Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)

Author: Siju Samuel

Welcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial. In this part, we will start with a Quantized TFLite graph and then compile and execute it via TVM.

For more details on quantizing the model using TFLite, readers are encouraged to go through Converting Quantized Models.

The TFLite models can be downloaded from this link.

To get started, Tensorflow and TFLite package needs to be installed as prerequisite.

# install tensorflow and tflite
pip install tensorflow==2.1.0
pip install tflite==2.1.0

Now please check if TFLite package is installed successfully, python -c "import tflite"

Necessary imports

import os

import numpy as np
import tflite

import tvm
from tvm import relay

Download pretrained Quantized TFLite model

# Download mobilenet V2 TFLite model provided by Google
from import download_testdata

model_url = (

# Download model tar file and extract it to get mobilenet_v2_1.0_224.tflite
model_path = download_testdata(
    model_url, "mobilenet_v2_1.0_224_quant.tgz", module=["tf", "official"]
model_dir = os.path.dirname(model_path)


File /workspace/.tvm_test_data/tf/official/mobilenet_v2_1.0_224_quant.tgz exists, skip.

Utils for downloading and extracting zip files

def extract(path):
    import tarfile

    if path.endswith("tgz") or path.endswith("gz"):
        dir_path = os.path.dirname(path)
        tar =
        raise RuntimeError("Could not decompress the file: " + path)


Load a test image

Get a real image for e2e testing

def get_real_image(im_height, im_width):
    from PIL import Image

    repo_base = ""
    img_name = "elephant-299.jpg"
    image_url = os.path.join(repo_base, img_name)
    img_path = download_testdata(image_url, img_name, module="data")
    image =, im_width))
    x = np.array(image).astype("uint8")
    data = np.reshape(x, (1, im_height, im_width, 3))
    return data

data = get_real_image(224, 224)


File /workspace/.tvm_test_data/data/elephant-299.jpg exists, skip.

Load a tflite model

Now we can open mobilenet_v2_1.0_224.tflite

tflite_model_file = os.path.join(model_dir, "mobilenet_v2_1.0_224_quant.tflite")
tflite_model_buf = open(tflite_model_file, "rb").read()

# Get TFLite model from buffer
    import tflite

    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
    import tflite.Model

    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)

Lets run TFLite pre-quantized model inference and get the TFLite prediction.

def run_tflite_model(tflite_model_buf, input_data):
    """ Generic function to execute TFLite """
        from tensorflow import lite as interpreter_wrapper
    except ImportError:
        from tensorflow.contrib import lite as interpreter_wrapper

    input_data = input_data if isinstance(input_data, list) else [input_data]

    interpreter = interpreter_wrapper.Interpreter(model_content=tflite_model_buf)

    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    # set input
    assert len(input_data) == len(input_details)
    for i in range(len(input_details)):
        interpreter.set_tensor(input_details[i]["index"], input_data[i])

    # Run

    # get output
    tflite_output = list()
    for i in range(len(output_details)):

    return tflite_output

Lets run TVM compiled pre-quantized model inference and get the TVM prediction.

def run_tvm(lib):
    from tvm.contrib import graph_executor

    rt_mod = graph_executor.GraphModule(lib["default"](tvm.cpu(0)))
    rt_mod.set_input("input", data)
    tvm_res = rt_mod.get_output(0).asnumpy()
    tvm_pred = np.squeeze(tvm_res).argsort()[-5:][::-1]
    return tvm_pred, rt_mod

TFLite inference

Run TFLite inference on the quantized model.

tflite_res = run_tflite_model(tflite_model_buf, data)
tflite_pred = np.squeeze(tflite_res).argsort()[-5:][::-1]

TVM compilation and inference

We use the TFLite-Relay parser to convert the TFLite pre-quantized graph into Relay IR. Note that frontend parser call for a pre-quantized model is exactly same as frontend parser call for a FP32 model. We encourage you to remove the comment from print(mod) and inspect the Relay module. You will see many QNN operators, like, Requantize, Quantize and QNN Conv2D.

dtype_dict = {"input":}
shape_dict = {"input": data.shape}

mod, params = relay.frontend.from_tflite(tflite_model, shape_dict=shape_dict, dtype_dict=dtype_dict)
# print(mod)

Lets now the compile the Relay module. We use the “llvm” target here. Please replace it with the target platform that you are interested in.

target = "llvm"
with tvm.transform.PassContext(opt_level=3):
    lib =, target=target, params=params)

Finally, lets call inference on the TVM compiled module.

tvm_pred, rt_mod = run_tvm(lib)

Accuracy comparison

Print the top-5 labels for MXNet and TVM inference. Checking the labels because the requantize implementation is different between TFLite and Relay. This cause final output numbers to mismatch. So, testing accuracy via labels.

print("TVM Top-5 labels:", tvm_pred)
print("TFLite Top-5 labels:", tflite_pred)


TVM Top-5 labels: [387 102 386 349 341]
TFLite Top-5 labels: [387 102 386 341 880]

Measure performance

Here we give an example of how to measure performance of TVM compiled models.

n_repeat = 100  # should be bigger to make the measurement more accurate
dev = tvm.cpu(0)
ftimer = rt_mod.module.time_evaluator("run", dev, number=1, repeat=n_repeat)
prof_res = np.array(ftimer().results) * 1e3
print("Elapsed average ms:", np.mean(prof_res))


Elapsed average ms: 69.77875007


Unless the hardware has special support for fast 8 bit instructions, quantized models are not expected to be any faster than FP32 models. Without fast 8 bit instructions, TVM does quantized convolution in 16 bit, even if the model itself is 8 bit.

For x86, the best performance can be achieved on CPUs with AVX512 instructions set. In this case, TVM utilizes the fastest available 8 bit instructions for the given target. This includes support for the VNNI 8 bit dot product instruction (CascadeLake or newer). For EC2 C5.12x large instance, TVM latency for this tutorial is ~2 ms.

Intel conv2d NCHWc schedule on ARM gives better end-to-end latency compared to ARM NCHW conv2d spatial pack schedule for many TFLite networks. ARM winograd performance is higher but it has a high memory footprint.

Moreover, the following general tips for CPU performance equally applies:

  • Set the environment variable TVM_NUM_THREADS to the number of physical cores

  • Choose the best target for your hardware, such as “llvm -mcpu=skylake-avx512” or “llvm -mcpu=cascadelake” (more CPUs with AVX512 would come in the future)

  • Perform autotuning - Auto-tuning a convolution network for x86 CPU.

  • To get best inference performance on ARM CPU, change target argument according to your device and follow Auto-tuning a convolution network for ARM CPU.

Total running time of the script: ( 2 minutes 23.146 seconds)

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