MLBEDSW-2569:Support 1x1 IFM ResizeBilinear
Signed-off-by: Charles Xu <charles.xu@arm.com>
Change-Id: I44428d77b2e8e44a477e5c4dfe28ab8dd1792838
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index c805be5..355b16f 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -27,6 +27,7 @@
from .numeric_util import full_shape
from .operation import NpuBlockType
from .operation import Operation
+from .tensor import QuantizationParameters
from .tensor import Tensor
passthrough_nodes = set(("Identity",))
@@ -181,6 +182,39 @@
return op
+# Convert the op to an elementwise add
+def convert_resizebilinear_1x1_to_add(op):
+ op.type = "AddAct"
+ op.name = op.name + "_add"
+ op.attrs.update({"npu_block_type": NpuBlockType.ElementWise})
+ op.attrs["resizebilinear"] = True
+ # Create an input tensor filled with zeros
+ shape = op.outputs[0].shape
+ tens = Tensor(shape, op.inputs[0].dtype, op.inputs[1].name + "_add")
+ tens.values = np.zeros(shape)
+ tens.quant_values = np.zeros(shape, np.uint8)
+ tens.quantization = QuantizationParameters(0.0, 255.0)
+ tens.quantization.scale_f32 = 1.0
+ tens.quantization.zero_point = 0
+ tens.consumer_list = [op]
+ tens_op = op.inputs[1].ops[0]
+ tens_op.outputs = [tens]
+ tens.ops = [tens_op]
+ # Set the add inputs
+ op.inputs[1] = op.inputs[0]
+ op.inputs[0] = tens
+
+ return op
+
+
+def fixup_resizebilinear(op, arch):
+ if op.type == "ResizeBilinear":
+ if op.inputs[0].shape[1] == 1 and op.inputs[0].shape[2] == 1:
+ convert_resizebilinear_1x1_to_add(op)
+
+ return op
+
+
def fixup_fully_connected_input(op, arch):
if op.type == "FullyConnectedAct":
inp = op.inputs[0]
@@ -614,8 +648,7 @@
# produce a (M * 2 - 1, N * 2 - 1) sized output
op.attrs["padding"] = b"VALID"
else:
- # If this exception is raised, something is wrong with the supported op check
- raise UnsupportedFeatureError("Unsupported upscaling factor")
+ return op
input_tensor.resampling_mode = resampling_mode.NEAREST
op.attrs.update({"strides": (1, 1, 1, 1), "ksize": (1, 2, 2, 1)})
return op
@@ -647,6 +680,7 @@
mark_npu_block_type,
fixup_elementwise_with_scalars,
reorder_depthwise_weights,
+ fixup_resizebilinear,
# convert_mul_max_to_abs_or_lrelu # TODO: enable optimisation once quantisation issues are resolved
]
diff --git a/ethosu/vela/npu_serialisation.py b/ethosu/vela/npu_serialisation.py
index 2d1c6b1..4b5a888 100644
--- a/ethosu/vela/npu_serialisation.py
+++ b/ethosu/vela/npu_serialisation.py
@@ -51,8 +51,9 @@
def copy_ifm_values_to_memory_tensor(memory_tensor, src_tensor):
start_addr = src_tensor.address
- end_addr = start_addr + src_tensor.quant_values.size
- memory_tensor.values[start_addr:end_addr] = src_tensor.quant_values
+ values = src_tensor.quant_values.flatten()
+ end_addr = start_addr + values.size
+ memory_tensor.values[start_addr:end_addr] = values
def serialise_npu_subgraph_into_tensors(nng, sg, arch, scratch_tens, scratch_fast_tens, flash_tens):
diff --git a/ethosu/vela/register_command_stream_generator.py b/ethosu/vela/register_command_stream_generator.py
index d32766b..38b40ba 100644
--- a/ethosu/vela/register_command_stream_generator.py
+++ b/ethosu/vela/register_command_stream_generator.py
@@ -478,6 +478,11 @@
if (faf == "Sigmoid") or (faf == "Tanh"):
output_scale = 1 / 0x3000
+ # Force output scale same as the input scale for
+ # resizebiliner 1x1 that is converted to add
+ if "resizebilinear" in primary_op.attrs:
+ output_scale = input2_scale
+
if input_scale == input2_scale:
opa_scale, opb_scale, ofm_scale, shift = scaling.simplified_elementwise_add_sub_scale(
input_scale, input2_scale, output_scale
@@ -860,7 +865,17 @@
emit.cmd0_with_param(zero_point_op, 0)
else:
assert tens.quantization.zero_point is not None, "need an actual zero point set"
- emit.cmd0_with_param(zero_point_op, int(tens.quantization.zero_point))
+ if (
+ "resizebilinear" in primary_op.attrs
+ and primary_op.type == "AddAct"
+ and cmd0.NPU_SET_OFM_ZERO_POINT == zero_point_op
+ ):
+ # Force output zero point same as the input zero point
+ # for resizebiliner 1x1 that is converted to add
+ zero_point = cmd.ifm2_tensor.quantization.zero_point
+ else:
+ zero_point = tens.quantization.zero_point
+ emit.cmd0_with_param(zero_point_op, int(zero_point))
if tens.shape == []:
# Empty shape, elementwise constant
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index e8e8d85..3ec3429 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -236,6 +236,8 @@
def check_resize_restrictions(self, op):
# check unsupported upscaling factor
if op.type == "ResizeBilinear":
+ if op.inputs[0].shape[1] == 1 and op.inputs[0].shape[2] == 1:
+ return True
upscaled_shape = [op.inputs[0].shape[1] * 2, op.inputs[0].shape[2] * 2]
out_shape = op.outputs[0].shape[1:3]
if not op.attrs["align_corners"] and out_shape != upscaled_shape: