def test_brevitas_qlinear(bias, out_features, in_features, w_bits, i_dtype):
i_shape = (1, in_features)
w_shape = (out_features, in_features)
b_linear = QuantLinear(
out_features=out_features,
in_features=in_features,
bias=bias,
bias_quant_type=QuantType.FP,
weight_bit_width=w_bits,
weight_quant_type=QuantType.INT,
weight_scaling_per_output_channel=True,
)
weight_tensor_fp = np.random.uniform(low=-1.0, high=1.0,
size=w_shape).astype(np.float32)
b_linear.weight.data = torch.from_numpy(weight_tensor_fp)
b_linear.eval()
bo.export_finn_onnx(b_linear, i_shape, export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
inp_tensor = gen_finn_dt_tensor(i_dtype, i_shape)
idict = {model.graph.input[0].name: inp_tensor}
odict = oxe.execute_onnx(model, idict, True)
produced = odict[model.graph.output[0].name]
inp_tensor = torch.from_numpy(inp_tensor).float()
expected = b_linear.forward(inp_tensor).detach().numpy()
assert np.isclose(produced, expected, atol=1e-3).all()
os.remove(export_onnx_path)
def test_quant_linear(bias, bias_quant, out_features, in_features, w_bits,
channel_scaling, i_bits):
# required to generated quantized inputs, not part of the exported model to test
quant_inp = QuantIdentity(bit_width=i_bits, return_quant_tensor=True)
inp_tensor = quant_inp(torch.randn(1, in_features))
linear = QuantLinear(out_features=out_features,
in_features=in_features,
bias=bias,
bias_quant=bias_quant,
weight_bit_width=w_bits,
weight_scaling_per_output_channel=channel_scaling)
linear.eval()
model = bo.export_finn_onnx(linear,
input_t=inp_tensor,
export_path='linear.onnx')
model = ModelWrapper(model)
model = model.transform(InferShapes())
# the quantized input tensor passed to FINN should be in integer form
int_inp_array = inp_tensor.int(float_datatype=True).numpy()
idict = {model.graph.input[0].name: int_inp_array}
odict = oxe.execute_onnx(model, idict, True)
produced = odict[model.graph.output[0].name]
expected = linear(inp_tensor).detach().numpy()
assert np.isclose(produced, expected, atol=1e-3).all()