the motivation of the feature.
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Related resources
+    description: |
+      If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful.
\ No newline at end of file

.github/ISSUE_TEMPLATE/-feature2-.yaml

+name: 🚀 新功能请求
+description: 为项目提出新功能建议
+title: "[Feature] "
+
+body:
+- type: checkboxes
+  attributes:
+    label: 检查清单
+    options:
+    - label: 1. 如果您提出的不是新功能而是问题，请在讨论区发起讨论 https://github.com/kvcache-ai/ktransformers/discussions。否则该 issue 将被关闭
+    - label: 2. 为方便社区交流，我将使用中文/英文或附上英文/中文翻译（如使用其他语言）。未附带翻译的非英文/中文内容可能会被关闭
+- type: textarea
+  attributes:
+    label: 需求背景
+    description: |
+      清晰简洁地描述该功能的背景需求
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: 相关资源
+    description: |
+      如果有官方代码实现或第三方实现，请在此提供相关信息，这将非常有帮助
\ No newline at end of file

.github/workflows/docker-image.yml

@@ -3,6 +3,14 @@ name: DockerHub CI
 on:
   release:
     types: [published]
+  workflow_dispatch:
+    inputs:
+      choose:
+        description: 'Will you push the image to DockerHub? 0 for No, 1 for Yes'
+        required: true
+        default: '0'
+        type: string
+
   # push:
   #   branches:
   #     - main

.gitignore

@@ -25,3 +25,4 @@ book
 ktransformers/tests/chat_txt.txt
 mmlu_result*
 ktransformers/ktransformers_ext/cuda_musa/
+test_prompt.txt

Dockerfile

@@ -10,7 +10,7 @@ EOF
 
 
 
-FROM pytorch/pytorch:2.3.1-cuda12.1-cudnn8-devel as compile_server
+FROM pytorch/pytorch:2.5.1-cuda12.1-cudnn9-devel as compile_server
 ARG CPU_INSTRUCT=NATIVE
 WORKDIR /workspace
 ENV CUDA_HOME /usr/local/cuda
@@ -27,6 +27,7 @@ rm -rf /var/lib/apt/lists/* &&
 cd ktransformers &&
 git submodule init &&
 git submodule update &&
+pip install --upgrade pip &&
 pip install ninja pyproject numpy cpufeature &&
 pip install flash-attn &&
 CPU_INSTRUCT=${CPU_INSTRUCT}  KTRANSFORMERS_FORCE_BUILD=TRUE TORCH_CUDA_ARCH_LIST="8.0;8.6;8.7;8.9;9.0+PTX" pip install . --no-build-isolation --verbose &&

doc/en/DeepseekR1_V3_tutorial.md

@@ -160,6 +160,7 @@ is speed up which is inspiring. So our showcase makes use of this finding*
 ### V0.2.2 longer context & FP8 kernel
 #### longer context
 To use this feature, [install flashinfer](https://github.com/flashinfer-ai/flashinfer) first.
+
 Note: The latest MLA kernel in FlashInfer still has a few minor issues. They are continuously fixing them on the main branch. If you are using FlashInfer, please install it from the main source code.
 
 If you want to use long context(longer than 20K) for prefill, enable the matrix absorption MLA during the prefill phase, which will significantly reduce the size of the kv cache. Modify yaml file like this:
@@ -173,6 +174,8 @@ If you want to use long context(longer than 20K) for prefill, enable the matrix
       prefill_device: "cuda"
       absorb_for_prefill: True # change this to True to enable long context(prefill may slower).
 ```
+
+If the VRAM is still insufficient, try reducing the `chunk_prefill_size` parameter (default is 8192) to further decrease the intermediate results during chunk prefill.
 #### FP8 kernel
 
 The DeepSeek-AI team provides FP8 safetensors for DeepSeek-R1/V3 models. We achieve performance optimization through the following works:

ktransformers/ktransformers_ext/CMakeLists.txt

@@ -209,6 +209,7 @@ add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../../third_party/llama.cpp ${CMAKE
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/../../third_party)
 if (WIN32)
     include_directories("$ENV{CUDA_PATH}/include")
+    add_compile_definitions(KTRANSFORMERS_USE_CUDA=1)
 elseif (UNIX)
     if (KTRANSFORMERS_USE_CUDA)
         find_package(CUDA REQUIRED)

ktransformers/ktransformers_ext/operators/kvcache/kvcache_attn.cpp

@@ -10,6 +10,8 @@
 
 #include "kvcache.h"
 
+#include <chrono>
+
 void KVCache::attention_kvhead_(const uint16_t *q_in_data, ggml_fp16_t *output,
                                 float *attn_lse, int batch_size,
                                 Backend *backend) {

ktransformers/ktransformers_ext/operators/kvcache/kvcache_load_dump.cpp

@@ -9,6 +9,9 @@
  **/
 
 #include "kvcache.h"
+
+#include <chrono>
+
 void KVCache::load_kvcache(std::string tensor_file_path, Backend *backend) {
     // Timer start
     auto start = std::chrono::high_resolution_clock::now();

ktransformers/ktransformers_ext/operators/kvcache/kvcache_read_write.cpp

@@ -10,6 +10,8 @@
 
 #include "kvcache.h"
 
+#include <chrono>
+
 void KVCache::get_anchor_one_block(ggml_fp16_t *anchor, int layer_id,
                                    int block_idx, Backend *backend) {
     // Timer start

ktransformers/ktransformers_ext/operators/kvcache/kvcache_utils.cpp

@@ -10,6 +10,8 @@
 
 #include "kvcache.h"
 
+#include <chrono>
+
 std::string ggml_type_to_string(ggml_type type) {
     switch (type) {
     case GGML_TYPE_F32:

ktransformers/local_chat.py

@@ -62,6 +62,7 @@ def local_chat(
     prompt_file : str | None = None,
     mode: str = "normal",
     force_think: bool = False,
+    chunk_prefill_size: int = 8192
 ):
 
     torch.set_grad_enabled(False)
@@ -110,15 +111,15 @@ def local_chat(
     optimize_and_load_gguf(model, optimize_config_path, gguf_path, config)
     
     try:
-            model.generation_config = GenerationConfig.from_pretrained(model_path)
-    except:
-            gen_config = GenerationConfig(
-                max_length=128,
-                temperature=0.7,
-                top_p=0.9,
-                do_sample=True
-            )
-            model.generation_config = gen_config
+        model.generation_config = GenerationConfig.from_pretrained(model_path)
+    except Exception as e:
+        print(f"generation config can't auto create, make default. Message: {e}")
+        gen_config = GenerationConfig(
+            temperature=0.6,
+            top_p=0.95,
+            do_sample=True
+        )
+        model.generation_config = gen_config
     # model.generation_config = GenerationConfig.from_pretrained(model_path)
     if model.generation_config.pad_token_id is None:
         model.generation_config.pad_token_id = model.generation_config.eos_token_id
@@ -168,16 +169,16 @@ def local_chat(
             assert Config().long_context_config['max_seq_len'] > input_tensor.shape[1] + max_new_tokens, \
             "please change max_seq_len in  ~/.ktransformers/config.yaml"
         
-        if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or "DeepseekV3ForCausalLM") and flashinfer_enabled and get_compute_capability() >= 8:
+        if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or config.architectures[0] == "DeepseekV3ForCausalLM") and flashinfer_enabled and get_compute_capability() >= 8:
             generated = prefill_and_generate(
-                model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think,
+                model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_prefill_size = chunk_prefill_size,
                 use_flashinfer_mla = True, num_heads = config.num_attention_heads, head_dim_ckv = config.kv_lora_rank, head_dim_kpe = config.qk_rope_head_dim, q_head_dim = config.qk_rope_head_dim + config.qk_nope_head_dim
             )
         else:
             generated = prefill_and_generate(
-                model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think,
+                model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_prefill_size = chunk_prefill_size,
             )
 
 
 if __name__ == "__main__":
-    fire.Fire(local_chat)
+    fire.Fire(local_chat)
\ No newline at end of file

ktransformers/operators/flashinfer_wrapper.py

@@ -122,7 +122,7 @@ class MLAWrapper():
         if kv_indices is None:
             assert self.max_batch_size == 1
             kv_indices = self.kv_indices_buf
-        
+
         self.wrapper.plan(
             qo_indptr,
             kv_indptr,
@@ -139,6 +139,11 @@ class MLAWrapper():
         )
 
     def run(self, q_nope, q_pe, ckv, k_pe, return_lse = False):
+        #print("run")
+        #print(self.wrapper._qo_indptr_buf)
+        #print(self.wrapper._kv_indptr_buf)
+        #print(self.wrapper._kv_indices_buf)
+        #print(self.wrapper._kv_len_arr_buf)
         return self.wrapper.run(q_nope, q_pe, ckv, k_pe, return_lse = return_lse)
 
 class MLAWrapperSingleton():
@@ -200,12 +205,14 @@ class MLAWrapperSingleton():
             
 
 if __name__ == "__main__":
+    torch.set_default_dtype(torch.bfloat16)
     max_batch_size = 1
-    max_pages = 1
+    max_pages = 128
     page_size = 64
     num_heads = 128
 
-    q_len = 10
+    kv_len = 4023
+    q_len = 1
     q_nope = torch.randn((q_len, num_heads, 512), dtype=torch.bfloat16, device="cuda")
     q_pe = torch.randn((q_len, num_heads, 64), dtype=torch.bfloat16, device="cuda")
     ckv = torch.randn((max_pages, page_size, 512), dtype=torch.bfloat16, device="cuda")
@@ -218,7 +225,7 @@ if __name__ == "__main__":
         max_pages,
     )
     
-    kv_len_arr = torch.tensor([q_len], dtype=torch.int32, device="cuda")
+    kv_len_arr = torch.tensor([kv_len], dtype=torch.int32, device="cuda")
     qo_indptr = torch.tensor([0, q_len], dtype=torch.int32, device="cuda")
     wrapper.plan(
         qo_indptr,
@@ -236,6 +243,29 @@ if __name__ == "__main__":
 
     attn_output = wrapper.run(q_nope, q_pe, ckv, k_pe)
     print(attn_output.shape)
+    
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        attn_output = wrapper.run(q_nope, q_pe, ckv, k_pe)
+        
+    kv_len = 6789
+    kv_len_arr = torch.tensor([kv_len], dtype=torch.int32, device="cuda")
+    qo_indptr = torch.tensor([0, q_len], dtype=torch.int32, device="cuda")
+    wrapper.plan(
+        qo_indptr,
+        None,
+        None,
+        kv_len_arr,
+        128,
+        512,
+        64,
+        page_size,
+        192 ** (-0.5),
+        torch.bfloat16,
+        torch.bfloat16,
+    )
+    
+    graph.replay()
 
     k = (
         torch.cat([ckv, k_pe], dim=-1)
@@ -244,15 +274,15 @@ if __name__ == "__main__":
     )
     v = ckv.view(-1, 1, 512).repeat_interleave(num_heads, dim=1)
 
-    print(k[:10].shape)
-    print(v[:10].shape)
+    print(k[:kv_len].shape)
+    print(v[:kv_len].shape)
 
     attn_ref, lse_ref = attention_ref(
         max_batch_size,
         torch.cat([q_nope, q_pe], dim=-1),
-        k[:10],
-        v[:10],
-        False,
+        k[:kv_len],
+        v[:kv_len],
+        True,
         192 ** (-0.5)
     )
     print(attn_ref.shape)

ktransformers/server/api/openai/endpoints/chat.py

@@ -31,13 +31,13 @@ async def chat_completion(request:Request,create:ChatCompletionCreate):
     if create.stream:
         async def inner():
             chunk = ChatCompletionChunk(id=id,object='chat.completion.chunk',created=int(time()))
-            async for token in interface.inference(input_message,id):
+            async for token in interface.inference(input_message,id,create.temperature,create.top_p):
                 chunk.set_token(token)
                 yield chunk
         return chat_stream_response(request,inner())
     else:
         comp = ChatCompletionObject(id=id,object='chat.completion',created=int(time()))
         comp.usage = Usage(completion_tokens=1, prompt_tokens=1, total_tokens=2)
-        async for token in interface.inference(input_message,id):
+        async for token in interface.inference(input_message,id,create.temperature,create.top_p):
             comp.append_token(token)
         return comp

ktransformers/server/api/openai/legacy/completions.py

@@ -20,7 +20,7 @@ async def create_completion(request:Request,create:CompletionCreate):
 
     if create.stream:
         async def inner():
-            async for token in interface.inference(create.prompt,id):     
+            async for token in interface.inference(create.prompt,id,create.temperature,create.top_p):     
                 d = {'choices':[{'delta':{'content':token}}]}
                 yield f"data:{json.dumps(d)}\n\n"
             d = {'choices':[{'delta':{'content':''},'finish_reason':''}]}
@@ -28,6 +28,6 @@ async def create_completion(request:Request,create:CompletionCreate):
         return stream_response(request,inner())
     else:
         comp = CompletionObject(id=id,object='text_completion',created=int(time()))
-        async for token in interface.inference(create.prompt,id):     
+        async for token in interface.inference(create.prompt,id,create.temperature,create.top_p):     
             comp.append_token(token)
         return comp

ktransformers/server/args.py

@@ -24,13 +24,13 @@ class ArgumentParser:
         parser.add_argument("--optimize_config_path", default=self.cfg.optimize_config_path, type=str, required=False)
         parser.add_argument("--cpu_infer", type=int, default=self.cfg.cpu_infer)
         parser.add_argument("--type", type=str, default=self.cfg.backend_type)
+        parser.add_argument("--chunk_prefill_size", type=int, default=8192)
 
         # model configs
         # parser.add_argument("--model_cache_lens", type=int, default=self.cfg.cache_lens)  # int?
         parser.add_argument("--paged", type=bool, default=self.cfg.paged)
         parser.add_argument("--total_context", type=int, default=self.cfg.total_context)
         parser.add_argument("--max_batch_size", type=int, default=self.cfg.max_batch_size)
-        parser.add_argument("--max_chunk_size", type=int, default=self.cfg.max_chunk_size)
         parser.add_argument("--max_new_tokens", type=int, default=self.cfg.max_new_tokens)
         parser.add_argument("--json_mode", type=bool, default=self.cfg.json_mode)
         parser.add_argument("--healing", type=bool, default=self.cfg.healing)

ktransformers/server/backend/args.py

@@ -23,7 +23,7 @@ class ConfigArgs(BaseModel):
     max_batch_size: int = Field(
         None, description="Max number of batches to run at once, assuming the sequences will fit within total_context"
     )
-    max_chunk_size: int = Field(
+    chunk_prefill_size: int = Field(
         None,
         description=(
             "Max chunk size. Determines the size of prefill operations. Can be reduced to reduce pauses whenever a new"