feat: lora blog (#29)

* feat: lora blog


Summary:

Test Plan:

* readme updates

* updated readme

* change folder

* fixed link

---------

Co-authored-by: KevinMusgrave <[email protected]>

Author: szewaiyuen6

README.md

@@ -15,6 +15,7 @@ This repository contains a variety of Determined examples that are not actively
 | [LLM Finetuning](blog/llm-finetuning) | Finetuning TinyLlama-1.1B on Text-to-SQL. |
 | [LLM Finetuning 2](blog/llm-finetuning-2) | Finetuning Mistral-7B on Text-to-SQL using LoRA and DeepSpeed. |
 | [LLM Finetuning 3](blog/llm-finetuning-3) | Finetuning Gemma-2B using DPO. |
+| [LoRA Parameters](blog/lora-parameters) | Finding the best LoRA parameters. |
 | [Python SDK demo](blog/python_sdk_demo) | Example usage of the Determined Python SDK to run and administer experiments. |
 | [Tensor Parallelism](blog/tp) | Profiling tensor parallelism in PyTorch. |
 

blog/lora-parameters/.detignore

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

blog/lora-parameters/.gitignore

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

blog/lora-parameters/README.md

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+# Finding the best LoRA parameters
+
+We finetune [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 40 GB A100 GPUs utilizing DeepSpeed.  
+
+See our [blog post](https://www.determined.ai/blog/lora-parameters) for our experiment results.
+
+To get started, first install Determined on your local machine:
+```bash
+pip install determined
+```
+
+Then finetune with LoRA:
+```bash
+det e create lora.yaml . 
+```
+
+You can view the actual training code in `finetune.py`.
+
+
+## Configuration
+
+Change configuration options in `lora.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 files are in the `ds_configs` folder.
+
+
+## Contributors
+
+- By [Sze Wai Yuen](https://github.com/szewaiyuen6)
+- Built on `llm-finetuning` code by [Agnieszka Ciborowska](https://github.com/aciborowska) and [Kevin Musgrave](https://github.com/KevinMusgrave).

blog/lora-parameters/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/lora-parameters/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/lora-parameters/ds_configs/ds_config_stage_3.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
+    },
+    "bf16": {
+      "enabled": "auto"
+    },
+    "optimizer": {
+      "type": "AdamW",
+      "params": {
+        "lr": "auto",
+        "betas": "auto",
+        "eps": "auto",
+        "weight_decay": "auto"
+      }
+    },
+    "scheduler": {
+      "type": "WarmupDecayLR",
+      "params": {
+        "warmup_min_lr": "auto",
+        "warmup_max_lr": "auto",
+        "warmup_num_steps": "auto",
+        "total_num_steps": "auto"
+      }
+    },
+    "zero_optimization": {
+      "stage": 3,
+      "overlap_comm": true,
+      "contiguous_gradients": true,
+      "sub_group_size": 1e9,
+      "reduce_bucket_size": "auto",
+      "stage3_prefetch_bucket_size": "auto",
+      "stage3_param_persistence_threshold": "auto",
+      "stage3_max_live_parameters": 1e9,
+      "stage3_max_reuse_distance": 1e9,
+      "stage3_gather_16bit_weights_on_model_save": true
+    },
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": "auto",
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto"
+  }