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-rw-r--r--SUPPORTED_OPS.md11
-rw-r--r--ethosu/vela/operation.py13
-rw-r--r--ethosu/vela/tflite_graph_optimiser.py8
-rw-r--r--ethosu/vela/tflite_model_semantic.py11
-rw-r--r--ethosu/vela/tflite_supported_operators.py10
5 files changed, 34 insertions, 19 deletions
diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md
index 83429b7a..d16d5f8e 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: `3.4.0rc3.dev1+g5e0ae55`
+Vela version: `3.4.1.dev3+g5c30971e`
This file complies with
[**Gitiles Markdown syntax**](https://github.com/google/gitiles/blob/master/Documentation/markdown.md)
@@ -42,6 +42,7 @@ Please check the supported operator list for your chosen runtime for further inf
| RELU_N1_TO_1 | [Generic](#tflite-generic-constraints) |
| RESHAPE | [Generic](#tflite-generic-constraints), [Specific](#tflite-reshape-constraints) |
| RESIZE_BILINEAR | [Generic](#tflite-generic-constraints), [Specific](#tflite-resize_bilinear-constraints) |
+| SHAPE | [Generic](#tflite-generic-constraints) |
| SLICE | [Generic](#tflite-generic-constraints) |
| SOFTMAX | [Generic](#tflite-generic-constraints), [Specific](#tflite-softmax-constraints) |
| SPLIT | [Generic](#tflite-generic-constraints) |
@@ -61,14 +62,14 @@ This is a list of constraints most NPU operators must satisfy in order to be sch
- Input(s) and Output tensors must not be dynamic - [Quantize]
- Input(s) and Output tensors must have a defined shape
- Output tensors cannot be scalar - [Quantize]
-- Scalar Input tensors are only valid for op type: ADD, EXPAND_DIMS, MAXIMUM, MEAN, MINIMUM, MUL, SPLIT, SPLIT_V, SUB - [Quantize]
+- Scalar Input tensors are only valid for op type: ADD, EXPAND_DIMS, MAXIMUM, MEAN, MINIMUM, MUL, QUANTIZE, 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]
+- 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
- Tensors must be of type: int16, int32, int8, uint8
-- Tensors which are int32 are only valid when op type is: ADD, MUL, SUB
+- Tensors which are int32 are only valid when op type is: ADD, MUL, SHAPE, SUB
- Tensor dimensions must be in the range [1, 65535]
- Per-axis quantization is only supported for the following op types: CONV_2D, DEPTHWISE_CONV_2D, TRANSPOSE_CONV
- The fused activation function (if present) must be one of type: LOGISTIC, RELU, RELU6, RELU_N1_TO_1, TANH
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index 5ed18621..db1c6f18 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -834,7 +834,16 @@ class Operation:
self.ifm_shapes = []
self.ofm_shapes = []
- ifm_tensor, ifm2_tensor, weight_tensor, ofm_tensor = self.get_ifm_ifm2_weights_ofm()
+ ifm_tensor, ifm2_tensor, ofm_tensor = self.get_ifm_ifm2_ofm()
+
+ if self.type == Op.Reshape:
+ # Set ofm shape
+ if len(self.inputs) > 1 and self.inputs[1].values is not None:
+ ofm_tensor.shape = self.inputs[1].values.flatten().tolist()
+ ofm_elements = ofm_tensor.elements()
+ # Stretch dimension
+ if ofm_elements < 0:
+ ofm_tensor.shape[ofm_tensor.shape.index(-1)] = int(ifm_tensor.elements() / abs(ofm_elements))
# set all shapes to op, as 4D
if self.type == Op.FullyConnected:
@@ -847,7 +856,7 @@ class Operation:
self.ofm_shapes.append(Shape4D([self.ofm.shape[0], 1, 1, self.ofm.shape[1]]))
else:
self.ofm_shapes.append(Shape4D(ofm_tensor.get_full_shape()))
- if self.type == Op.Softmax:
+ elif self.type == Op.Softmax:
self.ifm_shapes.append(Shape4D(ifm_tensor.get_full_shape()))
self.ofm_shapes.append(Shape4D(ofm_tensor.get_full_shape()))
elif self.type.is_split_op() or self.type.is_concat_op():
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py
index f2a8c803..b1a56605 100644
--- a/ethosu/vela/tflite_graph_optimiser.py
+++ b/ethosu/vela/tflite_graph_optimiser.py
@@ -1408,10 +1408,8 @@ def optimise_quantize(op: Operation, arch, nng):
if input_values.ndim == 0:
input_values = np.array([input_values])
- # requantized int8 to int8
- if (ifm.dtype == DataType.int8 and ofm.dtype == DataType.int8) or (
- ifm.dtype == DataType.int16 and ofm.dtype == DataType.int16
- ):
+ # requantized int8 to int8 or int16 to int16
+ if ifm.dtype == ofm.dtype == DataType.int8 or ifm.dtype == ofm.dtype == DataType.int16:
# scale needs to use double precision to match TFLite reference kernel
effective_scale = np.float64(ifm.quantization.scale_f32) / np.float64(ofm.quantization.scale_f32)
@@ -1495,7 +1493,7 @@ def supported_operator_check(op, arch, nng):
def tflite_optimise_graph(nng, arch):
- # Compile time optimisations
+ # Compile time static optimisations
optimisation_list = [optimise_quantize, convert_shape_op_to_constant_tensor]
for idx, sg in enumerate(nng.subgraphs):
diff --git a/ethosu/vela/tflite_model_semantic.py b/ethosu/vela/tflite_model_semantic.py
index 9408e0ce..16ca2797 100644
--- a/ethosu/vela/tflite_model_semantic.py
+++ b/ethosu/vela/tflite_model_semantic.py
@@ -76,7 +76,7 @@ class TFLiteSemantic:
)
binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops
- shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV, Op.Mean, Op.ExpandDims))
+ shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV, Op.Mean, Op.ExpandDims, Op.Quantize))
reshape_ops = set(
(
Op.Reshape,
@@ -214,13 +214,10 @@ class TFLiteSemantic:
generic_constraints_exclude_list = {
Op.Shape: [
TFLiteSemantic.constraint_tens_quant_none_check,
- TFLiteSemantic.constraint_tens_quant_scale,
- TFLiteSemantic.constraint_quant_scale_inf,
],
Op.Quantize: [
TFLiteSemantic.constraint_tens_no_dynamic,
TFLiteSemantic.constraint_tens_output_scalar,
- TFLiteSemantic.constraint_tens_input_scalar,
],
}
return generic_constraints_exclude_list
@@ -314,7 +311,11 @@ class TFLiteSemantic:
extra = []
tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
for tens in tensors:
- if (tens.quantization.scale_f32 is not None) and np.isinf(tens.quantization.scale_f32).any():
+ if (
+ tens.quantization
+ and tens.quantization.scale_f32 is not None
+ and np.isinf(tens.quantization.scale_f32).any()
+ ):
valid = False
extra.append(f"Tensor '{tens.name}' has quantization scale: {tens.quantization.scale_f32}")
return valid, ", ".join(extra)
diff --git a/ethosu/vela/tflite_supported_operators.py b/ethosu/vela/tflite_supported_operators.py
index 6328a4e5..25a34e82 100644
--- a/ethosu/vela/tflite_supported_operators.py
+++ b/ethosu/vela/tflite_supported_operators.py
@@ -39,7 +39,12 @@ def _optype_formatter(op_list):
class TFLiteSupportedOperators:
# Categorised lists of supported operators
- npu_pre_ops = set((Op.SplitSliceRead,))
+ npu_pre_ops = set(
+ (
+ Op.SplitSliceRead,
+ Op.Shape,
+ )
+ )
convolution_ops = set(
(
Op.Conv2DBias,
@@ -103,6 +108,7 @@ class TFLiteSupportedOperators:
(
Op.ReduceSum,
Op.CLZ,
+ Op.Shape,
)
)
| binary_elem_wise_add_mul_sub
@@ -363,7 +369,7 @@ class TFLiteSupportedOperators:
if op.type not in cls.per_axis_quant_ops:
tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
for tens in tensors:
- if tens.quantization.is_per_axis():
+ if tens.quantization and tens.quantization.is_per_axis():
valid = False
extra.append(tens.name)
return valid, "The following tensor(s) have per-axis quantization parameters: " + ", ".join(extra)
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-02 10:14:05 +0000