MLBEDSW-3148: Refactor Operation
- op.type is now an enum instead of a string
- Removed unused operator codes
- Refactored some attributes like npu_block_type, fused_activation_function
- Refactored operator index calculation
- Refactored a number of operator sets
Change-Id: I641f65ee375794b7aec42abc0664251ae37d78e8
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 4f435dc..1966a82 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -32,6 +32,7 @@
from .numeric_util import round_away_zero
from .operation import create_avgpool_nop
from .operation import NpuBlockType
+from .operation import Op
from .operation import Operation
from .softmax import SoftMax
from .tensor import create_const_tensor
@@ -39,33 +40,9 @@
from .tensor import QuantizationParameters
from .tensor import Tensor
-passthrough_nodes = set(("Identity",))
+passthrough_nodes = set((Op.Identity,))
-conv_op = set(("Conv2D", "QuantizedConv2D", "Conv2DBackpropInputSwitchedBias", "Conv2DBiasAct"))
-fc_op = set(
- (
- "MatMul",
- "QuantizedMatMul",
- "BlockLSTM",
- "RnnAct",
- "UnidirectionalSequenceRnnAct",
- "BidirectionalSequenceRnnAct",
- "LstmAct",
- "UnidirectionalSequenceLstmAct",
- "BidirectionalSequenceLstmAct",
- "FullyConnectedAct",
- )
-)
-depthwise_op = set(("DepthwiseConv2dNative", "DepthwiseConv2dBiasAct",))
-pool_op = set(
- ("AvgPool", "MaxPool", "QuantizedAvgPool", "QuantizedMaxPool", "AvgPoolAct", "MaxPoolAct", "ResizeBilinear")
-)
-reduce_sum_ops = set(("ReduceSum",))
-binary_elementwise_op = set(("AddAct", "MulAct", "SubAct", "Maximum", "Minimum"))
-elementwise_op = set(("LeakyRelu", "Abs", "CLZ", "SHL", "SHR")) | binary_elementwise_op
-relu_ops = set(("Relu", "Relu6", "ReluN1To1"))
-activation_ops = set(("Sigmoid", "Tanh")) | relu_ops
-memory_only_ops = set(("Reshape",))
+memory_only_ops = set((Op.Reshape,))
def remove_passthrough_tensor(tens, arch, nng):
@@ -76,7 +53,7 @@
def rewrite_concat(tens, arch, nng):
- if len(tens.ops) == 1 and tens.ops[0].is_concat_op():
+ if len(tens.ops) == 1 and tens.ops[0].type.is_concat_op():
concat_op = tens.ops[0]
if tens != concat_op.outputs[0]:
return tens # don't attempt to rewrite the min/max outputs of QuantizedConcat
@@ -90,7 +67,7 @@
tens.ops = []
offset = 0
for idx, inp in enumerate(inputs):
- new_op = Operation("ConcatSliceWrite", concat_op.name + str(idx))
+ new_op = Operation(Op.ConcatSliceWrite, concat_op.name + str(idx))
new_op.inputs = [inp]
new_op.outputs = [tens]
new_op.attrs["concat_axis"] = axis
@@ -116,7 +93,7 @@
def rewrite_split(tens, arch, nng):
- if len(tens.ops) == 1 and tens.ops[0].is_split_op():
+ if len(tens.ops) == 1 and tens.ops[0].type.is_split_op():
split_op = tens.ops[0]
# Not supported so leave it and run on CPU
@@ -126,7 +103,7 @@
inp, outputs, axis, offset_start, offset_end = split_op.get_split_inputs_axis()
tens.ops = []
- new_op = Operation("SplitSliceRead", split_op.name)
+ new_op = Operation(Op.SplitSliceRead, split_op.name)
new_op.inputs = [inp]
# For Split the offset cannot be extracted from the tensor so it has to
@@ -206,10 +183,10 @@
def fixup_conv2d_backprop(op, arch, nng):
- if op.type == "Conv2DBackpropInput":
+ if op.type == Op.Conv2DBackpropInput:
# flip the inputs
op.inputs[0], op.inputs[2] = op.inputs[2], op.inputs[0]
- op.type = "Conv2DBackpropInputSwitchedBias"
+ op.type = Op.Conv2DBackpropInputSwitchedBias
# Update strides
op.attrs.update({"stride_w": 1, "stride_h": 1, "strides": (1, 1, 1, 1)})
@@ -219,9 +196,8 @@
# Convert the op to an elementwise add
def convert_resizebilinear_1x1_to_add(op):
- op.type = "AddAct"
+ op.type = Op.Add
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
@@ -296,11 +272,11 @@
def fixup_resizebilinear(op, arch, nng):
- if op.type == "ResizeBilinear" and op.run_on_npu:
+ if op.type == Op.ResizeBilinear and op.run_on_npu:
if op.inputs[0].shape == op.outputs[0].shape:
# Bypass nop resizebilinear
op.inputs = op.inputs[:1]
- op.type = "Identity"
+ op.type = Op.Identity
elif op.inputs[0].shape[1] == 1 and op.inputs[0].shape[2] == 1:
convert_resizebilinear_1x1_to_add(op)
else:
@@ -310,16 +286,16 @@
def convert_nop_split_to_identity(op, arch, nng):
- if op.type == "Split" and op.attrs.get("num_splits") == 1:
+ if op.type == Op.Split and op.attrs.get("num_splits") == 1:
# the list comprehension should return a list with a single tensor
# if it shouldn't, remove_passthrough_tensor will fail appropriately
op.inputs = [i for i in op.inputs if i.shape == op.outputs[0].shape]
- op.type = "Identity"
+ op.type = Op.Identity
return op
def fixup_fully_connected_input(op, arch, nng):
- if op.type == "FullyConnectedAct":
+ if op.type == Op.FullyConnected:
inp = op.inputs[0]
weights = op.inputs[1]
@@ -337,7 +313,7 @@
def convert_batched_fc_to_conv(op, arch, nng):
- if op.type == "FullyConnectedAct":
+ if op.type == Op.FullyConnected:
ifm = op.inputs[0]
ofm = op.outputs[0]
# Check if the FC is 2D and first dimension indicates batching
@@ -348,14 +324,11 @@
# Convert to convolution
op.name += "_conv"
- op.type = "Conv2DBiasAct"
- faf = op.attrs.get("fused_activation_function", None)
+ op.type = Op.Conv2DBias
op.attrs = {
"dilation": (1, 1, 1, 1),
"dilation_h_factor": 1,
"dilation_w_factor": 1,
- "fused_activation_function": faf,
- "npu_block_type": NpuBlockType.ConvolutionMxN,
"padding": b"SAME",
"stride_h": 1,
"stride_w": 1,
@@ -364,7 +337,7 @@
prev_op = ifm.ops[0]
desired_shape = [1, h, w, ifm.shape[-1]]
- if len(ifm.consumer_list) == 1 and prev_op is not None and prev_op.type == "Reshape":
+ if len(ifm.consumer_list) == 1 and prev_op is not None and prev_op.type == Op.Reshape:
# There is a preceding Reshape
# Compare input of prev_op and input of op, to see if prev_op can be removed
ifm_prev_op = prev_op.inputs[0]
@@ -391,7 +364,7 @@
if (
len(ofm.consumer_list) == 1
and ofm.consumer_list[0] is not None
- and ofm.consumer_list[0].type == "Reshape"
+ and ofm.consumer_list[0].type == Op.Reshape
):
# There is a subsequent Reshape
# Compare desired shape and output of consumer op, to see if consumer op can be removed
@@ -408,7 +381,7 @@
def fixup_pack_input(op, arch, nng):
- if op.type == "Pack":
+ if op.type == Op.Pack:
# Pack is also referred to as Stack
# Requires the rewrite_concat function to be called on the op afterwards
axis = int(op.attrs["axis"])
@@ -421,24 +394,22 @@
reshape_out = inp.clone("_reshaped")
reshape_out.set_all_shapes(desired_shape)
- reshape_op = Operation("Reshape", "{}{}_reshape".format(op.name, idx))
+ reshape_op = Operation(Op.Reshape, "{}{}_reshape".format(op.name, idx))
reshape_op.attrs["new_shape"] = desired_shape
reshape_op.inputs = [inp, new_shape_tens]
reshape_op.set_output_tensor(reshape_out)
op.inputs[idx] = reshape_out
- op.type = "PackReshaped"
+ op.type = Op.PackReshaped
return op
def unfuse_activation_function(op, arch, nng):
- unfuse_ops = ("ConcatTFLite",)
- if op.type in unfuse_ops and op.run_on_npu and op.attrs.get("fused_activation_function", None) is not None:
- act = op.attrs["fused_activation_function"]
- del op.attrs["fused_activation_function"]
- act_op = Operation(act, op.name + act)
+ if op.type == Op.ConcatTFLite and op.run_on_npu and op.activation is not None:
+ act_op = Operation(op.activation, op.name + op.activation.name)
+ op.activation = None
out_tens = op.outputs[0]
intermediate_tens = out_tens.clone("_act_intermediate")
act_op.set_output_tensor(out_tens)
@@ -450,12 +421,12 @@
def fixup_unpack_output(tens, arch, nng):
op = tens.ops[0]
- if op.type in set(("Unpack", "StridedSlice")):
+ if op.type in set((Op.Unpack, Op.StridedSlice)):
# Unpack is also referred to as Unstack
# Requires the rewrite_split function to be called on the op afterwards
reshape_input_shape = tens.shape
- if op.type == "StridedSlice":
+ if op.type == Op.StridedSlice:
new_axis_mask = op.attrs["new_axis_mask"]
shrink_axis_mask = op.attrs["shrink_axis_mask"]
ellipsis_mask = op.attrs["ellipsis_mask"]
@@ -494,7 +465,7 @@
op.attrs["new_axis_mask"] = 0
else:
axis = int(op.attrs["axis"])
- op.type = "UnpackReshaped"
+ op.type = Op.UnpackReshaped
reshape_input_shape = tens.shape[:axis] + [1] + tens.shape[axis:]
# Construct 1 shape tensor to be used by all inserted reshape ops
@@ -505,7 +476,7 @@
reshape_in.set_all_shapes(reshape_input_shape)
reshape_in.ops = [op]
- reshape_op = Operation("Reshape", "{}{}_reshape".format(op.name, idx))
+ reshape_op = Operation(Op.Reshape, "{}{}_reshape".format(op.name, idx))
reshape_op.attrs["new_shape"] = reshape_input_shape
reshape_op.inputs = [reshape_in, new_shape_tens]
reshape_op.set_output_tensor(out_tens)
@@ -518,19 +489,16 @@
def add_padding_fields(op, arch, nng):
if op.run_on_npu:
if "padding" in op.attrs:
- if op.type in conv_op | depthwise_op:
+ if op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op():
kernel_size = op.inputs[1].shape[:2]
input_shape = op.inputs[0].shape
- elif op.type in pool_op | reduce_sum_ops:
+ elif op.type.is_pool_op() or op.type.npu_block_type == NpuBlockType.ReduceSum:
kernel_size = op.attrs["ksize"][1:3]
input_shape = op.inputs[0].shape
- elif op.type == "ExtractImagePatches":
- kernel_size = op.attrs["ksizes"][1:3]
- input_shape = op.inputs[0].shape
else:
raise UnsupportedFeatureError("Unknown operation that uses padding: {}".format(op.type))
- if op.type == "Conv2DBackpropInputSwitchedBias":
+ if op.type == Op.Conv2DBackpropInputSwitchedBias:
upscaling_factor = op.outputs[0].shape[1] // input_shape[1]
padding, skirt = calc_upscaled_padding_and_skirt(
op.attrs["padding"], kernel_size, op.attrs["strides"], input_shape, upscaling_factor
@@ -564,38 +532,19 @@
return None
-def mark_npu_block_type(op, arch, nng):
- npu_block_type = NpuBlockType.Default
- if op.type in conv_op:
- npu_block_type = NpuBlockType.ConvolutionMxN
- elif op.type in fc_op:
- npu_block_type = NpuBlockType.VectorProduct
- elif op.type in depthwise_op:
- npu_block_type = NpuBlockType.ConvolutionDepthWise
- elif op.type in pool_op:
- npu_block_type = NpuBlockType.Pooling
- elif op.type in elementwise_op:
- npu_block_type = NpuBlockType.ElementWise
- elif op.type in reduce_sum_ops:
- npu_block_type = NpuBlockType.ReduceSum
-
- op.attrs["npu_block_type"] = npu_block_type
- return op
-
-
def convert_depthwise_to_conv(op, arch, nng):
# Depthwise is equivalent to a single conv2d if the ifm depth is 1 and
# the ofm depth equals the depth multipler.
# If those conditions are true, then we can perform a simple
# switch of the operator type (and weight order)
- if (op.type in depthwise_op) and (op.attrs["depth_multiplier"] != 1):
+ if op.type == Op.DepthwiseConv2DBias and (op.attrs["depth_multiplier"] != 1):
ifm_tensor = op.inputs[0]
weight_tensor = op.inputs[1]
ofm_tensor = op.outputs[0]
if (ifm_tensor.shape[3] == 1) and (ofm_tensor.shape[3] == op.attrs["depth_multiplier"]):
# Change op type to Conv2d
- op.type = op.type.replace("DepthwiseConv2d", "Conv2D")
+ op.type = Op.Conv2DBias
del op.attrs["channel_multiplier"]
del op.attrs["depth_multiplier"]
@@ -611,7 +560,7 @@
def reorder_depthwise_weights(op, arch, nng):
- if op.type in depthwise_op:
+ if op.type.is_depthwise_conv2d_op():
weight_tensor = op.inputs[1]
weight_tensor.quant_values = np.transpose(weight_tensor.quant_values, (0, 1, 3, 2))
weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape))
@@ -625,18 +574,15 @@
# By representing certain convs as fully connected layers, Vela can better determine wether or not to use
# caching/double buffering for the weights.
# (Weights dont need to be reloaded for convs when IFM H and W are 1)
- if op.type == "Conv2DBiasAct":
+ if op.type == Op.Conv2DBias:
_, h, w, _ = op.inputs[0].shape
kh, kw, _, _ = op.inputs[1].shape
if h == 1 and w == 1 and kh == 1 and kw == 1:
# Overwrite this op as a Fully Connected Op
op.name += "_fc"
- op.type = "FullyConnectedAct"
- faf = op.attrs.get("fused_activation_function", None)
+ op.type = Op.FullyConnected
op.attrs = {
- "fused_activation_function": faf,
"weights_format": 0,
- "npu_block_type": NpuBlockType.VectorProduct,
}
# Reshape Weights to be 2D. HWIO becomes just IO (as H and W are 1, they can just be dropped)
weight_tensor = op.inputs[1]
@@ -652,7 +598,7 @@
# Add a reshape after the new OFM to convert it back to the original 4D shape
reshape_name = op.name + "_reshape"
new_shape_tens = create_const_tensor(reshape_name + "_shape", [1], DataType.int32, orig_ofm_tensor.shape)
- reshape_op = Operation("Reshape", reshape_name)
+ reshape_op = Operation(Op.Reshape, reshape_name)
reshape_op.attrs["new_shape"] = orig_ofm_tensor.shape
reshape_op.inputs = [fc_ofm_tensor, new_shape_tens]
reshape_op.set_output_tensor(orig_ofm_tensor)
@@ -662,7 +608,7 @@
def fixup_relus_with_differing_ifm_ofm_scaling(op, arch, nng):
- if op.run_on_npu and op.type in relu_ops:
+ if op.run_on_npu and op.type.is_relu_op():
ifm = op.inputs[0]
ofm = op.outputs[0]
# Relu with differing IFM and OFM scaling cannot be fused with another primary op
@@ -671,7 +617,7 @@
# Override this op with its own primary op (avgpool)
relu_fused_op = create_avgpool_nop(op.name + "_avgpool")
# And fuse the original activation function to it
- relu_fused_op.attrs["fused_activation_function"] = op.type
+ relu_fused_op.activation = op.type
# Tidy up and assign the ifm and ofm to the new op
ifm.consumer_list.remove(op)
@@ -691,7 +637,7 @@
# Reorder activation op if it's after the memory only operations
def fixup_act_reorder(op, arch, nng):
- if op.type in activation_ops:
+ if op.type.is_relu_op() or op in set((Op.Sigmoid, Op.Tanh)):
prep_op = get_prepend_op(op)
if prep_op is not None:
act_op = op.clone("_reordered")
@@ -711,12 +657,12 @@
prep_op.outputs[0].quantization = act_op_out.quantization.clone()
# Mark the op so that it will be removed as passthrough later on
- op.type = "Identity"
+ op.type = Op.Identity
return op
def fixup_elementwise_with_scalars(op, arch, nng):
- if op.type in binary_elementwise_op:
+ if op.type.is_binary_elementwise_op():
ifm_tensor, ifm2_tensor, _, _ = op.get_ifm_ifm2_weights_ofm()
if ifm2_tensor.shape != [] and ifm_tensor.shape != []:
diff = len(ifm_tensor.shape) - len(ifm2_tensor.shape)
@@ -745,7 +691,7 @@
def convert_softmax(op, arch, nng):
- if op.type == "Softmax" and op.run_on_npu:
+ if op.type == Op.Softmax and op.run_on_npu:
softmax = SoftMax(op)
op = softmax.get_graph()
return op
@@ -761,9 +707,9 @@
Max
"""
- if op.type == "Maximum":
+ if op.type == Op.Maximum:
# finds the Mul input(s) to the Max
- muls = [i for i in op.inputs if i.ops[0].type == "MulAct"]
+ muls = [i for i in op.inputs if i.ops[0].type == Op.Mul]
if len(muls) == 1:
mul = muls[0].ops[0]
elif len(muls) == 2:
@@ -777,10 +723,10 @@
mul_ofm = mul.outputs[0]
if len(mul_ofm.consumers()) != 1:
return op
- # make sure the Mul doesn't have a faf
- if mul.attrs["fused_activation_function"]:
+ # make sure the Mul doesn't have a fused activation function
+ if mul.activation:
return op
- ifm, _, _, ofm = op.get_ifm_weights_biases_ofm()
+ ifm, ofm = op.get_ifm_ofm()
if ifm.dtype not in (DataType.uint8, DataType.int8) or ifm.dtype != ofm.dtype:
return op
if not ifm.is_scaling_equal(ofm) or not ifm.is_scaling_equal(mul_ofm):
@@ -798,7 +744,7 @@
return op
const = const_tens.ops[0]
# check that it is a constant
- if const.type != "Const":
+ if const.type != Op.Const:
return op
# Remove the Mul from the shared input's consumers
shared_in.consumer_list.remove(mul)
@@ -807,7 +753,7 @@
val = const.outputs[0].values
if val >= 0:
- new_op = "LeakyRelu"
+ new_op = Op.LeakyRelu
op.attrs["alpha"] = val
# to produce bit exact results, the alpha is not enough;
# save additional scaling info in attr "alpha_scale", to be used as input
@@ -819,13 +765,13 @@
alpha_scale, alpha_shift = scaling.elementwise_mul_scale(mul_ifm_scale, mul_ifm2_scale, mul_ofm_scale)
op.attrs["alpha_scaling"] = (alpha_scalar, alpha_scale, alpha_shift)
elif val == -1:
- new_op = "Abs"
+ new_op = Op.Abs
else:
return op
- op.type = op.type.replace("Maximum", new_op)
- op.name = op.name.replace("Maximum", new_op)
- op.outputs[0].name = op.outputs[0].name.replace("Maximum", new_op)
+ op.type = new_op
+ op.name = op.name.replace("Maximum", new_op.name)
+ op.outputs[0].name = op.outputs[0].name.replace("Maximum", new_op.name)
op.inputs = [shared_in]
return op
@@ -833,10 +779,10 @@
def convert_lrelu_to_mul_max(op, arch):
# Converts LeakyRelu to Max(alpha * IFM, identity * IFM)
# (the opposite of convert_mul_max_to_abs_or_lrelu)
- ifm, _, _, ofm = op.get_ifm_weights_biases_ofm()
+ ifm, ofm = op.get_ifm_ofm()
# Add multiplication with alpha
- mul_alpha = Operation("MulAct", op.name + "_mul_alpha")
+ mul_alpha = Operation(Op.Mul, op.name + "_mul_alpha")
mul_alpha.add_input_tensor(ifm)
# Create const tensor containing alpha as scalar
alpha = op.attrs["alpha"]
@@ -855,7 +801,7 @@
fm_id = ifm
else:
# Add multiplication with identity
- mul_identity = Operation("MulAct", op.name + "_mul_identity")
+ mul_identity = Operation(Op.Mul, op.name + "_mul_identity")
mul_identity.add_input_tensor(ifm)
# Create const tensor containing identity as scalar
quantization = ifm.quantization.clone()
@@ -871,7 +817,7 @@
mul_identity.set_output_tensor(fm_id)
# Convert LeakyRelu to Max, add the results of the multiplication(s) as inputs
- op.type = "Maximum"
+ op.type = Op.Maximum
op.name = op.name.replace("LeakyRelu", "Maximum")
op.inputs = []
ifm.consumer_list.remove(op)
@@ -884,9 +830,8 @@
# Rewrite the operation by Add with scalar 0 + LUT activation
ifm = op.inputs[0]
assert ifm.dtype.size_in_bytes() == 1
- op.type = "AddAct"
+ op.type = Op.Add
op.name = op.name + "_add"
- op.attrs.update({"npu_block_type": NpuBlockType.ElementWise})
# Mark as no-op to enable potential fusing optimizations
op.attrs["is_nop"] = True
# Create an input tensor containing scalar zero
@@ -898,7 +843,7 @@
# The LUT must be applied without any preceding rescaling (the LUT itself performs the rescale),
# so even if the OFM has a different scale than the IFM, the generated OFM scale instructions
# should be the same as the IFM
- op.attrs["forced_output_quantization"] = ifm.quantization
+ op.forced_output_quantization = ifm.quantization
lut_tensor = lut.create_lut_tensor(op.name + "_lut", lut_values, DataType.int8)
op.set_activation_lut(lut_tensor)
return op
@@ -907,7 +852,7 @@
def convert_to_lut8(op, fn):
# Converts op to a no-op + int8/uint8 LUT which is generated with the given function.
# fn is a function(real) -> real
- ifm, _, _, ofm = op.get_ifm_weights_biases_ofm()
+ ifm, ofm = op.get_ifm_ofm()
if ifm.dtype not in (DataType.uint8, DataType.int8) or ifm.dtype != ofm.dtype:
return op
# Generate the LUT
@@ -929,7 +874,7 @@
def convert_lrelu_to_lut(op, arch):
- ifm, _, _, ofm = op.get_ifm_weights_biases_ofm()
+ ifm, ofm = op.get_ifm_ofm()
# Generate the LUT
alpha = op.attrs["alpha"]
ifm_scale = np.double(ifm.quantization.scale_f32)
@@ -960,9 +905,9 @@
def convert_lrelu(op, arch, nng):
# Converts LeakyRelu to a LUT based solution if possible, otherwise a mul + max
- if op.type != "LeakyRelu":
+ if op.type != Op.LeakyRelu:
return op
- ifm, _, _, ofm = op.get_ifm_weights_biases_ofm()
+ ifm, ofm = op.get_ifm_ofm()
if ifm.dtype in (DataType.uint8, DataType.int8) and ifm.dtype == ofm.dtype:
# use LUT for int8/uint8
return convert_lrelu_to_lut(op, arch)
@@ -974,20 +919,20 @@
def convert_tanh_sigmoid_to_lut(op, arch, nng):
# Converts int8/uint8 Sigmoid and Tanh to a LUT based solution
- if op.type == "Sigmoid":
+ if op.type == Op.Sigmoid:
return convert_to_lut8(op, clamp_sigmoid)
- elif op.type == "Tanh":
+ elif op.type == Op.Tanh:
return convert_to_lut8(op, math.tanh)
return op
def remove_unwanted_reshapes(op, arch, nng):
# Try to remove reshapes enclosing ElementWise operator with only one non-constant input
- if not op.run_on_npu or op.attrs["npu_block_type"] != NpuBlockType.ElementWise:
+ if not op.run_on_npu or not op.type.is_elementwise_op():
return op
# Check if the ElementWise operator only have one non-constant input
- non_const_tens = [x for x in op.inputs if x.ops[0].type != "Const"]
+ non_const_tens = [x for x in op.inputs if x.ops[0].type != Op.Const]
if len(non_const_tens) != 1:
return op
ifm = non_const_tens[0]
@@ -997,12 +942,12 @@
prev_op = ifm.ops[0]
if (
len(ifm.consumer_list) == 1
- and prev_op.type == "Reshape"
+ and prev_op.type == Op.Reshape
and len(ofm.consumer_list) == 1
- and ofm.consumer_list[0].type == "Reshape"
+ and ofm.consumer_list[0].type == Op.Reshape
):
# Operation is enclosed by reshapes, check if they can be removed
- prev_op_ifm, _, _, prev_op_ofm = prev_op.get_ifm_weights_biases_ofm()
+ prev_op_ifm, prev_op_ofm = prev_op.get_ifm_ofm()
cons_op = ofm.consumer_list[0]
cons_op_ifm = ofm
cons_op_ofm = cons_op.outputs[0]
@@ -1018,19 +963,18 @@
def fuse_activation_function_with_prev(op, arch, nng):
# if op is a no-op: attempts to move the activation function to the preceding op
- if not op.attrs.get("is_nop", False) or op.attrs.get("fused_activation_function", None) is None:
+ if not op.attrs.get("is_nop", False) or op.activation is None:
return op
- ifm, _, _, ofm = op.get_ifm_weights_biases_ofm()
+ ifm, ofm = op.get_ifm_ofm()
# finds the input(s) to the operation
prev_op = ifm.ops[0]
# Note: the below checks on prev_op require that a first optimize pass on the full graph has been performed
fuse = (
prev_op.run_on_npu
- and "npu_block_type" in prev_op.attrs
- and prev_op.attrs["npu_block_type"] != NpuBlockType.Default
+ and prev_op.type.npu_block_type != NpuBlockType.Default
and len(ifm.ops) == 1
and len(prev_op.outputs[0].consumers()) == 1
- and prev_op.attrs.get("fused_activation_function", None) is None
+ and prev_op.activation is None
)
if op.activation_lut is not None and arch.shram_reserved_unused_banks == 0:
# TODO: if SHRAM LUT space is shared with SHRAM ACC (32, 64 MAC),
@@ -1039,9 +983,8 @@
if not fuse:
return op
# Move the fused activation function + corresponding info to prev_op
- for attr in ("fused_activation_function", "forced_output_quantization"):
- if attr in op.attrs:
- prev_op.attrs[attr] = op.attrs[attr]
+ prev_op.activation = op.activation
+ prev_op.forced_output_quantization = op.forced_output_quantization
if op.activation_lut is not None:
prev_op.set_activation_lut(op.activation_lut)
# Bypass op
@@ -1050,7 +993,7 @@
def add_attrs_to_resizebilinear(op, arch, nng):
- if op.type == "ResizeBilinear" and op.run_on_npu:
+ if op.type == Op.ResizeBilinear and op.run_on_npu:
input_tensor = op.inputs[0]
upscaled_shape = [input_tensor.shape[1] * 2, input_tensor.shape[2] * 2]
out_shape = op.outputs[0].shape[1:3]
@@ -1070,7 +1013,7 @@
def fixup_bias_tensors(op, arch, nng):
- if op.needs_bias() and not op.inputs[-1]:
+ if op.type.needs_bias() and op.bias is None:
# Op has no bias, add bias tensor filled with zeros
nr_biases = op.inputs[1].shape[-1]
bias_values = [0] * nr_biases
@@ -1091,8 +1034,6 @@
nng.print_graph()
op_rewrite_list = [
- # mark block type and check if the operations are supported
- mark_npu_block_type,
set_tensor_equivalence,
supported_operator_check,
# then do any rewrites of supported operators
@@ -1106,7 +1047,6 @@
fixup_conv2d_backprop,
fixup_relus_with_differing_ifm_ofm_scaling,
fixup_act_reorder,
- mark_npu_block_type,
fixup_elementwise_with_scalars,
reorder_depthwise_weights,
fixup_resizebilinear,