📻 (AST) Audio data classification optimization and data pre-process (#…

…762)

## Describe your changes

For this issue #735, I tried to
add an example for AST model optimization with Olive data configs on
huggingface examples to achieve script-free for model optimization.

## Checklist before requesting a review
- [ ] Add unit tests for this change.
- [ ] Make sure all tests can pass.
- [ ] Update documents if necessary.
- [ ] Lint and apply fixes to your code by running `lintrunner -a`
- [ ] Is this a user-facing change? If yes, give a description of this
change to be included in the release notes.

## (Optional) Issue link

examples/AST/README.md

@@ -0,0 +1,28 @@
+# AST Optimization
+This folder contains examples of AST(Audio Spectrogram Transformer) optimization using olive workflows.
+
+- Model: https://huggingface.co/MIT/ast-finetuned-speech-commands-v2
+- Dataset: https://huggingface.co/datasets/speech_commands
+
+### Run example using config
+
+The `ast.json` is used on CPU optimization which tries to quantize the model and tune the inference config for better performance.
+
+First, install required packages according to passes.
+```sh
+python -m olive.workflows.run --config ast.json --setup
+```
+
+Then, optimize the model
+```sh
+python -m olive.workflows.run --config ast.json
+```
+
+or run simply with python code:
+```python
+from olive.workflows import run as olive_run
+olive_run("ast.json")
+```
+
+After running the above command, the model candidates and corresponding config will be saved in the output directory.
+You can then select the best model and config from the candidates and run the model with the selected config.

examples/AST/ast.json

@@ -0,0 +1,106 @@
+{
+    "input_model":{
+        "type": "PyTorchModel",
+        "config": {
+            "hf_config": {
+                "model_class": "ASTForAudioClassification",
+                "model_name": "MIT/ast-finetuned-speech-commands-v2",
+                "task": "audio-classification",
+                "dataset": {
+                    "data_name":"speech_commands",
+                    "subset": "v0.02",
+                    "split": "validation",
+                    "input_cols": ["audio"],
+                    "label_cols": ["label"],
+                    "max_samples": 100,
+                    "batch_size": 1,
+                    "component_kwargs": {
+                        "pre_process_data": {
+                            "labels_to_filter": ["_silence_"]
+                        }
+                    }
+                }
+            },
+            "io_config": {
+                "input_names": ["input_values"],
+                "output_names": ["logits"],
+                "dynamic_axes": {
+                    "input_values": {
+                        "0": "batch_size", "1": "max_length", "2": "num_mel_bins"
+                    },
+                    "logits": {
+                        "0": "batch_size"
+                    }
+                }
+
+            }
+        }
+    },
+    "evaluators": {
+        "common_evaluator": {
+            "metrics":[
+                {
+                    "name": "accuracy",
+                    "type": "accuracy",
+                    "backend": "huggingface_metrics",
+                    "sub_types": [
+                        {"name": "accuracy", "priority": 1, "goal": {"type": "max-degradation", "value": 0.05}},
+                        {"name": "f1", "metric_config": {"compute_params": {"average": "macro"}}}
+                    ]
+                },
+                {
+                    "name": "latency",
+                    "type": "latency",
+                    "sub_types": [
+                        {"name": "avg", "priority": 2, "goal": {"type": "percent-min-improvement", "value": 5}},
+                        {"name": "max"},
+                        {"name": "min"}
+                    ]
+                }
+            ]
+        }
+    },
+    "passes": {
+        "conversion": {
+            "type": "OnnxConversion"
+        },
+        "transformers_optimization": {
+            "type": "OrtTransformersOptimization",
+            "disable_search": true,
+            "config": {
+                "model_type": "vit"
+            }
+        },
+        "quantization": {
+            "type": "OnnxQuantization",
+            "disable_search": true,
+            "config": {
+                "quant_mode": "static",
+                "quant_preprocess": true,
+                "per_channel": false,
+                "reduce_range": false,
+                "data_config": "__input_model_data_config__"
+            }
+        },
+        "perf_tuning": {
+            "type": "OrtPerfTuning",
+            "config": {
+                "data_config": "__input_model_data_config__"
+            }
+        }
+    },
+    "engine": {
+        "search_strategy": {
+            "execution_order": "joint",
+            "search_algorithm": "tpe",
+            "search_algorithm_config": {
+                "num_samples": 3,
+                "seed": 0
+            }
+        },
+        "evaluator": "common_evaluator",
+        "execution_providers": ["CPUExecutionProvider"],
+        "cache_dir": "cache",
+        "output_dir" : "models/ast_cpu"
+    }
+}

olive/data/component/pre_process_data.py

@@ -5,7 +5,7 @@
 
 
 from copy import deepcopy
-from typing import Optional
+from typing import Any, Dict, List, Optional
 
 from olive.data.component.dataset import BaseDataset
 from olive.data.component.text_generation import (
@@ -197,3 +197,71 @@ def text_generation_huggingface_pre_process(
         return text_gen_corpus_pre_process(dataset, tokenizer, all_kwargs)
     else:
         return text_gen_pair_pre_process(dataset, tokenizer, all_kwargs)
+
+
+@Registry.register_pre_process()
+def audio_classification_pre_process(
+    dataset,
+    model_name: str,
+    input_cols: List,
+    label_cols: List,
+    max_samples: Optional[int] = None,
+    trust_remote_code: Optional[bool] = None,
+    feature_extractor_args: Optional[Dict[str, Any]] = None,
+    **kwargs
+):
+    """Pre-process data for audio classification task.
+
+    Args:
+        dataset (object): Data to be pre-processed, reserved for internal dataset assignment.
+        model_name (str): Name of the huggingface model.
+        input_cols (list): List of input columns.
+        label_cols (list): List of label columns.
+        max_samples (int, optional): Max number of samples to use. Defaults to None.
+        trust_remote_code (bool, optional): Whether or not to allow for custom models defined on the Hub in their own
+            modeling files. Defaults to None.
+        feature_extractor_args (dict, optional): Additional arguments for feature extractor.
+        **kwargs: Additional arguments.
+            The common arguments are the fields in olive.data.component.audio_classification.AudioClassificationParams.
+            Extra arguments:
+                - max_duration (int, optional): Max duration of audio in seconds. Defaults to 30.
+                - labels_to_filter (list, optional): List of labels to filter. Defaults to None.
+            Note: the AudioClassificationParams subclass already includes the common arguments.
+    """
+    from datasets import Audio
+    from transformers import AutoConfig, AutoFeatureExtractor
+
+    assert len(input_cols) == 1, "Only one input column is supported for audio classification task."
+
+    # align labels with model configs
+    model_config = AutoConfig.from_pretrained(model_name, trust_remote_code=trust_remote_code)
+    labels_to_filter = kwargs.get("labels_to_filter", None) or []
+    dataset = dataset.filter(
+        lambda x: x not in dataset.features["label"].str2int(labels_to_filter), input_columns=label_cols[0]
+    )
+    dataset = dataset.align_labels_with_mapping(model_config.label2id, label_cols[0])
+
+    fe_args = feature_extractor_args or {}
+    fea_extractor = AutoFeatureExtractor.from_pretrained(model_name, trust_remote_code=trust_remote_code, **fe_args)
+    dataset.cast_column(input_cols[0], Audio(sampling_rate=fea_extractor.sampling_rate))
+
+    def _tokenizer_and_align_labels(examples):
+        max_duration = kwargs.get("max_duration", 30)
+
+        audio_arrays = [x["array"] for x in examples[input_cols[0]]]
+        tokenized_inputs = fea_extractor(
+            audio_arrays,
+            sampling_rate=fea_extractor.sampling_rate,
+            max_length=int(fea_extractor.sampling_rate * max_duration),
+            truncation=True,
+            return_attention_mask=True,
+        )
+
+        tokenized_inputs["label"] = examples[label_cols[0]]
+
+        return tokenized_inputs
+
+    tokenized_datasets = _huggingface_pre_process_helper(
+        dataset, model_name, input_cols, label_cols, _tokenizer_and_align_labels, **kwargs
+    )
+    return BaseDataset(tokenized_datasets, label_cols=label_cols, max_samples=max_samples)

olive/data/container/huggingface_container.py

@@ -31,4 +31,8 @@ class HuggingfaceContainer(DataContainer):
             DataComponentType.PRE_PROCESS_DATA.value: "text_generation_huggingface_pre_process",
             DataComponentType.POST_PROCESS_DATA.value: "text_generation_post_process",
         },
+        "audio-classification": {
+            DataComponentType.PRE_PROCESS_DATA.value: "audio_classification_pre_process",
+            DataComponentType.POST_PROCESS_DATA.value: "text_classification_post_process",
+        },
     }