executorch/backends/arm/test/ops/test_exp.py at 76e5af8433a9c1bf8a73ca479e2fe6d3db8a1bc2 · pytorch/executorch · GitHub

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# Copyright 2024-2026 Arm Limited and/or its affiliates.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.


from typing import Tuple

import torch

from executorch.backends.arm.test import common
from executorch.backends.arm.test.tester.test_pipeline import (
    EthosU55PipelineINT,
    EthosU85PipelineINT,
    TosaPipelineFP,
    TosaPipelineINT,
    VgfPipeline,
)

test_data_suite = {
    # (test_name, test_data)
    "zeros": lambda: torch.zeros(1, 10, 10, 10),
    "ones": lambda: torch.ones(10, 10, 10),
    "rand": lambda: torch.rand(10, 10) - 0.5,
    "randn_pos": lambda: torch.randn(1, 4, 4, 4) + 10,
    "randn_neg": lambda: torch.randn(10) - 10,
    "ramp": lambda: torch.arange(-16, 16, 0.2),
}

test_data_suite_bf16 = {
    "rand_bf16": lambda: torch.rand(6, 6, dtype=torch.bfloat16) - 0.5,
    "ramp_bf16": lambda: torch.arange(-8, 8, 0.5, dtype=torch.bfloat16),
}
test_data_suite_fp16 = {
    "rand_fp16": lambda: torch.rand(6, 6, dtype=torch.float16) - 0.5,
    "ramp_fp16": lambda: torch.arange(-8, 8, 0.5, dtype=torch.float16),
}

aten_op = "torch.ops.aten.exp.default"
input_t1 = Tuple[torch.Tensor]  # Input x


class Exp(torch.nn.Module):
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return torch.exp(x)


@common.parametrize(
    "test_data", test_data_suite | test_data_suite_bf16 | test_data_suite_fp16
)
def test_exp_tosa_FP(test_data: Tuple):
    pipeline = TosaPipelineFP[input_t1](
        Exp(),
        (test_data(),),
        aten_op,
        exir_op=[],
        tosa_extensions=["bf16"],
    )
    pipeline.run()


@common.parametrize("test_data", test_data_suite)
def test_exp_tosa_INT(test_data: Tuple):
    pipeline = TosaPipelineINT[input_t1](
        Exp(),
        (test_data(),),
        aten_op,
        exir_op=[],
    )
    pipeline.run()


@common.parametrize("test_data", test_data_suite)
@common.XfailIfNoCorstone300
def test_exp_u55_INT(test_data: Tuple):
    pipeline = EthosU55PipelineINT[input_t1](
        Exp(),
        (test_data(),),
        aten_op,
        exir_ops=[],
    )
    pipeline.run()


@common.parametrize("test_data", test_data_suite)
@common.XfailIfNoCorstone320
def test_exp_u85_INT(test_data: Tuple):
    pipeline = EthosU85PipelineINT[input_t1](
        Exp(),
        (test_data(),),
        aten_op,
        exir_ops=[],
    )
    pipeline.run()


@common.parametrize("test_data", test_data_suite | test_data_suite_fp16)
@common.SkipIfNoModelConverter
def test_exp_vgf_no_quant(test_data: Tuple):
    data = test_data()
    if data.dtype == torch.float16:
        atol = 2e-2
        rtol = 2e-2
    else:
        atol = 1e-3
        rtol = 1e-3
    pipeline = VgfPipeline[input_t1](
        Exp(),
        (data,),
        aten_op,
        exir_op=[],
        quantize=False,
        atol=atol,
        rtol=rtol,
    )
    pipeline.run()


@common.parametrize("test_data", test_data_suite)
@common.SkipIfNoModelConverter
def test_exp_vgf_quant(test_data: Tuple):
    pipeline = VgfPipeline[input_t1](
        Exp(),
        (test_data(),),
        aten_op,
        exir_op=[],
        quantize=True,
    )
    pipeline.run()


a16w8_exp_test_parameters = {
    "rank1_rand": lambda: torch.rand(10),
    "rank2_rand": lambda: torch.rand(8, 8) - 0.5,
    "rank3_rand": lambda: torch.rand(1, 4, 4) * 2 - 1,
}


@common.parametrize("test_data", a16w8_exp_test_parameters)
@common.XfailIfNoCorstone300
def test_exp_a16w8_u55_INT(test_data: Tuple):
    pipeline = EthosU55PipelineINT[input_t1](
        Exp(),
        (test_data(),),
        aten_op,
        exir_ops=[],
        symmetric_io_quantization=True,
        a16w8_quantization=True,
        qtol=128,
        epsilon=2**-16,
    )
    pipeline.run()


@common.parametrize("test_data", a16w8_exp_test_parameters)
@common.XfailIfNoCorstone320
def test_exp_a16w8_u85_INT(test_data: Tuple):
    pipeline = EthosU85PipelineINT[input_t1](
        Exp(),
        (test_data(),),
        aten_op,
        exir_ops=[],
        symmetric_io_quantization=True,
        a16w8_quantization=True,
        qtol=128,
        epsilon=2**-16,
    )
    pipeline.run()