[Relay][BugFix] fix a bug about ReLu in the threshold attribute which causes a different results with keras

[jikechao]

Expected Behavior

TVM rejects the invalid attribute value threshold=None immediately rather than accepting it and producing a different result with Keras.

Actual Behavior

Steps to reproduce

import tvm
import tvm.relay as relay
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers, models


input_shape = (1, 2, 3, 4)
input_data = np.random.random(input_shape)
x = layers.Input(shape=input_shape[1:], dtype='float32')

layer = keras.layers.ReLU(threshold=None)  # threshold of a ReLU layer cannot be the None value. Received: None
layer.set_weights(layer.get_weights())

y = layer(x)
model = models.Model(x, y)
print(model.summary())
# model.save('model.h5')
res_keras = model.predict(input_data)

shape_dict = {'input_1': input_shape}
mod, params = relay.frontend.from_keras(model, shape_dict)

with tvm.transform.PassContext(opt_level=1):
    model = relay.build_module.create_executor("graph", mod, tvm.cpu(0), 'llvm', params).evaluate()  # crash

test_x_tvm = input_data
res_tvm = model(tvm.nd.array(test_x_tvm.astype('float32'))).numpy()

np.testing.assert_allclose(res_keras, res_tvm, atol=1e-3, rtol=1e-3)

cc @echuraev @Hzfengsy

[tvm-bot]

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• cc @shingjan _{See #10317 for details}

_{Generated by tvm-bot}

[echuraev]

Thank you for this PR and clarification about keras version.