diff options
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 44 |
1 files changed, 29 insertions, 15 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index f68e0cf9..daaca8dd 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -27,7 +27,6 @@ import numpy as np from . import fp_math from . import rewrite_graph from . import scaling -from .api import NpuRoundingMode from .data_type import BaseType from .data_type import DataType from .debug_database import DebugDatabase @@ -56,6 +55,7 @@ from .operation import NpuBlockType from .operation import Op from .operation import Operation from .operation import Padding +from .operation import RoundingMode from .operation_util import create_add_nop from .operation_util import create_avgpool_nop from .operation_util import create_cast_op @@ -295,7 +295,7 @@ def convert_resize_1x1_to_add(op): return op -# Convert ResizeNearestNeightbor with align corners to a depthwise convolution. The IFM will already have been upscaled +# Convert ResizeNearestNeighbor with align corners to a depthwise convolution. The IFM will already have been upscaled # apart from the final x2 scaling which will be done as part of this operation. The kernel contains a single coefficient # to select the appropriate nearest neighbor value def convert_resizenn_ac_to_depthwise_conv(op, upscale_factor): @@ -314,7 +314,7 @@ def convert_resizenn_ac_to_depthwise_conv(op, upscale_factor): "dilation": (1, 1, 1, 1), } - # change resizebilinear to depthwise + # change ResizeNearestNeighbor to Depthwise op.type = Op.DepthwiseConv2DBias op.attrs.update(dw_op_attrs) op.set_input_tensor(ifm, 0) # ifm tensor index @@ -695,12 +695,8 @@ def convert_resizebilinear_to_depthwise_convolutions(op, half_pixel_centers=True dw_conv.write_shape = Shape4D(n, h, w, c) dw_conv.write_offset = Shape4D(0, 0, 0, 0) - # Set the output rounding mode. Resize bilinear requires rounding away from zero. Therefore, we need to - # adjust the accumulated value by a "small" amount before applying natural rounding. The "small" amount - # should be big enough to cause a x.5 to be rounded correctly but small enough not to cause smaller - # values to be incorrectly rounded - ofm.quantization.next_after = True - dw_conv.rounding_mode = NpuRoundingMode.NATURAL + # Resize bilinear requires rounding away from zero + dw_conv.rounding_mode = RoundingMode.AwayZero # Double height and width stride to write the output of each of the four depthwise convolutions below # interleaved with each other when combined with OFM tile base offsets. @@ -1730,12 +1726,30 @@ def replace_pad_by_hw_pad(op: Operation, arch, nng): op.inputs = [] op.add_input_tensor(ifm) op.add_input_tensor(weight_tens) - # Add bias tensor, all biases set to 0 - op.inputs.append(None) - fixup_bias_tensors(op, arch, nng, DataType.int32) + + if op.ifm.dtype == DataType.uint8: + op.rounding_mode = RoundingMode.HalfUp + + # Add bias tensor, all biases set to 0 + op.inputs.append(None) + fixup_bias_tensors(op, arch, nng, DataType.int32) + + else: + op.rounding_mode = RoundingMode.AwayZero + + # The DepthwiseConv needs to be performed with the IFM zero point set appropriately so that the correct + # pad values are used. However, in order to use the rounding away from zero mode the zero point needs to + # have been removed so that the zero point is at zero. This is done by adding a kernel sized amount of + # the zero point as a bias. The datatype of the bias needs to be set to int32, even for an int16 IFM, + # because this will cause full precision scaling to be used (see weight compression). Finally, the OFM + # zero point will need forcing to zero (as it has already been removed) + nr_biases = op.inputs[1].shape[-1] + bias_values = [op.ifm.quantization.zero_point * k_h * k_w] * nr_biases + bias_tensor = create_const_tensor(op.name + "_bias", [nr_biases], DataType.int32, bias_values) + op.add_input_tensor(bias_tensor) + # Add other inputs op.inputs.extend(other_inputs) - op.rounding_mode = NpuRoundingMode.NATURAL # Bypass the PAD operator op.set_input_tensor(pad_op.ifm, 0) @@ -1946,7 +1960,7 @@ def convert_mean_to_depthwise_conv(op, arch, nng): # Set weight shape to [H,W,C,B] weight_shape = [h, w, shape[3], shape[0]] - op.rounding_mode = NpuRoundingMode.NATURAL + op.rounding_mode = RoundingMode.HalfUp identity_quant = QuantizationParameters(scale_f32=1.0, zero_point=0) op.forced_input_quantization = identity_quant op.forced_output_quantization = identity_quant @@ -2016,7 +2030,7 @@ def convert_mean_to_depthwise_conv(op, arch, nng): mul_op.set_ifm_ofm_shapes() # Reference using TFL rounding for the multiply - mul_op.rounding_mode = NpuRoundingMode.TFL + mul_op.rounding_mode = RoundingMode.TFLite # Need to use explicit scaling to get the wanted shift mul_op.explicit_scaling = ExplicitScaling(False, [output_shift_vela], [1]) |