📝 add doc support and fix bug in qwen2

.github/workflows/book-ci.yml

+name: Book-CI
+
+on:
+  push:
+    branches:
+      - main
+      - server_support
+
+  pull_request:
+    branches:
+      - main
+      - server_support
+jobs:
+  test:
+    name: test
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install Rust
+        run: |
+          rustup set profile minimal
+          rustup toolchain install stable
+          rustup default stable
+      - name: Setup mdBook
+        uses: peaceiris/actions-mdbook@v2
+        with:
+          mdbook-version: "latest"
+      # - name: Run tests
+      #   run: mdbook test
\ No newline at end of file

.github/workflows/deploy.yml

+name: Deploy
+
+on:
+  push:
+    branches:
+      - main
+      - server_support
+
+  pull_request:
+    branches:
+      - main
+      - server_support
+
+defaults:
+  run:
+    shell: bash
+
+permissions:
+  contents: write
+
+jobs:
+  deploy:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install Rust
+        run: |
+          rustup set profile minimal
+          rustup toolchain install stable
+          rustup default stable
+      - name: Setup mdBook
+        uses: peaceiris/actions-mdbook@v2
+        with:
+          mdbook-version: "latest"
+      - run: mdbook build
+      # - name: Copy Assets
+      #   run: |
+      #     chmod +x ci/copy-assets.sh
+      #     ci/copy-assets.sh ${{ matrix.os }}
+      - name: Deploy
+        uses: peaceiris/actions-gh-pages@v3
+        if: ${{ github.ref == 'refs/heads/main' }} or || github.ref == 'refs/heads/server_support'
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./book
\ No newline at end of file

.gitignore

@@ -22,3 +22,4 @@ img/
 tmp1.txt
 test_65_300_1536.txt
 test.txt
+book

book.toml

+[book]
+authors = ["kvcache-ai"]
+language = "zh-CN"
+title = "Ktransformers"
+src = "doc"
+
+[output.html]
+git-repository-url = "https://github.com/kvcache-ai/ktransformers"
+edit-url-template = "https://github.com/kvcache-ai/ktransformers/edit/main/{path}"
+
+[output.html.playground]
+editable = true
+copy-js = true
+# line-numbers = true
+
+[output.html.fold]
+enable = true
+level = 0
\ No newline at end of file

doc/README.md

+<div align="center">
+  <!-- <h1>KTransformers</h1> -->
+  <p align="center">
+
+<picture>
+    <img alt="KTransformers" src="https://github.com/user-attachments/assets/d5a2492f-a415-4456-af99-4ab102f13f8b" width=50%>
+
+</picture>
+
+</p>
+
+</div>
+
+<h2 id="intro">🎉 Introduction</h2>
+KTransformers, pronounced as Quick Transformers, is designed to enhance your 🤗 <a href="https://github.com/huggingface/transformers">Transformers</a> experience with advanced kernel optimizations and placement/parallelism strategies.
+<br/><br/>
+KTransformers is a flexible, Python-centric framework designed with extensibility at its core. 
+By implementing and injecting an optimized module with a single line of code, users gain access to a Transformers-compatible
+interface, RESTful APIs compliant with OpenAI and Ollama, and even a simplified ChatGPT-like web UI. 
+<br/><br/>
+Our vision for KTransformers is to serve as a flexible platform for experimenting with innovative LLM inference optimizations. Please let us know if you need any other features.
+
+<h2 id="Updates">🔥 Updates</h2>
+
+* **Feb 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. The detailed tutorial is [here](./doc/en/DeepseekR1_V3_tutorial.md).
+* **Aug 28, 2024**: Support 1M context under the InternLM2.5-7B-Chat-1M model, utilizing 24GB of VRAM and 150GB of DRAM. The detailed tutorial is [here](./doc/en/long_context_tutorial.md).
+* **Aug 28, 2024**: Decrease DeepseekV2's required VRAM from 21G to 11G.
+* **Aug 15, 2024**: Update detailed [TUTORIAL](doc/en/injection_tutorial.md) for injection and multi-GPU. 
+* **Aug 14, 2024**: Support llamfile as linear backend. 
+* **Aug 12, 2024**: Support multiple GPU; Support new model: mixtral 8\*7B  and 8\*22B; Support q2k, q3k, q5k dequant on gpu.
+* **Aug 9, 2024**: Support windows native.
\ No newline at end of file

doc/SUMMARY.md

+# Ktransformer
+
+[Introduction](./README.md)
+# DeepSeek
+- [DeepseekR1_V3_tutorial](en/DeepseekR1_V3_tutorial.md)
+- [deepseek-v2-injection](en/deepseek-v2-injection.md)
+- [Makefile_usage](en/makefile_usage.md)
+# Server
+- [Server](zh/api/server/README.md)
+  - [Server](zh/api/server/server.md)
+  - [Website](zh/api/server/website.md)
+  - [Tabby](zh/api/server/tabby.md)
+# FAQ
+- [FAQ](en/FAQ.md)
\ No newline at end of file

doc/basic/note1.md

+# basic-first20

doc/basic/note2.md

+# basic-data_structure

doc/zh/api/server/README.md

+# Server
+Still Under Construction... (May have bugs and lack of documentation)
\ No newline at end of file

ktransformers/operators/experts.py

@@ -576,6 +576,8 @@ class KQwen2MoeSparseMoeBlock(BaseInjectedModule, Qwen2MoeSparseMoeBlock):
         routing_weights_expert = routing_weights.to(self.experts.device) if isinstance(self.experts, KExpertsBase) else routing_weights_expert.cpu()
 
         shared_expert_output = self.shared_expert(hidden_states)
+        tmp  = self.shared_expert_gate(hidden_states) 
+        print("shared_expert_gate shape ", tmp.shape)
         shared_expert_output = (
             F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output
         )

ktransformers/operators/linear.py

@@ -54,15 +54,15 @@ class KLinearBase(ABC):
 
         self.has_bias = False
         self.dtype = torch.get_default_dtype()
-        # if orig_module is not None:
-        #     self.in_features = orig_module.in_features
-        #     self.out_features = orig_module.out_features
-        # else:
-        shape = self.gguf_loader.tensor_info[key + ".weight"]["shape"]
-        if len(shape) == 1:
-            print("Warning: orig_module is not set, but has in_features or out_features equals to 1, can't get in_features and out_features from GGUF")
-        self.in_features  = self.gguf_loader.tensor_info[key + ".weight"]["shape"][0]
-        self.out_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][1]
+        if orig_module is not None:
+            self.in_features = orig_module.in_features
+            self.out_features = orig_module.out_features
+        else:
+            shape = self.gguf_loader.tensor_info[key + ".weight"]["shape"]
+            if len(shape) == 1:
+                print("Warning: orig_module is not set, but has in_features or out_features equals to 1, can't get in_features and out_features from GGUF")
+            self.in_features  = self.gguf_loader.tensor_info[key + ".weight"]["shape"][0]
+            self.out_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][1]
 
     @abstractmethod
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -136,12 +136,19 @@ class KLinearTorch(KLinearBase):
     def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
         if device is None: device = self.device
         if w is None: w = self.load_weight(device=device)
+        # else: self.out_features = w.shape[0], self.in_features = w.shape[1]
         
         if isinstance(w, nn.Parameter):
-            self.w = w.to(dtype=self.dtype).T
+            try:
+                self.w = w.to(dtype=self.dtype).view(self.out_features, self.in_features).T
+            except: 
+                self.w = w.to(dtype=self.dtype).T
             self.has_bias = False
         elif isinstance(w, tuple):
-            self.w = w[0].to(dtype=self.dtype).T
+            try:
+                self.w = w[0].to(dtype=self.dtype).view(self.out_features, self.in_features).T
+            except:
+                self.w = w[0].to(dtype=self.dtype).T
             self.bias = w[1].to(dtype=self.dtype)
             self.has_bias = True
         else:
@@ -187,7 +194,8 @@ class KLinearMarlin(KLinearBase):
     def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
         if device is None: device = self.device
         assert device.lower() != "cpu", "Marlin quantized linear only supports GPU device"
-        if w is None: w = self.load_weight(device=device)
+        if w is None: 
+            w = self.load_weight(device=device) 
 
         if isinstance(w, nn.Parameter):
             # pad weight