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[Keras] Enable Dense operator for any input dims #16526

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Merged
masahi merged 1 commit into from
Feb 7, 2024
Merged

[Keras] Enable Dense operator for any input dims #16526

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14 changes: 8 additions & 6 deletions python/tvm/relay/frontend/keras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -266,11 +266,12 @@ def _convert_dense(
# In case of RNN dense, input shape will be (1, 1, n)
if input_dim > 2:
input_shape = tuple(dim if dim else 1 for dim in _as_list(input_shape)[0])
if input_dim != 3 or input_shape[0] != 1 or input_shape[1] != 1:
raise tvm.error.OpAttributeInvalid(
f"Input shape {input_shape} is not valid for operator Dense."
)
inexpr = _op.squeeze(inexpr, axis=[0])
# Keras has no limitations on the shape of the input tensor. But our
# dense op expects 2D input. All inputs with number of dimensions > 2
# are reshaped all "batch" axes into one.
# For example: (N, d1, d2, d3) -> (N * d1 * d2, d3)
new_batch_size = np.prod(input_shape[:-1])
inexpr = _op.reshape(inexpr, newshape=(new_batch_size, input_shape[-1]))
out = _op.nn.dense(data=inexpr, **params)
if keras_layer.use_bias:
bias = etab.new_const(weightList[1])
Expand All @@ -283,7 +284,8 @@ def _convert_dense(
if act_type != "linear":
out = _convert_activation(out, act_type, etab, data_layout)
if input_dim > 2:
out = _op.expand_dims(out, axis=0)
out_shape = (*input_shape[:-1], units)
out = _op.reshape(out, newshape=out_shape)
return out


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10 changes: 10 additions & 0 deletions tests/python/frontend/keras/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -285,6 +285,16 @@ def test_forward_dense(self, keras_mod):
keras_model = keras_mod.models.Model(data, x)
verify_keras_frontend(keras_model, need_transpose=False)

data = keras_mod.layers.Input(shape=(120, 2560), name="image_set")
x = keras_mod.layers.Dense(1, activation="linear", name="e")(data)
keras_model = keras_mod.models.Model(data, x)
verify_keras_frontend(keras_model, need_transpose=False)

data = keras_mod.layers.Input(shape=(10, 12, 2560), name="image_set")
x = keras_mod.layers.Dense(32, activation="linear", name="e")(data)
keras_model = keras_mod.models.Model(data, x)
verify_keras_frontend(keras_model, need_transpose=False)

def test_forward_permute(self, keras_mod):
data = keras_mod.layers.Input(shape=(2, 3, 4))
x = keras_mod.layers.Permute([2, 3, 1])(data)
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