ColossalAI/tests/test_analyzer/test_fx/test_bias_addition.py

import pytest
import torch
from packaging import version
from torch.utils.checkpoint import checkpoint

from colossalai.testing.utils import clear_cache_before_run, parameterize

try:
    from colossalai._analyzer.fx import symbolic_trace
except:
    pass


class LinearModel(torch.nn.Module):
    def __init__(self, in_features, out_features, bias):
        super().__init__()
        self.linear = torch.nn.Linear(in_features, out_features, bias=bias)

    def forward(self, x):
        x = self.linear(x)
        return x


class ConvModel(torch.nn.Module):
    def __init__(self, in_channel, out_channels, kernel_size, bias) -> None:
        super().__init__()
        self.conv = torch.nn.Conv2d(
            in_channel, out_channels, kernel_size, bias=bias, padding=1, stride=2, dilation=2, groups=3
        )
        self.conv_transpose = torch.nn.ConvTranspose2d(
            in_channel, out_channels, kernel_size, bias=bias, padding=1, stride=2, dilation=2, groups=3
        )

    def forward(self, x, select=0):
        if select == 0:
            x = self.conv(x)
        else:
            x = self.conv_transpose(x)
        return x


class SiuModel(torch.nn.Module):
    def __init__(self, bias) -> None:
        super().__init__()
        self.linear = LinearModel(3, 3, bias)
        self.conv = ConvModel(3, 6, 3, bias)

    def forward(self, x, select=torch.Tensor([0])):
        x = self.linear(x)
        if select:
            x = checkpoint(self.conv, x, 0)
        else:
            x = checkpoint(self.conv, x, 1)

        return x


class AddmmModel(torch.nn.Module):
    def __init__(self, alpha, beta) -> None:
        super().__init__()
        self.alpha = alpha
        self.beta = beta

    def forward(self, x):
        x = torch.addmm(x, x, x, alpha=self.alpha, beta=self.beta)
        return x


@pytest.mark.skipif(version.parse(torch.__version__) < version.parse("1.12.0"), reason="torch version < 12")
@clear_cache_before_run()
@parameterize("bias", [True, False])
@parameterize("bias_addition_split", [True, False])
@parameterize("shape", [(3, 3, 3), (3, 3, 3, 3)])
@parameterize("select", [torch.Tensor([0]), torch.Tensor([1])])
def test_siu_model(bias, bias_addition_split, shape, select):
    model = SiuModel(bias=bias)
    x = torch.rand(shape)
    gm = symbolic_trace(
        model,
        meta_args={"x": x},
        concrete_args={"select": select},
        trace_act_ckpt=True,
        bias_addition_split=bias_addition_split,
    )
    assert torch.allclose(model(x, select), gm(x)), "original model and traced model should be the same!"
    if bias and bias_addition_split:
        assert "+" in gm.code, "bias addition should be split!"
    else:
        assert "+" not in gm.code, "bias addition should not be split!"


@pytest.mark.skipif(version.parse(torch.__version__) < version.parse("1.12.0"), reason="torch version < 12")
@parameterize("alpha", [1, 2])
@parameterize("beta", [1, 2])
@parameterize("bias_addition_split", [True, False])
@parameterize("shape", [(3, 3), (5, 5)])
def test_addmm_model(alpha, beta, bias_addition_split, shape):
    model = AddmmModel(alpha=alpha, beta=beta)
    x = torch.rand(shape)
    gm = symbolic_trace(model, meta_args={"x": x}, trace_act_ckpt=True, bias_addition_split=bias_addition_split)
    assert torch.allclose(model(x), gm(x)), "original model and traced model should be the same!"
    if (alpha == 1 and beta == 1) or not bias_addition_split:
        assert "*" not in gm.code, "bias addition should not be split!"
    elif bias_addition_split:
        assert "+" in gm.code, "bias addition should be split!"


if __name__ == "__main__":
    test_siu_model()
    test_addmm_model()