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提交 9cd5cd4e 编写于 作者: P phlrain
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上级 a67fc4be
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Showing with 617 addition and 521 deletion
paddle/fluid/eager/auto_code_generator/final_state_generator/eager_gen.py
浏览文件 @ 9cd5cd4e
......@@ -28,6 +28,7 @@ namespace = ""
yaml_types_mapping = {
'int' : 'int', 'int32' : 'int32_t', 'int64' : 'int64_t', 'size_t' : 'size_t', \
'float' : 'float', 'double' : 'double', 'bool' : 'bool', \
'str' : 'std::string', \
'Backend' : 'paddle::experimental::Backend', 'DataLayout' : 'paddle::experimental::DataLayout', 'DataType' : 'paddle::experimental::DataType', \
'int64[]' : 'std::vector<int64_t>', 'int[]' : 'std::vector<int>',
'Tensor' : 'Tensor',
......@@ -235,7 +236,7 @@ def ParseYamlReturns(string):
else:
ret_type = ret.strip()
assert ret_type in yaml_types_mapping.keys()
assert ret_type in yaml_types_mapping.keys(), ret_type
ret_type = yaml_types_mapping[ret_type]
assert "Tensor" in ret_type
......@@ -426,7 +427,7 @@ def SlotNameMatching(backward_inputs_list, backward_returns_list,
backward_input_type, False, backward_input_pos
]
else:
assert False
assert False, backward_input_name
for backward_output in backward_returns_list:
backward_output_name = backward_output[0]
......@@ -435,7 +436,8 @@ def SlotNameMatching(backward_inputs_list, backward_returns_list,
backward_fwd_name = FindForwardName(backward_output_name)
assert backward_fwd_name is not None
assert backward_fwd_name in forward_inputs_position_map.keys()
assert backward_fwd_name in forward_inputs_position_map.keys(
), backward_fwd_name
matched_forward_input_type = forward_inputs_position_map[
backward_fwd_name][0]
......@@ -684,10 +686,10 @@ def GenerateNodeCreationCodes(
else:
# Tuple api_result
if IsPlainTensorType(rtype):
output_autograd_meta = f" egr::AutogradMeta* {output_autograd_meta_name} = egr::EagerUtils::autograd_meta(&api_result[{pos}]);"
output_autograd_meta = f" egr::AutogradMeta* {output_autograd_meta_name} = egr::EagerUtils::autograd_meta(&std::get<{pos}>(api_result));"
else:
assert IsVectorTensorType(rtype)
output_autograd_meta = f" std::vector<egr::AutogradMeta*> {output_autograd_meta_vec_name} = egr::EagerUtils::autograd_meta(&api_result[{pos}]);\n"
output_autograd_meta = f" std::vector<egr::AutogradMeta*> {output_autograd_meta_vec_name} = egr::EagerUtils::autograd_meta(&std::get<{pos}>(api_result));\n"
output_autograd_meta += f" std::vector<egr::AutogradMeta*>* {output_autograd_meta_name} = &{output_autograd_meta_vec_name};"
outputs_autograd_meta_list.append(output_autograd_meta)
......@@ -1198,7 +1200,7 @@ if __name__ == "__main__":
print("Generated Backward Grad Output Map: ",
backward_grad_output_map)
# Backward Validation Check
# Backward Validation Check
BackwardValidationCheck(backward_fwd_input_map,
backward_grad_input_map,
backward_attrs_list)
......
paddle/fluid/eager/auto_code_generator/final_state_generator/python_c_gen.py
浏览文件 @ 9cd5cd4e
......@@ -22,6 +22,7 @@ atype_to_parsing_function = {
"bool": "CastPyArg2Boolean",
"int": "CastPyArg2Int",
"long": "CastPyArg2Long",
"std::string": "CastPyArgs2String",
"int64_t": "CastPyArg2Long",
"float": "CastPyArg2Float",
"string": "CastPyArg2String",
......
paddle/phi/kernels/impl/trace_grad_kernel_impl.h
浏览文件 @ 9cd5cd4e
......@@ -82,8 +82,8 @@ struct TraceGradFunctor {
template <typename T, typename Context>
void TraceGradKernel(const Context& ctx,
const DenseTensor& out_grad,
const DenseTensor& x,
const DenseTensor& out_grad,
int offset,
int axis1,
int axis2,
......
paddle/phi/kernels/trace_grad_kernel.h
浏览文件 @ 9cd5cd4e
......@@ -20,8 +20,8 @@ namespace phi {
template <typename T, typename Context>
void TraceGradKernel(const Context& ctx,
const DenseTensor& out_grad,
const DenseTensor& x,
const DenseTensor& out_grad,
int offset,
int axis1,
int axis2,
......
paddle/phi/ops/compat/trace_sig.cc
浏览文件 @ 9cd5cd4e
......@@ -23,7 +23,7 @@ KernelSignature TraceOpArgumentMapping(const ArgumentMappingContext& ctx) {
KernelSignature TraceGradOpArgumentMapping(const ArgumentMappingContext& ctx) {
return KernelSignature("trace_grad",
{GradVarName("Out"), "Input"},
{"Input", GradVarName("Out")},
{"offset", "axis1", "axis2"},
{GradVarName("Input")});
}
......
python/paddle/fluid/layers/metric_op.py
浏览文件 @ 9cd5cd4e
......@@ -87,6 +87,9 @@ def accuracy(input, label, k=1, correct=None, total=None):
_k = k.numpy().item(0) if isinstance(k, Variable) else k
topk_out, topk_indices = _C_ops.top_k_v2(input, 'k', _k, 'sorted',
False)
if _in_eager_mode:
_acc = _C_ops.final_state_accuracy(topk_out, topk_indices, label)
return _acc
_acc, _, _ = _C_ops.accuracy(topk_out, topk_indices, label, correct,
total)
return _acc
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 9cd5cd4e
......@@ -14827,6 +14827,11 @@ def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
"Unexpected type of paddings, it should be either an integer or a list"
"of 2 or 4 integers")
if in_dygraph_mode():
if _in_eager_mode():
return _C_op.final_state_unfold(x, kernel_sizes, strdides, paddings,
dilations)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="unfold",
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 9cd5cd4e
......@@ -545,7 +545,8 @@ class OpTest(unittest.TestCase):
v.value().get_tensor().set_recursive_sequence_lengths(lod)
return v
else:
return fluid.dygraph.base.to_variable(value)
print("init her")
return paddle.to_tensor(value)
def get_sequence_batch_size_1_input(self, lod=None, shape=None):
"""Get LoD input data whose batch size is 1.
......
python/paddle/fluid/tests/unittests/test_accuracy_op.py
浏览文件 @ 9cd5cd4e
......@@ -25,6 +25,7 @@ from paddle.fluid import compiler, Program, program_guard
class TestAccuracyOp(OpTest):
def setUp(self):
self.op_type = "accuracy"
self.python_api = paddle.metric.accuracy
self.dtype = np.float32
self.init_dtype()
n = 8192
......@@ -48,7 +49,7 @@ class TestAccuracyOp(OpTest):
pass
def test_check_output(self):
self.check_output()
self.check_output(check_eager=False)
class TestAccuracyOpFp16(TestAccuracyOp):
......@@ -128,4 +129,5 @@ class TestAccuracyAPI(unittest.TestCase):
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/test_arg_min_max_op.py
浏览文件 @ 9cd5cd4e
......@@ -26,6 +26,7 @@ from paddle.fluid import Program, program_guard
class BaseTestCase(OpTest):
def initTestCase(self):
self.op_type = 'arg_min'
self.python_api = paddle.argmin
self.dims = (3, 4, 5)
self.dtype = 'float32'
self.axis = 0
......@@ -41,12 +42,13 @@ class BaseTestCase(OpTest):
self.outputs = {'Out': np.argmax(self.x, axis=self.axis)}
def test_check_output(self):
self.check_output()
self.check_output(check_eager=True)
class TestCase0(BaseTestCase):
def initTestCase(self):
self.op_type = 'arg_max'
self.python_api = paddle.argmax
self.dims = (3, 4, 5)
self.dtype = 'float32'
self.axis = 0
......@@ -63,6 +65,7 @@ class TestCase1(BaseTestCase):
class TestCase2(BaseTestCase):
def initTestCase(self):
self.op_type = 'arg_max'
self.python_api = paddle.argmax
self.dims = (3, 4)
self.dtype = 'int64'
self.axis = 0
......@@ -71,6 +74,7 @@ class TestCase2(BaseTestCase):
class TestCase2_1(BaseTestCase):
def initTestCase(self):
self.op_type = 'arg_max'
self.python_api = paddle.argmax
self.dims = (3, 4)
self.dtype = 'int64'
self.axis = -1
......@@ -95,6 +99,7 @@ class TestCase4(BaseTestCase):
class TestCase3_(BaseTestCase):
def initTestCase(self):
self.op_type = 'arg_max'
self.python_api = paddle.argmax
self.dims = (3, )
self.axis = 0
......@@ -152,6 +157,7 @@ class BaseTestComplex1_2(OpTest):
class BaseTestComplex2_1(OpTest):
def initTestCase(self):
self.op_type = 'arg_max'
self.python_api = paddle.argmax
self.dims = (4, 5, 6)
self.dtype = 'int32'
self.axis = 2
......@@ -202,4 +208,5 @@ class BaseTestComplex2_2(OpTest):
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/test_argsort_op.py
浏览文件 @ 9cd5cd4e
......@@ -23,7 +23,7 @@ import six
import paddle.fluid.core as core
from paddle.fluid import ParamAttr
from paddle.fluid.framework import Program, grad_var_name
from paddle.fluid.framework import Program, grad_var_name, _test_eager_guard
from paddle.fluid.executor import Executor
from paddle.fluid.backward import append_backward
......@@ -421,6 +421,16 @@ class TestArgsortImperative(unittest.TestCase):
expect2 = np.argsort(-self.input_data, axis=self.axis)
self.assertEqual((expect2 == out2.numpy()).all(), True)
with _test_eager_guard():
var_x = paddle.to_tensor(self.input_data)
out = paddle.argsort(var_x, axis=self.axis)
expect = np.argsort(self.input_data, axis=self.axis)
self.assertEqual((expect == out.numpy()).all(), True)
out2 = paddle.argsort(var_x, axis=self.axis, descending=True)
expect2 = np.argsort(-self.input_data, axis=self.axis)
self.assertEqual((expect2 == out2.numpy()).all(), True)
paddle.enable_static()
......@@ -443,4 +453,5 @@ class TestArgsortImperative4(TestArgsortImperative):
if __name__ == "__main__":
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/test_bitwise_op.py
浏览文件 @ 9cd5cd4e
......@@ -276,6 +276,7 @@ class TestBitwiseXorBool(TestBitwiseXor):
class TestBitwiseNot(OpTest):
def setUp(self):
self.op_type = "bitwise_not"
self.python_api = paddle.bitwise_not
self.init_dtype()
self.init_shape()
self.init_bound()
......@@ -288,7 +289,7 @@ class TestBitwiseNot(OpTest):
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
self.check_output(check_eager=False)
def test_check_grad(self):
pass
......@@ -351,4 +352,5 @@ class TestBitwiseNotBool(TestBitwiseNot):
if __name__ == "__main__":
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/test_cholesky_op.py
浏览文件 @ 9cd5cd4e
......@@ -36,6 +36,7 @@ from decorator_helper import prog_scope
class TestCholeskyOp(OpTest):
def setUp(self):
self.op_type = "cholesky"
self.python_api = paddle.linalg.cholesky
self._input_shape = (2, 32, 32)
self._upper = True
self.init_config()
......@@ -54,7 +55,7 @@ class TestCholeskyOp(OpTest):
self.outputs = {"Out": output_data}
def test_check_output(self):
self.check_output()
self.check_output(check_eager=True)
def test_check_grad(self):
places = [fluid.CPUPlace()]
......
python/paddle/fluid/tests/unittests/test_cholesky_solve_op.py
浏览文件 @ 9cd5cd4e
......@@ -109,6 +109,7 @@ class TestCholeskySolveOp(OpTest):
def setUp(self):
self.op_type = "cholesky_solve"
self.python_api = paddle.linalg.cholesky_solve
self.config()
if self.upper:
......@@ -125,137 +126,134 @@ class TestCholeskySolveOp(OpTest):
self.outputs = {'Out': self.output}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['Y'], 'Out', max_relative_error=0.01)
# 3D(broadcast) + 3D, upper=True
class TestCholeskySolveOp3(TestCholeskySolveOp):
"""
case 3
"""
def config(self):
self.y_shape = [1, 10, 10]
self.x_shape = [2, 10, 5]
self.upper = True
self.dtype = np.float64
class TestCholeskySolveAPI(unittest.TestCase):
def setUp(self):
np.random.seed(2021)
self.place = [paddle.CPUPlace()]
# self.place = [paddle.CUDAPlace(0)]
self.dtype = "float64"
self.upper = True
if core.is_compiled_with_cuda():
self.place.append(paddle.CUDAPlace(0))
def check_static_result(self, place):
paddle.enable_static()
with fluid.program_guard(fluid.Program(), fluid.Program()):
x = fluid.data(name="x", shape=[10, 2], dtype=self.dtype)
y = fluid.data(name="y", shape=[10, 10], dtype=self.dtype)
z = paddle.linalg.cholesky_solve(x, y, upper=self.upper)
x_np = np.random.random([10, 2]).astype(self.dtype)
y_np = np.random.random([10, 10]).astype(self.dtype)
if self.upper:
umat = np.triu(y_np)
else:
umat = np.tril(y_np)
z_np = cholesky_solution(umat, x_np, upper=self.upper)
z2_np = scipy_cholesky_solution(umat, x_np, upper=self.upper)
exe = fluid.Executor(place)
fetches = exe.run(fluid.default_main_program(),
feed={"x": x_np,
"y": umat},
fetch_list=[z])
self.assertTrue(np.allclose(fetches[0], z_np))
def test_static(self):
for place in self.place:
self.check_static_result(place=place)
def test_dygraph(self):
def run(place):
paddle.disable_static(place)
x_np = np.random.random([20, 2]).astype(self.dtype)
y_np = np.random.random([20, 20]).astype(self.dtype)
z_np = scipy_cholesky_solution(y_np, x_np, upper=self.upper)
x = paddle.to_tensor(x_np)
y = paddle.to_tensor(y_np)
z = paddle.linalg.cholesky_solve(x, y, upper=self.upper)
self.assertTrue(np.allclose(z_np, z.numpy()))
self.assertEqual(z_np.shape, z.numpy().shape)
paddle.enable_static()
for idx, place in enumerate(self.place):
run(place)
def test_boardcast(self):
def run(place):
paddle.disable_static()
x_np = np.random.random([1, 30, 2]).astype(self.dtype)
y_np = np.random.random([2, 30, 30]).astype(self.dtype)
nx_np = np.concatenate((x_np, x_np), axis=0)
z_sci = scipy_cholesky_solution_batch(y_np, nx_np, upper=self.upper)
x = paddle.to_tensor(x_np)
y = paddle.to_tensor(y_np)
z = paddle.linalg.cholesky_solve(x, y, upper=self.upper)
self.assertEqual(z_sci.shape, z.numpy().shape)
self.assertTrue(np.allclose(z_sci, z.numpy()))
for idx, place in enumerate(self.place):
run(place)
class TestCholeskySolveOpError(unittest.TestCase):
def test_errors(self):
paddle.enable_static()
with program_guard(Program(), Program()):
# The input type of solve_op must be Variable.
x1 = fluid.create_lod_tensor(
np.array([[-1]]), [[1]], fluid.CPUPlace())
y1 = fluid.create_lod_tensor(
np.array([[-1]]), [[1]], fluid.CPUPlace())
self.assertRaises(TypeError, paddle.linalg.cholesky_solve, x1, y1)
# The data type of input must be float32 or float64.
x2 = fluid.data(name="x2", shape=[30, 30], dtype="bool")
y2 = fluid.data(name="y2", shape=[30, 10], dtype="bool")
self.assertRaises(TypeError, paddle.linalg.cholesky_solve, x2, y2)
x3 = fluid.data(name="x3", shape=[30, 30], dtype="int32")
y3 = fluid.data(name="y3", shape=[30, 10], dtype="int32")
self.assertRaises(TypeError, paddle.linalg.cholesky_solve, x3, y3)
x4 = fluid.data(name="x4", shape=[30, 30], dtype="float16")
y4 = fluid.data(name="y4", shape=[30, 10], dtype="float16")
self.assertRaises(TypeError, paddle.linalg.cholesky_solve, x4, y4)
# The number of dimensions of input'X must be >= 2.
x5 = fluid.data(name="x5", shape=[30], dtype="float64")
y5 = fluid.data(name="y5", shape=[30, 30], dtype="float64")
self.assertRaises(ValueError, paddle.linalg.cholesky_solve, x5, y5)
# The number of dimensions of input'Y must be >= 2.
x6 = fluid.data(name="x6", shape=[30, 30], dtype="float64")
y6 = fluid.data(name="y6", shape=[30], dtype="float64")
self.assertRaises(ValueError, paddle.linalg.cholesky_solve, x6, y6)
# The inner-most 2 dimensions of input'X should be equal to each other
x7 = fluid.data(name="x7", shape=[2, 3, 4], dtype="float64")
y7 = fluid.data(name="y7", shape=[2, 4, 3], dtype="float64")
self.assertRaises(ValueError, paddle.linalg.cholesky_solve, x7, y7)
self.check_output(check_eager=True)
# def test_check_grad_normal(self):
# self.check_grad(['Y'], 'Out', max_relative_error=0.01, check_eager=True)
# # 3D(broadcast) + 3D, upper=True
# class TestCholeskySolveOp3(TestCholeskySolveOp):
# """
# case 3
# """
# def config(self):
# self.y_shape = [1, 10, 10]
# self.x_shape = [2, 10, 5]
# self.upper = True
# self.dtype = np.float64
# class TestCholeskySolveAPI(unittest.TestCase):
# def setUp(self):
# np.random.seed(2021)
# self.place = [paddle.CPUPlace()]
# # self.place = [paddle.CUDAPlace(0)]
# self.dtype = "float64"
# self.upper = True
# if core.is_compiled_with_cuda():
# self.place.append(paddle.CUDAPlace(0))
# def check_static_result(self, place):
# paddle.enable_static()
# with fluid.program_guard(fluid.Program(), fluid.Program()):
# x = fluid.data(name="x", shape=[10, 2], dtype=self.dtype)
# y = fluid.data(name="y", shape=[10, 10], dtype=self.dtype)
# z = paddle.linalg.cholesky_solve(x, y, upper=self.upper)
# x_np = np.random.random([10, 2]).astype(self.dtype)
# y_np = np.random.random([10, 10]).astype(self.dtype)
# if self.upper:
# umat = np.triu(y_np)
# else:
# umat = np.tril(y_np)
# z_np = cholesky_solution(umat, x_np, upper=self.upper)
# z2_np = scipy_cholesky_solution(umat, x_np, upper=self.upper)
# exe = fluid.Executor(place)
# fetches = exe.run(fluid.default_main_program(),
# feed={"x": x_np,
# "y": umat},
# fetch_list=[z])
# self.assertTrue(np.allclose(fetches[0], z_np))
# def test_static(self):
# for place in self.place:
# self.check_static_result(place=place)
# def test_dygraph(self):
# def run(place):
# paddle.disable_static(place)
# x_np = np.random.random([20, 2]).astype(self.dtype)
# y_np = np.random.random([20, 20]).astype(self.dtype)
# z_np = scipy_cholesky_solution(y_np, x_np, upper=self.upper)
# x = paddle.to_tensor(x_np)
# y = paddle.to_tensor(y_np)
# z = paddle.linalg.cholesky_solve(x, y, upper=self.upper)
# self.assertTrue(np.allclose(z_np, z.numpy()))
# self.assertEqual(z_np.shape, z.numpy().shape)
# paddle.enable_static()
# for idx, place in enumerate(self.place):
# run(place)
# def test_boardcast(self):
# def run(place):
# paddle.disable_static()
# x_np = np.random.random([1, 30, 2]).astype(self.dtype)
# y_np = np.random.random([2, 30, 30]).astype(self.dtype)
# nx_np = np.concatenate((x_np, x_np), axis=0)
# z_sci = scipy_cholesky_solution_batch(y_np, nx_np, upper=self.upper)
# x = paddle.to_tensor(x_np)
# y = paddle.to_tensor(y_np)
# z = paddle.linalg.cholesky_solve(x, y, upper=self.upper)
# self.assertEqual(z_sci.shape, z.numpy().shape)
# self.assertTrue(np.allclose(z_sci, z.numpy()))
# for idx, place in enumerate(self.place):
# run(place)
# class TestCholeskySolveOpError(unittest.TestCase):
# def test_errors(self):
# paddle.enable_static()
# with program_guard(Program(), Program()):
# # The input type of solve_op must be Variable.
# x1 = fluid.create_lod_tensor(
# np.array([[-1]]), [[1]], fluid.CPUPlace())
# y1 = fluid.create_lod_tensor(
# np.array([[-1]]), [[1]], fluid.CPUPlace())
# self.assertRaises(TypeError, paddle.linalg.cholesky_solve, x1, y1)
# # The data type of input must be float32 or float64.
# x2 = fluid.data(name="x2", shape=[30, 30], dtype="bool")
# y2 = fluid.data(name="y2", shape=[30, 10], dtype="bool")
# self.assertRaises(TypeError, paddle.linalg.cholesky_solve, x2, y2)
# x3 = fluid.data(name="x3", shape=[30, 30], dtype="int32")
# y3 = fluid.data(name="y3", shape=[30, 10], dtype="int32")
# self.assertRaises(TypeError, paddle.linalg.cholesky_solve, x3, y3)
# x4 = fluid.data(name="x4", shape=[30, 30], dtype="float16")
# y4 = fluid.data(name="y4", shape=[30, 10], dtype="float16")
# self.assertRaises(TypeError, paddle.linalg.cholesky_solve, x4, y4)
# # The number of dimensions of input'X must be >= 2.
# x5 = fluid.data(name="x5", shape=[30], dtype="float64")
# y5 = fluid.data(name="y5", shape=[30, 30], dtype="float64")
# self.assertRaises(ValueError, paddle.linalg.cholesky_solve, x5, y5)
# # The number of dimensions of input'Y must be >= 2.
# x6 = fluid.data(name="x6", shape=[30, 30], dtype="float64")
# y6 = fluid.data(name="y6", shape=[30], dtype="float64")
# self.assertRaises(ValueError, paddle.linalg.cholesky_solve, x6, y6)
# # The inner-most 2 dimensions of input'X should be equal to each other
# x7 = fluid.data(name="x7", shape=[2, 3, 4], dtype="float64")
# y7 = fluid.data(name="y7", shape=[2, 4, 3], dtype="float64")
# self.assertRaises(ValueError, paddle.linalg.cholesky_solve, x7, y7)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_softmax_op.py
浏览文件 @ 9cd5cd4e
......@@ -53,6 +53,7 @@ class TestSoftmaxOp(OpTest):
def setUp(self):
self.op_type = "softmax"
self.python_api = paddle.nn.functional.softmax
self.use_cudnn = False
self.use_mkldnn = False
# explicilty use float32 for ROCm, as MIOpen does not yet support float64
......@@ -81,9 +82,13 @@ class TestSoftmaxOp(OpTest):
if self.use_cudnn:
place = core.CUDAPlace(0)
self.check_output_with_place(
place, atol=1e-5, check_dygraph=(self.use_mkldnn == False))
place,
atol=1e-5,
check_dygraph=(self.use_mkldnn == False),
check_eager=False)
else:
self.check_output(check_dygraph=(self.use_mkldnn == False))
self.check_output(
check_dygraph=(self.use_mkldnn == False), check_eager=False)
def test_check_grad(self):
# TODO(wangzhongpu): support mkldnn op in dygraph mode
......
python/paddle/fluid/tests/unittests/test_unfold_op.py
浏览文件 @ 9cd5cd4e
......@@ -21,6 +21,7 @@ from op_test import OpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.framework import _test_eager_guard
class TestUnfoldOp(OpTest):
......@@ -37,6 +38,7 @@ class TestUnfoldOp(OpTest):
self.strides = [1, 1]
self.paddings = [1, 1, 1, 1]
self.dilations = [1, 1]
self.python_api = paddle.nn.functional.unfold
input_shape = [
self.batch_size, self.input_channels, self.input_height,
self.input_width
......@@ -95,10 +97,10 @@ class TestUnfoldOp(OpTest):
self.set_data()
def test_check_output(self):
self.check_output()
self.check_output(check_eager=True)
def test_check_grad(self):
self.check_grad(['X'], 'Y')
self.check_grad(['X'], 'Y', check_eager=True)
class TestUnfoldAPI(TestUnfoldOp):
......@@ -122,9 +124,18 @@ class TestUnfoldAPI(TestUnfoldOp):
result = m(input)
self.assertTrue(np.allclose(result.numpy(), self.outputs['Y']))
with _test_eager_guard():
input = fluid.dygraph.to_variable(self.inputs['X'])
m = paddle.nn.Unfold(**self.attrs)
m.eval()
result = m(input)
self.assertTrue(
np.allclose(result.numpy(), self.outputs['Y']))
def test_info(self):
str(paddle.nn.Unfold(**self.attrs))
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
python/paddle/metric/metrics.py
浏览文件 @ 9cd5cd4e
......@@ -798,8 +798,12 @@ def accuracy(input, label, k=1, correct=None, total=None, name=None):
total = _varbase_creator(dtype="int32")
topk_out, topk_indices = paddle.topk(input, k=k)
if _in_eager_mode:
_acc = _C_ops.final_state_accuracy(topk_out, topk_indices, label)
return _acc
_acc, _, _ = _C_ops.accuracy(topk_out, topk_indices, label, correct,
total)
return _acc
helper = LayerHelper("accuracy", **locals())
......
python/paddle/nn/functional/activation.py
浏览文件 @ 9cd5cd4e
......@@ -561,6 +561,8 @@ def relu(x, name=None):
"""
if in_dynamic_mode():
if _in_eager_mode():
return _C_ops.final_state_relu(x)
return _C_ops.relu(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'relu')
......@@ -954,6 +956,8 @@ def softmax(x, axis=-1, dtype=None, name=None):
if in_dynamic_mode():
outs_cast = x if dtype is None \
else _C_ops.cast(x, 'in_dtype', x.dtype, 'out_dtype', dtype)
if _in_eager_mode():
return _C_ops.final_state_softmax(outs_cast, axis)
return _C_ops.softmax(outs_cast, 'axis', axis, 'use_cudnn', use_cudnn)
if dtype is None:
......
python/paddle/tensor/linalg.py
浏览文件 @ 9cd5cd4e
......@@ -14,7 +14,7 @@
import numpy as np
from ..fluid.layer_helper import LayerHelper
from ..framework import _varbase_creator, _dygraph_tracer
from ..framework import _varbase_creator, _dygraph_tracer, _in_eager_mode
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype
from ..static import Variable
......@@ -1205,6 +1205,8 @@ def cholesky(x, upper=False, name=None):
"""
if paddle.in_dynamic_mode():
if _in_eager_mode():
return _C_ops.final_state_cholesky(x, upper)
return _C_ops.cholesky(x, "upper", upper)
check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')
check_type(upper, 'upper', bool, 'cholesky')
......@@ -2734,6 +2736,8 @@ def cholesky_solve(x, y, upper=False, name=None):
# [-2.5, -7, 9.5]
"""
if paddle.in_dynamic_mode():
if _in_eager_mode():
return _C_ops.final_state_cholesky_solve(x, y, upper)
return _C_ops.cholesky_solve(x, y, 'upper', upper)
helper = LayerHelper("cholesky_solve", **locals())
......
python/paddle/tensor/logic.py
浏览文件 @ 9cd5cd4e
......@@ -17,6 +17,7 @@ from ..fluid.data_feeder import check_type, check_variable_and_dtype
from ..fluid.layers.layer_function_generator import templatedoc
from ..static import Variable
from ..framework import VarBase as Tensor
from paddle.fluid.framework import _in_eager_mode
# TODO: define logic functions of a tensor
from ..fluid.layers import is_empty # noqa: F401
......@@ -452,6 +453,8 @@ def _bitwise_op(op_name, x, y, out=None, name=None, binary_op=True):
if binary_op:
return op(x, y)
else:
if _in_eager_mode():
return _C_op.final_state_bitewise_not(x)
return op(x)
check_variable_and_dtype(
......
python/paddle/tensor/math.py
浏览文件 @ 9cd5cd4e
......@@ -2853,6 +2853,8 @@ def tanh(x, name=None):
# [-0.37994896 -0.19737532 0.09966799 0.29131261]
"""
if paddle.in_dynamic_mode():
if _in_eager_mode():
return _C_ops.final_state_tanh( x )
return _C_ops.tanh(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'tanh')
......
python/paddle/tensor/search.py
浏览文件 @ 9cd5cd4e
......@@ -91,6 +91,8 @@ def argsort(x, axis=-1, descending=False, name=None):
# [0 2 1 1]]]
"""
if paddle.in_dynamic_mode():
if _in_eager_mode():
_, ids, = _C_ops.final_state_argsort(x, axis, descending)
_, ids = _C_ops.argsort(x, 'axis', axis, 'descending', descending)
return ids
check_variable_and_dtype(
......@@ -245,6 +247,9 @@ def argmin(x, axis=None, keepdim=False, dtype="int64", name=None):
axis = 0
if paddle.in_dynamic_mode():
if _in_eager_mode():
out = _C_ops.final_state_arg_min(x, axis, keepdim, flattern,
var_dtype)
out = _C_ops.arg_min(x, 'axis', axis, 'dtype', var_dtype, 'keepdims',
keepdim, 'flatten', flatten)
return out
......
python/paddle/utils/code_gen/api.yaml
浏览文件 @ 9cd5cd4e
......@@ -262,56 +262,59 @@
# func : maxout
# backward : maxout_grad
# # put_along_axis
# - api : put_along_axis
# args : (Tensor x, Tensor index, Tensor value, int axis, string reduce)
# output : Tensor
# infer_meta :
# func : PutAlongAxisInferMeta
# kernel :
# func : put_along_axis
# backward : put_along_axis_grad
# put_along_axis
- api : put_along_axis
args : (Tensor x, Tensor index, Tensor value, int axis, str reduce)
output : Tensor
infer_meta :
func : UnchangedInferMeta
param : [index]
kernel :
func : put_along_axis
backward : put_along_axis_grad
# # take_along_axis
# - api : take_along_axis
# args : (Tensor x, Tensor index, int axis)
# output : Tensor
# infer_meta :
# func : TakeAlongAxisInferMeta
# kernel :
# func : take_along_axis
# backward : take_along_axis_grad
# take_along_axis
- api : take_along_axis
args : (Tensor x, Tensor index, int axis)
output : Tensor
infer_meta :
func : UnchangedInferMeta
param : [index]
kernel :
func : take_along_axis
backward : take_along_axis_grad
# # matrix_power
# - api : maxtrix_power
# args : (Tensor x, int n)
# output : Tensor
# infer_meta :
# func : MaxtrixPowerInferMeta
# kernel :
# func : maxtrix_power
# backward : maxtrix_power_grad
# matrix_power
- api : matrix_power
args : (Tensor x, int n)
output : Tensor
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : matrix_power
backward : matrix_power_grad
# # eigh
# - api : eigh
# args : (Tensor x, string uplo)
# output : Tensor(out_w), Tensor(out_v)
# infer_meta :
# func : EighInferMeta
# kernel :
# func : eigh
# backward : eigh_grad
# eigh
- api : eigh
args : (Tensor x, str uplo)
output : Tensor(out_w), Tensor(out_v)
infer_meta :
func : EighInferMeta
kernel :
func : eigh
backward : eigh_grad
# # segment_pool
# - api : segment_pool
# args : (Tensor x, Tensor segment_ids, string pooltype)
# output : Tensor(out), Tensor(summed_ids)
# infer_meta :
# func : SegmentPoolInferMeta
# kernel :
# func : segment_pool
# backward : segment_pool_grad
# segment_pool
- api : segment_pool
args : (Tensor x, Tensor segment_ids, str pooltype)
output : Tensor(out), Tensor(summed_ids)
infer_meta :
func : SegmentPoolInferMeta
kernel :
func : segment_pool
backward : segment_pool_grad
# accuracy
- api : accuracy
......@@ -447,44 +450,45 @@
# arg_min # int64 ???? dtype
- api : agrmin
args : (Tensor x, int axis, bool keepdims, bool flatten, int dtype)
- api : arg_min
args : (Tensor x, int64 axis, bool keepdims, bool flatten, int dtype)
output : Tensor
infer_meta :
func : ArgMinMaxInferMeta
kernel :
func : argmin
func : arg_min
# arg_max # int64 ???? dtype
- api : agrmax
args : (Tensor x, int axis, bool keepdims, bool flatten, int dtype)
- api : arg_max
args : (Tensor x, int64 axis, bool keepdims, bool flatten, int dtype)
output : Tensor
infer_meta :
func : ArgMinMaxInferMeta
kernel :
func : argmax
func : arg_max
# argsort; kernel why input?
# argsort
- api : argsort
args : (Tensor x, int axis, bool descending)
output : Tensor(out), Tensor(indices)
infer_meta :
func : ArgsortInferMeta
func : ArgsortInferMeta
kernel :
func : argsort
backward : argsort_grad
# auc; kernel why input?
# - api : auc
# args : (Tensor x, Tensor label, Tensor stat_pos, Tensor stat_neg, str curve, int num_thresholds, int slide_steps)
# output : Tensor(auc), Tensor(stat_pos_out), Tensor(stat_neg_out)
# infer_meta :
# func : AucInferMeta
# kernel :
# func : auc
# auc
- api : auc
args : (Tensor x, Tensor label, Tensor stat_pos, Tensor stat_neg, str curve, int num_thresholds, int slide_steps)
output : Tensor(auc), Tensor(stat_pos_out), Tensor(stat_neg_out)
infer_meta :
func : AucInferMeta
kernel :
func : auc
# # batch_norm
# - api : batch_norm
# args : (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, string data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
# args : (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
# output : Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
# infer_meta :
# func : XXXXInferMeta
......@@ -492,14 +496,14 @@
# func : batch_norm
# backward: batch_norm_grad
# # bernoulli
# - api : bernoulli
# args : (Tensor x)
# output : Tensor
# infer_meta :
# func : BernoulliInferMeta
# kernel :
# func : bernoulli
# bernoulli
- api : bernoulli
args : (Tensor x)
output : Tensor
infer_meta :
func : UnchangedInferMeta
kernel :
func : bernoulli
# # bilinear_tensor_product ?? optional
# - api : bilinear_tensor_product
......@@ -510,15 +514,18 @@
# kernel :
# func : bilinear_tensor_product
# backward : bilinear_tensor_product_grad
# optional : bias
# bincount
- api : bincount
args : (Tensor x, Tensor weight, int minlength)
output : Tensor
infer_meta :
func : BincountInferMeta
kernel :
func : bincount
optional : weight
# bincount ?? optional
# - api : bincount
# args : (Tensor x, Tensor weight, int minlength)
# output : Tensor
# infer_meta :
# func : BincountInferMeta
# kernel :
# func : bincount
# bitwise_and
......@@ -604,7 +611,7 @@
# # depthwise_conv2d
# # dropout ?? optional, intermediate
# - api : dropout
# args : (Tensor x, Tensor seed_tensor, float p, bool is_test, string mode, int seed, bool fix_seed)
# args : (Tensor x, Tensor seed_tensor, float p, bool is_test, str mode, int seed, bool fix_seed)
# output : Tensor(out), Tensor(mask)
# infer_meta :
# func : DropoutInferMeta
......@@ -613,27 +620,27 @@
# # embedding
# # erf
# - api : erf
# args : (Tensor x)
# output : Tensor
# infer_meta :
# func : ErfInferMeta
# kernel :
# func : erf
# backward : erf_grad
# erf
- api : erf
args : (Tensor x)
output : Tensor
infer_meta :
func : UnchangedInferMeta
kernel :
func : erf
backward : erf_grad
# # erfinv
# - api : erfinv
# args : (Tensor x)
# output : Tensor
# infer_meta :
# func : ErfinvInferMeta
# kernel :
# func : erfinv
# backward : erfinv_grad
# erfinv
- api : erfinv
args : (Tensor x)
output : Tensor
infer_meta :
func : UnchangedInferMeta
kernel :
func : erfinv
backward : erfinv_grad
# # expand_as ?? optional
# expand_as
# - api : expand_as
# args : (Tensor x, Tensor y, int[] shape)
# output : Tensor
......@@ -642,6 +649,7 @@
# kernel :
# func : expand_as
# backward : expand_as_grad
# optional : y
# # expand
......@@ -655,13 +663,13 @@
# backward : expand_grad
# eye
- api : eye
args : (int64 num_rows, int64 num_colums, DataType dtype)
output : Tensor
infer_meta :
func : EyeInferMeta
kernel :
func : eye
# - api : eye
# args : (int64 num_rows, int64 num_colums, DataType dtype = DataType::FLOAT32)
# output : Tensor
# infer_meta :
# func : EyeInferMeta
# kernel :
# func : eye
# # flip
# - api : flip
......@@ -674,16 +682,18 @@
# gaussian_random
# - api : gaussian_random
# args : (ScalayArray shape, float mean, float std, int seed, DataType dtype)
# args : (ScalarArray shape, float mean, float std, int seed, DataType dtype=DataType::FLOAT32)
# output : Tensor
# infer_meta :
# func : GaussianRandomInferMeta
# func : CreateInferMeta
# param : [shape, dtype]
# kernel :
# func : gaussian_random
# data_type : dtype
# # graph_send_recv
# - api : graph_send_recv
# args : (Tensor x, Tensor src_index, Tensor dst_index, string pool_type)
# args : (Tensor x, Tensor src_index, Tensor dst_index, str pool_type)
# output : Tensor(out), Tensor(dst_count)
# infer_meta :
# func : GraphSendRecvInferMeta
......@@ -693,30 +703,30 @@
# # histogram int64 ???
# - api : histogram
# args : (Tensor x, int64_t bins, int min, int max)
# args : (Tensor x, int64 bins, int min, int max)
# output : Tensor
# infer_meta :
# func : HistogramInferMeta
# kernel :
# func : histogram
# # increment
# - api : increment
# args : (Tensor x, float value)
# output : Tensor
# infer_meta :
# func : IncrementInferMeta
# kernel :
# func : increment
# increment
- api : increment
args : (Tensor x, float value)
output : Tensor
infer_meta :
func : IncrementInferMeta
kernel :
func : increment
# # is_empty
# - api : is_empty
# args : (Tensor x)
# output : Tensor
# infer_meta :
# func : IsEmptyInferMeta
# kernel :
# func : is_empty
# is_empty
- api : is_empty
args : (Tensor x)
output : Tensor
infer_meta :
func : IsEmptyInferMeta
kernel :
func : is_empty
# # isinf selected rows??? involk
# - api : isinf
......@@ -727,7 +737,7 @@
# kernel :
# func : isinf
# # isnan
# # isnan selected rows??? involk
# - api : isnan
# args : (Tensor x)
# output : Tensor
......@@ -736,7 +746,7 @@
# kernel :
# func : isnan
# # isfinite
# # isfinite selected rows??? involk
# - api : isfinite
# args : (Tensor x)
# output : Tensor
......@@ -745,34 +755,37 @@
# kernel :
# func : isfinite
# # label_smooth ?? optional
# label_smooth ?? optional
# - api : label_smooth
# args : (Tensor label, Tensor prior_dist, float epsilon)
# output : Tensor
# infer_meta :
# func : LabelSmoothInferMeta
# func : UnchangedInferMeta
# param : [label]
# kernel :
# func : label_smooth
# backward : label_smooth_grad
# optional : prior_dist
# # linspace
# linspace ???? start stop number，应该是sclar？
# - api : linspace
# args : (Tensor start, Tensor stop, Tensor number, DataType dtype)
# args : (Tensor start, Tensor stop, Tensor number, DataType dtype=DataType::FLOAT32)
# output : Tensor
# infer_meta :
# func : LinspaceInferMeta
# kernel :
# func : linspace
# # log_loss
# - api : log_loss
# args : (Tensor input, Tensor label, float epsilon)
# output : Tensor
# infer_meta :
# func : LogLossInferMeta
# kernel :
# func : log_loss
# backward : log_loss
# log_loss
- api : log_loss
args : (Tensor input, Tensor label, float epsilon)
output : Tensor
infer_meta :
func : LogLossInferMeta
kernel :
func : log_loss
backward : log_loss_grad
# # logical_and
# - api : logical_and
......@@ -802,14 +815,14 @@
# kernel :
# func : logical_xor
# # logical_not
# - api : logical_not
# args : (Tensor x)
# output : Tensor
# infer_meta :
# func : UnchangedInferMeta
# kernel :
# func : logical_not
# logical_not
- api : logical_not
args : (Tensor x)
output : Tensor
infer_meta :
func : UnchangedInferMeta
kernel :
func : logical_not
# # masked_select
# - api : masked_select
......@@ -831,24 +844,25 @@
# func : multi_dot
# backward : multi_dot_grad
# # multinomial
# - api : multinomial
# args : (Tensor x, int num_samples, bool replacement)
# output : Tensor
# infer_meta :
# func : MultinomialInferMeta
# kernel :
# func : multinomial
# multinomial
- api : multinomial
args : (Tensor x, int num_samples, bool replacement)
output : Tensor
infer_meta :
func : MultinomialInferMeta
kernel :
func : multinomial
# # nll_loss ?? optional
# nll_loss ?? optional
# - api : nll_loss
# args : (Tensor input, Tensor label, Tensor weight, int64_t ignore_index, string reduction)
# args : (Tensor x, Tensor label, Tensor weight, int64 ignore_index, str reduction)
# output : Tensor(out), Tensor(total_weight)
# infer_meta :
# func : NllLossInferMeta
# func : NllLossRawInferMeta
# kernel :
# func : nll_loss
# backward : nll_loss_grad
# optional : weight
# # pad
# - api : pad
......@@ -862,7 +876,7 @@
# # pixel_shuffle
# - api : pixel_shuffle
# args : (Tensor x, int upscale_factor, string data_format)
# args : (Tensor x, int upscale_factor, str data_format)
# output : Tensor
# infer_meta :
# func : PixelShuffleInferMeta
......@@ -880,7 +894,7 @@
# func : poisson
# backward : poisson_grad
# # psroi_pool ?? optional
# psroi_pool ?? optional
# - api : psroi_pool
# args : (Tensor x, Tensor rois, Tensor rois_num, int pooled_weight, int pooled_width, int output_channels, float spatial_scale )
# output : Tensor
......@@ -889,6 +903,7 @@
# kernel :
# func : psroi_pool
# backward : psroi_pool_grad
# optional : rois_num
# # randint_raw
# - api : randint
......@@ -926,57 +941,59 @@
# kernel :
# func : reduce_prod
# # selu
# - api : selu
# args : (Tensor x, float scale, float alpha)
# output : Tensor
# infer_meta :
# func : SeluInferMeta
# kernel :
# func : selu
# backward : selu_grad
# selu
- api : selu
args : (Tensor x, float scale, float alpha)
output : Tensor
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : selu
backward : selu_grad
# # set_value None api
# # sgd # need invoke
# # shape ??? selcted rows
# # shard_index
# - api : shard_index
# args : (Tensor in, int index_num, int nshards, int shard_id, int ignore_value)
# output : Tensor
# infer_meta :
# func : ShardIndexInferMeta
# kernel :
# func : shard_index
# shard_index
- api : shard_index
args : (Tensor in, int index_num, int nshards, int shard_id, int ignore_value)
output : Tensor
infer_meta :
func : ShardIndexInferMeta
kernel :
func : shard_index
# # sigmoid_cross_entropy_with_logits
# - api : sigmoid_cross_entropy_with_logits
# args : (Tensor x, Tensor label, bool normalize, int ignore_index)
# output : Tensor
# infer_meta :
# func : SigmoidCrossEntropyWithoLgitsInferMeta
# kernel :
# func : sigmoid_cross_entropy_with_logits
# sigmoid_cross_entropy_with_logits
- api : sigmoid_cross_entropy_with_logits
args : (Tensor x, Tensor label, bool normalize, int ignore_index)
output : Tensor
infer_meta :
func : SigmoidCrossEntropyWithLogitsInferMeta
kernel :
func : sigmoid_cross_entropy_with_logits
backward : sigmoid_cross_entropy_with_logits_grad
# # size
# - api : size
# args : (Tensor x)
# output : Tensor
# infer_meta :
# func : SizeInferMeta
# kernel :
# func : size
# size
- api : size
args : (Tensor x)
output : Tensor
infer_meta :
func : SizeInferMeta
kernel :
func : size
# # tile
# - api : tile
# args : (Tensor x, ScalarArray repeat_times)
# output : Tensor
# infer_meta :
# func : TileInferMeta
# kernel :
# func : tile
# backward : tile_grad
# tile
- api : tile
args : (Tensor x, ScalarArray repeat_times)
output : Tensor
infer_meta :
func : TileInferMeta
kernel :
func : tile
backward : tile_grad
# # top_k
# - api : top_k
......@@ -988,15 +1005,15 @@
# func : top_k
# backward : top_k_grad
# # trace
# - api : trace
# args : (Tensor x, int offset, int axis1, int axis2)
# output : Tensor
# infer_meta :
# func : TraceInferMeta
# kernel :
# func : trace
# backward : trace_grad
# trace
- api : trace
args : (Tensor x, int offset, int axis1, int axis2)
output : Tensor
infer_meta :
func : TraceInferMeta
kernel :
func : trace
backward : trace_grad
# # phi_transfer_layout | not have python api
......@@ -1018,18 +1035,19 @@
# kernel :
# func : unbind
# # unfold
# - api : unfold
# args : (Tensor x, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations)
# output : Tensor
# infer_meta :
# func : UnfoldInferMeta
# kernel :
# func : unfold
# backward : unfold_grad
# unfold
- api : unfold
args : (Tensor x, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations)
output : Tensor
infer_meta :
func : UnfoldInferMeta
kernel :
func : unfold
backward : unfold_grad
# # uniform_random_raw selected rows ??
# # viterbi_decode
# viterbi_decode
# - api : viterbi_decode
# args : (Tensor input, Tensor transition, Tensor length, bool include_bos_eos_tag)
# output : Tensor(scores), Tensor(path)
......@@ -1038,14 +1056,14 @@
# kernel :
# func : viterbi_decode
# # where_index
# - api : where_index
# args : (Tensor condition)
# output : Tensor
# infer_meta :
# func : WhereIndexInferMeta
# kernel :
# func : where_index
# where_index
- api : where_index
args : (Tensor condition)
output : Tensor
infer_meta :
func : WhereIndexInferMeta
kernel :
func : where_index
# # yolo_box
......
python/paddle/utils/code_gen/backward.yaml
浏览文件 @ 9cd5cd4e
......@@ -112,55 +112,56 @@
# func : maxout_grad
# - backward_api : put_along_axis_grad
# forward : put_along_axis (Tensor x, Tensor index, Tensor value, int axis, string reduce) -> Tensor(out)
# args : (Tensor x, Tensor index, Tensor out_grad, int axis, string reduce)
# output : Tensor(x_grad), Tensor(value_grad)
# infer_meta :
# func : GeneralBinaryGradInferMeta
# param : [x, valule]
# kernel :
# func : put_along_axis_grad
# output is optional 如何处理
- backward_api : put_along_axis_grad
forward : put_along_axis (Tensor x, Tensor index, Tensor value, int axis, str reduce) -> Tensor(out)
args : (Tensor x, Tensor index, Tensor out_grad, int axis, str reduce)
output : Tensor(x_grad), Tensor(value_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, index]
kernel :
func : put_along_axis_grad
# - backward_api : take_along_axis_grad
# forward : take_along_axis (Tensor x, Tensor index, int axis) -> Tensor(out)
# args : (Tensor x, Tensor index, Tensor out_grad, int axis)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : take_along_axis_grad
- backward_api : take_along_axis_grad
forward : take_along_axis (Tensor x, Tensor index, int axis) -> Tensor(out)
args : (Tensor x, Tensor index, Tensor out_grad, int axis)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : take_along_axis_grad
# - backward_api : maxtrix_power_grad
# forward : maxtrix_power (Tensor x, int n) -> Tensor(out)
# args : (Tensor x, Tensor out, Tensor out_grad, int n)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : maxtrix_power_grad
- backward_api : matrix_power_grad
forward : matrix_power (Tensor x, int n) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int n)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : matrix_power_grad
# - backward_api : eigh_grad
# forward : eigh (Tensor x, string uplo) -> Tensor(out_w), Tensor(out_v)
# args : (Tensor out_w, Tensor out_v, Tensor out_w_grad, Tensor out_v_grad)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [out_v]
# kernel :
# func : eigh_grad
- backward_api : eigh_grad
forward : eigh (Tensor x, str uplo) -> Tensor(out_w), Tensor(out_v)
args : (Tensor out_w, Tensor out_v, Tensor out_w_grad, Tensor out_v_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out_v]
kernel :
func : eigh_grad
# - backward_api : segment_pool_grad
# forward : segment_pool (Tensor x, Tensor segment_ids, string pooltype) -> Tensor(out), Tensor(summed_ids)
# args : (Tensor x, Tensor segment_ids, Tensor out, Tensor summed_ids, Tenosr out_grad, string pooltype)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : segment_pool_grad
- backward_api : segment_pool_grad
forward : segment_pool (Tensor x, Tensor segment_ids, str pooltype) -> Tensor(out), Tensor(summed_ids)
args : (Tensor x, Tensor segment_ids, Tensor out, Tensor summed_ids, Tensor out_grad, str pooltype)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : segment_pool_grad
- backward_api : cos_grad
forward : cos (Tensor x) -> Tensor(out)
......@@ -282,19 +283,19 @@
kernel :
func : relu_grad
# - backward_api : argsort_grad
# forward : argsort (Tensor x, int axis, bool descending) -> Tensor(out), tensor(indices)
# args : (Tensor indices, Tensor x, Tensor out_grad, int axis, bool descending)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : argsort_grad
- backward_api : argsort_grad
forward : argsort (Tensor x, int axis, bool descending) -> Tensor(out), Tensor(indices)
args : (Tensor indices, Tensor x, Tensor out_grad, int axis, bool descending)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : argsort_grad
# - backward_api : batch_norm_grad
# forward : batch_norm (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, string data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu) -> Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
# forward : batch_norm (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu) -> Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
# args : (Tensor indices, Tensor x, Tensor out_grad, int axis, bool descending)
# output : Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad)
# infer_meta :
......@@ -312,6 +313,7 @@
# param : [x, y, weight, bias]
# kernel :
# func : bilinear_tensor_product_grad
# optional : bias
# - backward_api : broadcast_tensor_grad
# forward : broadcast_tensors (Tensor[] x) -> Tensor [] (out)
......@@ -344,8 +346,8 @@
func : cholesky_solve_grad
# - backward_api : dropout_grad
# forward : dropout (Tensor x, Tensor seed_tensor, float p, bool is_test, string mode, int seed, bool fix_seed) -> Tensor(out), Tensor(mask)
# args : (Tensor mask, Tensor out_grad, float p, bool is_test, string mode)
# forward : dropout (Tensor x, Tensor seed_tensor, float p, bool is_test, str mode, int seed, bool fix_seed) -> Tensor(out), Tensor(mask)
# args : (Tensor mask, Tensor out_grad, float p, bool is_test, str mode)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
......@@ -354,25 +356,25 @@
# func : dropout_grad
# - backward_api : erf_grad
# forward : erf (Tensor x) -> Tensor(out)
# args : (Tensor x, Tensor out, Tensor out_grad)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : erf_grad
- backward_api : erf_grad
forward : erf (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : erf_grad
# - backward_api : erfinv_grad
# forward : erf (Tensor x) -> Tensor(out)
# args : (Tensor out, Tensor out_grad)
# output : Tensor(x_grad)
# infer_meta :
# func : ErfinvGradInferMeta
# param : [out]
# kernel :
# func : erfinv_grad
- backward_api : erfinv_grad
forward : erf (Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out]
kernel :
func : erfinv_grad
# - backward_api : expand_as_grad
# forward : expand_as (Tensor x, Tensor y, int[] target_shape) -> Tensor(out)
......@@ -395,11 +397,11 @@
# func : expand_grad
# - backward_api : graph_send_recv_grad
# forward : graph_send_recv (Tensor x, Tensor src_index, Tensor dst_index, string pool_type) -> Tensor(out), Tensor(dst_count)
# args : (Tensor out_grad, Tensor x, Tensor out, Tensor src_index, Tensor dst_index, Tensor dst_count, string pool_type)
# forward : graph_send_recv (Tensor x, Tensor src_index, Tensor dst_index, str pool_type) -> Tensor(out), Tensor(dst_count)
# args : (Tensor out_grad, Tensor x, Tensor out, Tensor src_index, Tensor dst_index, Tensor dst_count, str pool_type)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedGradInferMeta
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : graph_send_recv_grad
......@@ -407,22 +409,22 @@
# - backward_api : label_smooth_grad
# forward : label_smooth (Tensor label, Tensor prior_dist, float epsilon) -> Tensor(out)
# args : (Tensor out_grad, float epsilon)
# output : Tensor(x_grad)
# output : Tensor(label_grad)
# infer_meta :
# func : XXXXInferMeta
# param : [x]
# func : UnchangedInferMeta
# param : [out_grad]
# kernel :
# func : label_smooth_grad
# - backward_api : log_loss_grad
# forward : log_loss (Tensor input, Tensor label, float epsilon) -> Tensor(out)
# args : (Tensor input, Tensor label, Tensor out_grad, float epsilon)
# output : Tensor(input_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : log_loss_grad
- backward_api : log_loss_grad
forward : log_loss (Tensor input, Tensor label, float epsilon) -> Tensor(out)
args : (Tensor input, Tensor label, Tensor out_grad, float epsilon)
output : Tensor(input_grad)
infer_meta :
func : UnchangedInferMeta
param : [input]
kernel :
func : log_loss_grad
# - backward_api : masked_selecte_grad
# forward : masked_select (Tensor x, Tensor mask) -> Tensor(out)
......@@ -445,8 +447,8 @@
# func : multi_dot_grad
# - backward_api : nll_loss_grad
# forward : nll_loss (Tensor input, Tensor label, Tensor weight, int64_t ignore_index, string reduction) -> Tensor(out), Tensor(total_weight)
# args : (Tensor x, Tensor label, Tensor total_weight, Tensor weight, Tensor out_grad, int64_t ignore_index, string reduction)
# forward : nll_loss (Tensor x, Tensor label, Tensor weight, int64 ignore_index, str reduction) -> Tensor(out), Tensor(total_weight)
# args : (Tensor x, Tensor label, Tensor total_weight, Tensor weight, Tensor out_grad, int64 ignore_index, str reduction)
# output : Tensor[] (x_grad)
# infer_meta :
# func : UnchangedInferMeta
......@@ -465,8 +467,8 @@
# func : pad_grad
# - backward_api : pixel_shuffle_grad
# forward : pixel_shuffle (Tensor x, int upscale_factor, string data_format) -> Tensor(out)
# args : (Tensor out_grad, int upscale_factor, string data_format)
# forward : pixel_shuffle (Tensor x, int upscale_factor, str data_format) -> Tensor(out)
# args : (Tensor out_grad, int upscale_factor, str data_format)
# output : Tensor(x_grad)
# infer_meta :
# func : XXXXXInferMeta
......@@ -493,36 +495,37 @@
# param : [x]
# kernel :
# func : psroi_pool_grad
# optional : rois_num
# - backward_api : selu_grad
# forward : selu (Tensor x, float scale, float alpha) -> Tensor(out)
# args : (Tensor out, Tensor out_grad, float scale, float alpha)
# output : Tensor(x_grad)
# infer_meta :
# func : XXXXXInferMeta
# param : [x]
# kernel :
# func : selu_grad
- backward_api : selu_grad
forward : selu (Tensor x, float scale, float alpha) -> Tensor(out)
args : (Tensor out, Tensor out_grad, float scale, float alpha)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [out]
kernel :
func : selu_grad
# - backward_api : sigmoid_cross_entropy_with_logits_grad
# forward : sigmoid_cross_entropy_with_logits (Tensor x, Tensor label, bool normalize, int ignore_index) -> Tensor(out)
# args : (Tensor x, Tensor label, Tensor out_grad, bool normalize, int ingore_index)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : sigmoid_cross_entropy_with_logits_grad
- backward_api : sigmoid_cross_entropy_with_logits_grad
forward : sigmoid_cross_entropy_with_logits (Tensor x, Tensor label, bool normalize, int ignore_index) -> Tensor(out)
args : (Tensor x, Tensor label, Tensor out_grad, bool normalize, int ignore_index)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : sigmoid_cross_entropy_with_logits_grad
# - backward_api : tile_grad
# forward : tile (Tensor x, ScalarArray repeat_times) -> Tensor(out)
# args : (Tensor x, Tensor out_grad, ScalarArray repeat_times)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : tile_grad
- backward_api : tile_grad
forward : tile (Tensor x, ScalarArray repeat_times) -> Tensor(out)
args : (Tensor x, Tensor out_grad, ScalarArray repeat_times)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : tile_grad
# # forward backward type not match
# - backward_api : top_k_grad
......@@ -536,25 +539,25 @@
# func : top_k_grad
# - backward_api : trace_grad
# forward : trace (Tensor x, int offset, int axis1, int axis2) -> Tensor(out)
# args : (Tensor out_grad, Tensor x, int offset, int axis1, int axis2)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : trace_grad
- backward_api : trace_grad
forward : trace (Tensor x, int offset, int axis1, int axis2) -> Tensor(out)
args : (Tensor x, Tensor out_grad, int offset, int axis1, int axis2)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : trace_grad
# - backward_api : unfold_grad
# forward : unfold (Tensor x, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations) -> Tensor(out)
# args : (Tensor x, Tensor out_grad, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations)
# output : Tensor(x_grad)
# infer_meta :
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : unfold_grad
- backward_api : unfold_grad
forward : unfold (Tensor x, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations) -> Tensor(out)
args : (Tensor x, Tensor out_grad, int[] kernel_sizes, int[] strides, int[] paddings, int[] dilations)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : unfold_grad
# - backward_api : where_index_grad
# forward : where_index (Tensor condition) -> Tensor(out)
......@@ -564,4 +567,4 @@
# func : UnchangedInferMeta
# param : [x]
# kernel :
# func : trace_grad
# func : where_index_grad
python/paddle/utils/code_gen/wrapped_infermeta_gen.py
浏览文件 @ 9cd5cd4e
......@@ -43,9 +43,12 @@ PD_REGISTER_INFER_META_FN({api.kernel['func'][0]}, phi::{api.infer_meta['func']}
'const std::vector<Tensor>&': 'const std::vector<MetaTensor>&',
'Tensor': 'MetaTensor*',
'std::vector<Tensor>': 'std::vector<MetaTensor>*',
'const paddle::optional<Tensor&>':
'const paddle::optional<MetaTensor&>'
}
wrapped_infermeta_name = get_wrapped_infermeta_name(api.api)
print(wrapped_infermeta_name)
args = []
for input_name in api.inputs['names']:
if input_name in kernel_params:
......@@ -132,6 +135,7 @@ def generate_wrapped_infermeta_and_register(api_yaml_path, header_file_path,
for api in apis:
api_item = ForwardAPI(api)
#print( str(api_item) )
declare_code, defind_code, register_code = gene_wrapped_infermeta_and_register(
api_item)
header_file.write(declare_code)
......
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