| # SPDX-FileCopyrightText: Copyright 2020-2022 Arm Limited and/or its affiliates <open-source-office@arm.com> |
| # |
| # SPDX-License-Identifier: Apache-2.0 |
| # |
| # Licensed under the Apache License, Version 2.0 (the License); you may |
| # not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| # Description: |
| # Unit tests for tflite support_operators |
| import numpy as np |
| |
| from ethosu.vela.data_type import DataType |
| from ethosu.vela.operation import ActivationFunction |
| from ethosu.vela.operation import Op |
| from ethosu.vela.operation import Padding |
| from ethosu.vela.tensor import create_const_tensor |
| from ethosu.vela.tensor import QuantizationParameters |
| from ethosu.vela.tensor import Tensor |
| from ethosu.vela.test import testutil |
| from ethosu.vela.tflite_supported_operators import TFLiteSupportedOperators |
| |
| support = TFLiteSupportedOperators() |
| |
| |
| def test_constraint_tens_dtype(): |
| # Tensors can only be of type uint8, int8, int16 and int32 |
| op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.float32) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_tens_int32_ops(): |
| # For int32, only select op types are allowed: |
| op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], datatype=DataType.int32) |
| assert support.is_operator_supported(op) |
| op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_tens_dimension(): |
| # Tensors can only have values in the inclusive range of 1-65535 |
| op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 0], [1, 8, 8, 65536]) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_tens_quant_per_axis_not_supp(): |
| # Quantization scale cannot be array-valued for elemwise ops |
| qp = QuantizationParameters() |
| qp.zero_point = np.zeros((1, 3)) |
| qp.scale_f32 = np.ones((1, 3)) |
| op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm_quant=qp) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_tens_quant_per_axis_is_supp(): |
| op = testutil.create_op_with_quant_tensors( |
| Op.Conv2DBias, [1, 1, 1, 3], [1, 1, 1, 3], weights_shape=[1, 1, 1, 3], bias_shape=[1, 1, 1, 3] |
| ) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| assert support.is_operator_supported(op) |
| qp = QuantizationParameters() |
| qp.zero_point = np.zeros((1, 3)) |
| qp.scale_f32 = np.ones((1, 3)) |
| op.bias.quantization = qp |
| assert support.is_operator_supported(op) |
| |
| |
| def test_constraint_fc_output_2d_is_supp(): |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 8, 8, 4], [32, 32], weights_shape=[4, 8, 8, 4]) |
| assert support.is_operator_supported(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [1, 1024], [16, 64], weights_shape=[1, 1024]) |
| assert support.is_operator_supported(op) |
| |
| |
| def test_constraint_faf(): |
| # Fused activation functions, if set, must be a valid op type |
| op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.activation = ActivationFunction(Op.Conv2D) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_faf_ofm_dtype(): |
| # If fused activation function is present, OFM must be 8 or 16 bit |
| shp = [1, 8, 8, 8] |
| for dtype in [DataType.int8, DataType.uint8, DataType.int16, DataType.int32]: |
| op = testutil.create_elemwise_op(Op.Add, "op", shp, shp, shp, datatype=dtype) |
| op.activation = ActivationFunction(Op.Relu) |
| expected = dtype.size_in_bytes() <= 2 |
| assert support.is_operator_supported(op) == expected, f"Data type: {dtype}" |
| |
| |
| def test_constraint_conv_pass(): |
| # First test a simple conv passes |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1]) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| assert support.is_operator_supported(op) |
| |
| |
| def test_constraint_stride_range(): |
| # Stride width and height must lie within a certain range |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 0, "stride_h": 20} |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_dilated_height_range(): |
| # Dilated kernel height must lie within a certain range |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[65, 64, 1, 1]) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_dilated_product_range(): |
| # Dilated kernel width x height must lie within a certain range |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[64, 65, 1, 1]) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_weights_type(): |
| # Weight tensor must be 8-bit |
| op = testutil.create_op_with_quant_tensors( |
| Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1], datatype=DataType.int16 |
| ) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_weights_const(): |
| # Weight tensor cannot be non-const tensors |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| weights = Tensor([64, 64, 1, 1], DataType.uint8, "weights") |
| weights.quantization = testutil.default_quant_params() |
| op.add_input_tensor(weights) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_weights_limit(): |
| # Sum of weights has a limit |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1]) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| op.weights.quantization.zero_point = np.array([[[[(127 * 65536) + 1]]]]) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_bias_type(): |
| # Bias must have a certain datatype |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1]) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| bias = Tensor([1, 8, 8, 8], DataType.uint8, "bias") |
| op.add_input_tensor(bias) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_bias_40bit(): |
| # Bias must not exceed 40-bit |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1]) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| bias = Tensor([1, 1, 1, 1], DataType.int64, "bias") |
| bias.values = np.array([0x01FF_FFFF_FFFF]) |
| op.add_input_tensor(bias) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_batch_size(): |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [2, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1]) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_depth_multiplier(): |
| # Valid. Depth multiplier is 1 so no further constraints |
| op = testutil.create_op_with_quant_tensors( |
| Op.DepthwiseConv2DBias, [1, 1, 1, 1], [1, 1, 1, 2], weights_shape=[1, 1, 1, 1] |
| ) |
| op.attrs = {"stride_w": 1, "stride_h": 1, "depth_multiplier": 1} |
| assert support.is_operator_supported(op) |
| # Invalid. Depth multiplier doesnt equal ofm channel |
| op = testutil.create_op_with_quant_tensors( |
| Op.DepthwiseConv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1] |
| ) |
| op.attrs = {"stride_w": 1, "stride_h": 1, "depth_multiplier": 2} |
| assert not support.is_operator_supported(op) |
| # Valid. Depth multiplier is equal to ofm channel |
| op = testutil.create_op_with_quant_tensors( |
| Op.DepthwiseConv2DBias, [1, 1, 1, 1], [1, 1, 1, 2], weights_shape=[1, 1, 1, 1] |
| ) |
| op.attrs = {"stride_w": 1, "stride_h": 1, "depth_multiplier": 2} |
| assert support.is_operator_supported(op) |
| |
| |
| def test_constraint_tconv_stride(): |
| # Strides must be 2 |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 2, 2, 1], weights_shape=[1, 1, 1, 1]) |
| op.attrs = {"stride_w": 1, "stride_h": 1, "padding": Padding.SAME} |
| ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm") |
| ifm.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_tconv_same(): |
| # Valid |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 2, 2, 1], weights_shape=[1, 1, 1, 1]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.SAME} |
| ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm") |
| ifm.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm) |
| assert support.is_operator_supported(op) |
| # Invalid |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[1, 1, 1, 1]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.SAME} |
| ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm") |
| ifm.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_tconv_valid(): |
| # Valid |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[4, 4, 1, 1]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.VALID} |
| ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm") |
| ifm.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm) |
| assert support.is_operator_supported(op) |
| # Invalid |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[2, 2, 1, 1]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.VALID} |
| ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm") |
| ifm.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_filter_range(): |
| # Avg pool restrictions are dependent on padding: |
| # SAME padding restricts both W and H to max 8 |
| op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 20, "filter_height": 20, "padding": Padding.SAME} |
| assert not support.is_operator_supported(op) |
| # VALID padding limits are much larger |
| op.attrs["padding"] = Padding.VALID |
| assert support.is_operator_supported(op) |
| |
| |
| def test_constraint_filter_height_range_valid_pad(): |
| # Avg pool restrictions are dependent on padding: |
| op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 256, "padding": Padding.VALID} |
| assert support.is_operator_supported(op) |
| # VALID padding restricts to 256 in filter height |
| op.attrs["filter_height"] = 257 |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_filter_product_height_range_valid_pad(): |
| # Avg pool restrictions are dependent on padding: |
| op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 256, "filter_height": 256, "padding": Padding.VALID} |
| assert support.is_operator_supported(op) |
| # VALID padding restricts filter W x H to 256x256 |
| op.attrs["filter_width"] = 257 |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_filter_height_range(): |
| # Max pool restrictions arent dependent on padding |
| op = testutil.create_op_with_quant_tensors(Op.MaxPool, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 256, "padding": Padding.SAME} |
| assert support.is_operator_supported(op) |
| # Restricts to 256 in filter height |
| op.attrs["filter_height"] = 257 |
| assert not support.is_operator_supported(op) |
| # Doesnt matter if SAME or VALID |
| op.attrs["padding"] = Padding.VALID |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_filter_product_height_range(): |
| # Max pool restrictions arent dependent on padding |
| op = testutil.create_op_with_quant_tensors(Op.MaxPool, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 256, "filter_height": 256, "padding": Padding.SAME} |
| assert support.is_operator_supported(op) |
| # Restricts filter W x H to 256x256 |
| op.attrs["filter_width"] = 257 |
| assert not support.is_operator_supported(op) |
| # Doesnt matter if SAME or VALID |
| op.attrs["padding"] = Padding.VALID |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_resize(): |
| for resize_op in Op.op_set(Op.is_resize_op): |
| # IFM W and H == 1 |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 1, 1, 8], [1, 8, 8, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) |
| assert support.is_operator_supported(op) |
| |
| # IFM == OFM |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) |
| assert support.is_operator_supported(op) |
| |
| # IFM x2 == OFM ; align_corners = False |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 8, 8, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) |
| assert support.is_operator_supported(op) |
| |
| # IFM x4 == OFM ; align_corners = False |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 16, 16, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [16, 16], np.int32)) |
| assert support.is_operator_supported(op) |
| |
| # IFM x8 == OFM ; align_corners = False |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 32, 32, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [32, 32], np.int32)) |
| assert support.is_operator_supported(op) |
| |
| # IFM -1 x2 == OFM -1 ; align_corners = True |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 7, 7, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [7, 7], np.int32)) |
| op.attrs["align_corners"] = True |
| assert support.is_operator_supported(op) |
| |
| # IFM -1 x4 == OFM -1 ; align_corners = True |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 13, 13, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [13, 13], np.int32)) |
| op.attrs["align_corners"] = True |
| assert support.is_operator_supported(op) |
| |
| # IFM -1 x8 == OFM -1 ; align_corners = True |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 25, 25, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [25, 25], np.int32)) |
| op.attrs["align_corners"] = True |
| assert support.is_operator_supported(op) |
| |
| # Invalid case - upscale size |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 17, 17, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [17, 17], np.int32)) |
| assert not support.is_operator_supported(op) |
| |
| # Invalid case - upscale size with align corners |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 15, 15, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [15, 15], np.int32)) |
| op.attrs["align_corners"] = True |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_resize_size(): |
| for resize_op in Op.op_set(Op.is_resize_op): |
| # Invalid case - size != ofm size |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 8, 8, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [7, 7], np.int32)) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_resize_attrs(): |
| for resize_op in Op.op_set(Op.is_resize_op): |
| # Invalid case - both align corners and half-pixel centers |
| op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 8, 8, 8]) |
| op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) |
| op.attrs["align_corners"] = True |
| op.attrs["half_pixel_centers"] = True |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_concat_pass(): |
| # A working concat |
| op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8]) |
| ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2") |
| ifm2.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm2) |
| op.attrs["axis"] = 3 |
| assert support.is_operator_supported(op) |
| |
| |
| def create_pad_op( |
| in_shape, |
| out_shape, |
| padding, |
| in_dtype=DataType.int8, |
| out_dtype=DataType.int8, |
| pad_dtype=DataType.int32, |
| ): |
| qp = testutil.default_quant_params() |
| in0 = Tensor(in_shape, in_dtype, "in") |
| in0.quantization = qp |
| pad_tensor = create_const_tensor(name="pad", shape=list(np.shape(padding)), values=padding, dtype=pad_dtype) |
| out = Tensor(out_shape, out_dtype, "out") |
| out.quantization = qp.clone() |
| op = testutil.create_op(Op.Pad, [in0, pad_tensor], out) |
| return op |
| |
| |
| def test_constraint_padded_dimensions(): |
| # Incorrect padding dimensions, can only pad width and height |
| op = create_pad_op( |
| in_shape=[1, 1, 1, 1], |
| out_shape=[1, 3, 3, 1], |
| padding=[[1, 1], [1, 1], [1, 1], [0, 0]], |
| ) |
| assert not support.is_operator_supported(op) |
| op = create_pad_op( |
| in_shape=[1, 1, 1, 1], |
| out_shape=[1, 3, 3, 1], |
| padding=[[1, 1], [1, 1], [0, 0]], |
| ) |
| assert support.is_operator_supported(op) |
| op = create_pad_op( |
| in_shape=[1, 1, 1, 1], |
| out_shape=[1, 3, 3, 1], |
| padding=[[1, 1], [1, 1], [0, 1]], |
| ) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_pad_shape(): |
| # PAD operator must be of shape (3,2) or (4,2) |
| op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [0, 0]]) |
| assert support.is_operator_supported(op) |
| op = create_pad_op( |
| in_shape=[1, 1, 1, 1], |
| out_shape=[1, 3, 3, 1], |
| padding=[[0, 0], [1, 1], [1, 1], [0, 0], [0, 0]], |
| ) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_pad_none(): |
| op = create_pad_op( |
| in_shape=[1, 1, 1, 1], |
| out_shape=[1, 3, 3, 1], |
| padding=[], |
| ) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_pad_dtype(): |
| # PAD operator dtype should be int32 or int64 |
| op = create_pad_op( |
| in_shape=[1, 1, 1, 1], |
| out_shape=[1, 3, 3, 1], |
| padding=[[0, 0], [1, 1], [1, 1], [0, 0], [0, 0]], |
| pad_dtype=DataType.int16, |
| ) |
| assert not support.is_operator_supported(op) |
| |
| |
| def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets): |
| qp = testutil.default_quant_params() |
| in0 = Tensor(in_shape, DataType.uint8, "in") |
| in0.quantization = qp |
| in1 = create_const_tensor("begin", [len(start_offsets)], DataType.uint8, start_offsets, quantization=qp) |
| in2 = create_const_tensor("end", [len(end_offsets)], DataType.uint8, end_offsets, quantization=qp) |
| in3 = create_const_tensor("strides", [len(end_offsets)], DataType.uint8, len(end_offsets) * [1], quantization=qp) |
| out = Tensor(out_shape, DataType.uint8, "out") |
| out.quantization = qp |
| attrs = {"ellipsis_mask": 0, "new_axis_mask": 0, "shrink_axis_mask": 0, "begin_mask": 0, "end_mask": 0} |
| return testutil.create_op(Op.StridedSlice, [in0, in1, in2, in3], out, attrs=attrs) |
| |
| |
| def create_strided_slice(): |
| # Creates a valid strided slice operator with some valid inputs/outputs |
| op = create_strided_slice_op([1, 10, 10, 10], [1, 5, 5, 10], [127, 2, 2, 0], [0, 7, -3, 0]) |
| op.attrs["begin_mask"] = 1 |
| op.attrs["end_mask"] = 9 |
| assert support.is_operator_supported(op) |
| return op |
| |
| |
| def test_constraint_stridedslice_stride_values(): |
| # Unsupported strides |
| op = create_strided_slice() |
| op.inputs[3].values = [1, 1, 2, 1] |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_inputs_int32(): |
| # both inputs must be type int32 |
| op = testutil.create_elemwise_op(Op.SHL, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8]) |
| assert not support.is_operator_supported(op) |
| op = testutil.create_elemwise_op(Op.SHL, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32) |
| assert support.is_operator_supported(op) |
| op.ifm2.dtype = DataType.int16 |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_output_int32(): |
| # output must be type int32 |
| op = testutil.create_elemwise_op(Op.SHL, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32) |
| assert support.is_operator_supported(op) |
| op.ofm.dtype = DataType.int16 |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_matching_quantization_parameters(): |
| qp = QuantizationParameters() |
| qp.scale_f32 = np.float32(1.5) |
| qp.zero_point = 128 |
| # valid - all matching (uses default quant params) |
| op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8]) |
| assert support.is_operator_supported(op) |
| # invalid - ifm mismatch ofm |
| op.ifm.quantization = qp |
| assert not support.is_operator_supported(op) |
| # invalid - ifm2 mismatch ofm |
| op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.ifm2.quantization = qp |
| assert not support.is_operator_supported(op) |
| # invalid - both ifm and ifm2 mismatch ofm |
| op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.ifm.quantization = qp |
| op.ifm2.quantization = qp |
| assert not support.is_operator_supported(op) |
| # valid - all matching |
| op.ofm.quantization = qp |
| assert support.is_operator_supported(op) |
| op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], None, [1, 8, 8, 8]) |
| assert support.is_operator_supported(op) |
| |
| |
| def test_constraint_elemwise_batch_size(): |
| # BINARY CASE |
| # Batch can be >1 if dims is <=3D |
| op = testutil.create_elemwise_op(Op.Add, "op", [2, 2, 2], [2, 2, 2], [2, 2, 2]) |
| assert support.is_operator_supported(op) |
| # For dims >3D, batch must be 1 |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 2, 2, 2], [1, 2, 2, 2], [1, 2, 2, 2]) |
| assert support.is_operator_supported(op) |
| # invalid case |
| op = testutil.create_elemwise_op(Op.Add, "op", [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2]) |
| assert not support.is_operator_supported(op) |
| |
| # UNARY CASE |
| # Batch can be >1 if dims is <=3D |
| op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2, 2], None, [2, 2, 2], datatype=DataType.int32) |
| assert support.is_operator_supported(op) |
| # For dims >3D, batch must be 1 |
| op = testutil.create_elemwise_op(Op.CLZ, "op", [1, 2, 2, 2], None, [1, 2, 2, 2], datatype=DataType.int32) |
| assert support.is_operator_supported(op) |
| # invalid case |
| op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2, 2, 2], None, [2, 2, 2, 2], datatype=DataType.int32) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_constraint_broadcast_shapes(): |
| # BINARY CASE |
| # Only allow broadcast to 1 dim, for 1 rank index |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 1, 4], [1, 2, 4], [1, 2, 4]) |
| assert support.is_operator_supported(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 2, 4], [1, 1, 4], [1, 2, 4]) |
| assert support.is_operator_supported(op) |
| # Only allow broadcast to 1 dim, for 3 rank indexes |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 1, 1, 1], [1, 4, 8, 16], [1, 4, 8, 16]) |
| assert support.is_operator_supported(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 4, 8, 16], [1, 1, 1, 1], [1, 4, 8, 16]) |
| assert support.is_operator_supported(op) |
| # One broadcast dim not 1 |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 2, 4], [1, 4, 4], [1, 4, 4]) |
| assert not support.is_operator_supported(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 4, 4], [1, 2, 4], [1, 4, 4]) |
| assert not support.is_operator_supported(op) |
| # OFM shape dim largest ifm/ifm2 shape dim |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 4], [4, 4], [1, 4]) |
| assert not support.is_operator_supported(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 4], [4, 4], [1, 4]) |
| assert not support.is_operator_supported(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 4, 1, 16], [1, 1, 4, 1], [1, 4, 1, 16]) |
| assert not support.is_operator_supported(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 1, 4, 1], [1, 4, 1, 16], [1, 4, 1, 16]) |
| assert not support.is_operator_supported(op) |
| |
| |
| def create_mean(input_shape, output_shape, axis, datatype, attrs): |
| ifm = Tensor(input_shape, datatype, "in") |
| ifm.quantization = testutil.default_quant_params() |
| ofm = Tensor(output_shape, datatype, "out") |
| ofm.quantization = testutil.default_quant_params() |
| if type(axis) is list: |
| indices = create_const_tensor("indices", [len(axis)], DataType.int32, axis, np.uint8) |
| elif type(axis) is int: |
| indices = create_const_tensor("indices", [], DataType.int32, axis, np.uint8) |
| op = testutil.create_op(Op.Mean, [ifm, indices], ofm, attrs) |
| return op |
| |
| |
| def test_mean_hw_product(): |
| op = create_mean([1, 64, 64, 16], [1, 16], [1, 2], DataType.uint8, {}) |
| assert support.is_operator_supported(op) |
| op = create_mean([1, 65, 64, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True}) |
| assert not support.is_operator_supported(op) |
| |
| |
| def test_mean_hw_product_avgpool(): |
| op = create_mean([1, 200, 200, 16], [1, 16], [1, 2], DataType.uint8, {"keep_dims": False}) |
| assert support.is_operator_supported(op) |
| op = create_mean([1, 200, 200, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True}) |
| assert not support.is_operator_supported(op) |