Llmblog2

README.md

@@ -12,6 +12,7 @@ This repository contains a variety of Determined examples that are not actively
 | Example                                 | Description                                                                  |
 |:---------------------------------------:|:----------------------------------------------------------------------------:|
 | [LLM Finetuning](blog/llm-finetuning) | Finetuning the TinyLlama-1.1B Model on Text-to-SQL. |
+| [LLM Finetuning 2](blog/llm-finetuning-2) | Finetuning the Mistral-7B Model on Text-to-SQL using LoRA and DeepSpeed. |
 | [Python SDK demo](blog/python_sdk_demo) | Example usage of the Determined Python SDK to run and administer experiments. |
 
 ## Computer Vision

blog/llm-finetuning-2/.detignore

@@ -0,0 +1,2 @@
+text-to-sql*
+checkpoints

blog/llm-finetuning-2/.gitignore

@@ -0,0 +1,5 @@
+__pycache__
+.DS_STORE
+text-to-sql*
+checkpoints
+*.png

blog/llm-finetuning-2/README.md

@@ -0,0 +1,60 @@
+# Finetuning Mistral-7B using LoRA and DeepSpeed
+
+In this demo, we finetune the [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) using [LoRA](https://arxiv.org/abs/2106.09685) and [DeepSpeed](https://github.com/microsoft/DeepSpeed). We ran LoRA on two 80 GB A100 GPUs, and DeepSpeed on two, four, and eight 80 GB A100 GPUs.
+
+To get started, first install Determined on your local machine:
+```bash
+pip install determined
+```
+
+Finetune with LoRA:
+```bash
+det e create distributed.yaml . 
+```
+
+Finetune with DeepSpeed:
+```bash
+det e create deepspeed.yaml . 
+```
+
+## Configuration
+
+Change configuration options in `distributed.yaml`. Some important options are:
+- `slots_per_trial`: the number of GPUs to use.
+- `dataset_subset`: the difficulty subset to train on.
+- `per_device_train_batch_size`: the batch size per GPU.
+
+
+DeepSpeed configuration options are in the `ds_configs` folder.
+
+## Testing
+
+Test your model's generation capabilities:
+
+```bash
+python test_model.py --exp_id <exp_id> --dataset_subset <dataset_subset>
+```
+
+Where 
+- `<exp_id>` is the id of your finetuning experiment in the Determined UI.
+- `<dataset_subset>` is one of "easy", "medium", or "hard".
+
+To test the pretrained model (not finetuned), leave out `--exp_id`. For example:
+
+```bash
+python test_model.py --dataset_subset easy
+```
+
+## Validating the tokenizer
+
+Plot the distribution of dataset sample lengths, and see how many samples will be truncated by the tokenizer:
+
+```bash
+python validate_tokenizer.py
+```
+
+
+## Contributors
+
+- [Kevin Musgrave](https://github.com/KevinMusgrave)
+- [Agnieszka Ciborowska](https://github.com/aciborowska)

blog/llm-finetuning-2/chat_format.py

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+CHAT_ML_TEMPLATE = """
+{% for message in messages %}
+{% if message['role'] == 'user' %}
+{{'<|im_start|>user\n' + message['content'].strip() + '<|im_end|>' }}
+{% elif message['role'] == 'system' %}
+{{'<|im_start|>system\n' + message['content'].strip() + '<|im_end|>' }}
+{% elif message['role'] == 'assistant' %}
+{{'<|im_start|>assistant\n'  + message['content'] + '<|im_end|>' }}
+{% endif %}
+{% endfor %}
+"""
+
+
+CHAT_ML_EOS_TOKEN = "<|im_end|>"
+
+
+def get_chat_format(element, model_name, with_assistant_response=True):
+    system_prompt = (
+        "You are a helpful programmer assistant that excels at SQL. "
+        "When prompted with a task and a definition of an SQL table, you "
+        "respond with a SQL query to retrieve information from the table. "
+        "Don't explain your reasoning, only provide the SQL query."
+    )
+
+    user_prompt = "Task: {instruction}\nSQL table: {input}\nSQL query: "
+
+    if model_name == "mistralai/Mistral-7B-Instruct-v0.2":
+        user_prompt = f"{system_prompt}\n{user_prompt}"
+        output = [
+            {"role": "user", "content": user_prompt.format_map(element)},
+        ]
+    else:
+        output = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt.format_map(element)},
+        ]
+
+    if with_assistant_response:
+        output.append({"role": "assistant", "content": element["response"]})
+
+    return output
+
+
+def set_special_tokens(tokenizer, model_name):
+    if model_name == "TinyLlama/TinyLlama-1.1B-Chat-v0.4":
+        tokenizer.chat_template = CHAT_ML_TEMPLATE
+        tokenizer.eos_token = CHAT_ML_EOS_TOKEN
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+
+
+def get_assistant_prompt(model_name):
+    if model_name == "TinyLlama/TinyLlama-1.1B-Chat-v0.4":
+        return "<|im_start|>assistant\n"
+    else:
+        return "[/INST]"
+
+
+def get_response_template_ids(tokenizer, model_name):
+    return tokenizer.encode(get_assistant_prompt(model_name), add_special_tokens=False)
+
+
+def maybe_add_generation_prompt(x, model_name):
+    if model_name == "TinyLlama/TinyLlama-1.1B-Chat-v0.4":
+        return x + get_assistant_prompt(model_name)
+    else:
+        return x

blog/llm-finetuning-2/dataset_utils.py

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+import datasets
+import pandas as pd
+
+
+def add_length_column(dataset) -> pd.DataFrame:
+    df = dataset.to_pandas()
+    df["total_length"] = 0
+    for column_name in ["instruction", "input", "response"]:
+        num_words = df[column_name].astype(str).str.split().apply(len)
+        df["total_length"] += num_words
+
+    return df
+
+
+def filter_by_total_length(df, difficulty, number_of_samples):
+    if difficulty == "easy":
+        return df[df["total_length"].between(10, 100)].iloc[:number_of_samples]
+    elif difficulty == "medium":
+        return df[df["total_length"].between(101, 200)].iloc[:number_of_samples]
+    elif difficulty == "hard":
+        return df[df["total_length"].between(201, 800)].iloc[:number_of_samples]
+
+
+def get_dataset_subset_name(difficulty: str) -> str:
+    return f"text-to-sql-v1-{difficulty}"
+
+
+def create_and_save_datasets(
+    df, difficulty, train_ratio=0.8, val_ratio=0.1, test_ratio=0.1
+):
+    seed = 123
+    # remove total_length column because we don't need it anymore
+    df = df.drop(columns=["total_length"])
+    dataset = datasets.Dataset.from_pandas(df, preserve_index=False)
+
+    # split into training and "the rest"
+    train_valtest = dataset.train_test_split(train_size=train_ratio, seed=seed)
+
+    # split "the rest" into validation and testing
+    val_test = train_valtest["test"].train_test_split(
+        test_size=test_ratio / (test_ratio + val_ratio), seed=seed
+    )
+
+    dataset = datasets.DatasetDict(
+        {
+            "train": train_valtest["train"],
+            "valid": val_test["train"],
+            "test": val_test["test"],
+        }
+    )
+    dataset_name = get_dataset_subset_name(difficulty)
+    dataset.save_to_disk(dataset_name)
+    return dataset
+
+
+def load_dataset(difficulty):
+    return datasets.load_from_disk(get_dataset_subset_name(difficulty))
+
+
+def load_or_create_dataset(difficulty, num_samples=10000):
+    try:
+        return load_dataset(difficulty)
+    except FileNotFoundError:
+        dataset = datasets.load_dataset("Clinton/Text-to-sql-v1")
+        dataset = dataset["train"]
+        dataset = dataset.remove_columns(["text", "source"])
+        df = add_length_column(dataset)
+        df = filter_by_total_length(df, difficulty, num_samples)
+        return create_and_save_datasets(df, difficulty)

blog/llm-finetuning-2/deepspeed.yaml

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+name: mistral deepspeed easy
+debug: false
+environment:
+  environment_variables:
+    - NCCL_DEBUG=INFO
+  image: determinedai/genai-train:latest
+resources:
+  slots_per_trial: 2
+searcher:
+  name: single
+  max_length:
+    batches: 5000
+  metric: eval_accuracy
+  smaller_is_better: false
+hyperparameters:
+  model: "mistralai/Mistral-7B-Instruct-v0.2"
+  dataset_subset: "easy"
+  lora: false
+  training_args:
+    output_dir: "/tmp/llm_finetuning"
+    max_steps: 5000
+    per_device_train_batch_size: 2
+    per_device_eval_batch_size: 4
+    bf16: true
+    evaluation_strategy: "steps"
+    eval_steps: 1000
+    logging_strategy: "steps"
+    logging_steps: 100
+    save_strategy: "steps"
+    save_steps: 5000
+    learning_rate: 1e-5
+    deepspeed: "ds_configs/ds_config_stage_3.json"
+entrypoint: >-
+  python -m determined.launch.deepspeed
+  python finetune.py
+max_restarts: 0

blog/llm-finetuning-2/distributed.yaml

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+name: mistral lora easy
+debug: false
+environment:
+  environment_variables:
+    - NCCL_DEBUG=INFO
+  image: determinedai/environments-dev:python-3.10-pytorch-2.0-deepspeed-0.10.0-smartsim
+resources:
+  slots_per_trial: 2
+searcher:
+  name: single
+  max_length:
+    batches: 5000
+  metric: eval_accuracy
+  smaller_is_better: false
+hyperparameters:
+  model: "mistralai/Mistral-7B-Instruct-v0.2"
+  dataset_subset: "easy"
+  lora: true
+  training_args:
+    output_dir: "/tmp/llm_finetuning"
+    max_steps: 5000
+    per_device_train_batch_size: 8
+    per_device_eval_batch_size: 4
+    bf16: true
+    evaluation_strategy: "steps"
+    eval_steps: 1000
+    logging_strategy: "steps"
+    logging_steps: 100
+    save_strategy: "steps"
+    save_steps: 1000
+    learning_rate: 1e-5
+entrypoint: >-
+  python -m determined.launch.torch_distributed
+  python finetune.py
+max_restarts: 0

blog/llm-finetuning-2/ds_configs/ds_config_stage_1.json

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+{
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
+  "scheduler": {
+    "type": "WarmupLR",
+    "params": {
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto"
+    }
+  },
+  "zero_optimization": {
+    "stage": 1,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 2e8,
+    "overlap_comm": true,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 2e8,
+    "contiguous_gradients": true
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "flops_profiler": {
+    "enabled": true,
+    "profile_step": 1,
+    "module_depth": -1,
+    "top_modules": 1,
+    "detailed": true,
+    "output_file": null
+  }
+}

blog/llm-finetuning-2/ds_configs/ds_config_stage_2.json

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+{
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
+  "scheduler": {
+    "type": "WarmupLR",
+    "params": {
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto"
+    }
+  },
+  "zero_optimization": {
+    "stage": 2,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 2e8,
+    "overlap_comm": true,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 2e8,
+    "contiguous_gradients": true
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "flops_profiler": {
+    "enabled": true,
+    "profile_step": 1,
+    "module_depth": -1,
+    "top_modules": 1,
+    "detailed": true,
+    "output_file": null
+  }
+}