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# Owner(s): ["oncall: jit"]
import io
import unittest
import torch
from torch.testing._internal.common_utils import IS_WINDOWS, TEST_MKL
from torch.testing._internal.jit_utils import JitTestCase
class TestSparse(JitTestCase):
def test_freeze_sparse_coo(self):
class SparseTensorModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.rand(3, 4).to_sparse()
self.b = torch.rand(3, 4).to_sparse()
def forward(self, x):
return x + self.a + self.b
x = torch.rand(3, 4).to_sparse()
m = SparseTensorModule()
unfrozen_result = m.forward(x)
m.eval()
frozen = torch.jit.freeze(torch.jit.script(m))
frozen_result = frozen.forward(x)
self.assertEqual(unfrozen_result, frozen_result)
buffer = io.BytesIO()
torch.jit.save(frozen, buffer)
buffer.seek(0)
loaded_model = torch.jit.load(buffer)
loaded_result = loaded_model.forward(x)
self.assertEqual(unfrozen_result, loaded_result)
def test_serialize_sparse_coo(self):
class SparseTensorModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.rand(3, 4).to_sparse()
self.b = torch.rand(3, 4).to_sparse()
def forward(self, x):
return x + self.a + self.b
x = torch.rand(3, 4).to_sparse()
m = SparseTensorModule()
expected_result = m.forward(x)
buffer = io.BytesIO()
torch.jit.save(torch.jit.script(m), buffer)
buffer.seek(0)
loaded_model = torch.jit.load(buffer)
loaded_result = loaded_model.forward(x)
self.assertEqual(expected_result, loaded_result)
@unittest.skipIf(IS_WINDOWS or not TEST_MKL, "Need MKL to run CSR matmul")
def test_freeze_sparse_csr(self):
class SparseTensorModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.rand(4, 4).to_sparse_csr()
self.b = torch.rand(4, 4).to_sparse_csr()
def forward(self, x):
return x.matmul(self.a).matmul(self.b)
x = torch.rand(4, 4).to_sparse_csr()
m = SparseTensorModule()
unfrozen_result = m.forward(x)
m.eval()
frozen = torch.jit.freeze(torch.jit.script(m))
frozen_result = frozen.forward(x)
self.assertEqual(unfrozen_result.to_dense(), frozen_result.to_dense())
buffer = io.BytesIO()
torch.jit.save(frozen, buffer)
buffer.seek(0)
loaded_model = torch.jit.load(buffer)
loaded_result = loaded_model.forward(x)
self.assertEqual(unfrozen_result.to_dense(), loaded_result.to_dense())
@unittest.skipIf(IS_WINDOWS or not TEST_MKL, "Need MKL to run CSR matmul")
def test_serialize_sparse_csr(self):
class SparseTensorModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.rand(4, 4).to_sparse_csr()
self.b = torch.rand(4, 4).to_sparse_csr()
def forward(self, x):
return x.matmul(self.a).matmul(self.b)
x = torch.rand(4, 4).to_sparse_csr()
m = SparseTensorModule()
expected_result = m.forward(x)
buffer = io.BytesIO()
torch.jit.save(torch.jit.script(m), buffer)
buffer.seek(0)
loaded_model = torch.jit.load(buffer)
loaded_result = loaded_model.forward(x)
self.assertEqual(expected_result.to_dense(), loaded_result.to_dense())
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