Seq2seq pytorch Inference slow

Question

I tried the seq2seq pytorch implementation available here seq2seq . After profiling the evaluation(evaluate.py) code, the piece of code taking longer time was the decode_minibatch method

def decode_minibatch(
    config,
    model,
    input_lines_src,
    input_lines_trg,
    output_lines_trg_gold
):
    """Decode a minibatch."""
    for i in xrange(config['data']['max_trg_length']):

        decoder_logit = model(input_lines_src, input_lines_trg)
        word_probs = model.decode(decoder_logit)
        decoder_argmax = word_probs.data.cpu().numpy().argmax(axis=-1)
        next_preds = Variable(
            torch.from_numpy(decoder_argmax[:, -1])
        ).cuda()

        input_lines_trg = torch.cat(
            (input_lines_trg, next_preds.unsqueeze(1)),
            1
        )

return input_lines_trg

Trained the model on GPU and loaded the model in CPU mode to make inference. But unfortunately, every sentence seems to take ~10sec. Is slow prediction expected on pytorch?

Any fixes, suggestions to speed up would be much appreciated. Thanks.

Answer 1

One solution for slow performance may be to use a toolkit optimized for the inference, such as OpenVINO. OpenVINO is optimized for Intel hardware but it should work with any CPU. It optimizes the inference performance by e.g. graph pruning or fusing some operations together.

You can find a full tutorial on how to convert the PyTorch model here (FastSeg) and here (BERT). Some snippets below.

Install OpenVINO

The easiest way to do it is using PIP. Alternatively, you can use this tool to find the best way in your case.

pip install openvino-dev[pytorch,onnx]

Save your model to ONNX

OpenVINO cannot convert PyTorch model directly for now but it can do it with ONNX model. This sample code assumes the model is for computer vision.

dummy_input = torch.randn(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH)
torch.onnx.export(model, dummy_input, "model.onnx", opset_version=11)

Use Model Optimizer to convert ONNX model

The Model Optimizer is a command line tool which comes from OpenVINO Development Package so be sure you have installed it. It converts the ONNX model to OV format (aka IR), which is a default format for OpenVINO. It also changes the precision to FP16 (to further increase performance). Run in command line:

mo --input_model "model.onnx" --input_shape "[1, 3, 224, 224]" --mean_values="[123.675, 116.28 , 103.53]" --scale_values="[58.395, 57.12 , 57.375]" --data_type FP16 --output_dir "model_ir"

Run the inference on the CPU

The converted model can be loaded by the runtime and compiled for a specific device e.g. CPU or GPU (integrated into your CPU like Intel HD Graphics). If you don't know what is the best choice for you, just use AUTO.

# Load the network
ie = Core()
model_ir = ie.read_model(model="model_ir/model.xml")
compiled_model_ir = ie.compile_model(model=model_ir, device_name="CPU")

# Get output layer
output_layer_ir = compiled_model_ir.output(0)

# Run inference on the input image
result = compiled_model_ir([input_image])[output_layer_ir]

Disclaimer: I work on OpenVINO.

Answer 2

Following on dragon7 answer, I'd recommend using the ONNX export from Optimum that can handle out of the box the export for encoder/decoder models, as well as make use of past key values in the decoder:

optimum-cli export onnx --model gpt2 --task causal-lm-with-past --for-ort gpt2_onnx/

If you want to use OpenVINO, a good option can be the OVModel that handle the inference with OpenVINO (especially for seq2seq models) out of the box!

Disclaimer: I am a contributor to Optimum library