zhaojer
/

bert-hot-path-predictor

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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 ## Training Details
@@ -129,71 +118,3 @@ Use the code below to get started with the model.
 [More Information Needed]
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 ---
 library_name: transformers
+tags:
+- Compiler
+- LLVM
+- Intermediate Representation
+- IR
+- Path
+- Hot Path
+datasets:
+- zhaojer/compiler_hot_paths
+language:
+- en
+base_model:
+- google-bert/bert-base-uncased
 ---
+# Model Card for BERT Hot Path Predictor
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
+This BERT model performs hot path prediction: Given a path (i.e. a sequence of LLVM IR instructions), predict whether it is "hot" (1) or "cold" (0).
+It was fine-tuned on the [hot paths dataset](https://huggingface.co/datasets/zhaojer/compiler_hot_paths) for 3 epochs with standard learning hyperparameters.
+- **Model type:** Binary Sequence Classification
+- **Language(s) (NLP):** English, Compiler/LLVM
+- **Finetuned from model:** google-bert/bert-base-uncased
+- **Dataset used:** zhaojer/compiler_hot_paths
 ## Uses
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+The model can be used to predict whether a path is hot or cold, which is important information for compiler optimizations. Here is an instance of the prediction pipeline:
+1. Given a program (written in C, C++, Fortran, or other languages supported by LLVM), compile it into LLVM IR (e.g., `clang -S -emit-llvm program.c -o program.ll`)
+2. Select a sequence of instructions (in the unit of basic blocks) from the IR file; use this as the input to the model.
+3. Load the present model and feed it the selected input, the model will then output either 0 (cold path) or 1 (hot path).
+The model can be further fine-tuned using additional data. Please see zhaojer/compiler_hot_paths dataset card for more information on the expected data used for fine-tuning.
+## How to Get Started with the Model
+Use the code below to get started with the model.
+```
+from transformers import BertForSequenceClassification, BertTokenizer, pipeline
+# Load saved model
+saved_model = BertForSequenceClassification.from_pretrained("zhaojer/bert-hot-path-predictor")
+saved_tokenizer = BertTokenizer.from_pretrained("zhaojer/bert-hot-path-predictor")
+# Pipeline for predictions
+classifier = pipeline("text-classification", model=saved_model, tokenizer=saved_tokenizer)
+# Example prediction
+new_path = "%26 = load i32, ptr %21, align 4\n%27 = load i32, ptr %11, align\n%28 = icmp slt i32 %26, %27\nbr i1 %28, label %29, label %59\n\nstore i32 0, ptr %22, align 4\nbr label %30"
+prediction = classifier(new_path)
+print(prediction)
+```
 ## Training Details
 [More Information Needed]
 #### Summary