Skip to content

Fix nn.functional.gelu #249

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
kulinseth merged 2 commits into from
Jan 23, 2023
Merged

Fix nn.functional.gelu #249

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
cef8adf
Fix nn.functional.gelu
Ronian526 Jan 20, 2023
68839b0
- add test_gelu_tanh test
Ronian526 Jan 23, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
54 changes: 52 additions & 2 deletions aten/src/ATen/native/mps/operations/Activation.mm
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -744,6 +744,51 @@ Tensor relu_mps(const Tensor& self) {
return erfTensor;
}

MPSGraphTensor* tanh (MPSGraph* mpsGraph, MPSGraphTensor *inputTensor) {
// 0.5 * x * (1 + text{Tanh}(sqrt(2 / pi) * (x + 0.044715 * x^3)))
auto dataType = [inputTensor dataType];
const float SQRT2_PI = 0.797884523868560791015625f;
const float VAL = 0.044715f;
MPSGraphTensor *onef = [mpsGraph constantWithScalar: 1.0f
shape: @[@1]
dataType: dataType];
MPSGraphTensor *halff = [mpsGraph constantWithScalar: 0.5f
shape: @[@1]
dataType: dataType];
MPSGraphTensor *sqrt2_pi = [mpsGraph constantWithScalar: SQRT2_PI
shape: @[@1]
dataType: dataType];
MPSGraphTensor *valf = [mpsGraph constantWithScalar: VAL
shape: @[@1]
dataType: dataType];

MPSGraphTensor *erfTensor = [mpsGraph multiplicationWithPrimaryTensor: inputTensor
secondaryTensor: inputTensor
name : nil];
erfTensor = [mpsGraph multiplicationWithPrimaryTensor: erfTensor
secondaryTensor: inputTensor
name : nil];
erfTensor = [mpsGraph multiplicationWithPrimaryTensor: erfTensor
secondaryTensor: valf
name : nil];
erfTensor = [mpsGraph additionWithPrimaryTensor: erfTensor
secondaryTensor: inputTensor
name : nil];
erfTensor = [mpsGraph multiplicationWithPrimaryTensor: erfTensor
secondaryTensor: sqrt2_pi
name : nil];
erfTensor = [mpsGraph tanhWithTensor: erfTensor
name : nil];
erfTensor = [mpsGraph additionWithPrimaryTensor: erfTensor
secondaryTensor: onef
name : nil];
erfTensor = [mpsGraph multiplicationWithPrimaryTensor: erfTensor
secondaryTensor: halff
name : nil];

return erfTensor;
}

TORCH_IMPL_FUNC(gelu_out_mps) (
const Tensor& self, c10::string_view approximate, const Tensor& output
) {
Expand All @@ -767,7 +812,7 @@ Tensor relu_mps(const Tensor& self) {
MPSStream* stream = getCurrentMPSStream();

@autoreleasepool {
string key = "gelu_out_mps" + getTensorsStringKey({self});
string key = "gelu_out_mps" + getTensorsStringKey({self}) + ":" + c10::str(approximate);
CachedGraph* cachedGraph = static_cast<CachedGraph *>(cache_->LookUp(key));
if(!cachedGraph) {
MPSCachedGraph *tmpCachedGraph = cache_->CreateCachedGraph(key, ^ MPSCachedGraph * () {
Expand All @@ -782,7 +827,12 @@ Tensor relu_mps(const Tensor& self) {
getMPSDataType(self.scalar_type()),
getMPSShape(self));

MPSGraphTensor* outputTensor = normcdf(mpsGraph, inputTensor);
MPSGraphTensor* outputTensor = nil;
if(approximate == "tanh") {
outputTensor = tanh(mpsGraph, inputTensor);
} else {
outputTensor = normcdf(mpsGraph, inputTensor);
}
outputTensor = [mpsGraph multiplicationWithPrimaryTensor:outputTensor
secondaryTensor:inputTensor
name:nil];
Expand Down
12 changes: 11 additions & 1 deletion test/test_mps.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2197,6 +2197,17 @@ def helper(dtype):
e_string = str(e)
self.assertEqual(e_string, "MPS does not support cumsum op with int64 input")

def test_gelu_tanh(self):
def helper(shape):
cpu_x = torch.randn(shape, device='cpu', dtype=torch.float)
x = cpu_x.detach().clone().to('mps')

gelu_tanh_result = torch.nn.functional.gelu(x, approximate='tanh')
gelu_tanh_result_cpu = torch.nn.functional.gelu(cpu_x, approximate='tanh')
self.assertEqual(gelu_tanh_result, gelu_tanh_result_cpu)

helper((2, 8, 4, 5))


class TestLogical(TestCase):
def _wrap_tensor(self, x, device="cpu", dtype=None, requires_grad=False):
Expand Down Expand Up @@ -9315,7 +9326,6 @@ class TestConsistency(TestCase):
'as_stridedpartial_views': [torch.bool, torch.float16, torch.float32, torch.int16, torch.int32, torch.int64, torch.uint8],
'trace': [torch.int64],
'normalnumber_mean': [torch.float16, torch.float32],
'nn.functional.gelu': [torch.float32],
'new_empty_strided': [torch.bool, torch.float16, torch.float32, torch.int16, torch.int32, torch.int64, torch.uint8],
'multinomial': [torch.float32],
'floor_divide': [torch.int16, torch.int32, torch.int64],
Expand Down