This model is a FP16 optimized version of SamLowe/roberta-base-go_emotions. It runs exclusively on the GPU.
On an RTX 4090, it is about 2x faster than the base ONNX version (SamLowe/roberta-base-go_emotions-onnx) and 3x faster than the pytorch version. The speedup depends chiefly on your GPU's FP16:FP32 ratio. For more comparison benchmarks and sample code, check here: https://github.com/joaopn/gpu_benchmark_goemotions.
Accuracy: On a test set of 10K reddit comments, the mean label probability difference from the pytorch version was ~1E-4. Metrics (accuracy, F1) are essentially identical to the original model.
Usage
The model was generated with
from optimum.onnxruntime import ORTOptimizer, ORTModelForSequenceClassification, AutoOptimizationConfig
model_id_onnx = "SamLowe/roberta-base-go_emotions-onnx"
file_name = "onnx/model.onnx"
model = ORTModelForSequenceClassification.from_pretrained(model_id_onnx, file_name=file_name, provider="CUDAExecutionProvider", provider_options={'device_id': 0})
optimizer = ORTOptimizer.from_pretrained(model)
optimization_config = AutoOptimizationConfig.O4()
optimizer.optimize(save_dir='roberta-base-go_emotions-onnx-fp16', optimization_config=optimization_config)
You will need the GPU version of the ONNX Runtime. It can be installed with
pip install optimum[onnxruntime-gpu] --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
For convenience, the benchmark repo provides an environment.yml
file to create a conda env with all the requirements. Below is an optimized, batched usage example:
import pandas as pd
import torch
from tqdm import tqdm
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
def sentiment_analysis_batched(df, batch_size, field_name):
model_id = 'joaopn/roberta-base-go_emotions-onnx-fp16'
file_name = 'model.onnx'
gpu_id = 0
model = ORTModelForSequenceClassification.from_pretrained(model_id, file_name=file_name, provider="CUDAExecutionProvider", provider_options={'device_id': gpu_id})
device = torch.device(f"cuda:{gpu_id}")
tokenizer = AutoTokenizer.from_pretrained(model_id)
results = []
# Precompute id2label mapping
id2label = model.config.id2label
total_samples = len(df)
with tqdm(total=total_samples, desc="Processing samples") as pbar:
for start_idx in range(0, total_samples, batch_size):
end_idx = start_idx + batch_size
texts = df[field_name].iloc[start_idx:end_idx].tolist()
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt", max_length=512)
input_ids = inputs['input_ids'].to(device)
attention_mask = inputs['attention_mask'].to(device)
with torch.no_grad():
outputs = model(input_ids, attention_mask=attention_mask)
predictions = torch.sigmoid(outputs.logits) # Use sigmoid for multi-label classification
# Collect predictions on GPU
results.append(predictions)
pbar.update(end_idx - start_idx)
# Concatenate all results on GPU
all_predictions = torch.cat(results, dim=0).cpu().numpy()
# Convert to DataFrame
predictions_df = pd.DataFrame(all_predictions, columns=[id2label[i] for i in range(all_predictions.shape[1])])
# Add prediction columns to the original DataFrame
combined_df = pd.concat([df.reset_index(drop=True), predictions_df], axis=1)
return combined_df
df = pd.read_csv('https://github.com/joaopn/gpu_benchmark_goemotions/raw/main/data/random_sample_10k.csv.gz')
df = sentiment_analysis_batched(df, batch_size=8, field_name='body')
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