add paddle.tensor interface (#23801)

* add paddle.tensor
test=develop

* polish gpu where codes
test=develop

* polish test code
test=develop

paddle/fluid/operators/where_op.cu

@@ -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];
     }
   }
 }

python/paddle/__init__.py

@@ -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

python/paddle/fluid/tests/unittests/test_where_op.py

@@ -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]]:
+    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
-                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)
+                    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():
-                        return
-                    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+                            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={'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)
+                            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)
 

python/paddle/tensor/search.py

@@ -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',