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ont hot support tensor depth (#16972)

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* support some input tensor remain on cpu; test=develop

* fix input = none; test=develop

* fix unfound bug; test=develop

* fix proto None case; test=develop

* fix bug; test=develop

* fix proto null bug; test=develop

* remove conv check; test=develop

* fix test bug; test=develop

* move fill constant; test=develop

* no change in proto; test=develop

* fix bug; test=develop

* change attr detph name; test=develop

* remove remain cpu; test=develop

* fix bug; test=develop

* merge develop; test=develop

* fix one_hot bug; test=develop

* fix bug; test=develop

* fix bug; test=develop

* fix bug; test=develop

* fix python api bug; test=develop
dependabot/pip/python/requests-2.20.0
Hongyu Liu 6 years ago committed by GitHub
parent 7cfddf22c8
commit 82358bfdc1
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 141 additions and 15 deletions
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33
paddle/fluid/operators/one_hot_op.cc
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@ -13,6 +13,8 @@
// limitations under the License.
#include "paddle/fluid/operators/one_hot_op.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/framework.pb.h"
namespace paddle {
@ -34,15 +36,34 @@ class OneHotOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GE(x_dims[x_dims.size() - 1], 1U,
"Last dimension of Input(X) should be 1.");
}
int depth = ctx->Attrs().Get<int>("depth");
PADDLE_ENFORCE_GT(depth, 0, "Should provide a positive depth (%d).", depth);
framework::DDim out_dims(x_dims);
int depth = ctx->Attrs().Get<int>("depth");
if (ctx->HasInput("depth_tensor")) {
depth = -1;
}
out_dims[out_dims.size() - 1] = depth;
ctx->SetOutputDim("Out", out_dims);
ctx->ShareLoD("X", /* --> */ "Out");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.device_context());
}
framework::OpKernelType GetKernelTypeForVar(
const std::string& var_name, const Tensor& tensor,
const framework::OpKernelType& expected_kernel_type) const override {
if (var_name == "depth_tensor") {
return expected_kernel_type;
}
return framework::OpKernelType(expected_kernel_type.data_type_,
tensor.place(), tensor.layout());
}
};
class OneHotOpMaker : public framework::OpProtoAndCheckerMaker {
@ -52,11 +73,15 @@ class OneHotOpMaker : public framework::OpProtoAndCheckerMaker {
"(LoDTensor, LoDTensor<int>) Input variable with rank at least 2. "
"The last dimension of X should be 1. Each value of X is an index "
"to indicate the position.");
AddInput("depth_tensor", "(Tensor, Tensor<int>), Length of one-hot vector")
.AsDispensable();
AddOutput("Out",
"(Tensor, Tensor<float>) Output tensor with same rank as X. "
"The tensor consists of one-hot representations of values in X.");
AddAttr<int>("depth",
"A positive integer to specify the length of one-hot vector.");
"A positive integer to specify the length of one-hot vector.")
.SetDefault(-1);
AddAttr<int>("dtype",
"An integer to specify the data type of one-hot "
"vector. The default value is FP32.")

19
paddle/fluid/operators/one_hot_op.cu
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@ -62,8 +62,25 @@ class OneHotCUDAKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
int depth = context.Attr<int>("depth");
int depth = -1;
if (context.HasInput("depth_tensor")) {
auto* depth_tensor = context.Input<framework::Tensor>("depth_tensor");
if (platform::is_gpu_place(depth_tensor->place())) {
framework::Tensor temp;
TensorCopySync(*depth_tensor, platform::CPUPlace(), &temp);
depth = *temp.data<int32_t>();
} else {
depth = *depth_tensor->data<int32_t>();
}
auto in_dims = in->dims();
framework::DDim out_dims(in_dims);
out_dims[out_dims.size() - 1] = depth;
out->Resize(out_dims);
} else {
depth = context.Attr<int>("depth");
}
framework::VisitDataType(
static_cast<framework::proto::VarType::Type>(
context.Attr<int>("dtype")),

10
paddle/fluid/operators/one_hot_op.h
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@ -49,6 +49,7 @@ struct OneHotOpFunctor {
};
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
template <typename DeviceContext, typename T>
class OneHotKernel : public framework::OpKernel<T> {
public:
@ -56,6 +57,15 @@ class OneHotKernel : public framework::OpKernel<T> {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
int depth = context.Attr<int>("depth");
if (context.HasInput("depth_tensor")) {
auto* depth_tensor = context.Input<Tensor>("depth_tensor");
auto* depth_data = depth_tensor->data<int32_t>();
depth = depth_data[0];
auto in_dims = in->dims();
framework::DDim out_dims(in_dims);
out_dims[out_dims.size() - 1] = depth;
out->Resize(out_dims);
}
framework::VisitDataType(
static_cast<framework::proto::VarType::Type>(

11
python/paddle/fluid/framework.py
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@ -1013,7 +1013,7 @@ class Operator(object):
return
if type is None:
raise ValueError(
"`type` to initilized an Operator can not be None.")
"`type` to initialized an Operator can not be None.")
else:
callstack_var_name = op_maker.kOpCreationCallstackAttrName()
op_attrs[callstack_var_name] = list(
@ -1036,7 +1036,6 @@ class Operator(object):
found = find_name(inputs, in_proto.name)
assert found or in_proto.dispensable, "Input {} not found".format(
in_proto.name)
if found:
in_args = inputs[in_proto.name]
if not isinstance(in_args, list):
@ -1046,13 +1045,17 @@ class Operator(object):
"Input %s expects only one input, but %d are given."
% (in_proto.name, len(in_args)))
in_arg_names = []
for arg in in_args:
for index, arg in enumerate(in_args):
if isinstance(arg, six.string_types):
in_arg_names.append(arg)
elif isinstance(arg, six.binary_type):
in_arg_names.append(arg.decode())
else:
elif isinstance(arg, Variable):
in_arg_names.append(cpt.to_text(arg.name))
else:
raise ValueError(
"not suprt args type , should be[ string_type, binary_type, Varibale]"
)
self.desc.set_input(in_proto.name, in_arg_names)
else:
self.desc.set_input(in_proto.name, [])

17
python/paddle/fluid/layers/nn.py
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@ -6564,11 +6564,24 @@ def one_hot(input, depth):
one_hot_label = fluid.layers.one_hot(input=label, depth=10)
"""
helper = LayerHelper("one_hot", **locals())
one_hot_out = helper.create_variable_for_type_inference(dtype='float32')
if in_dygraph_mode():
inputs = {'X': input}
attrs = {'depth': depth}
else:
if not isinstance(depth, Variable):
# user attribute
inputs = {'X': input}
attrs = {'depth': depth}
else:
inputs = {'X': input, 'depth_tensor': depth}
attrs = {}
helper.append_op(
type="one_hot",
inputs={'X': input},
attrs={'depth': depth},
inputs=inputs,
attrs=attrs,
outputs={'Out': one_hot_out},
stop_gradient=True)
return one_hot_out

6
python/paddle/fluid/tests/unittests/test_layers.py
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@ -1267,6 +1267,12 @@ class TestBook(LayerTest):
out = layers.scatter(input=x, index=idx, updates=updates)
return (out)
def make_one_hot(self):
with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
label = self._get_data(name="label", shape=[1], dtype="int32")
one_hot_label = layers.one_hot(input=label, depth=10)
return (one_hot_label)
def make_label_smooth(self):
# TODO(minqiyang): support gpu ut
self._force_to_use_cpu = True

54
python/paddle/fluid/tests/unittests/test_one_hot_op.py
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@ -28,10 +28,34 @@ class TestOneHotOp(OpTest):
def setUp(self):
self.op_type = 'one_hot'
depth = 10
depth_np = np.array(10).astype('int32')
dimension = 12
x_lod = [[4, 1, 3, 3]]
x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
x = np.array(x).astype('int').reshape([sum(x_lod[0]), 1])
x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
out = np.zeros(shape=(np.product(x.shape[:-1]),
depth)).astype('float32')
for i in range(np.product(x.shape)):
out[i, x[i]] = 1.0
self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
self.attrs = {'dtype': int(core.VarDesc.VarType.FP32)}
self.outputs = {'Out': (out, x_lod)}
def test_check_output(self):
self.check_output()
class TestOneHotOp_attr(OpTest):
def setUp(self):
self.op_type = 'one_hot'
depth = 10
dimension = 12
x_lod = [[4, 1, 3, 3]]
x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
out = np.zeros(shape=(np.product(x.shape[:-1]),
depth)).astype('float32')
@ -40,7 +64,7 @@ class TestOneHotOp(OpTest):
out[i, x[i]] = 1.0
self.inputs = {'X': (x, x_lod)}
self.attrs = {'depth': depth, 'dtype': int(core.VarDesc.VarType.FP32)}
self.attrs = {'dtype': int(core.VarDesc.VarType.FP32), 'depth': depth}
self.outputs = {'Out': (out, x_lod)}
def test_check_output(self):
@ -48,13 +72,37 @@ class TestOneHotOp(OpTest):
class TestOneHotOp_default_dtype(OpTest):
def setUp(self):
self.op_type = 'one_hot'
depth = 10
depth_np = np.array(10).astype('int32')
dimension = 12
x_lod = [[4, 1, 3, 3]]
x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
out = np.zeros(shape=(np.product(x.shape[:-1]),
depth)).astype('float32')
for i in range(np.product(x.shape)):
out[i, x[i]] = 1.0
self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
self.attrs = {}
self.outputs = {'Out': (out, x_lod)}
def test_check_output(self):
self.check_output()
class TestOneHotOp_default_dtype_attr(OpTest):
def setUp(self):
self.op_type = 'one_hot'
depth = 10
dimension = 12
x_lod = [[4, 1, 3, 3]]
x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
x = np.array(x).astype('int').reshape([sum(x_lod[0]), 1])
x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
out = np.zeros(shape=(np.product(x.shape[:-1]),
depth)).astype('float32')

2
python/paddle/fluid/tests/unittests/test_operator_desc.py
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@ -33,7 +33,7 @@ class TestOperator(unittest.TestCase):
except ValueError as v_err:
self.assertEqual(
cpt.get_exception_message(v_err),
"`type` to initilized an Operator can not be None.")
"`type` to initialized an Operator can not be None.")
try:
block.append_op(type="no_such_op")
self.assertFail()

4
python/paddle/fluid/tests/unittests/test_recordio_reader.py
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@ -86,3 +86,7 @@ class TestRecordIO(unittest.TestCase):
def test_double_buffer_reader(self):
self.test_main(decorator_callback=lambda reader: fluid.layers.io.double_buffer(reader,
place='cuda:0' if fluid.core.is_compiled_with_cuda() else 'cpu'))
if __name__ == '__main__':
unittest.main()

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