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# 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)
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