MLBEDSW-4892: Fix crash affecting biases without quantization.
Remove quant_values attribute from Tensor class.
It only needs a single values attribute, holding either
quantized or unquantized values as appropriate.
Change-Id: Ie96f80ac58061b6077e0f7048dc60209fdfbcafa
Signed-off-by: James Peet <james.peet@arm.com>
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py
index 3d9eeb8..9fdff8f 100644
--- a/ethosu/vela/tflite_graph_optimiser.py
+++ b/ethosu/vela/tflite_graph_optimiser.py
@@ -354,8 +354,7 @@
# Create an input tensor filled with zeros
shape = op.ofm_shapes[0].as_list()
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.values = np.zeros(shape, tens.dtype.as_numpy_type())
tens.quantization = QuantizationParameters(0.0, 255.0)
tens.quantization.scale_f32 = 1.0
tens.quantization.zero_point = 0
@@ -470,8 +469,8 @@
# Reshape Weights to be 4D. IO becomes HWIO
weight_tensor = op.inputs[1]
- weight_tensor.quant_values = np.expand_dims(np.expand_dims(weight_tensor.quant_values, axis=0), axis=0)
- weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape))
+ weight_tensor.values = np.expand_dims(np.expand_dims(weight_tensor.values, axis=0), axis=0)
+ weight_tensor.set_all_shapes(list(weight_tensor.values.shape))
n = op.ofm_shapes[0].batch
h, w = batching_split.get(n, (1, n))
@@ -608,8 +607,8 @@
del op.attrs["channel_multiplier"]
del op.attrs["depth_multiplier"]
- 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))
+ weight_tensor.values = np.transpose(weight_tensor.values, (0, 1, 3, 2))
+ weight_tensor.set_all_shapes(list(weight_tensor.values.shape))
else:
raise UnsupportedFeatureError(
f"Unsupported 'DEPTHWISE_CONV_2D' with depth_multiplier = {op.attrs['depth_multiplier']},",
@@ -622,8 +621,8 @@
def reorder_depthwise_weights(op, arch, nng):
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))
+ weight_tensor.values = np.transpose(weight_tensor.values, (0, 1, 3, 2))
+ weight_tensor.set_all_shapes(list(weight_tensor.values.shape))
weight_tensor.weight_transpose_depthwise = True
return op
@@ -654,14 +653,14 @@
for i in range(weight_shape[0]):
padded_array[i] = np.vstack(
[
- weight_tensor.quant_values[i],
+ weight_tensor.values[i],
np.full((1, weight_shape[2], weight_shape[3]), weight_tensor.quantization.zero_point),
]
)
- weight_tensor.quant_values = padded_array
+ weight_tensor.values = padded_array
weight_shape[1] //= 2
weight_shape[2] *= 2
- weight_tensor.quant_values = np.reshape(weight_tensor.quant_values, weight_shape)
+ weight_tensor.values = np.reshape(weight_tensor.values, weight_shape)
weight_tensor.set_all_shapes(weight_shape)
# If multiple copies of the weights are used, we could avoid
# them having the same address by changing the value_id
@@ -692,8 +691,8 @@
}
# 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]
- weight_tensor.quant_values = weight_tensor.quant_values.squeeze(axis=(0, 1))
- weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape))
+ weight_tensor.values = weight_tensor.values.squeeze(axis=(0, 1))
+ weight_tensor.set_all_shapes(list(weight_tensor.values.shape))
DebugDatabase.add_optimised(op, op)
return op
@@ -729,11 +728,11 @@
ifm2_tensor.shape = full_shape(len(ifm_tensor.shape), ifm2_tensor.shape, 1)
elif diff < 0:
ifm_tensor.shape = full_shape(len(ifm2_tensor.shape), ifm_tensor.shape, 1)
- elif ifm_tensor.shape == [] and ifm_tensor.quant_values is None:
+ elif ifm_tensor.shape == [] and ifm_tensor.values is None:
# IFM is marked as a scalar, but is a result of an operation; change it to a shape of size 1
ifm_tensor.shape = len(ifm2_tensor.shape) * [1]
ifm_tensor.storage_shape = ifm_tensor.shape
- elif ifm2_tensor.shape == [] and ifm2_tensor.quant_values is None:
+ elif ifm2_tensor.shape == [] and ifm2_tensor.values is None:
# IFM2 is marked as a scalar, but is a result of an operation; change it to a shape of size 1
ifm2_tensor.shape = len(ifm_tensor.shape) * [1]
ifm2_tensor.storage_shape = ifm2_tensor.shape
@@ -811,7 +810,7 @@
# to produce bit exact results, the alpha is not enough;
# save additional scaling info in attr "alpha_scale", to be used as input
# to the LUT construction
- alpha_scalar = const_tens.quant_values - const_tens.quantization.zero_point
+ alpha_scalar = const_tens.values - const_tens.quantization.zero_point
mul_ifm_scale = np.double(ifm.quantization.scale_f32)
mul_ifm2_scale = np.double(const_tens.quantization.scale_f32)
mul_ofm_scale = np.double(mul_ofm.quantization.scale_f32)
@@ -912,7 +911,7 @@
alpha_tens = create_const_tensor(
op.name + "_alpha_scalar", [], ifm.dtype, [scalar], np.float32, quantization=quantization
)
- alpha_tens.quant_values = np.array([1])
+ alpha_tens.values = np.array([1])
mul_alpha.add_input_tensor(alpha_tens)
fm_alpha = ofm.clone(op.name + "_alpha", set_unique=True)
mul_alpha.set_output_tensor(fm_alpha)
@@ -1209,7 +1208,7 @@
purpose=TensorPurpose.Weights,
quantization=quantization,
)
- weight_tens.quant_values = weights
+ weight_tens.values = weights
op.type = Op.DepthwiseConv2DBias
op.inputs = []
op.add_input_tensor(ifm)
@@ -1331,7 +1330,6 @@
nr_biases = op.inputs[1].shape[-1]
bias_values = [0] * nr_biases
bias_tensor = create_const_tensor(op.name + "_bias", [nr_biases], DataType.int32, bias_values)
- bias_tensor.quant_values = bias_tensor.values
op.set_input_tensor(bias_tensor, op.type.info.indices.biases[0])
return op
@@ -1409,13 +1407,7 @@
quant = QuantizationParameters()
quant.zero_point = 0
bias_term_tens = create_const_tensor(
- op.name + "_bias",
- [1, 1, 1, 1],
- DataType.int16,
- [bias_term],
- np.int16,
- quantization=quant,
- quant_value_dtype=np.int16,
+ op.name + "_bias", [1, 1, 1, 1], DataType.int16, [bias_term], np.int16, quantization=quant,
)
add_op.add_input_tensor(bias_term_tens)
add_op.set_output_tensor(op.ofm)
@@ -1514,7 +1506,7 @@
),
1,
)
- op.weights.quant_values = np.reshape(op.inputs[1].quant_values, weight_shape)
+ op.weights.values = np.reshape(op.inputs[1].values, weight_shape)
# Add None bias tensor
op.inputs.append(None)