Internal Error (Could not find any implementation for node {...Tensordot/Reshape}) failure of TensorRT 8.6 when running generate INT8 engine on GPU RTX3080

Hi,
I'm using TensorRT8.6 to conduct int8 calibration and genereate tensorrt engine with polygraphy tools like this

polygraphy convert onnx_model/model.onnx --trt-min-shapes xs:[1,1120] xlen:[1] --trt-opt-shapes xs:[1,160000] xlen:[1] --trt-max-shapes xs:[1,480000] xlen:[1] --int8 --data-loader-script data_loader.py --calibration-cache trt86_minmax_calib.cache --calib-base-cls IInt8MinMaxCalibrator --output trt_model/trt86_minmax_int8.plan

, but it always give the following error:

[W] 'colored' module is not installed, will not use colors when logging. To enable colors, please install the 'colored' module: python3 -m pip install colored
[W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[I]     Configuring with profiles: [Profile().add('xs', min=[1, 1120], opt=[1, 160000], max=[1, 480000]).add('xlen', min=[1], opt=[1], max=[1])]
[W] TensorRT does not currently support using dynamic shapes during calibration. The `OPT` shapes from the calibration profile will be used for tensors with dynamic shapes. Calibration data is expected to conform to those shapes. 
[I] Building engine with configuration:
    Flags                  | [INT8]
    Engine Capability      | EngineCapability.DEFAULT
    Memory Pools           | [WORKSPACE: 10002.44 MiB, TACTIC_DRAM: 10002.44 MiB]
    Tactic Sources         | [CUBLAS, CUBLAS_LT, CUDNN, EDGE_MASK_CONVOLUTIONS, JIT_CONVOLUTIONS]
    Profiling Verbosity    | ProfilingVerbosity.DETAILED
    Preview Features       | [FASTER_DYNAMIC_SHAPES_0805, DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
    Calibrator             | Calibrator(<generator object load_data at 0x7fd95cd5c430>, cache='trt86_minmax_calib.cache', BaseClass=<class 'tensorrt.tensorrt.IInt8MinMaxCalibrator'>)
[I] Saving calibration cache to trt86_minmax_calib.cache
[W] Missing scale and zero-point for tensor xlen, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor (Unnamed Layer* 1) [Shuffle]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor sub:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor (Unnamed Layer* 9) [Shuffle]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor floordiv:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor stft/frame/range_1:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor (Unnamed Layer* 59) [Shuffle]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor stft/frame/mul_1:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor stft/frame/Reshape_2:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor (Unnamed Layer* 70) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor stft/frame/add_2:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor (Unnamed Layer* 82) [Shuffle]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor add:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor SequenceMask/ExpandDims:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor SequenceMask/Cast:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor (Unnamed Layer* 107) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor (Unnamed Layer* 112) [Constant]_output, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor SequenceMask/Range:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[W] Missing scale and zero-point for tensor SequenceMask/Less:0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
formats.cpp:2379: DCHECK(desired_so.size() == t->dim_count()) failed. 
[E] 10: Could not find any implementation for node {ForeignNode[(Unnamed Layer* 82) [Shuffle]_output[Constant]...Tensordot/Reshape]}.
[E] 10: [optimizer.cpp::computeCosts::3869] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[(Unnamed Layer* 82) [Shuffle]_output[Constant]...Tensordot/Reshape]}.)
[!] Invalid Engine. Please ensure the engine was built correctly

The convert onnx model is generated from the saved model, which produced with codes as below:

import librosa
import tensorflow as tf

xs = tf.placeholder(tf.float32, shape=[None, None], name='xs')
xlen = tf.placeholder(tf.int32, shape=[None], name='xlen')
spectrogram = tf.square(tf.abs(tf.signal.stft(xs, frame_length=400, frame_step=160, fft_length=512)))
weight = librosa.filters.mel(sr=16000, n_fft=512, n_mels=80).T  # (257,80)
weight = tf.convert_to_tensor(weight, tf.float32)
mel = tf.tensordot(spectrogram, weight, axes=1)
mel_length = (xlen - 400) // 160 + 1
mask = tf.sequence_mask(mel_length, maxlen=tf.shape(mel)[1], dtype=tf.float32)
mel = mel * tf.expand_dims(mask, axis=-1)
ys = tf.identity(mel, name='ys')

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())

	tf.saved_model.simple_save(sess,
		'./saved_model',
		inputs={"xs": xs, "xlen": xlen},
		outputs={"ys": ys})

Then, convert saved model to onnx model with python3 -m tf2onnx.convert --opset 13 --saved-model ./saved_model/ --output ./onnx_model/model.onnx , what's wrong with it ?

By the way, the file data_loader.py is used for int8 calibration, you can reproduce it like this:

import numpy as np

def load_data(calib_num=100):
	for _ in range(calib_num):
		n = np.random.randint(1000, 5000)
		x = np.random.rand(1, n).astype(np.float32)
		x_len = np.array([n], dtype=np.int32)
		yield {"xs": x, "xlen": x_len}

Anyone can give some helps? Thanks a lot for the help!!!