Update run_linter.sh

Fix spelling error `notebooked` → `notebooks`

.cloud-build/execute_changed_notebooks_helper.py

@@ -238,7 +238,7 @@ def _get_notebook_python_version(notebook_path: str) -> str:
 
             # Look for the python version specification pattern
             re_match = re.search(
-                r"python version = (\d+\.\d+)", markdown, flags=re.IGNORECASE
+                "python version = (\d+\.\d+)", markdown, flags=re.IGNORECASE
             )
             if re_match:
                 # get the version number

.cloud-build/test_notebook_vm.txt

@@ -1,3 +1,5 @@
+notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb
+notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb
 notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb
 notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb
 .cloud-build/tests/python_version_test.ipynb

.github/dependabot.yml

@@ -1,10 +0,0 @@
-version: 2
-updates:
-
-  # Ignore model garden dockerfiles:
-  - package-ecosystem: "npm"
-    directory: "/community-content/vertex_model_garden"
-    schedule:
-      interval: "monthly"
-    ignore:
-      - dependency-name: "*"

.github/workflows/ci.yaml

@@ -7,9 +7,9 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
       with:
-        python-version: '3.13'      
+        python-version: '3.x'      
     - name: Fetch pull request branch
       uses: actions/checkout@v4
       with:

.github/workflows/linter/Dockerfile

@@ -4,7 +4,7 @@
 # 2. To lint specific notebooks:
 # docker run -v ${PWD}:/setup/app gcr.io/python-docs-samples-tests/notebook_linter:latest notebooks/1.ipynb notebooks/2.ipynb
 
-FROM python:3.14
+FROM python:3.12
 
 WORKDIR setup
 

.github/workflows/linter/requirements.txt

@@ -2,9 +2,9 @@ git+https://github.com/tensorflow/docs
 ipython
 jupyter
 nbconvert
-black==25.1.0
-pyupgrade==3.21.0
-isort==6.0.1
-flake8==7.3.0
-nbqa==1.9.1
+black==24.4.2
+pyupgrade==3.16.0
+isort==5.13.2
+flake8==7.1.0
+nbqa==1.8.5
 

community-content/CODEOWNERS

@@ -20,7 +20,6 @@
 /vertex_model_garden/model_oss/movinet @KCFindstr
 /vertex_model_garden/model_oss/data_converter @KCFindstr
 /vertex_model_garden/model_oss/peft @weigary
-/vertex_model_garden/model_oss/peft/templates @rayandasoriya
 /vertex_model_garden/model_oss/lm-evaluation-harness @kathyyu-google
 /vertex_model_garden/model_oss/tfvision @dstnluong-google
 /vertex_model_garden/model_oss/fvlm @minwoo33park
@@ -29,5 +28,4 @@
 /vertex_model_garden/model_oss/vllm @kathyyu-google
 /vertex_model_garden/benchmarking_reports @lavraicse
 /vertex_model_garden/model_oss/autogluon @lavraicse
-/vertex_distributed_training/a3mega/llama-3-8b-nemo-pretraining @mstyer-google @erwinh85 @mchrestkha
 

community-content/pytorch_image_classification_distributed_data_parallel_training_with_vertex_sdk/trainer/Dockerfile

@@ -1,40 +1,16 @@
-# Stage 1: Build Environment
-FROM pytorch/pytorch:1.8.1-cuda11.1-cudnn8-runtime AS builder
-
-# Install necessary tools and dependencies
-RUN apt-get update && \
-    apt-get install -y curl gnupg && \
-    echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
-    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && \
-    apt-get update -y && \
-    apt-get install -y google-cloud-sdk
-
-# Copy application code
-COPY . /trainer
-
-# Set working directory
-WORKDIR /trainer
-
-# Install Python dependencies
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Stage 2: Runtime Environment
 FROM pytorch/pytorch:1.8.1-cuda11.1-cudnn8-runtime
 
-# Install Google Cloud SDK
 RUN apt-get update && \
     apt-get install -y curl gnupg && \
     echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
-    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && \
+    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg  add - && \
     apt-get update -y && \
-    apt-get install -y google-cloud-sdk && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
+    apt-get install google-cloud-sdk -y
 
-# Copy from the builder stage
-COPY --from=builder /trainer /trainer
+COPY . /trainer
 
-# Set working directory
 WORKDIR /trainer
 
-# Set the entry point
-ENTRYPOINT ["python", "-m", "task"]
+RUN pip install -r requirements.txt
+
+ENTRYPOINT ["python", "-m", "task"]

community-content/pytorch_image_classification_distributed_data_parallel_training_with_vertex_sdk/trainer/requirements.txt

@@ -1,3 +1,3 @@
-torch==2.8.0
+torch==1.13.1
 torchvision==0.9.1
 tensorboard==2.5.0

community-content/pytorch_image_classification_single_gpu_with_vertex_sdk_and_torchserve/trainer/requirements.txt

@@ -1,3 +1,3 @@
-torch==2.7.0
+torch==2.2.0
 torchvision==0.9.1
 tensorboard==2.5.0

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/logger/requirements.txt

@@ -1,4 +1,4 @@
 google-cloud-bigquery==2.20.0
-tensorflow==2.12.1
+tensorflow==2.7.2
 pillow==10.3.0
 tf-agents==0.8.0

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/prediction_container/requirements.txt

@@ -1,4 +1,4 @@
 google-cloud-pubsub==2.5.0
 pillow==10.3.0
 tf-agents==0.8.0
-tensorflow==2.12.1
+tensorflow==2.7.2

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/simulator/requirements.txt

@@ -1,5 +1,5 @@
 dataclasses==0.6
 google-cloud-aiplatform==1.8.1
-tensorflow==2.12.1
+tensorflow==2.7.2
 pillow==10.3.0
 tf-agents==0.8.0

community-content/tf_keras_image_classification_distributed_multi_worker_with_vertex_sdk/trainer/requirements.txt

@@ -1 +1 @@
-tensorflow==2.12.1
+tensorflow==2.7.2

community-content/tf_keras_text_classification_distributed_single_worker_gpus_with_gcloud_local_run_and_vertex_sdk/trainer/requirements.txt

@@ -1 +1 @@
-tensorflow==2.12.1
+tensorflow==2.7.2

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/README.md

@@ -1,126 +0,0 @@
-# Vertex AI Training: Llama 3.1 8B pre-training using Nvidia A3 Mega VMs (H100)
-This document provides a step-by-step guide for pre-training a Llama 3.1 8B model on the `en-wiki` dataset using multiple [Vertex AI Custom Training](https://cloud.google.com/vertex-ai/docs/training/overview) `a3-megagpu-8g` nodes.
-
-We will use a custom container based on NVIDIA's [NeMo Framework](https://docs.nvidia.com/nemo-framework/user-guide/24.07/overview.html) to demonstrate a scalable, multi-node training workflow. All required artifacts and commands are included.
-
-## 1. Prerequisites
-
-### 1.1. Google Cloud Project setup
-- **Enable APIs:** Ensure the Vertex AI API is [enabled for your project](http://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).
-- **H100 Mega Quota:** A3 Mega VMs are powered by H100 GPUs. Request quota for `custom_model_training_nvidia_h100_mega_gpus` in one of the [supported regions](https://cloud.google.com/vertex-ai/docs/general/locations#accelerator_support). If using Spot VMs, request `custom_model_training_preemptible_nvidia_h100_mega_gpus` quota instead.
-- **Reservations (Optional but recommended):** For guaranteed capacity, [create a reservation](https://cloud.google.com/compute/docs/instances/reservations-shared) and ensure the reservation is shared with the Vertex AI service account. This guide requires a minimum of **16 H100 GPUs** (2 full A3 Mega nodes).
-
-### 1.2. GCS bucket
-Create a [Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) in the same region where you have quota. If you're using Hierarchical Namespace for your bucket, you may need to update permissions of the Vertex AI Custom Code Service Agent .
-
-This bucket is used for:
-- Staging the training application.
-- Storing model checkpoints and logs.
-- Storing data if you use your own data.
-
-
-## 2. Setup & configuration
-
-### 2.1. Clone the repo
-First clone the repo into your development environment.
-
-```bash
-git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
-```
-
-Navigate to the root folder for this sample.
-
-### 2.2. Environment Setup
-First, configure your local environment. These variables are used in subsequent commands.
-
-```bash
-# Required: Update with your values
-export PROJECT_ID="<your-project-id>"
-export REPOSITORY="<your-artifact-registry-repo-name>" # e.g., "my-containers"
-export BUCKET="<your-gcs-bucket-name>"
-
-# Optional: Change if needed
-export REGION="us-central1"
-
-# --- Do not change the lines below ---
-export ARTIFACT_REGISTRY="${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}"
-export REPO_ROOT=$(git rev-parse --show-toplevel)
-```
-
-## 3. Build and push a docker container image to Artifact Registry
-Normally, you can use any custom training container on Vertex AI Training. In this example you build a NeMo Docker image that is based on the [Nvidia’s NeMo 24.09](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags) image. Use Cloud Build to build and push the container image.
-
-This document picked NeMo as the demonstrating container since it’s a widely adopted GPU LLM training framework providing high performance and versatile training functionalities.
-
-In addition to the base image, some customizations are included to form the final prebuilt image:
-- Some dependencies are installed to integrate with Vertex AI Training.
-- An entrypoint script that sets up required environments and calls the training job.
-- Some patches are applied to the NeMo code to let it  load the dataset from a GCS bucket.
-
-Run this command to build the container and push the container into the Google Artifact Registry.
-
-```bash
-cd "${REPO_ROOT}/community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining"
-export IMAGE_NAME="vertex-nemo-llama"
-gcloud builds submit . \
-    --project="${PROJECT_ID}" \
-    --region="${REGION}" \
-    --config=docker/cloudbuild.yml \
-    --substitutions="_ARTIFACT_REGISTRY=${ARTIFACT_REGISTRY},_IMAGE_NAME=${IMAGE_NAME}" \
-    --timeout="2h" \
-    --machine-type="e2-highcpu-32"
-```
-
-## 4. Launch the Training Job
-
-
-###  4.1. Job Configuration File
-Once the container is built, update the job_config.json to set up the training job.
-File: job_config.json
-```json
-{
-  "project_id": "<project-id>",
-  "region": "<region>",
-  "zone": "<zone if using reservation>",
-  "bucket": "<bucket>",
-  "dataset_bucket": "github-repo/data/third-party/enwiki-latest-pages-articles",
-  "image_uri": "<docker image uri from artifact registry>",
-  "strategy": "spot",
-  "nodes": "2",
-  "machine_type": "a3-megagpu-8g",
-  "gpu_type": "NVIDIA_H100_MEGA_80GB",
-  "gpus_per_node": "8",
-  "recipe_name": "llama3_1_8b_pretrain_a3mega",
-  "job_prefix": "vertex-spot-",
-  "reservation_name": ""
-}
-```
-
-### 4.2 Launch the Training Job
-
-First, create a Python virtual environment using your tool of choice, then install
-the requirements specified in `requirements.txt`. Using `pip`, the command would be:
-```bash
-pip install -r requirements.txt
-```
-
-Now launch the Vertex AI training job using the provided Python script.
-
-```bash
-python3 scripts/launch.py --config_file=job_config.json
-```
-
-This script reads job_config.json, defines the cluster specification (2 nodes, 8 GPUs each), and submits the custom training job to Vertex AI.
-
-## 5. Monitor and Clean Up
-
-### 5.1. Monitoring
-Vertex AI Console: Track the job's status in the Google Cloud Console under Vertex AI > Training > Custom Jobs.
-Logs: View detailed logs in Cloud Logging by filtering for your job name.
-Checkpoints: Model checkpoints are saved to your GCS bucket at the path specified in your training script's configuration.
-
-### 5.2. Cleaning Up
-To avoid ongoing charges, delete the resources you created:
-- The Artifact Registry image.
-- The contents of the GCS bucket (checkpoints, logs).
-- The Vertex AI Custom Job will eventually complete or fail, incurring no further cost.

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/configs/llama3_1_8b_pretrain_a3mega.yaml

@@ -1,265 +0,0 @@
-# Reference:
-# https://github.com/NVIDIA/NeMo-Framework-Launcher/blob/24.07/launcher_scripts/conf/training/llama/llama3_1_8b.yaml
-name: llama3_1_8b_pretrain_a3mega
-restore_from_path: null  # used when starting from a .nemo file
-
-trainer:
-  devices: 8
-  num_nodes: 1
-  accelerator: gpu
-  precision: bf16
-  logger: false  # logger provided by exp_manager
-  enable_checkpointing: false
-  use_distributed_sampler: false
-  max_epochs: -1  # PTL default. In practice, max_steps will be reached first.
-  max_steps: 30  # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches
-  log_every_n_steps: 1
-  val_check_interval: null
-  limit_val_batches: 1
-  limit_test_batches: 1
-  accumulate_grad_batches: 1  # do not modify, grad acc is automatic for training megatron models
-  gradient_clip_val: 1.0
-  benchmark: false
-  enable_model_summary: false  # default PTL callback for this does not support model parallelism, instead we log manually
-
-exp_manager:
-  explicit_log_dir: null
-  exp_dir: /data
-  name: ${name}
-  create_dllogger_logger: true
-  dllogger_logger_kwargs:
-    verbose: true
-    stdout: true
-    json_file: "/data/dllogger.json"
-  create_wandb_logger: false
-  wandb_logger_kwargs:
-    project: null
-    name: null
-  resume_if_exists: true
-  resume_ignore_no_checkpoint: true
-  create_checkpoint_callback: false
-  checkpoint_callback_params:
-    monitor: val_loss
-    save_top_k: 3
-    mode: min
-    always_save_nemo: false  # saves nemo file during validation, not implemented for model parallel
-    save_nemo_on_train_end: false  # not recommended when training large models on clusters with short time limits
-    filename: 'megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}'
-    model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}
-  seconds_to_sleep: 5  # Allows node_rank!=0 to sleep and let node0 to init, like preparing data
-
-model:
-  mcore_gpt: true
-  # specify micro_batch_size, global_batch_size, and model parallelism
-  # gradient accumulation will be done automatically based on data_parallel_size
-  micro_batch_size: 1  # limited by GPU memory
-  global_batch_size: 1024  # will use more micro batches to reach global batch size
-  tensor_model_parallel_size: 1  # intra-layer model parallelism
-  pipeline_model_parallel_size: 2  # inter-layer model parallelism
-  context_parallel_size: 1
-  virtual_pipeline_model_parallel_size: null  # interleaved pipeline
-  ## Sequence Parallelism
-  # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially
-  # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
-  sequence_parallel: false
-
-  fsdp: false
-  fsdp_cpu_offload: true
-  fsdp_sharding_strategy: "full"  # Method to shard model states. Available options are 'full', 'hybrid', and 'grad'.
-  fsdp_grad_reduce_dtype: "16"  # Gradient reduction data type.
-  fsdp_sharded_checkpoint: false  # Store and load FSDP shared checkpoint.
-  fsdp_use_orig_params: false  # Set to True to use FSDP for specific peft scheme.
-
-  # Distributed checkpoint setup
-  dist_ckpt_format: "torch_dist"  # Set to 'torch_dist' to use PyTorch distributed checkpoint format.
-  dist_ckpt_load_on_device: true  # whether to load checkpoint weights directly on GPU or to CPU
-  dist_ckpt_parallel_save: true  # if true, each worker will write its own part of the dist checkpoint
-  dist_ckpt_parallel_save_within_dp: false  # if true, save will be parallelized only within a DP group (whole world otherwise), which might slightly reduce the save overhead
-  dist_ckpt_parallel_load: false  # if true, each worker will load part of the dist checkpoint and exchange with NCCL. Might use some extra GPU memory
-  dist_ckpt_torch_dist_multiproc: 2  # number of extra processes per rank used during ckpt save with PyTorch distributed format
-  dist_ckpt_assume_constant_structure: false  # set to True only if the state dict structure doesn't change within a single job. Allows caching some computation across checkpoint saves.
-  dist_ckpt_parallel_dist_opt: true  # parallel save/load of a DistributedOptimizer. 'True' allows performant save and reshardable checkpoints. Set to 'False' only in order to minimize the number of checkpoint files.
-  dist_ckpt_load_strictness: null  # defines checkpoint keys mismatch behavior (only during dist-ckpt load). Choices: assume_ok_unexpected (default - try loading without any check), log_all (log mismatches), raise_all (raise mismatches)
-
-  # model architecture
-  encoder_seq_length: 8192
-  max_position_embeddings: ${.encoder_seq_length}
-  num_layers: 32  # 8b: 32 | 70b: 80 | 405b: 126
-  hidden_size: 4096  # 8b: 4096 | 70b: 8192 | 405b: 16384
-  ffn_hidden_size: 14336  # 8b: 14336 | 70b: 28672 | 405b: 53248
-  num_attention_heads: 32  # 8b: 32 | 70b: 64 | 405b: 128
-  num_query_groups: 8  # Number of query groups for group query attention. If None, normal attention is used. 8b: 8 | 70b: 8 | 405b: 16
-  init_method_std: 0.01  # Standard deviation of the zero mean normal distribution used for weight initialization.  8b: 0.01 | 70b: 0.008944 | 405b: 0.02
-  use_scaled_init_method: true  # use scaled residuals initialization
-  hidden_dropout: 0.0  # Dropout probability for hidden state transformer.
-  attention_dropout: 0.0  # Dropout probability for attention
-  ffn_dropout: 0.0  # Dropout probability in the feed-forward layer.
-  kv_channels: null  # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null
-  apply_query_key_layer_scaling: true  # scale Q * K^T by 1 / layer-number.
-  normalization: 'rmsnorm'  # Normalization layer to use. Options are 'layernorm', 'rmsnorm'
-  layernorm_epsilon: 1e-5
-  do_layer_norm_weight_decay: false  # True means weight decay on all params
-  make_vocab_size_divisible_by: 128  # Pad the vocab size to be divisible by this value for computation efficiency.
-  pre_process: true  # add embedding
-  post_process: true  # add pooler
-  persist_layer_norm: true  # Use of persistent fused layer norm kernel.
-  bias: false  # Whether to use bias terms in all weight matrices.
-  activation: 'fast-swiglu'  # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu']
-  headscale: false  # Whether to learn extra parameters that scale the output of the each self-attention head.
-  transformer_block_type: 'pre_ln'  # Options ['pre_ln', 'post_ln', 'normformer']
-  openai_gelu: false  # Use OpenAI's GELU instead of the default GeLU
-  normalize_attention_scores: true  # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True.
-  position_embedding_type: 'rope'  # Position embedding type. Options ['learned_absolute', 'rope']
-  rotary_percentage: 1.0  # If using position_embedding_type=rope, then the per head dim is multiplied by this.
-  attention_type: 'multihead'  # Attention type. Options ['multihead']
-  share_embeddings_and_output_weights: false  # Share embedding and output layer weights.
-  scale_positional_embedding: true  # This is false for llama3 models. Only used for >= llama3.1.
-
-  # Use GPT2BPETokenizer for test, because the testing dataset is tokenized by this tokenizer.
-  # https://docs.nvidia.com/nemo-framework/user-guide/24.07/playbooks/singlenodepretrain.html#data-download-and-pre-processing
-  tokenizer:
-    library: megatron
-    type: GPT2BPETokenizer
-    model: null  # /path/to/tokenizer.model
-    vocab_file: null
-    merge_file: null
-    delimiter: null  # only used for tabular tokenizer
-    sentencepiece_legacy: false  # Legacy=True allows you to add special tokens to sentencepiece tokenizers.
-
-  # Mixed precision
-  native_amp_init_scale: 4294967296  # 2 ** 32
-  native_amp_growth_interval: 1000
-  hysteresis: 2  # Gradient scale hysteresis
-  fp32_residual_connection: false  # Move residual connections to fp32
-  fp16_lm_cross_entropy: false  # Move the cross entropy unreduced loss calculation for lm head to fp16
-
-  # Megatron O2-style half-precision
-  megatron_amp_O2: true  # Enable O2-level automatic mixed precision using main parameters
-  grad_allreduce_chunk_size_mb: 125
-
-  # Fusion
-  grad_div_ar_fusion: true  # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism..
-  gradient_accumulation_fusion: true  # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2.
-  bias_activation_fusion: true  # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function.
-  bias_dropout_add_fusion: true  # Use a kernel that fuses the bias addition, dropout and residual connection addition.
-  masked_softmax_fusion: true  # Use a kernel that fuses the attention softmax with it's mask.
-  apply_rope_fusion: true  # Use a kernel to add rotary positional embeddings. Only used if position_embedding_type=rope
-  cross_entropy_loss_fusion: true
-
-  # Miscellaneous
-  seed: 1234
-  resume_from_checkpoint: null  # manually set the checkpoint file to load from
-  use_cpu_initialization: false  # Init weights on the CPU (slow for large models)
-  onnx_safe: false  # Use work-arounds for known problems with Torch ONNX exporter.
-  apex_transformer_log_level: 30  # Python logging level displays logs with severity greater than or equal to this
-  gradient_as_bucket_view: true  # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory)
-  sync_batch_comm: false  # Enable stream synchronization after each p2p communication between pipeline stages
-
-  ## Activation Checkpointing
-  # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed.
-  # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+).
-  # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
-  # 'full' will checkpoint the entire transformer layer.
-  activations_checkpoint_granularity: null  # 'selective' or 'full'
-  activations_checkpoint_method: null  # 'uniform', 'block'
-  # 'uniform' divides the total number of transformer layers and checkpoints the input activation
-  # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model.
-  # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity
-  activations_checkpoint_num_layers: null
-  # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory.
-  # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage.
-  num_micro_batches_with_partial_activation_checkpoints: null
-  # This feature is valid only when used with pipeline-model-parallelism.
-  # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed
-  # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is
-  # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint
-  # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'.
-  # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage.
-  activations_checkpoint_layers_per_pipeline: null
-  # This feature is valid only when used with pipeline-model-parallelism.
-  # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later
-  # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than
-  # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage
-  # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints',
-  # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path.
-
-  ## Transformer Engine
-  transformer_engine: true
-  fp8: false  # enables fp8 in TransformerLayer forward
-  fp8_e4m3: false  # sets fp8_format = recipe.Format.E4M3
-  fp8_hybrid: false  # sets fp8_format = recipe.Format.HYBRID
-  fp8_margin: 0  # scaling margin
-  fp8_interval: 1  # scaling update interval
-  fp8_amax_history_len: 1024  # Number of steps for which amax history is recorded per tensor
-  fp8_amax_compute_algo: 'max'  # 'most_recent' or 'max'. Algorithm for computing amax from history
-  ub_tp_comm_overlap: false  # do not turn on because of b/397797926
-  use_flash_attention: true
-  gc_interval: 100
-
-  ## Offloading Activations/Weights to CPU
-  cpu_offloading: false
-  cpu_offloading_num_layers: ${sum:${.num_layers},-1}  # This value should be between [1,num_layers-1] as we don't want to offload the final layer's activations and expose any offloading duration for the final layer
-  cpu_offloading_activations: true
-  cpu_offloading_weights: true
-
-  data:
-    # Path to data must be specified by the user.
-    # Supports List, String and Dictionary
-    # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]",
-    # Or see example below:
-    # data_prefix:
-    # - .5
-    # - /raid/data/pile/my-gpt3_00_text_document
-    # - .5
-    # - /raid/data/pile/my-gpt3_01_text_document
-    # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test}
-    # Or see example below:
-    # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}"
-    data_prefix: [1.0, /data/hfbpe_gpt_training_data_text_document]
-    index_mapping_dir: null  # path to save index mapping .npy files, by default will save in the same location as data_prefix
-    data_impl: mmap
-    splits_string: 900,50,50
-    seq_length: ${model.encoder_seq_length}
-    skip_warmup: true
-    num_workers: 2
-    dataloader_type: single  # cyclic
-    reset_position_ids: false  # Reset position ids after end-of-document token
-    reset_attention_mask: false  # Reset attention mask after end-of-document token
-    eod_mask_loss: false  # Mask loss for the end of document tokens
-    validation_drop_last: true  # Set to false if the last partial validation samples is to be consumed
-    no_seqlen_plus_one_input_tokens: false  # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token
-    pad_samples_to_global_batch_size: false  # Set to True if you want to pad the last partial batch with -1's to equal global batch size
-    shuffle_documents: true  # Set to False to disable documents shuffling. Sample index will still be shuffled
-
-  # Nsys profiling options
-  nsys_profile:
-    enabled: false
-    start_step: 0  # Global batch to start profiling
-    end_step: 1  # Global batch to end profiling
-    ranks: [0]  # Global rank IDs to profile
-    gen_shape: false  # Generate model and kernel details including input shapes
-
-  memory_profile:
-    enabled: false
-    start_step: 0
-    end_step: 1
-    ranks: [0]
-    output_path: /data  # Must be a dir
-
-  optim:
-    name: distributed_fused_adam  # E.g., fused_adam or set _target_: torch.optim.AdamW field
-    lr: 2e-5
-    weight_decay: 0.01
-    betas:
-    - 0.9
-    - 0.98
-    bucket_cap_mb: 125
-    overlap_grad_sync: true
-    overlap_param_sync: true
-    contiguous_grad_buffer: true
-    contiguous_param_buffer: true
-    sched:
-      name: CosineAnnealing
-      warmup_steps: 400
-      constant_steps: 0
-      min_lr: 2e-6

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/cloudbuild.yml

@@ -1,26 +0,0 @@
-# Copyright 2024 Google LLC
-#
-# 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.
-
-steps:
-- name: 'gcr.io/cloud-builders/docker'
-  args:
-  - 'build'
-  - '--tag=${_ARTIFACT_REGISTRY}/${_IMAGE_NAME}'
-  - '--file=docker/vertex-dist-recipes.Dockerfile'
-  - '.'
-  automapSubstitutions: true
-  env:
-  - 'DOCKER_BUILDKIT=1'
-images:
-- '${_ARTIFACT_REGISTRY}/${_IMAGE_NAME}'

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/gpu_stats.patch

@@ -1,41 +0,0 @@
-diff --git a/nemo/collections/nlp/parts/megatron_trainer_builder.py b/nemo/collections/nlp/parts/megatron_trainer_builder.py
-index b2c85cde4..a3a9670c3 100644
---- a/nemo/collections/nlp/parts/megatron_trainer_builder.py
-+++ b/nemo/collections/nlp/parts/megatron_trainer_builder.py
-@@ -19,6 +19,7 @@ from lightning_fabric.utilities.exceptions import MisconfigurationException
- from omegaconf import DictConfig
- from pytorch_lightning import Trainer
- from pytorch_lightning.callbacks import ModelSummary
-+from pytorch_lightning.callbacks import Callback
- from pytorch_lightning.plugins.environments import TorchElasticEnvironment
-
- from nemo.collections.common.metrics.perf_metrics import FLOPsMeasurementCallback
-@@ -38,6 +39,23 @@ from nemo.utils.callbacks.dist_ckpt_io import (
-     AsyncFinalizerCallback,
-     DistributedCheckpointIO,
- )
-+from vmg.util.device_stats import gpu_stats_str
-+
-+class GpuStatsMon(Callback):
-+    def on_train_start(self, trainer, pl_module) -> None:
-+        rank=pl_module.global_rank
-+        print(f'train_start: {rank=} {gpu_stats_str()}', flush=True)
-+
-+    def on_train_batch_start(self, trainer, pl_module, batch, batch_idx) -> None:
-+        rank=pl_module.global_rank
-+        print(f'batch_start: {rank=} {gpu_stats_str()}', flush=True)
-+
-+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx) -> None:
-+        rank=pl_module.global_rank
-+        print(f'batch_end: {rank=} {gpu_stats_str()}', flush=True)
-
-
- class MegatronTrainerBuilder:
-@@ -178,6 +196,7 @@ class MegatronTrainerBuilder:
-         if self.cfg.get('exp_manager', {}).get('log_tflops_per_sec_per_gpu', True):
-             callbacks.append(FLOPsMeasurementCallback(self.cfg))
-
-+        callbacks.append(GpuStatsMon())
-         return callbacks
-
-     def create_trainer(self, callbacks=None) -> Trainer:

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/local_rank.patch

@@ -1,41 +0,0 @@
-diff -ruN old-datasets/blended_megatron_dataset_builder.py datasets/blended_megatron_dataset_builder.py
---- old-datasets/blended_megatron_dataset_builder.py	2025-05-02 04:08:45.369199665 +0000
-+++ datasets/blended_megatron_dataset_builder.py	2025-05-02 04:10:47.369119891 +0000
-@@ -2,6 +2,7 @@
-
- import logging
- import math
-+import os
- from concurrent.futures import ThreadPoolExecutor
- from typing import Any, Callable, Iterable, List, Optional, Type, Union
-
-@@ -353,7 +354,7 @@
-         num_dataset_builder_threads = self.config.num_dataset_builder_threads
-
-         if torch.distributed.is_initialized():
--            rank = torch.distributed.get_rank()
-+            rank = int(os.getenv("LOCAL_RANK", "0"))
-             # First, build on rank 0
-             if rank == 0:
-                 num_workers = num_dataset_builder_threads
-@@ -475,7 +476,7 @@
-             Optional[Union[DistributedDataset, Iterable]]: The DistributedDataset instantion, the Iterable instantiation, or None
-         """
-         if torch.distributed.is_initialized():
--            rank = torch.distributed.get_rank()
-+            rank = int(os.getenv("LOCAL_RANK", "0"))
-
-             dataset = None
-
-diff -ruN old-datasets/gpt_dataset.py datasets/gpt_dataset.py
---- old-datasets/gpt_dataset.py	2025-05-02 04:08:45.369199665 +0000
-+++ datasets/gpt_dataset.py	2025-05-02 04:09:30.309170278 +0000
-@@ -351,7 +351,7 @@
-
-         if not path_to_cache or (
-             not cache_hit
--            and (not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0)
-+            and (not torch.distributed.is_initialized() or int(os.getenv("LOCAL_RANK", "0")) == 0)
-         ):
-
-             log_single_rank(

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/nemo2hf.patch

@@ -1,13 +0,0 @@
-diff --git a/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py b/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
-index 8da15148d..005cae6c9 100644
---- a/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
-+++ b/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
-@@ -104,6 +104,8 @@ def convert(input_nemo_file, output_hf_file, precision=None, cpu_only=False) ->
-     dummy_trainer = Trainer(devices=1, accelerator='cpu', strategy=NLPDDPStrategy())
-     model_config = MegatronGPTModel.restore_from(input_nemo_file, trainer=dummy_trainer, return_config=True)
-     model_config.tensor_model_parallel_size = 1
-+    model_config.virtual_pipeline_model_parallel_size = None
-+    model_config.sequence_parallel = False
-     model_config.pipeline_model_parallel_size = 1
-     if cpu_only:
-         map_location = torch.device('cpu')

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/sigabort.patch

@@ -1,24 +0,0 @@
-diff --git a/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py b/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py
-index bfe8ea359..dfeaf93b5 100644
---- a/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py
-+++ b/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py
-@@ -13,6 +13,8 @@
- # limitations under the License.
-
- import torch.multiprocessing as mp
-+import torch.distributed as dist
-+
- from omegaconf.omegaconf import OmegaConf
-
- from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel
-@@ -76,6 +78,10 @@ def main(cfg) -> None:
-
-     trainer.fit(model)
-
-+    if dist.is_available() and dist.is_initialized():
-+        dist.barrier()
-+        dist.destroy_process_group()
-+
-
- if __name__ == '__main__':
-     main()

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/throughput_calc.patch

@@ -1,13 +0,0 @@
-diff --git a/src/utils/training_metrics/process_training_results.py b/src/utils/training_metrics/process_training_results.py
-index 3e82a66..e61e1d8 100644
---- a/src/utils/training_metrics/process_training_results.py
-+++ b/src/utils/training_metrics/process_training_results.py
-@@ -134,7 +134,7 @@ def get_average_step_time(file: str, start_step: int, end_step: int) -> float:
-     for line in datajson:
-         if line.get("step") != "PARAMETER":
-             step = line.get("step")
--            if step >= start_step and step <= end_step:
-+            if step >= start_step and step <= end_step and "train_step_timing in s" in line["data"]:
-                 time_step_accumulator += line["data"].get("train_step_timing in s")
-                 num_steps += 1
-     if num_steps == 0:

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/requirements.txt

@@ -1,10 +0,0 @@
-dllogger@git+https://github.com/NVIDIA/dllogger@v1.0.0
-
-# Fixing these libraries versions to avoid conflicting or broken packages.
-immutabledict==4.2.1
-protobuf==4.25.8
-opencv-python-headless==4.11.0.86
-docutils==0.16
-urllib3==2.5.0
-google-cloud-storage==3.0.0
-retrying

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/uninstall.txt

@@ -1,18 +0,0 @@
-# cuml-cu12==24.8.0 was installed in nemo:24.09
-# Removing cuml=24.4.0 to avoid conflicting packages.
-cudf==24.4.0
-cugraph==24.4.0
-cugraph-service-server==24.4.0
-cuml==24.4.0
-dask-cudf==24.4.0
-raft-dask==24.4.0
-cugraph-dgl==24.4.0
-cugraph-pyg==24.4.0
-# The following packages are removed temporarily to avoid conflicting packages
-# and can be brought back if needed.
-tensorrt-llm==0.12.0
-img2dataset==1.45.0
-Sphinx==8.1.3
-sphinxcontrib-bibtex==2.6.3
-torchx==0.7.0
-nemo-run

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/vertex-dist-recipes.Dockerfile

@@ -1,66 +0,0 @@
-# Dockerfile wrapping NeMo.
-#
-# To workaround base nemo docker image using too many layers, we use Multi-stage
-# build to first collect the additional files we'll need.
-FROM alpine:latest AS prep_files
-WORKDIR /workspace
-RUN mkdir -p configs vdt vdt/util
-COPY scripts/*.py vdt/
-COPY scripts/util/*.py vdt/util/
-COPY configs/* configs/
-COPY docker/patches/24.09/* vdt/patches/
-RUN chmod a+rwX -R vdt
-# Copy license.
-RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
-
-# Available tags
-# https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags
-# It installs NeMo source code in /opt/NeMo folder, with tag=r2.0.0
-FROM nvcr.io/nvidia/nemo:24.09
-
-RUN apt-get update && apt-get install -y sudo zsh tmux && \
-    rm -rf /var/lib/apt/lists*
-
-RUN echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | \
-    tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
-    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | \
-    apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && \
-    apt-get update -y && apt-get install google-cloud-sdk -y && \
-    rm -rf /var/lib/apt/lists*
-
-# Install libraries with pip
-ENV PIP_ROOT_USER_ACTION=ignore
-
-# We expect this will be run in the root directory of the vertex-dist-recipes repo
-ARG HOST_SRC_DIR="."
-
-# The pre-installed NeMo introduces a lot of deps conflicts.
-# We uninstall the confilicting libs and reinstall some of them as needed.
-COPY ${HOST_SRC_DIR}/docker/uninstall.txt /tmp/uninstall.txt
-RUN cat /tmp/uninstall.txt | grep -v '#' | xargs pip uninstall -y
-COPY ${HOST_SRC_DIR}/docker/requirements.txt /tmp/requirements.txt
-RUN pip install -r /tmp/requirements.txt
-
-# Make sure there's no inconsistent pip libraries.
-RUN pip check
-
-WORKDIR /workspace
-
-# Copy configs
-COPY ${HOST_SRC_DIR}/configs/* /opt/NeMo/examples/nlp/language_modeling/conf/
-
-# Copy all additional files we need from `prep_files` image.
-COPY --from=prep_files /workspace/ .
-
-# Install for `src/utils/training_metrics/process_training_results.py` to report
-# throughput and MFU numbers.
-RUN git clone https://github.com/AI-Hypercomputer/gpu-recipes.git
-
-# This hack is needed for multi-node training while not using a sharing file system.
-RUN patch --verbose -l -d /opt/megatron-lm/megatron/core/datasets -p1 -i /workspace/vdt/patches/local_rank.patch; \
-    git -C /workspace/gpu-recipes apply /workspace/vdt/patches/throughput_calc.patch; \
-    git -C /opt/NeMo apply /workspace/vdt/patches/nemo2hf.patch; \
-    git -C /opt/NeMo apply /workspace/vdt/patches/sigabort.patch;
-# git -C /opt/NeMo apply /workspace/vdt/patches/gpu_stats.patch;
-
-# Do not put an entrypoint here. Specify the entrypoint in the docker run script.

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/job_config.json

@@ -1,16 +0,0 @@
-{
-  "project_id": "<your_project_id>",
-  "region": "us-central1",
-  "zone": "us-central1-c",
-  "bucket": "<your_bucket",
-  "dataset_bucket": "github-repo/data/third-party/enwiki-latest-pages-articles",
-  "image_uri": "<your_image_uri>",
-  "strategy": "spot",
-  "nodes": "2",
-  "machine_type": "a3-megagpu-8g",
-  "gpu_type": "NVIDIA_H100_MEGA_80GB",
-  "gpus_per_node": "8",
-  "recipe_name": "llama3_1_8b_pretrain_a3mega",
-  "job_prefix": "vertex-ai",
-  "reservation_name": ""
-}

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/requirements.txt

@@ -1,49 +0,0 @@
-absl-py==2.2.2
-annotated-types==0.7.0
-anyio==4.9.0
-black==25.1.0
-cachetools==5.5.2
-certifi==2025.4.26
-charset-normalizer==3.4.2
-click==8.1.8
-docstring_parser==0.16
-google-api-core==2.24.2
-google-auth==2.40.1
-google-cloud-aiplatform==1.92.0
-google-cloud-bigquery==3.31.0
-google-cloud-core==2.4.3
-google-cloud-resource-manager==1.14.2
-google-cloud-storage==2.19.0
-google-crc32c==1.7.1
-google-genai==1.14.0
-google-resumable-media==2.7.2
-googleapis-common-protos==1.70.0
-grpc-google-iam-v1==0.14.2
-grpcio==1.71.0
-grpcio-status==1.71.0
-h11==0.16.0
-httpcore==1.0.9
-httpx==0.28.1
-idna==3.10
-mypy_extensions==1.1.0
-numpy==2.2.5
-packaging==25.0
-pathspec==0.12.1
-platformdirs==4.3.8
-proto-plus==1.26.1
-protobuf==5.29.4
-pyasn1==0.6.1
-pyasn1_modules==0.4.2
-pydantic==2.11.4
-pydantic_core==2.33.2
-python-dateutil==2.9.0.post0
-pytz==2025.2
-requests==2.32.4
-rsa==4.9.1
-shapely==2.1.0
-six==1.17.0
-sniffio==1.3.1
-typing-inspection==0.4.0
-typing_extensions==4.13.2
-urllib3==2.5.0
-websockets==15.0.1

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/scripts/launch.py

@@ -1,173 +0,0 @@
-"""Launch script for Vertex distributed training"""
-
-# Copy the sample_job_config.json file to job_config.json
-# to define the job parameters.
-#
-# Run like this:
-#
-#   python3 vertex_dist_train/launch.py --config_file=job_config.json
-#
-
-import datetime
-import json
-import os
-import pprint
-from collections.abc import Sequence
-from typing import Any, List
-
-from absl import app, flags
-from google.cloud import aiplatform
-from google.cloud.aiplatform_v1.types.custom_job import Scheduling
-from pytz import timezone
-
-FLAGS = flags.FLAGS
-flags.DEFINE_string("config_file", None, "Path to JSON config file")
-flags.DEFINE_boolean(
-    "debug", False, "Debug mode: just print the command, don't run it."
-)
-
-
-def launch_job(
-    job_name: str,
-    project: str,
-    region: str,
-    gcs_bucket: str,
-    image_uri: str,
-    entrypoint_cmd: List[str],
-    trainer_args: List[Any],
-    num_nodes: int,
-    machine_type: str,
-    num_gpus_per_node: int,
-    gpu_type: str,
-    strategy: str,
-    reservation_name: str = "",
-):
-    assert strategy in ("dws", "spot", "reservation")
-    aiplatform.init(
-        project=project, location=region, staging_bucket=gcs_bucket
-    )
-
-    train_job = aiplatform.CustomContainerTrainingJob(
-        display_name=job_name,
-        container_uri=image_uri,
-        command=entrypoint_cmd,
-    )
-
-    job_args = dict(
-        args=trainer_args,
-        enable_web_access=True,
-        replica_count=num_nodes,
-        machine_type=machine_type,
-        accelerator_type=gpu_type,
-        accelerator_count=num_gpus_per_node,
-        boot_disk_size_gb=1000,
-        restart_job_on_worker_restart=True,
-        #restart_job_on_worker_restart=False,
-    )
-
-    if strategy == "spot":
-        job_args.update({"scheduling_strategy": Scheduling.Strategy.SPOT.name})
-    elif strategy == "dws":
-        job_args.update(
-            {"scheduling_strategy": Scheduling.Strategy.FLEX_START.name}
-        )
-    elif strategy == "reservation":
-        assert reservation_name != "", (
-            "If using a reservation, provide the reservation_name in the "
-            "format `projects/{project_id_or_number}/zones/{zone}/"
-            "reservations/{reservation_name}`"
-        )
-        job_args.update(
-            {
-                "reservation_affinity_type": "SPECIFIC_RESERVATION",
-                "reservation_affinity_key": "compute.googleapis.com/reservation-name",
-                "reservation_affinity_values": [reservation_name],
-            }
-        )
-
-    pprint.pprint(job_args)
-    if not FLAGS.debug:
-        train_job.submit(**job_args)
-
-
-def main(argv: Sequence[str]) -> None:
-    config_file_path = FLAGS.config_file
-    print(f"Reading job config from {config_file_path}")
-    with open(config_file_path, encoding="utf-8") as config_file:
-        config = json.load(config_file)
-
-    project_id = config["project_id"]
-    region = config["region"]
-    zone = config["zone"]
-    bucket = config["bucket"]
-    dataset_bucket = config["dataset_bucket"]
-    n_nodes = int(config["nodes"])
-    machine_type = config["machine_type"]
-    num_gpus_per_node = int(config["gpus_per_node"])
-    gpu_type = config["gpu_type"]
-    reservation_name = config.get("reservation_name")
-    reservation_full_name = (
-        f"projects/{project_id}/zones/{zone}/reservations/{reservation_name}"
-        if "reservation_name" in config
-        else ""
-    )
-
-    strategy = config["strategy"]
-    recipe_name = config["recipe_name"]
-    job_prefix = config["job_prefix"]
-    image_uri = config["image_uri"]
-
-    # Job name
-    timestamp = (
-        datetime.datetime.now()
-        .astimezone(timezone("US/Pacific"))
-        .strftime("%Y%m%d_%H%M%S")
-    )
-    job_name = f"{recipe_name}-{timestamp}"
-    if job_prefix:
-        job_name = f"{job_prefix}-{job_name}"
-
-    base_output_dir = os.path.join("/gcs", bucket, job_name)
-
-    # Training command and args
-    entrypoint_cmd = ["python3", "vdt/run.py"]
-
-    dataset_bucket = f"gs://{config['dataset_bucket']}"
-
-    trainer_args = [
-        f"--train_data_gcs={dataset_bucket}",
-        "/opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py",
-        "--config-path=conf/",
-        f"--config-name={recipe_name}.yaml",
-        f"exp_manager.explicit_log_dir={base_output_dir}",
-        f"exp_manager.dllogger_logger_kwargs.json_file={base_output_dir}/dllogger.json",
-        "+exp_manager.create_tensorboard_logger=true",
-        "exp_manager.create_checkpoint_callback=false",
-        f"trainer.num_nodes={n_nodes}",
-        f"trainer.devices={num_gpus_per_node}",
-        "trainer.max_steps=10",
-        "trainer.log_every_n_steps=1",
-        "model.tokenizer.vocab_file=/data/gpt2-vocab.json",
-        "model.tokenizer.merge_file=/data/gpt2-merges.txt",
-        "model.data.data_prefix=[1.0,/data/hfbpe_gpt_training_data_text_document]",
-    ]
-
-    launch_job(
-        job_name=job_name,
-        project=project_id,
-        region=region,
-        gcs_bucket=bucket,
-        image_uri=image_uri,
-        entrypoint_cmd=entrypoint_cmd,
-        trainer_args=trainer_args,
-        num_nodes=n_nodes,
-        machine_type=machine_type,
-        num_gpus_per_node=num_gpus_per_node,
-        gpu_type=gpu_type,
-        strategy=strategy,
-        reservation_name=reservation_full_name,
-    )
-
-
-if __name__ == "__main__":
-    app.run(main)

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/scripts/run.py

@@ -1,85 +0,0 @@
-"""Entrypoint for Vertex Distributed Training container."""
-
-import argparse
-import os
-import sys
-from collections.abc import Sequence
-from subprocess import STDOUT, check_output, run
-
-from absl import app, flags, logging
-from util import cluster_spec
-
-from retrying import retry
-
-# PyTorch barrier call which synchronizes all of the nodes before launching the training process.
-# This makes sure that processes will block until all processes are ready.
-# Improves the reliability of spot VM usage for multi-node training jobs
-
-@retry(stop_max_attempt_number=100, wait_exponential_multiplier=1000)
-def barrier_with_retry() -> None:
-    import torch
-    logging.info("Starting barrier on RANK {}".format(os.environ["RANK"]))
-    torch.distributed.init_process_group()
-    torch.distributed.barrier()
-    torch.distributed.destroy_process_group()
-    logging.info("Finished barrier on RANK {}".format(os.environ["RANK"]))
-
-def main(unused_argv: Sequence[str]) -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--train_data_gcs",
-        type=str,
-        help="Download training data from gcs path",
-    )
-    args, unknown = parser.parse_known_args()
-
-    for key, val in os.environ.items():
-        logging.info("ENV %s=%s", key, val)
-
-    if args.train_data_gcs:
-        local_dir = "/data"
-        if not os.path.exists(local_dir):
-            os.mkdir(local_dir)
-        logging.info("downloading %s to %s...", args.train_data_gcs, local_dir)
-        check_output(
-            [
-                "gcloud",
-                "storage",
-                "cp",
-                "-r",
-                f"{args.train_data_gcs}/*",
-                local_dir,
-            ],
-            stderr=STDOUT,
-        )
-        logging.info("%s downloaded.", args.train_data_gcs)
-
-    primary_node_addr, primary_node_port, node_rank, num_nodes = (
-        cluster_spec.get_cluster_spec()
-    )
-
-    cmd = [
-        "torchrun",
-        "--nproc-per-node=8",
-        f"--nnodes={num_nodes}",
-        f"--node_rank={node_rank}",
-    ]
-    if num_nodes > 1:
-        cmd += [
-            "--max-restarts=3",
-            "--rdzv-backend=static",
-            f'--rdzv_id={os.getenv("CLOUD_ML_JOB_ID", primary_node_port)}',
-            f"--rdzv-endpoint={primary_node_addr}:{primary_node_port}",
-        ]
-    cmd += unknown
-
-    logging.info("launching with cmd: \n%s", " \\\n".join(cmd))
-    barrier_with_retry()
-    run(cmd, stdout=sys.stdout, stderr=sys.stdout, check=True)
-
-
-if __name__ == "__main__":
-    logging.get_absl_handler().python_handler.stream = sys.stdout
-    app.run(
-        main, flags_parser=lambda _args: flags.FLAGS(_args, known_only=True)
-    )

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/scripts/util/cluster_spec.py

@@ -1,81 +0,0 @@
-"""Get cluster info from environment variables."""
-
-import dataclasses
-import json
-import os
-
-from absl import logging
-
-
-@dataclasses.dataclass
-class ClusterInfo:
-    """Contains information about the cluster.
-
-    Attributes:
-      primary_node_addr: The address of the primary node.
-      primary_node_port: The port of the primary node.
-      node_rank: The rank of the node.
-      num_nodes: The number of nodes in the cluster.
-    """
-
-    primary_node_addr: str | None = None
-    primary_node_port: str | None = None
-    node_rank: int = 0
-    num_nodes: int = 1
-
-    # Allows unpacking operation like
-    # primary_node_addr, primary_node_port, _, _ = ClusterInfo()
-    # See https://stackoverflow.com/a/70753113
-    def __iter__(self):
-        return iter(dataclasses.astuple(self))
-
-
-def get_cluster_spec() -> ClusterInfo:
-    """Parses CLUSTER_SPEC environment variable and returns the cluster info.
-
-    Returns:
-      A ClusterInfo object.
-    """
-    cluster_spec = os.getenv("CLUSTER_SPEC", None)
-
-    # If CLUSTER_SPEC is not set, use individual vars to construct cluster info.
-    if not cluster_spec:
-        cluster_info = ClusterInfo(
-            primary_node_addr=os.getenv("MASTER_ADDR", None),
-            primary_node_port=os.getenv("MASTER_PORT", None),
-            node_rank=int(os.getenv("RANK", "0")),
-            num_nodes=int(os.getenv("NNODES", "1")),
-        )
-        return cluster_info
-
-    cluster_data = json.loads(cluster_spec)
-    # Get primary node info
-    primary_node = cluster_data["cluster"]["workerpool0"][0]
-    logging.info("primary node: %s", primary_node)
-    primary_node_addr, primary_node_port = primary_node.split(":")
-    logging.info("primary node address: %s", primary_node_addr)
-    logging.info("primary node port: %s", primary_node_port)
-
-    # Determine node rank of this machine
-    workerpool = cluster_data["task"]["type"]
-    if workerpool == "workerpool0":
-        node_rank = 0
-    elif workerpool == "workerpool1":
-        # Add 1 for the primary node, since `index` is the index of workerpool1.
-        node_rank = cluster_data["task"]["index"] + 1
-    else:
-        raise ValueError(
-            "Only workerpool0 and workerpool1 are supported. Unknown workerpool:"
-            f" {workerpool}"
-        )
-    logging.info("node rank: %s", node_rank)
-
-    # Calculate total nodes.
-    num_nodes = 1  # For the primary node.
-    if "workerpool1" in cluster_data["cluster"]:
-        num_nodes += len(cluster_data["cluster"]["workerpool1"])
-    logging.info("num nodes: %s", num_nodes)
-
-    return ClusterInfo(
-        primary_node_addr, primary_node_port, node_rank, num_nodes
-    )

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/scripts/util/cluster_spec_test.py

@@ -1,59 +0,0 @@
-"""Add tests for cluster_spec.py."""
-
-import os
-
-from . import cluster_spec
-
-
-# TODO(styer): Use pytest instead
-class ClusterSpecTest(googletest.TestCase):
-
-    def setUp(self):
-        super().setUp()
-        self.curr_env_var = os.environ.copy()
-
-    def tearDown(self):
-        super().tearDown()
-        os.environ = self.curr_env_var
-
-    def test_get_cluster_spec_from_env_vars(self):
-        os.environ["CLUSTER_SPEC"] = ""
-        os.environ["MASTER_ADDR"] = "127.0.0.1"
-        os.environ["MASTER_PORT"] = "8080"
-        os.environ["RANK"] = "0"
-        os.environ["NNODES"] = "2"
-        cluster_info = cluster_spec.get_cluster_spec()
-        self.assertEqual(cluster_info.primary_node_addr, "127.0.0.1")
-        self.assertEqual(cluster_info.primary_node_port, "8080")
-        self.assertEqual(cluster_info.node_rank, 0)
-        self.assertEqual(cluster_info.num_nodes, 2)
-
-    def test_get_cluster_spec_from_cluster_spec(self):
-        os.environ[
-            "CLUSTER_SPEC"
-        ] = """
-    {
-      "cluster": {
-        "workerpool0": [
-          "127.0.0.1:8080"
-        ],
-        "workerpool1": [
-          "127.0.0.2:8080",
-          "127.0.0.3:8080"
-        ]
-      },
-      "task": {
-        "type": "workerpool1",
-        "index": 0
-      }
-    }
-    """
-        cluster_info = cluster_spec.get_cluster_spec()
-        self.assertEqual(cluster_info.primary_node_addr, "127.0.0.1")
-        self.assertEqual(cluster_info.primary_node_port, "8080")
-        self.assertEqual(cluster_info.node_rank, 1)
-        self.assertEqual(cluster_info.num_nodes, 3)
-
-
-if __name__ == "__main__":
-    googletest.main()

community-content/vertex_cpr_samples/sklearn/predictor_Classifier.py

@@ -1,33 +0,0 @@
-import numpy as np
-import os
-import pickle
-
-from google.cloud.aiplatform.constants import prediction
-from google.cloud.aiplatform.utils import prediction_utils
-from google.cloud.aiplatform.prediction.predictor import Predictor
-from sklearn.datasets import load_breast_cancer
-from sklearn.linear_model import RidgeClassifier
-
-class LinearRegressionPredictor(Predictor):
-
-    def __init__(self):
-        return
-
-    def load(self, artifacts_uri: str) -> None:
-        prediction_utils.download_model_artifacts(artifacts_uri)
-        if os.path.exists(prediction.MODEL_FILENAME_PKL):
-            self._model = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
-        else:
-            self._model = RidgeClassifier()
-        X, y = load_breast_cancer(return_X_y=True)
-        self._model.fit(X, y)
-
-    def preprocess(self, prediction_input: dict) -> np.ndarray:
-        instances = prediction_input["instances"]
-        return np.asarray(instances)
-
-    def predict(self, instances: np.ndarray) -> np.ndarray:
-        return self._model.predict(instances)
-
-    def postprocess(self, prediction_results: np.ndarray) -> dict:
-        return {"predictions": prediction_results.tolist()}

community-content/vertex_cpr_samples/sklearn/predictor_LinearRegression.py

@@ -1,33 +0,0 @@
-import numpy as np
-import os
-import pickle
-
-from google.cloud.aiplatform.constants import prediction
-from google.cloud.aiplatform.utils import prediction_utils
-from google.cloud.aiplatform.prediction.predictor import Predictor
-from sklearn.datasets import make_blobs
-from sklearn.linear_model import LinearRegression 
-
-class LinearRegressionPredictor(Predictor):
-
-    def __init__(self):
-        return
-
-    def load(self, artifacts_uri: str) -> None:
-        prediction_utils.download_model_artifacts(artifacts_uri)
-        if os.path.exists(prediction.MODEL_FILENAME_PKL):
-            self._model = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
-        else:
-            self._model = LogisticRegression()
-        X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)
-        self._model.fit(X, y)
-
-    def preprocess(self, prediction_input: dict) -> np.ndarray:
-        instances = prediction_input["instances"]
-        return np.asarray(instances)
-
-    def predict(self, instances: np.ndarray) -> np.ndarray:
-        return self._model.predict_proba(instances)
-
-    def postprocess(self, prediction_results: np.ndarray) -> dict:
-        return {"predictions": prediction_results.tolist()}

community-content/vertex_cpr_samples/sklearn/predictor_SGDClassifier.py

@@ -1,33 +0,0 @@
-import numpy as np
-import os
-import pickle
-
-from google.cloud.aiplatform.constants import prediction
-from google.cloud.aiplatform.utils import prediction_utils
-from google.cloud.aiplatform.prediction.predictor import Predictor
-from sklearn.linear_model import SGDClassifier
-
-class SGDClassifierPredictor(Predictor):
-
-    def __init__(self):
-        return
-
-    def load(self, artifacts_uri: str) -> None:
-        prediction_utils.download_model_artifacts(artifacts_uri)
-        if os.path.exists(prediction.MODEL_FILENAME_PKL):
-            self._model = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
-        else:
-            self._model = SGDClassifier(max_iter=5)
-        X = [[0., 0.], [1., 1.]]
-        y = [0, 1]
-        self._model.fit(X, y)
-
-    def preprocess(self, prediction_input: dict) -> np.ndarray:
-        instances = prediction_input["instances"]
-        return np.asarray(instances)
-
-    def predict(self, instances: np.ndarray) -> np.ndarray:
-        return self._model.predict(instances)
-
-    def postprocess(self, prediction_results: np.ndarray) -> dict:
-        return {"predictions": prediction_results.tolist()}

community-content/vertex_cpr_samples/torch/predictor_resnet.py

@@ -1,34 +0,0 @@
-import os
-import torch
-
-from google.cloud.aiplatform.utils import prediction_utils
-from google.cloud.aiplatform.prediction.predictor import Predictor
-from torchvision.models import detection, resnet50, ResNet50_Weights
-from typing import Dict, List
-
-class ResNetPredictor(Predictor):
-
-    def __init__(self):
-        return
-
-    def load(self, artifacts_uri: str) -> None:
-        prediction_utils.download_model_artifacts(artifacts_uri)
-        if os.path.exists("model.pth.tar"):
-            self.model = detection.fasterrcnn_resnet50_fpn(pretrained=True)
-            stat_dic = torch.load("model.pth.tar")
-            self.model.load_state_dict(stat_dic['state_dict'])
-        else:
-            weights = ResNet50_Weights.DEFAULT
-            self.model = resnet50(weights=weights)
-        self.model.eval()
-
-    def preprocess(self, prediction_input: dict) -> torch.Tensor:
-        instances = prediction_input["instances"]
-        return torch.Tensor(instances)
-
-    @torch.inference_mode()
-    def predict(self, instances: torch.Tensor) -> List[str]:
-        return self._model(instances)
-
-    def postprocess(self, prediction_results: List[str]) -> Dict:
-        return {"predictions": prediction_results}

community-content/vertex_cpr_samples/xgboost/predictor_Classifier.py

@@ -1,37 +0,0 @@
-import os
-import numpy as np
-import pickle
-import xgboost as xgb
-
-from google.cloud.aiplatform.constants import prediction
-from google.cloud.aiplatform.utils import prediction_utils
-from google.cloud.aiplatform.prediction.predictor import Predictor
-from sklearn.datasets import make_blobs
-from xgboost import XGBClassifier
-
-
-class ClassifierPredictor(Predictor):
-
-    def __init__(self):
-        return
-
-    def load(self, artifacts_uri: str) -> None:
-        prediction_utils.download_model_artifacts(artifacts_uri)
-        if os.path.exists(prediction.MODEL_FILENAME_PKL):
-            booster = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
-        else:
-            X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)
-            model = XGBClassifier()
-            model.fit(X, y)
-            booster = model.get_booster()
-        self._booster = booster
-
-    def preprocess(self, prediction_input: dict) -> xgb.DMatrix:
-        instances = prediction_input["instances"]
-        return xgb.DMatrix(instances)
-
-    def predict(self, instances: xgb.DMatrix) -> np.ndarray:
-        return self._booster.predict(instances)
-
-    def postprocess(self, prediction_results: np.ndarray) -> dict:
-        return {"predictions": prediction_results.tolist()}

community-content/vertex_cpr_samples/xgboost/predictor_xgbranker.py

@@ -1,41 +0,0 @@
-import os
-import numpy as np
-import pandas as pd
-import pickle
-import xgboost as xgb
-
-from google.cloud.aiplatform.constants import prediction
-from google.cloud.aiplatform.utils import prediction_utils
-from google.cloud.aiplatform.prediction.predictor import Predictor
-
-class XGBRankerPredictor(Predictor):
-
-    def __init__(self):
-        return
-
-    def load(self, artifacts_uri: str) -> None:
-        prediction_utils.download_model_artifacts(artifacts_uri)
-        if os.path.exists(prediction.MODEL_FILENAME_PKL):
-            booster = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
-            self._booster = booster
-        else:
-            N = 500
-            dates = pd.date_range(start='2023-01-01', end='2023-01-12', periods=N)
-            X = pd.DataFrame(np.random.randn(N, 5), columns=list('ABCDE'), index=dates)
-            y = pd.Series(np.random.randint(0, 10, size=N), index=dates, name='label')
-            group = X.groupby(dates + pd.offsets.MonthEnd(0)).size()
-            sample_weight = pd.Series(np.arange(len(group)), index=group.index)
-            model = xgb.XGBRanker(objective='rank:pairwise', max_depth=3, learning_rate=0.1, booster='gbtree', tree_method='hist', n_jobs=4, n_estimators=50, enable_categorical=False, random_state=42)
-            model.fit(X=X, y=y, group=group, sample_weight=sample_weight, verbose=True)
-            booster = model.get_booster()
-            self._booster = booster
-
-    def preprocess(self, prediction_input: dict) -> xgb.DMatrix:
-        instances = prediction_input["instances"]
-        return xgb.DMatrix(instances)
-
-    def predict(self, instances: xgb.DMatrix) -> np.ndarray:
-        return self._booster.predict(instances, output_margin=False, ntree_limit=0)
-
-    def postprocess(self, prediction_results: np.ndarray) -> dict:
-        return {"predictions": prediction_results.tolist()}

community-content/vertex_model_garden/model_oss/cloudnerf/requirements.txt

@@ -1,6 +1,6 @@
 --find-links https://download.pytorch.org/whl/torch_stable.html
 
-torch==2.2.0
+torch==2.0.1+cu118
 numpy==1.26.1
 absl_py==2.0.0
 accelerate==0.24.0

community-content/vertex_model_garden/model_oss/notebook_util/common_util.py

@@ -1,16 +1,13 @@
 """Common util functions for notebook."""
 
 import base64
-from collections.abc import Sequence
 import datetime
 import io
 import json
 import os
 import subprocess
-import time
-from typing import Any
+from typing import Any, Dict, Sequence
 
-from google import auth
 from google.cloud import storage
 import matplotlib.pyplot as plt
 import numpy as np
@@ -88,9 +85,7 @@ def get_job_name_with_datetime(prefix: str) -> str:
   Returns:
     A job name.
   """
-  now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-  job_name = f"{prefix}-{now}".replace("_", "-")
-  return job_name
+  return prefix + datetime.datetime.now().strftime("_%Y%m%d_%H%M%S")
 
 
 def create_job_name(prefix: str) -> str:
@@ -104,7 +99,7 @@ def create_job_name(prefix: str) -> str:
   """
   user = os.environ.get("USER")
   now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-  job_name = f"{prefix}-{user}-{now}".replace("_", "-")
+  job_name = f"{prefix}-{user}-{now}"
   return job_name
 
 
@@ -233,23 +228,23 @@ def download_image(url: str) -> str:
     base64 encoded image.
   """
   response = requests.get(url)
-  return Image.open(io.BytesIO(response.content))  # pytype: disable=bad-return-type  # pillow-102-upgrade
+  return Image.open(io.BytesIO(response.content))
 
 
 def resize_image(image: Any, new_width: int = 1000) -> Any:
-  """Resizes an image to a certain width.
+    """Resizes an image to a certain width.
 
-  Args:
-    image: The image which has to be resized.
-    new_width: New width of the image.
+    Args:
+      image: The image which has to be resized.
+      new_width: New width of the image.
 
-  Returns:
-    New resized image.
-  """
-  width, height = image.size
-  new_height = int(height * new_width / width)
-  new_img = image.resize((new_width, new_height))
-  return new_img
+    Returns:
+      New resized image.
+    """
+    width, height = image.size
+    new_height = int(height * new_width / width)
+    new_img = image.resize((new_width, new_height))
+    return new_img
 
 
 def load_img(path: str) -> Any:
@@ -284,7 +279,7 @@ def decode_image(
   return image
 
 
-def get_label_map(label_map_yaml_filepath: str) -> dict[int, str]:
+def get_label_map(label_map_yaml_filepath: str) -> Dict[int, str]:
   """Returns class id to label mapping given a filepath to the label map.
 
   Args:
@@ -328,150 +323,6 @@ def get_prediction_instances(test_filepath: str, new_width: int = -1) -> Any:
   return instances
 
 
-def vqa_predict(
-    endpoint: Any,
-    question_prompts: Sequence[str],
-    image: Any,
-    language_code: str = "en",
-    new_width: int = 1000,
-    use_dedicated_endpoint: bool = False,
-) -> Sequence[str]:
-  """Predicts the answer to a question about an image using an Endpoint."""
-  # Resize and convert image to base64 string.
-  resized_image = resize_image(image, new_width)
-  resized_image_base64 = image_to_base64(resized_image)
-
-  instances = []
-  if question_prompts:
-    # Format question prompt
-    question_prompt_format = "answer {} {}\n"
-    for question_prompt in question_prompts:
-      if question_prompt:
-        instances.append({
-            "prompt": question_prompt_format.format(
-                language_code, question_prompt
-            ),
-            "image": resized_image_base64,
-        })
-  else:
-    instances.append({
-        "image": resized_image_base64,
-    })
-
-  response = endpoint.predict(
-      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
-  )
-  return [pred.get("response") for pred in response.predictions]
-
-
-def caption_predict(
-    endpoint: Any,
-    language_code: str,
-    image: Any,
-    caption_prompt: bool = False,
-    new_width: int = 1000,
-    use_dedicated_endpoint: bool = False,
-) -> str:
-  """Predicts a caption for a given image using an Endpoint."""
-  # Resize and convert image to base64 string.
-  resized_image = resize_image(image, new_width)
-  resized_image_base64 = image_to_base64(resized_image)
-
-  instance = {"image": resized_image_base64}
-
-  if caption_prompt:
-    # Format caption prompt
-    caption_prompt_format = "caption {}\n"
-    instance["prompt"] = caption_prompt_format.format(language_code)
-
-  instances = [instance]
-  response = endpoint.predict(
-      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
-  )
-  return response.predictions[0].get("response")
-
-
-def ocr_predict(
-    endpoint: Any,
-    ocr_prompt: str,
-    image: Any,
-    new_width: int = 1000,
-    use_dedicated_endpoint: bool = False,
-) -> str:
-  """Extracts text from a given image using an Endpoint."""
-  # Resize and convert image to base64 string.
-  resized_image = resize_image(image, new_width)
-  resized_image_base64 = image_to_base64(resized_image)
-
-  instance = {"image": resized_image_base64}
-  if ocr_prompt:
-    instance["prompt"] = ocr_prompt
-  instances = [instance]
-
-  response = endpoint.predict(
-      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
-  )
-  return response.predictions[0].get("response")
-
-
-def detect_predict(
-    endpoint: Any,
-    detect_prompt: str,
-    image: Any,
-    new_width: int = 1000,
-    use_dedicated_endpoint: bool = False,
-) -> str:
-  """Predicts the answer to a question about an image using an Endpoint."""
-  # Resize and convert image to base64 string.
-  resized_image = resize_image(image, new_width)
-  resized_image_base64 = image_to_base64(resized_image)
-
-  instance = {"image": resized_image_base64}
-  if detect_prompt:
-    instance["prompt"] = detect_prompt
-  instances = [instance]
-
-  response = endpoint.predict(
-      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
-  )
-  return response.predictions[0].get("response")
-
-
-def copy_model_artifacts(
-    model_id: str,
-    model_source: str,
-    model_destination: str,
-) -> None:
-  """Copies model artifacts from model_source to model_destination.
-
-  model_source and model_destination should be GCS path.
-
-  Args:
-    model_id: The model id.
-    model_source: The source of the model artifact.
-    model_destination: The destination of the model artifact.
-  """
-  if not model_source.startswith(GCS_URI_PREFIX):
-    raise ValueError(
-        f"{model_source} is not a GCS path starting with {GCS_URI_PREFIX}."
-    )
-  if not model_destination.startswith(GCS_URI_PREFIX):
-    raise ValueError(
-        f"{model_destination} is not a GCS path starting with {GCS_URI_PREFIX}."
-    )
-  model_source = f"{model_source}/{model_id}"
-  model_destination = f"{model_destination}/{model_id}"
-  print("Copying model artifact from ", model_source, " to ", model_destination)
-  subprocess.check_output([
-      "gcloud",
-      "storage",
-      "cp",
-      "-r",
-      model_source,
-      model_destination,
-  ])
-
-
 def get_quota(project_id: str, region: str, resource_id: str) -> int:
   """Returns the quota for a resource in a region.
 
@@ -510,17 +361,6 @@ def get_quota(project_id: str, region: str, resource_id: str) -> int:
   ):
     return -1
   all_regions_data = quota_data[0]["consumerQuotaLimits"][0]["quotaBuckets"]
-
-  # If the quota data does not have dimensions, it is global quota. However,
-  # global quota may be overridden by regional quota. So we need to check the
-  # global quota first.
-  global_quota = -1
-  if (
-      all_regions_data
-      and "dimensions" not in all_regions_data[0]
-      and "effectiveLimit" in all_regions_data[0]
-  ):
-    global_quota = int(all_regions_data[0]["effectiveLimit"])
   for region_data in all_regions_data:
     if (
         region_data.get("dimensions")
@@ -530,92 +370,47 @@ def get_quota(project_id: str, region: str, resource_id: str) -> int:
         return int(region_data["effectiveLimit"])
       else:
         return 0
-  return global_quota
+  return -1
 
 
-def get_resource_id(
-    accelerator_type: str,
-    is_for_training: bool,
-    is_spot: bool = False,
-    is_restricted_image: bool = False,
-    is_dynamic_workload_scheduler: bool = False,
-) -> str:
+def get_resource_id(accelerator_type: str, is_for_training: bool) -> str:
   """Returns the resource id for a given accelerator type and the use case.
 
   Args:
     accelerator_type: The accelerator type.
     is_for_training: Whether the resource is used for training. Set false for
       serving use case.
-    is_spot: Whether the resource is used with Spot.
-    is_restricted_image: Whether the image is hosted in `vertex-ai-restricted`.
-    is_dynamic_workload_scheduler: Whether the resource is used with Dynamic
-      Workload Scheduler.
 
   Returns:
     The resource id.
   """
-  accelerator_suffix_map = {
-      "NVIDIA_TESLA_V100": "nvidia_v100_gpus",
-      "NVIDIA_TESLA_P100": "nvidia_p100_gpus",
-      "NVIDIA_L4": "nvidia_l4_gpus",
-      "NVIDIA_TESLA_A100": "nvidia_a100_gpus",
-      "NVIDIA_A100_80GB": "nvidia_a100_80gb_gpus",
-      "NVIDIA_H100_80GB": "nvidia_h100_gpus",
-      "NVIDIA_H100_MEGA_80GB": "nvidia_h100_mega_gpus",
-      "NVIDIA_H200_141GB": "nvidia_h200_gpus",
-      "NVIDIA_TESLA_T4": "nvidia_t4_gpus",
-      "TPU_V6e": "tpu_v6e",
-      "TPU_V5e": "tpu_v5e",
-      "TPU_V3": "tpu_v3",
-  }
-  default_training_accelerator_map = {
-      key: f"custom_model_training_{accelerator_suffix_map[key]}"
-      for key in accelerator_suffix_map
-  }
-  dws_training_accelerator_map = {
-      key: f"custom_model_training_preemptible_{accelerator_suffix_map[key]}"
-      for key in accelerator_suffix_map
-  }
-  restricted_image_training_accelerator_map = {
-      "NVIDIA_A100_80GB": "restricted_image_training_nvidia_a100_80gb_gpus",
-  }
-  spot_serving_accelerator_map = {
-      key: f"custom_model_serving_preemptible_{accelerator_suffix_map[key]}"
-      for key in accelerator_suffix_map
+  training_accelerator_map = {
+      "NVIDIA_TESLA_V100": "custom_model_training_nvidia_v100_gpus",
+      "NVIDIA_L4": "custom_model_training_nvidia_l4_gpus",
+      "NVIDIA_TESLA_A100": "custom_model_training_nvidia_a100_gpus",
+      "NVIDIA_A100_80GB": "custom_model_training_nvidia_a100_80gb_gpus",
+      "NVIDIA_TESLA_T4": "custom_model_training_nvidia_t4_gpus",
+      "TPU_V5e": "custom_model_training_tpu_v5e",
+      "TPU_V3": "custom_model_training_tpu_v3",
   }
   serving_accelerator_map = {
-      key: f"custom_model_serving_{accelerator_suffix_map[key]}"
-      for key in accelerator_suffix_map
+      "NVIDIA_TESLA_V100": "custom_model_serving_nvidia_v100_gpus",
+      "NVIDIA_L4": "custom_model_serving_nvidia_l4_gpus",
+      "NVIDIA_TESLA_A100": "custom_model_serving_nvidia_a100_gpus",
+      "NVIDIA_A100_80GB": "custom_model_serving_nvidia_a100_80gb_gpus",
+      "NVIDIA_TESLA_T4": "custom_model_serving_nvidia_t4_gpus",
+      "TPU_V5e": "custom_model_serving_tpu_v5e",
   }
-
   if is_for_training:
-    if is_restricted_image and is_dynamic_workload_scheduler:
-      raise ValueError(
-          "Dynamic Workload Scheduler does not work for restricted image"
-          " training."
-      )
-    training_accelerator_map = (
-        restricted_image_training_accelerator_map
-        if is_restricted_image
-        else default_training_accelerator_map
-    )
     if accelerator_type in training_accelerator_map:
-      if is_dynamic_workload_scheduler:
-        return dws_training_accelerator_map[accelerator_type]
-      else:
-        return training_accelerator_map[accelerator_type]
+      return training_accelerator_map[accelerator_type]
     else:
       raise ValueError(
           f"Could not find accelerator type: {accelerator_type} for training."
       )
   else:
-    if is_dynamic_workload_scheduler:
-      raise ValueError("Dynamic Workload Scheduler does not work for serving.")
-    accelerator_map = (
-        spot_serving_accelerator_map if is_spot else serving_accelerator_map
-    )
-    if accelerator_type in accelerator_map:
-      return accelerator_map[accelerator_type]
+    if accelerator_type in serving_accelerator_map:
+      return serving_accelerator_map[accelerator_type]
     else:
       raise ValueError(
           f"Could not find accelerator type: {accelerator_type} for serving."
@@ -628,31 +423,9 @@ def check_quota(
     accelerator_type: str,
     accelerator_count: int,
     is_for_training: bool,
-    is_spot: bool = False,
-    is_restricted_image: bool = False,
-    is_dynamic_workload_scheduler: bool = False,
-) -> None:
-  """Checks if the project and the region has the required quota.
-
-  Args:
-    project_id: The project id.
-    region: The region.
-    accelerator_type: The accelerator type.
-    accelerator_count: The number of accelerators to check quota for.
-    is_for_training: Whether the resource is used for training. Set false for
-      serving use case.
-    is_spot: Whether the resource is used with Spot.
-    is_restricted_image: Whether the image is hosted in `vertex-ai-restricted`.
-    is_dynamic_workload_scheduler: Whether the resource is used with Dynamic
-      Workload Scheduler.
-  """
-  resource_id = get_resource_id(
-      accelerator_type,
-      is_for_training=is_for_training,
-      is_spot=is_spot,
-      is_restricted_image=is_restricted_image,
-      is_dynamic_workload_scheduler=is_dynamic_workload_scheduler,
-  )
+):
+  """Checks if the project and the region has the required quota."""
+  resource_id = get_resource_id(accelerator_type, is_for_training)
   quota = get_quota(project_id, region, resource_id)
   quota_request_instruction = (
       "Either use "
@@ -672,76 +445,3 @@ def check_quota(
         f"Quota not enough for {resource_id} in {region}: {quota} <"
         f" {accelerator_count}. {quota_request_instruction}"
     )
-
-
-def get_deploy_source() -> str:
-  """Gets deploy_source string based on running environment."""
-  vertex_product = os.environ.get("VERTEX_PRODUCT", "")
-  match vertex_product:
-    case "COLAB_ENTERPRISE":
-      return "notebook_colab_enterprise"
-    case "WORKBENCH_INSTANCE":
-      return "notebook_workbench"
-    case _:
-      # Legacy workbench, legacy colab, or other custom environments.
-      return "notebook_environment_unspecified"
-
-
-def _is_operation_done(op_name: str, region: str) -> bool:
-  """Checks if the operation is done.
-
-  Args:
-    op_name: The name of the operation to poll.
-    region: The region of the operation.
-
-  Returns:
-    True if the operation is done, False otherwise.
-
-  Raises:
-    ValueError: If the operation failed.
-  """
-  creds, _ = auth.default()
-  auth_req = auth.transport.requests.Request()
-  creds.refresh(auth_req)
-  headers = {
-      "Authorization": f"Bearer {creds.token}",
-  }
-  url = f"https://{region}-aiplatform.googleapis.com/ui/{op_name}"
-  response = requests.get(url, headers=headers)
-  operation_data = response.json()
-  if "error" in operation_data:
-    raise ValueError(f"Operation failed: {operation_data['error']}")
-  return operation_data.get("done", False)
-
-
-def poll_and_wait(
-    op_name: str, region: str, total_wait: int, interval: int = 60
-) -> None:
-  """Polls the operation and waits for it to complete.
-
-  Args:
-    op_name: The name of the operation to poll.
-    region: The region of the operation.
-    total_wait: The total wait time in seconds.
-    interval: The interval between each poll in seconds.
-
-  Raises:
-    TimeoutError: If the operation times out.
-  """
-  start_time = time.time()
-  while True:
-    if _is_operation_done(op_name, region):
-      break
-    time_elapsed = time.time() - start_time
-    if time_elapsed > total_wait:
-      raise TimeoutError(
-          f"Operation timed out after {int(time_elapsed)} seconds."
-      )
-    print(
-        "\rStill waiting for operation... Elapsed time in seconds:"
-        f" {int(time_elapsed):<6}",
-        end="",
-        flush=True,
-    )
-    time.sleep(interval)
-

community-content/vertex_model_garden/model_oss/notebook_util/dataset_validation_util.py

@@ -1,570 +0,0 @@
-"""Functions for dataset validation.
-
-This tool is used to validate the dataset against the given template.
-"""
-
-import json
-import multiprocessing
-import os
-import subprocess
-from typing import Any, Callable, Dict, Tuple, Union
-from absl import logging
-import accelerate
-import datasets
-import transformers
-
-GCS_URI_PREFIX = "gs://"
-GCSFUSE_URI_PREFIX = "/gcs/"
-LOCAL_BASE_MODEL_DIR = "/tmp/base_model_dir"
-LOCAL_TEMPLATE_DIR = "/tmp/template_dir"
-_TEMPLATE_DIRNAME = "templates"
-_VERTEX_AI_SAMPLES_GITHUB_REPO_NAME = "vertex-ai-samples"
-_VERTEX_AI_SAMPLES_GITHUB_TEMPLATE_DIR = (
-    "community-content/vertex_model_garden/model_oss/peft/train/vmg/templates"
-)
-_MODELS_REQUIRING_PAD_TOKEN = ("llama", "falcon", "mistral", "mixtral")
-_MODELS_REQUIRING_EOS_TOEKN = ("gemma-2b", "gemma-7b")
-_DESCRIPTION_KEY = "description"
-_SOURCE_KEY = "source"
-_PROMPT_INPUT_KEY = "prompt_input"
-_PROMPT_NO_INPUT_KEY = "prompt_no_input"
-_RESPONSE_SEPARATOR = "response_separator"
-_INSTRUCTION_SEPARATOR = "instruction_separator"
-_CHAT_TEMPLATE_KEY = "chat_template"
-_KNOWN_KEYS = (
-    _DESCRIPTION_KEY,
-    _SOURCE_KEY,
-    _PROMPT_INPUT_KEY,
-    _PROMPT_NO_INPUT_KEY,
-    _RESPONSE_SEPARATOR,
-    _INSTRUCTION_SEPARATOR,
-    _CHAT_TEMPLATE_KEY,
-)
-
-
-def is_gcs_path(input_path: str) -> bool:
-  """Checks if the input path is a Google Cloud Storage (GCS) path.
-
-  Args:
-      input_path: The input path to be checked.
-
-  Returns:
-      True if the input path is a GCS path, False otherwise.
-  """
-  return input_path is not None and input_path.startswith(GCS_URI_PREFIX)
-
-
-def force_gcs_fuse_path(gcs_uri: str) -> str:
-  """Converts gs:// uris to their /gcs/ equivalents. No-op for other uris.
-
-  Args:
-    gcs_uri: The GCS URI to convert.
-
-  Returns:
-    The converted GCS URI.
-  """
-  if is_gcs_path(gcs_uri):
-    return GCSFUSE_URI_PREFIX + gcs_uri[len(GCS_URI_PREFIX) :]
-  else:
-    return gcs_uri
-
-
-def download_gcs_uri_to_local(
-    gcs_uri: str,
-    destination_dir: str = LOCAL_BASE_MODEL_DIR,
-    check_path_exists: bool = True,
-) -> str:
-  """Downloads GCS URI to local.
-
-  If GCS URI is a directory, gs://some/folder is downloaded to
-  /destination_dir/folder. If GCS URI is a file, gs://some/file is downloaded to
-  /destination_dir/file.
-
-  Args:
-    gcs_uri: GCS URI to download.
-    destination_dir: Local directory directory.
-    check_path_exists: Whether to check if the path exists.
-
-  Returns:
-    Local path to target folder/file.
-  """
-  target = os.path.join(
-      destination_dir,
-      os.path.basename(os.path.normpath(gcs_uri)),
-  )
-  if check_path_exists and os.path.exists(target):
-    logging.info("File %s already exists.", target)
-    return target
-  if accelerate.PartialState().is_local_main_process:
-    logging.info(
-        "Downloading file(s) from %s to %s...", gcs_uri, destination_dir
-    )
-    if not os.path.exists(destination_dir):
-      os.mkdir(destination_dir)
-    subprocess.check_output([
-        "gsutil",
-        "-m",
-        "cp",
-        "-r",
-        gcs_uri,
-        destination_dir,
-    ])
-    logging.info("Downloaded file(s) from %s to %s.", gcs_uri, destination_dir)
-  # Make sure ALL processes process to next step after data downloading is done.
-  # It matters for the main process to wait for other processes as well.
-  accelerate.PartialState().wait_for_everyone()
-  return target
-
-
-def get_template(template_path: str) -> Dict[str, str]:
-  """Gets the template dictionary given the file path.
-
-  Args:
-    template_path: Path to the template file.
-
-  Returns:
-    A dictionary of the template.
-
-  Raises:
-    ValueError: If the template file does not exist or contains unknown keys.
-  """
-  if is_gcs_path(template_path):
-    template_path = force_gcs_fuse_path(template_path)
-  elif not os.path.isfile(template_path):
-    template_path = os.path.join(
-        os.path.dirname(__file__),
-        _TEMPLATE_DIRNAME,
-        template_path + ".json",
-    )
-  if not os.path.isfile(template_path):
-    raise ValueError(f"Template file {template_path} does not exist.")
-  with open(template_path, "r") as f:
-    template_json: dict[str, str] = json.load(f)
-    for key in template_json:
-      if key not in _KNOWN_KEYS:
-        raise ValueError(f"Unknown key {key} in template {template_path}.")
-    return template_json
-
-
-def get_response_separator(template_json: Dict[str, str]) -> Union[str, None]:
-  return template_json.get(_RESPONSE_SEPARATOR, None)
-
-
-def get_instruction_separator(
-    template_json: Dict[str, str],
-) -> Union[str, None]:
-  return template_json.get(_INSTRUCTION_SEPARATOR, None)
-
-
-def _format_template_fn(
-    template: str,
-    input_column: str,
-    tokenizer: transformers.PreTrainedTokenizer | None = None,
-) -> Callable[[Dict[str, str]], Dict[str, str]]:
-  """Formats a dataset example according to a template.
-
-  Args:
-    template: Name of the JSON template file under `templates/` or GCS path to
-      the template file.
-    input_column: The input column in the dataset to be used or updated by the
-      template. If it does not exist, the template's `prompt_no_input` will be
-      used, and the input_column will be created.
-    tokenizer: The tokenizer to use for chat_template templates.
-
-  Returns:
-    A function that formats data according to the template.
-  """
-  template_json = get_template(template)
-
-  if _CHAT_TEMPLATE_KEY not in template_json:
-
-    def format_fn(example: Dict[str, str]) -> Dict[str, str]:
-      format_dict = {key: value for key, value in example.items()}
-      if format_dict.get(input_column):
-        format_str = template_json[_PROMPT_INPUT_KEY]
-      elif _PROMPT_NO_INPUT_KEY in template_json:
-        format_str = template_json[_PROMPT_NO_INPUT_KEY]
-      else:
-        raise KeyError(
-            f"The template {os.path.basename(template)} does not contain"
-            f" {_PROMPT_INPUT_KEY} or {_PROMPT_NO_INPUT_KEY} key."
-        )
-      try:
-        return {input_column: format_str.format(**format_dict)}
-      except KeyError as e:
-        raise KeyError(
-            f"The template {os.path.basename(template)} contains a key {e} in"
-            f" {_PROMPT_INPUT_KEY} or {_PROMPT_NO_INPUT_KEY} that does not"
-            " exist in the dataset example. The dataset example looks like"
-            f" {format_dict}."
-        ) from e
-
-    return format_fn
-  elif (
-      _PROMPT_INPUT_KEY in template_json
-      or _PROMPT_NO_INPUT_KEY in template_json
-  ):
-    raise ValueError(
-        f"chat_template templates do not support {_PROMPT_INPUT_KEY} or"
-        f" {_PROMPT_NO_INPUT_KEY} templates."
-    )
-  else:
-    if tokenizer is None:
-      raise ValueError("A tokenizer is required for chat_template templates.")
-    # Assign HuggingFace jinja template.
-    tokenizer.chat_template = template_json[_CHAT_TEMPLATE_KEY]
-
-    def format_fn(example: Dict[str, str]) -> Dict[str, str]:
-      try:
-        return {
-            input_column: tokenizer.apply_chat_template(
-                example[input_column],
-                tokenize=False,
-                add_generation_prompt=False,
-            )
-        }
-      except KeyError as e:
-        raise KeyError(
-            f"The template {os.path.basename(template)} contains a key {e} in"
-            f" {_CHAT_TEMPLATE_KEY} that does not exist in the dataset example."
-        ) from e
-
-    return format_fn
-
-
-def _get_split_string(
-    split: str,
-    dataset_percent: int | None = None,
-    dataset_k_rows: int | None = None,
-) -> str:
-  """Gets the formatted split string for the dataset.
-
-  This is used to format the split string as per
-  https://huggingface.co/docs/datasets/v2.21.0/loading#slice-splits. Also, this
-  function will only be used to load the partial dataset for validating the
-  dataset against the template.
-
-  Args:
-    split: Split of the dataset.
-    dataset_percent: The percentage of the dataset to load.
-    dataset_k_rows: The top k sequences to load from the dataset.
-
-  Returns:
-    A formatted split string.
-  """
-  # Validate the dataset_percent and dataset_k_rows values.
-  if dataset_percent and dataset_k_rows:
-    raise ValueError(
-        "You can set either validate_percentage_of_dataset or"
-        " validate_k_rows_of_dataset, but not both."
-    )
-
-  if dataset_percent:
-    logging.info("Loading %d percent of the dataset...", dataset_percent)
-    return f"{split}[:{dataset_percent}%]"
-
-  if dataset_k_rows:
-    logging.info("Loading top %d rows of the dataset...", dataset_k_rows)
-    return f"{split}[:{dataset_k_rows}]"
-
-  return split
-
-
-def _github_template_path(template: str) -> str:
-  """Generates the path to the template in the Vertex AI Samples GitHub repo.
-
-  Args:
-    template: Name of the template.
-
-  Returns:
-    The path to the template in the Vertex AI Samples GitHub repo.
-  """
-  # vertex-ai-samples directory may lie under separate directory depending on
-  # the scratch_dir parameter in the notebook execution environment.
-  vertex_ai_samples_abs_path = os.getcwd().split(
-      _VERTEX_AI_SAMPLES_GITHUB_REPO_NAME
-  )[0]
-  return os.path.join(
-      vertex_ai_samples_abs_path,
-      _VERTEX_AI_SAMPLES_GITHUB_REPO_NAME,
-      _VERTEX_AI_SAMPLES_GITHUB_TEMPLATE_DIR,
-      template + ".json",
-  )
-
-
-def _get_dataset(
-    dataset_name: str,
-    split: str,
-    num_proc: int | None = None,
-) -> datasets.DatasetDict:
-  """Gets a dataset.
-
-  Args:
-    dataset_name: Name of the dataset or path to a custom dataset.
-    split: Split of the dataset.
-    num_proc: Number of processors to use.
-
-  Returns:
-    A dataset.
-  """
-  dataset_name = force_gcs_fuse_path(dataset_name)
-  if os.path.isfile(dataset_name):
-    # Custom dataset.
-    return datasets.load_dataset(
-        "json",
-        data_files=[dataset_name],
-        split=split,
-        num_proc=num_proc,
-    )
-  # HF dataset.
-  return datasets.load_dataset(dataset_name, split=split, num_proc=num_proc)
-
-
-def should_add_pad_token(model_id: str) -> bool:
-  """Returns whether the model requires adding a special pad token.
-
-  Args:
-    model_id: The name of the model.
-
-  Returns:
-    True if the model requires adding a special pad token, False otherwise.
-  """
-  return any(s.lower() in model_id.lower() for s in _MODELS_REQUIRING_PAD_TOKEN)
-
-
-def should_add_eos_token(model_id: str) -> bool:
-  """Returns whether the model requires adding a special eos token.
-
-  Args:
-    model_id: The name of the model.
-
-  Returns:
-    True if the model requires adding a special eos token, False otherwise.
-  """
-  return any(m in model_id for m in _MODELS_REQUIRING_EOS_TOEKN)
-
-
-def load_tokenizer(
-    pretrained_model_id: str,
-    padding_side: str | None = None,
-    access_token: str | None = None,
-) -> transformers.AutoTokenizer:
-  """Loads tokenizer based on `pretrained_model_id`.
-
-  Args:
-    pretrained_model_id: The name of the pretrained model.
-    padding_side: The side to pad the input on.
-    access_token: The access token to use for the tokenizer.
-
-  Returns:
-    The tokenizer.
-  """
-  tokenizer_kwargs = {}
-  if should_add_eos_token(pretrained_model_id):
-    tokenizer_kwargs["add_eos_token"] = True
-  if padding_side:
-    tokenizer_kwargs["padding_side"] = padding_side
-
-  with accelerate.PartialState().local_main_process_first():
-    tokenizer = transformers.AutoTokenizer.from_pretrained(
-        pretrained_model_id,
-        trust_remote_code=False,
-        use_fast=True,
-        token=access_token,
-        **tokenizer_kwargs,
-    )
-
-  if should_add_pad_token(pretrained_model_id):
-    tokenizer.add_special_tokens({"pad_token": "[PAD]"})
-
-  return tokenizer
-
-
-def get_filtered_dataset(
-    dataset: Any,
-    input_column: str,
-    max_seq_length: int,
-    tokenizer: transformers.PreTrainedTokenizer,
-) -> Any:
-  """Returns the dataset by removing examples that are longer than max_seq_length.
-
-  Args:
-    dataset: The dataset to filter.
-    input_column: The input column in the dataset to be used.
-    max_seq_length: The maximum sequence length.
-    tokenizer: The tokenizer.
-  """
-  actual_dataset_length = len(dataset)
-  filtered_dataset = dataset.filter(
-      lambda x: len(tokenizer(x[input_column])["input_ids"]) <= max_seq_length
-  )
-  filtered_dataset_length = len(filtered_dataset)
-  if actual_dataset_length != filtered_dataset_length:
-    examples_removed_percent = (
-        (actual_dataset_length - filtered_dataset_length)
-        * 100
-        / actual_dataset_length
-    )
-    logging.info(
-        "(%.2f%%) of examples token length is <= max-seq-length(%d); (%.2f%%) >"
-        " max-seq-length. Filtering out %d example(s) which are longer than"
-        " max-seq-length.",
-        100 - examples_removed_percent,
-        max_seq_length,
-        examples_removed_percent,
-        actual_dataset_length - filtered_dataset_length,
-    )
-
-  return filtered_dataset
-
-
-def format_dataset(
-    dataset: datasets.Dataset,
-    input_column: str,
-    template: str = None,
-    tokenizer: transformers.PreTrainedTokenizer | None = None,
-) -> datasets.Dataset:
-  """Takes a raw dataset and formats it using a template and tokenizer.
-
-  Args:
-    dataset: The raw (unprocessed) dataset to format.
-    input_column: The input column in the dataset to be used or updaded by the
-      template. If it does not exist, the template's `prompt_no_input` will be
-      used, and the input_column will be created.
-    template: Name of the JSON template file under `templates/` or GCS path to
-      the template file.
-    tokenizer: The tokenizer to use for chat_template templates.
-
-  Returns:
-    A dataset compatible with the template.
-  """
-  return dataset.map(
-      _format_template_fn(
-          template,
-          input_column=input_column,
-          tokenizer=tokenizer,
-      )
-  )
-
-
-def load_dataset_with_template(
-    dataset_name: str,
-    split: str,
-    input_column: str,
-    template: str = None,
-    tokenizer: transformers.PreTrainedTokenizer | None = None,
-) -> Tuple[Any, Any]:
-  """Loads dataset with templates.
-
-  Args:
-    dataset_name: Name of the dataset or path to a custom dataset.
-    split: Split of the dataset.
-    input_column: The input column in the dataset to be used or updaded by the
-      template. If it does not exist, the template's `prompt_no_input` will be
-      used, and the input_column will be created.
-    template: Name of the JSON template file under `templates/` or GCS path to
-      the template file.
-    tokenizer: The tokenizer to use for chat_template templates.
-
-  Returns:
-    The raw dataset and the dataset compatible with the template.
-  """
-  raw = _get_dataset(dataset_name, split=split)
-  if template:
-    templated = format_dataset(raw, input_column, template, tokenizer)
-  else:
-    templated = None
-
-  return raw, templated
-
-
-def validate_dataset_with_template(
-    dataset_name: str,
-    split: str,
-    input_column: str,
-    template: str,
-    tokenizer: transformers.PreTrainedTokenizer | None = None,
-    max_seq_length: int | None = None,
-    use_multiprocessing: bool = False,
-    validate_percentage_of_dataset: int | None = None,
-    validate_k_rows_of_dataset: int | None = None,
-) -> Any:
-  """Validates dataset with templates.
-
-  This function will be used to load the dataset and validate it against the
-  template. In case of validation, we also allow the users to load the dataset
-  partially by allowing them to read x% or top k rows of the dataset. To
-  validate the dataset, the template file must be available in the GCS bucket
-  and the dataset must be available either in the GCS bucket or Hugging Face.
-
-  Args:
-    dataset_name: Name of the dataset or path to a custom dataset.
-    split: Split of the dataset.
-    input_column: The input column in the dataset to be used or updaded by the
-      template. If it does not exist, the template's `prompt_no_input` will be
-      used, and the input_column will be created.
-    template: Name of the JSON template file under `templates/` or GCS path to
-      the template file.
-    tokenizer: The tokenizer to use for chat_template templates.
-    max_seq_length: The maximum sequence length.
-    use_multiprocessing: If True, it will use multiprocessing to load the
-      dataset.
-    validate_percentage_of_dataset: The percentage of the dataset to load.
-    validate_k_rows_of_dataset: The top k sequences to load from the dataset.
-
-  Returns:
-    None if the validation is successful, otherwise returns the error message.
-  """
-  if not template:
-    raise ValueError("template is required for validate_dataset.")
-
-  if not dataset_name:
-    raise ValueError("dataset_name is empty.")
-
-  if not split:
-    raise ValueError("split is empty.")
-
-  split = _get_split_string(
-      split,
-      validate_percentage_of_dataset,
-      validate_k_rows_of_dataset,
-  )
-
-  num_proc = multiprocessing.cpu_count() if use_multiprocessing else 1
-
-  # gcsfuse cannot be used from the notebook runtime env. Hence, we have
-  # to download dataset and template from gcs to local.
-  if is_gcs_path(dataset_name):
-    dataset_name = download_gcs_uri_to_local(dataset_name, LOCAL_BASE_MODEL_DIR)
-
-  if is_gcs_path(template):
-    template_path = download_gcs_uri_to_local(template, LOCAL_TEMPLATE_DIR)
-  elif os.path.isfile(_github_template_path(template)):
-    template_path = _github_template_path(template)
-  else:
-    raise ValueError(
-        f"Template file {template} does not exist. To validate the"
-        " dataset, please provide a valid GCS path for the template or a valid"
-        " template name from"
-        f" https://github.com/GoogleCloudPlatform/{_VERTEX_AI_SAMPLES_GITHUB_REPO_NAME}/tree/main/{_VERTEX_AI_SAMPLES_GITHUB_TEMPLATE_DIR}."
-    )
-
-  dataset = format_dataset(
-      _get_dataset(dataset_name, split, num_proc),
-      input_column,
-      template_path,
-      tokenizer,
-  )
-
-  if tokenizer is not None:
-    get_filtered_dataset(
-        dataset=dataset,
-        input_column=input_column,
-        max_seq_length=max_seq_length,
-        tokenizer=tokenizer,
-    )
-  print(
-      "Dataset {} is compatible with the {} template.".format(
-          os.path.basename(dataset_name), os.path.basename(template)
-      )
-  )

community-content/vertex_model_garden/model_oss/peft/causal_language_modeling_lora.py

@@ -0,0 +1,142 @@
+"""Causal language modeling with LoRA models."""
+
+# pylint: disable=g-importing-member
+
+from datasets import load_dataset
+from peft import get_peft_model
+from peft import LoraConfig
+import torch
+from torch import nn
+import transformers
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+from transformers import BitsAndBytesConfig
+from transformers import TrainingArguments
+from util import constants
+
+
+def finetune_causal_language_modeling(
+    pretrained_model_id: str,
+    dataset_name: str,
+    output_dir: str,
+    precision_mode: str = None,
+    lora_rank: int = 16,
+    lora_alpha: int = 32,
+    lora_dropout: float = 0.05,
+    warmup_steps: int = 10,
+    max_steps: int = 10,
+    learning_rate: float = 2e-4,
+    local_pretrained_model_id: str = None,
+) -> None:
+  """Finetunes causal language modelings."""
+  if precision_mode == constants.PRECISION_MODE_32:
+    model = AutoModelForCausalLM.from_pretrained(
+        local_pretrained_model_id
+        if local_pretrained_model_id
+        else pretrained_model_id,
+        torch_dtype=torch.float32,
+        device_map="auto",
+    )
+  elif precision_mode == constants.PRECISION_MODE_16:
+    model = AutoModelForCausalLM.from_pretrained(
+        local_pretrained_model_id
+        if local_pretrained_model_id
+        else pretrained_model_id,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+    )
+  elif precision_mode == constants.PRECISION_MODE_8:
+    quantization_config = BitsAndBytesConfig(
+        load_in_8bit=True, int8_threshold=0
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        local_pretrained_model_id
+        if local_pretrained_model_id
+        else pretrained_model_id,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        quantization_config=quantization_config,
+    )
+  else:
+    quantization_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16,
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        local_pretrained_model_id
+        if local_pretrained_model_id
+        else pretrained_model_id,
+        device_map="auto",
+        torch_dtype=torch.bfloat16,
+        quantization_config=quantization_config,
+    )
+
+  tokenizer = AutoTokenizer.from_pretrained(
+      local_pretrained_model_id
+      if local_pretrained_model_id
+      else pretrained_model_id
+  )
+  if "llama" in pretrained_model_id:
+    tokenizer.pad_token = "[PAD]"
+
+  for param in model.parameters():
+    # Freezes the model - train adapters later.
+    param.requires_grad = False
+    if param.ndim == 1:
+      # Casts the small parameters (e.g. layernorm) to fp32 for stability.
+      param.data = param.data.to(torch.float32)
+
+  # Reduces the number of stored activations.
+  model.gradient_checkpointing_enable()
+  model.enable_input_require_grads()
+
+  class CastOutputToFloat(nn.Sequential):
+
+    def forward(self, x):
+      return super().forward(x).to(torch.float32)
+
+  model.lm_head = CastOutputToFloat(model.lm_head)
+
+  config = LoraConfig(
+      r=lora_rank,
+      lora_alpha=lora_alpha,
+      target_modules=["q_proj", "v_proj"],
+      lora_dropout=lora_dropout,
+      bias="none",
+      task_type="CAUSAL_LM",
+  )
+
+  model = get_peft_model(model, config)
+  model.print_trainable_parameters()
+
+  data = load_dataset(dataset_name)
+  data = data.map(
+      lambda samples: tokenizer(samples["quote"]),
+      batched=True,
+  )
+
+  trainer = transformers.Trainer(
+      model=model,
+      train_dataset=data["train"],
+      args=TrainingArguments(
+          per_device_train_batch_size=4,
+          gradient_accumulation_steps=4,
+          warmup_steps=warmup_steps,
+          max_steps=max_steps,
+          learning_rate=learning_rate,
+          fp16=True,
+          logging_steps=1,
+          output_dir=output_dir,
+          ddp_find_unused_parameters=False,
+      ),
+      data_collator=transformers.DataCollatorForLanguageModeling(
+          tokenizer,
+          mlm=False,
+      ),
+  )
+  # Silence the warnings. Please re-enable for inference!
+  model.config.use_cache = False
+  trainer.train()
+
+  model.save_pretrained(output_dir)

community-content/vertex_model_garden/model_oss/peft/dockerfile/code_llama_convert.Dockerfile

@@ -1,28 +0,0 @@
-# Base on pytorch-cuda image.
-FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
-
-# Install tools.
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update
-RUN apt-get install -y --no-install-recommends apt-utils
-RUN apt-get install -y --no-install-recommends curl
-RUN apt-get install -y --no-install-recommends wget
-RUN apt-get install -y --no-install-recommends git
-
-# Install libraries.
-ENV PIP_ROOT_USER_ACTION=ignore
-RUN python3 -m pip install --upgrade pip
-RUN pip install tokenizers==0.13.3
-RUN pip install accelerate==0.21.0
-RUN pip install sentencepiece==0.1.99
-RUN pip install datasets==2.14.4
-RUN pip install protobuf==4.24.1
-
-# Install transformers
-RUN git clone https://github.com/huggingface/transformers.git
-WORKDIR transformers
-# Pin the commit to add-code-llama 08/25/2023
-RUN git reset --hard 015f8e110d270a0ad42de4ae5b98198d69eb1964
-RUN pip install -e .
-
-ENTRYPOINT ["python","src/transformers/models/llama/convert_llama_weights_to_hf.py"]

community-content/vertex_model_garden/model_oss/peft/dockerfile/conversion.Dockerfile

@@ -1,22 +0,0 @@
-# Dockerfile for Language Model Conversion.
-#
-# To build:
-# docker build -f model_oss/peft/dockerfile/conversion.Dockerfile . -t ${YOUR_IMAGE_TAG}
-#
-# To push to gcr:
-# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
-# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
-
-FROM tensorflow/build:2.14-python3.8
-
-RUN git clone https://github.com/facebookresearch/llama-recipes.git && \
-    cd llama-recipes && \
-    pip install -r requirements.txt && \
-    pip freeze | grep transformers && \
-    git clone https://github.com/huggingface/transformers.git && \
-    cd transformers && \
-    pip install protobuf
-
-WORKDIR /llama-recipes/transformers
-
-ENTRYPOINT ["python","src/transformers/models/llama/convert_llama_weights_to_hf.py"]

community-content/vertex_model_garden/model_oss/peft/dockerfile/serve.Dockerfile

@@ -7,40 +7,39 @@
 # docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
 # docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
 
-FROM pytorch/torchserve:0.11.0-gpu
+FROM pytorch/torchserve:0.7.0-gpu
 
 USER root
 
-ENV INFER_PORT=7080
-ENV MNG_PORT=7081
-ENV MODEL="peft_serving"
+ENV infer_port=7080
+ENV mng_port=7081
+ENV model_name="peft_serving"
 ENV PATH="/home/model-server/:${PATH}"
 
-RUN apt-get update && apt-get -y upgrade && apt-get install -y --no-install-recommends \
+RUN apt-get update && apt-get install -y --no-install-recommends \
         curl \
         wget \
         vim \
         git \
         git-lfs
 RUN git lfs install
-RUN apt-get autoremove -y
 
 # Install libraries.
 ENV PIP_ROOT_USER_ACTION=ignore
 RUN python3 -m pip install --upgrade pip
-RUN pip install --upgrade torch==2.0.1 --index-url https://download.pytorch.org/whl/cu118
+RUN pip install --upgrade torch==2.0.1
 RUN pip install torchvision==0.15.2
 RUN pip install tokenizers==0.13.3
 RUN pip install accelerate==0.21.0
 RUN pip install sentencepiece==0.1.99
 RUN pip install grpcio-status==1.33.2
 RUN pip install protobuf==3.19.6
-RUN pip install peft==0.5.0
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft.git
 RUN pip install datasets==2.14.4
-RUN pip install triton==3.0.0
+RUN pip install triton==2.0.0.dev20221120
 RUN pip install xformers==0.0.20
-RUN pip install google-cloud-storage
-RUN pip install absl-py
+RUN pip install google-cloud-storage==2.7.0
+RUN pip install absl-py==1.4.0
 RUN pip install scipy==1.10.1
 RUN pip install evaluate==0.4.0
 RUN pip install scikit-learn==1.2.2
@@ -48,43 +47,52 @@ RUN pip install loralib==0.1.1
 RUN pip install bitsandbytes==0.39.0
 RUN pip install trl==0.4.4
 RUN pip install einops==0.6.1
-RUN pip install optimum==1.13.2
-RUN pip install auto-gptq==0.4.2
-RUN pip install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.7/autoawq-0.1.7+cu118-cp39-cp39-linux_x86_64.whl
-RUN pip install diffusers==0.27.2
-RUN pip install tiktoken==0.6.0
-RUn pip install git+https://github.com/huggingface/transformers.git@76fa17c1663a0efeca7208c20579833365584889
-RUN pip install pynvml==11.4.0
-RUN pip install -i https://test.pypi.org/simple/ bitsandbytes
+
+# Install diffusers from source.
+RUN git clone --depth 1 --branch v0.16.1 https://github.com/huggingface/diffusers.git
+WORKDIR diffusers
+RUN pip install -e .
+WORKDIR /home/model-server
+
+# Install transformers from source.
+RUN git clone --depth 1 --branch v4.31.0 https://github.com/huggingface/transformers.git
+# The patch is used to change the transformers loading model behavior:
+# 1) For models on Huggingface hub: if the model has multiple shards, each shard
+#    will be downloaded separately and get deleted after loading to GPU.
+# 2) For models on local disk: if a model bin file is actually a text file
+#    recording a GCS path, the model file will be downloaded and get deleted
+#    after loading to GPU.
+COPY model_oss/peft/hf_transformers_lazy_download.patch /home/model-server/hf_transformers_lazy_download.patch
+WORKDIR transformers
+RUN git apply /home/model-server/hf_transformers_lazy_download.patch
+RUN pip install -e .
+WORKDIR /home/model-server
 
 # Copy license.
-WORKDIR /home/model-server
 RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
 
 # Copy model artifacts.
 COPY model_oss/peft/handler.py /home/model-server/handler.py
 COPY model_oss/peft/config.properties /home/model-server/config.properties
 COPY model_oss/util/ /home/model-server/util/
-COPY model_oss/util/pytorch_startup_prober.sh /model_garden/scripts/pytorch_startup_prober.sh
 ENV PYTHONPATH /home/model-server/
 
 # Expose ports.
-EXPOSE ${INFER_PORT}
-EXPOSE ${MNG_PORT}
+EXPOSE ${infer_port}
+EXPOSE ${mng_port}
 
 # Set environments.
 ENV TASK "causal-language-modeling-lora"
-ENV BASE_MODEL_ID ""
-ENV MODEL_ID ""
+ENV MODEL_ID "openlm-research/open_llama_7b"
 ENV PRECISION_LOADING_MODE "float16"
 ENV FINETUNED_LORA_MODEL_PATH ""
-ENV TRUST_REMOTE_CODE ""
+
 
 # Archive model artifacts and dependencies.
 # Do not set --model-file and --serialized-file because model and checkpoint
 # will be dynamically loaded in handler.py.
 RUN torch-model-archiver \
---model-name=${MODEL} \
+--model-name=${model_name} \
 --version=1.0 \
 --handler=/home/model-server/handler.py \
 --runtime=python3 \
@@ -95,5 +103,5 @@ RUN torch-model-archiver \
 # Run Torchserve HTTP serve to respond to prediction requests.
 CMD ["torchserve", "--start", \
      "--ts-config", "/home/model-server/config.properties", \
-     "--models", "${MODEL}=${MODEL}.mar", \
+     "--models", "${model_name}=${model_name}.mar", \
      "--model-store", "/home/model-server/model-store"]

community-content/vertex_model_garden/model_oss/peft/dockerfile/train.Dockerfile

@@ -0,0 +1,111 @@
+# Dockerfile for PEFT Training.
+#
+# To build:
+# docker build -f model_oss/peft/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+# Builds GPU docker image of PyTorch
+# Uses multi-staged approach to reduce size
+# Stage 1
+# Use base conda image to reduce time
+FROM continuumio/miniconda3:latest AS compile-image
+# Specify py version
+ENV PYTHON_VERSION=3.8
+# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+RUN apt-get update && \
+    apt-get install -y curl git wget software-properties-common git-lfs && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+# Install audio-related libraries
+RUN apt-get update && \
+    apt install -y ffmpeg
+
+RUN apt install -y libsndfile1-dev
+RUN git lfs install
+
+# Create our conda env - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+RUN conda create --name peft python=${PYTHON_VERSION} ipython jupyter pip
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+
+# Below is copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+# We don't install pytorch here yet since CUDA isn't available
+# instead we use the direct torch wheel
+ENV PATH /opt/conda/envs/peft/bin:$PATH
+# Activate our bash shell
+RUN chsh -s /bin/bash
+SHELL ["/bin/bash", "-c"]
+# Activate the conda env and install transformers + accelerate from source
+RUN source activate peft
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/transformers
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft#egg=peft[test]
+RUN python3 -m pip install --no-cache-dir bitsandbytes
+
+# Stage 2
+FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04 AS build-image
+COPY --from=compile-image /opt/conda /opt/conda
+ENV PATH /opt/conda/bin:$PATH
+
+# Install apt libs
+RUN apt-get update && \
+    apt-get install -y curl git wget vim && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+RUN echo "source activate peft" >> ~/.profile
+
+# Install libraries.
+RUN pip install --upgrade torch==2.0.1
+RUN pip install torchvision==0.15.2
+RUN pip install git+https://github.com/huggingface/transformers@de9255de27abfcae4a1f816b904915f0b1e23cd9
+RUN pip install transformers -U
+RUN pip install accelerate==0.21.0
+RUN pip install sentencepiece==0.1.99
+RUN pip install grpcio-status==1.33.2
+RUN pip install protobuf==3.19.6
+RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft.git
+RUN pip install datasets==2.9.0
+RUN pip install triton==2.0.0.dev20221120
+RUN pip install xformers==0.0.20
+RUN pip install Jinja2==3.1.2
+RUN pip install ftfy==6.1.1
+RUN pip install cloudml-hypertune==0.1.0.dev6
+RUN pip install tensorboard==2.12.0
+RUN pip install scipy==1.10.1
+RUN pip install evaluate==0.4.0
+RUN pip install scikit-learn==1.2.2
+RUN pip install loralib==0.1.1
+RUN pip install bitsandbytes==0.39.0
+RUN pip install trl==0.4.4
+RUN pip install einops==0.6.1
+RUN pip install google-cloud-storage==2.7.0
+
+RUN git clone --depth 1 --branch v0.16.1 https://github.com/huggingface/diffusers.git
+WORKDIR diffusers
+RUN pip install -e .
+
+# Switch to diffusers examples folder.
+WORKDIR examples
+
+# NOTE: use 'sed' to modify train_text_to_image_lora.py to
+#       fix the bug for accelerator.
+RUN sed -i \
+"s#logging_dir=logging_dir#project_dir=logging_dir#g" \
+text_to_image/train_text_to_image_lora.py
+
+# Config accelerate.
+RUN mkdir -p ./vertex_vision_model_garden_peft/
+COPY model_oss/peft/train.sh ./vertex_vision_model_garden_peft/train.sh
+COPY model_oss/peft/*.py     ./vertex_vision_model_garden_peft/
+COPY model_oss/util /diffusers/examples/util
+ENV PYTHONPATH /diffusers/examples/
+
+# Generate accelerate config at the beginning of docker run.
+ENTRYPOINT ["python3", "vertex_vision_model_garden_peft/main.py"]

community-content/vertex_model_garden/model_oss/peft/handler.py

@@ -72,39 +72,15 @@ class PeftHandler(BaseHandler):
         "PRECISION_LOADING_MODE", constants.PRECISION_MODE_16
     )
     self.task = os.environ.get("TASK", CAUSAL_LANGUAGE_MODELING_LORA)
-    trust_remote_code = os.environ.get("TRUST_REMOTE_CODE", None)
-    if trust_remote_code == "false":
-      self.trust_remote_code = False
-    else:
-      self.trust_remote_code = True
-
-    # If present, the path of the model in the container.
-    aip_storage_dir = os.environ.get("AIP_STORAGE_DIR", None)
-
-    # If present, the URI of the model in a google owned GCS bucket.
-    aip_storage_uri = os.environ.get("AIP_STORAGE_URI", None)
-
-    model_id = os.environ.get("MODEL_ID", None)
-    base_model_id = os.environ.get("BASE_MODEL_ID", None)
-
-    self.model_id = None
-    if aip_storage_dir:
-      self.model_id = aip_storage_dir
-      logging.info(f"Loaded base model from AIP_STORAGE_DIR: {self.model_id}.")
-    elif aip_storage_uri:
-      self.model_id = aip_storage_uri
-      logging.info(f"Loaded base model from AIP_STORAGE_URI: {self.model_id}.")
-    elif model_id:
-      self.model_id = model_id
-      logging.info(f"Loaded base model from MODEL_ID: {self.model_id}.")
-    elif base_model_id:
-      # Note: BASE_MODEL_ID has been unified with MODEL_ID.
-      # MODEL_ID should be used whenever possible.
-      self.model_id = base_model_id
-      logging.info(f"Loaded base model from BASE_MODEL_ID: {self.model_id}.")
-
+    self.base_model_id = os.environ.get("BASE_MODEL_ID", None)
+    self.model_id = self.base_model_id
+    if not self.base_model_id:
+      self.model_id = os.environ.get("MODEL_ID", "")
     self.quantization = os.environ.get("QUANTIZATION", None)
-
+    logging.info(f"Load base model id from MODEL_ID:{self.model_id}.")
+    if not self.model_id:
+      self.model_id = os.environ.get("AIP_STORAGE_URI", "")
+      logging.info(f"Load base model id from AIP_STORAGE_URI: {self.model_id}.")
     if not self.model_id:
       raise ValueError("Base model id is must be set.")
     if fileutils.is_gcs_path(self.model_id):
@@ -125,7 +101,8 @@ class PeftHandler(BaseHandler):
 
     logging.info(
         f"Using task:{self.task}, base model:{self.model_id}, lora model:"
-        f" {self.finetuned_lora_model_path}, precision {self.precision_mode}."
+        f" {self.finetuned_lora_model_path}, and precision"
+        f" {self.precision_mode}."
     )
 
     self.pipeline = None
@@ -168,18 +145,11 @@ class PeftHandler(BaseHandler):
     elif (
         self.task == CAUSAL_LANGUAGE_MODELING_LORA or self.task == INSTRUCT_LORA
     ):
-      tokenizer = AutoTokenizer.from_pretrained(
-          self.model_id,
-          trust_remote_code=self.trust_remote_code,
-      )
-
+      tokenizer = AutoTokenizer.from_pretrained(self.model_id)
       logging.debug("Initialized the tokenizer.")
       if self.task == CAUSAL_LANGUAGE_MODELING_LORA:
         if self.quantization == constants.AWQ:
-          model = AutoAWQForCausalLM.from_quantized(
-              self.model_id,
-              trust_remote_code=self.trust_remote_code,
-          )
+          model = AutoAWQForCausalLM.from_quantized(self.model_id)
         elif self.quantization == constants.GPTQ or not self.quantization:
           if self.precision_mode == constants.PRECISION_MODE_32:
             model = AutoModelForCausalLM.from_pretrained(
@@ -187,7 +157,6 @@ class PeftHandler(BaseHandler):
                 return_dict=True,
                 torch_dtype=torch.float32,
                 device_map="auto",
-                trust_remote_code=self.trust_remote_code,
             )
           elif self.precision_mode == constants.PRECISION_MODE_16B:
             model = AutoModelForCausalLM.from_pretrained(
@@ -195,7 +164,6 @@ class PeftHandler(BaseHandler):
                 return_dict=True,
                 torch_dtype=torch.bfloat16,
                 device_map="auto",
-                trust_remote_code=self.trust_remote_code,
             )
           elif self.precision_mode == constants.PRECISION_MODE_16:
             model = AutoModelForCausalLM.from_pretrained(
@@ -203,7 +171,6 @@ class PeftHandler(BaseHandler):
                 return_dict=True,
                 torch_dtype=torch.float16,
                 device_map="auto",
-                trust_remote_code=self.trust_remote_code,
             )
           elif self.precision_mode == constants.PRECISION_MODE_8:
             quantization_config = BitsAndBytesConfig(
@@ -215,7 +182,6 @@ class PeftHandler(BaseHandler):
                 torch_dtype=torch.float16,
                 device_map="auto",
                 quantization_config=quantization_config,
-                trust_remote_code=self.trust_remote_code,
             )
           else:
             quantization_config = BitsAndBytesConfig(
@@ -229,7 +195,6 @@ class PeftHandler(BaseHandler):
                 device_map="auto",
                 torch_dtype=torch.bfloat16,
                 quantization_config=quantization_config,
-                trust_remote_code=self.trust_remote_code,
             )
         else:
           raise ValueError(f"Invalid QUANTIZATION value: {self.quantization}")
@@ -238,14 +203,14 @@ class PeftHandler(BaseHandler):
           model = AutoModelForCausalLM.from_pretrained(
               self.model_id,
               torch_dtype=torch.bfloat16,
-              trust_remote_code=self.trust_remote_code,
+              trust_remote_code=True,
               device_map="auto",
           )
         except:  # pylint: disable=bare-except
           model = AutoModelForCausalLM.from_pretrained(
               self.model_id,
               torch_dtype=torch.bfloat16,
-              trust_remote_code=self.trust_remote_code,
+              trust_remote_code=True,
               device_map="auto",
           )
       logging.debug("Initialized the base model.")
@@ -364,4 +329,4 @@ class PeftHandler(BaseHandler):
     return f"Prompt:\n{prompt.strip()}\nOutput:\n{output}"
 
 
-# pylint: enable=logging-fstring-interpolation
+# pylint: enable=logging-fstring-interpolation

community-content/vertex_model_garden/model_oss/peft/hf_transformers_lazy_download.patch

@@ -0,0 +1,131 @@
+diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
+index 45459ed..32527f4 100644
+--- a/src/transformers/modeling_utils.py
++++ b/src/transformers/modeling_utils.py
+@@ -32,6 +32,8 @@ import torch
+ from packaging import version
+ from torch import Tensor, nn
+ from torch.nn import CrossEntropyLoss
++from huggingface_hub import hf_hub_download
++from google.cloud import storage
+
+ from .activations import get_activation
+ from .configuration_utils import PretrainedConfig
+@@ -442,6 +444,29 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
+     """
+     Reads a PyTorch checkpoint file, returning properly formatted errors if they arise.
+     """
++    delete_download = False
++    tmp_dir = "/tmp/model"
++    os.makedirs(tmp_dir, exist_ok=True)
++    if isinstance(checkpoint_file, dict):
++        # Download model file from huggingface
++        print(f"==> Download model from HF: {checkpoint_file}")
++        checkpoint_file = hf_hub_download(
++            local_dir=tmp_dir, local_dir_use_symlinks=False, force_download=True, resume_download=True, **checkpoint_file)
++        delete_download = True
++    else:
++        with open(checkpoint_file, "rb") as f:
++            is_gcs_file = (f.read(2) == b"gs")
++        if is_gcs_file:
++            # Download model file from GCS
++            with open(checkpoint_file, "r") as f:
++                gcs_file = f.read()
++            checkpoint_file = os.path.join(tmp_dir, gcs_file.split("/")[-1])
++            print(f"==> Download model from GCS: {gcs_file} to: {checkpoint_file}")
++            client = storage.Client()
++            with open(checkpoint_file, 'wb') as f:
++                client.download_blob_to_file(gcs_file, f)
++            delete_download = True
++
+     if checkpoint_file.endswith(".safetensors") and is_safetensors_available():
+         # Check format of the archive
+         with safe_open(checkpoint_file, framework="pt") as f:
+@@ -455,9 +480,9 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
+             raise NotImplementedError(
+                 f"Conversion from a {metadata['format']} safetensors archive to PyTorch is not implemented yet."
+             )
+-        return safe_load_file(checkpoint_file)
++        state_dict = safe_load_file(checkpoint_file)
+     try:
+-        return torch.load(checkpoint_file, map_location="cpu")
++        state_dict = torch.load(checkpoint_file, map_location="cpu")
+     except Exception as e:
+         try:
+             with open(checkpoint_file) as f:
+@@ -478,6 +503,10 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
+                 f"at '{checkpoint_file}'. "
+                 "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True."
+             )
++    if delete_download:
++        print(f"==> Delete downloaded model: {checkpoint_file}")
++        os.remove(checkpoint_file)
++    return state_dict
+
+
+ def set_initialized_submodules(model, state_dict_keys):
+@@ -3179,7 +3208,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
+             return mismatched_keys
+
+         if resolved_archive_file is not None:
+-            folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
++            if isinstance(resolved_archive_file, str):
++                folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
++            else:
++                folder = None
+         else:
+             folder = None
+         if device_map is not None and is_safetensors:
+diff --git a/src/transformers/utils/hub.py b/src/transformers/utils/hub.py
+index ffed743..4b15770 100644
+--- a/src/transformers/utils/hub.py
++++ b/src/transformers/utils/hub.py
+@@ -414,20 +414,34 @@ def cached_file(
+     user_agent = http_user_agent(user_agent)
+     try:
+         # Load from URL or cache if already cached
+-        resolved_file = hf_hub_download(
+-            path_or_repo_id,
+-            filename,
+-            subfolder=None if len(subfolder) == 0 else subfolder,
+-            repo_type=repo_type,
+-            revision=revision,
+-            cache_dir=cache_dir,
+-            user_agent=user_agent,
+-            force_download=force_download,
+-            proxies=proxies,
+-            resume_download=resume_download,
+-            use_auth_token=use_auth_token,
+-            local_files_only=local_files_only,
+-        )
++        if filename.endswith(".bin"):
++            # NOTE: To save disk we do not download bin file eagerly. Do not support safetensors.
++            resolved_file = dict(
++                repo_id=path_or_repo_id,
++                filename=filename,
++                subfolder=None if len(subfolder) == 0 else subfolder,
++                repo_type=repo_type,
++                revision=revision,
++                user_agent=user_agent,
++                proxies=proxies,
++                use_auth_token=use_auth_token,
++            )
++            print(f"--> Apply lazy download to bin file: {resolved_file}")
++        else:
++            resolved_file = hf_hub_download(
++                path_or_repo_id,
++                filename,
++                subfolder=None if len(subfolder) == 0 else subfolder,
++                repo_type=repo_type,
++                revision=revision,
++                cache_dir=cache_dir,
++                user_agent=user_agent,
++                force_download=force_download,
++                proxies=proxies,
++                resume_download=resume_download,
++                use_auth_token=use_auth_token,
++                local_files_only=local_files_only,
++            )
+
+     except RepositoryNotFoundError:
+         raise EnvironmentError(

community-content/vertex_model_garden/model_oss/peft/instruct_lora.py

@@ -0,0 +1,97 @@
+"""Instruct/Chat with LoRA models."""
+
+# pylint: disable=g-importing-member
+from datasets import load_dataset
+from peft import LoraConfig
+import torch
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+from transformers import BitsAndBytesConfig
+from transformers import TrainingArguments
+from trl import SFTTrainer
+
+
+def finetune_instruct(
+    pretrained_model_id: str,
+    dataset_name: str,
+    output_dir: str,
+    lora_rank: int = 64,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.1,
+    warmup_ratio: int = 0.03,
+    max_steps: int = 10,
+    max_seq_length: int = 512,
+    learning_rate: float = 2e-4,
+) -> None:
+  """Finetunes instruct."""
+  dataset = load_dataset(dataset_name, split="train")
+
+  bnb_config = BitsAndBytesConfig(
+      load_in_4bit=True,
+      bnb_4bit_quant_type="nf4",
+      bnb_4bit_compute_dtype=torch.float16,
+  )
+
+  model = AutoModelForCausalLM.from_pretrained(
+      pretrained_model_id,
+      quantization_config=bnb_config,
+      trust_remote_code=True,
+  )
+  model.config.use_cache = False
+
+  tokenizer = AutoTokenizer.from_pretrained(
+      pretrained_model_id, trust_remote_code=True
+  )
+  tokenizer.pad_token = tokenizer.eos_token
+
+  peft_config = LoraConfig(
+      lora_alpha=lora_alpha,
+      lora_dropout=lora_dropout,
+      r=lora_rank,
+      bias="none",
+      task_type="CAUSAL_LM",
+      target_modules=[
+          "query_key_value",
+          "dense",
+          "dense_h_to_4h",
+          "dense_4h_to_h",
+      ],
+  )
+
+  per_device_train_batch_size = 4
+  gradient_accumulation_steps = 4
+  optim = "paged_adamw_32bit"
+  save_steps = 10
+  logging_steps = 10
+  max_grad_norm = 0.3
+  lr_scheduler_type = "constant"
+
+  training_arguments = TrainingArguments(
+      output_dir=output_dir,
+      per_device_train_batch_size=per_device_train_batch_size,
+      gradient_accumulation_steps=gradient_accumulation_steps,
+      optim=optim,
+      save_steps=save_steps,
+      logging_steps=logging_steps,
+      learning_rate=learning_rate,
+      fp16=True,
+      max_grad_norm=max_grad_norm,
+      max_steps=max_steps,
+      warmup_ratio=warmup_ratio,
+      group_by_length=True,
+      lr_scheduler_type=lr_scheduler_type,
+  )
+
+  trainer = SFTTrainer(
+      model=model,
+      train_dataset=dataset,
+      peft_config=peft_config,
+      dataset_text_field="text",
+      max_seq_length=max_seq_length,
+      tokenizer=tokenizer,
+      args=training_arguments,
+  )
+  for name, module in trainer.model.named_modules():
+    if "norm" in name:
+      module = module.to(torch.float32)
+  trainer.train()

community-content/vertex_model_garden/model_oss/peft/main.py

@@ -0,0 +1,177 @@
+"""Main function to start PEFT finetuning."""
+import subprocess
+
+from absl import app
+from absl import flags
+from absl import logging
+
+from peft import causal_language_modeling_lora
+from peft import instruct_lora
+from peft import sequence_classification_lora
+from util import constants
+from util import fileutils
+
+_TASK = flags.DEFINE_string(
+    'task',
+    constants.CAUSAL_LANGUAGE_MODELING_LORA,
+    'The supported PEFT tasks.',
+)
+
+_PRETRAINED_MODEL_ID = flags.DEFINE_string(
+    'pretrained_model_id',
+    None,
+    'The pretrained model id. Supported models can be causal language modeling'
+    ' models from https://github.com/huggingface/peft/tree/main.',
+    required=True,
+)
+
+_DATASET_NAME = flags.DEFINE_string(
+    'dataset_name',
+    None,
+    'The dataset name in huggingface.',
+    required=True,
+)
+
+_OUTPUT_DIR = flags.DEFINE_string(
+    'output_dir',
+    None,
+    'The output directory.',
+    required=True,
+)
+
+_PRECISION_MODE = flags.DEFINE_string(
+    'precision_mode',
+    constants.PRECISION_MODE_16,
+    'Supported finetuning precision_modes are `{}` and `{}`.'.format(
+        constants.PRECISION_MODE_8, constants.PRECISION_MODE_16
+    ),
+)
+
+_LORA_RANK = flags.DEFINE_integer(
+    'lora_rank',
+    16,
+    'The rank of the update matrices, expressed in int. Lower rank results in'
+    ' smaller update matrices with fewer trainable parameters, referring to'
+    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
+)
+
+_LORA_ALPHA = flags.DEFINE_integer(
+    'lora_alpha',
+    32,
+    'LoRA scaling factor, referring to'
+    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
+)
+
+_LORA_DROPOUT = flags.DEFINE_float(
+    'lora_dropout',
+    0.05,
+    'dropout probability of the LoRA layers, referring to'
+    ' https://huggingface.co/docs/peft/task_guides/token-classification-lora.',
+)
+
+_WARMUP_STEPS = flags.DEFINE_integer(
+    'warmup_steps',
+    10,
+    'Number of steps for the warmup in the learning rate scheduler.',
+)
+
+_WARMUP_RATIO = flags.DEFINE_float(
+    'warmup_ratio',
+    0.03,
+    'The warmup ratio in the learning rate scheduler.',
+)
+
+_MAX_STEPS = flags.DEFINE_integer(
+    'max_steps',
+    10,
+    'Total number of training steps.',
+)
+
+_MAX_SEQ_LENGTH = flags.DEFINE_integer(
+    'max_seq_length',
+    512,
+    'The maximum sequence length.',
+)
+
+_NUM_EPOCHS = flags.DEFINE_integer(
+    'num_epochs',
+    20,
+    'The number of training epochs.',
+)
+
+_BATCH_SIZE = flags.DEFINE_integer(
+    'batch_size',
+    32,
+    'The batch size.',
+)
+
+_LEARNING_RATE = flags.DEFINE_float(
+    'learning_rate',
+    2e-4,
+    'The learning rate after the potential warmup period.',
+)
+
+
+def main(_) -> None:
+  task = _TASK.value
+  pretrained_model_id = _PRETRAINED_MODEL_ID.value
+  local_pretrained_model_id = None
+  if pretrained_model_id.startswith(constants.GCS_URI_PREFIX):
+    logging.info(
+        'Start to copy pretrained models locally: %s.', pretrained_model_id
+    )
+    fileutils.download_gcs_dir_to_local(
+        pretrained_model_id, constants.LOCAL_BASE_MODEL_DIR
+    )
+    local_pretrained_model_id = constants.LOCAL_BASE_MODEL_DIR
+    logging.info(
+        'Finished copying pretrained models locally to: %s.',
+        local_pretrained_model_id,
+    )
+  if task == constants.TEXT_TO_IMAGE_LORA:
+    subprocess.run(['/bin/bash', 'train.sh'], check=True)
+  elif task == constants.SEQUENCE_CLASSIFICATION_LORA:
+    sequence_classification_lora.finetune_sequence_classification(
+        pretrained_model_id=pretrained_model_id,
+        dataset_name=_DATASET_NAME.value,
+        output_dir=_OUTPUT_DIR.value,
+        lora_rank=_LORA_RANK.value,
+        lora_alpha=_LORA_ALPHA.value,
+        lora_dropout=_LORA_DROPOUT.value,
+        num_epochs=_NUM_EPOCHS.value,
+        batch_size=_BATCH_SIZE.value,
+        learning_rate=_LEARNING_RATE.value,
+    )
+  elif task == constants.CAUSAL_LANGUAGE_MODELING_LORA:
+    causal_language_modeling_lora.finetune_causal_language_modeling(
+        pretrained_model_id=pretrained_model_id,
+        dataset_name=_DATASET_NAME.value,
+        output_dir=_OUTPUT_DIR.value,
+        precision_mode=_PRECISION_MODE.value,
+        lora_rank=_LORA_RANK.value,
+        lora_alpha=_LORA_ALPHA.value,
+        lora_dropout=_LORA_DROPOUT.value,
+        warmup_steps=_WARMUP_STEPS.value,
+        max_steps=_MAX_STEPS.value,
+        learning_rate=_LEARNING_RATE.value,
+        local_pretrained_model_id=local_pretrained_model_id,
+    )
+  elif task == constants.INSTRUCT_LORA:
+    instruct_lora.finetune_instruct(
+        pretrained_model_id=pretrained_model_id,
+        dataset_name=_DATASET_NAME.value,
+        output_dir=_OUTPUT_DIR.value,
+        lora_rank=_LORA_RANK.value,
+        lora_alpha=_LORA_ALPHA.value,
+        lora_dropout=_LORA_DROPOUT.value,
+        warmup_ratio=_WARMUP_RATIO.value,
+        max_steps=_MAX_STEPS.value,
+        max_seq_length=_MAX_SEQ_LENGTH.value,
+        learning_rate=_LEARNING_RATE.value,
+    )
+  else:
+    raise ValueError('The task {} is not supported.'.format(task))
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_model_garden/model_oss/peft/sequence_classification_lora.py

@@ -0,0 +1,133 @@
+"""Sequence classification with LoRA models."""
+
+# pylint: disable=g-importing-member
+
+from datasets import load_dataset
+import evaluate
+from peft import get_peft_model
+from peft import LoraConfig
+import torch
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from transformers import get_linear_schedule_with_warmup
+
+
+def finetune_sequence_classification(
+    pretrained_model_id: str,
+    dataset_name: str,
+    output_dir: str,
+    lora_rank: int = 8,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.1,
+    num_epochs: int = 20,
+    batch_size: int = 32,
+    learning_rate: float = 3e-4,
+) -> None:
+  """Finetunes sequence classification."""
+  task = "mrpc"
+  device = "cuda"
+
+  peft_config = LoraConfig(
+      task_type="SEQ_CLS",
+      inference_mode=False,
+      r=lora_rank,
+      lora_alpha=lora_alpha,
+      lora_dropout=lora_dropout,
+  )
+  if any(k in pretrained_model_id for k in ("gpt", "opt", "bloom")):
+    padding_side = "left"
+  else:
+    padding_side = "right"
+
+  tokenizer = AutoTokenizer.from_pretrained(
+      pretrained_model_id, padding_side=padding_side
+  )
+  if getattr(tokenizer, "pad_token_id") is None:
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+  datasets = load_dataset(dataset_name, task)
+  metric = evaluate.load(dataset_name, task)
+
+  def tokenize_function(examples):
+    # max_length=None => use the model max length (it's actually the default)
+    outputs = tokenizer(
+        examples["sentence1"],
+        examples["sentence2"],
+        truncation=True,
+        max_length=None,
+    )
+    return outputs
+
+  tokenized_datasets = datasets.map(
+      tokenize_function,
+      batched=True,
+      remove_columns=["idx", "sentence1", "sentence2"],
+  )
+
+  # We also rename the 'label' column to 'labels' which is the expected name for
+  # labels by the models of the transformers library.
+  tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+
+  def collate_fn(examples):
+    return tokenizer.pad(examples, padding="longest", return_tensors="pt")
+
+  # Instantiate dataloaders.
+  train_dataloader = DataLoader(
+      tokenized_datasets["train"],
+      shuffle=True,
+      collate_fn=collate_fn,
+      batch_size=batch_size,
+  )
+  eval_dataloader = DataLoader(
+      tokenized_datasets["validation"],
+      shuffle=False,
+      collate_fn=collate_fn,
+      batch_size=batch_size,
+  )
+
+  model = AutoModelForSequenceClassification.from_pretrained(
+      pretrained_model_id, return_dict=True
+  )
+  model = get_peft_model(model, peft_config)
+  model.print_trainable_parameters()
+
+  optimizer = AdamW(params=model.parameters(), lr=learning_rate)
+
+  # Instantiate scheduler
+  lr_scheduler = get_linear_schedule_with_warmup(
+      optimizer=optimizer,
+      num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),
+      num_training_steps=(len(train_dataloader) * num_epochs),
+  )
+
+  model.to(device)
+  for epoch in range(num_epochs):
+    model.train()
+    for _, batch in enumerate(tqdm(train_dataloader)):
+      batch.to(device)
+      outputs = model(**batch)
+      loss = outputs.loss
+      loss.backward()
+      optimizer.step()
+      lr_scheduler.step()
+      optimizer.zero_grad()
+
+    model.eval()
+    for _, batch in enumerate(tqdm(eval_dataloader)):
+      batch.to(device)
+      with torch.no_grad():
+        outputs = model(**batch)
+      predictions = outputs.logits.argmax(dim=-1)
+      references = batch["labels"]
+      metric.add_batch(
+          predictions=predictions,
+          references=references,
+      )
+
+    eval_metric = metric.compute()
+    print(f"epoch {epoch}:", eval_metric)
+
+  model.save_pretrained(output_dir)

community-content/vertex_model_garden/model_oss/peft/train.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+
+# Setup accelerate config before running trainer.
+python -c "from accelerate.utils import write_basic_config; write_basic_config(mixed_precision='fp16')"
+
+accelerate launch "$@"

community-content/vertex_model_garden/model_oss/peft/train/axolotl/dockerfile/train.Dockerfile

@@ -1,16 +0,0 @@
-# Dockerfile for axolotl training.
-#
-# To build:
-# docker build -f model_oss/peft/train/axolotol/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
-#
-# To push to gcr:
-# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
-# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
-
-FROM winglian/axolotl:main-latest
-
-RUN mkdir -p ./vertex_vision_model_garden/
-
-COPY model_oss/peft/train/axolotl/*.py ./vertex_vision_model_garden/
-
-ENTRYPOINT ["python3", "./vertex_vision_model_garden/train_entrypoint.py"]

community-content/vertex_model_garden/model_oss/peft/train/axolotl/scripts/build_train_docker.sh

@@ -1,20 +0,0 @@
-#!/bin/bash
-
-# Run copybara first:
-# cloud/ml/applications/vision/model_garden/copybara/run_copybara_local.sh
-# Run docker build:
-# cloud/ml/applications/vision/model_garden/model_oss/peft/train/axolotl/scripts/build_train_docker.sh
-
-set -x
-
-COPYBARA_DIR="/tmp/train_docker/"
-
-pushd "${COPYBARA_DIR}"
-
-PROJECT="cloud-nas-260507"
-IMAGE_TAG="gcr.io/${PROJECT}/axolotl-train:${USER}-test"
-
-docker build -f model_oss/peft/train/axolotl/dockerfile/train.Dockerfile . -t "${IMAGE_TAG}"
-docker push "${IMAGE_TAG}"
-
-popd

community-content/vertex_model_garden/model_oss/peft/train/axolotl/train_entrypoint.py

@@ -1,88 +0,0 @@
-"""Entrypoint for axolotl train docker."""
-
-import argparse
-import json
-import os
-import subprocess
-
-
-def _get_multi_node_flags(cluster_spec: str) -> list[str]:
-  """Returns the multi-node flags."""
-  print(f'CLUSTER_SPEC: {cluster_spec}')
-
-  cluster_data = json.loads(cluster_spec)
-
-  # Get primary node info
-  primary_node = cluster_data['cluster']['workerpool0'][0]
-  print(f'primary node: {primary_node}')
-  primary_node_addr, primary_node_port = primary_node.split(':')
-  print(f'primary node address: {primary_node_addr}')
-  print(f'primary node port: {primary_node_port}')
-
-  # Determine node rank of this machine
-  workerpool = cluster_data['task']['type']
-  if workerpool == 'workerpool0':
-    node_rank = 0
-  else:
-    node_rank = cluster_data['task']['index'] + 1
-  print(f'node rank: {node_rank}')
-
-  # Calculate total nodes
-  num_worker_nodes = len(cluster_data['cluster']['workerpool1'])
-  num_nodes = num_worker_nodes + 1  # Add 1 for the primary node
-  print(f'num nodes: {num_nodes}')
-
-  return [
-      f'--machine_rank={node_rank}',
-      f'--num_machines={num_nodes}',
-      f'--main_process_ip={primary_node_addr}',
-      f'--main_process_port={primary_node_port}',
-      '--max_restarts=0',
-      '--monitor_interval=120',
-      '--dynamo_backend=no',
-  ]
-
-
-def main() -> None:
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--config_file')
-  parser.add_argument('--huggingface_access_token')
-  args, unknown = parser.parse_known_args()
-
-  accelerate_flags = []
-
-  if args.config_file:
-    accelerate_flags.append(f'--config_file={args.config_file}')
-
-  if cluster_spec := os.getenv('CLUSTER_SPEC', default=None):
-    print('========== Launch on cloud multi nodes ==========')
-    accelerate_flags.extend(_get_multi_node_flags(cluster_spec))
-
-  cmd = (
-      [
-          'accelerate',
-          'launch',
-      ]
-      + accelerate_flags
-      + [
-          '-m',
-          'axolotl.cli.train',
-      ]
-      + unknown
-  )
-  print(f'{cmd=}', flush=True)
-
-  env = os.environ.copy()
-
-  if args.huggingface_access_token:
-    env['HF_TOKEN'] = args.huggingface_access_token
-
-  subprocess.run(
-      cmd,
-      check=True,
-      env=env,
-  )
-
-
-if __name__ == '__main__':
-  main()

community-content/vertex_model_garden/model_oss/peft/train/test_utils/command_builder.py

@@ -1,95 +0,0 @@
-"""Class that bundles docker related flags."""
-
-import getpass
-import os
-import pwd
-
-
-class CommandBuilder:
-  """Base class for building commands."""
-
-  def __init__(self):
-    self._defaults = []
-    self._env_vars = {}
-
-  def add_env_var(self, var: str, val: str) -> None:
-    """Add environment variable to the command.
-
-    Args:
-      var: environment variable name.
-      val: environment variable value.
-    """
-    self._env_vars[var] = val
-
-  def add_mount_map(self, host_path, docker_path):
-    pass
-
-
-class DockerCommandBuilder(CommandBuilder):
-  """Bundle docker related flags."""
-
-  def __init__(self, docker_uri: str, shm_size: str = '128gb'):
-    super().__init__()
-    self._docker_uri = [docker_uri]
-    self.privilege_mode = []
-    self.entrypoint = []
-
-    self._defaults = [
-        'docker',
-        'run',
-        '--gpus=all',
-        '--net=host',
-        '--rm',
-        f'--shm-size={shm_size}',
-    ]
-
-    self._mount_maps = []
-    user = getpass.getuser()
-    # username ends with `_google_com` is managed by ldap and does not have a
-    # corresponding entry in /etc/passwd or /etc/group file. We cannot enable
-    # non-root docker user with below method.
-    if not user.endswith('_google_com'):
-      uid = os.getuid()
-      gid = pwd.getpwuid(uid).pw_gid
-      self._defaults += [
-          f'--user={uid}:{gid}',
-          '--volume=/etc/group:/etc/group:ro',
-          '--volume=/etc/passwd:/etc/passwd:ro',
-      ]
-
-  def add_mount_map(self, host_path, docker_path):
-    self._mount_maps.append(f'--volume={host_path}:{docker_path}')
-
-  def add_privilege_mode(self):
-    self.privilege_mode = ['--privileged']
-
-  def add_entrypoint(self, entrypoint: list[str]):
-    self.entrypoint = entrypoint
-
-  def build_cmd(self) -> str:
-    return (
-        self._defaults
-        + [f'--env={var}={val}' for var, val in self._env_vars.items()]
-        + self._mount_maps
-        + self.privilege_mode
-        + self._docker_uri
-        + self.entrypoint
-    )
-
-
-class PythonCommandBuilder(CommandBuilder):
-  """Bundle Python test command related flags."""
-
-  def __init__(self):
-    super().__init__()
-    self._defaults = [
-        'python3',
-        './vertex_vision_model_garden_peft/train/vmg/train_entrypoint.py',
-    ]
-
-  def build_cmd(self) -> str:
-    os.environ.update(self._env_vars)
-    return self._defaults
-
-  def add_entrypoint(self, entrypoint: list[str]):
-    self._defaults = entrypoint

community-content/vertex_model_garden/model_oss/peft/train/test_utils/test_util.py

@@ -1,471 +0,0 @@
-"""Test util class."""
-
-import copy
-import dataclasses
-import datetime
-import inspect
-import os
-import signal
-import subprocess
-import sys
-from absl import flags
-from absl import logging
-from absl.testing import parameterized
-import command_builder
-import immutabledict
-import torch
-
-_DOCKER_URI = flags.DEFINE_string('docker_uri', None, 'docker image uri')
-
-_DRY_RUN = flags.DEFINE_bool('dry_run', False, 'dry-run the commands')
-
-_LOCAL_INPUT_DIR = flags.DEFINE_string(
-    'local_input_dir',
-    os.path.expanduser('~/test_input'),
-    'local directory for storing input data.',
-)
-
-_LOCAL_OUTPUT_DIR = flags.DEFINE_string(
-    'local_output_dir',
-    '/tmp',
-    'local directory for storing test output.',
-)
-
-
-_GCS_INPUT_DIR = flags.DEFINE_string(
-    'gcs_input_dir',
-    'gs://vmg-tuning-docker-test',
-    'GCS directory that stores model checkpoint, dataset and etc.',
-)
-
-_GCS_OUTPUT_DIR = flags.DEFINE_string(
-    'gcs_output_dir',
-    'gs://vmg-tuning-docker-test/output',
-    'GCS directory that stores test output.',
-)
-
-_GCS_TESTDATA_DIR = 'peft-train-image-test'
-
-_THROUGHPUT_TEST_EXCEPTIONS = immutabledict.immutabledict({
-    ('bm_deepspeed_zero3_8gpu_gemma-2-9b-it_4bit.txt', '12.0'): float('inf'),
-    ('bm_fsdp_8gpu_llama3.1-70b-hf_4bit.txt', '20.0'): float('inf'),
-    ('bm_deepspeed_zero2_8gpu_gemma-2-2b-it_bfloat16.txt', '12.0'): 20.0,
-    ('bm_deepspeed_zero3_8gpu_gemma-2-2b-it_4bit.txt', '4.0'): 20.0,
-    ('bm_deepspeed_zero3_8gpu_gemma-2-27b-it_4bit.txt', '4.0'): 20.0,
-})
-
-
-@dataclasses.dataclass
-class BenchmarkStats:
-  """Class to store the benchmark result.
-
-  Attributes:
-    peak_mem: peak memory in GB.
-    throughput: throughput in tokens/sec.
-  """
-
-  peak_mem: float
-  throughput: float
-
-
-class TestBase(parameterized.TestCase):
-  """Test base class that defines how to run commands."""
-
-  def setUp(self):
-    super().setUp()
-
-    # Create a copy of the environment variables
-    self.old_env_var = copy.deepcopy(os.environ)
-    if _DOCKER_URI.value:
-      self.command_builder = command_builder.DockerCommandBuilder(
-          _DOCKER_URI.value
-      )
-    else:
-      self.command_builder = command_builder.PythonCommandBuilder()
-
-    self.command_builder.add_mount_map(
-        os.path.expanduser('~'), os.path.expanduser('~')
-    )
-    self.command_builder.add_mount_map(
-        self.local_input_dir(), self.local_input_dir()
-    )
-
-    self.task_cmd_builder = None
-
-  def tearDown(self):
-    super().tearDown()
-    # Restore the original environment variables
-    os.environ.clear()
-    os.environ.update(self.old_env_var)
-
-  def cmd(self):
-    return self.command_builder.build_cmd() + self.task_cmd_builder.build_cmd()
-
-  def run_cmd(self) -> int:
-    return run_cmd(self.cmd(), output_file=None)
-
-  def gcs_output_dir(self):
-    return _GCS_OUTPUT_DIR.value
-
-  def local_input_dir(self):
-    """Returns local input dir in host/docker."""
-    return _LOCAL_INPUT_DIR.value
-
-  def local_output_dir(self):
-    """Returns local output dir in host/docker."""
-    return _LOCAL_OUTPUT_DIR.value
-
-  def get_testcase_name(self):
-    """Returns the function name at the calling site."""
-    # https://docs.python.org/3/library/inspect.html#inspect.FrameInfo
-    cur_frame = inspect.currentframe()
-    # https://stackoverflow.com/a/17366561
-    return cur_frame.f_back.f_code.co_name
-
-
-def get_timestamp():
-  return datetime.datetime.now(datetime.timezone.utc).strftime(
-      '%Y%m%d_%H%M%S%Z'
-  )
-
-
-def download_from_gcs(gcs_uri: str, local_dir: str):
-  if not os.path.exists(local_dir):
-    os.mkdir(local_dir)
-  subprocess.check_output([
-      'gcloud',
-      'storage',
-      'cp',
-      '-r',
-      gcs_uri,
-      local_dir,
-  ])
-
-
-def get_test_data_path(name: str, download: bool = True) -> str:
-  """Gets test data path.
-
-  Args:
-    name: name of the test data
-    download: if True, then download data from GCS and returns its local path.
-
-  Returns:
-    test data path.
-  """
-  if not download:
-    return os.path.join(_GCS_INPUT_DIR.value, name)
-
-  local_data = os.path.join(_LOCAL_INPUT_DIR.value, name)
-  if not os.path.exists(local_data):
-    # If `name` is a file in sub-folders, then create the sub-folders under
-    # `_LOCAL_INPUT_DIR`.
-    local_data_dir = os.path.dirname(local_data)
-    if not os.path.exists(local_data_dir):
-      os.makedirs(local_data_dir)
-
-    download_from_gcs(os.path.join(_GCS_INPUT_DIR.value, name), local_data_dir)
-
-  return local_data
-
-
-def run_cmd(cmd: list[str], output_file: str = None) -> int:
-  """Runs the command and returns the return code.
-
-  Args:
-    cmd: The command to run.
-    output_file: The file to write the output to.
-
-  Returns:
-    The return code of the command.
-  """
-  logging.info('running command: \n%s', ' \\\n'.join(cmd))
-  if _DRY_RUN.value:
-    return 0
-  stdout = sys.stdout if output_file is None else open(output_file, 'w')
-  p = subprocess.Popen(cmd, stdout=stdout, stderr=sys.stderr)
-  try:
-    unused_output, unused_error = p.communicate()
-    return_code = p.returncode
-  except KeyboardInterrupt:
-    p.send_signal(signal.SIGINT)
-    return_code = 0
-  finally:
-    if output_file is not None:
-      stdout.close()
-  return return_code
-
-
-def get_pretrained_model_name_or_path(model_id: str) -> str:
-  # If `model_id` contains `/`, it is assumed to be HF model or model from GCS.
-  if '/' in model_id:
-    return model_id
-
-  return get_test_data_path(model_id, download=True)
-
-
-def is_gpu_h100():
-  """Checks if the GPU is H100."""
-  return 'H100' in torch.cuda.get_device_name()
-
-
-def is_gpu_a100():
-  """Checks if the GPU is A100."""
-  return 'A100' in torch.cuda.get_device_name()
-
-
-def _get_formatted_string(max_seq_length: int) -> str:
-  """Returns the formatted string for max_seq_length.
-
-  Args:
-    max_seq_length: max sequence length to get the formatted string.
-
-  Returns:
-    formatted string for max_seq_length.
-  """
-  return f'{max_seq_length/1024.0:.1f}'
-
-
-def get_benchmark_results(
-    benchmark_file_path: str, max_seq_length: int
-) -> BenchmarkStats:
-  """Gets benchmark results from the benchmark file.
-
-  Args:
-    benchmark_file_path: path to the benchmark file.
-    max_seq_length: max sequence length to get the benchmark results.
-
-  Returns:
-    peak_mem: peak memory in GB.
-    throughput: throughput in tokens/sec.
-  """
-  formatted_max_seq_length = _get_formatted_string(max_seq_length)
-  peak_mem, throughput = None, None
-  with open(benchmark_file_path, 'r') as f:
-    for line in f:
-      if line.startswith(formatted_max_seq_length):
-        metrics = line.split('|')
-        try:
-          peak_mem = float(metrics[1].strip())
-        except ValueError:
-          pass
-        try:
-          throughput = float(metrics[2].strip())
-        except ValueError:
-          pass
-        break
-    else:
-      logging.error(
-          'No metrics found for max_seq_length %s in %s',
-          formatted_max_seq_length,
-          benchmark_file_path,
-      )
-  return BenchmarkStats(peak_mem, throughput)
-
-
-def print_benchmark_file(file_path: str) -> None:
-  """Prints the contents of the file.
-
-  Args:
-    file_path: path to the file.
-  """
-  with open(file_path, 'r') as f:
-    for line in f:
-      logging.info(line.strip())
-
-
-def print_benchmark_results(
-    benchmark_file_path: str, benchmark_type: str
-) -> None:
-  """Prints the benchmark results.
-
-  Args:
-    benchmark_file_path: path to the benchmark file.
-    benchmark_type: type of the benchmark.
-  """
-  benchmark_filename = os.path.basename(benchmark_file_path)
-  logging.info('--------------------------------------------------------------')
-  logging.info('%s benchmark for %s', benchmark_type, benchmark_filename)
-  logging.info('--------------------------------------------------------------')
-  print_benchmark_file(benchmark_file_path)
-
-
-def _calculate_percent_change(
-    actual_value: float, expected_value: float
-) -> float:
-  """Calculates the percent change between the actual and expected values.
-
-  Args:
-    actual_value: actual value to compare.
-    expected_value: expected value to compare.
-
-  Returns:
-    percent change between the actual and expected values.
-  """
-  return ((actual_value - expected_value) / expected_value) * 100.0
-
-
-def compare_benchmark_results(
-    expected_benchmark_file_path: str,
-    actual_benchmark_file_path: str,
-    allowed_threshold: float,
-    max_seq_length: int,
-) -> bool:
-  """Compares if the benchmark results are the similar.
-
-  Args:
-    expected_benchmark_file_path: path to the expected benchmark file.
-    actual_benchmark_file_path: path to the actual benchmark file.
-    allowed_threshold: allowed percent range of the benchmark results.
-    max_seq_length: max sequence length to get the benchmark results.
-
-  Returns:
-    True if the benchmark results are the similar, False otherwise.
-  """
-  benchmark_filename = os.path.basename(expected_benchmark_file_path)
-  expected_results = get_benchmark_results(
-      expected_benchmark_file_path, max_seq_length
-  )
-  expected_peak_mem, expected_throughput = (
-      expected_results.peak_mem,
-      expected_results.throughput,
-  )
-  actual_results = get_benchmark_results(
-      actual_benchmark_file_path, max_seq_length
-  )
-  actual_peak_mem, actual_throughput = (
-      actual_results.peak_mem,
-      actual_results.throughput,
-  )
-  formatted_max_seq_length = _get_formatted_string(max_seq_length)
-
-  # Case 1: both peak mem and throughput are None(ideally due to OOM)
-  if expected_peak_mem is None and actual_peak_mem is None:
-    logging.info(
-        'Both peak mem and throughput are None for max_seq_length %d.',
-        max_seq_length,
-    )
-    return True
-
-  check_oom_exception = _THROUGHPUT_TEST_EXCEPTIONS.get(
-      (benchmark_filename, formatted_max_seq_length), 0.0
-  ) == float('inf')
-  # Case 2: When something strated to fail recently, or something which failed
-  # before but is working now.
-  if expected_peak_mem is None and actual_peak_mem is not None:
-    if check_oom_exception:
-      return True
-    logging.error(
-        'One of the failing benchmarks in %s is passing now for max_seq_length'
-        ' %d. The expected peak mem and throughput are None, but the actual'
-        ' peak mem is %f and actual throughput is %f',
-        benchmark_filename,
-        max_seq_length,
-        actual_peak_mem,
-        actual_throughput,
-    )
-    return False
-  if actual_peak_mem is None and expected_peak_mem is not None:
-    if check_oom_exception:
-      return True
-    logging.error(
-        'One of the passing benchmarks in %s is failing now for max_seq_length'
-        ' %d. The actual peak mem and throughput are None, but the expected'
-        ' peak mem is %f and expected throughput is %f',
-        benchmark_filename,
-        max_seq_length,
-        expected_peak_mem,
-        expected_throughput,
-    )
-    return False
-  # Case 3: When both actual peak mem and throughput lies within the range
-  # of their respective expected values.
-  mem_percent_change = _calculate_percent_change(
-      actual_peak_mem, expected_peak_mem
-  )
-  throughput_percent_change = _calculate_percent_change(
-      actual_throughput, expected_throughput
-  )
-  allowed_threshold = _THROUGHPUT_TEST_EXCEPTIONS.get(
-      (benchmark_filename, formatted_max_seq_length), allowed_threshold
-  )
-
-  if abs(mem_percent_change) > allowed_threshold:
-    logging.error(
-        'The peak memory is changing by more than %f%% for max_seq_length %d.'
-        ' Expected: %f, Actual: %f',
-        allowed_threshold,
-        max_seq_length,
-        expected_peak_mem,
-        actual_peak_mem,
-    )
-    return False
-  if abs(throughput_percent_change) > allowed_threshold:
-    logging.error(
-        'The throughput is changing by more than %f%% for max_seq_length %d.'
-        ' Expected throughput: %f, Actual throughput: %f',
-        allowed_threshold,
-        max_seq_length,
-        expected_throughput,
-        actual_throughput,
-    )
-    return False
-
-  return True
-
-
-def check_benchmark_results(
-    actual_benchmark_file_path: str,
-    model_family: str,
-    allowed_threshold: float,
-    max_seq_length: int,
-) -> bool:
-  """Checks the benchmark result between the actual and expected benchmark files.
-
-  Args:
-    actual_benchmark_file_path: path to the actual benchmark file.
-    model_family: family of the model.
-    allowed_threshold: allowed range of the benchmark results in percent.
-    max_seq_length: max sequence length to get the benchmark results.
-
-  Returns:
-    True if the benchmark results are the similar, False otherwise.
-  """
-  benchmark_filename = os.path.basename(actual_benchmark_file_path)
-  get_test_data_path(_GCS_TESTDATA_DIR)
-  expected_benchmark_file_path = os.path.join(
-      _LOCAL_INPUT_DIR.value,
-      _GCS_TESTDATA_DIR,
-      model_family,
-      benchmark_filename,
-  )
-  print_benchmark_results(expected_benchmark_file_path, 'Expected')
-  print_benchmark_results(actual_benchmark_file_path, 'Actual')
-
-  return compare_benchmark_results(
-      expected_benchmark_file_path,
-      actual_benchmark_file_path,
-      allowed_threshold,
-      max_seq_length,
-  )
-
-
-def list_gcs_directories(bucket: str, directory: str) -> list[str]:
-  """Lists GCS files."""
-  output = subprocess.check_output([
-      'gcloud',
-      'storage',
-      'ls',
-      f'gs://{bucket}/{directory}',
-  ])
-  return output.decode('utf-8').splitlines()
-
-
-def delete_gcs_object(gcs_directory: str):
-  """Deletes GCS object."""
-  subprocess.check_output([
-      'gcloud',
-      'storage',
-      'rm',
-      '-r',
-      f'{gcs_directory}',
-  ])

community-content/vertex_model_garden/model_oss/peft/train/util/cluster_spec.py

@@ -1,79 +0,0 @@
-"""Get cluster info from environment variables."""
-
-import dataclasses
-import json
-import os
-
-from absl import logging
-
-
-@dataclasses.dataclass
-class ClusterInfo:
-  """Contains information about the cluster.
-
-  Attributes:
-    primary_node_addr: The address of the primary node.
-    primary_node_port: The port of the primary node.
-    node_rank: The rank of the node.
-    num_nodes: The number of nodes in the cluster.
-  """
-
-  primary_node_addr: str | None = None
-  primary_node_port: str | None = None
-  node_rank: int = 0
-  num_nodes: int = 1
-
-  # Allows unpacking operation like
-  # primary_node_addr, primary_node_port, _, _ = ClusterInfo()
-  # See https://stackoverflow.com/a/70753113
-  def __iter__(self):
-    return iter(dataclasses.astuple(self))
-
-
-def get_cluster_spec() -> ClusterInfo:
-  """Parses CLUSTER_SPEC environment variable and returns the cluster info.
-
-  Returns:
-    A ClusterInfo object.
-  """
-  cluster_spec = os.getenv('CLUSTER_SPEC', None)
-
-  # If CLUSTER_SPEC is not set, use individual vars to construct cluster info.
-  if not cluster_spec:
-    cluster_info = ClusterInfo(
-        primary_node_addr=os.getenv('MASTER_ADDR', None),
-        primary_node_port=os.getenv('MASTER_PORT', None),
-        node_rank=int(os.getenv('RANK', '0')),
-        num_nodes=int(os.getenv('NNODES', '1')),
-    )
-    return cluster_info
-
-  cluster_data = json.loads(cluster_spec)
-  # Get primary node info
-  primary_node = cluster_data['cluster']['workerpool0'][0]
-  logging.info('primary node: %s', primary_node)
-  primary_node_addr, primary_node_port = primary_node.split(':')
-  logging.info('primary node address: %s', primary_node_addr)
-  logging.info('primary node port: %s', primary_node_port)
-
-  # Determine node rank of this machine
-  workerpool = cluster_data['task']['type']
-  if workerpool == 'workerpool0':
-    node_rank = 0
-  elif workerpool == 'workerpool1':
-    # Add 1 for the primary node, since `index` is the index of workerpool1.
-    node_rank = cluster_data['task']['index'] + 1
-  else:
-    raise ValueError(
-        'Only workerpool0 and workerpool1 are supported. Unknown workerpool:'
-        f' {workerpool}'
-    )
-  logging.info('node rank: %s', node_rank)
-
-  # Calculate total nodes.
-  num_nodes = 1  # For the primary node.
-  if 'workerpool1' in cluster_data['cluster']:
-    num_nodes += len(cluster_data['cluster']['workerpool1'])
-  logging.info('num nodes: %s', num_nodes)
-
-  return ClusterInfo(primary_node_addr, primary_node_port, node_rank, num_nodes)

community-content/vertex_model_garden/model_oss/peft/train/util/common_util.py

@@ -1,24 +0,0 @@
-"""Utility functions."""
-
-import logging
-import subprocess
-import sys
-import time
-
-
-def run_cmd(cmd: list[str]) -> float:
-  """Runs the command and logs the output.
-
-  Args:
-    cmd: The command to run.
-
-  Returns:
-    The time it took to run the command.
-  """
-  cmd_str = ' \\\n'.join(cmd)
-  logging.info('launching cmd: \n%s', cmd_str)
-  start_time = time.time()
-  subprocess.run(cmd, stdout=sys.stdout, stderr=sys.stdout, check=True)
-  elapsed_time = round(time.time() - start_time, 2)
-  logging.info('Command %s finished in %0.2f seconds.', cmd_str, elapsed_time)
-  return elapsed_time

community-content/vertex_model_garden/model_oss/peft/train/util/dataset_stats.py

@@ -1,197 +0,0 @@
-"""Calculate dataset statistics like token, example and character counts."""
-
-from collections.abc import Mapping, Sequence
-import dataclasses
-import json
-from typing import Any
-import datasets
-import numpy as np
-import transformers
-from util import dataset_validation_util
-
-_MAX_NUM_DATASET_SAMPLES = 6
-
-
-@dataclasses.dataclass
-class SupervisedTuningDatasetBucket:
-  """Represents a histogram bucket for tuning dataset distribution stats."""
-
-  count: float = 0
-  left: float = 0
-  right: float = 0
-
-
-@dataclasses.dataclass
-class SupervisedTuningDatasetDistribution:
-  """Represents a histogram with summary statistics for tuning dataset distribution stats."""
-
-  sum: int = 0
-  billable_sum: int = 0
-  min: float = 0
-  max: float = 0
-  mean: float = 0
-  median: float = 0
-  p5: float = 0
-  p95: float = 0
-  buckets: list[SupervisedTuningDatasetBucket] = dataclasses.field(
-      default_factory=list
-  )
-
-
-# Represents detailed tuning dataset statistics.
-@dataclasses.dataclass
-class SupervisedTuningDataStats:
-  """Represents detailed tuning dataset stats."""
-
-  tuning_dataset_example_count: int = 0
-  total_tuning_character_count: int = 0
-  total_billable_token_count: int = 0
-  tuning_step_count: int = 0
-  # Represents a histogram and some summary statistics of the number of input
-  # tokens across examples.
-  user_input_token_distribution: SupervisedTuningDatasetDistribution | None = (
-      None
-  )
-  # Represents a histogram and some summary statistics for the number of output
-  # tokens across examples.
-  user_output_token_distribution: SupervisedTuningDatasetDistribution | None = (
-      None
-  )
-  # Represents the number of "messages" (a single-turn conversation will have a
-  # single message) across examples.
-  user_message_per_example_distribution: (
-      SupervisedTuningDatasetDistribution | None
-  ) = None
-  user_dataset_examples: list[str] = dataclasses.field(default_factory=list)
-
-
-def get_dataset_stats(
-    *,
-    raw: Any,
-    templated: Any,
-    template: str,
-    tokenizer: transformers.PreTrainedTokenizer,
-    column: str,
-    effective_batch_size: int,
-) -> Mapping[str, Any]:
-  """Calculates dataset statistics for managed fine-tuning, e.g., total number of tokens."""
-  tokenized_dataset = templated.map(lambda x: tokenizer(x[column]))
-  inputs = tokenized_dataset["input_ids"]
-  tuning_dataset_example_count = int(len(inputs))
-  total_billable_token_count = int(np.sum([len(ex) for ex in inputs]))
-  total_tuning_character_count = int(
-      np.sum([len(ex[column]) for ex in templated])
-  )
-  tuning_step_count = (
-      tuning_dataset_example_count + effective_batch_size - 1
-  ) // effective_batch_size
-
-  # Assume that data is represented as ChatCompletions or Vertex Text-Bison
-  # formats to extract per-example input/output tokens.
-  user_inputs = []
-  user_outputs = []
-  user_input_messages_counts = []
-
-  for ex in raw:
-    if "messages" in ex:
-      messages = ex["messages"]
-      if messages:
-        # For ChatCompletions assume the last turn (i.e. the instruction
-        # response) is the expected output.
-        user_inputs.append({**ex, "messages": messages[:-1]})
-        user_outputs.append({**ex, "messages": messages[-1:]})
-        # Exclude everything but the last message for the number of input
-        # messages.
-        user_input_messages_counts.append(len(messages[:-1]))
-    elif "input_text" in ex:
-      # For Vertex Text-Bison, the `output_text` field is the expected output.
-      user_inputs.append({**ex, "output_text": ""})
-      user_outputs.append(
-          {**ex, "input_text": ex["output_text"], "output_text": ""}
-      )
-      # Vertex Text-Bison goes from input -> output; i.e. there is only a single
-      # input "message".
-      user_input_messages_counts.append(1)
-
-  def calc_histogram(
-      counts: Sequence[int],
-  ) -> SupervisedTuningDatasetDistribution:
-    mean = np.mean(counts)
-    median = np.median(counts).item()
-    max_count = np.max(counts).item()
-    min_count = np.min(counts).item()
-    count_sum = np.sum(counts).item()
-    p5 = np.percentile(counts, 0.05).item()
-    p95 = np.percentile(counts, 0.95).item()
-    hist, bin_edges = np.histogram(counts, bins=10)
-
-    return SupervisedTuningDatasetDistribution(
-        sum=count_sum,
-        billable_sum=count_sum,
-        min=min_count,
-        max=max_count,
-        mean=mean,
-        median=median,
-        p5=p5,
-        p95=p95,
-        buckets=[
-            SupervisedTuningDatasetBucket(
-                count=hist[i].item(),
-                left=bin_edges[i].item(),
-                right=bin_edges[i + 1].item(),
-            )
-            for i in range(len(hist))
-        ],
-    )
-
-  # Tokenize input and output messages separately to generate separate summary
-  # statistics about them.
-  user_input_token_distribution = None
-  if user_inputs:
-    user_input_dataset = dataset_validation_util.format_dataset(
-        datasets.Dataset.from_list(user_inputs), column, template, tokenizer
-    )
-    user_input_tokenized_dataset = user_input_dataset.map(
-        lambda x: tokenizer(x[column])
-    )
-    user_input_tokens = user_input_tokenized_dataset["input_ids"]
-    user_input_token_counts = np.array([len(ex) for ex in user_input_tokens])
-    user_input_token_distribution = calc_histogram(user_input_token_counts)
-
-  user_output_token_distribution = None
-  if user_outputs:
-    user_output_dataset = dataset_validation_util.format_dataset(
-        datasets.Dataset.from_list(user_outputs), column, template, tokenizer
-    )
-    user_output_tokenized_dataset = user_output_dataset.map(
-        lambda x: tokenizer(x[column])
-    )
-    user_output_tokens = user_output_tokenized_dataset["input_ids"]
-    user_output_token_counts = np.array([len(ex) for ex in user_output_tokens])
-    user_output_token_distribution = calc_histogram(user_output_token_counts)
-
-  user_messages_per_example_distribution = None
-  if user_input_messages_counts:
-    user_input_messages_counts = np.array(user_input_messages_counts)
-    user_messages_per_example_distribution = calc_histogram(
-        user_input_messages_counts
-    )
-
-  user_dataset_examples = [
-      json.dumps(ex)
-      for ex in raw.shuffle().select(
-          range(min(len(raw), _MAX_NUM_DATASET_SAMPLES))
-      )
-  ]
-
-  dataset_stats = SupervisedTuningDataStats(
-      tuning_dataset_example_count=tuning_dataset_example_count,
-      total_tuning_character_count=total_tuning_character_count,
-      total_billable_token_count=total_billable_token_count,
-      tuning_step_count=tuning_step_count,
-      user_input_token_distribution=user_input_token_distribution,
-      user_output_token_distribution=user_output_token_distribution,
-      user_message_per_example_distribution=user_messages_per_example_distribution,
-      user_dataset_examples=user_dataset_examples,
-  )
-  return dataclasses.asdict(dataset_stats)

community-content/vertex_model_garden/model_oss/peft/train/util/device_stats.py

@@ -1,140 +0,0 @@
-"""Util functions for reporting device (GPU, CPU) stats."""
-
-import dataclasses
-
-import psutil
-import pynvml
-import torch
-
-
-@dataclasses.dataclass
-class GpuStats:
-  """Holds information about GPU usage stats.
-
-  For memory related, see
-  https://pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management
-  """
-
-  # device id
-  device_id: int
-  # memory reserved.
-  reserved: float
-  # memory occupied.
-  occupied: float
-  # memory reserved, but not used.
-  unused: float
-  # nvidia-smi usually reports more memory usages than pytorch (for driver,
-  # kernel and etc). `smi_diff` tracks this difference.
-  smi_diff: float
-  # Gpu utilization.
-  util: float
-
-  # Allows unpacking operation like
-  # device_id, reserved, occupied, unused, smi_diff, util = GpuStats(...)
-  # See https://stackoverflow.com/a/70753113
-  def __iter__(self):
-    return iter(dataclasses.astuple(self))
-
-
-def gpu_stats() -> GpuStats:
-  """Reports GPU memory usage and utilization."""
-  # See https://pytorch.org/docs/stable/notes/cuda.html#memory-management
-  bytes_per_gb = 1024.0**3
-  device = torch.cuda.current_device()
-  occupied = torch.cuda.memory_allocated(device) / bytes_per_gb
-  reserved = torch.cuda.memory_reserved(device) / bytes_per_gb
-  unused = reserved - occupied
-
-  def smi_mem(device):
-    try:
-      pynvml.nvmlInit()
-      handle = pynvml.nvmlDeviceGetHandleByIndex(device)
-      info = pynvml.nvmlDeviceGetMemoryInfo(handle)
-      return info.used / bytes_per_gb
-    except pynvml.NVMLError:
-      return 0.0
-
-  mem_used_smi = smi_mem(device)
-  smi_diff = mem_used_smi - reserved
-
-  util = torch.cuda.utilization(device)
-  return GpuStats(device, reserved, occupied, unused, smi_diff, util)
-
-
-def gpu_stats_str(stats: GpuStats | None = None) -> str:
-  if stats is None:
-    stats = gpu_stats()
-  device, reserved, occupied, unused, smi_diff, util = stats
-  return (
-      f"GPU ({device=}) memory: {reserved:.2f}({occupied=:.2f}, {unused=:.2f}),"
-      f" {smi_diff=:.2f} GB. Utilization: {util:.2f}%"
-  )
-
-
-@dataclasses.dataclass
-class CpuStats:
-  """Holds information about CPU usage stats."""
-
-  # Total CPU virtual memory i.e. virtual memory allocated + unallocated.
-  total_virtual_mem: float
-  # CPU virtual memory available for use.
-  unallocated_virtual_mem: float
-  # CPU virtual memory already used.
-  allocated_virtual_mem: float
-  # Total CPU swap memory i.e. swap memory allocated + unallocated.
-  total_swap_mem: float
-  # CPU swap memory available for use.
-  unallocated_swap_mem: float
-  # CPU swap memory already used.
-  allocated_swap_mem: float
-  # CPU utilization percentage.
-  utilization: float
-
-
-def cpu_stats() -> CpuStats:
-  """Reports CPU memory usage and utilization."""
-
-  # https://psutil.readthedocs.io/en/latest/#memory
-  gb = 1024.0**3
-  vmem = psutil.virtual_memory()
-  vmem_total = vmem.total / gb
-  vmem_available = vmem.available / gb
-  vmem_used = vmem_total - vmem_available
-  smem = psutil.swap_memory()
-  swap_total = smem.total / gb
-  swap_free = smem.free / gb
-  swap_used = smem.used / gb
-  # https://psutil.readthedocs.io/en/latest/#psutil.cpu_percent
-  cpu_util = psutil.cpu_percent(interval=1e-6)
-  return CpuStats(
-      total_virtual_mem=vmem_total,
-      unallocated_virtual_mem=vmem_available,
-      allocated_virtual_mem=vmem_used,
-      total_swap_mem=swap_total,
-      unallocated_swap_mem=swap_free,
-      allocated_swap_mem=swap_used,
-      utilization=cpu_util,
-  )
-
-
-def cpu_stats_str(stats: CpuStats | None = None) -> str:
-  """Returns a string representation of the CPU stats."""
-
-  if stats is None:
-    stats = cpu_stats()
-  total, occupied, unused = (
-      stats.total_virtual_mem,
-      stats.allocated_virtual_mem,
-      stats.unallocated_virtual_mem,
-  )
-  virtual_mem = (
-      f"CPU virtual memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
-  )
-  total, occupied, unused = (
-      stats.total_swap_mem,
-      stats.allocated_swap_mem,
-      stats.unallocated_swap_mem,
-  )
-  swap_mem = f"CPU swap memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
-  percent = stats.utilization
-  return f"{virtual_mem} {swap_mem} CPU Utilization: {percent:.2f}%"

community-content/vertex_model_garden/model_oss/peft/train/vmg/callbacks.py

@@ -1,126 +0,0 @@
-"""Different trainer callbacks for PEFT Trainer."""
-
-from collections.abc import MutableMapping
-import math
-import time
-
-from absl import logging
-import accelerate
-from transformers import TrainingArguments
-from transformers.trainer_callback import TrainerCallback
-from transformers.trainer_callback import TrainerControl
-from transformers.trainer_callback import TrainerState
-
-from util import device_stats
-
-
-class TrainerStatsCallback(TrainerCallback):
-  """Trainer callback to report trainer stats."""
-
-  def __init__(self, max_seq_length, filename=None):
-    self._max_seq_length = max_seq_length
-    self._filename = filename
-
-    self._partial_state = accelerate.PartialState()
-    self._start_time = float('nan')
-    self._prev_time = float('nan')
-    self._peak_mem = 0.0
-    self._avg_throughput = 0.0
-
-  def on_log(
-      self,
-      args: TrainingArguments,
-      state: TrainerState,
-      control: TrainerControl,
-      logs: MutableMapping[str, float] | None = None,
-      **kwargs,
-  ) -> None:
-    """Calculates perplexity from train loss.
-
-    Args:
-      args: Arguments passed to the trainer.
-      state: State of the trainer.
-      control: Control of the trainer.
-      logs: A dict of logs from the training loop.
-      **kwargs: Additional keyword arguments, not used in this callback.
-    """
-    del kwargs  # Unused.
-    if self._partial_state.is_main_process:
-      train_loss = logs.get('loss') if logs is not None else None
-      if train_loss is not None:
-        perplexity = round(float(math.exp(train_loss)), 4)
-        logs['perplexity'] = perplexity
-
-  def on_step_end(
-      self,
-      args: TrainingArguments,
-      state: TrainerState,
-      control: TrainerControl,
-      **kwargs,
-  ):
-    if self._partial_state.is_main_process:
-      if state.global_step == 1:
-        self._prev_time = time.time()
-        self._prev_num_token = state.num_input_tokens_seen
-        throughput = 0.0
-      else:
-        cur_time = time.time()
-        cur_num_token = state.num_input_tokens_seen
-        throughput = (cur_num_token - self._prev_num_token) / (
-            cur_time - self._prev_time
-        )
-        self._prev_time = cur_time
-        self._prev_num_token = cur_num_token
-        self._avg_throughput += (throughput - self._avg_throughput) / (
-            state.global_step - 1
-        )
-
-      gpu_stats = device_stats.gpu_stats()
-      self._peak_mem = max(
-          gpu_stats.reserved + gpu_stats.smi_diff, self._peak_mem
-      )
-      logging.info(
-          'on_step_end: Throughput: %.2f token/s. %s, %s',
-          throughput,
-          device_stats.gpu_stats_str(gpu_stats),
-          device_stats.cpu_stats_str(),
-      )
-
-  def on_train_begin(
-      self,
-      args: TrainingArguments,
-      state: TrainerState,
-      control: TrainerControl,
-      **kwargs,
-  ):
-    if self._partial_state.is_main_process:
-      self._start_time = time.time()
-      logging.info(
-          'on_train_begin: %s, %s',
-          device_stats.gpu_stats_str(),
-          device_stats.cpu_stats_str(),
-      )
-
-  def on_train_end(
-      self,
-      args: TrainingArguments,
-      state: TrainerState,
-      control: TrainerControl,
-      **kwargs,
-  ):
-    if self._partial_state.is_main_process:
-      train_time = time.time() - self._start_time
-      throughput = state.num_input_tokens_seen / train_time
-      logging.info(
-          'training time %.2f s, throughput (including overhead, e.g., ckpt'
-          ' saving): %.2f token/s, peak_mem: %.2f GB',
-          train_time,
-          throughput,
-          self._peak_mem,
-      )
-      if self._filename:
-        with open(self._filename, 'a') as out_f:
-          out_f.write(
-              f'{self._max_seq_length/1024.0:.1f} | {self._peak_mem:.2f} |'
-              f' {self._avg_throughput:.2f}\n'
-          )

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/custom_loglikelihood.yaml

@@ -1,18 +0,0 @@
-group:
-- vertex
-task: custom_loglikelihood
-dataset_path: json
-dataset_name: null
-output_type: loglikelihood
-training_split: null
-validation_split: null
-test_split: test
-doc_to_text: "Request: {{prompt}}\nResponse:"
-doc_to_target: " {{ground_truth}}"
-metric_list:
-- metric: perplexity
-  aggregation: perplexity
-  higher_is_better: false
-- metric: acc
-  aggregation: mean
-  higher_is_better: true

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/ddp_4gpu.yaml

@@ -1,17 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: MULTI_GPU
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-gpu_ids: all
-machine_rank: 0
-main_training_function: main
-mixed_precision: fp16
-num_machines: 1
-num_processes: 4
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/ddp_8gpu.yaml

@@ -1,17 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: MULTI_GPU
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-gpu_ids: all
-machine_rank: 0
-main_training_function: main
-mixed_precision: fp16
-num_machines: 1
-num_processes: 8
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/deepspeed_zero2_4gpu.yaml

@@ -1,17 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-deepspeed_config:
-  deepspeed_config_file: /diffusers/examples/vertex_vision_model_garden_peft/zero2.json
-  zero3_init_flag: true
-distributed_type: DEEPSPEED
-downcast_bf16: 'no'
-machine_rank: 0
-main_training_function: main
-num_machines: 1
-num_processes: 4
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/deepspeed_zero2_8gpu.yaml

@@ -1,17 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-deepspeed_config:
-  deepspeed_config_file: /diffusers/examples/vertex_vision_model_garden_peft/zero2.json
-  zero3_init_flag: true
-distributed_type: DEEPSPEED
-downcast_bf16: 'no'
-machine_rank: 0
-main_training_function: main
-num_machines: 1
-num_processes: 8
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/deepspeed_zero3_4gpu.yaml

@@ -1,17 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-deepspeed_config:
-  deepspeed_config_file: /diffusers/examples/vertex_vision_model_garden_peft/zero3.json
-  zero3_init_flag: true
-distributed_type: DEEPSPEED
-downcast_bf16: 'no'
-machine_rank: 0
-main_training_function: main
-num_machines: 1
-num_processes: 4
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/deepspeed_zero3_8gpu.yaml

@@ -1,17 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-deepspeed_config:
-  deepspeed_config_file: /diffusers/examples/vertex_vision_model_garden_peft/zero3.json
-  zero3_init_flag: true
-distributed_type: DEEPSPEED
-downcast_bf16: 'no'
-machine_rank: 0
-main_training_function: main
-num_machines: 1
-num_processes: 8
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/gemma2_fsdp_8gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: Gemma2DecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 1
-num_processes: 8
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_128gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 16
-num_processes: 128
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_16gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 2
-num_processes: 16
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_24gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 3
-num_processes: 24
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_32gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 4
-num_processes: 32
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_8gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 1
-num_processes: 8
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_hsdp_16gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: HYBRID_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 2
-num_processes: 16
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_hsdp_24gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: HYBRID_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 3
-num_processes: 24
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_hsdp_32gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: HYBRID_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 4
-num_processes: 32
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/qwen2_fsdp_8gpu.yaml

@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 1
-num_processes: 8
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/zero2.json

@@ -1,24 +0,0 @@
-{
-  "zero_optimization": {
-    "stage": 2,
-    "contiguous_gradients": false,
-    "overlap_comm": false
-  },
-  "bf16": {
-    "enabled": "auto"
-  },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
-  "gradient_accumulation_steps": "auto",
-  "gradient_clipping": "auto",
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "wall_clock_breakdown": false
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/zero3.json

@@ -1,31 +0,0 @@
-{
-  "zero_optimization": {
-    "stage": 3,
-    "overlap_comm": false,
-    "contiguous_gradients": false,
-    "sub_group_size": 0,
-    "reduce_bucket_size": "auto",
-    "stage3_prefetch_bucket_size": "auto",
-    "stage3_param_persistence_threshold": "auto",
-    "stage3_max_live_parameters": 0,
-    "stage3_max_reuse_distance": 0,
-    "stage3_gather_16bit_weights_on_model_save": true
-  },
-  "bf16": {
-    "enabled": "auto"
-  },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
-  "gradient_accumulation_steps": "auto",
-  "gradient_clipping": "auto",
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "wall_clock_breakdown": false
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/dockerfile/merge_env.yaml

@@ -1,43 +0,0 @@
-# Doc about format of conda environment file
-# https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually
-name: merge
-channels:
-- nodefaults
-- conda-forge
-dependencies:
-- _libgcc_mutex=0.1=conda_forge
-- _openmp_mutex=4.5=2_gnu
-- bzip2=1.0.8=h4bc722e_7
-- ca-certificates=2024.7.4=hbcca054_0
-- ld_impl_linux-64=2.40=hf3520f5_7
-- libffi=3.4.2=h7f98852_5
-- libgcc-ng=14.1.0=h77fa898_0
-- libgomp=14.1.0=h77fa898_0
-- libnsl=2.0.1=hd590300_0
-- libsqlite=3.46.0=hde9e2c9_0
-- libuuid=2.38.1=h0b41bf4_0
-- libxcrypt=4.4.36=hd590300_1
-- libzlib=1.3.1=h4ab18f5_1
-- ncurses=6.5=h59595ed_0
-- openssl=3.3.1=h4bc722e_2
-- pip=24.2=pyhd8ed1ab_0
-- python=3.10.14=hd12c33a_0_cpython
-- readline=8.2=h8228510_1
-- setuptools=72.1.0=pyhd8ed1ab_0
-- tk=8.6.13=noxft_h4845f30_101
-- tzdata=2024a=h0c530f3_0
-- wheel=0.44.0=pyhd8ed1ab_0
-- xz=5.2.6=h166bdaf_0
-- pip:
-  - --extra-index-url https://download.pytorch.org/whl/cu121
-  - absl-py==2.1.0
-  - accelerate==0.34.2  # Needed for fp8
-  - datasets==2.19.2
-  - fbgemm-gpu==0.8.0+cu121  # Needed for fp8
-  - kfp==2.5.0
-  - peft==0.12.0
-  - protobuf==3.20.3
-  - pynvml==11.5.3
-  - torch==2.4.0+cu121  # Needed for fp8
-  - transformers==4.47.1
-  - trl==0.11.2

community-content/vertex_model_garden/model_oss/peft/train/vmg/dockerfile/requirements.txt

@@ -1,32 +0,0 @@
-# Doc about format of requirement file
-# https://pip.pypa.io/en/stable/reference/requirements-file-format
-
---extra-index-url https://download.pytorch.org/whl/cu118
---extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
-
-# keep sorted
-accelerate==0.34.2
-auto_gptq==0.7.1+cu118
-autoawq==0.2.8
-bitsandbytes==0.43.2
-cloudml-hypertune==0.1.0.dev6
-datasets==2.20.0
-deepspeed==0.15.2
-diffusers==0.25.1
-evaluate==0.4.3
-fsspec==2024.3.1
-gcsfs==2024.3.1
-immutabledict==4.2.1
-ninja==1.11.1 # Needed to avoid `ninja 1.11.1.1 is not supported on this platform` error
-nltk==3.9.1
-optimum==1.17.1
-peft==0.12.0
-pynvml==11.5.3
-rouge_score==0.1.2
-torch==2.2.2+cu118
-torchvision==0.17.2+cu118
-transformers==4.47.1
-trl==0.11.2
-wandb==0.17.1
-ydata-profiling==4.7.0 # Upgrade the version from 4.6.0 to 4.7.0 to fix the old `pydantic` package error.
-psutil==6.0.0

community-content/vertex_model_garden/model_oss/peft/train/vmg/dockerfile/train.Dockerfile

@@ -1,83 +0,0 @@
-# Dockerfile for PEFT Training.
-#
-# To build:
-# docker build -f model_oss/peft/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
-#
-# To push to gcr:
-# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
-# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
-
-# Picked from https://cloud.google.com/deep-learning-containers/docs/choosing-container#pytorch
-FROM us-docker.pkg.dev/deeplearning-platform-release/gcr.io/pytorch-cu121.2-2.py310:m123
-RUN apt-get update && \
-    apt-get upgrade -y && \
-    apt-get install -y curl git wget software-properties-common vim libaio-dev && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists*
-
-# Copy license.
-RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
-
-
-# Install libraries.
-ENV PIP_ROOT_USER_ACTION=ignore
-RUN pip install --upgrade pip
-
-# Remove packages that are not needed and are causing conflicts.
-# dataproc_jupyter_plugin was installed as a part of pytorch-cu121.2-2.py310
-# container which we don't need. It depends on ibis-framework and bigframes.
-# The package and its dependencies request lower versions of pyarrow/pydantic
-# than deepspeed/datasets. So, dataproc_jupyter_plugin conflicts with
-# deepspeed/datasets.
-RUN pip uninstall -y dataproc_jupyter_plugin ibis-framework bigframes
-
-# Prefer to install with requirement file as much as possible for reasons
-# described in b/355034754.
-COPY model_oss/peft/train/vmg/dockerfile/requirements.txt /tmp/requirements.txt
-RUN pip install -r /tmp/requirements.txt
-
-# flash-attn cannot be installed with the requirement file approach above
-# because of the `no-build-isolation` requirement.
-#
-# It is OK to install it after other packages FOR NOW because it only has
-# limited dependencies. And there's no concern about it overwriting previously
-# installed packages.
-# https://github.com/Dao-AILab/flash-attention/blob/v2.6.3/setup.py#L523
-RUN pip install flash-attn==2.6.3 --no-build-isolation
-
-# Install `diffusers` library as editable and in root folder (/) on purpose.
-RUN git clone --depth 1 --branch v0.25.1 https://github.com/huggingface/diffusers.git
-# Remove `diffusers` (NOTE that the dependency libraries are kept).
-RUN pip uninstall -y diffusers
-# Using `--no-deps` option to make sure previously installed packages are not
-# overwritten.
-RUN pip install --no-deps -e /diffusers
-
-# Make sure there's no inconsistent pip libraries.
-RUN pip check
-
-# Install merge related packages in a separate env.
-COPY model_oss/peft/train/vmg/dockerfile/merge_env.yaml /tmp/merge_env.yaml
-RUN conda env create -n merge --yes --file /tmp/merge_env.yaml
-RUN conda init
-
-# Switch to diffusers examples folder.
-WORKDIR /diffusers/examples
-
-RUN mkdir -p ./vertex_vision_model_garden_peft/
-COPY model_oss/peft/train/vmg/configs/* ./vertex_vision_model_garden_peft/
-COPY model_oss/peft/train/vmg/*.py ./vertex_vision_model_garden_peft/train/vmg/
-COPY model_oss/peft/train/vmg/templates /diffusers/examples/util/templates
-COPY model_oss/peft/train/util/*.py /diffusers/examples/util/
-COPY model_oss/util/* /diffusers/examples/util/
-COPY model_oss/notebook_util/dataset_validation_util.py /diffusers/examples/util
-COPY model_oss/peft/train/vmg/tests/*.py ./vertex_vision_model_garden_peft/tests/
-COPY model_oss/peft/train/test_utils/test_util.py ./vertex_vision_model_garden_peft/tests/
-COPY model_oss/peft/train/test_utils/command_builder.py ./vertex_vision_model_garden_peft/tests/
-
-RUN chmod a+rwX -R /diffusers/examples/
-ENV PYTHONPATH /diffusers/examples/
-# Must disable torch XLA, otherwise runtime uses CPU even if GPU exists.
-ENV USE_TORCH_XLA 0
-
-ENTRYPOINT ["python3", "./vertex_vision_model_garden_peft/train/vmg/train_entrypoint.py"]

community-content/vertex_model_garden/model_oss/peft/train/vmg/eval_lib.py

@@ -1,284 +0,0 @@
-"""Library for running evaluations during training."""
-
-from collections.abc import Callable, Mapping, MutableMapping, Sequence
-import dataclasses
-import string
-from typing import Type
-
-from absl import logging
-import evaluate
-import numpy as np
-import torch
-import transformers
-
-from util import dataset_validation_util
-from util import constants
-
-
-_STRING_TRANSLATOR = str.maketrans("", "", string.punctuation)
-
-_GREATER_IS_BETTER_MAP = {
-    "loss": False,
-    "perplexity": False,
-    "bleu": True,
-    "google_bleu": True,
-    "rouge1": True,
-    "rouge2": True,
-    "rougeL": True,
-    "rougeLsum": True,
-}
-
-
-@dataclasses.dataclass(frozen=True)
-class EvalConfig:
-  """Configuration for running evaluations during training.
-
-  Attributes:
-    steps: The number of steps to run evaluation.
-    tasks: The list of tasks to run evaluation on.
-    per_device_batch_size: The per device batch size for evaluation.
-    limit: The maximum number of examples to evaluate.
-    metric_name: The name of the metric to compute.
-    tokenize_dataset: Whether to tokenize the dataset.
-    dataset_path: The path to the dataset.
-    split: The split of the dataset to evaluate.
-    template: The template to use for the dataset.
-    column: The column name of the dataset.
-    metric_for_best_model: The metric to use for loading the best model.
-  """
-
-  steps: int
-  per_device_batch_size: int
-  limit: float | None
-  metric_name: Sequence[str]
-  tokenize_dataset: bool
-  dataset_path: str = ""
-  split: str = "test"
-  template: str = ""
-  column: str = constants.DEFAULT_TRAIN_COLUMN
-  metric_for_best_model: str | None = None
-
-
-def create_trainer(
-    cls: Type[transformers.Trainer],
-    eval_config: EvalConfig | None,
-    tokenizer: transformers.PreTrainedTokenizerBase | None,
-    args: transformers.TrainingArguments,
-    **kwargs,
-) -> transformers.Trainer:
-  """Creates a trainer. If eval config is provided, injects evaluation loop.
-
-  Args:
-    cls: The trainer class.
-    eval_config: The evaluation config.
-    tokenizer: The tokenizer.
-    args: The training arguments.
-    **kwargs: The keyword arguments.
-
-  Returns:
-    A trainer.
-  """
-  if not eval_config:
-    return cls(args=args, **kwargs)
-
-  args.eval_strategy = "steps"
-  args.eval_steps = eval_config.steps
-  args.per_device_eval_batch_size = eval_config.per_device_batch_size
-  args.metric_for_best_model = eval_config.metric_for_best_model
-  args.greater_is_better = _GREATER_IS_BETTER_MAP.get(
-      eval_config.metric_for_best_model, None
-  )
-  args.save_strategy = (
-      transformers.trainer_utils.SaveStrategy.STEPS
-      if eval_config.metric_for_best_model is None
-      else transformers.trainer_utils.SaveStrategy.BEST
-  )
-
-  kwargs["tokenizer"] = tokenizer
-
-  try:
-    _, eval_dataset = dataset_validation_util.load_dataset_with_template(
-        dataset_name=eval_config.dataset_path,
-        split=eval_config.split,
-        input_column=eval_config.column,
-        template=eval_config.template,
-        tokenizer=tokenizer,
-    )
-    if eval_config.limit is not None:
-      if eval_config.limit >= 1:
-        limit = int(eval_config.limit)
-      else:
-        limit = int(eval_config.limit * len(eval_dataset))
-      eval_dataset = eval_dataset.select(range(limit))
-    if tokenizer is not None:
-      eval_dataset = dataset_validation_util.get_filtered_dataset(
-          dataset=eval_dataset,
-          input_column=eval_config.column,
-          max_seq_length=kwargs["max_seq_length"],
-          tokenizer=tokenizer,
-      )
-    if eval_config.tokenize_dataset:
-      eval_dataset = eval_dataset.map(
-          lambda samples: tokenizer(samples[eval_config.column])
-      )
-    kwargs["eval_dataset"] = eval_dataset
-  except (OSError, ValueError, IndexError) as e:
-    logging.warning(
-        "Failed to load eval dataset %s. Evaluation will be skipped.\n%s",
-        eval_config.dataset_path,
-        e,
-    )
-    del args.evaluation_strategy
-    del args.eval_steps
-    del args.per_device_eval_batch_size
-  return cls(args=args, **kwargs)
-
-
-def _cleanup_text(text: str) -> str:
-  """Cleans up the prediction and references text.
-
-  Args:
-    text: The text to clean up.
-
-  Returns:
-    Cleaned up text.
-  """
-  text = text.translate(_STRING_TRANSLATOR)
-  text = text.strip()
-  text = " ".join(text.split())
-  return text.lower()
-
-
-def create_compute_metrics(
-    tokenizer: transformers.PreTrainedTokenizerBase,
-    eval_metrics: Mapping[str, evaluate.EvaluationModule],
-) -> Callable[[transformers.EvalPrediction], MutableMapping[str, float]]:
-  """Creates a compute_metrics function using Hugging Face evaluate library.
-
-  Args:
-    tokenizer: The tokenizer for decoding predictions.
-    eval_metrics: The eval metrics to compute.
-
-  Returns:
-    Function that computes comprehensive metrics.
-  """
-
-  def _preprocess_data(
-      predictions: np.ndarray, labels: np.ndarray
-  ) -> tuple[Sequence[str], Sequence[str]]:
-    """Preprocesses predictions and lavels before evaluation.
-
-    Args:
-      predictions: The predictions to preprocess.
-      labels: The labels to preprocess.
-
-    Returns:
-      A tuple (preprocessed predictions, labels).
-    """
-    # Handle padding and special tokens.
-    predictions = np.where(
-        predictions != -100, predictions, tokenizer.pad_token_id
-    )
-    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
-
-    # Decode to text.
-    pred_texts = tokenizer.batch_decode(predictions, skip_special_tokens=True)
-    label_texts = tokenizer.batch_decode(labels, skip_special_tokens=True)
-
-    # Clean up text.
-    cleaned_pred_texts = [_cleanup_text(text) for text in pred_texts]
-    cleaned_label_texts = [_cleanup_text(text) for text in label_texts]
-
-    return cleaned_pred_texts, cleaned_label_texts
-
-  def _compute_metrics_with_tokenizer(
-      eval_pred: transformers.EvalPrediction,
-  ) -> MutableMapping[str, float]:
-    """Computes metrics using Hugging Face evaluate library.
-
-    Args:
-      eval_pred: The evaluation prediction.
-
-    Returns:
-      A dictionary of metrics.
-    """
-    predictions, perplexities = eval_pred.predictions
-    labels = eval_pred.label_ids
-
-    pred_texts, label_texts = _preprocess_data(predictions, labels)
-
-    metrics = {}
-
-    for eval_metric, computed_eval_metric in eval_metrics.items():
-      match eval_metric:
-        case "perplexity":
-          # We don't use the perplexity from HF Evaluate since it loads the
-          # model again. This causes an increase in the GPU utilization and
-          # hence an OOM. Due to this, we compute the perplexity ourselves
-          # using the eval_loss over the unmasked tokens in
-          # preprocess_logits_for_metrics fn.
-          metrics[eval_metric] = np.mean(perplexities)
-        case "bleu" | "google_bleu":
-          num_valid_labels = len(list(filter(None, label_texts)))
-          if num_valid_labels:
-            eval_score = computed_eval_metric.compute(
-                predictions=pred_texts,
-                references=[[text] for text in label_texts],
-            )
-            metrics[eval_metric] = eval_score[eval_metric]
-          else:
-            metrics[eval_metric] = 0.0
-        case "rouge1" | "rouge2" | "rougeL" | "rougeLsum":
-          rouge_scores = computed_eval_metric.compute(
-              predictions=pred_texts,
-              references=label_texts,
-              use_stemmer=True,
-          )
-          metrics[eval_metric] = rouge_scores[eval_metric]
-
-    pred_lengths = [len(pred.split()) for pred in pred_texts]
-    label_lengths = [len(label.split()) for label in label_texts]
-
-    metrics["gen_len"] = np.mean(pred_lengths)
-    metrics["ref_len"] = np.mean(label_lengths)
-    metrics["length_ratio"] = np.mean(
-        [len(p) / len(r) if r else 0 for p, r in zip(pred_texts, label_texts)]
-    )
-
-    # Round all metrics to 4 decimal places.
-    return {k: round(float(v), 4) for k, v in metrics.items()}
-
-  return _compute_metrics_with_tokenizer
-
-
-def preprocess_logits_for_metrics(
-    logits: torch.Tensor, labels: torch.Tensor
-) -> tuple[torch.Tensor, torch.Tensor]:
-  """Preprocesses the logits before caching them for eval metric calculation.
-
-  Args:
-    logits: Logits predicted by the model.
-    labels: Ground truth labels.
-
-  Returns:
-    A tuple (pred_ids, perplexities).
-  """
-  # Calculate prediction IDs.
-  pred_ids = logits.argmax(dim=-1)
-
-  # This step shifts the logits and labels to align them correctly, where we are
-  # predicting the next token in a sequence. The last logit doesn't have a
-  # corresponding label, and the first label doesn't have a preceding logit to
-  # predict it. This calculation of perplexity is inspired from
-  # https://github.com/huggingface/evaluate/blob/main/metrics/perplexity/perplexity.py.
-  shift_logits = logits[..., :-1, :].contiguous()
-  shift_labels = labels[..., 1:].contiguous()
-  attn_mask = shift_labels != -100
-  loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
-
-  perplexities = torch.exp(
-      (loss_fct(shift_logits.transpose(1, 2), shift_labels) * attn_mask).sum(1)
-      / attn_mask.sum(1)
-  )
-
-  return (pred_ids, perplexities)

community-content/vertex_model_garden/model_oss/peft/train/vmg/instruct_lora.py

@@ -1,905 +0,0 @@
-"""Instruct/Chat with LoRA models."""
-
-from collections.abc import Callable, Mapping, Sequence
-import datetime
-import json
-import os
-from typing import Any
-import warnings
-
-from absl import app
-from absl import flags
-from absl import logging
-from accelerate import DistributedType
-from accelerate import PartialState
-import bitsandbytes as bnb
-import evaluate
-from peft import get_peft_model
-from peft import LoraConfig
-import torch
-import transformers
-import trl
-import wandb
-
-from util import dataset_validation_util
-from util import dataset_stats
-from util import device_stats
-from vertex_vision_model_garden_peft.train.vmg import callbacks
-from vertex_vision_model_garden_peft.train.vmg import eval_lib
-from vertex_vision_model_garden_peft.train.vmg import utils
-from util import constants
-from util import fileutils
-
-
-_PRETRAINED_MODEL_NAME_OR_PATH = flags.DEFINE_string(
-    'pretrained_model_name_or_path',
-    None,
-    'The pretrained model name or path. Supported models can be causal language'
-    ' modeling models from https://github.com/huggingface/peft/tree/main. Note,'
-    ' there might be different paddings for different models. This tool assumes'
-    ' the pretrained_model_name_or_path contains model name, and then choose'
-    ' proper padding methods. e.g. it must contain `llama` for `Llama2'
-    ' models`.',
-    required=True,
-)
-
-_HUGGINGFACE_ACCESS_TOKEN = flags.DEFINE_string(
-    'huggingface_access_token',
-    None,
-    'The access token for loading huggingface gated models.',
-)
-
-_TRAIN_DATASET = flags.DEFINE_string(
-    'train_dataset',
-    None,
-    'The training dataset name in huggingface or path.',
-)
-
-_OUTPUT_DIR = flags.DEFINE_string(
-    'output_dir',
-    None,
-    'The output directory.',
-)
-
-_LOGGING_OUTPUT_DIR = flags.DEFINE_string(
-    'logging_output_dir',
-    '',
-    'The logging output directory, which defaults to same as output_dir.',
-)
-
-_PRECISION_MODE = flags.DEFINE_enum(
-    'precision_mode',
-    constants.PRECISION_MODE_16,
-    [
-        constants.PRECISION_MODE_4,
-        constants.PRECISION_MODE_8,
-        constants.PRECISION_MODE_16,
-        constants.PRECISION_MODE_16B,
-        constants.PRECISION_MODE_32,
-    ],
-    'Precision to load model weights for finetuning.',
-)
-
-_LORA_RANK = flags.DEFINE_integer(
-    'lora_rank',
-    16,
-    'The rank of the update matrices, expressed in int. Lower rank results in'
-    ' smaller update matrices with fewer trainable parameters, referring to'
-    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
-)
-
-_LORA_ALPHA = flags.DEFINE_integer(
-    'lora_alpha',
-    32,
-    'LoRA scaling factor, referring to'
-    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
-)
-
-_LORA_DROPOUT = flags.DEFINE_float(
-    'lora_dropout',
-    0.05,
-    'dropout probability of the LoRA layers, referring to'
-    ' https://huggingface.co/docs/peft/task_guides/token-classification-lora.',
-)
-
-_WARMUP_STEPS = flags.DEFINE_integer(
-    'warmup_steps',
-    10,
-    'Number of steps for the warmup in the learning rate scheduler.',
-)
-
-_WARMUP_RATIO = flags.DEFINE_float(
-    'warmup_ratio',
-    0.03,
-    'The warmup ratio in the learning rate scheduler.',
-)
-
-_WEIGHT_DECAY = flags.DEFINE_float(
-    'weight_decay',
-    0.001,
-    'The weight decay in the learning rate scheduler.',
-)
-
-_NUM_TRAIN_EPOCHS = flags.DEFINE_float(
-    'num_train_epochs',
-    None,
-    'The number of training epochs. Only used for'
-    ' "sequence-classification-lora" with an integer value and for'
-    ' "instruct-lora" with a float value allowed.',
-)
-
-_MAX_STEPS = flags.DEFINE_integer(
-    'max_steps',
-    None,
-    'Total number of training steps. Overrides num_train_epochs if set. Only'
-    ' used for "instruct-lora."',
-)
-
-_MAX_SEQ_LENGTH = flags.DEFINE_integer(
-    'max_seq_length',
-    512,
-    'The maximum sequence length.',
-)
-
-_LEARNING_RATE = flags.DEFINE_float(
-    'learning_rate',
-    2e-4,
-    'The learning rate after the potential warmup period.',
-)
-
-_TRAIN_COLUMN = flags.DEFINE_string(
-    'train_column',
-    constants.DEFAULT_TRAIN_COLUMN,
-    'The instruct column in dataset.',
-)
-
-_REPORT_TO = flags.DEFINE_string(
-    'report_to',
-    constants.REPORT_TO_NONE,
-    'Where logging is reported to, which can be tensorboard or none.',
-)
-
-_PER_DEVICE_TRAIN_BATCH_SIZE = flags.DEFINE_integer(
-    'per_device_train_batch_size',
-    4,
-    'The per device train batch size.',
-)
-
-_GRADIENT_ACCUMULATION_STEPS = flags.DEFINE_integer(
-    'gradient_accumulation_steps',
-    4,
-    'The gradient accumulation steps.',
-)
-
-_GRADIENT_CHECKPOINTING = flags.DEFINE_boolean(
-    'gradient_checkpointing',
-    False,
-    'Whether to enable gradient checkpointing.',
-)
-
-_ENABLE_PEFT = flags.DEFINE_boolean(
-    'enable_peft',
-    True,
-    'Whether to enable peft.',
-)
-_TRAIN_TEMPLATE = flags.DEFINE_string(
-    'train_template',
-    None,
-    'Template for formatting language model training data. Must be a filename'
-    ' under `templates` folder, without `.json` extension, e.g. `alpaca`, or a'
-    ' Cloud Storage URI to a JSON file.',
-)
-
-_OPTIMIZER = flags.DEFINE_string(
-    'optimizer',
-    'adamw_torch',
-    'The optimizer.',
-)
-
-_LR_SCHEDULER_TYPE = flags.DEFINE_string(
-    'lr_scheduler_type',
-    'cosine',
-    'The learning rate scheduler type.',
-)
-
-_SAVE_STEPS = flags.DEFINE_integer(
-    'save_steps',
-    10,
-    'The save steps.',
-)
-
-_LOGGING_STEPS = flags.DEFINE_integer(
-    'logging_steps',
-    10,
-    'The logging steps.',
-)
-
-_EVAL_STEPS = flags.DEFINE_integer(
-    'eval_steps',
-    10,
-    'The number of training steps between evaluations.',
-)
-
-_TRAIN_SPLIT = flags.DEFINE_string(
-    'train_split',
-    'train',
-    'The train split name.',
-)
-
-_PER_DEVICE_EVAL_BATCH_SIZE = flags.DEFINE_integer(
-    'per_device_eval_batch_size',
-    1,
-    'The per device batch size for model evaluation.',
-)
-
-
-_EVAL_LIMIT = flags.DEFINE_float(
-    'eval_limit',
-    None,
-    'Limit the number of examples per task. If <1, limit is a percentage of the'
-    ' total number of examples.',
-)
-
-_EVAL_METRIC_NAME = flags.DEFINE_list(
-    'eval_metric_name',
-    ['loss'],
-    'A comma-separated list of metric names to aggregate during model'
-    ' evaluation. The supported metrics are: '
-    + ', '.join(constants.SUPPORTED_EVAL_METRICS),
-)
-
-_EVAL_DATASET = flags.DEFINE_string(
-    'eval_dataset',
-    None,
-    'The Hugging Face dataset name or path to use for evaluation.',
-)
-
-# We set the default eval split as `test`, based on observation from
-# https://huggingface.co/datasets/timdettmers/openassistant-guanaco/viewer/default/test.
-_EVAL_SPLIT = flags.DEFINE_string(
-    'eval_split',
-    'test',
-    'Eval split name in the eval dataset.',
-)
-
-_EVAL_TEMPLATE = flags.DEFINE_string(
-    'eval_template',
-    None,
-    'Template for formatting language model evaluation data.'
-    ' Must be a filename under `templates` folder, without `.json` extension,'
-    ' e.g. `alpaca`, or a Cloud Storage URI to a JSON file.',
-)
-
-_EVAL_COLUMN = flags.DEFINE_string(
-    'eval_column',
-    None,
-    'Eval column name in the eval dataset.',
-)
-
-_METRIC_FOR_BEST_MODEL = flags.DEFINE_string(
-    'metric_for_best_model',
-    None,
-    'If set, the best model is saved at the end of training based on the'
-    ' metric',
-)
-
-_TRAIN_PRECISION = flags.DEFINE_enum(
-    'train_precision',
-    constants.PRECISION_MODE_16B,
-    [
-        constants.PRECISION_MODE_16,
-        constants.PRECISION_MODE_16B,
-        constants.PRECISION_MODE_32,
-    ],
-    'Precision to train the model.',
-)
-
-_EXAMPLE_PACKING = flags.DEFINE_boolean(
-    'example_packing',
-    False,
-    'Enables example packing during training, which uses '
-    '`ConstantLengthDataset` under the hood.',
-)
-
-_INPUT_MASKING = flags.DEFINE_boolean(
-    'input_masking',
-    False,
-    'If set, it uses DataCollatorForCompletionOnlyLM to train the model on the'
-    ' generated prompts only, i.e., masking out the input',
-)
-
-_ATTN_IMPLEMENTATION = flags.DEFINE_string(
-    'attn_implementation',
-    None,
-    'Attention implementation, can be `eager`, `sdpa` or `flash_attention_2`',
-)
-
-_MAX_GRAD_NORM = flags.DEFINE_float(
-    'max_grad_norm',
-    0.3,
-    'Maximum gradient norm used for gradient clipping',
-)
-
-_WARNINGS_FILTER = flags.DEFINE_string(
-    'warnings_filter',
-    'ignore',
-    'Warning filter as defined in '
-    'https://docs.python.org/3/library/warnings.html#the-warnings-filter',
-)
-
-_LOGGER_LEVEL = flags.DEFINE_string(
-    'logger_level',
-    'passive',
-    'logging level passed to TrainingArguments. Note that this is for python'
-    ' logging module, NOT the one from absl',
-)
-
-_BENCHMARK_OUT_FILE = flags.DEFINE_string(
-    'benchmark_out_file', None, 'file path for writing benchmark result'
-)
-
-
-_NCCL_TIMEOUT = flags.DEFINE_integer(
-    'nccl_timeout', 6000, 'nccl timeout in seconds'
-)
-
-_TUNING_DATA_STATS_FILE = flags.DEFINE_string(
-    'tuning_data_stats_file', None, 'file path for writing tuning data stats.'
-)
-
-_TARGET_MODULES = flags.DEFINE_list(
-    'target_modules', None, 'The names of the modules to apply LoRA adapter to.'
-)
-
-_MAX_GPU_MEMORY_FRACTION = flags.DEFINE_float(
-    'max_gpu_memory_fraction',
-    '0.9',
-    'Maximum GPU memory a caching allocator is allowed to use per GPU.',
-)
-
-
-@flags.multi_flags_validator(
-    [
-        _INPUT_MASKING.name,
-        _EXAMPLE_PACKING.name,
-    ],
-    message='`example_packing=True` does not work with `input_masking=True`',
-)
-def check_example_packing(flags_dict: Mapping[str, Any]) -> bool:
-  """Check to make sure example packing is enabled properly.
-
-  Args:
-    flags_dict: Dictionary containing flags to check.
-
-  Returns:
-    If `example_packing` is set properly.
-  """
-  if flags_dict[_INPUT_MASKING.name] and flags_dict[_EXAMPLE_PACKING.name]:
-    return False
-  return True
-
-
-@flags.multi_flags_validator(
-    [
-        _INPUT_MASKING.name,
-        _TRAIN_TEMPLATE.name,
-    ],
-    message='`train_template` should be provided if using `input_masking=True`',
-)
-def check_input_masking(flags_dict: Mapping[str, Any]) -> bool:
-  """Check to make sure input_masking is enabled properly.
-
-  Args:
-    flags_dict: Dictionary containing flags to check.
-
-  Returns:
-    If `input_masking` is set properly
-  """
-  if (
-      flags_dict[_INPUT_MASKING.name]
-      and flags_dict[_TRAIN_TEMPLATE.name] is None
-  ):
-    return False
-  return True
-
-
-@flags.multi_flags_validator(
-    [
-        _EVAL_DATASET.name,
-        _EVAL_METRIC_NAME.name,
-    ],
-    message=(
-        '`eval_metric_name` should be a valid metric name and present when'
-        ' eval_dataset is provided.'
-    ),
-)
-def _validate_eval_metrics(flags_dict: Mapping[str, Any]) -> bool:
-  """Validates the eval metric name.
-
-  Args:
-    flags_dict: Dictionary containing flags to check.
-
-  Returns:
-    If the eval metrics are valid.
-  """
-  if flags_dict[_EVAL_DATASET.name] is None:
-    return True
-  eval_metrics = flags_dict[_EVAL_METRIC_NAME.name]
-  for eval_metric in eval_metrics:
-    if eval_metric not in constants.SUPPORTED_EVAL_METRICS:
-      raise flags.ValidationError(f'Invalid eval metric: {eval_metric}')
-  if 'perplexity' in eval_metrics and 'loss' not in eval_metrics:
-    _EVAL_METRIC_NAME.value.append('loss')
-    logging.warning(
-        'Adding `loss` to eval_metric_name because `perplexity` is present.'
-    )
-  return True
-
-
-@flags.multi_flags_validator(
-    [
-        _METRIC_FOR_BEST_MODEL.name,
-        _EVAL_METRIC_NAME.name,
-    ],
-    message='`metric_for_best_model` should be in `eval_metric_name`.',
-)
-def _validate_metric_for_best_model(flags_dict: Mapping[str, Any]) -> bool:
-  """Validates the metric for best model.
-
-  Args:
-    flags_dict: Dictionary containing flags to check.
-
-  Returns:
-    If the metric for best model is valid.
-  """
-  if flags_dict[_METRIC_FOR_BEST_MODEL.name] is None:
-    return True
-
-  metric_for_best_model = flags_dict[_METRIC_FOR_BEST_MODEL.name]
-  eval_metric_name = flags_dict[_EVAL_METRIC_NAME.name]
-
-  if metric_for_best_model not in eval_metric_name:
-    raise flags.ValidationError(
-        'Invalid metric for picking the best model:'
-        f' {metric_for_best_model}. The metric should be one'
-        f' of the {eval_metric_name}.'
-    )
-  return True
-
-
-# References:
-# Huggingface SFT trainer example:
-# https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py.
-# Huggingface sagemaker example:
-# https://github.com/huggingface/notebooks/blob/main/sagemaker/28_train_llms_with_qlora/scripts/run_clm.py.
-
-
-def _calculate_hf_eval_metrics(
-    tokenizer: transformers.PreTrainedTokenizerBase,
-    eval_config: eval_lib.EvalConfig | None,
-) -> tuple[
-    Callable[[transformers.EvalPrediction], Mapping[str, float]], torch.Tensor
-]:
-  """Calculates the HF evaluation metrics.
-
-  Args:
-    tokenizer: The tokenizer to use for evaluation.
-    eval_config: The evaluation config to use.
-
-  Returns:
-    The compute metrics and preprocess logits for metrics.
-  """
-  if eval_config is None:
-    return None, None
-  hf_eval_metrics = {}
-  for metric in eval_config.metric_name:
-    if metric in constants.SUPPORTED_HF_EVAL_METRICS:
-      if metric in constants.ROUGE_VARIANTS:
-        hf_eval_metrics[metric] = evaluate.load('rouge')
-      else:
-        hf_eval_metrics[metric] = evaluate.load(metric)
-
-  if not hf_eval_metrics:
-    return None, None
-  return (
-      eval_lib.create_compute_metrics(tokenizer, hf_eval_metrics),
-      eval_lib.preprocess_logits_for_metrics,
-  )
-
-
-# Copied from https://github.com/artidoro/qlora/blob/main/qlora.py.
-def find_all_linear_names(
-    model: transformers.AutoModelForCausalLM, precision_mode: str
-) -> Sequence[str]:
-  """Finds all linear module names."""
-  if precision_mode == constants.PRECISION_MODE_4:
-    cls = bnb.nn.Linear4bit
-  elif precision_mode == constants.PRECISION_MODE_8:
-    cls = bnb.nn.Linear8bitLt
-  else:
-    cls = torch.nn.Linear
-  lora_module_names = set()
-  for name, module in model.named_modules():
-    if isinstance(module, cls):
-      names = name.split('.')
-      lora_module_names.add(names[0] if len(names) == 1 else names[-1])
-  if 'lm_head' in lora_module_names:  # needed for 16-bit
-    lora_module_names.remove('lm_head')
-  return list(lora_module_names)
-
-
-def finetune_instruct(
-    pretrained_model_name_or_path: str,
-    train_dataset: str,
-    output_dir: str,
-    logging_output_dir: str,
-    lora_rank: int = 64,
-    lora_alpha: int = 16,
-    lora_dropout: float = 0.1,
-    warmup_ratio: int = 0.03,
-    num_train_epochs: float | None = None,
-    max_steps: int | None = None,
-    warmup_steps: int = 10,
-    max_seq_length: int = 512,
-    learning_rate: float = 2e-4,
-    precision_mode: str = None,
-    train_column: str = constants.DEFAULT_TRAIN_COLUMN,
-    per_device_train_batch_size: int = 4,
-    gradient_accumulation_steps: int = 4,
-    optim: str = 'paged_adamw_32bit',
-    weight_decay: float = 0.001,
-    gradient_checkpointing: bool = False,
-    enable_peft: bool = True,
-    train_template: str = None,
-    lr_scheduler_type: str = 'constant',
-    save_steps: int = 10,
-    logging_steps: int = 10,
-    train_split: str = 'train',
-    eval_config: eval_lib.EvalConfig | None = None,
-    report_to: str = constants.REPORT_TO_NONE,
-    access_token: str | None = None,
-    train_precision: str = constants.PRECISION_MODE_16B,
-    example_packing: bool = False,
-    attn_implementation: str | None = None,
-    max_grad_norm: float = 0.3,
-    input_masking: bool = False,
-    logger_level: str = 'passive',
-    benchmark_out_file: str | None = None,
-    tuning_data_stats_file: str | None = None,
-    target_modules: str | None = None,
-) -> None:
-  """Finetunes instruct."""
-  logging.info(
-      'on entering instruct_lora, %s,\n%s',
-      device_stats.gpu_stats_str(),
-      device_stats.cpu_stats_str(),
-  )
-  gradient_checkpointing_kwargs = {}
-  # DDP provides limited support with the reentrant variant of gradient
-  # checkpoint [1]. Below is an indirect way of checking whether DDP will be
-  # used. It is "indirect" because there are complex logic under the hood of
-  # `SFTTrainer` and since those are not public API, they might change as we
-  # update the library.
-  if PartialState().distributed_type == DistributedType.MULTI_GPU:
-    gradient_checkpointing_kwargs['use_reentrant'] = False
-
-  tokenizer = dataset_validation_util.load_tokenizer(
-      pretrained_model_name_or_path,
-      'right',
-      access_token=access_token,
-  )
-
-  train_dataset, train_dataset_with_template = (
-      dataset_validation_util.load_dataset_with_template(
-          train_dataset,
-          split=train_split,
-          input_column=train_column,
-          template=train_template,
-          tokenizer=tokenizer,
-      )
-  )
-  train_dataset_with_template = dataset_validation_util.get_filtered_dataset(
-      dataset=train_dataset_with_template,
-      input_column=train_column,
-      max_seq_length=max_seq_length,
-      tokenizer=tokenizer,
-  )
-
-  if tuning_data_stats_file:
-    with PartialState().main_process_first():
-      effective_batch_size = (
-          per_device_train_batch_size
-          * gradient_accumulation_steps
-          * PartialState().num_processes
-      )
-      logging.info(
-          'getting tuning data stats with effective batch size %s',
-          effective_batch_size,
-      )
-      train_dataset_stats = dataset_stats.get_dataset_stats(
-          raw=train_dataset,
-          templated=train_dataset_with_template,
-          template=train_template,
-          tokenizer=tokenizer,
-          column=train_column,
-          effective_batch_size=effective_batch_size,
-      )
-      logging.info('stats: %s', train_dataset_stats)
-      tuning_data_stats_file = dataset_validation_util.force_gcs_fuse_path(
-          tuning_data_stats_file
-      )
-      with open(tuning_data_stats_file, 'w') as out_f:
-        json.dump(train_dataset_stats, out_f)
-
-  model = utils.load_model(
-      pretrained_model_name_or_path=pretrained_model_name_or_path,
-      tokenizer=tokenizer,
-      precision_mode=precision_mode,
-      gradient_checkpointing=gradient_checkpointing,
-      access_token=access_token,
-      attn_implementation=attn_implementation,
-      train_precision=train_precision,
-  )
-
-  if enable_peft:
-    if target_modules is None:
-      target_modules = find_all_linear_names(
-          model, precision_mode=precision_mode
-      )
-    logging.info('applying lora adapters to modules: %s', target_modules)
-    peft_config = LoraConfig(
-        lora_alpha=lora_alpha,
-        lora_dropout=lora_dropout,
-        r=lora_rank,
-        bias='none',
-        task_type='CAUSAL_LM',
-        target_modules=target_modules,
-    )
-    # If we pass in `peft_config` to SFTTrainer, it does a lot of magic under
-    # the hood, e.g., calling `prepare_model_for_kbit_training` before calling
-    # `get_peft_model`, which may revert other changes we did before. That's why
-    # we are calling `get_peft_model` explicitly here.
-    model = get_peft_model(model, peft_config)
-    adapter_for_eval_dir = os.path.join(output_dir, 'adapter_for_eval')
-    logging.info('saving adapter for evaluation to %s...', adapter_for_eval_dir)
-    peft_config.save_pretrained(adapter_for_eval_dir)
-    # This is to work-around mix-precision training. This issue is not fixed as
-    # of transformers==4.41.2.
-    # See b/332760883#comment30 for more details.
-    if precision_mode in (
-        constants.PRECISION_MODE_16,
-        constants.PRECISION_MODE_16B,
-    ):
-      for param in filter(lambda p: p.requires_grad, model.parameters()):
-        param.data = param.data.to(torch.float32)
-
-  if not logging_output_dir:
-    logging_output_dir = output_dir
-
-  # To use singleton PartialState() without re-initializing it. See
-  # b/357970482#comment3
-  accelerator_config = {'use_configured_state': True}
-
-  training_arguments = transformers.TrainingArguments(
-      report_to=report_to,
-      output_dir=output_dir,
-      per_device_train_batch_size=per_device_train_batch_size,
-      gradient_accumulation_steps=gradient_accumulation_steps,
-      optim=optim,
-      save_steps=save_steps,
-      save_total_limit=3,
-      logging_dir=os.path.join(logging_output_dir, 'logs'),
-      logging_steps=logging_steps,
-      learning_rate=learning_rate,
-      fp16=(train_precision == constants.PRECISION_MODE_16),
-      bf16=(train_precision == constants.PRECISION_MODE_16B),
-      max_grad_norm=max_grad_norm,
-      num_train_epochs=num_train_epochs if num_train_epochs else -1,
-      max_steps=max_steps if max_steps else -1,
-      warmup_ratio=warmup_ratio,
-      warmup_steps=warmup_steps,
-      group_by_length=False,
-      lr_scheduler_type=lr_scheduler_type,
-      gradient_checkpointing=gradient_checkpointing,
-      gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
-      weight_decay=weight_decay,
-      log_level=logger_level,
-      accelerator_config=accelerator_config,
-      include_num_input_tokens_seen=True,
-  )
-  trainer_kwargs = {}
-  if input_masking and train_template:
-    template_json = dataset_validation_util.get_template(
-        template_path=train_template
-    )
-    instruction_sep = dataset_validation_util.get_instruction_separator(
-        template_json
-    )
-    response_sep = dataset_validation_util.get_response_separator(template_json)
-    if not response_sep:
-      raise ValueError(
-          '`response_separator` must be provided to use'
-          ' `DataCollatorForCompletionOnlyLM`'
-      )
-
-    trainer_kwargs['data_collator'] = trl.DataCollatorForCompletionOnlyLM(
-        instruction_template=instruction_sep,
-        response_template=response_sep,
-        tokenizer=tokenizer,
-    )
-    logging.info('using DataCollatorForCompletionOnlyLM')
-
-  trainer_stats_callback = callbacks.TrainerStatsCallback(
-      max_seq_length, benchmark_out_file
-  )
-  compute_metrics, preprocess_logits = _calculate_hf_eval_metrics(
-      tokenizer, eval_config
-  )
-
-  trainer = eval_lib.create_trainer(
-      cls=trl.SFTTrainer,
-      eval_config=eval_config,
-      model=model,
-      train_dataset=train_dataset_with_template,
-      dataset_text_field=train_column,
-      max_seq_length=max_seq_length,
-      tokenizer=tokenizer,
-      args=training_arguments,
-      packing=example_packing,
-      callbacks=[trainer_stats_callback],
-      compute_metrics=compute_metrics,
-      preprocess_logits_for_metrics=preprocess_logits,
-      **trainer_kwargs,
-  )
-
-  # `eval_lib.create_trainer` might modify the training args. Printing here
-  # should capture what will be used by the trainer.
-  if PartialState().is_main_process:
-    logging.info('training args: %s', trainer.args)
-
-  if enable_peft:
-    trainer.model.print_trainable_parameters()
-
-  if trainer.is_fsdp_enabled:
-    logging.info('Trainer running with FSDP.')
-  elif trainer.is_deepspeed_enabled:
-    logging.info('Trainer running with DeepSpeed.')
-  else:
-    logging.info('Trainer running without parallelism.')
-
-  trainer.train()
-
-  # Always save the final checkpoint.
-  final_checkpoint = utils.get_final_checkpoint_path(output_dir)
-  logging.info('The final checkpoint is: %s.', final_checkpoint)
-
-  if trainer.is_fsdp_enabled:
-    trainer.accelerator.state.fsdp_plugin.set_state_dict_type('FULL_STATE_DICT')
-    # This method saves the sharded weights like `accelerator.save_state`, see
-    # https://huggingface.co/docs/accelerate/en/usage_guides/fsdp#saving-and-loading
-    trainer.save_model(output_dir)
-    model = trainer.model
-    state_dict = trainer.accelerator.get_state_dict(model)
-    # To aggregate the weights from all the devices, we need to use
-    # `state_dict=state_dict`.
-    model.save_pretrained(
-        final_checkpoint,
-        state_dict=state_dict,
-        is_main_process=PartialState().is_main_process,
-        save_embedding_layers=False,  # Only pad token is added. See go/lora-adapter-pad-token #pylint: disable=line-too-long
-    )
-  else:
-    trainer.model.save_pretrained(
-        final_checkpoint,
-        is_main_process=PartialState().is_main_process,
-        save_embedding_layers=False,  # Only pad token is added. See go/lora-adapter-pad-token #pylint: disable=line-too-long
-    )
-
-  if eval_config is not None and trainer.eval_dataset is not None:
-    metrics = trainer.evaluate(metric_key_prefix='eval')
-    # Both `log_metrics` and `save_metrics` are multiple process safe.
-    # https://github.com/huggingface/transformers/blob/v4.38.2/src/transformers/trainer_pt_utils.py#L911 #pylint: disable=line-too-long
-    # https://github.com/huggingface/transformers/blob/v4.38.2/src/transformers/trainer_pt_utils.py#L1001 #pylint: disable=line-too-long
-    trainer.log_metrics('eval', metrics)
-    trainer.save_metrics('eval', metrics)
-
-  if not enable_peft:
-    tokenizer.save_pretrained(
-        final_checkpoint, is_main_process=PartialState().is_main_process
-    )
-
-
-def main(unused_argv: Sequence[str]) -> None:
-  # This needs to be called before any other PartialState() calls.
-  utils.init_partial_state(
-      timeout=datetime.timedelta(seconds=_NCCL_TIMEOUT.value)
-  )
-
-  torch.cuda.set_per_process_memory_fraction(
-      _MAX_GPU_MEMORY_FRACTION.value, device=PartialState().local_process_index
-  )
-
-  utils.print_library_versions()
-  warnings.simplefilter(_WARNINGS_FILTER.value)
-
-  pretrained_model_name_or_path = fileutils.force_gcs_path(
-      _PRETRAINED_MODEL_NAME_OR_PATH.value
-  )
-  if dataset_validation_util.is_gcs_path(pretrained_model_name_or_path):
-    pretrained_model_name_or_path = (
-        dataset_validation_util.download_gcs_uri_to_local(
-            pretrained_model_name_or_path
-        )
-    )
-
-  # GCS Fuse does not sync flushed files if not closed. See b/361771727.
-  logging_output_dir = fileutils.force_gcs_path(_LOGGING_OUTPUT_DIR.value)
-
-  # Creates evaluation config.
-  if _EVAL_DATASET.value:
-    eval_config = eval_lib.EvalConfig(
-        per_device_batch_size=_PER_DEVICE_EVAL_BATCH_SIZE.value,
-        limit=_EVAL_LIMIT.value,
-        metric_name=_EVAL_METRIC_NAME.value,
-        steps=_EVAL_STEPS.value,
-        dataset_path=dataset_validation_util.force_gcs_fuse_path(
-            _EVAL_DATASET.value
-        ),
-        split=_EVAL_SPLIT.value,
-        template=_EVAL_TEMPLATE.value,
-        column=_EVAL_COLUMN.value,
-        tokenize_dataset=False,
-        metric_for_best_model=_METRIC_FOR_BEST_MODEL.value,
-    )
-  else:
-    eval_config = None
-
-  if _REPORT_TO.value == constants.REPORT_TO_WANDB:
-    wandb.login()
-
-  finetune_instruct(
-      pretrained_model_name_or_path=pretrained_model_name_or_path,
-      train_dataset=_TRAIN_DATASET.value,
-      output_dir=_OUTPUT_DIR.value,
-      logging_output_dir=logging_output_dir,
-      precision_mode=_PRECISION_MODE.value,
-      lora_rank=_LORA_RANK.value,
-      lora_alpha=_LORA_ALPHA.value,
-      lora_dropout=_LORA_DROPOUT.value,
-      warmup_ratio=_WARMUP_RATIO.value,
-      num_train_epochs=_NUM_TRAIN_EPOCHS.value,
-      warmup_steps=_WARMUP_STEPS.value,
-      max_steps=_MAX_STEPS.value,
-      max_seq_length=_MAX_SEQ_LENGTH.value,
-      learning_rate=_LEARNING_RATE.value,
-      train_column=_TRAIN_COLUMN.value,
-      per_device_train_batch_size=_PER_DEVICE_TRAIN_BATCH_SIZE.value,
-      optim=_OPTIMIZER.value,
-      weight_decay=_WEIGHT_DECAY.value,
-      gradient_accumulation_steps=_GRADIENT_ACCUMULATION_STEPS.value,
-      gradient_checkpointing=_GRADIENT_CHECKPOINTING.value,
-      enable_peft=_ENABLE_PEFT.value,
-      train_template=_TRAIN_TEMPLATE.value,
-      lr_scheduler_type=_LR_SCHEDULER_TYPE.value,
-      save_steps=_SAVE_STEPS.value,
-      logging_steps=_LOGGING_STEPS.value,
-      train_split=_TRAIN_SPLIT.value,
-      eval_config=eval_config,
-      report_to=_REPORT_TO.value,
-      access_token=_HUGGINGFACE_ACCESS_TOKEN.value,
-      train_precision=_TRAIN_PRECISION.value,
-      example_packing=_EXAMPLE_PACKING.value,
-      attn_implementation=_ATTN_IMPLEMENTATION.value,
-      max_grad_norm=_MAX_GRAD_NORM.value,
-      input_masking=_INPUT_MASKING.value,
-      logger_level=_LOGGER_LEVEL.value,
-      benchmark_out_file=_BENCHMARK_OUT_FILE.value,
-      tuning_data_stats_file=_TUNING_DATA_STATS_FILE.value,
-      target_modules=_TARGET_MODULES.value,
-  )
-  # Frees the model from GPU.
-  utils.force_gc()
-
-
-if __name__ == '__main__':
-  app.run(main)

community-content/vertex_model_garden/model_oss/peft/train/vmg/merge_causal_language_model_lora.py

@@ -1,108 +0,0 @@
-"""Script to merge PEFT adapter with base model."""
-
-from collections.abc import Mapping, Sequence
-from typing import Any
-
-from absl import app
-from absl import flags
-
-from util import dataset_validation_util
-from vertex_vision_model_garden_peft.train.vmg import utils
-from util import constants
-from util import fileutils
-
-
-_PRETRAINED_MODEL_NAME_OR_PATH = flags.DEFINE_string(
-    'pretrained_model_name_or_path',
-    None,
-    'The pretrained model id. Supported models can be causal language modeling'
-    ' models from https://github.com/huggingface/peft/tree/main. Note, there'
-    ' might be different paddings for different models. This tool assumes the'
-    ' pretrained_model_name_or_path contains model name, and then choose proper'
-    ' padding methods. e.g. it must contain `llama` for `Llama2 models`.',
-    required=True,
-)
-
-_MERGE_BASE_AND_LORA_OUTPUT_DIR = flags.DEFINE_string(
-    'merge_base_and_lora_output_dir',
-    None,
-    'The directory to store the merged model with the base and lora adapter.',
-)
-
-_MERGE_MODEL_PRECISION_MODE = flags.DEFINE_enum(
-    'merge_model_precision_mode',
-    constants.PRECISION_MODE_16B,
-    [
-        constants.PRECISION_MODE_4,
-        constants.PRECISION_MODE_8,
-        constants.PRECISION_MODE_FP8,
-        constants.PRECISION_MODE_16,
-        constants.PRECISION_MODE_16B,
-        constants.PRECISION_MODE_32,
-    ],
-    'Merging model precision mode.',
-)
-
-_FINETUNED_LORA_MODEL_DIR = flags.DEFINE_string(
-    'finetuned_lora_model_dir',
-    None,
-    'The directory storing finetuned LoRA model weights.',
-)
-
-_HUGGINGFACE_ACCESS_TOKEN = flags.DEFINE_string(
-    'huggingface_access_token',
-    None,
-    'The access token for loading huggingface gated models.',
-)
-
-
-@flags.multi_flags_validator(
-    [
-        _PRETRAINED_MODEL_NAME_OR_PATH.name,
-        _FINETUNED_LORA_MODEL_DIR.name,
-        _MERGE_BASE_AND_LORA_OUTPUT_DIR.name,
-    ],
-)
-def check_merge_lora_model_flags(flags_dict: Mapping[str, Any]) -> bool:
-  """Check if required flags are set on merge model LoRA task.
-
-  Args:
-    flags_dict: Dictionary containing task and flags to check.
-
-  Returns:
-    If required flags are not None.
-  """
-  return all(map(lambda x: x is not None, flags_dict.values()))
-
-
-def main(unused_argv: Sequence[str]) -> None:
-  pretrained_model_name_or_path = fileutils.force_gcs_path(
-      _PRETRAINED_MODEL_NAME_OR_PATH.value
-  )
-  if dataset_validation_util.is_gcs_path(pretrained_model_name_or_path):
-    pretrained_model_name_or_path = (
-        dataset_validation_util.download_gcs_uri_to_local(
-            pretrained_model_name_or_path
-        )
-    )
-
-  finetuned_lora_model_dir = fileutils.force_gcs_path(
-      _FINETUNED_LORA_MODEL_DIR.value
-  )
-  if dataset_validation_util.is_gcs_path(finetuned_lora_model_dir):
-    finetuned_lora_model_dir = (
-        dataset_validation_util.download_gcs_uri_to_local(
-            finetuned_lora_model_dir
-        )
-    )
-  utils.merge_causal_language_model_with_lora(
-      pretrained_model_name_or_path=pretrained_model_name_or_path,
-      precision_mode=_MERGE_MODEL_PRECISION_MODE.value,
-      finetuned_lora_model_dir=finetuned_lora_model_dir,
-      merged_model_output_dir=_MERGE_BASE_AND_LORA_OUTPUT_DIR.value,
-      access_token=_HUGGINGFACE_ACCESS_TOKEN.value,
-  )
-
-
-if __name__ == '__main__':
-  app.run(main)

community-content/vertex_model_garden/model_oss/peft/train/vmg/scripts/build_train_docker.sh

@@ -1,21 +0,0 @@
-#!/bin/bash
-
-# Run copybara first:
-# cloud/ml/applications/vision/model_garden/copybara/run_copybara_local.sh
-# Run docker build:
-# cloud/ml/applications/vision/model_garden/model_oss/peft/train/vmg/scripts/build_train_docker.sh
-
-set -x
-set -e
-
-COPYBARA_DIR="/tmp/train_docker/"
-
-pushd "${COPYBARA_DIR}"
-
-PROJECT="cloud-nas-260507"
-IMAGE_TAG="gcr.io/${PROJECT}/pytorch-peft-train:${USER}-test"
-
-docker build -f model_oss/peft/train/vmg/dockerfile/train.Dockerfile . -t "${IMAGE_TAG}"
-docker push "${IMAGE_TAG}"
-
-popd

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/README.md

@@ -1,98 +0,0 @@
-# Vertex Model Garden Training Dataset Template
-
-## Overview
-
-Vertex Model Garden training provides templates for streamlined preprocessing of
-datasets. Although datasets often have intricate structures, the supported LLM
-models accept only flat strings. A template facilitates parsing a dataset and
-preprocessing it to be compatible with the model.
-
-When fine-tuning a pretrained model, it is advisable to maintain the same format
-as the original training data. A template helps replicate the format, ensuring
-consistency and potentially enhancing the fine-tuning process.
-
-Both multi-turn messages and single instruction-response pairs are supported.
-Multi-turn messages are accommodated using a more general `chat_template` field,
-whereas simple instruction-response pair datasets are supported through the
-`prompt_input` field.
-
-A template is a JSON file consisting of string key-value pairs. Refer to the
-following for the definitions of the supported fields.
-
-## Template field documentation
-
-**description**: An explanation of the template.
-
-**source**: Information about the origin of the template.
-
-**chat_template**: A
-[jinja template](https://jinja.palletsprojects.com/en/3.1.x/templates/) that can
-be used to parse a chat dataset. This is the same format as
-[HF chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating).
-To create a chat_template, use the `messages` variable to be filled with the
-sample. The flag `--instruct_column_in_dataset` identifies which column will be
-passed to the `messages` variable in the chat_template. This field is mutually
-exclusive with `prompt_input` and `prompt_no_input`.
-
-**prompt_input**: A string template that is used when value for the input column
-exists in the sample. It should be able to be formatted with the
-[str.format](https://docs.python.org/3/library/stdtypes.html#str.format) method.
-The input column is specified with the flag `--instruct_column_in_dataset`. Used
-for instruction dataset. This field is mutually exclusive with `chat_template`.
-
-**prompt_no_input**: A string template that is used when value for the input
-column does not exist in the sample. It should be able to be formatted with the
-[str.format](https://docs.python.org/3/library/stdtypes.html#str.format) method.
-The input column is specified with the flag `--instruct_column_in_dataset`. Used
-for instruction dataset. This field is mutually exclusive with `chat_template`.
-
-**instruction_separator**: A unique string used to indicate the start of the
-instructions. If not specified, every token after response_separator will be
-treated as a response, and every token before the first response_separator will
-be treated as instruction.
-
-**response_separator**: A unique string used to indicate the start of the
-response. This field is required if `--completion_only` flag is set to `True`.
-
-## Example templates
-
--   See the list of all supported templates [here](https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/community-content/vertex_model_garden/model_oss/peft/train/vmg/templates).
--   For an example with `chat_template` see the JSON template below.
-
-```
-{
-  "description": "Chat template used by Llama 3.",
-  "source": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct/blob/a5a71a7527eac1d651bb145436c72026887fb68e/tokenizer_config.json#L2053",
-  "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
-  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
-  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
-}
-```
-
--   For an example with `prompt_input` see the JSON template below. In this case
-    the flag `--instruct_column_in_dataset=text` should be set, and there must
-    be a column named `text` in the dataset.
-
-```
-{
-  "description": "Template for openassistant-guanaco dataset.",
-  "source": "https://huggingface.co/datasets/timdettmers/openassistant-guanaco",
-  "prompt_input": "{text}",
-  "instruction_separator": "### Human:",
-  "response_separator": "### Assistant:"
-}
-```
-
--   For an example with `prompt_no_input` see the JSON template below. In this
-    case the flag `--instruct_column_in_dataset=input` should be set, and there
-    must be columns named `input` and `instruction` in the dataset.
-
-```
-{
-  "description": "Template used by Alpaca-LoRA.",
-  "source": "https://github.com/tloen/alpaca-lora/blob/main/templates/alpaca.json",
-  "prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
-  "prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
-  "response_separator": "### Response:"
-}
-```

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/alpaca.json

@@ -1,7 +0,0 @@
-{
-  "description": "Template used by Alpaca-LoRA.",
-  "source": "https://github.com/tloen/alpaca-lora/blob/main/templates/alpaca.json",
-  "prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
-  "prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
-  "response_separator": "### Response:"
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/alpaca_short.json

@@ -1,7 +0,0 @@
-{
-  "description": "A shorter template to experiment with.",
-  "source": "https://github.com/tloen/alpaca-lora/blob/main/templates/alpaca_short.json",
-  "prompt_input": "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
-  "prompt_no_input": "### Instruction:\n{instruction}\n\n### Response:\n",
-  "response_separator": "### Response:"
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/gemma.json

@@ -1,7 +0,0 @@
-{
-  "description": "Chat template used by Gemma. 'assistant' role is replaced by 'model'",
-  "source": "https://huggingface.co/google/gemma-1.1-2b-it/blob/bf4924f313df5166dee1467161e886e55f2eb4d4/tokenizer_config.json#L1507",
-  "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
-  "instruction_separator": "<start_of_turn>user\n",
-  "response_separator": "<start_of_turn>model\n"
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/llama3-text-bison.json

@@ -1,7 +0,0 @@
-{
-  "description": "Template used by Llama 3, accepting text-bison format.",
-  "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/models/tune-text-models-supervised#dataset-format",
-  "prompt_input": "\n\n<|start_header_id|>user<|end_header_id|>\n\n{input_text}<|eot_id|>\n\n<|start_header_id|>assistant<|end_header_id|>\n\n{output_text}<|eot_id|>",
-  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
-  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/llama3.json

@@ -1,7 +0,0 @@
-{
-  "description": "Chat template used by Llama 3.",
-  "source": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct/blob/a5a71a7527eac1d651bb145436c72026887fb68e/tokenizer_config.json#L2053",
-  "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '\n\n<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '\n\n<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
-  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
-  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/mistral.json

@@ -1,7 +0,0 @@
-{
-  "description": "Chat template used by Mistral.",
-  "source": "https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/src/axolotl/utils/chat_templates.py",
-  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
-  "instruction_separator": "[INST]",
-  "response_separator": "[/INST]"
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/openai-chat.json

@@ -1,7 +0,0 @@
-{
-  "description": "Template used by openai chat.",
-  "source": "https://platform.openai.com/docs/api-reference/fine-tuning/chat-input",
-  "chat_template": "{% set loop_messages = messages %}{% set content = '' %}{% for message in loop_messages %}{% set content = content ~ '\n\n<|start_header_id|>' ~ message.role ~ '<|end_header_id|>\n\n' %}{% if message.content is string %}{% set content = content ~ message.content|trim ~ '<|eot_id|>' %}{% else %}{% set content = content ~ message.content|join(' ', attribute='text')|trim ~ '<|eot_id|>' %}{% endif %}{% if loop.index0 == 0 %}{% set content = bos_token ~ content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
-  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
-  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/openai-completion.json

@@ -1,7 +0,0 @@
-{
-  "description": "Template used by openai completion.",
-  "source": "https://platform.openai.com/docs/api-reference/fine-tuning/completions-input",
-  "prompt_input": "\n\n<|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|>\n\n<|start_header_id|>assistant<|end_header_id|>\n\n{completion}<|eot_id|>",
-  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
-  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/openassistant-guanaco.json

@@ -1,7 +0,0 @@
-{
-  "description": "Template for openassistant-guanaco dataset.",
-  "source": "https://huggingface.co/datasets/timdettmers/openassistant-guanaco",
-  "prompt_input": "{text}",
-  "instruction_separator": "### Human:",
-  "response_separator": "### Assistant:"
-}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/qwen2_5.json

@@ -1,7 +0,0 @@
-{
-  "description": "Chat template used by Qwen 2.5.",
-  "source": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/tokenizer_config.json#L198",
-  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
-  "instruction_separator": "<|im_start|>user\n",
-  "response_separator": "<|im_start|>assistant\n"
-}