MLBEDSW-6313 Static optimisation for Shape OP
*Shape OP value is available at compile time hence
it can be optimised
*Disconnected shape OP at compile time from parent
tensor
*Transformed shape OP tensor into constant
Change-Id: I0a024269e2b592c6146dd72e62d7a41951fb727a
Signed-off-by: Ayaan Masood <Ayaan.Masood@arm.com>
diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md
index 3e76670..c258dfb 100644
--- a/SUPPORTED_OPS.md
+++ b/SUPPORTED_OPS.md
@@ -1,7 +1,7 @@
# Supported Ops
This file was automatically generated by Vela using the `--supported-ops-report` parameter.
-Vela version: `3.4.0`
+Vela version: `3.4.0rc3.dev1+g5e0ae55`
This file complies with
[**Gitiles Markdown syntax**](https://github.com/google/gitiles/blob/master/Documentation/markdown.md)
@@ -55,17 +55,18 @@
### TFLite Generic Constraints
-This is a list of constraints that all NPU operators must satisfy in order to be scheduled on the NPU.
+This is a list of constraints most NPU operators must satisfy in order to be scheduled on the NPU.
+(Operators excluded from certain constraints are shown in brackets [ ] )
-- Input(s) and Output tensors must not be dynamic
-- Input(s) and Output tensors must have a defined shape
-- Output tensors cannot be scalar
-- Scalar Input tensors are only valid for op type: ADD, EXPAND_DIMS, MAXIMUM, MEAN, MINIMUM, MUL, SPLIT, SPLIT_V, SUB
-- Input(s) and Output tensors must not be greater than 4D
-- Input(s), Output and Weight tensors must have quantization parameters
-- Input(s), Output and Weight tensors with quantization scales must be finite
-- Input and Output tensors must have quantization scales that fit within float32 precision
-- Constant tensors should not have NoneType-values
+- Input(s) and Output tensors must not be dynamic
+- Input(s) and Output tensors must have a defined shape
+- Output tensors cannot be scalar
+- Scalar Input tensors are only valid for op type: ADD, EXPAND_DIMS, MAXIMUM, MEAN, MINIMUM, MUL, SPLIT, SPLIT_V, SUB
+- Input(s) and Output tensors must not be greater than 4D
+- Input(s), Output and Weight tensors must have quantization parameters - [Shape]
+- Input(s), Output and Weight tensors with quantization scales must be finite - [Shape]
+- Input and Output tensors must have quantization scales that fit within float32 precision - [Shape]
+- Constant tensors should not have NoneType-values
- Tensors must be of type: int16, int32, int8, uint8
- Tensors which are int32 are only valid when op type is: ADD, MUL, SUB
- Tensor dimensions must be in the range [1, 65535]
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index 8e06b1e..6b6671b 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -261,7 +261,7 @@
SegmentSum = OperatorInfo()
Select = OperatorInfo()
SelectV2 = OperatorInfo()
- Shape = OperatorInfo()
+ Shape = OperatorInfo(indices=NNG_IFM_INDICES)
Sigmoid = OperatorInfo(indices=NNG_IFM_INDICES)
SignBit = OperatorInfo()
Sin = OperatorInfo()
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py
index 0639578..cf3985e 100644
--- a/ethosu/vela/tflite_graph_optimiser.py
+++ b/ethosu/vela/tflite_graph_optimiser.py
@@ -1391,12 +1391,43 @@
return op
+def convert_shape_op_to_constant_tensor(op: Operation, arch, nng):
+ """Static optimisation for SHAPE operator output value known at compile time"""
+
+ # Disconnect SHAPE operator from its parent and transform SHAPE OP into constant
+
+ if op.type == Op.Shape and op.run_on_npu:
+
+ ifm, ofm = op.get_ifm_ofm()
+
+ if len(ifm.shape) != ofm.shape[0]:
+ return op
+
+ # Remove reference of the current shape op from the parent tensor's consumer list
+ ifm.consumer_list = [consumer for consumer in ifm.consumer_list if consumer.op_index != op.op_index]
+
+ # Clear any references to parent node
+ op.inputs = []
+
+ # Convert this SHAPE op to const
+ op.type = Op.Const
+
+ # Add size calculation to shape output tensors
+ ofm.values = np.array(ifm.shape)
+
+ return op
+
+
def supported_operator_check(op, arch, nng):
op.run_on_npu = arch.tflite_supported_operators.is_operator_supported(op)
return op
def tflite_optimise_graph(nng, arch):
+
+ # Compile time optimisations
+ optimisation_list = [convert_shape_op_to_constant_tensor]
+
# Pre-processing step
pre_process_list = [
supported_operator_check,
@@ -1409,6 +1440,16 @@
sg,
arch,
[],
+ optimisation_list,
+ rewrite_unsupported=False,
+ )
+
+ for idx, sg in enumerate(nng.subgraphs):
+ nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
+ nng,
+ sg,
+ arch,
+ [],
pre_process_list,
rewrite_unsupported=False,
)
diff --git a/ethosu/vela/tflite_mapping.py b/ethosu/vela/tflite_mapping.py
index 7b487ae..bf155b9 100644
--- a/ethosu/vela/tflite_mapping.py
+++ b/ethosu/vela/tflite_mapping.py
@@ -767,7 +767,7 @@
BuiltinOperator.SHAPE: (
Op.Shape,
OptionsSerializer("ShapeOptions", (("out_type", datatype_deserialize, datatype_serialize),)),
- TFLITE_NO_INDICES,
+ TFLITE_IFM_INDICES,
),
BuiltinOperator.POW: (Op.Pow, OptionsSerializer("PowOptions"), TFLITE_NO_INDICES),
BuiltinOperator.ARG_MIN: (
diff --git a/ethosu/vela/tflite_model_semantic.py b/ethosu/vela/tflite_model_semantic.py
index e0541df..ee66d4c 100644
--- a/ethosu/vela/tflite_model_semantic.py
+++ b/ethosu/vela/tflite_model_semantic.py
@@ -186,7 +186,15 @@
if op.type in (Op.Placeholder, Op.SubgraphInput, Op.Const):
return True
- for constraint in self.generic_constraints + self.specific_constraints[op.type]:
+ # Generic constraints list filtered out to exclude certain constraints depending on op.type
+ filtered_generic_constraints = []
+
+ for constraint in self.generic_constraints:
+ # Check constraint not in dictionary otherwise return empty array
+ if constraint not in self.get_generic_constraint_exclude_list().get(op.type, []):
+ filtered_generic_constraints.append(constraint)
+
+ for constraint in filtered_generic_constraints + self.specific_constraints[op.type]:
valid, extra = constraint(op)
if not valid:
print(
@@ -200,6 +208,19 @@
return True
@staticmethod
+ def get_generic_constraint_exclude_list():
+
+ # Not all generic constraints can be applied to each operator
+ generic_constraints_exclude_list = {
+ Op.Shape: [
+ TFLiteSemantic.constraint_tens_quant_none_check,
+ TFLiteSemantic.constraint_tens_quant_scale,
+ TFLiteSemantic.constraint_quant_scale_inf,
+ ]
+ }
+ return generic_constraints_exclude_list
+
+ @staticmethod
def constraint_none_const_tensors(op):
"Constant tensors should not have NoneType-values"
valid = True
diff --git a/ethosu/vela/vela.py b/ethosu/vela/vela.py
index 90cff03..1de437b 100644
--- a/ethosu/vela/vela.py
+++ b/ethosu/vela/vela.py
@@ -250,13 +250,22 @@
"",
f"### {network_type.name} Generic Constraints",
"",
- "This is a list of constraints that all NPU operators must satisfy in order to be scheduled on the NPU.",
- "",
+ "This is a list of constraints most NPU operators must satisfy in order to be scheduled on the NPU.",
+ "(Operators excluded from certain constraints are shown in brackets [ ] )\n" "",
]
for constraint in semantic_checker.generic_constraints:
# Markdown needs two spaces at the end of a line to render it as a separate line
reason = constraint.__doc__.replace("\n", " \n")
- lines.append(f"- {reason}")
+
+ exclude_list = TFLiteSemantic.get_generic_constraint_exclude_list().items()
+ constraints_excluded_names = [
+ op.name for op, exclude_constraint in exclude_list if constraint in exclude_constraint
+ ]
+ excluded_constraint_text = ""
+ if constraints_excluded_names:
+ excluded_constraint_text = f"- [{', '.join(constraints_excluded_names)}]"
+
+ lines.append(f"- {reason} {excluded_constraint_text}")
for constraint in supported.generic_constraints:
# Markdown needs two spaces at the end of a line to render it as a separate line
reason = constraint.__doc__.replace("\n", " \n")