torch-mlir/e2e_testing/torchscript/constant_alloc.py

# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.

import torch

from torch_mlir_e2e_test.torchscript.framework import TestUtils
from torch_mlir_e2e_test.torchscript.registry import register_test_case
from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export

# ==============================================================================

class ZerosModuleInt2D(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.zeros(3, 4, dtype=torch.int64)

@register_test_case(module_factory=lambda: ZerosModuleInt2D())
def ZerosModuleInt2D_basic(module, tu: TestUtils):
    module.forward()


class ZerosModuleInt3D(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.zeros(3, 4, 5, dtype=torch.int64)

@register_test_case(module_factory=lambda: ZerosModuleInt3D())
def ZerosModuleInt3D_basic(module, tu: TestUtils):
    module.forward()


class ZerosModuleFloat2D(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.zeros(3, 4, dtype=torch.float32)

@register_test_case(module_factory=lambda: ZerosModuleFloat2D())
def ZerosModuleFloat2D_basic(module, tu: TestUtils):
    module.forward()


class ZerosModuleFloat3D(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.zeros(3, 4, 5, dtype=torch.float32)

@register_test_case(module_factory=lambda: ZerosModuleFloat3D())
def ZerosModuleFloat3D_basic(module, tu: TestUtils):
    module.forward()


class ZerosModuleFalsePinMemory(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.zeros(3, 4, dtype=torch.float32, pin_memory=False)

@register_test_case(module_factory=lambda: ZerosModuleFalsePinMemory())
def ZerosModuleFalsePinMemory_basic(module, tu: TestUtils):
    module.forward()

# ==============================================================================

class OnesModuleInt(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.ones(3, 4, dtype=torch.int64)

@register_test_case(module_factory=lambda: OnesModuleInt())
def OnesModuleInt_basic(module, tu: TestUtils):
    module.forward()


class OnesModuleFloat(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.ones(3, 4, dtype=torch.float32)

@register_test_case(module_factory=lambda: OnesModuleFloat())
def OnesModuleFloat_basic(module, tu: TestUtils):
    module.forward()


class OnesModuleFalsePinMemory(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.ones(3, 4, dtype=torch.float32, pin_memory=False)

@register_test_case(module_factory=lambda: OnesModuleFalsePinMemory())
def OnesModuleFalsePinMemory_basic(module, tu: TestUtils):
    module.forward()

# ==============================================================================

class EmptyIntModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return 0 * torch.empty((3, 4), dtype=torch.int64)

@register_test_case(module_factory=lambda: EmptyIntModule())
def EmptyModule_int(module, tu: TestUtils):
    module.forward()


class EmptyFloatModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.pow(torch.empty((3, 4), dtype=torch.float32), 0)

@register_test_case(module_factory=lambda: EmptyFloatModule())
def EmptyModule_float(module, tu: TestUtils):
    module.forward()


class EmptyFalsePinMemoryModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
    ])
    def forward(self):
        return torch.pow(torch.empty((3, 4), dtype=torch.float32, 
                                     pin_memory=False), 0)

@register_test_case(module_factory=lambda: EmptyFalsePinMemoryModule())
def EmptyModule_falsePinMemory(module, tu: TestUtils):
    module.forward()

# ==============================================================================

class EmptyLikeIntModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1], torch.int64, True),
    ])
    def forward(self, a):
        return 0 * torch.empty_like(a, dtype=torch.int32)

@register_test_case(module_factory=lambda: EmptyLikeIntModule())
def EmptyLikeModule_int(module, tu: TestUtils):
    module.forward(torch.randint(10, (3, 5)))


class EmptyLikeFloatModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1], torch.float32, True),
    ])
    def forward(self, a):
        return torch.pow(torch.empty_like(a, dtype=torch.float32), 0)

@register_test_case(module_factory=lambda: EmptyLikeFloatModule())
def EmptyLikeModule_float(module, tu: TestUtils):
    module.forward(tu.rand(4, 5))


class EmptyLikeFalsePinMemoryModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1, -1], torch.float32, True),
    ])
    def forward(self, a):
        return torch.pow(torch.empty_like(a, dtype=torch.float64, 
                                     pin_memory=False), 0)

@register_test_case(module_factory=lambda: EmptyLikeFalsePinMemoryModule())
def EmptyLikeModule_falsePinMemory(module, tu: TestUtils):
    module.forward(tu.rand(2, 3, 4))

# ==============================================================================

class ZerosLikeIntModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1], torch.int64, True),
    ])
    def forward(self, a):
        return torch.zeros_like(a, dtype=torch.int32)

@register_test_case(module_factory=lambda: ZerosLikeIntModule())
def ZerosLikeModule_int(module, tu: TestUtils):
    module.forward(torch.randint(10, (3, 5)))


class ZerosLikeFloatModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1], torch.float32, True),
    ])
    def forward(self, a):
        return torch.zeros_like(a, dtype=torch.float32)

@register_test_case(module_factory=lambda: ZerosLikeFloatModule())
def ZerosLikeModule_float(module, tu: TestUtils):
    module.forward(tu.rand(4, 5))


class ZerosLikeFalsePinMemoryModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1, -1], torch.float32, True),
    ])
    def forward(self, a):
        return torch.zeros_like(a, dtype=torch.float64, pin_memory=False)

@register_test_case(module_factory=lambda: ZerosLikeFalsePinMemoryModule())
def ZerosLikeModule_falsePinMemory(module, tu: TestUtils):
    module.forward(tu.rand(2, 3, 4))

# ==============================================================================

class OnesLikeIntModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1], torch.int64, True),
    ])
    def forward(self, a):
        return torch.ones_like(a, dtype=torch.int32)

@register_test_case(module_factory=lambda: OnesLikeIntModule())
def OnesLikeModule_int(module, tu: TestUtils):
    module.forward(torch.randint(10, (3, 5)))


class OnesLikeFloatModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1], torch.float32, True),
    ])
    def forward(self, a):
        return torch.ones_like(a, dtype=torch.float32)

@register_test_case(module_factory=lambda: OnesLikeFloatModule())
def OnesLikeModule_float(module, tu: TestUtils):
    module.forward(tu.rand(4, 5))


class OnesLikeFalsePinMemoryModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1, -1], torch.float32, True),
    ])
    def forward(self, a):
        return torch.ones_like(a, dtype=torch.float64, pin_memory=False)

@register_test_case(module_factory=lambda: OnesLikeFalsePinMemoryModule())
def OnesLikeModule_falsePinMemory(module, tu: TestUtils):
    module.forward(tu.rand(2, 3, 4))

# ==============================================================================

class Fill_TensorFloat64WithFloat32(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1, -1], torch.float32, True),
    ])
    def forward(self, tensor):
        return torch.ops.aten.fill_(tensor, 3.0)

@register_test_case(module_factory=lambda: Fill_TensorFloat64WithFloat32())
def Fill_TensorFloat64WithFloat32_basic(module, tu: TestUtils):
    module.forward(torch.randn(3, 2, 4))


class Fill_TensorFloat64WithFloat64(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1, -1], torch.float64, True),
    ])
    def forward(self, tensor):
        return torch.ops.aten.fill_(tensor, 3.0)

@register_test_case(module_factory=lambda: Fill_TensorFloat64WithFloat64())
def Fill_TensorFloat64WithFloat64_basic(module, tu: TestUtils):
    module.forward(torch.randn(3, 2, 4).to(torch.float64))


class Fill_TensorFloat64WithInt64(torch.nn.Module):
    def __init__(self):
        super().__init__()

    @export
    @annotate_args([
        None,
        ([-1, -1, -1], torch.float64, True),
    ])
    def forward(self, tensor):
        return torch.ops.aten.fill_(tensor, 3)

@register_test_case(module_factory=lambda: Fill_TensorFloat64WithInt64())
def Fill_TensorFloat64WithInt64_basic(module, tu: TestUtils):
    module.forward(torch.randn(3, 2, 4).to(torch.float64))