components text_generation_pipeline_singularity_standard_high - Azure/azureml-assets GitHub Wiki

Text Generation Pipeline Singularity Standard High

text_generation_pipeline_singularity_standard_high

Overview

Pipeline component for text generation

Version: 0.0.53

View in Studio: https://ml.azure.com/registries/azureml/components/text_generation_pipeline_singularity_standard_high/version/0.0.53

Inputs

Compute parameters

Name Description Type Default Optional Enum
instance_type_model_import Instance type to be used for model_import component in case of virtual cluster compute, eg. Singularity.D8_v3. The parameter compute_model_import must be set to 'virtual cluster' for instance_type to be used string Singularity.D8_v3 True
instance_type_preprocess Instance type to be used for model_import component in case of virtual cluster compute, eg. Singularity.D8_v3. The parameter compute_model_import must be set to 'virtual cluster' for instance_type to be used string Singularity.D8_v3 True
instance_type_finetune Instance type to be used for finetune component in case of virtual cluster compute, eg. Singularity.ND40_v2. The parameter compute_finetune must be set to 'virtual cluster' for instance_type to be used string Singularity.ND40_v2 True

instance_type_model_evaluation: type: string optional: true default: Singularity.ND40_v2 description: Instance type to be used for finetune component in case of virtual cluster compute, eg. Singularity.ND40_v2. The parameter compute_finetune must be set to 'virtual cluster' for instance_type to be used

Name Description Type Default Optional Enum
num_nodes_finetune number of nodes to be used for finetuning (used for distributed training) integer 1 True
number_of_gpu_to_use_finetuning number of gpus to be used per node for finetuning, should be equal to number of gpu per node in the compute SKU used for finetune integer 1 True

ModelSelector parameters

Name Description Type Default Optional Enum
huggingface_id Input HuggingFace model id. Incase of continual finetuning provide proper id. Models from Hugging Face are subject to third party license terms available on the Hugging Face model details page. It is your responsibility to comply with the model's license terms. string True

Continual-Finetuning model path

Name Description Type Default Optional Enum
pytorch_model_path Pytorch model asset path. Special characters like \ and ' are invalid in the parameter value. custom_model True
mlflow_model_path MLflow model asset path. Special characters like \ and ' are invalid in the parameter value. mlflow_model True

Preprocessing parameters

Name Description Type Default Optional Enum
task_name TextGeneration task type string TextGeneration False ['TextGeneration']
text_key key for text in an example. format your data keeping in mind that text is concatenated with ground_truth while finetuning in the form - text + groundtruth. for eg. "text"="knock knock\n", "ground_truth"="who's there"; will be treated as "knock knock\nwho's there" string False
ground_truth_key key for ground_truth in an example. we take separate column for ground_truth to enable use cases like summarization, translation, question_answering, etc. which can be repurposed in form of text-generation where both text and ground_truth are needed. This separation is useful for calculating metrics. for eg. "text"="Summarize this dialog:\n{input_dialogue}\nSummary:\n", "ground_truth"="{summary of the dialogue}" string True
batch_size Number of examples to batch before calling the tokenization function integer 1000 True
pad_to_max_length If set to True, the returned sequences will be padded according to the model's padding side and padding index, up to their max_seq_length. If no max_seq_length is specified, the padding is done up to the model's max length. string false True ['true', 'false']
max_seq_length Default is -1 which means the padding is done up to the model's max length. Else will be padded to max_seq_length. integer -1 True

Dataset path Parameters

Name Description Type Default Optional Enum
train_file_path Path to the registered training data asset. The supported data formats are jsonl, json, csv, tsv and parquet. Special characters like \ and ' are invalid in the parameter value. uri_file True
validation_file_path Path to the registered validation data asset. The supported data formats are jsonl, json, csv, tsv and parquet. Special characters like \ and ' are invalid in the parameter value. uri_file True
test_file_path Path to the registered test data asset. The supported data formats are jsonl, json, csv, tsv and parquet. Special characters like \ and ' are invalid in the parameter value. uri_file True
train_mltable_path Path to the registered training data asset in mltable format. Special characters like \ and ' are invalid in the parameter value. mltable True
validation_mltable_path Path to the registered validation data asset in mltable format. Special characters like \ and ' are invalid in the parameter value. mltable True
test_mltable_path Path to the registered test data asset in mltable format. Special characters like \ and ' are invalid in the parameter value. mltable True

Finetuning parameters Lora parameters

Name Description Type Default Optional Enum
apply_lora lora enabled string false True ['true', 'false']
merge_lora_weights if set to true, the lora trained weights will be merged to base model before saving string true True ['true', 'false']
lora_alpha lora attention alpha integer 128 True
lora_r lora dimension integer 8 True
lora_dropout lora dropout value number 0.0 True

Training parameters

Name Description Type Default Optional Enum
num_train_epochs training epochs integer 1 True
max_steps If set to a positive number, the total number of training steps to perform. Overrides 'epochs'. In case of using a finite iterable dataset the training may stop before reaching the set number of steps when all data is exhausted. integer -1 True
per_device_train_batch_size Train batch size integer 1 True
per_device_eval_batch_size Validation batch size integer 1 True
auto_find_batch_size Flag to enable auto finding of batch size. If the provided 'per_device_train_batch_size' goes into Out Of Memory (OOM) enabling auto_find_batch_size will find the correct batch size by iteratively reducing 'per_device_train_batch_size' by a factor of 2 till the OOM is fixed string false True ['true', 'false']
optim Optimizer to be used while training string adamw_hf True ['adamw_hf', 'adamw_torch', 'adafactor']
learning_rate Start learning rate. Defaults to linear scheduler. number 2e-05 True
warmup_steps Number of steps used for a linear warmup from 0 to learning_rate integer 0 True
weight_decay The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in AdamW optimizer number 0.0 True
adam_beta1 The beta1 hyperparameter for the AdamW optimizer number 0.9 True
adam_beta2 The beta2 hyperparameter for the AdamW optimizer number 0.999 True
adam_epsilon The epsilon hyperparameter for the AdamW optimizer number 1e-08 True
gradient_accumulation_steps Number of updates steps to accumulate the gradients for, before performing a backward/update pass integer 1 True
eval_accumulation_steps Number of predictions steps to accumulate before moving the tensors to the CPU, will be passed as None if set to -1 integer -1 True
lr_scheduler_type learning rate scheduler to use. string linear True ['linear', 'cosine', 'cosine_with_restarts', 'polynomial', 'constant', 'constant_with_warmup']
precision Apply mixed precision training. This can reduce memory footprint by performing operations in half-precision. string 32 True ['32', '16']
seed Random seed that will be set at the beginning of training integer 42 True
enable_full_determinism Ensure reproducible behavior during distributed training string false True ['true', 'false']
dataloader_num_workers Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. integer 0 True
ignore_mismatched_sizes Whether or not to raise an error if some of the weights from the checkpoint do not have the same size as the weights of the model string true True ['true', 'false']
max_grad_norm Maximum gradient norm (for gradient clipping) number 1.0 True
evaluation_strategy The evaluation strategy to adopt during training string epoch True ['epoch', 'steps']
evaluation_steps_interval The evaluation steps in fraction of an epoch steps to adopt during training. Overwrites evaluation_steps if not 0. number 0.0 True
eval_steps Number of update steps between two evals if evaluation_strategy='steps' integer 500 True
logging_strategy The logging strategy to adopt during training. string steps True ['epoch', 'steps']
logging_steps Number of update steps between two logs if logging_strategy='steps' integer 10 True
metric_for_best_model Specify the metric to use to compare two different models string loss True ['loss']
resume_from_checkpoint Loads Optimizer, Scheduler and Trainer state for finetuning if true string false True ['true', 'false']
save_total_limit If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir. If the value is -1 saves all checkpoints integer -1 True

Early Stopping Parameters

Name Description Type Default Optional Enum
apply_early_stopping Enable early stopping string false True ['true', 'false']
early_stopping_patience Stop training when the specified metric worsens for early_stopping_patience evaluation calls integer 1 True
early_stopping_threshold Denotes how much the specified metric must improve to satisfy early stopping conditions number 0.0 True

Deepspeed Parameters

Name Description Type Default Optional Enum
apply_deepspeed If set to true, will enable deepspeed for training string false True ['true', 'false']
deepspeed Deepspeed config to be used for finetuning uri_file True
deepspeed_stage This parameter configures which DEFAULT deepspeed config to be used - stage2 or stage3. The default choice is stage2. Note that, this parameter is ONLY applicable when user doesn't pass any config information via deepspeed port. string 2 True ['2', '3']

ORT Parameters

Name Description Type Default Optional Enum
apply_ort If set to true, will use the ONNXRunTime training string false True ['true', 'false']

Model Evaluation parameters evaluation_config: type: uri_file optional: true description: Additional parameters for Computing Metrics. Special characters like \ and ' are invalid in the parameter value. evaluation_config_params: type: string optional: true description: Additional parameters as JSON serielized string Validation parameters

Name Description Type Default Optional Enum
system_properties Validation parameters propagated from pipeline. string True

Compute parameters

Name Description Type Default Optional Enum
compute_model_import compute to be used for model_import eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used string virtual cluster True
compute_preprocess compute to be used for preprocess eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used string virtual cluster True
compute_finetune compute to be used for finetune eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used string virtual cluster True

compute_model_evaluation: type: string optional: true default: 'virtual cluster' description: >- compute to be used for model_eavaluation eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used

Name Description Type Default Optional Enum

Outputs

Name Description Type
pytorch_model_folder output folder containing best model as defined by metric_for_best_model. Along with the best model, output folder contains checkpoints saved after every evaluation which is defined by the evaluation_strategy. Each checkpoint contains the model weight(s), config, tokenizer, optimzer, scheduler and random number states. uri_folder
mlflow_model_folder output folder containing best finetuned model in mlflow format. mlflow_model

evaluation_result: type: uri_folder description: Test Data Evaluation Results mode: rw_mount

Name Description Type
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