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Added support for an "add" operation implemented via ACL for fp32 and quantized uint8 data types
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| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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 | ||
| # | ||
| # http://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. | ||
| """Arm Compute Library integration reshape tests.""" | ||
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| import numpy as np | ||
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| import tvm | ||
| import tvm.testing | ||
| from tvm import relay | ||
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| from test_arm_compute_lib.infrastructure import ( | ||
| skip_runtime_test, | ||
| skip_codegen_test, | ||
| build_and_run, | ||
| verify, | ||
| verify_codegen, | ||
| ) | ||
| from test_arm_compute_lib.infrastructure import Device | ||
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| _qnn_params = { | ||
| "lhs_scale": relay.const(0.0156863, "float32"), | ||
| "lhs_zero_point": relay.const(127, "int32"), | ||
| "rhs_scale": relay.const(0.0117647, "float32"), | ||
| "rhs_zero_point": relay.const(85, "int32"), | ||
| "output_scale": relay.const(0.0235294, "float32"), | ||
| "output_zero_point": relay.const(128, "int32"), | ||
| } | ||
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| def _get_model(shape, dtype, var_names, op, op_params): | ||
| a = relay.var(next(var_names), shape=shape, dtype=dtype) | ||
| b = relay.var(next(var_names), shape=shape, dtype=dtype) | ||
| return op(a, b, **op_params) | ||
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| def _get_expected_codegen(shape, dtype, op_name, qnn_params): | ||
| input_a = {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}} | ||
| input_b = {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}} | ||
| input_qnn = [ | ||
| { | ||
| "op": "const", | ||
| "name": "", | ||
| "attrs": { | ||
| "shape": [[list(qnn_params[_].data.shape)]], | ||
| "dtype": [[qnn_params[_].data.dtype]], | ||
| }, | ||
| } | ||
| for _ in qnn_params | ||
| ] | ||
| inputs = [input_a, input_b, *input_qnn] | ||
| node = { | ||
| "op": "kernel", | ||
| "name": op_name, | ||
| "inputs": [[_, 0, 0] for _ in range(len(inputs))], | ||
| "attrs": { | ||
| "num_inputs": str(len(inputs)), | ||
| "num_outputs": "1", | ||
| "shape": [[list(shape)]], | ||
| "dtype": [[dtype]], | ||
| }, | ||
| } | ||
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| return [*inputs, node] | ||
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| def test_runtime_add(): | ||
| Device.load("test_config.json") | ||
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| if skip_runtime_test(): | ||
| return | ||
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| device = Device() | ||
| np.random.seed(0) | ||
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| for dtype, low, high, atol, rtol, op, op_params in [ | ||
| ("float32", -127, 128, 1e-7, 1e-7, relay.add, {}), | ||
| ("uint8", 0, 255, 0.0, 1.0, relay.qnn.op.add, _qnn_params), | ||
| ]: | ||
| shape = (2, 2) | ||
| for inputs in [ | ||
| { | ||
| "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)), | ||
| "b": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)), | ||
| } | ||
| ]: | ||
| outputs = [] | ||
| func = _get_model(shape, dtype, iter(inputs), op, op_params) | ||
| for acl in [True, False]: | ||
| outputs.append(build_and_run(func, inputs, 1, None, device, enable_acl=acl)[0]) | ||
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| config = { | ||
| "shape": shape, | ||
| "dtype": dtype, | ||
| "inputs": inputs, | ||
| "operation": op, | ||
| "op_params": op_params, | ||
| } | ||
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| # verify_saturation=False as the result of add_QASYMM8_QASYMM8_QASYMM8 | ||
| # is always saturated currently. | ||
| verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=False) | ||
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| def test_runtime_codegen_add(): | ||
| if skip_codegen_test(): | ||
| return | ||
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| inputs = {"a", "b"} | ||
| for dtype, op_name, op, qnn_params in [ | ||
| ("float32", "add", relay.add, {}), | ||
| ("uint8", "qnn.add", relay.qnn.op.add, _qnn_params), | ||
| ]: | ||
| for shape in [(1, 1), (2, 2, 2), (3, 3, 3, 3)]: | ||
| func = _get_model(shape, dtype, iter(inputs), op, qnn_params) | ||
| exp_codegen = _get_expected_codegen(shape, dtype, op_name, qnn_params) | ||
| verify_codegen(func, exp_codegen, 1) | ||
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| if __name__ == "__main__": | ||
| test_runtime_codegen_add() | ||
| test_runtime_add() |