test_pytorch_onnx_onnxruntime_cuda.py

# Owner(s): ["module: onnx"]

import unittest
import onnxruntime  # noqa: F401
import torch

from torch.cuda.amp import autocast

from test_pytorch_common import disableScriptTest, skipIfUnsupportedMinOpsetVersion
from test_pytorch_common import skipIfNoCuda, skipIfNoBFloat16Cuda

from test_pytorch_onnx_onnxruntime import TestONNXRuntime

class TestONNXRuntime_cuda(unittest.TestCase):
    from torch.onnx.symbolic_helper import _export_onnx_opset_version
    opset_version = _export_onnx_opset_version
    keep_initializers_as_inputs = True
    onnx_shape_inference = True

    @skipIfUnsupportedMinOpsetVersion(9)
    @skipIfNoCuda
    def test_gelu_fp16(self):
        class GeluModel(torch.nn.Module):
            def forward(self, x):
                return torch.nn.functional.gelu(x)

        x = torch.randn(2, 4, 5, 6, requires_grad=True, dtype=torch.float16, device=torch.device("cuda"))
        self.run_test(GeluModel(), x, rtol=1e-3, atol=1e-5)

    @skipIfUnsupportedMinOpsetVersion(9)
    @skipIfNoCuda
    @disableScriptTest()
    def test_layer_norm_fp16(self):
        class LayerNormModel(torch.nn.Module):
            def __init__(self):
                super(LayerNormModel, self).__init__()
                self.layer_norm = torch.nn.LayerNorm([10, 10])

            @autocast()
            def forward(self, x):
                return self.layer_norm(x)

        x = torch.randn(20, 5, 10, 10, requires_grad=True, dtype=torch.float16, device=torch.device("cuda"))
        self.run_test(LayerNormModel().cuda(), x, rtol=1e-3, atol=1e-5)


    @skipIfUnsupportedMinOpsetVersion(12)
    @skipIfNoCuda
    @disableScriptTest()
    def test_softmaxCrossEntropy_fusion_fp16(self):
        class FusionModel(torch.nn.Module):
            def __init__(self):
                super(FusionModel, self).__init__()
                self.loss = torch.nn.NLLLoss(reduction="none")
                self.m = torch.nn.LogSoftmax(dim=1)

            @autocast()
            def forward(self, input, target):
                output = self.loss(self.m(2 * input), target)
                return output

        N, C = 5, 4
        input = torch.randn(N, 16, dtype=torch.float16, device=torch.device("cuda"))
        target = torch.empty(N, dtype=torch.long, device=torch.device("cuda")).random_(0, C)

        # using test data containing default ignore_index=-100
        target[target == 1] = -100
        self.run_test(FusionModel(), (input, target))

    @skipIfNoCuda
    @disableScriptTest()
    def test_apex_o2(self):
        class LinearModel(torch.nn.Module):
            def __init__(self):
                super(LinearModel, self).__init__()
                self.linear = torch.nn.Linear(3, 5)

            def forward(self, x):
                return self.linear(x)
        try:
            from apex import amp
        except Exception:
            raise unittest.SkipTest("Apex is not available")
        input = torch.randn(3, 3, device=torch.device("cuda"))
        model = amp.initialize(LinearModel(), opt_level="O2")
        self.run_test(model, input)

    # ONNX supports bfloat16 for opsets >= 13
    # Add, Sub and Mul ops don't support bfloat16 cpu in onnxruntime.
    @skipIfUnsupportedMinOpsetVersion(13)
    @skipIfNoBFloat16Cuda
    def test_arithmetic_bfp16(self):
        class MyModule(torch.nn.Module):
            def forward(self, x):
                y = torch.ones(3, 4, dtype=torch.bfloat16, device=torch.device("cuda"))
                x = x.type_as(y)
                return torch.mul(torch.add(x, y), torch.sub(x, y)).to(dtype=torch.float16)

        x = torch.ones(3, 4, requires_grad=True, dtype=torch.float16, device=torch.device("cuda"))
        self.run_test(MyModule(), x, rtol=1e-3, atol=1e-5)

TestONNXRuntime_cuda.setUp = TestONNXRuntime.setUp
TestONNXRuntime_cuda.run_test = TestONNXRuntime.run_test

if __name__ == "__main__":
    unittest.main(TestONNXRuntime_cuda())