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[te] Ban uint8 tensors from fusion groups #49247

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[te] Ban uint8 tensors from fusion groups #49247

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89c94b9
[te] Ban uint8 tensors from fusion groups
bertmaher Dec 11, 2020
31dee70
clang-format on "[te] Ban uint8 tensors from fusion groups"
bertmaher Dec 11, 2020
f5d0ab4
actually enable tests on "[te] Ban uint8 tensors from fusion groups"
bertmaher Dec 11, 2020
2861e19
clean up test dtypes on "[te] Ban uint8 tensors from fusion groups"
bertmaher Dec 11, 2020
dbd3326
fix test on "[te] Ban uint8 tensors from fusion groups"
bertmaher Dec 12, 2020
52899c3
no uint8 on cuda test on "[te] Ban uint8 tensors from fusion groups"
bertmaher Dec 14, 2020
f5bdf85
fix scalar ops again on "[te] Ban uint8 tensors from fusion groups"
bertmaher Dec 14, 2020
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28 changes: 19 additions & 9 deletions test/test_jit_fuser_te.py
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Original file line number Diff line number Diff line change
Expand Up @@ -490,7 +490,8 @@ def apply(fn):
try:
t = torch.jit.trace(fn, (x, y, z))
self.assertEqual(ref, t(x, y, z))
self.assertAllFused(t.graph_for(x, y, z))
if dtype != torch.uint8:
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can we move the dtypes to self.dtypes and exclude torch.uint8 ?

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Oh yeah that's a better idea than this. I had kind of wanted to test a few uint cases just to make sure nothing was broken but no reason to exhaustively go through them here.

self.assertAllFused(t.graph_for(x, y, z))
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
Expand Down Expand Up @@ -528,7 +529,8 @@ def apply(fn):
try:
t = torch.jit.trace(fn, (x, y, z))
self.assertEqual(ref, t(x, y, z))
self.assertAllFused(t.graph_for(x, y, z))
if dtype != torch.uint8:
self.assertAllFused(t.graph_for(x, y, z))
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
Expand Down Expand Up @@ -1275,7 +1277,8 @@ def apply(fn):
try:
t = torch.jit.trace(fn, (x,))
torch.testing.assert_allclose(ref, t(x))
self.assertAllFused(t.graph_for(x))
if dtype != torch.uint8:
self.assertAllFused(t.graph_for(x))
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device, str(size)])
Expand Down Expand Up @@ -1344,7 +1347,8 @@ def apply(fn):
t = torch.jit.trace(fn, (x, y))
self.assertEqual(ref, t(x, y))
if op not in fp_only or dtype.is_floating_point:
self.assertAllFused(t.graph_for(x, y))
if dtype != torch.uint8:
self.assertAllFused(t.graph_for(x, y))
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
Expand Down Expand Up @@ -1403,7 +1407,8 @@ def apply_with_scalar(fn, scalar):
try:
t = torch.jit.trace(fn, (x))
self.assertEqual(ref, t(x))
self.assertAllFused(t.graph_for(x))
if dtype != torch.uint8:
self.assertAllFused(t.graph_for(x))
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
Expand Down Expand Up @@ -1487,7 +1492,8 @@ def apply_with_scalar(fn, scalar):
try:
t = torch.jit.trace(fn, (x))
self.assertEqual(ref, t(x))
self.assertAllFused(t.graph_for(x))
if dtype != torch.uint8:
self.assertAllFused(t.graph_for(x))
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
Expand Down Expand Up @@ -1529,7 +1535,8 @@ def apply(fn):
try:
t = torch.jit.trace(fn, (x, y, z))
self.assertEqual(ref, t(x, y, z))
self.assertAllFused(t.graph_for(x, y, z))
if dtype != torch.uint8:
self.assertAllFused(t.graph_for(x, y, z))
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
Expand Down Expand Up @@ -1570,7 +1577,8 @@ def apply(fn):
try:
t = torch.jit.trace(fn, (x, y, z))
self.assertEqual(ref, t(x, y, z))
self.assertAllFused(t.graph_for(x, y, z))
if dtype != torch.uint8:
self.assertAllFused(t.graph_for(x, y, z))
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
Expand Down Expand Up @@ -1612,7 +1620,8 @@ def apply(fn):
try:
t = torch.jit.trace(fn, (cond, x, y))
self.assertEqual(ref, t(cond, x, y))
self.assertAllFused(t.graph_for(cond, x, y))
if dtype != torch.uint8:
self.assertAllFused(t.graph_for(cond, x, y))
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
Expand All @@ -1624,6 +1633,7 @@ def fn(x):
return x * x + x

unsupported_dtypes = [
torch.uint8,
torch.bfloat16,
torch.complex32,
torch.complex64,
Expand Down
25 changes: 16 additions & 9 deletions torch/csrc/jit/passes/tensorexpr_fuser.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -739,15 +739,22 @@ class TensorExprFuser {
};
// clang-format on

// Value is only supported if operands are floats.
if (node->isMemberOf(float_only_operator_set)) {
for (const Value* v : node->inputs()) {
if (auto const& tt = v->type()->cast<TensorType>()) {
auto const& st = tt->scalarType();
if (!st || !isFloatingType(*st)) {
return false;
}
} else if (!v->type()->cast<FloatType>()) {
for (const Value* v : node->inputs()) {
if (auto const& tt = v->type()->cast<TensorType>()) {
auto const& st = tt->scalarType();

// All tensors must be typed.
if (!st) {
return false;
}

// Byte tensors introduce too many corner cases in type promotion. Better not to try to handle them.
if (*st == c10::ScalarType::Byte) {
return false;
}

// These operators only support floats, because integer divisors need to raise ZeroDivisionError.
if (node->isMemberOf(float_only_operator_set) && !isFloatingType(*st)) {
return false;
}
}
Expand Down