Opt the compilation of sparse kernel (#41086)

cmake/phi.cmake

@@ -116,6 +116,9 @@ function(kernel_library TARGET)
     if ("${kernel_library_SUB_DIR}" STREQUAL "selected_rows")
         set(target_suffix "_sr")
     endif()
+    if ("${kernel_library_SUB_DIR}" STREQUAL "sparse")
+        set(target_suffix "_sp")
+    endif()
 
     list(LENGTH kernel_library_SRCS kernel_library_SRCS_len)
     # one kernel only match one impl file in each backend

paddle/phi/kernels/sparse/sparse_activation_grad_kernel.cc → paddle/phi/kernels/sparse/activation_grad_kernel.cc

@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/phi/kernels/sparse/sparse_activation_grad_kernel.h"
+#include "paddle/phi/kernels/sparse/activation_grad_kernel.h"
 #include "paddle/phi/kernels/activation_grad_kernel.h"
 #include "paddle/phi/kernels/copy_kernel.h"
 #include "paddle/phi/kernels/empty_kernel.h"

paddle/phi/kernels/sparse/sparse_activation_kernel.cc → paddle/phi/kernels/sparse/activation_kernel.cc

@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/phi/kernels/sparse/sparse_activation_kernel.h"
+#include "paddle/phi/kernels/sparse/activation_kernel.h"
 #include "paddle/phi/kernels/copy_kernel.h"
 #include "paddle/phi/kernels/empty_kernel.h"
 

paddle/phi/tests/kernels/test_sparse_activation_dev_api.cc

@@ -24,8 +24,8 @@ limitations under the License. */
 #include "paddle/phi/kernels/activation_grad_kernel.h"
 #include "paddle/phi/kernels/activation_kernel.h"
 #include "paddle/phi/kernels/empty_kernel.h"
-#include "paddle/phi/kernels/sparse/sparse_activation_grad_kernel.h"
-#include "paddle/phi/kernels/sparse/sparse_activation_kernel.h"
+#include "paddle/phi/kernels/sparse/activation_grad_kernel.h"
+#include "paddle/phi/kernels/sparse/activation_kernel.h"
 #include "paddle/phi/kernels/sparse/sparse_utils_kernel.h"
 
 namespace phi {

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

@@ -33,6 +33,7 @@ class TestSparseUtils(unittest.TestCase):
             stop_gradient = False
             coo = core.eager.sparse_coo_tensor(dense_indices, dense_elements,
                                                dense_shape, stop_gradient)
+
             print(coo)
 
     def test_create_sparse_csr_tensor(self):
@@ -49,6 +50,7 @@ class TestSparseUtils(unittest.TestCase):
             csr = core.eager.sparse_csr_tensor(dense_crows, dense_cols,
                                                dense_elements, dense_shape,
                                                stop_gradient)
+
             print(csr)
 
     def test_to_sparse_coo(self):
@@ -58,6 +60,7 @@ class TestSparseUtils(unittest.TestCase):
             non_zero_elements = [1, 2, 3, 4, 5]
             dense_x = paddle.to_tensor(x)
             out = dense_x.to_sparse_coo(2)
+            print(out)
             assert np.array_equal(out.non_zero_indices().numpy(),
                                   non_zero_indices)
             assert np.array_equal(out.non_zero_elements().numpy(),
@@ -81,6 +84,7 @@ class TestSparseUtils(unittest.TestCase):
                                   non_zero_elements)
 
             dense_tensor = out.to_dense()
+            print(dense_tensor)
             assert np.array_equal(dense_tensor.numpy(), x)
 
 

python/paddle/tensor/to_string.py

@@ -286,30 +286,45 @@ def _format_dense_tensor(tensor, indent):
 
 def sparse_tensor_to_string(tensor, prefix='Tensor'):
     indent = len(prefix) + 1
-    _template = "{prefix}(shape={shape}, dtype={dtype}, place={place}, stop_gradient={stop_gradient}, \n{indent}{data})"
     if tensor.is_sparse_coo():
+        _template = "{prefix}(shape={shape}, dtype={dtype}, place={place}, stop_gradient={stop_gradient}, \n{indent}{indices}, \n{indent}{values})"
         indices_tensor = tensor.non_zero_indices()
         elements_tensor = tensor.non_zero_elements()
-        indices_data = _format_dense_tensor(indices_tensor, indent)
-        elements_data = _format_dense_tensor(elements_tensor, indent)
-        data = 'non_zero_indices=' + indices_data + ',\nnon_zero_elements=' + elements_data
+        indices_data = 'indices=' + _format_dense_tensor(indices_tensor, indent
+                                                         + len('indices='))
+        values_data = 'values=' + _format_dense_tensor(elements_tensor, indent +
+                                                       len('values='))
+        return _template.format(
+            prefix=prefix,
+            shape=tensor.shape,
+            dtype=tensor.dtype,
+            place=tensor._place_str,
+            stop_gradient=tensor.stop_gradient,
+            indent=' ' * indent,
+            indices=indices_data,
+            values=values_data)
     else:
+        _template = "{prefix}(shape={shape}, dtype={dtype}, place={place}, stop_gradient={stop_gradient}, \n{indent}{crows}, \n{indent}{cols}, \n{indent}{values})"
         crows_tensor = tensor.non_zero_crows()
         cols_tensor = tensor.non_zero_cols()
         elements_tensor = tensor.non_zero_elements()
-        crows_data = _format_dense_tensor(crows_tensor, indent)
-        cols_data = _format_dense_tensor(cols_tensor, indent)
-        elements_data = _format_dense_tensor(elements_tensor, indent)
-        data = 'non_zero_crows=' + crows_data + ',\nnon_zero_cols=' + cols_data + ',\nnon_zero_elements=' + elements_data
+        crows_data = 'crows=' + _format_dense_tensor(crows_tensor, indent +
+                                                     len('crows='))
+        cols_data = 'cols=' + _format_dense_tensor(cols_tensor, indent +
+                                                   len('cols='))
+        values_data = 'values=' + _format_dense_tensor(elements_tensor, indent +
+                                                       len('values='))
 
-    return _template.format(
-        prefix=prefix,
-        shape=tensor.shape,
-        dtype=tensor.dtype,
-        place=tensor._place_str,
-        stop_gradient=tensor.stop_gradient,
-        indent=' ' * indent,
-        data=data)
+        return _template.format(
+            prefix=prefix,
+            shape=tensor.shape,
+            dtype=tensor.dtype,
+            place=tensor._place_str,
+            stop_gradient=tensor.stop_gradient,
+            indent=' ' * indent,
+            crows=crows_data,
+            cols=cols_data,
+            values=values_data)
 
 
 def tensor_to_string(tensor, prefix='Tensor'):
@@ -317,11 +332,11 @@ def tensor_to_string(tensor, prefix='Tensor'):
 
     _template = "{prefix}(shape={shape}, dtype={dtype}, place={place}, stop_gradient={stop_gradient},\n{indent}{data})"
 
-    if not tensor._is_initialized():
-        return "Tensor(Not initialized)"
-
     if tensor.is_sparse():
         return sparse_tensor_to_string(tensor, prefix)
+
+    if not tensor._is_dense_tensor_hold_allocation():
+        return "Tensor(Not initialized)"
     else:
         data = _format_dense_tensor(tensor, indent)
         return _template.format(