def test_embedding_converter(self):
input_dim = (1, 1, 1, 1)
output_dim = (1, 2, 1, 1)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
weights = numpy.zeros(shape=(2))
weights[:] = [-1, 1]
bias = numpy.zeros(shape=(2))
bias[:] = [-100, 100]
builder.add_embedding(name='Embed', input_dim=1, W=weights, b=bias, output_channels=2, has_bias=True,
input_name='input', output_name='output')
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
def test_linear_quant_embedding_7bit(self):
embed_size = 2
vocab_size = 3
W = np.zeros((embed_size, vocab_size), dtype=np.uint8)
W[:, 0] = [100, 127]
W[:, 1] = [20, 40]
W[:, 2] = [90, 1]
quant_scale = np.reshape(np.array([10.0, 2.0]), (2, 1))
quant_bias = np.reshape(np.array([-2.0, -10.0]), (2, 1))
W_unquantized = np.broadcast_to(quant_scale, (2, 3)) * W + np.broadcast_to(
quant_bias, (2, 3)
)
bias = np.reshape(np.array([1.0, 2.0]), (2, 1))
W_unquantized = W_unquantized + np.broadcast_to(bias, (2, 3))
input_features = [("data", datatypes.Array(4, 1, 1, 1))]
output_features = [("out", None)]
builder = NeuralNetworkBuilder(
input_features, output_features, disable_rank5_shape_mapping=True
)
builder.add_embedding(
name="embed",
W=_convert_array_to_nbit_quantized_bytes(W.flatten(), 7).tobytes(),
b=bias,
input_dim=vocab_size,
output_channels=embed_size,
has_bias=True,
input_name="data",
output_name="out",
is_quantized_weight=True,
quantization_type="linear",
nbits=7,
quant_scale=quant_scale,
quant_bias=quant_bias,
)
mlmodel = MLModel(builder.spec)
data = np.reshape(np.array([2.0, 2.0, 1.0, 0.0]), (4, 1, 1, 1))
data_dict = {"data": data}
out = mlmodel.predict(data_dict, useCPUOnly=True)["out"]
self.assertTrue(out.shape == (4, embed_size, 1, 1))
expected_out = np.zeros((4, embed_size), dtype=np.float32)
expected_out[0, :] = W_unquantized[:, 2].flatten()
expected_out[1, :] = W_unquantized[:, 2].flatten()
expected_out[2, :] = W_unquantized[:, 1].flatten()
expected_out[3, :] = W_unquantized[:, 0].flatten()
self.assertTrue(np.allclose(out.flatten(), expected_out.flatten()))
def test_embedding_converter(self):
input_dim = (1, 1, 1, 1)
output_dim = (1, 2, 1, 1)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
weights = numpy.zeros(shape=(2))
weights[:] = [-1, 1]
bias = numpy.zeros(shape=(2))
bias[:] = [-100, 100]
builder.add_embedding(name='Embed',
input_dim=1,
W=weights,
b=bias,
output_channels=2,
has_bias=True,
input_name='input',
output_name='output')
context = ConvertContext()
node = EmbeddingLayerConverter.convert(
context, builder.spec.neuralNetwork.layers[0], ['input'],
['output'])
self.assertTrue(node is not None)
