test/jit/test_graph_rewrite_passes.py

# Owner(s): ["oncall: jit"]

from torch.testing._internal.jit_utils import JitTestCase
import torch
import torch._C
from torch.testing import FileCheck


class TestGraphRewritePasses(JitTestCase):
    def test_fuse_linear(self):
        class FunctionalLinear(torch.nn.Module):
            def __init__(self, weight, bias):
                super(FunctionalLinear, self).__init__()
                self.weight = weight
                self.bias = bias

            def forward(self, x):
                res = torch.matmul(x, self.weight.t())
                if self.bias is not None:
                    res.add_(self.bias)
                return res

        x1 = torch.rand(3)
        w1 = torch.rand(5, 3)
        b1 = torch.rand(5)
        for has_bias in [True, False]:
            bias = b1 if has_bias else None
            model = torch.jit.trace(FunctionalLinear(w1, bias), [x1])
            for node in model.graph.nodes():
                if node.kind() == "aten::matmul":
                    source_range_1 = node.sourceRange()
            torch._C._jit_pass_fuse_linear(model.graph)
            for node in model.graph.nodes():
                if node.kind() == "aten::linear":
                    source_range_2 = node.sourceRange()
            FileCheck().check("aten::linear").run(model.graph)
            check_not = ["aten::matmul", "aten::addmm", "aten::add_", "aten::t("]
            for cn in check_not:
                FileCheck().check_not(cn).run(model.graph)
            self.assertTrue(source_range_1 == source_range_2)
            # make sure it runs
            model(x1)

        # check matmuls are not fused
        class Matmul(torch.nn.Module):
            def __init__(self, weight):
                super(Matmul, self).__init__()
                self.weight = weight

            def forward(self, x):
                return torch.matmul(x, self.weight)

        x = torch.rand(5, 6, 5)
        w = torch.rand(5, 5, 100)
        model = torch.jit.trace(Matmul(w), [x])
        torch._C._jit_pass_fuse_linear(model.graph)
        # check 3d matmul is not fused
        FileCheck().check("aten::matmul").run(model.graph)
        FileCheck().check_not("aten::linear").run(model.graph)
        # make sure it runs
        model(x)