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Ngraph op tests skip check grad ci (#22688)

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* ngraph op test skip check grad ci, test=develop
revert-22710-feature/integrated_ps_api
cc 5 years ago committed by GitHub
parent 1b561da1f7
commit fb15aa1c5b
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2 changed files with 49 additions and 36 deletions
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3
python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.py
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@ -17,11 +17,10 @@ from __future__ import print_function
import unittest, sys
sys.path.append("../")
import numpy as np
from op_test import OpTest, skip_check_grad_ci
from op_test import OpTest
from test_activation_op import TestAbs, TestGelu, TestSigmoid, TestSquare, TestRelu, TestTanh
@skip_check_grad_ci(reason="Use float32 in ngraph relu op.")
class TestNGRAPHReluDim4(TestRelu):
def setUp(self):
super(TestNGRAPHReluDim4, self).setUp()

82
python/paddle/fluid/tests/unittests/op_test.py
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@ -147,12 +147,12 @@ def get_numeric_gradient(place,
def skip_check_grad_ci(reason=None):
"""Decorator to skip check_grad CI.
Check_grad is required for Op test cases. However, there are some special
cases that do not need to do check_grad. This decorator is used to skip the
cases that do not need to do check_grad. This decorator is used to skip the
check_grad of the above cases.
Note: the execution of unit test will not be skipped. It just avoids check_grad
Note: the execution of unit test will not be skipped. It just avoids check_grad
checking in tearDownClass method by setting a `no_need_check_grad` flag.
Example:
@ -210,6 +210,9 @@ class OpTest(unittest.TestCase):
def is_mkldnn_op_test():
return hasattr(cls, "use_mkldnn") and cls.use_mkldnn == True
def is_ngraph_op_test():
return hasattr(cls, "use_ngraph") and cls.use_ngraph == True
if not hasattr(cls, "op_type"):
raise AssertionError(
"This test do not have op_type in class attrs, "
@ -229,6 +232,7 @@ class OpTest(unittest.TestCase):
if cls.dtype in [np.float32, np.float64] \
and cls.op_type not in op_accuracy_white_list.NO_FP64_CHECK_GRAD_OP_LIST \
and not hasattr(cls, 'exist_fp64_check_grad') \
and not is_ngraph_op_test() \
and not is_mkldnn_op_test():
raise AssertionError(
"This test of %s op needs check_grad with fp64 precision." %
@ -320,6 +324,10 @@ class OpTest(unittest.TestCase):
(hasattr(self, "attrs") and "use_mkldnn" in self.attrs and \
self.attrs["use_mkldnn"] == True):
self.__class__.use_mkldnn = True
if fluid.core.is_compiled_with_ngraph() and \
fluid.core.globals()['FLAGS_use_ngraph']:
self.__class__.use_ngraph = True
op_proto = OpProtoHolder.instance().get_op_proto(self.op_type)
"infer datatype from inputs and outputs for this test case"
self.infer_dtype_from_inputs_outputs(self.inputs, self.outputs)
@ -341,7 +349,7 @@ class OpTest(unittest.TestCase):
inputs=inputs,
outputs=outputs,
attrs=self.attrs if hasattr(self, "attrs") else dict())
# infer variable type and infer shape in compile-time
# infer variable type and infer shape in compile-time
op.desc.infer_var_type(block.desc)
op.desc.infer_shape(block.desc)
@ -555,8 +563,8 @@ class OpTest(unittest.TestCase):
feed_map = self.feed_var(inputs, place)
if for_inplace_test:
# Some variables' tensors hold no buffer (tensor's _holder is NULL), like XShape in reshape2 op,
# and the shapes of those variables contain 0 (eg. Xshape.shape = [0, 2, 5]).
# Some variables' tensors hold no buffer (tensor's _holder is NULL), like XShape in reshape2 op,
# and the shapes of those variables contain 0 (eg. Xshape.shape = [0, 2, 5]).
# Set persistable for those variables in order to get them from global_scope for inplace grad test directly other than feed them,
# since feed op calls check_memory_size() which fails when tensor's holder_ is NULL.
for out_name in op.output_arg_names:
@ -617,7 +625,7 @@ class OpTest(unittest.TestCase):
"""Compare expect outs and actual outs of an tested op.
Args:
place (CPUPlace | CUDAPlace): The place where the op runs.
place (CPUPlace | CUDAPlace): The place where the op runs.
fetch_list (list): The outputs of tested op.
expect_outs (list): The expect outs of tested op.
actual_outs (list): The actual outs of tested op.
@ -628,7 +636,7 @@ class OpTest(unittest.TestCase):
"""
# compare expect_outs and actual_outs
for i, name in enumerate(fetch_list):
# Note(zhiqiu): inplace_atol should be only set when op doesn't ensure
# Note(zhiqiu): inplace_atol should be only set when op doesn't ensure
# computational consistency.
# When inplace_atol is not None, the inplace check uses numpy.allclose
# to check inplace result instead of numpy.array_equal.
@ -658,7 +666,7 @@ class OpTest(unittest.TestCase):
Args:
fwd_program (tuple): The program that contains grad_op_desc's corresponding forward op.
grad_op_desc (OpDesc): The OpDesc of grad op.
op_grad_to_var (dict): The relation of variables in grad op and its forward op.
op_grad_to_var (dict): The relation of variables in grad op and its forward op.
Returns:
grad_program (program): The program which contains the grad_op.
@ -685,8 +693,8 @@ class OpTest(unittest.TestCase):
type=fwd_var.type,
persistable=False)
# Some variables' tensors hold no buffer (tensor's _holder is NULL), like XShape in reshape2 op,
# and the shapes of those variables contain 0 (eg. Xshape.shape = [0, 2, 5]).
# Some variables' tensors hold no buffer (tensor's _holder is NULL), like XShape in reshape2 op,
# and the shapes of those variables contain 0 (eg. Xshape.shape = [0, 2, 5]).
# Set persistable for those variables in order to get them from global_scope for inplace grad test directly other than feed them,
# since feed op calls check_memory_size() which fails when tensor's holder_ is NULL.
if 0 in grad_var.shape:
@ -702,11 +710,11 @@ class OpTest(unittest.TestCase):
we use fwd outs (also inputs sometimes) to construct grad inputs.
Args:
place (CPUPlace | CUDAPlace): The place where the op runs.
place (CPUPlace | CUDAPlace): The place where the op runs.
fwd_res (tuple): The outputs of its forward op, in the same form as returns of _calc_outputs() when for_inplace_test is True.
i.e., tuple(fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc)
grad_op_desc (OpDesc): The OpDesc of grad op.
op_grad_to_var (dict): The relation of variables in grad op and its fwd_op.
op_grad_to_var (dict): The relation of variables in grad op and its fwd_op.
Returns:
grad_feed_map (dict): The feed_map of grad_op.
@ -738,12 +746,12 @@ class OpTest(unittest.TestCase):
An op needs to run druing inplace check if,
(1) it has infer_inplace,
(2) it has infer_inplace in its grad descendants. (since we need its outputs as to construct its grad's inputs)
Args:
op_desc (OpDesc): The op_desc of current op.
fwd_op_desc (OpDesc): The op_desc of current op's forward op, None if current op has no forward op.
op_desc (OpDesc): The op_desc of current op.
fwd_op_desc (OpDesc): The op_desc of current op's forward op, None if current op has no forward op.
Eg. relu's fwd_op is None, relu_grad's fwd_op is relu, relu_grad_grad's fwd_op is relu_grad, etc.
Returns:
need_run_ops (list[(op_desc, fwd_op_desc)]): The ops that need to run during inplace test.
"""
@ -758,7 +766,7 @@ class OpTest(unittest.TestCase):
if not has_grad_op_maker:
has_infer_inplace_in_descendants = False
else:
# get grad_op_desc
# get grad_op_desc
grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(
op_desc, set(), [])
if not grad_op_desc_list:
@ -784,15 +792,15 @@ class OpTest(unittest.TestCase):
inplace_atol=None):
"""Chech the inplace correctness of given op (self.op_type).
Run the op twice with same inputs, one enable inplace and another disable, compare their outputs.
Args:
place (CPUPlace | CUDAPlace): The place where the op runs.
place (CPUPlace | CUDAPlace): The place where the op runs.
no_check_set (list): The names of outputs that needn't check, like XShape of reshape op.
inplace_atol (float): The tolerable error, only set when op doesn't ensure computational consistency, like group_norm op.
Returns:
expect_res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given op.
We return this to construct grad_program and grad_feed_map for grad inplace check.
expect_res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given op.
We return this to construct grad_program and grad_feed_map for grad inplace check.
"""
# _calc_output() returns in the form tuple(outs, fetch_list, feed_map, program, op_desc) when for_inplace_test=True.
expect_res = self._calc_output(
@ -825,7 +833,7 @@ class OpTest(unittest.TestCase):
we use fwd outs (also inputs sometimes) to construct grad inputs.
Args:
place (CPUPlace | CUDAPlace): The place where the op runs.
place (CPUPlace | CUDAPlace): The place where the op runs.
fwd_res (tuple): The outputs of its forward op, in the same form as returns of _calc_outputs() when for_inplace_test is True.
i.e., tuple(fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc).
grad_op_desc (OpDesc): The OpDesc of grad op.
@ -869,15 +877,15 @@ class OpTest(unittest.TestCase):
So we define a new function for grad, grad_grad, etc.
Args:
place (CPUPlace | CUDAPlace): The place where the op runs.
place (CPUPlace | CUDAPlace): The place where the op runs.
fwd_res (tuple): The outputs of its forward op, in the same form as returns of _calc_outputs() when for_inplace_test is True.
i.e., tuple(fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc).
grad_op_desc (OpDesc): The OpDesc of grad op.
inplace_atol (float): The tolerable error, only set when op doesn't ensure computational consistency, like group_norm op.
Returns:
expect_res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given op.
We return this to construct grad_program and grad_feed_map for grad inplace check.
expect_res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given op.
We return this to construct grad_program and grad_feed_map for grad inplace check.
"""
expect_res = self._calc_grad_output(
place, fwd_res, grad_op_desc, enable_inplace=False)
@ -901,7 +909,7 @@ class OpTest(unittest.TestCase):
(2) Run op in need_run_ops, and do inplace check if it has infer_inplace.
Args:
place (CPUPlace | CUDAPlace): The place where the op runs.
place (CPUPlace | CUDAPlace): The place where the op runs.
no_check_set (list): The names of outputs that needn't check, like XShape of reshape op.
inplace_atol (float): The tolerable error, only set when op doesn't ensure computational consistency, like group_norm op.
@ -936,14 +944,16 @@ class OpTest(unittest.TestCase):
attrs_use_mkldnn = hasattr(
self,
'attrs') and bool(self.attrs.get('use_mkldnn', False))
flags_use_ngraph = fluid.core.globals()["FLAGS_use_ngraph"]
attrs_use_ngraph = hasattr(
self,
'attrs') and bool(self.attrs.get('use_ngraph', False))
if flags_use_mkldnn or attrs_use_mkldnn:
warnings.warn(
"check inplace_grad for ops using mkldnn is not supported"
)
continue
use_ngraph = fluid.core.is_compiled_with_ngraph(
) and fluid.core.globals()["FLAGS_use_ngraph"]
if use_ngraph:
if flags_use_ngraph or attrs_use_ngraph:
warnings.warn(
"check inplace_grad for ops using ngraph is not supported"
)
@ -1093,10 +1103,10 @@ class OpTest(unittest.TestCase):
"Output (" + out_name + ") has different lod at " +
str(place) + " in dygraph mode")
# Note(zhiqiu): inplace_atol should be only set when op doesn't ensure
# Note(zhiqiu): inplace_atol should be only set when op doesn't ensure
# computational consistency.
# For example, group_norm uses AtomicAdd on CUDAPlace, which do not ensure
# computation order when multiple threads write the same address. So the
# computation order when multiple threads write the same address. So the
# result of group_norm is non-deterministic when datatype is float.
# When inplace_atol is not None, the inplace check uses numpy.allclose
# to check inplace result instead of numpy.array_equal.
@ -1105,7 +1115,7 @@ class OpTest(unittest.TestCase):
"inplace_atol should only be set when op doesn't ensure computational consistency, please check it!"
)
# Check inplace for given op, its grad op, its grad_grad op, etc.
# No effect on original OpTest
# No effect on original OpTest
self.check_inplace_output_with_place(
place, no_check_set=no_check_set, inplace_atol=inplace_atol)
@ -1190,6 +1200,10 @@ class OpTest(unittest.TestCase):
(hasattr(self, "attrs") and "use_mkldnn" in self.attrs and \
self.attrs["use_mkldnn"] == True):
self.__class__.use_mkldnn = True
if fluid.core.is_compiled_with_ngraph() and \
fluid.core.globals()['FLAGS_use_ngraph']:
self.__class__.use_ngraph = True
places = self._get_places()
for place in places:
res = self.check_output_with_place(place, atol, no_check_set,

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