feat(framework) Add FlowerTune templates to flwr new

src/py/flwr/cli/new/new.py

@@ -41,6 +41,16 @@ class MlFramework(str, Enum):
     HUGGINGFACE = "HF"
     MLX = "MLX"
     SKLEARN = "sklearn"
+    FLOWERTUNE = "FlowerTune"
+
+
+class LlmChallengeName(str, Enum):
+    """Available LLM challenges."""
+
+    GENERALNLP = "GeneralNLP"
+    FINANCE = "Finance"
+    MEDICAL = "Medical"
+    CODE = "Code"
 
 
 class TemplateNotFound(Exception):
@@ -81,6 +91,7 @@ def render_and_create(file_path: str, template: str, context: Dict[str, str]) ->
     create_file(file_path, content)
 
 
+# pylint: disable=too-many-locals,too-many-branches,too-many-statements
 def new(
     project_name: Annotated[
         Optional[str],
@@ -125,6 +136,19 @@ def new(
 
     framework_str = framework_str.lower()
 
+    if framework_str == "flowertune":
+        llm_challenge_value = prompt_options(
+            "Please select LLM challenge by typing in the number",
+            sorted([challenge.value for challenge in LlmChallengeName]),
+        )
+        selected_value = [
+            name
+            for name, value in vars(LlmChallengeName).items()
+            if value == llm_challenge_value
+        ]
+        llm_challenge_str = selected_value[0]
+        llm_challenge_str = llm_challenge_str.lower()
+
     print(
         typer.style(
             f"\n🔨 Creating Flower project {project_name}...",
@@ -139,40 +163,92 @@ def new(
     import_name = package_name.replace("-", "_")
     project_dir = os.path.join(cwd, package_name)
 
-    # List of files to render
-    files = {
-        ".gitignore": {"template": "app/.gitignore.tpl"},
-        "README.md": {"template": "app/README.md.tpl"},
-        "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"},
-        f"{import_name}/__init__.py": {"template": "app/code/__init__.py.tpl"},
-        f"{import_name}/server.py": {
-            "template": f"app/code/server.{framework_str}.py.tpl"
-        },
-        f"{import_name}/client.py": {
-            "template": f"app/code/client.{framework_str}.py.tpl"
-        },
-    }
-
-    # Depending on the framework, generate task.py file
-    frameworks_with_tasks = [
-        MlFramework.PYTORCH.value.lower(),
-        MlFramework.JAX.value.lower(),
-        MlFramework.HUGGINGFACE.value.lower(),
-        MlFramework.MLX.value.lower(),
-        MlFramework.TENSORFLOW.value.lower(),
-    ]
-    if framework_str in frameworks_with_tasks:
-        files[f"{import_name}/task.py"] = {
-            "template": f"app/code/task.{framework_str}.py.tpl"
-        }
-
     context = {
         "project_name": project_name,
         "package_name": package_name,
         "import_name": import_name.replace("-", "_"),
         "username": username,
     }
 
+    # List of files to render
+    if framework_str == "flowertune":
+        files = {
+            ".gitignore": {"template": "app/.gitignore.tpl"},
+            "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"},
+            "README.md": {"template": f"app/README.{framework_str}.md.tpl"},
+            f"{import_name}/__init__.py": {"template": "app/code/__init__.py.tpl"},
+            f"{import_name}/server.py": {
+                "template": "app/code/flwr_tune/server.py.tpl"
+            },
+            f"{import_name}/client.py": {
+                "template": "app/code/flwr_tune/client.py.tpl"
+            },
+            f"{import_name}/app.py": {"template": "app/code/flwr_tune/app.py.tpl"},
+            f"{import_name}/models.py": {
+                "template": "app/code/flwr_tune/models.py.tpl"
+            },
+            f"{import_name}/dataset.py": {
+                "template": "app/code/flwr_tune/dataset.py.tpl"
+            },
+            f"{import_name}/conf/config.yaml": {
+                "template": "app/code/flwr_tune/config.yaml.tpl"
+            },
+            f"{import_name}/conf/static_config.yaml": {
+                "template": "app/code/flwr_tune/static_config.yaml.tpl"
+            },
+        }
+
+        # Challenge specific context
+        fraction_fit = "0.2" if llm_challenge_str == "code" else "0.1"
+        if llm_challenge_str == "generalnlp":
+            challenge_name = "General NLP"
+            num_clients = "20"
+            dataset_name = "vicgalle/alpaca-gpt4"
+        elif llm_challenge_str == "finance":
+            challenge_name = "Finance"
+            num_clients = "50"
+            dataset_name = "FinGPT/fingpt-sentiment-train"
+        elif llm_challenge_str == "medical":
+            challenge_name = "Medical"
+            num_clients = "20"
+            dataset_name = "medalpaca/medical_meadow_medical_flashcards"
+        else:
+            challenge_name = "Code"
+            num_clients = "10"
+            dataset_name = "lucasmccabe-lmi/CodeAlpaca-20k"
+
+        context["llm_challenge_str"] = llm_challenge_str
+        context["fraction_fit"] = fraction_fit
+        context["challenge_name"] = challenge_name
+        context["num_clients"] = num_clients
+        context["dataset_name"] = dataset_name
+    else:
+        files = {
+            ".gitignore": {"template": "app/.gitignore.tpl"},
+            "README.md": {"template": "app/README.md.tpl"},
+            "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"},
+            f"{import_name}/__init__.py": {"template": "app/code/__init__.py.tpl"},
+            f"{import_name}/server.py": {
+                "template": f"app/code/server.{framework_str}.py.tpl"
+            },
+            f"{import_name}/client.py": {
+                "template": f"app/code/client.{framework_str}.py.tpl"
+            },
+        }
+
+        # Depending on the framework, generate task.py file
+        frameworks_with_tasks = [
+            MlFramework.PYTORCH.value.lower(),
+            MlFramework.JAX.value.lower(),
+            MlFramework.HUGGINGFACE.value.lower(),
+            MlFramework.MLX.value.lower(),
+            MlFramework.TENSORFLOW.value.lower(),
+        ]
+        if framework_str in frameworks_with_tasks:
+            files[f"{import_name}/task.py"] = {
+                "template": f"app/code/task.{framework_str}.py.tpl"
+            }
+
     for file_path, value in files.items():
         render_and_create(
             file_path=os.path.join(project_dir, file_path),

src/py/flwr/cli/new/templates/app/README.flowertune.md.tpl

@@ -0,0 +1,56 @@
+# FlowerTune LLM on $challenge_name Dataset
+
+This directory conducts federated instruction tuning with a pretrained [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.3) model on a [$challenge_name dataset](https://huggingface.co/datasets/$dataset_name).
+We use [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the dataset.
+Flower's Simulation Engine is used to simulate the LLM fine-tuning process in federated way,
+which allows users to perform the training on a single GPU.
+
+
+## Methodology
+
+This baseline performs federated LLM fine-tuning with [LoRA](https://arxiv.org/pdf/2106.09685) using the [🤗PEFT](https://huggingface.co/docs/peft/en/index) library.
+The clients' models are aggregated with FedAvg strategy.
+This provides a baseline performance for the leaderboard of $challenge_name challenge.
+
+
+## Environments setup
+
+Project dependencies are defined in `pyproject.toml`. Install them in an activated Python environment with:
+
+```shell
+pip install -e .
+```
+
+## Experimental setup
+
+The dataset is partitioned into $num_clients shards with IID fashion serving as clients.
+We randomly sample $fraction_fit clients to be available for each round,
+and the federated fine-tuning lasts for `200` rounds.
+All settings are defined in `$project_name/conf/static_config.yaml`, which is not allowed to be modified for fair competition if you plan to participated in the [LLM leaderboard](https://flower.ai/benchmarks/llm-leaderboard).
+
+
+## Running the challenge
+
+First make sure that you have got the access to [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.3) model with your Hugging-Face account. You can request access directly from the Hugging-Face website.
+Then, follow the instruction [here](https://huggingface.co/docs/huggingface_hub/en/quick-start#login-command) to log in your account. Note you only need to complete this stage once in your development machine:
+
+```bash
+huggingface-cli login
+```
+
+Run the challenge with default config values.
+The configs are in `$project_name/conf/config.yaml` and `$project_name/conf/static_config.yaml`, and are loaded automatically.
+
+```bash
+flwr run
+```
+
+## VRAM consumption
+
+We use Mistral-7B model with 4-bit quantization as default. The estimated VRAM consumption per client for each challenge is shown below:
+
+| Challenges | GeneralNLP |   Finance  |   Medical  |    Code    |
+| :--------: | :--------: | :--------: | :--------: | :--------: |
+|    VRAM    | ~25.50 GB  | ~17.30 GB  | ~22.80 GB  | ~17.40 GB  |
+
+You can adjust the CPU/GPU resources you assign to each of the clients based on your device, which is specified with `flower.engine.simulation` in `pyproject.toml`.

src/py/flwr/cli/new/templates/app/code/flwr_tune/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+"""Flower CLI `new` command app / code / flwr_tune templates."""

src/py/flwr/cli/new/templates/app/code/flwr_tune/app.py.tpl

@@ -0,0 +1,86 @@
+"""$project_name: A Flower / FlowerTune app."""
+
+import os
+import warnings
+from datetime import datetime
+
+from flwr_datasets import FederatedDataset
+from hydra import compose, initialize
+from hydra.utils import instantiate
+
+from flwr.client import ClientApp
+from flwr.common import ndarrays_to_parameters
+from flwr.server import ServerApp, ServerConfig
+
+from $import_name.client import gen_client_fn, get_parameters
+from $import_name.dataset import get_tokenizer_and_data_collator_and_propt_formatting
+from $import_name.models import get_model
+from $import_name.server import fit_weighted_average, get_evaluate_fn, get_on_fit_config
+
+# Avoid warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+os.environ["TOKENIZERS_PARALLELISM"] = "true"
+os.environ["RAY_DISABLE_DOCKER_CPU_WARNING"] = "1"
+
+# Initialise regular config
+with initialize(config_path="conf", version_base="1.1"):
+    cfg = compose(config_name="config")
+
+# Initialise static config
+with initialize(config_path="conf", version_base="1.1"):
+    cfg_static = compose(config_name="static_config")
+
+cfg.train.num_rounds = cfg_static.num_rounds
+
+# Create output directory given current timestamp
+current_time = datetime.now()
+folder_name = current_time.strftime("%Y-%m-%d_%H-%M-%S")
+save_path = os.path.join(os.getcwd(), f"results/{folder_name}")
+os.makedirs(save_path, exist_ok=True)
+
+# Partition dataset and get dataloaders
+partitioner = instantiate(cfg_static.partitioner)
+fds = FederatedDataset(
+    dataset=cfg_static.dataset.name, partitioners={"train": partitioner}
+)
+(
+    tokenizer,
+    data_collator,
+    formatting_prompts_func,
+) = get_tokenizer_and_data_collator_and_propt_formatting(cfg.model.name)
+
+# ClientApp for Flower Next
+client = ClientApp(
+    client_fn=gen_client_fn(
+        fds,
+        tokenizer,
+        formatting_prompts_func,
+        data_collator,
+        cfg.model,
+        cfg.train,
+        save_path,
+    ),
+)
+
+# Get initial model weights
+init_model = get_model(cfg.model)
+init_model_parameters = get_parameters(init_model)
+init_model_parameters = ndarrays_to_parameters(init_model_parameters)
+
+# Instantiate strategy according to config. Here we pass other arguments
+# that are only defined at runtime.
+strategy = instantiate(
+    cfg.strategy,
+    on_fit_config_fn=get_on_fit_config(),
+    fit_metrics_aggregation_fn=fit_weighted_average,
+    initial_parameters=init_model_parameters,
+    evaluate_fn=get_evaluate_fn(
+        cfg.model, cfg.train.save_every_round, cfg_static.num_rounds, save_path
+    ),
+)
+
+# ServerApp for Flower Next
+server = ServerApp(
+    config=ServerConfig(num_rounds=cfg_static.num_rounds),
+    strategy=strategy,
+)