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Fix tensor shapes for elementwise binary operations with broadcasting #1234

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merged 13 commits into from
Dec 1, 2023
Merged

Fix tensor shapes for elementwise binary operations with broadcasting #1234

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2dbc18c
Fix shapes in keras
soumyac1999 Oct 13, 2023
575cc2a
remove extra lines
soumyac1999 Oct 13, 2023
b37faaa
Merge branch 'inference' into broadcast_shapes
soumyac1999 Nov 3, 2023
421b687
Add python<3.12 requirement to fix CI errors
soumyac1999 Nov 4, 2023
2e7257b
Merge branch 'broadcast_shapes' of https://github.com/soumyac1999/Fle…
soumyac1999 Nov 4, 2023
fe88f4b
Add python<=3.11 requirement to fix CI errors
soumyac1999 Nov 4, 2023
a347295
Tweak around requirement to fix CI errors
soumyac1999 Nov 4, 2023
4d49c0b
Merge branch 'inference' into broadcast_shapes
soumyac1999 Nov 10, 2023
f8558a2
Restore flexflow.yml
soumyac1999 Nov 10, 2023
bac6e24
Restore pytorch-gpu.yml
soumyac1999 Nov 10, 2023
55038b1
Merge branch 'inference' into broadcast_shapes
soumyac1999 Nov 14, 2023
20ea349
Merge branch 'inference' into soumya/fix_broadcast_shapes
soumyac1999 Nov 24, 2023
734a15a
Merge branch 'inference' into soumya/fix_broadcast_shapes
jiazhihao Nov 30, 2023
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33 changes: 30 additions & 3 deletions python/flexflow/keras/layers/merge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,16 @@ def __init__(self, **kwargs):
def _calculate_inout_shape(self, input_tensors):
assert len(input_tensors) == 2, "check input_tensors"
self.input_shape = input_tensors[0].batch_shape
self.output_shape = input_tensors[0].batch_shape
self.output_shape = list(input_tensors[0].batch_shape)
for i, d in enumerate(input_tensors[1].batch_shape):
if self.output_shape[i] != d:
if self.output_shape[i] == 1 or d == 1:
self.output_shape[i] *= d
else:
raise AssertionError(
f"Tensor with shape {input_tensors[0].batch_shape} and "
f"{input_tensors[1].batch_shape} cannot be added")
self.output_shape = tuple(self.output_shape)
fflogger.debug("add output %s" %( str(self.output_shape)))

def subtract(input_tensors):
Expand All @@ -114,7 +123,16 @@ def __init__(self, **kwargs):
def _calculate_inout_shape(self, input_tensors):
assert len(input_tensors) == 2, "check input_tensors"
self.input_shape = input_tensors[0].batch_shape
self.output_shape = input_tensors[0].batch_shape
self.output_shape = list(input_tensors[0].batch_shape)
for i, d in enumerate(input_tensors[1].batch_shape):
if self.output_shape[i] != d:
if self.output_shape[i] == 1 or d == 1:
self.output_shape[i] *= d
else:
raise AssertionError(
f"Tensor with shape {input_tensors[0].batch_shape} and "
f"{input_tensors[1].batch_shape} cannot be subtracted")
self.output_shape = tuple(self.output_shape)
fflogger.debug("subtract output %s" %( str(self.output_shape)))

def multiply(input_tensors):
Expand All @@ -127,7 +145,16 @@ def __init__(self, **kwargs):
def _calculate_inout_shape(self, input_tensors):
assert len(input_tensors) == 2, "check input_tensors"
self.input_shape = input_tensors[0].batch_shape
self.output_shape = input_tensors[0].batch_shape
self.output_shape = list(input_tensors[0].batch_shape)
for i, d in enumerate(input_tensors[1].batch_shape):
if self.output_shape[i] != d:
if self.output_shape[i] == 1 or d == 1:
self.output_shape[i] *= d
else:
raise AssertionError(
f"Tensor with shape {input_tensors[0].batch_shape} and "
f"{input_tensors[1].batch_shape} cannot be multiplied")
self.output_shape = tuple(self.output_shape)
fflogger.debug("multiply output %s" %( str(self.output_shape)))

class Maximum(_Merge):
Expand Down
15 changes: 14 additions & 1 deletion src/ops/element_binary.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -89,8 +89,21 @@ Tensor FFModel::binary(OperatorType op,
}
// Assert type match after broadcast
assert(ele->inputs[0]->data_type == ele->inputs[1]->data_type);

int numdim = in1->num_dims;
int dims[MAX_TENSOR_DIM];
for (int i = 0; i < numdim; i++) {
if (in1->dims[i] == 1) {
dims[i] = in2->dims[i];
} else if (in2->dims[i] == 1) {
dims[i] = in1->dims[i];
} else {
dims[i] = in1->dims[i];
}
}

ele->outputs[0] = create_tensor_legion_ordering(
in1->num_dims, in1->dims, ele->data_type, ele, 0, true /*create_grad*/);
in1->num_dims, dims, ele->data_type, ele, 0, true /*create_grad*/);
ele->add_int_property("inplace_a", inplace_a);
layers.push_back(ele);
return ele->outputs[0];
Expand Down
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