Imatrix

Dockerfile

@@ -60,4 +60,4 @@ ENV PYTHONPATH=${HOME}/app \
     PATH=/usr/local/nvidia/bin:${PATH}
 
 
-ENTRYPOINT ["/bin/bash", "-c", "cd llama.cpp && LLAMA_CUDA=1 make -j && cd .. && /bin/sh start.sh"]
+ENTRYPOINT ["/bin/bash", "-c", "cd llama.cpp && LLAMA_CUDA=1 make -j quantize gguf-split imatrix && cd .. && /bin/sh start.sh"]

Dockerfile.bak

@@ -0,0 +1,63 @@
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -y --no-install-recommends \
+    git \
+    git-lfs \
+    wget \
+    curl \
+    # python build dependencies \
+    build-essential \
+    libssl-dev \
+    zlib1g-dev \
+    libbz2-dev \
+    libreadline-dev \
+    libsqlite3-dev \
+    libncursesw5-dev \
+    xz-utils \
+    tk-dev \
+    libxml2-dev \
+    libxmlsec1-dev \
+    libffi-dev \
+    liblzma-dev \
+    ffmpeg \
+    nvidia-driver-515
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:${PATH}
+WORKDIR ${HOME}/app
+
+RUN curl https://pyenv.run | bash
+ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
+ARG PYTHON_VERSION=3.10.13
+RUN pyenv install ${PYTHON_VERSION} && \
+    pyenv global ${PYTHON_VERSION} && \
+    pyenv rehash && \
+    pip install --no-cache-dir -U pip setuptools wheel && \
+    pip install "huggingface-hub" "hf-transfer" "gradio[oauth]>=4.28.0" "gradio_huggingfacehub_search==0.0.7" "APScheduler"
+
+COPY --chown=1000 . ${HOME}/app
+RUN git clone https://github.com/ggerganov/llama.cpp
+RUN pip install -r llama.cpp/requirements.txt
+
+COPY imatrix_calibration.txt ${HOME}/app/llama.cpp/
+
+ENV PYTHONPATH=${HOME}/app \
+    PYTHONUNBUFFERED=1 \
+    HF_HUB_ENABLE_HF_TRANSFER=1 \
+    GRADIO_ALLOW_FLAGGING=never \
+    GRADIO_NUM_PORTS=1 \
+    GRADIO_SERVER_NAME=0.0.0.0 \
+    GRADIO_THEME=huggingface \
+    TQDM_POSITION=-1 \
+    TQDM_MININTERVAL=1 \
+    SYSTEM=spaces \
+    LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH} \
+    PATH=/usr/local/nvidia/bin:${PATH}
+
+
+ENTRYPOINT ["/bin/bash", "-c", "cd llama.cpp && LLAMA_CUDA=1 make -j && cd .. && /bin/sh start.sh"]

app.py

@@ -1,6 +1,7 @@
 import os
 import shutil
 import subprocess
+import signal
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
 import gradio as gr
 
@@ -18,16 +19,10 @@ from textwrap import dedent
 HF_TOKEN = os.environ.get("HF_TOKEN")
 
 def generate_importance_matrix(model_path, train_data_path):
-    imatrix_command = f"./imatrix -m ../{model_path} -f {train_data_path} -ngl 99"  
+    imatrix_command = f"./imatrix -m ../{model_path} -f {train_data_path} -ngl 99 --output-frequency 10"
 
     os.chdir("llama.cpp")
 
-    compile_command = "LLAMA_CUDA=1 make -j"
-    compile_result = subprocess.run(compile_command, shell=True, capture_output=True, text=True)
-    if compile_result.returncode != 0:
-        raise Exception(f"Error compiling imatrix: {compile_result.stderr}")
-
-
     print(f"Current working directory: {os.getcwd()}")
     print(f"Files in the current directory: {os.listdir('.')}")
 
@@ -35,13 +30,22 @@ def generate_importance_matrix(model_path, train_data_path):
         raise Exception(f"Model file not found: {model_path}")
 
     print("Running imatrix command...")
-    result = subprocess.run(imatrix_command, shell=True, capture_output=True, text=True)
+    process = subprocess.Popen(imatrix_command, shell=True)
+
+    try:
+        process.wait(timeout=60)  # added wait
+    except subprocess.TimeoutExpired:
+        print("Imatrix computation timed out. Sending SIGINT to allow graceful termination...")
+        process.send_signal(signal.SIGINT)
+        try:
+            process.wait(timeout=5)  # grace period
+        except subprocess.TimeoutExpired:
+            print("Imatrix proc still didn't term. Forecfully terming process...")
+            process.kill()
 
     os.chdir("..")
 
-    if result.returncode != 0:
-        raise Exception(f"Error generating importance matrix: {result.stderr}")
-    print("Importance matrix generated successfully!")
+    print("Importance matrix generation completed.")
 
 def split_upload_model(model_path, repo_id, oauth_token: gr.OAuthToken | None, split_max_tensors=256, split_max_size=None):
     if oauth_token.token is None:

app.py.bak

@@ -0,0 +1,375 @@
+import os
+import shutil
+import subprocess
+os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+import gradio as gr
+
+from huggingface_hub import create_repo, HfApi
+from huggingface_hub import snapshot_download
+from huggingface_hub import whoami
+from huggingface_hub import ModelCard
+
+from gradio_huggingfacehub_search import HuggingfaceHubSearch
+
+from apscheduler.schedulers.background import BackgroundScheduler
+
+from textwrap import dedent
+
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+def generate_importance_matrix(model_path, train_data_path):
+    imatrix_command = f"./imatrix -m ../{model_path} -f {train_data_path} -ngl 99"  
+
+    os.chdir("llama.cpp")
+
+    compile_command = "LLAMA_CUDA=1 make -j"
+    compile_result = subprocess.run(compile_command, shell=True, capture_output=True, text=True)
+    if compile_result.returncode != 0:
+        raise Exception(f"Error compiling imatrix: {compile_result.stderr}")
+
+
+    print(f"Current working directory: {os.getcwd()}")
+    print(f"Files in the current directory: {os.listdir('.')}")
+
+    if not os.path.isfile(f"../{model_path}"):
+        raise Exception(f"Model file not found: {model_path}")
+
+    print("Running imatrix command...")
+    result = subprocess.run(imatrix_command, shell=True, capture_output=True, text=True)
+
+    os.chdir("..")
+
+    if result.returncode != 0:
+        raise Exception(f"Error generating importance matrix: {result.stderr}")
+    print("Importance matrix generated successfully!")
+
+def split_upload_model(model_path, repo_id, oauth_token: gr.OAuthToken | None, split_max_tensors=256, split_max_size=None):
+    if oauth_token.token is None:
+        raise ValueError("You have to be logged in.")
+    
+    split_cmd = f"llama.cpp/gguf-split --split --split-max-tensors {split_max_tensors}"
+    if split_max_size:
+        split_cmd += f" --split-max-size {split_max_size}"
+    split_cmd += f" {model_path} {model_path.split('.')[0]}"
+    
+    print(f"Split command: {split_cmd}") 
+    
+    result = subprocess.run(split_cmd, shell=True, capture_output=True, text=True)
+    print(f"Split command stdout: {result.stdout}") 
+    print(f"Split command stderr: {result.stderr}") 
+    
+    if result.returncode != 0:
+        raise Exception(f"Error splitting the model: {result.stderr}")
+    print("Model split successfully!")
+     
+    
+    sharded_model_files = [f for f in os.listdir('.') if f.startswith(model_path.split('.')[0])]
+    if sharded_model_files:
+        print(f"Sharded model files: {sharded_model_files}")
+        api = HfApi(token=oauth_token.token)
+        for file in sharded_model_files:
+            file_path = os.path.join('.', file)
+            print(f"Uploading file: {file_path}")
+            try:
+                api.upload_file(
+                    path_or_fileobj=file_path,
+                    path_in_repo=file,
+                    repo_id=repo_id,
+                )
+            except Exception as e:
+                raise Exception(f"Error uploading file {file_path}: {e}")
+    else:
+        raise Exception("No sharded files found.")
+    
+    print("Sharded model has been uploaded successfully!")
+
+def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_repo, train_data_file, split_model, split_max_tensors, split_max_size, oauth_token: gr.OAuthToken | None):
+    if oauth_token.token is None:
+        raise ValueError("You must be logged in to use GGUF-my-repo")
+    model_name = model_id.split('/')[-1]
+    fp16 = f"{model_name}.fp16.gguf"
+
+    try:
+        api = HfApi(token=oauth_token.token)
+
+        dl_pattern = ["*.md", "*.json", "*.model"]
+
+        pattern = (
+            "*.safetensors"
+            if any(
+                file.path.endswith(".safetensors")
+                for file in api.list_repo_tree(
+                    repo_id=model_id,
+                    recursive=True,
+                )
+            )
+            else "*.bin"
+        )
+
+        dl_pattern += pattern
+
+        api.snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False, allow_patterns=dl_pattern)
+        print("Model downloaded successfully!")
+        print(f"Current working directory: {os.getcwd()}")
+        print(f"Model directory contents: {os.listdir(model_name)}")
+
+        conversion_script = "convert-hf-to-gguf.py"
+        fp16_conversion = f"python llama.cpp/{conversion_script} {model_name} --outtype f16 --outfile {fp16}"
+        result = subprocess.run(fp16_conversion, shell=True, capture_output=True)
+        print(result)
+        if result.returncode != 0:
+            raise Exception(f"Error converting to fp16: {result.stderr}")
+        print("Model converted to fp16 successfully!")
+        print(f"Converted model path: {fp16}")
+
+        imatrix_path = "llama.cpp/imatrix.dat"
+
+        if use_imatrix:
+            if train_data_file:
+                train_data_path = train_data_file.name
+            else:
+                train_data_path = "imatrix_calibration.txt"
+
+            print(f"Training data file path: {train_data_path}")
+
+            if not os.path.isfile(train_data_path):
+                raise Exception(f"Training data file not found: {train_data_path}")
+
+            generate_importance_matrix(fp16, train_data_path)
+        else:
+            print("Not using imatrix quantization.")
+        username = whoami(oauth_token.token)["name"]
+        quantized_gguf_name = f"{model_name.lower()}-{imatrix_q_method.lower()}-imat.gguf" if use_imatrix else f"{model_name.lower()}-{q_method.lower()}.gguf"
+        quantized_gguf_path = quantized_gguf_name
+        if use_imatrix:
+            quantise_ggml = f"./llama.cpp/quantize --imatrix {imatrix_path} {fp16} {quantized_gguf_path} {imatrix_q_method}"
+        else:
+            quantise_ggml = f"./llama.cpp/quantize {fp16} {quantized_gguf_path} {q_method}"
+        result = subprocess.run(quantise_ggml, shell=True, capture_output=True)
+        if result.returncode != 0:
+            raise Exception(f"Error quantizing: {result.stderr}")
+        print(f"Quantized successfully with {imatrix_q_method if use_imatrix else q_method} option!")
+        print(f"Quantized model path: {quantized_gguf_path}")
+
+        # Create empty repo
+        new_repo_url = api.create_repo(repo_id=f"{username}/{model_name}-{imatrix_q_method if use_imatrix else q_method}-GGUF", exist_ok=True, private=private_repo)
+        new_repo_id = new_repo_url.repo_id
+        print("Repo created successfully!", new_repo_url)
+
+        try:
+            card = ModelCard.load(model_id, token=oauth_token.token)
+        except:
+            card = ModelCard("")
+        if card.data.tags is None:
+            card.data.tags = []
+        card.data.tags.append("llama-cpp")
+        card.data.tags.append("gguf-my-repo")
+        card.data.base_model = model_id
+        card.text = dedent(
+            f"""
+            # {new_repo_id}
+            This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
+            Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model.
+            
+            ## Use with llama.cpp
+            Install llama.cpp through brew (works on Mac and Linux)
+            
+            ```bash
+            brew install llama.cpp
+            
+            ```
+            Invoke the llama.cpp server or the CLI.
+            
+            ### CLI:
+            ```bash
+            llama --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
+            ```
+            
+            ### Server:
+            ```bash
+            llama-server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
+            ```
+            
+            Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
+
+            Step 1: Clone llama.cpp from GitHub.
+            ```
+            git clone https://github.com/ggerganov/llama.cpp
+            ```
+
+            Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
+            ```
+            cd llama.cpp && LLAMA_CURL=1 make
+            ```
+
+            Step 3: Run inference through the main binary.
+            ```
+            ./main --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
+            ```
+            or 
+            ```
+            ./server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
+            ```
+            """
+        )
+        card.save(f"README.md")
+
+        if split_model:
+            split_upload_model(quantized_gguf_path, new_repo_id, oauth_token, split_max_tensors, split_max_size)
+        else:
+            try:
+                print(f"Uploading quantized model: {quantized_gguf_path}")
+                api.upload_file(
+                    path_or_fileobj=quantized_gguf_path,
+                    path_in_repo=quantized_gguf_name,
+                    repo_id=new_repo_id,
+                )
+            except Exception as e:
+                raise Exception(f"Error uploading quantized model: {e}")
+        
+        
+        imatrix_path = "llama.cpp/imatrix.dat"
+        if os.path.isfile(imatrix_path):
+            try:
+                print(f"Uploading imatrix.dat: {imatrix_path}")
+                api.upload_file(
+                    path_or_fileobj=imatrix_path,
+                    path_in_repo="imatrix.dat",
+                    repo_id=new_repo_id,
+                )
+            except Exception as e:
+                raise Exception(f"Error uploading imatrix.dat: {e}")
+
+        api.upload_file(
+            path_or_fileobj=f"README.md",
+            path_in_repo=f"README.md",
+            repo_id=new_repo_id,
+        )
+        print(f"Uploaded successfully with {imatrix_q_method if use_imatrix else q_method} option!")
+
+        return (
+            f'Find your repo <a href=\'{new_repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
+            "llama.png",
+        )
+    except Exception as e:
+        return (f"Error: {e}", "error.png")
+    finally:
+        shutil.rmtree(model_name, ignore_errors=True)
+        print("Folder cleaned up successfully!")
+
+
+# Create Gradio interface
+with gr.Blocks(css=".gradio-container {max-height: 600px; overflow-y: auto;}") as demo: 
+    gr.Markdown("You must be logged in to use GGUF-my-repo.")
+    gr.LoginButton(min_width=250)
+
+    model_id = HuggingfaceHubSearch(
+        label="Hub Model ID",
+        placeholder="Search for model id on Huggingface",
+        search_type="model",
+    )
+
+    q_method = gr.Dropdown(
+        ["Q2_K", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"],
+        label="Quantization Method",
+        info="GGML quantization type",
+        value="Q4_K_M",
+        filterable=False,
+        visible=True
+    )
+
+    imatrix_q_method = gr.Dropdown(
+        ["IQ3_M", "IQ3_XXS", "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", "Q5_K_S"],
+        label="Imatrix Quantization Method",
+        info="GGML imatrix quants type",
+        value="IQ4_NL", 
+        filterable=False,
+        visible=False
+    )
+
+    use_imatrix = gr.Checkbox(
+        value=False,
+        label="Use Imatrix Quantization",
+        info="Use importance matrix for quantization."
+    )
+
+    private_repo = gr.Checkbox(
+        value=False,
+        label="Private Repo",
+        info="Create a private repo under your username."
+    )
+
+    train_data_file = gr.File(
+        label="Training Data File",
+        file_types=["txt"],
+        visible=False
+    )
+
+    split_model = gr.Checkbox(
+        value=False,
+        label="Split Model",
+        info="Shard the model using gguf-split."
+    )
+
+    split_max_tensors = gr.Number(
+        value=256,
+        label="Max Tensors per File",
+        info="Maximum number of tensors per file when splitting model.",
+        visible=False
+    )
+
+    split_max_size = gr.Textbox(
+        label="Max File Size",
+        info="Maximum file size when splitting model (--split-max-size). May leave empty to use the default.",
+        visible=False
+    )
+
+    def update_visibility(use_imatrix):
+        return gr.update(visible=not use_imatrix), gr.update(visible=use_imatrix), gr.update(visible=use_imatrix)
+    
+    use_imatrix.change(
+        fn=update_visibility,
+        inputs=use_imatrix,
+        outputs=[q_method, imatrix_q_method, train_data_file]
+    )
+
+    iface = gr.Interface(
+        fn=process_model,
+        inputs=[
+            model_id,
+            q_method,
+            use_imatrix,
+            imatrix_q_method,
+            private_repo,
+            train_data_file,
+            split_model,
+            split_max_tensors,
+            split_max_size,
+        ],
+        outputs=[
+            gr.Markdown(label="output"),
+            gr.Image(show_label=False),
+        ],
+        title="Create your own GGUF Quants, blazingly fast ⚡!",
+        description="The space takes an HF repo as an input, quantizes it and creates a Public repo containing the selected quant under your HF user namespace.",
+        api_name=False
+    )
+
+    def update_split_visibility(split_model):
+        return gr.update(visible=split_model), gr.update(visible=split_model)
+
+    split_model.change(
+        fn=update_split_visibility,
+        inputs=split_model,
+        outputs=[split_max_tensors, split_max_size]
+    )
+
+def restart_space():
+    HfApi().restart_space(repo_id="ggml-org/gguf-my-repo", token=HF_TOKEN, factory_reboot=True)
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=21600)
+scheduler.start()
+
+# Launch the interface
+demo.queue(default_concurrency_limit=1, max_size=5).launch(debug=True, show_api=False)

start.sh

@@ -1,5 +1 @@
-cd llama.cpp
-make -j quantize gguf-split imatrix
-
-cd ..
-python app.py
+python app.py

start.sh.bak

@@ -0,0 +1,5 @@
+cd llama.cpp
+make -j quantize gguf-split imatrix
+
+cd ..
+python app.py