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【Hackathon 6th Fundable Projects 3 No.18】Remove fluid operator bernoulli_p #63236

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merged 3 commits into from
Apr 11, 2024
Merged

【Hackathon 6th Fundable Projects 3 No.18】Remove fluid operator bernoulli_p #63236

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f4fa64a
Fix
co63oc Apr 5, 2024
af6a506
Fix
co63oc Apr 5, 2024
21a0c6d
Fix
co63oc Apr 5, 2024
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82 changes: 0 additions & 82 deletions paddle/fluid/operators/prim_ops/bernoulli_p_op.cc
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This file was deleted.

9 changes: 0 additions & 9 deletions python/paddle/incubate/autograd/primops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,15 +72,6 @@ def fill_const(value, shape, dtype, out=None):
return out


def bernoulli(shape, dtype, p, out=None):
attrs = {'shape': shape, 'dtype': dtype, 'p': p}
helper = LayerHelper('bernoulli_p', **locals())
if out is None:
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(type=helper.layer_type, outputs={'Y': out}, attrs=attrs)
return out


def neg(x, out=None):
zero = fill_const(0.0, x.shape, x.dtype)
return sub(zero, x)
Expand Down
29 changes: 0 additions & 29 deletions python/paddle/incubate/autograd/primrules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,6 @@
from . import primops
from .primops import (
add,
bernoulli,
broadcast,
concat,
cos,
Expand Down Expand Up @@ -460,34 +459,6 @@ def gelu_orig2prim(op, x):
)


@REGISTER_ORIG2PRIM('dropout')
def dropout_orig2prim(op, seed_t, x):
assert (
seed_t is None
), 'Can not lower dropout into prim ops with seedtensor.'
mask = bernoulli(shape=x.shape, dtype=x.dtype, p=op.attr('dropout_prob'))
if op.attr('dropout_implementation') == 'upscale_in_train':
if not op.attr('is_test'):
out = div(
mul(x, mask),
fill_const(1.0 - op.attr('dropout_prob'), x.shape, x.dtype),
)
return primops.cast(mask, dtype=paddle.uint8), out
else:
return primops.cast(mask, dtype=paddle.uint8), x
elif op.attr('dropout_implementation') == 'downgrade_in_infer':
if not op.attr('is_test'):
return primops.cast(mask, dtype=paddle.uint8), mul(x, mask)
else:
return primops.cast(mask, dtype=paddle.uint8), mul(
x, fill_const(1.0 - op.attr('dropout_prob'), x.shape, x.dtype)
)
else:
raise RuntimeError(
'Unsupported dropout_implementation, only support upscale_in_train and downgrade_in_infer'
)


@REGISTER_ORIG2PRIM('uniform_random')
def uniform_random_orig2prim(op, shape_t, shape_tl):
if shape_t or shape_tl:
Expand Down
117 changes: 0 additions & 117 deletions test/autograd/test_orig2prim.py
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Original file line number Diff line number Diff line change
Expand Up @@ -796,123 +796,6 @@ def init_data(self):
self.out_map = {0: self.output['Out']}


class TestDropoutOrig2PrimCase1(TestElementWiseAddOrig2Prim):
def init_data(self):
self.op_type = 'dropout'
X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

self.input = {'X': X}
self.output = {
'Mask': self.layer_help.create_variable_for_type_inference(
dtype=paddle.uint8
),
'Out': self.layer_help.create_variable_for_type_inference(
dtype=X.dtype
),
}
self.attrs = {
'dropout_prob': 0.5,
'is_test': False,
'dropout_implementation': 'upscale_in_train',
}

self.orig2prim_args = (None, X)
self.all_ops = [
'bernoulli_p',
'mul_p',
'fill_constant_p',
'div_p',
'cast_p',
'dropout',
]
# { prim_op_output_index: orig_op_output_var }
self.out_map = {0: self.output['Mask'], 1: self.output['Out']}


class TestDropoutOrig2PrimCase2(TestElementWiseAddOrig2Prim):
def init_data(self):
self.op_type = 'dropout'
X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

self.input = {'X': X}
self.output = {
'Mask': self.layer_help.create_variable_for_type_inference(
dtype=paddle.uint8
),
'Out': self.layer_help.create_variable_for_type_inference(
dtype=X.dtype
),
}
self.attrs = {
'dropout_prob': 0.5,
'is_test': False,
'dropout_implementation': 'downgrade_in_infer',
}

self.orig2prim_args = (None, X)
self.all_ops = ['bernoulli_p', 'mul_p', 'cast_p', 'dropout']
# { prim_op_output_index: orig_op_output_var }
self.out_map = {0: self.output['Mask'], 1: self.output['Out']}


class TestDropoutOrig2PrimCase3(TestElementWiseAddOrig2Prim):
def init_data(self):
self.op_type = 'dropout'
X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

self.input = {'X': X}
self.output = {
'Mask': self.layer_help.create_variable_for_type_inference(
dtype=paddle.uint8
),
'Out': self.layer_help.create_variable_for_type_inference(
dtype=X.dtype
),
}
self.attrs = {
'dropout_prob': 0.5,
'is_test': True,
'dropout_implementation': 'upscale_in_train',
}

self.orig2prim_args = (None, X)
self.all_ops = ['bernoulli_p', 'cast_p', 'dropout']
# { prim_op_output_index: orig_op_output_var }
self.out_map = {0: self.output['Mask'], 1: self.output['Out']}


class TestDropoutOrig2PrimCase4(TestElementWiseAddOrig2Prim):
def init_data(self):
self.op_type = 'dropout'
X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

self.input = {'X': X}
self.output = {
'Mask': self.layer_help.create_variable_for_type_inference(
dtype=paddle.uint8
),
'Out': self.layer_help.create_variable_for_type_inference(
dtype=X.dtype
),
}
self.attrs = {
'dropout_prob': 0.5,
'is_test': True,
'dropout_implementation': 'downgrade_in_infer',
}

self.orig2prim_args = (None, X)
self.all_ops = [
'bernoulli_p',
'fill_constant_p',
'mul_p',
'cast_p',
'dropout',
]
# { prim_op_output_index: orig_op_output_var }
self.out_map = {0: self.output['Mask'], 1: self.output['Out']}


class TestReduceSumOrig2Prim(TestElementWiseAddOrig2Prim):
def init_data(self):
self.op_type = 'reduce_sum'
Expand Down
17 changes: 0 additions & 17 deletions test/autograd/test_prim2orig.py
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Original file line number Diff line number Diff line change
Expand Up @@ -677,23 +677,6 @@ def init_data(self):
self.out_map = {self.output['Z']: 0}


class TestBernoulliPPrim2Orig(TestAddPPrim2Orig):
def init_data(self):
self.op_type = 'bernoulli_p'

self.input = {}
self.output = {
'Y': self.layer_help.create_variable_for_type_inference(
dtype=paddle.float64
)
}
self.attrs = {'shape': [7, 8], 'dtype': paddle.float64, 'p': 0.5}

self.prim2orig_args = ()
self.all_ops = ['bernoulli_p', 'fill_constant', 'bernoulli']
self.out_map = {self.output['Y']: 0}


class TestCastPPrim2Orig(TestAddPPrim2Orig):
def init_data(self):
self.op_type = 'cast_p'
Expand Down