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未验证 提交 bb89c0c8 编写于 作者: C Charles-hit 提交者： GitHub
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[AMP Prim OP]support some prim ops for bf16 dtype part5 (#54422) 

* support some prim ops for bf16 dtype

* remove useless code
上级 a5444592
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Showing with 165 addition and 61 deletion
test/legacy_test/CMakeLists.txt
浏览文件 @ bb89c0c8
......@@ -1197,7 +1197,9 @@ set(TEST_CINN_OPS
test_scatter_nd_op
test_strided_slice_op
test_instance_norm_op
test_cumsum_op)
test_cumsum_op
test_pad_op
test_split_op)
foreach(TEST_CINN_OPS ${TEST_CINN_OPS})
if(WITH_CINN)
......
test/legacy_test/test_elementwise_min_op.py
浏览文件 @ bb89c0c8
......@@ -127,18 +127,12 @@ class TestElementwiseMinOp_ZeroDim1(TestElementwiseOp):
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.minimum(self.inputs['X'], self.inputs['Y'])}
def if_enable_cinn(self):
self.enable_cinn = False
class TestElementwiseMinFP16Op_ZeroDim1(TestElementwiseFP16Op):
def init_data(self):
self.x = np.random.uniform(0.1, 1, []).astype(np.float16)
self.y = np.random.uniform(0.1, 1, []).astype(np.float16)
def if_enable_cinn(self):
self.enable_cinn = False
class TestElementwiseMinOp_ZeroDim2(TestElementwiseOp):
def setUp(self):
......@@ -146,24 +140,17 @@ class TestElementwiseMinOp_ZeroDim2(TestElementwiseOp):
self.python_api = paddle.minimum
self.public_python_api = paddle.minimum
self.prim_op_type = "prim"
self.if_enable_cinn()
x = np.random.uniform(0.1, 1, [13, 17]).astype("float64")
y = np.random.uniform(0.1, 1, []).astype("float64")
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.minimum(self.inputs['X'], self.inputs['Y'])}
def if_enable_cinn(self):
self.enable_cinn = False
class TestElementwiseMinFP16Op_ZeroDim2(TestElementwiseFP16Op):
def init_data(self):
self.x = np.random.uniform(0.1, 1, [13, 17]).astype("float16")
self.y = np.random.uniform(0.1, 1, []).astype("float16")
def if_enable_cinn(self):
self.enable_cinn = False
class TestElementwiseMinOp_ZeroDim3(TestElementwiseOp):
def setUp(self):
......@@ -177,18 +164,12 @@ class TestElementwiseMinOp_ZeroDim3(TestElementwiseOp):
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.minimum(self.inputs['X'], self.inputs['Y'])}
def if_enable_cinn(self):
self.enable_cinn = False
class TestElementwiseMinFP16Op_ZeroDim3(TestElementwiseFP16Op):
def init_data(self):
self.x = np.random.uniform(0.1, 1, []).astype("float16")
self.y = np.random.uniform(0.1, 1, [13, 17]).astype("float16")
def if_enable_cinn(self):
self.enable_cinn = False
@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast."
......@@ -388,7 +369,7 @@ class TestElementwiseBF16Op(OpTest):
def test_check_grad_ingore_x(self):
places = self._get_places()
for place in places:
if type(place) is paddle.fluid.libpaddle.CPUPlace:
if isinstance(place, paddle.fluid.libpaddle.CPUPlace):
check_prim = False
else:
check_prim = True
......@@ -413,7 +394,7 @@ class TestElementwiseBF16Op(OpTest):
def test_check_grad_ingore_y(self):
places = self._get_places()
for place in places:
if type(place) is paddle.fluid.libpaddle.CPUPlace:
if isinstance(place, paddle.fluid.libpaddle.CPUPlace):
check_prim = False
else:
check_prim = True
......@@ -436,7 +417,7 @@ class TestElementwiseBF16Op(OpTest):
)
def if_enable_cinn(self):
self.enable_cinn = False
pass
class TestElementwiseMinBF16Op_ZeroDim1(TestElementwiseBF16Op):
......
test/legacy_test/test_pad_op.py
浏览文件 @ bb89c0c8
......@@ -100,7 +100,7 @@ def create_test_fp16(parent):
return np.float16
def test_check_grad_normal(self):
self.check_grad(['X'], 'Out')
self.check_grad(['X'], 'Out', check_prim=True)
cls_name = "{}_{}".format(parent.__name__, "Fp16")
TestPadFp16.__name__ = cls_name
......@@ -238,9 +238,12 @@ class TestPadBP16Op(OpTest):
)
self.inputs = {'X': convert_float_to_uint16(x)}
self.outputs = {'Out': convert_float_to_uint16(out)}
self.enable_cinn = False
self.prim_op_type = "prim"
self.public_python_api = pad_wrapper
self.if_enable_cinn()
def if_enable_cinn(self):
pass
def initTestCase(self):
self.shape = (16, 16)
......
test/legacy_test/test_roll_op.py
浏览文件 @ bb89c0c8
......@@ -53,9 +53,6 @@ class TestRollOp(OpTest):
def test_check_grad_normal(self):
self.check_grad(['X'], 'Out', check_prim=True)
def test_check_grad(self):
self.check_grad(['X'], 'Out', check_prim=True)
class TestRollOpCase2(TestRollOp):
def init_dtype_type(self):
......
test/legacy_test/test_scatter_op.py
浏览文件 @ bb89c0c8
......@@ -31,6 +31,7 @@ class TestScatterOp(OpTest):
self.public_python_api = paddle.scatter
self.prim_op_type = "prim"
self._set_dtype()
self.if_enable_cinn()
target_dtype = "float16" if self.dtype == np.float16 else "float32"
ref_np = np.ones((3, 50)).astype(target_dtype)
index_np = np.array([1, 2]).astype("int32")
......@@ -44,11 +45,14 @@ class TestScatterOp(OpTest):
self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
self.outputs = {'Out': output_np}
def if_enable_cinn(self):
pass
def _set_dtype(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output(check_prim=True)
self.check_output()
def test_check_grad(self):
self.check_grad(["X", "Updates"], "Out", check_prim=True)
......@@ -67,12 +71,14 @@ class TestScatterFP16Op(TestScatterOp):
class TestScatterBF16Op(TestScatterOp):
def _set_dtype(self):
self.dtype = np.uint16
def if_enable_cinn(self):
self.enable_cinn = False
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, check_prim=True)
self.check_output_with_place(place)
def test_check_grad(self):
if core.is_compiled_with_cuda():
......@@ -91,6 +97,7 @@ class TestScatterOp0(OpTest):
self.python_api = paddle.scatter
self.public_python_api = paddle.scatter
self.prim_op_type = "prim"
self.if_enable_cinn()
self._set_dtype()
target_dtype = "float16" if self.dtype == np.float16 else "float32"
ref_np = np.ones((3, 3)).astype(target_dtype)
......@@ -106,11 +113,14 @@ class TestScatterOp0(OpTest):
self.attrs = {'overwrite': True}
self.outputs = {'Out': output_np}
def if_enable_cinn(self):
pass
def _set_dtype(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output(check_prim=True)
self.check_output()
def test_check_grad(self):
self.check_grad(["X", "Updates"], "Out", check_prim=True)
......@@ -129,12 +139,14 @@ class TestScatterFP16Op0(TestScatterOp0):
class TestScatterBF16Op0(TestScatterOp0):
def _set_dtype(self):
self.dtype = np.uint16
def if_enable_cinn(self):
self.enable_cinn = False
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, check_prim=True)
self.check_output_with_place(place)
def test_check_grad(self):
if core.is_compiled_with_cuda():
......@@ -154,6 +166,7 @@ class TestScatterOp1(OpTest):
self.public_python_api = paddle.scatter
self.prim_op_type = "prim"
self._set_dtype()
self.if_enable_cinn()
target_dtype = "float16" if self.dtype == np.float16 else "float32"
ref_np = np.ones((3, 3)).astype(target_dtype)
zeros_np = np.zeros([2, 3]).astype(target_dtype)
......@@ -171,11 +184,14 @@ class TestScatterOp1(OpTest):
self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
self.outputs = {'Out': output_np}
def if_enable_cinn(self):
pass
def _set_dtype(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output(check_prim=True)
self.check_output()
def test_check_grad(self):
self.check_grad(["X", "Updates"], "Out", check_prim=True)
......@@ -194,12 +210,14 @@ class TestScatterFP16Op1(TestScatterOp1):
class TestScatterBF16Op1(TestScatterOp1):
def _set_dtype(self):
self.dtype = np.uint16
def if_enable_cinn(self):
self.enable_cinn = False
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, check_prim=True)
self.check_output_with_place(place)
def test_check_grad(self):
if core.is_compiled_with_cuda():
......@@ -222,6 +240,7 @@ class TestScatterOp2(OpTest):
self.public_python_api = paddle.scatter
self.prim_op_type = "prim"
self._set_dtype()
self.if_enable_cinn()
target_dtype = "float16" if self.dtype == np.float16 else "float32"
ref_np = np.ones((3, 3)).astype(target_dtype)
index_np = np.array([1, 2]).astype("int32")
......@@ -235,13 +254,16 @@ class TestScatterOp2(OpTest):
self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
self.outputs = {'Out': output_np}
def if_enable_cinn(self):
pass
def _set_dtype(self):
self.dtype = np.float32
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, atol=1e-3, check_prim=True)
self.check_output_with_place(place, atol=1e-3)
def test_check_grad(self):
if core.is_compiled_with_cuda():
......@@ -270,6 +292,8 @@ class TestScatterFP16Op2(TestScatterOp2):
class TestScatterBF16Op2(TestScatterOp2):
def _set_dtype(self):
self.dtype = np.uint16
def if_enable_cinn(self):
self.enable_cinn = False
......@@ -283,6 +307,7 @@ class TestScatterOp3(OpTest):
self.public_python_api = paddle.scatter
self.prim_op_type = "prim"
self._set_dtype()
self.if_enable_cinn()
target_dtype = "float16" if self.dtype == np.float16 else "float32"
ref_np = np.ones((3, 3)).astype(target_dtype)
zeros_np = np.zeros([2, 3]).astype(target_dtype)
......@@ -300,13 +325,16 @@ class TestScatterOp3(OpTest):
self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
self.outputs = {'Out': output_np}
def if_enable_cinn(self):
pass
def _set_dtype(self):
self.dtype = np.float32
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, atol=1e-3, check_prim=True)
self.check_output_with_place(place, atol=1e-3)
def test_check_grad(self):
if core.is_compiled_with_cuda():
......@@ -335,6 +363,8 @@ class TestScatterFP16Op3(TestScatterOp3):
class TestScatterBF16Op3(TestScatterOp3):
def _set_dtype(self):
self.dtype = np.uint16
def if_enable_cinn(self):
self.enable_cinn = False
......@@ -345,6 +375,7 @@ class TestScatterOp4(OpTest):
self.public_python_api = paddle.scatter
self.prim_op_type = "prim"
self._set_dtype()
self.if_enable_cinn()
target_dtype = "float16" if self.dtype == np.float16 else "float32"
ref_np = np.ones((3, 3)).astype(target_dtype)
index_np = np.array([1, 2]).astype("int64")
......@@ -358,11 +389,14 @@ class TestScatterOp4(OpTest):
self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
self.outputs = {'Out': output_np}
def if_enable_cinn(self):
pass
def _set_dtype(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output(check_prim=True)
self.check_output()
def test_check_grad(self):
self.check_grad(['X', 'Updates'], 'Out', check_prim=True)
......@@ -381,12 +415,14 @@ class TestScatterFP16Op4(TestScatterOp4):
class TestScatterBF16Op4(TestScatterOp4):
def _set_dtype(self):
self.dtype = np.uint16
def if_enable_cinn(self):
self.enable_cinn = False
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, check_prim=True)
self.check_output_with_place(place)
def test_check_grad(self):
if core.is_compiled_with_cuda():
......@@ -409,6 +445,7 @@ class TestScatterOp5(OpTest):
self.public_python_api = paddle.scatter
self.prim_op_type = "prim"
self._set_dtype()
self.if_enable_cinn()
target_dtype = "float16" if self.dtype == np.float16 else "float32"
ref_np = np.ones((3, 3)).astype(target_dtype)
index_np = np.array([1, 2]).astype("int64")
......@@ -422,13 +459,16 @@ class TestScatterOp5(OpTest):
self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
self.outputs = {'Out': output_np}
def if_enable_cinn(self):
pass
def _set_dtype(self):
self.dtype = np.float32
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, atol=1e-3, check_prim=True)
self.check_output_with_place(place, atol=1e-3)
def test_check_grad(self):
if core.is_compiled_with_cuda():
......@@ -457,6 +497,8 @@ class TestScatterFP16Op5(TestScatterOp5):
class TestScatterBF16Op5(TestScatterOp5):
def _set_dtype(self):
self.dtype = np.uint16
def if_enable_cinn(self):
self.enable_cinn = False
......@@ -466,7 +508,7 @@ class TestScatterOp6(OpTest):
self.python_api = paddle.scatter
self.public_python_api = paddle.scatter
self.prim_op_type = "prim"
self.enable_cinn = False
self.if_enable_cinn()
self._set_dtype()
target_dtype = "float16" if self.dtype == np.float16 else "float32"
ref_np = np.ones((3, 50)).astype(target_dtype)
......@@ -481,11 +523,14 @@ class TestScatterOp6(OpTest):
self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
self.outputs = {'Out': output_np}
def if_enable_cinn(self):
pass
def _set_dtype(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output(check_prim=True)
self.check_output()
def test_check_grad(self):
self.check_grad(["X", "Updates"], "Out", check_prim=True)
......@@ -502,13 +547,16 @@ class TestScatterFP16Op6(TestScatterOp6):
"core is not complied with CUDA and not support the bfloat16",
)
class TestScatterBF16Op6(TestScatterOp6):
def if_enable_cinn(self):
self.enable_cinn = False
def _set_dtype(self):
self.dtype = np.uint16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, check_prim=True)
self.check_output_with_place(place)
def test_check_grad(self):
if core.is_compiled_with_cuda():
......
test/legacy_test/test_split_op.py
浏览文件 @ bb89c0c8
......@@ -32,7 +32,6 @@ class TestSplitOp(OpTest):
self.dtype = self.get_dtype()
axis = 1
if self.dtype == np.uint16:
self.enable_cinn = False
x = np.random.random((4, 5, 6)).astype(np.float32)
out = np.split(x, [2, 3], axis)
self.inputs = {'X': convert_float_to_uint16(x)}
......@@ -285,7 +284,7 @@ def create_test_bf16(parent):
def test_check_grad(self):
place = core.CUDAPlace(0)
self.check_grad_with_place(place, ['X'], 'out2')
self.check_grad_with_place(place, ['X'], 'out2', check_prim=True)
cls_name = "{}_{}".format(parent.__name__, "BF16Op")
TestSplitBF16Op.__name__ = cls_name
......
test/legacy_test/test_squeeze2_op.py
浏览文件 @ bb89c0c8
......@@ -16,10 +16,11 @@ import os
import unittest
import numpy as np
from eager_op_test import OpTest
from eager_op_test import OpTest, convert_float_to_uint16
from test_attribute_var import UnittestBase
import paddle
from paddle.fluid import core
from paddle.fluid.framework import Program, program_guard
paddle.enable_static()
......@@ -36,19 +37,32 @@ class TestSqueezeOp(OpTest):
"Out"
] # python out sig is customized output signature.
self.init_test_case()
self.inputs = {"X": np.random.random(self.ori_shape).astype("float64")}
self.init_dtype()
self.if_enable_cinn()
x = np.random.random(self.ori_shape).astype("float64")
xshape = np.random.random(self.ori_shape).astype("float64")
if hasattr(self, "dtype") and self.dtype == np.uint16:
x = convert_float_to_uint16(x.astype(np.float32))
xshape = convert_float_to_uint16(xshape.astype(np.float32))
self.inputs = {"X": x}
self.init_attrs()
self.outputs = {
"Out": self.inputs["X"].reshape(self.new_shape),
"XShape": np.random.random(self.ori_shape).astype("float64"),
"XShape": xshape,
}
def if_enable_cinn(self):
pass
def test_check_output(self):
self.check_output(no_check_set=['XShape'], check_prim=True)
def test_check_grad(self):
self.check_grad(["X"], "Out", check_prim=True)
def init_dtype(self):
self.dtype = np.float64
def init_test_case(self):
self.ori_shape = (1, 3, 1, 40)
self.axes = (0, 2)
......@@ -58,6 +72,16 @@ class TestSqueezeOp(OpTest):
self.attrs = {"axes": self.axes}
@unittest.skipIf(
not core.is_compiled_with_cuda()
or not core.is_bfloat16_supported(core.CUDAPlace(0)),
"core is not compiled with CUDA and do not support bfloat16",
)
class TestSqueezeOpBF16OP(TestSqueezeOp):
def init_dtype(self):
self.dtype = np.uint16
# Correct: There is mins axis.
class TestSqueezeOp1(TestSqueezeOp):
def init_test_case(self):
......@@ -66,6 +90,16 @@ class TestSqueezeOp1(TestSqueezeOp):
self.new_shape = (20, 5)
@unittest.skipIf(
not core.is_compiled_with_cuda()
or not core.is_bfloat16_supported(core.CUDAPlace(0)),
"core is not compiled with CUDA and do not support bfloat16",
)
class TestSqueezeOp1BF16Op(TestSqueezeOp):
def init_dtype(self):
self.dtype = np.uint16
# Correct: No axes input.
class TestSqueezeOp2(TestSqueezeOp):
def setUp(self):
......@@ -77,19 +111,42 @@ class TestSqueezeOp2(TestSqueezeOp):
"Out"
] # python out sig is customized output signature.
self.init_test_case()
self.inputs = {"X": np.random.random(self.ori_shape).astype("float64")}
self.init_dtype()
self.if_enable_cinn()
x = np.random.random(self.ori_shape).astype("float64")
xshape = np.random.random(self.ori_shape).astype("float64")
if hasattr(self, "dtype") and self.dtype == np.uint16:
x = convert_float_to_uint16(x.astype(np.float32))
xshape = convert_float_to_uint16(xshape.astype(np.float32))
self.inputs = {"X": x}
self.init_attrs()
self.outputs = {
"Out": self.inputs["X"].reshape(self.new_shape),
"XShape": np.random.random(self.ori_shape).astype("float64"),
"XShape": xshape,
}
def if_enable_cinn(self):
pass
def init_dtype(self):
self.dtype = np.float64
def init_test_case(self):
self.ori_shape = (1, 20, 1, 5)
self.axes = ()
self.new_shape = (20, 5)
@unittest.skipIf(
not core.is_compiled_with_cuda()
or not core.is_bfloat16_supported(core.CUDAPlace(0)),
"core is not compiled with CUDA and do not support bfloat16",
)
class TestSqueezeOp2BF16Op(TestSqueezeOp):
def init_dtype(self):
self.dtype = np.uint16
# Correct: Just part of axes be squeezed.
class TestSqueezeOp3(TestSqueezeOp):
def init_test_case(self):
......@@ -98,6 +155,16 @@ class TestSqueezeOp3(TestSqueezeOp):
self.new_shape = (6, 5, 1, 4)
@unittest.skipIf(
not core.is_compiled_with_cuda()
or not core.is_bfloat16_supported(core.CUDAPlace(0)),
"core is not compiled with CUDA and do not support bfloat16",
)
class TestSqueezeOp3BF16Op(TestSqueezeOp):
def init_dtype(self):
self.dtype = np.uint16
class TestSqueeze2AxesTensor(UnittestBase):
def init_info(self):
self.shapes = [[2, 3, 4]]
......
test/legacy_test/test_stack_op.py
浏览文件 @ bb89c0c8
......@@ -167,7 +167,6 @@ class TestStackBF16Op(OpTest):
self.initParameters()
self.op_type = 'stack'
self.prim_op_type = "comp"
self.enable_cinn = False
self.python_api = paddle.stack
self.public_python_api = paddle.stack
self.x = []
......@@ -191,8 +190,7 @@ class TestStackBF16Op(OpTest):
self.check_output(check_prim=True)
def test_check_grad(self):
# concat_grad unspport bfloat16 dtype, skip check_prim
self.check_grad(self.get_x_names(), 'Y')
self.check_grad(self.get_x_names(), 'Y', check_prim=True)
class TestStackAPIWithLoDTensorArray(unittest.TestCase):
......
test/legacy_test/test_tile_op.py
浏览文件 @ bb89c0c8
......@@ -30,15 +30,18 @@ class TestTileOpRank1(OpTest):
self.op_type = "tile"
self.python_api = paddle.tile
self.prim_op_type = "prim"
self.enable_cinn = True
self.public_python_api = paddle.tile
self.init_data()
self.if_enable_cinn()
self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")}
self.attrs = {'repeat_times': self.repeat_times}
output = np.tile(self.inputs['X'], self.repeat_times)
self.outputs = {'Out': output}
def if_enable_cinn(self):
pass
def init_data(self):
self.ori_shape = [100]
self.repeat_times = [2]
......@@ -52,24 +55,30 @@ class TestTileOpRank1(OpTest):
class TestTileOpRank_ZeroDim1(TestTileOpRank1):
def init_data(self):
self.enable_cinn = False
self.ori_shape = []
self.repeat_times = []
def if_enable_cinn(self):
self.enable_cinn = False
class TestTileOpRank_ZeroDim2(TestTileOpRank1):
def init_data(self):
self.enable_cinn = False
self.ori_shape = []
self.repeat_times = [2]
def if_enable_cinn(self):
self.enable_cinn = False
class TestTileOpRank_ZeroDim3(TestTileOpRank1):
def init_data(self):
self.enable_cinn = False
self.ori_shape = []
self.repeat_times = [2, 3]
def if_enable_cinn(self):
self.enable_cinn = False
# with dimension expanding
class TestTileOpRank2Expanding(TestTileOpRank1):
......@@ -240,7 +249,6 @@ class TestTileBF16OP(OpTest):
self.__class__.op_type = self.op_type
self.python_api = paddle.tile
self.prim_op_type = "prim"
self.enable_cinn = False
self.public_python_api = paddle.tile
self.init_data()
x = np.random.uniform(10, size=self.ori_shape).astype(np.float32)
......
test/legacy_test/test_unsqueeze2_op.py
浏览文件 @ bb89c0c8
......@@ -37,12 +37,16 @@ class TestUnsqueezeOp(OpTest):
"XShape": np.random.random(self.ori_shape).astype("float64"),
}
self.prim_op_type = "comp"
self.if_enable_cinn()
def if_enable_cinn(self):
pass
def test_check_output(self):
self.check_output(no_check_set=["XShape"], check_prim=True)
def test_check_grad(self):
self.check_grad(["X"], "Out")
self.check_grad(["X"], "Out", check_prim=True)
def init_test_case(self):
self.ori_shape = (3, 40)
......@@ -90,7 +94,6 @@ class TestUnsqueezeOp_ZeroDim1(TestUnsqueezeOp):
self.ori_shape = ()
self.axes = (-1,)
self.new_shape = 1
self.enable_cinn = False
class TestUnsqueezeOp_ZeroDim2(TestUnsqueezeOp):
......@@ -98,7 +101,6 @@ class TestUnsqueezeOp_ZeroDim2(TestUnsqueezeOp):
self.ori_shape = ()
self.axes = (-1, 1)
self.new_shape = (1, 1)
self.enable_cinn = False
class TestUnsqueezeOp_ZeroDim3(TestUnsqueezeOp):
......@@ -106,7 +108,6 @@ class TestUnsqueezeOp_ZeroDim3(TestUnsqueezeOp):
self.ori_shape = ()
self.axes = (0, 1, 2)
self.new_shape = (1, 1, 1)
self.enable_cinn = False
# axes is a list(with tensor)
......
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