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add paddle.tensor interface (#23801)

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* add paddle.tensor
test=develop

* polish gpu where codes
test=develop

* polish test code
test=develop
revert-22778-infer_var_type
GaoWei8 6 years ago committed by GitHub
parent e9289e8c22
commit 490db7f325
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 71 additions and 71 deletions
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12
paddle/fluid/operators/where_op.cu
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@ -30,15 +30,15 @@ __global__ void WhereCUDAKernel(const int N, const bool* cond, const T* x,
}
template <typename T>
__global__ void WhereGradCUDAKernel(const int N, const T* out, const bool* cond,
T* x, T* y) {
__global__ void WhereGradCUDAKernel(const int N, const T* dout,
const bool* cond, T* dx, T* dy) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
for (; idx < N; idx += blockDim.x * gridDim.x) {
if (x != nullptr) {
x[idx] = out[idx] * (cond[idx] ? 1. : 0.);
if (dx != nullptr) {
dx[idx] = cond[idx] ? dout[idx] : 0.;
}
if (y != nullptr) {
y[idx] = out[idx] * (cond[idx] ? 0. : 1.);
if (dy != nullptr) {
dy[idx] = cond[idx] ? 0. : dout[idx];
}
}
}

2
python/paddle/__init__.py
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@ -191,7 +191,7 @@ from .tensor.search import argmax #DEFINE_ALIAS
# from .tensor.search import has_nan #DEFINE_ALIAS
# from .tensor.search import masked_select #DEFINE_ALIAS
# from .tensor.search import topk #DEFINE_ALIAS
# from .tensor.search import where #DEFINE_ALIAS
from .tensor.search import where #DEFINE_ALIAS
from .tensor.search import index_select #DEFINE_ALIAS
from .tensor.search import index_sample #DEFINE_ALIAS
from .tensor.search import nonzero #DEFINE_ALIAS

123
python/paddle/fluid/tests/unittests/test_where_op.py
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@ -16,9 +16,9 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.tensor as tensor
import paddle.fluid.core as core
from op_test import OpTest
from paddle.fluid import compiler, Program, program_guard
@ -60,61 +60,64 @@ class TestWhereOp3(TestWhereOp):
class TestWhereAPI(unittest.TestCase):
def test_api(self, use_cuda=False):
main_program = Program()
with fluid.program_guard(main_program):
x = fluid.layers.data(name='x', shape=[4], dtype='float32')
y = fluid.layers.data(name='y', shape=[4], dtype='float32')
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float32")
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float32")
cond_i = np.array([False, False, True, True]).astype("bool")
result = tensor.where(x > 1, x=x, y=y)
def setUp(self):
self.init_data()
for use_cuda in [False, True]:
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
out = exe.run(fluid.default_main_program(),
feed={'x': x_i,
'y': y_i},
fetch_list=[result])
assert np.array_equal(out[0], np.where(cond_i, x_i, y_i))
def init_data(self):
self.shape = [10, 15]
self.cond = np.array(np.random.randint(2, size=self.shape), dtype=bool)
self.x = np.random.uniform(-2, 3, self.shape).astype(np.float32)
self.y = np.random.uniform(-2, 3, self.shape).astype(np.float32)
self.out = np.where(self.cond, self.x, self.y)
def test_grad(self, use_cuda=False):
main_program = Program()
with fluid.program_guard(main_program):
x = fluid.layers.data(name='x', shape=[4], dtype='float32')
y = fluid.layers.data(name='y', shape=[4], dtype='float32')
for x_stop_gradient, y_stop_gradient in [[False, False],
[True, False],
[False, True]]:
x.stop_gradient = x_stop_gradient
y.stop_gradient = y_stop_gradient
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float32")
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float32")
cond_i = np.array([False, False, True, True]).astype("bool")
result = tensor.where(x > 1, x=x, y=y)
x_mean = layers.mean(x)
append_backward(x_mean)
y_mean = layers.mean(y)
append_backward(y_mean)
for use_cuda in [False, True]:
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
out = exe.run(
fluid.default_main_program(),
feed={'x': x_i,
'y': y_i},
fetch_list=[result, x.grad_name, y.grad_name])
x_grad = [0.25] * 4
y_grad = [0.25] * 4
assert np.array_equal(out[0], np.where(cond_i, x_i, y_i))
assert np.array_equal(out[1], x_grad)
assert np.array_equal(out[2], y_grad)
def ref_x_backward(self, dout):
return np.where(self.cond == True, dout, 0)
def ref_y_backward(self, dout):
return np.where(self.cond == False, dout, 0)
def test_api(self, use_cuda=False):
for x_stop_gradient in [False, True]:
for y_stop_gradient in [False, True]:
with fluid.program_guard(Program(), Program()):
cond = fluid.layers.data(
name='cond', shape=self.shape, dtype='bool')
x = fluid.layers.data(
name='x', shape=self.shape, dtype='float32')
y = fluid.layers.data(
name='y', shape=self.shape, dtype='float32')
x.stop_gradient = x_stop_gradient
y.stop_gradient = y_stop_gradient
result = paddle.where(cond, x, y)
append_backward(layers.mean(result))
for use_cuda in [False, True]:
if use_cuda and not fluid.core.is_compiled_with_cuda():
break
place = fluid.CUDAPlace(
0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
fetch_list = [result, result.grad_name]
if x_stop_gradient is False:
fetch_list.append(x.grad_name)
if y_stop_gradient is False:
fetch_list.append(y.grad_name)
out = exe.run(
fluid.default_main_program(),
feed={'cond': self.cond,
'x': self.x,
'y': self.y},
fetch_list=fetch_list)
assert np.array_equal(out[0], self.out)
if x_stop_gradient is False:
assert np.array_equal(out[2],
self.ref_x_backward(out[1]))
if y.stop_gradient is False:
assert np.array_equal(
out[3], self.ref_y_backward(out[1]))
elif y.stop_gradient is False:
assert np.array_equal(out[2],
self.ref_y_backward(out[1]))
def test_api_broadcast(self, use_cuda=False):
main_program = Program()
@ -124,9 +127,7 @@ class TestWhereAPI(unittest.TestCase):
x_i = np.array([[0.9383, 0.1983, 3.2, 1.2]]).astype("float32")
y_i = np.array([[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0]]).astype("float32")
cond_i = np.array([[False, False, True, True],
[False, False, True, True]]).astype("bool")
result = tensor.where(x > 1, x=x, y=y)
result = paddle.where(x > 1, x=x, y=y)
for use_cuda in [False, True]:
if use_cuda and not fluid.core.is_compiled_with_cuda():
@ -137,7 +138,7 @@ class TestWhereAPI(unittest.TestCase):
feed={'x': x_i,
'y': y_i},
fetch_list=[result])
assert np.array_equal(out[0], np.where(cond_i, x_i, y_i))
assert np.array_equal(out[0], np.where(x_i > 1, x_i, y_i))
class TestWhereDygraphAPI(unittest.TestCase):
@ -149,7 +150,7 @@ class TestWhereDygraphAPI(unittest.TestCase):
x = fluid.dygraph.to_variable(x_i)
y = fluid.dygraph.to_variable(y_i)
cond = fluid.dygraph.to_variable(cond_i)
out = tensor.where(cond, x, y)
out = paddle.where(cond, x, y)
assert np.array_equal(out.numpy(), np.where(cond_i, x_i, y_i))
@ -161,7 +162,7 @@ class TestWhereOpError(unittest.TestCase):
cond_i = np.array([False, False, True, True]).astype("bool")
def test_Variable():
tensor.where(cond_i, x_i, y_i)
paddle.where(cond_i, x_i, y_i)
self.assertRaises(TypeError, test_Variable)
@ -169,7 +170,7 @@ class TestWhereOpError(unittest.TestCase):
x = fluid.layers.data(name='x', shape=[4], dtype='bool')
y = fluid.layers.data(name='y', shape=[4], dtype='float16')
cond = fluid.layers.data(name='cond', shape=[4], dtype='int32')
tensor.where(cond, x, y)
paddle.where(cond, x, y)
self.assertRaises(TypeError, test_type)

5
python/paddle/tensor/search.py
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@ -388,9 +388,9 @@ def where(condition, x, y, name=None):
Examples:
.. code-block:: python
import paddle
import numpy as np
import paddle.fluid as fluid
import paddle.tensor as paddle
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float32")
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float32")
@ -417,8 +417,7 @@ def where(condition, x, y, name=None):
return core.ops.where(condition, x, y)
else:
helper = LayerHelper("where", **locals())
dtype = helper.input_dtype()
out = helper.create_variable_for_type_inference(dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='where',

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