test_binary_folding.py

# Owner(s): ["module: inductor"]
import functools
import importlib
import itertools
import os
import sys
import unittest

import torch
from torch import nn
from torch._inductor import config as inductor_config
from torch.testing._internal.common_cuda import TEST_CUDNN

# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)

from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS, TEST_WITH_ASAN
from torch.testing._internal.inductor_utils import skipCUDAIf

if IS_WINDOWS and IS_CI:
    sys.stderr.write(
        "Windows CI does not have necessary dependencies for test_torchinductor yet\n"
    )
    if __name__ == "__main__":
        sys.exit(0)
    raise unittest.SkipTest("requires sympy/functorch/filelock")

from inductor.test_inductor_freezing import TestCase
from inductor.test_torchinductor import check_model, check_model_cuda, copy_tests

importlib.import_module("functorch")
importlib.import_module("filelock")

from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA

aten = torch.ops.aten


class BinaryFoldingTemplate(TestCase):
    @skipCUDAIf(TEST_CUDNN, "CUDNN has accuracy issues for this test")
    def test_conv_binary_folding(self):
        @torch.no_grad()
        def test_conv_fusion(use_bias, module, op, scalar, add_tensor, expect_success):
            class ConvOp(nn.Module):
                __constants__ = ["use_scalar"]

                def __init__(self, in_channels, out_channels, device, **kwargs):
                    super().__init__()
                    self.conv = module(
                        in_channels, out_channels, bias=use_bias, **kwargs
                    ).to(device)
                    self.conv2 = module(
                        in_channels, out_channels, bias=use_bias, **kwargs
                    ).to(device)
                    self.use_scalar = scalar
                    tensor_size = [1 for _ in range(self.conv.weight.ndim)]
                    tensor_size[1] = self.conv.weight.size(0)
                    self.tensor = (
                        add_tensor
                        if add_tensor is not None
                        else torch.rand(tensor_size).to(device)
                    )
                    self.op = op

                def forward(self, x):
                    x = self.conv(x)
                    if self.use_scalar:
                        return self.op(x, 2.0)
                    else:
                        return self.op(x, self.tensor)

            from torch._inductor.compile_fx import compile_fx, compile_fx_inner

            aten_binary = {
                torch.add: aten.add.Tensor,
                torch.sub: aten.sub.Tensor,
                torch.mul: aten.mul.Tensor,
                torch.div: aten.div.Tensor,
            }
            n_binary_ops = 0

            def my_inner_compile(gm, example_inputs, *args, **kwargs):
                out = compile_fx_inner(gm, example_inputs, *args, **kwargs)
                nonlocal n_binary_ops
                binarry_ops = [n for n in gm.graph.nodes if n.target == aten_binary[op]]
                n_binary_ops += len(binarry_ops)
                return out

            torch._dynamo.reset()
            mod_eager = ConvOp(3, 32, self.device, kernel_size=3, stride=2).eval()
            out_optimized = torch.compile(
                mod_eager,
                backend=functools.partial(compile_fx, inner_compile=my_inner_compile),
            )

            inps = [4, 3, 4]
            if module == nn.Conv2d:
                inps.append(inps[-1])
            if module == nn.Conv3d:
                inps.append(inps[-1])
                inps.append(inps[-1])

            torch.manual_seed(1234)
            inp = torch.rand(inps).to(self.device)
            out_eager = mod_eager(inp)
            out_optimized = out_optimized(inp)
            self.assertEqual(out_optimized, out_eager)
            if expect_success:
                self.assertTrue(n_binary_ops == 0)
            else:
                self.assertTrue(n_binary_ops == 1)

        conv_bias = [True, False]
        modules = [nn.Conv1d, nn.Conv2d, nn.Conv3d]
        use_scalar = [True, False]
        ops = [torch.add, torch.sub, torch.mul, torch.div]
        for use_bias, module, pytorch_op, scalar in itertools.product(
            conv_bias, modules, ops, use_scalar
        ):
            # TODO: support scalar case
            expect_success = not scalar
            test_conv_fusion(
                use_bias,
                module,
                pytorch_op,
                scalar,
                add_tensor=None,
                expect_success=expect_success,
            )

        for use_bias, pytorch_op in itertools.product(conv_bias, ops):
            # broadcasting add
            test_conv_fusion(
                use_bias,
                nn.Conv2d,
                pytorch_op,
                False,
                add_tensor=torch.rand(32, 1, 32).to(self.device),
                expect_success=False,
            )

            # broadcasting add
            test_conv_fusion(
                use_bias,
                nn.Conv2d,
                pytorch_op,
                False,
                add_tensor=torch.rand(1, 1).to(self.device),
                expect_success=True,
            )

            # add with different dtype
            test_conv_fusion(
                use_bias,
                nn.Conv2d,
                pytorch_op,
                False,
                add_tensor=torch.tensor([2]).to(torch.int).to(self.device),
                expect_success=False,
            )

    @inductor_config.patch({"freezing": True})
    def test_conv_bn_folding(self):
        @torch.no_grad()
        def test_conv_fusion(use_bias, module, expect_success):
            class ConvOp(nn.Module):
                def __init__(self, in_channels, out_channels, device, **kwargs):
                    super().__init__()
                    self.conv = module[0](
                        in_channels, out_channels, bias=use_bias, **kwargs
                    ).to(device)
                    self.bn = module[1](out_channels).to(device)

                def forward(self, x):
                    x = self.conv(x)
                    return self.bn(x)

            from torch._inductor.compile_fx import compile_fx, compile_fx_inner

            aten_binary = [
                aten.add.Tensor,
                aten.sub.Tensor,
                aten.mul.Tensor,
                aten.div.Tensor,
            ]
            n_binary_ops = 0

            def my_inner_compile(gm, example_inputs, *args, **kwargs):
                out = compile_fx_inner(gm, example_inputs, *args, **kwargs)
                nonlocal n_binary_ops
                binarry_ops = [n for n in gm.graph.nodes if n.target in aten_binary]
                n_binary_ops += len(binarry_ops)
                return out

            torch._dynamo.reset()
            mod_eager = ConvOp(3, 32, self.device, kernel_size=3, stride=2).eval()
            out_optimized = torch.compile(
                mod_eager,
                backend=functools.partial(compile_fx, inner_compile=my_inner_compile),
            )

            inps = [4, 3, 4]
            if module[0] == nn.Conv2d:
                inps.append(inps[-1])
            if module[0] == nn.Conv3d:
                inps.append(inps[-1])
                inps.append(inps[-1])

            inp = torch.rand(inps).to(self.device)
            out_eager = mod_eager(inp)
            out_optimized = out_optimized(inp)
            self.assertEqual(out_optimized, out_eager, atol=2e-04, rtol=1e-5)
            if expect_success:
                self.assertTrue(n_binary_ops == 0)
            else:
                self.assertTrue(n_binary_ops > 1)

        conv_bias = [True, False]
        modules = [
            (nn.Conv1d, nn.BatchNorm1d),
            (nn.Conv2d, nn.BatchNorm2d),
            (nn.Conv3d, nn.BatchNorm3d),
        ]
        for use_bias, module in itertools.product(conv_bias, modules):
            test_conv_fusion(
                use_bias,
                module,
                expect_success=True,
            )


if HAS_CPU and not torch.backends.mps.is_available():

    class FreezingCpuTests(TestCase):
        common = check_model
        device = "cpu"
        autocast = torch.cpu.amp.autocast

    copy_tests(BinaryFoldingTemplate, FreezingCpuTests, "cpu")

if HAS_CUDA and not TEST_WITH_ASAN:

    class FreezingCudaTests(TestCase):
        common = check_model_cuda
        device = "cuda"
        autocast = torch.cuda.amp.autocast

    copy_tests(BinaryFoldingTemplate, FreezingCudaTests, "cuda")


del BinaryFoldingTemplate

if __name__ == "__main__":
    from torch._inductor.test_case import run_tests

    if HAS_CPU or HAS_CUDA:
        run_tests(needs="filelock")