mirror of
https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
synced 2026-09-26 14:42:04 +00:00
Add hyperparameter tuning examples to #PEFT Llama2 and OpenLlama notebooks (#2564)
This commit is contained in:
KCFindstr
@@ -272,7 +272,7 @@
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"outputs": [],
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"source": [
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"# The pre-built training, serving and evaluation docker images.\n",
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"TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-train:20231129_0936_RC00\"\n",
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"TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-train:20231130_0936_RC00\"\n",
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"PREDICTION_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-serve:20231129_0948_RC00\"\n",
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"VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20231127_0916_RC00\"\n",
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"VLLM_GPTQ_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:gptq\"\n",
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@@ -938,12 +938,12 @@
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"# accelerator_type = \"NVIDIA_TESLA_A100\"\n",
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"# accelerator_count = 1\n",
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"\n",
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"# Finetunes and LLaMA2 70B with 8 L4 (24G).\n",
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"# Finetunes LLaMA2 70B with 8 L4 (24G).\n",
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"# machine_type = \"g2-standard-96\"\n",
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"# accelerator_type = \"NVIDIA_L4\"\n",
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"# accelerator_count = 8\n",
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"\n",
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"# Finetunes and LLaMA2 70B with 4 A100 (40G).\n",
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"# Finetunes LLaMA2 70B with 4 A100 (40G).\n",
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"# machine_type = \"a2-highgpu-4g\"\n",
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"# accelerator_type = \"NVIDIA_TESLA_A100\"\n",
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"# accelerator_count = 4\n",
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@@ -1023,7 +1023,65 @@
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"\n",
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"- `metric_spec`: Dictionary specifying the metric to optimize. The dictionary key is the hyperparameter_metric_tag that you set in your training application code, and the value is the optimization goal.\n",
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"\n",
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"The following example runs 4 trials on different learning rates, and evaluates the model on 100 examples selected from the `truthfulqa_mc2` dataset. You can customize the search space by extending the range of learning rates, adding other parameters such as LoRA rank, etc. Please refer to the [hyperparameter tuning documentation](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview) for more information."
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"The following 4bit QLoRA experiment results show the effectiveness of hyperparameter tuning evaluated on the ARC Challenge dataset (for reference only):\n",
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"\n",
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"| Model | Training time | Trials | Parallel Trials | GPU | ∆arc challenge | ∆hellaswag | ∆truthfulqa_mc | cost |\n",
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"|------------|---------------|--------|-----------------|------|----------------|------------|----------------|-------------|\n",
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"| Llama2-7b | 2d 10hrs | 8 | 1 | L4x1 | +0.73 | +1.61 | +5.34 | \\$49.5088 |\n",
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"| Llama2-13b | 4d 8hrs | 8 | 1 | L4x1 | +1.53 | +2.11 | +10.98 | \\$88.7744 |\n",
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"| Llama2-70b | 6d 10hrs | 8 | 2 | L4x4 | +0.77 | +0.93 | +10.63 | \\$1,312.5461 |\n",
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"\n",
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"The following example runs 8 trials on `timdettmers/openassistant-guanaco` with different learning rates, and evaluates the model on `arc_challenge` dataset. You can customize the search space by extending the range of learning rates, adding other parameters such as LoRA rank, etc. Please refer to the [hyperparameter tuning documentation](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview) for more information."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "e1289e21a9d3"
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},
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"outputs": [],
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"source": [
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"# Huggingface dataset name or gs:// URI to a custom JSONL dataset.\n",
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"dataset_name = \"timdettmers/openassistant-guanaco\" # @param {type:\"string\"}\n",
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"# Optional. Template name or gs:// URI to a custom template.\n",
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"template = \"\" # @param {type:\"string\"}\n",
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"\n",
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"hpt_precision_mode = \"4bit\"\n",
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"\n",
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"# Worker pool spec for 4bit finetuning.\n",
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"\n",
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"# Finetunes LLaMA2 7B with 1 L4 (24G).\n",
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"machine_type = \"g2-standard-8\"\n",
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"accelerator_type = \"NVIDIA_L4\"\n",
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"accelerator_count = 1\n",
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"\n",
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"# Finetunes LLaMA2 13B with 1 L4 (24G).\n",
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"# machine_type = \"g2-standard-8\"\n",
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"# accelerator_type = \"NVIDIA_L4\"\n",
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"# accelerator_count = 1\n",
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"\n",
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"# Finetunes LLaMA2 70B with 4 L4 (24G).\n",
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"# machine_type = \"g2-standard-48\"\n",
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"# accelerator_type = \"NVIDIA_L4\"\n",
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"# accelerator_count = 4"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "80a5f9508a95"
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},
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"source": [
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"### [Optional] Custom evaluation dataset\n",
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"\n",
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"To obtain a model with better performance on some specific tasks, you might want to run hyperparameter tuning with a custom evaluation dataset. The hyperparameter tuning service will pick the model according to the evaluation dataset and the metrics you selected. You can use any of the following tasks as the `eval_task` in the code cell below:\n",
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"\n",
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"1. The name of a [lm-evaluation-harness task](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor/lm_eval/tasks).\n",
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"\n",
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"2. `custom_likelihood`. Then, add a flag `--eval_dataset_path=<Cloud Storage URI to your JSONL dataset>`. The JSONL file must be in the format in Vertex AI language model's [prepare evaluation dataset](https://cloud.google.com/vertex-ai/docs/generative-ai/models/evaluate-models#classification) page.\n",
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"\n",
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"3. `builtin_eval`. The built-in evaluation loop of the trainer will be used to evaluate the model instead of the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) library. You can supply any eval dataset in the same format as the training dataset by specifying `--eval_dataset_path`, `--eval_split`, `--eval_template`, and `--eval_column`."
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]
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},
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{
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@@ -1036,26 +1094,29 @@
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"source": [
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"from google.cloud.aiplatform import hyperparameter_tuning as hpt\n",
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"\n",
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"# Refer to https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor/lm_eval/tasks\n",
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"# for supported tasks and eval metrics.\n",
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"eval_task = \"truthfulqa_mc2\" # @param {type:\"string\"}\n",
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"eval_metric_name = \"acc\" # @param {type:\"string\"}\n",
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"eval_task = \"arc_challenge\" # @param {type:\"string\"}\n",
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"eval_metric_name = \"acc_norm\" # @param {type:\"string\"}\n",
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"\n",
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"# Runs 10 training steps as a minimal example. Use 1000 to reproduce the experiment results.\n",
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"max_steps = 10 # @param {type:\"integer\"}\n",
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"# Evaluates the model on 10 examples. Use 10000 to reproduce the experiment results.\n",
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"eval_limit = 10 # @param {type:\"integer\"}\n",
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"\n",
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"flags = {\n",
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" \"learning_rate\": 2e-4,\n",
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" \"precision_mode\": \"float16\",\n",
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" \"task\": \"causal-language-modeling-lora\",\n",
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" \"learning_rate\": 1e-5,\n",
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" \"precision_mode\": hpt_precision_mode,\n",
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" \"task\": \"instruct-lora\",\n",
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" \"pretrained_model_id\": base_model_id,\n",
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" \"output_dir\": output_dir,\n",
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" \"warmup_steps\": 10,\n",
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" \"max_steps\": 10,\n",
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" \"lora_rank\": 16,\n",
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" \"lora_alpha\": 32,\n",
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" \"max_steps\": max_steps,\n",
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" \"lora_rank\": 32,\n",
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" \"lora_alpha\": 64,\n",
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" \"lora_dropout\": 0.05,\n",
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" \"dataset_name\": dataset_name,\n",
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" \"eval_steps\": 10,\n",
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" \"eval_steps\": max_steps + 1, # Only evaluates in the end.\n",
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" \"eval_tasks\": eval_task,\n",
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" \"eval_limit\": 100,\n",
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" \"eval_limit\": eval_limit,\n",
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" \"eval_metric_name\": eval_metric_name,\n",
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"}\n",
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"\n",
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@@ -1075,9 +1136,7 @@
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"]\n",
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"metric_spec = {\"model_performance\": \"maximize\"}\n",
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"parameter_spec = {\n",
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" \"learning_rate\": hpt.DiscreteParameterSpec(\n",
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" values=[4e-5, 4.3e-5, 4.6e-5, 5e-5], scale=\"linear\"\n",
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" ),\n",
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" \"learning_rate\": hpt.DoubleParameterSpec(min=1e-5, max=1e-4, scale=\"linear\"),\n",
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"}\n",
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"train_job = aiplatform.CustomJob(\n",
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" display_name=job_name,\n",
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@@ -1090,7 +1149,7 @@
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" custom_job=train_job,\n",
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" metric_spec=metric_spec,\n",
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" parameter_spec=parameter_spec,\n",
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" max_trial_count=4,\n",
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" max_trial_count=8,\n",
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" parallel_trial_count=2,\n",
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")\n",
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"\n",
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@@ -1116,14 +1175,9 @@
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"best_trial_id = (\n",
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" np.argmax(\n",
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" [trial.final_measurement.metrics[0].value for trial in train_hpt_job.trials]\n",
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" )\n",
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" + 1\n",
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")\n",
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"best_trial_id = max(\n",
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" train_hpt_job.trials, key=lambda trial: trial.final_measurement.metrics[0].value\n",
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").id\n",
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"output_dir = os.path.join(output_dir, f\"trial_{best_trial_id}\")\n",
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"output_dir_gcsfuse = output_dir.replace(\"gs://\", \"/gcs/\")\n",
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"print(f\"Best trial {best_trial_id} saved model in:\", output_dir)"
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@@ -1531,8 +1585,8 @@
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" desc_act = True\n",
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" quantization_args = [\n",
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" \"--task=quantize-model\",\n",
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" f\"--pretrained_model_id={base_model_id}\",\n",
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" f\"--quantization_method={quantization_method}\",\n",
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" f\"--pretrained_model_id={base_model_id}\",\n",
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" f\"--quantization_precision_mode={quantization_precision_mode}\",\n",
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" f\"--quantization_output_dir={quantization_output_dir_gcsfuse}\",\n",
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" f\"--quantization_dataset_name={gptq_dataset_name}\",\n",
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@@ -248,7 +248,7 @@
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"outputs": [],
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"source": [
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"# The pre-built training and serving docker images.\n",
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"TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-train:20231129_0936_RC00\"\n",
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"TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-train:20231130_0936_RC00\"\n",
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"PREDICTION_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-serve:20231130_0948_RC00\"\n",
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"VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20231127_0916_RC00\"\n",
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"VLLM_GPTQ_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:gptq\"\n",
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@@ -694,6 +694,175 @@
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "53b8a1ad6def"
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},
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"source": [
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"### [Optional] Hyperparameter tuning\n",
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"\n",
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"You can use the Vertex AI SDK to create and run the [hyperparameter tuning job](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview) to obtain a better performance by experimenting with different hyperparameters such as learning rates.\n",
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"\n",
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"Define the following specifications:\n",
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"\n",
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"- `worker_pool_specs`: Dictionary specifying the machine type and Docker image.\n",
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"\n",
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"- `parameter_spec`: Dictionary specifying the parameters to optimize. The dictionary key is the string assigned to the command line argument for each hyperparameter in your training application code, and the dictionary value is the parameter specification. The parameter specification includes the type, min/max values, and scale for the hyperparameter.\n",
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"\n",
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"- `metric_spec`: Dictionary specifying the metric to optimize. The dictionary key is the hyperparameter_metric_tag that you set in your training application code, and the value is the optimization goal.\n",
|
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"\n",
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"The following 4bit QLoRA experiment results show the effectiveness of hyperparameter tuning evaluated on the ARC Challenge dataset (for reference only):\n",
|
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"\n",
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"| Model | Training time | Trials | Parallel Trials | GPU | ∆arc challenge | ∆hellaswag | ∆truthfulqa_mc | cost |\n",
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"|---------------|---------------|--------|-----------------|------|----------------|------------|----------------|----------|\n",
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"| Openllama-3b | 2d 10hrs | 8 | 1 | L4x1 | +1.62 | +7.32 | +3.34 | \\$29.0232 |\n",
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"| Openllama-7b | 1d 4hrs | 8 | 2 | L4x1 | +2.82 | +3.55 | +6.68 | \\$47.8016 |\n",
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"| Openllama-13b | 6d 10hrs | 8 | 2 | L4x1 | +1.01 | +3.67 | +6.19 | \\$87.9208 |\n",
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"\n",
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"The following example runs 8 trials on `timdettmers/openassistant-guanaco` with different learning rates, and evaluates the model on `arc_challenge` dataset. You can customize the search space by extending the range of learning rates, adding other parameters such as LoRA rank, etc. Please refer to the [hyperparameter tuning documentation](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview) for more information."
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||||
"metadata": {
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||||
"id": "a81402f70641"
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},
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"outputs": [],
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"source": [
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"# Huggingface dataset name or gs:// URI to a custom JSONL dataset.\n",
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"dataset_name = \"timdettmers/openassistant-guanaco\" # @param {type:\"string\"}\n",
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"# Optional. Template name or gs:// URI to a custom template.\n",
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"template = \"\" # @param {type:\"string\"}\n",
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"\n",
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"hpt_precision_mode = \"4bit\"\n",
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"\n",
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"# Worker pool spec for 4bit finetuning.\n",
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"\n",
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"# Finetunes Openllama 3B / 7B / 13B with 1 L4 (24G).\n",
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"machine_type = \"g2-standard-8\"\n",
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"accelerator_type = \"NVIDIA_L4\"\n",
|
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"accelerator_count = 1"
|
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]
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},
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{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "55ed2a2a9d54"
|
||||
},
|
||||
"source": [
|
||||
"### [Optional] Custom evaluation dataset\n",
|
||||
"\n",
|
||||
"To obtain a model with better performance on some specific tasks, you might want to run hyperparameter tuning with a custom evaluation dataset. The hyperparameter tuning service will pick the model according to the evaluation dataset and the metrics you selected. You can use any of the following tasks as the `eval_task` in the code cell below:\n",
|
||||
"\n",
|
||||
"1. The name of a [lm-evaluation-harness task](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor/lm_eval/tasks).\n",
|
||||
"\n",
|
||||
"2. `custom_likelihood`. Then, add a flag `--eval_dataset_path=<Cloud Storage URI to your JSONL dataset>`. The JSONL file must be in the format in Vertex AI language model's [prepare evaluation dataset](https://cloud.google.com/vertex-ai/docs/generative-ai/models/evaluate-models#classification) page.\n",
|
||||
"\n",
|
||||
"3. `builtin_eval`. The built-in evaluation loop of the trainer will be used to evaluate the model instead of the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) library. You can supply any eval dataset in the same format as the training dataset by specifying `--eval_dataset_path`, `--eval_split`, `--eval_template`, and `--eval_column`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "7864cff27197"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from google.cloud.aiplatform import hyperparameter_tuning as hpt\n",
|
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"\n",
|
||||
"eval_task = \"arc_challenge\" # @param {type:\"string\"}\n",
|
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"eval_metric_name = \"acc_norm\" # @param {type:\"string\"}\n",
|
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"\n",
|
||||
"# Runs 10 training steps as a minimal example. Use 1000 to reproduce the experiment results.\n",
|
||||
"max_steps = 10 # @param {type:\"integer\"}\n",
|
||||
"# Evaluates the model on 10 examples. Use 10000 to reproduce the experiment results.\n",
|
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"eval_limit = 10 # @param {type:\"integer\"}\n",
|
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"\n",
|
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"flags = {\n",
|
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" \"learning_rate\": 1e-5,\n",
|
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" \"precision_mode\": hpt_precision_mode,\n",
|
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" \"task\": \"instruct-lora\",\n",
|
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" \"pretrained_model_id\": base_model_id,\n",
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" \"output_dir\": lora_output_dir_gcsfuse,\n",
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" \"warmup_steps\": 10,\n",
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" \"max_steps\": max_steps,\n",
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" \"lora_rank\": 32,\n",
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" \"lora_alpha\": 64,\n",
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" \"lora_dropout\": 0.05,\n",
|
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" \"dataset_name\": dataset_name,\n",
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" \"eval_steps\": max_steps + 1, # Only evaluates in the end.\n",
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" \"eval_tasks\": eval_task,\n",
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" \"eval_limit\": eval_limit,\n",
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" \"eval_metric_name\": eval_metric_name,\n",
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" \"merge_base_and_lora_output_dir\": merged_model_output_dir_gcsfuse,\n",
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"}\n",
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"\n",
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"worker_pool_specs = [\n",
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" {\n",
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" \"machine_spec\": {\n",
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" \"machine_type\": machine_type,\n",
|
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" \"accelerator_type\": accelerator_type,\n",
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" \"accelerator_count\": accelerator_count,\n",
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" },\n",
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" \"replica_count\": replica_count,\n",
|
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" \"container_spec\": {\n",
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" \"image_uri\": TRAIN_DOCKER_URI,\n",
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" \"args\": [\"--{}={}\".format(k, v) for k, v in flags.items()],\n",
|
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" },\n",
|
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" }\n",
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"]\n",
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"metric_spec = {\"model_performance\": \"maximize\"}\n",
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"parameter_spec = {\n",
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" \"learning_rate\": hpt.DoubleParameterSpec(min=1e-5, max=1e-4, scale=\"linear\"),\n",
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"}\n",
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"train_job = aiplatform.CustomJob(\n",
|
||||
" display_name=job_name,\n",
|
||||
" worker_pool_specs=worker_pool_specs,\n",
|
||||
" staging_bucket=STAGING_BUCKET,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"train_hpt_job = aiplatform.HyperparameterTuningJob(\n",
|
||||
" display_name=f\"{job_name}_hpt\",\n",
|
||||
" custom_job=train_job,\n",
|
||||
" metric_spec=metric_spec,\n",
|
||||
" parameter_spec=parameter_spec,\n",
|
||||
" max_trial_count=8,\n",
|
||||
" parallel_trial_count=2,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"train_hpt_job.run()\n",
|
||||
"\n",
|
||||
"print(\"Trained models were saved in: \", lora_output_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "afad2431f37f"
|
||||
},
|
||||
"source": [
|
||||
"Then, find the best trial from the hyperparameter tuning job."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "138b09ee6313"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_trial_id = max(\n",
|
||||
" train_hpt_job.trials, key=lambda trial: trial.final_measurement.metrics[0].value\n",
|
||||
").id\n",
|
||||
"lora_output_dir = os.path.join(lora_output_dir, f\"trial_{best_trial_id}\")\n",
|
||||
"lora_output_dir_gcsfuse = lora_output_dir.replace(\"gs://\", \"/gcs/\")\n",
|
||||
"print(f\"Best trial {best_trial_id} saved model in:\", lora_output_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
|
||||
Reference in New Issue
Block a user