def test_gru_cell_operator():
batch_size = 1
input_size = 16
hidden_size = 128
X_shape = [batch_size, input_size]
H_t_shape = [batch_size, hidden_size]
W_shape = [3 * hidden_size, input_size]
R_shape = [3 * hidden_size, hidden_size]
B_shape = [3 * hidden_size]
parameter_X = ng.parameter(X_shape, name="X", dtype=np.float32)
parameter_H_t = ng.parameter(H_t_shape, name="H_t", dtype=np.float32)
parameter_W = ng.parameter(W_shape, name="W", dtype=np.float32)
parameter_R = ng.parameter(R_shape, name="R", dtype=np.float32)
parameter_B = ng.parameter(B_shape, name="B", dtype=np.float32)
expected_shape = [1, 128]
node_default = ng.gru_cell(
parameter_X, parameter_H_t, parameter_W, parameter_R, parameter_B, hidden_size
)
assert node_default.get_type_name() == "GRUCell"
assert node_default.get_output_size() == 1
assert list(node_default.get_output_shape(0)) == expected_shape
activations = ["tanh", "relu"]
activations_alpha = [1.0, 2.0]
activations_beta = [1.0, 2.0]
clip = 0.5
linear_before_reset = True
# If *linear_before_reset* is set True, then B tensor shape must be [4 * hidden_size]
B_shape = [4 * hidden_size]
parameter_B = ng.parameter(B_shape, name="B", dtype=np.float32)
node_param = ng.gru_cell(
parameter_X,
parameter_H_t,
parameter_W,
parameter_R,
parameter_B,
hidden_size,
activations,
activations_alpha,
activations_beta,
clip,
linear_before_reset,
)
assert node_param.get_type_name() == "GRUCell"
assert node_param.get_output_size() == 1
assert list(node_param.get_output_shape(0)) == expected_shape
def test_dynamic_attributes_simple():
batch_size = 1
input_size = 16
hidden_size = 128
X_shape = [batch_size, input_size]
H_t_shape = [batch_size, hidden_size]
W_shape = [3 * hidden_size, input_size]
R_shape = [3 * hidden_size, hidden_size]
B_shape = [4 * hidden_size]
parameter_X = ng.parameter(X_shape, name="X", dtype=np.float32)
parameter_H_t = ng.parameter(H_t_shape, name="H_t", dtype=np.float32)
parameter_W = ng.parameter(W_shape, name="W", dtype=np.float32)
parameter_R = ng.parameter(R_shape, name="R", dtype=np.float32)
parameter_B = ng.parameter(B_shape, name="B", dtype=np.float32)
activations = ["tanh", "relu"]
activations_alpha = [1.0, 2.0]
activations_beta = [1.0, 2.0]
clip = 0.5
linear_before_reset = True
node = ng.gru_cell(
parameter_X,
parameter_H_t,
parameter_W,
parameter_R,
parameter_B,
hidden_size,
activations,
activations_alpha,
activations_beta,
clip,
linear_before_reset,
)
assert node.get_hidden_size() == hidden_size
assert all(map(lambda x, y: x == y, node.get_activations(), activations))
assert all(np.equal(node.get_activations_alpha(), activations_alpha))
assert all(np.equal(node.get_activations_beta(), activations_beta))
assert node.get_linear_before_reset() == linear_before_reset
assert np.isclose(node.get_clip(), clip)