MLBEDSW-2637 Utilise new tensor and operator funcs
add_input_tensor, set_output_tensor, create_const_tensor and
create_reshape_tensor have recently been added.
This replaces all found existing instances with these new helper
functions
Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: If33be8dbf237b2087b562b03cdeb51da1f99a786
diff --git a/ethosu/vela/extract_npu_subgraphs.py b/ethosu/vela/extract_npu_subgraphs.py
index 6747ec9..4adddc1 100644
--- a/ethosu/vela/extract_npu_subgraphs.py
+++ b/ethosu/vela/extract_npu_subgraphs.py
@@ -80,9 +80,8 @@
op_type = "Const"
op = Operation(op_type, orig_tens.name + "_input")
op.attrs["npu_block_type"] = NpuBlockType.Default
- op.outputs = [new_tens]
op.scheduled_pass = startup_init_ps
- new_tens.ops = [op]
+ op.set_output_tensor(new_tens)
startup_init_ps.ops.append(op)
startup_init_ps.outputs.append(new_tens)
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index a9d5cce..582924c 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -28,6 +28,8 @@
from .operation import NpuBlockType
from .operation import Operation
from .softmax import SoftMax
+from .tensor import create_const_tensor
+from .tensor import create_reshape_tensor
from .tensor import QuantizationParameters
from .tensor import Tensor
@@ -84,7 +86,6 @@
tens.ops = []
new_op = Operation("SplitSliceRead", split_op.name)
new_op.inputs = [inp]
- new_op.outputs = [tens]
# For Split the offset cannot be extracted from the tensor so it has to
# be calculated from the index of the output tensor
@@ -102,7 +103,7 @@
new_op.attrs["split_start"] = offset_start
new_op.attrs["split_end"] = offset_end
new_op.run_on_npu = True
- tens.ops.append(new_op)
+ new_op.set_output_tensor(tens)
return tens
@@ -168,14 +169,12 @@
if len(op.inputs) < 4:
# Add bias/scale tensor filled with zeros
- scale_op = Operation("Const", op.name + "_bias")
scale_tens = Tensor([weight_sets], DataType.int32, op.name + "_bias_tens")
scale_tens.values = [0] * weight_sets
scale_tens.quant_values = [0] * weight_sets
- scale_tens.ops = [scale_op]
- scale_op.outputs = [scale_tens]
- scale_tens.consumer_list = [op]
- op.inputs.append(scale_tens)
+ scale_op = Operation("Const", op.name + "_bias")
+ scale_op.set_output_tensor(scale_tens)
+ op.add_input_tensor(scale_tens)
# Update strides
op.attrs.update({"stride_w": 1, "stride_h": 1, "strides": (1, 1, 1, 1)})
@@ -199,8 +198,7 @@
tens.quantization.zero_point = 0
tens.consumer_list = [op]
tens_op = op.inputs[1].ops[0]
- tens_op.outputs = [tens]
- tens.ops = [tens_op]
+ tens_op.set_output_tensor(tens)
# Set the add inputs
op.inputs[1] = op.inputs[0]
op.inputs[0] = tens
@@ -233,22 +231,7 @@
desired_shape = [batch_size, n_in_elems]
if inp.shape != desired_shape:
# mismatch, insert a reshape to fix this.
- reshape_name = op.name + "_reshape"
- new_shape_tens = Tensor([1], DataType.int32, reshape_name + "_shape")
- new_shape_tens.values = np.array(desired_shape)
- new_shape_tens_const = Operation("Const", new_shape_tens.name + "_const")
- new_shape_tens.ops = [new_shape_tens_const]
- new_shape_tens_const.outputs = [new_shape_tens]
-
- reshape_op = Operation("Reshape", reshape_name)
- reshape_op.inputs = [inp, new_shape_tens]
- reshape_op.attrs["new_shape"] = desired_shape
- reshape_out = inp.clone("_reshaped")
- reshape_out.set_all_shapes(desired_shape)
- reshape_out.ops = [reshape_op]
- reshape_op.outputs = [reshape_out]
-
- op.inputs[0] = reshape_out
+ op.inputs[0] = create_reshape_tensor(inp, desired_shape)
return op
@@ -261,22 +244,16 @@
desired_shape = op.inputs[0].shape[:axis] + [1] + op.inputs[0].shape[axis:]
# Construct 1 shape tensor to be used by all inserted reshape ops
- new_shape_name = op.name + "_reshape_shape"
- new_shape_tens = Tensor([1], DataType.int32, new_shape_name)
- new_shape_tens.values = np.array(desired_shape)
- new_shape_tens_const = Operation("Const", new_shape_tens.name + "_const")
- new_shape_tens.ops = [new_shape_tens_const]
- new_shape_tens_const.outputs = [new_shape_tens]
+ new_shape_tens = create_const_tensor(op.name + "_reshape_shape", [1], DataType.int32, desired_shape)
for idx, inp in enumerate(op.inputs):
- reshape_name = op.name + str(idx) + "_reshape"
- reshape_op = Operation("Reshape", reshape_name)
- reshape_op.inputs = [inp, new_shape_tens]
- reshape_op.attrs["new_shape"] = desired_shape
reshape_out = inp.clone("_reshaped")
reshape_out.set_all_shapes(desired_shape)
- reshape_out.ops = [reshape_op]
- reshape_op.outputs = [reshape_out]
+
+ reshape_op = Operation("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
@@ -335,22 +312,17 @@
reshape_input_shape = tens.shape[:axis] + [1] + tens.shape[axis:]
# Construct 1 shape tensor to be used by all inserted reshape ops
- new_shape_name = op.name + "_reshape_shape"
- new_shape_tens = Tensor([1], DataType.int32, new_shape_name)
- new_shape_tens.values = np.array(tens.shape)
- new_shape_tens_const = Operation("Const", new_shape_tens.name + "_const")
- new_shape_tens.ops = [new_shape_tens_const]
- new_shape_tens_const.outputs = [new_shape_tens]
+ new_shape_tens = create_const_tensor(op.name + "_reshape_shape", [1], DataType.int32, tens.shape)
for idx, out_tens in enumerate(op.outputs):
- reshape_name = op.name + str(idx) + "_reshape"
- reshape_op = Operation("Reshape", reshape_name)
- reshape_op.outputs = [out_tens]
reshape_in = out_tens.clone("_reshaped")
reshape_in.set_all_shapes(reshape_input_shape)
reshape_in.ops = [op]
- out_tens.ops = [reshape_op]
+
+ reshape_op = Operation("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)
op.outputs[idx] = reshape_in
@@ -517,17 +489,12 @@
fc_ofm_tensor.set_all_shapes([1, fc_ofm_tensor.shape[-1]])
fc_ofm_tensor.ops = [op]
# Add a reshape after the new OFM to convert it back to the original 4D shape
- reshape_name = op.name + "_reshape_post"
- new_shape_tens = Tensor([1], DataType.int32, reshape_name + "_shape")
- new_shape_tens.values = np.array(orig_ofm_tensor.shape)
- new_shape_tens_const = Operation("Const", new_shape_tens.name + "_const")
- new_shape_tens.ops = [new_shape_tens_const]
- new_shape_tens_const.outputs = [new_shape_tens]
+ 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.inputs = [fc_ofm_tensor, new_shape_tens]
reshape_op.attrs["new_shape"] = orig_ofm_tensor.shape
- orig_ofm_tensor.ops = [reshape_op]
- reshape_op.outputs = [orig_ofm_tensor]
+ reshape_op.inputs = [fc_ofm_tensor, new_shape_tens]
+ reshape_op.set_output_tensor(orig_ofm_tensor)
# Replace this ops OFM to point to the 2D tensor
op.outputs[0] = fc_ofm_tensor
return op
@@ -542,8 +509,7 @@
act_op.inputs = [prep_op.inputs[0]]
act_op_out = act_op.inputs[0].clone("_acted")
act_op_out.quantization = op.outputs[0].quantization.clone()
- act_op_out.ops = [act_op]
- act_op.outputs = [act_op_out]
+ act_op.set_output_tensor(act_op_out)
prep_op.inputs[0] = act_op_out
prep_op.outputs[0].quantization = act_op_out.quantization.clone()
diff --git a/ethosu/vela/insert_dma.py b/ethosu/vela/insert_dma.py
index 76016f1..6c5c803 100644
--- a/ethosu/vela/insert_dma.py
+++ b/ethosu/vela/insert_dma.py
@@ -56,11 +56,10 @@
new_tens = tens.clone_into_fast_storage(arch)
dma_cmd = Operation("DMA", tens.ops[0].name + "_dma")
dma_cmd.inputs = [tens]
- dma_cmd.outputs = [new_tens]
+ dma_cmd.set_output_tensor(new_tens)
dma_cmd.attrs["source"] = tens.mem_area
dma_cmd.attrs["destination"] = new_tens.mem_area
dma_cmd.run_on_npu = True
- new_tens.ops = [dma_cmd]
if tens.purpose == TensorPurpose.LUT:
# TODO: Add support more than one LUT at a time
# Reserve last 2 blocks for LUT
diff --git a/ethosu/vela/npu_serialisation.py b/ethosu/vela/npu_serialisation.py
index 030503d..c6b0d87 100644
--- a/ethosu/vela/npu_serialisation.py
+++ b/ethosu/vela/npu_serialisation.py
@@ -149,8 +149,7 @@
def add_const_tens_to_startup_cascaded_pass(startup_cps, tens):
op = Operation("Const", tens.name + "_const")
- op.outputs = [tens]
- tens.ops = [op]
+ op.set_output_tensor(tens)
startup_cps.passes[0].ops.insert(0, op)
startup_cps.passes[0].outputs.insert(0, tens)
startup_cps.outputs.insert(0, tens)
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index adbbff5..0290e81 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -307,10 +307,9 @@
return input_tens, outputs, axis, offset_start, offset_end
def set_activation_lut(self, lut_tensor):
- lut_tensor.consumer_list.append(self)
self.attrs["fused_activation_function"] = "LUT"
self.activation_lut = lut_tensor
- self.inputs.append(lut_tensor)
+ self.add_input_tensor(lut_tensor)
def add_input_tensor(self, tens):
self.inputs.append(tens)
diff --git a/ethosu/vela/pass_packing.py b/ethosu/vela/pass_packing.py
index fab00e0..8e108db 100644
--- a/ethosu/vela/pass_packing.py
+++ b/ethosu/vela/pass_packing.py
@@ -456,8 +456,7 @@
avgpool_op.attrs["explicit_padding"] = [0, 0, 0, 0]
avgpool_out = inp.clone("_avgpooled")
avgpool_out.consumer_list.append(op)
- avgpool_out.ops = [avgpool_op]
- avgpool_op.outputs = [avgpool_out]
+ avgpool_op.set_output_tensor(avgpool_out)
op.inputs[0] = avgpool_out
ops_list.insert(0, avgpool_op)
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py
index bf3fe95..5e966d1 100644
--- a/ethosu/vela/tflite_reader.py
+++ b/ethosu/vela/tflite_reader.py
@@ -54,9 +54,7 @@
tens.quant_values = tens.quant_values.transpose(reorder)
op = Operation("Const", tens.name)
- op.outputs = [tens]
- tens.ops = [op]
-
+ op.set_output_tensor(tens)
return tens
@@ -81,14 +79,12 @@
TensorError(tens, "This subgraph input tensor has unexpected driving operators.")
op = Operation("Placeholder", tens.name)
- op.outputs = [tens]
- tens.ops = [op]
+ op.set_output_tensor(tens)
for tens in self.tensors:
if not tens.ops:
op = Operation("Const", tens.name)
- op.outputs = [tens]
- tens.ops = [op]
+ op.set_output_tensor(tens)
def get_tensors_from_indices_remove_duplicates(self, indices, warning_str):
tensors = []
@@ -190,8 +186,7 @@
act_op = Operation(activation_function_to_split_out, name + activation_function_to_split_out)
out_tens = op.outputs[0]
intermediate_tens = out_tens.clone("_act_intermediate")
- out_tens.ops = [act_op]
- act_op.outputs = [out_tens]
+ act_op.set_output_tensor(out_tens)
intermediate_tens.ops = [op]
op.outputs[0] = intermediate_tens
act_op.inputs = [intermediate_tens]