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Fix memory error in var #100

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Merged
abhudev merged 2 commits into from
Sep 6, 2022
Merged

Fix memory error in var #100

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b81f735
Fix memory corruption + wrong handling of negative dims
abhudev Aug 31, 2022
9a3ae54
Use vector for shape
abhudev Sep 2, 2022
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27 changes: 16 additions & 11 deletions aten/src/ATen/native/mps/operations/ReduceOps.mm
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Original file line number Diff line number Diff line change
Expand Up @@ -590,7 +590,7 @@ Tensor std_var_common_impl_mps(
NSMutableArray<NSNumber *> *axes = nil;
NSMutableArray<NSNumber*> *apparent_output_shape = nil;
NSMutableArray<NSNumber*> *apparent_input_shape = nil;
int64_t* output_shape = nil;
std::vector<int64_t> output_shape;

if ((!keepdim && !use_dim) || (!keepdim && use_dim && dim_value.size() <= 0))
{
Expand Down Expand Up @@ -630,7 +630,6 @@ Tensor std_var_common_impl_mps(
axes);

num_output_dims = (num_input_dims >= num_reduce_dims) ? (num_input_dims - num_reduce_dims) : 0; //num_input_dims;
output_shape = (int64_t *)malloc(num_output_dims * sizeof(int64_t));

unsigned int curr_i = 0;
for (int i = 0; i < num_input_dims; i++)
Expand All @@ -645,13 +644,17 @@ Tensor std_var_common_impl_mps(
}
}
if (found) continue;
output_shape[curr_i] = input_shape[i];
output_shape.push_back(input_shape[i]);
curr_i += 1;
// End loop when output shape is filled
if(curr_i == num_output_dims)
break;
}

for(int i = 0; i < num_reduce_dims; i++)
{
correction_n *= input_shape[dim_value[i]];
auto wrap_dim = maybe_wrap_dim(dim_value[i], input_shape.size());
correction_n *= input_shape[wrap_dim];
}
// (3, 4, 5) --> (3, 5)
}
Expand All @@ -668,10 +671,9 @@ Tensor std_var_common_impl_mps(
input_shape,
axes);
num_output_dims = num_input_dims;
output_shape = (int64_t *)malloc(num_output_dims * sizeof(int64_t));
for (int i = 0; i < num_input_dims; i++)
{
output_shape[i] = (int64_t) 1;
output_shape.push_back((int64_t) 1);
correction_n *= input_shape[i];
}
// scalar --> vector case [[1.0034567]]
Expand All @@ -691,21 +693,24 @@ Tensor std_var_common_impl_mps(
axes);

num_output_dims = num_input_dims;//(num_input_dims >= num_reduce_dims) ? (num_input_dims - num_reduce_dims) : 0;
output_shape = (int64_t *)malloc(num_output_dims * sizeof(int64_t));

for(int i = 0; i < num_reduce_dims; i++)
{
correction_n *= input_shape[dim_value[i]];
auto wrap_dim = maybe_wrap_dim(dim_value[i], input_shape.size());
correction_n *= input_shape[wrap_dim];
}

for (int i = 0; i < num_input_dims; i++)
{
output_shape[i] = [apparent_output_shape[i] longValue];
output_shape.push_back([apparent_output_shape[i] longValue]);
}
}

int64_t output_shape_array[output_shape.size()];
std::copy(output_shape.begin(), output_shape.end(), output_shape_array);

Tensor output_t = at::native::empty_mps(
IntArrayRef(output_shape, num_output_dims),
IntArrayRef(output_shape_array, num_output_dims),
input_t.scalar_type(),
c10::nullopt,
kMPS,
Expand Down Expand Up @@ -790,7 +795,7 @@ Tensor std_var_common_impl_mps(
};
native_mps::runMPSGraph(stream, cachedGraph->graph(), feeds, results);
}
free(output_shape);

return output_t;
}

Expand Down
108 changes: 26 additions & 82 deletions test/test_mps.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2948,104 +2948,48 @@ def helper(shape):
helper((9, 5, 6, 7))

# Test var
def test_var(self):
def helper(shape):
cpu_x = torch.randn(shape, device='cpu', dtype=torch.float, requires_grad=False)
x = cpu_x.detach().clone().to('mps')

all_var = torch.var(x, unbiased=False)
all_var_cpu = torch.var(cpu_x, unbiased=False)

self.assertEqual(all_var, all_var_cpu)

nil_dim_var = torch.var(x, dim=[], unbiased=False)
nil_dim_var_cpu = torch.var(cpu_x, dim=[], unbiased=False)

self.assertEqual(nil_dim_var, nil_dim_var_cpu)

nil_dim_var_keepdim = torch.var(x, dim=[], keepdim=True, unbiased=False)
nil_dim_var_cpu_keepdim = torch.var(cpu_x, dim=[], keepdim=True, unbiased=False)

self.assertEqual(nil_dim_var_keepdim, nil_dim_var_cpu_keepdim)

zero_dim_var = torch.var(x, dim=[0], unbiased=False)
zero_dim_var_cpu = torch.var(cpu_x, dim=[0], unbiased=False)

self.assertEqual(zero_dim_var, zero_dim_var_cpu)

zero_dim_var_keepdim = torch.var(x, dim=[0], keepdim=True, unbiased=False)
zero_dim_var_cpu_keepdim = torch.var(cpu_x, dim=[0], keepdim=True, unbiased=False)

self.assertEqual(zero_dim_var_keepdim, zero_dim_var_cpu_keepdim)

zero_one_dim_var = torch.var(x, dim=[0, 1], unbiased=False)
zero_one_dim_var_cpu = torch.var(cpu_x, dim=[0, 1], unbiased=False)

self.assertEqual(zero_one_dim_var, zero_one_dim_var_cpu)
def test_var_simple(self):
def helper():

zero_one_dim_var_keepdim = torch.var(x, dim=[0, 1], keepdim=True, unbiased=False)
zero_one_dim_var_cpu_keepdim = torch.var(cpu_x, dim=[0, 1], keepdim=True, unbiased=False)
shape = [2,3,4,5]

self.assertEqual(zero_one_dim_var_keepdim, zero_one_dim_var_cpu_keepdim)

two_three_dim_var = torch.var(x, dim=[2, 3], unbiased=False)
two_three_dim_var_cpu = torch.var(cpu_x, dim=[2, 3], unbiased=False)

self.assertEqual(two_three_dim_var, two_three_dim_var_cpu)

two_three_keepdim_var = torch.var(x, dim=[2, 3], keepdim=True, unbiased=False)
two_three_dim_keepvar_cpu = torch.var(cpu_x, dim=[2, 3], keepdim=True, unbiased=False)

self.assertEqual(two_three_keepdim_var, two_three_dim_keepvar_cpu)

all_var = torch.var(x, unbiased=True)
all_var_cpu = torch.var(cpu_x, unbiased=True)

self.assertEqual(all_var, all_var_cpu)

nil_dim_var = torch.var(x, dim=[], unbiased=True)
nil_dim_var_cpu = torch.var(cpu_x, dim=[], unbiased=True)

self.assertEqual(nil_dim_var, nil_dim_var_cpu)

nil_dim_var_keepdim = torch.var(x, dim=[], keepdim=True, unbiased=True)
nil_dim_var_cpu_keepdim = torch.var(cpu_x, dim=[], keepdim=True, unbiased=True)
cpu_x = torch.randn(shape, device='cpu', dtype=torch.float, requires_grad=False)
x = cpu_x.detach().clone().to('mps')

self.assertEqual(nil_dim_var_keepdim, nil_dim_var_cpu_keepdim)
for unbiased in [False, True]:
for keepdim in [False, True]:

zero_dim_var = torch.var(x, dim=[0], unbiased=True)
zero_dim_var_cpu = torch.var(cpu_x, dim=[0], unbiased=True)
zero_dim_var = x.var(-1, keepdim=keepdim, unbiased=unbiased)
zero_dim_var_cpu = cpu_x.var(-1, keepdim=keepdim, unbiased=unbiased)

self.assertEqual(zero_dim_var, zero_dim_var_cpu)
self.assertEqual(zero_dim_var, zero_dim_var_cpu)

zero_dim_var_keepdim = torch.var(x, dim=[0], keepdim=True, unbiased=True)
zero_dim_var_cpu_keepdim = torch.var(cpu_x, dim=[0], keepdim=True, unbiased=True)
all_var = torch.var(x, unbiased=unbiased)
all_var_cpu = torch.var(cpu_x, unbiased=unbiased)

self.assertEqual(zero_dim_var_keepdim, zero_dim_var_cpu_keepdim)
self.assertEqual(all_var, all_var_cpu)

zero_one_dim_var = torch.var(x, dim=[0, 1], unbiased=True)
zero_one_dim_var_cpu = torch.var(cpu_x, dim=[0, 1], unbiased=True)
nil_dim_var = torch.var(x, dim=[], keepdim=keepdim, unbiased=unbiased)
nil_dim_var_cpu = torch.var(cpu_x, dim=[], keepdim=keepdim, unbiased=unbiased)

self.assertEqual(zero_one_dim_var, zero_one_dim_var_cpu)
self.assertEqual(nil_dim_var, nil_dim_var_cpu)

zero_one_dim_var_keepdim = torch.var(x, dim=[0, 1], keepdim=True, unbiased=True)
zero_one_dim_var_cpu_keepdim = torch.var(cpu_x, dim=[0, 1], keepdim=True, unbiased=True)
zero_dim_var = torch.var(x, dim=[0], keepdim=keepdim, unbiased=unbiased)
zero_dim_var_cpu = torch.var(cpu_x, dim=[0], keepdim=keepdim, unbiased=unbiased)

self.assertEqual(zero_one_dim_var_keepdim, zero_one_dim_var_cpu_keepdim)
self.assertEqual(zero_dim_var, zero_dim_var_cpu)

two_three_dim_var = torch.var(x, dim=[2, 3], unbiased=True)
two_three_dim_var_cpu = torch.var(cpu_x, dim=[2, 3], unbiased=True)
zero_one_dim_var = torch.var(x, dim=[0, -1], keepdim=keepdim, unbiased=unbiased)
zero_one_dim_var_cpu = torch.var(cpu_x, dim=[0, -1], keepdim=keepdim, unbiased=unbiased)

self.assertEqual(two_three_dim_var, two_three_dim_var_cpu)
self.assertEqual(zero_one_dim_var, zero_one_dim_var_cpu)

two_three_keepdim_var = torch.var(x, dim=[2, 3], keepdim=True, unbiased=True)
two_three_dim_keepvar_cpu = torch.var(cpu_x, dim=[2, 3], keepdim=True, unbiased=True)
two_three_dim_var = torch.var(x, dim=[2, 3], keepdim=keepdim, unbiased=unbiased)
two_three_dim_var_cpu = torch.var(cpu_x, dim=[2, 3], keepdim=keepdim, unbiased=unbiased)

self.assertEqual(two_three_keepdim_var, two_three_dim_keepvar_cpu)
self.assertEqual(two_three_dim_var, two_three_dim_var_cpu)

helper((4, 5, 6, 7))
# verify if a change in shape of input would cause problems with graph caching
helper((9, 5, 6, 7))
helper()

# Test forward amax
def test_amax(self):
Expand Down