MLBEDSW-2813: Handle non-const weights and check shapes
- Added check for non-constant weights in supported operators
- Added check ifm & ifm2 shapes
- Handle None tensors for CPU operators
- Handle missing attributes for Cast operator
Signed-off-by: Andreas Nevalainen <andreas.nevalainen@arm.com>
Change-Id: I2f16d3d44d0c6da5237550b39273cdb9cc3c7607
diff --git a/ethosu/vela/mark_tensors.py b/ethosu/vela/mark_tensors.py
index 03ab83f..208b5b8 100644
--- a/ethosu/vela/mark_tensors.py
+++ b/ethosu/vela/mark_tensors.py
@@ -253,7 +253,6 @@
def mark_tensor_purpose(nng, arch, verbose_tensor_purpose=False):
def mark_tensor_helper(tens, purpose):
-
if tens.purpose == TensorPurpose.Unknown or tens.purpose == purpose:
tens.purpose = purpose
elif tens.purpose != TensorPurpose.LUT:
@@ -284,6 +283,8 @@
)
for idx, tens in enumerate(op.inputs):
+ if tens is None:
+ continue
purpose = input_purpose(op, idx) if tens.purpose == TensorPurpose.Unknown else tens.purpose
mark_tensor_helper(tens, purpose)
diff --git a/ethosu/vela/pass_packing.py b/ethosu/vela/pass_packing.py
index a1b03fe..f49f981 100644
--- a/ethosu/vela/pass_packing.py
+++ b/ethosu/vela/pass_packing.py
@@ -318,6 +318,8 @@
print("Warning:", curr_op.type, "operation is unknown or unsupported, placing on CPU")
for inp in reversed(curr_op.inputs):
+ if inp is None:
+ continue
can_pack = True
if len(inp.ops) == 1:
next_op = inp.ops[0]
@@ -390,6 +392,8 @@
# Check primary_op first
if primary_op is not None:
for inp in primary_op.inputs:
+ if inp is None:
+ continue
if len(inp.ops) == 1 and inp.ops[0].type == "DMA" and inp.purpose == TensorPurpose.FeatureMap:
src_op = inp.ops[0]
if src_op in input_ops_list:
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index b6551cf..b0afa2c 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -227,6 +227,12 @@
# check batch size
if ifm_tensor.shape[0] != 1:
return False
+
+ # check non const weights
+ if weight_tensor.values is None:
+ print("Warning:", op.type, "has non-const weights, placing on CPU")
+ return False
+
return True
def check_depthwise_convolution_restrictions(self, op):
@@ -317,6 +323,11 @@
if not self.check_bias_restrictions(bias_tensor):
return False
+ # check non const weights
+ if weight_tensor.values is None:
+ print("Warning:", op.type, "has non-const weights, placing on CPU")
+ return False
+
return True
def check_element_wise_restrictions(self, op):
@@ -362,6 +373,10 @@
if op.type == "LeakyRelu" and op.attrs["alpha"] < 0:
return False
+ # check if ifm or ifm2 has ofm shape
+ if ifm_tensor.shape != ofm_tensor.shape and ifm2_tensor.shape != ofm_tensor.shape:
+ return False
+
return True
def check_memory_only_restrictions(self, op):
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py
index a2f744d..7458b90 100644
--- a/ethosu/vela/tflite_reader.py
+++ b/ethosu/vela/tflite_reader.py
@@ -152,7 +152,8 @@
activation_function_to_split_out = None
if op_type.startswith("DepthwiseConv2d") or op_type.startswith("Conv2D"):
- inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 3, 0))
+ if inputs[1].values is not None:
+ inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 3, 0))
if len(inputs) < 3 or (len(inputs) < 4 and "Backprop" in op_type):
# No Bias tensor
inputs.append(None)
@@ -160,7 +161,8 @@
inputs[-1] = clone_and_reshape_tensor(inputs[-1], (0,))
if op_type.startswith("FullyConnected"):
- inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0))
+ if inputs[1].values is not None:
+ inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0))
if len(inputs) < 3:
# No Bias tensor
inputs.append(None)
@@ -174,6 +176,13 @@
# Reshape should have an attrib "new_shape" but if it is missing, add it based on the output shape
op.attrs["new_shape"] = outputs[0].shape
+ if op_type == "Cast":
+ # Cast op should have "in/out_data_type" attribs add if missing
+ if "in_data_type" not in op.attrs:
+ op.attrs["in_data_type"] = inputs[0].dtype
+ if "out_data_type" not in op.attrs:
+ op.attrs["out_data_type"] = outputs[0].dtype
+
if "stride_w" in op.attrs:
op.attrs["strides"] = (1, op.attrs["stride_h"], op.attrs["stride_w"], 1)
if "filter_width" in op.attrs:
diff --git a/ethosu/vela/tflite_writer.py b/ethosu/vela/tflite_writer.py
index cb208d7..68af487 100644
--- a/ethosu/vela/tflite_writer.py
+++ b/ethosu/vela/tflite_writer.py
@@ -90,9 +90,13 @@
if op.type not in self.ops_to_ignore:
all_ops.append(op)
if op.type.startswith("Conv2D") or op.type.startswith("DepthwiseConv2d"):
- self.tensors_to_reshape[op.inputs[1]] = (3, 0, 1, 2)
+ # If values are None op has non-constant weights
+ if op.inputs[1].values is not None:
+ self.tensors_to_reshape[op.inputs[1]] = (3, 0, 1, 2)
if op.type.startswith("FullyConnected"):
- self.tensors_to_reshape[op.inputs[1]] = (1, 0)
+ # If values are None op has non-constant weights
+ if op.inputs[1].values is not None:
+ self.tensors_to_reshape[op.inputs[1]] = (1, 0)
self.operator_codes = list(sorted(set(op.type for op in all_ops)))
self.operator_code_map = {}
@@ -314,7 +318,8 @@
# e.g. due to an empty graph containing no ops
for op in all_ops + placeholder_ops:
for tens in op.inputs + op.outputs:
- tensor_set.add(tens)
+ if tens is not None:
+ tensor_set.add(tens)
all_tensors = [tens for nm, idx, tens in sorted((tens.name, idx, tens) for idx, tens in enumerate(tensor_set))]