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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3: Prune the fine-tuned teacher model to create a pruned student\n",
"In this step, we will explore two methods to prune the fine-tuned teacher model - depth and width pruning. Refer to the [README.md](./README.md) to decide which pruning techniques you would like to explore. We use [NeMo Run](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/quickstart.html) to run the pruning recipe. For usage details of pruning recipe or alternative commandline script, please refer to the [pruning docs](https://docs.nvidia.com/nemo-framework/user-guide/latest/model-optimization/pruning/pruning.html).\n",
"\n",
"Let's define the common recipe setup for depth or width pruning."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import nemo_run as run\n",
"from nemo.collections import llm\n",
"from nemo.collections.llm.modelopt import PruningConfig\n",
"from nemo.collections.llm.modelopt.recipes import prune_recipe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Set path(s) if different:\n",
"ROOT_DIR = \"/workspace\"\n",
"TEACHER_MODEL_PATH = f\"{ROOT_DIR}/Llama-3.1-8B-nemo-ft/checkpoints/best\"\n",
"SEQ_LENGTH = 8192\n",
"DATA_PATH = f\"{ROOT_DIR}/wikitext-data\"\n",
"DATA_PATHS = {\n",
" \"train\": [1.0, f\"{DATA_PATH}/wikitext_tokenized_train_text_document\"],\n",
" \"validation\": [f\"{DATA_PATH}/wikitext_tokenized_test_text_document\"],\n",
" \"test\": [f\"{DATA_PATH}/wikitext_tokenized_val_text_document\"],\n",
"}\n",
"INDEX_MAPPING_DIR = f\"{DATA_PATH}/index_mappings\"\n",
"\n",
"# Change these to accommodate resources:\n",
"DEVICES = 8\n",
"NODES = 1\n",
"TENSOR_PARALLEL_SIZE = 1 # Pruning only supports tensor parallelism 1\n",
"PIPELINE_PARALLEL_SIZE = DEVICES\n",
"MICRO_BATCH_SIZE = 4\n",
"\n",
"# Reduce this number to speed up the pruning process but may result in a slightly worse pruned model\n",
"# Not used if pruning_config.drop_layers is set\n",
"NUM_TRAIN_SAMPLES = 1024\n",
"\n",
"\n",
"def configure_recipe(pruning_config: PruningConfig, save_path: str):\n",
" # Define the recipe\n",
" recipe = prune_recipe(\n",
" nemo_checkpoint=TEACHER_MODEL_PATH,\n",
" save_path=save_path,\n",
" )\n",
"\n",
" # Change dataset (default is Squad dataset)\n",
" recipe.data = run.Config(\n",
" llm.PreTrainingDataModule,\n",
" paths=DATA_PATHS,\n",
" index_mapping_dir=INDEX_MAPPING_DIR,\n",
" seq_length=SEQ_LENGTH,\n",
" micro_batch_size=MICRO_BATCH_SIZE,\n",
" global_batch_size=MICRO_BATCH_SIZE, # Global batch size has no effect on pruning\n",
" )\n",
"\n",
" recipe.devices = DEVICES\n",
" recipe.num_nodes = NODES\n",
" recipe.tp_size = TENSOR_PARALLEL_SIZE\n",
" recipe.pp_size = PIPELINE_PARALLEL_SIZE\n",
" recipe.legacy_ckpt = True # For compatibility with newer versions of TransformerEngine\n",
" recipe.num_train_samples = NUM_TRAIN_SAMPLES\n",
"\n",
" for k, v in pruning_config.__dict__.items():\n",
" if v is not None:\n",
" setattr(recipe.pruning_config, k, v)\n",
"\n",
" return recipe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 3a: Using depth-pruning \n",
"To depth-prune, we will trim the layers 16-31 (leaving 1-15 and 32) in the finetined teacher model. Per the [blog](https://developer.nvidia.com/blog/how-to-prune-and-distill-llama-3-1-8b-to-an-nvidia-llama-3-1-minitron-4b-model/) and [tech report](https://arxiv.org/pdf/2408.11796), removing contiguous layers from the second last block (layers 16 to 31 continuously) yields the best overall results. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pruning_config = PruningConfig(\n",
" # To drop specific layers\n",
" drop_layers=[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n",
" # To drop layers automatically based on the cosine similarity\n",
" # target_num_layers=16,\n",
")\n",
"save_path = f\"{ROOT_DIR}/Llama-3.1-8B-nemo-ft-depth-pruned\"\n",
"\n",
"recipe = configure_recipe(pruning_config=pruning_config, save_path=save_path)\n",
"print(recipe)\n",
"\n",
"env_vars = {\n",
" \"TORCH_NCCL_AVOID_RECORD_STREAMS\": \"1\", # Disable caching NCCL communication buffer memory\n",
" \"NCCL_NVLS_ENABLE\": \"0\", # Disable NVLink SHARP to save memory\n",
"}\n",
"executor = run.LocalExecutor(ntasks_per_node=recipe.devices, launcher=\"torchrun\", env_vars=env_vars)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.run(recipe, executor=executor, name=\"depth_pruning\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running this script will save the depth-pruned model to your workspace at `/workspace/Llama-3.1-8B-nemo-ft-depth-pruned`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 3b: Using width-pruning \n",
"To width-prune, we will trim the ffn_hidden_size to 9216 and hidden_size to 3072. We can also trim the `num_attention_heads` and `num_query_groups` if needed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pruning_config = PruningConfig(\n",
" target_ffn_hidden_size=9216,\n",
" target_hidden_size=3072,\n",
" target_num_attention_heads=None,\n",
" target_num_query_groups=None,\n",
")\n",
"save_path = f\"{ROOT_DIR}/Llama-3.1-8B-nemo-ft-width-pruned\"\n",
"\n",
"recipe = configure_recipe(pruning_config=pruning_config, save_path=save_path)\n",
"print(recipe)\n",
"\n",
"env_vars = {\n",
" \"TORCH_NCCL_AVOID_RECORD_STREAMS\": \"1\", # Disable caching NCCL communication buffer memory\n",
" \"NCCL_NVLS_ENABLE\": \"0\", # Disable NVLink SHARP to save memory\n",
"}\n",
"executor = run.LocalExecutor(ntasks_per_node=recipe.devices, launcher=\"torchrun\", env_vars=env_vars)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.run(recipe, executor=executor, name=\"width_pruning\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running this script will save the width-pruned model to your workspace at `/workspace/Llama-3.1-8B-nemo-ft-width-pruned`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have the depth and width pruned models, we can distill them from the finetuned teacher model in next step."
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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