OSSForks/tensorflow
1
0
Fork 0
mirror of https://github.com/zebrajr/tensorflow.git synced 2025-12-06 12:20:11 +01:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

Add bool type supports for GPU kernels (#11927)

Browse Source 
* Add bool type supports for GPU kernels

* Add bool type test codes for GPU kernels
This commit is contained in:
Taehoon Lee 2017-08-11 13:00:12 +09:00 committed by Rasmus Munk Larsen
parent de01be952d
commit 881de45c2d
8 changed files with 15 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
tensorflow/core/kernels/concat_lib_gpu.cc
View File

@ -117,6 +117,7 @@ TF_CALL_complex64(REGISTER);
TF_CALL_complex128(REGISTER); TF_CALL_complex128(REGISTER);
TF_CALL_int64(REGISTER); TF_CALL_int64(REGISTER);
REGISTER(bfloat16); REGISTER(bfloat16);
REGISTER(bool);
#undef REGISTER #undef REGISTER

4
tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc
View File

@ -203,24 +203,28 @@ TF_CALL_complex64(REGISTER_GPUCONCAT32);
TF_CALL_complex128(REGISTER_GPUCONCAT32); TF_CALL_complex128(REGISTER_GPUCONCAT32);
TF_CALL_int64(REGISTER_GPUCONCAT32); TF_CALL_int64(REGISTER_GPUCONCAT32);
REGISTER_GPUCONCAT32(bfloat16); REGISTER_GPUCONCAT32(bfloat16);
REGISTER_GPUCONCAT32(bool);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPUCONCAT64); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPUCONCAT64);
TF_CALL_complex64(REGISTER_GPUCONCAT64); TF_CALL_complex64(REGISTER_GPUCONCAT64);
TF_CALL_complex128(REGISTER_GPUCONCAT64); TF_CALL_complex128(REGISTER_GPUCONCAT64);
TF_CALL_int64(REGISTER_GPUCONCAT64); TF_CALL_int64(REGISTER_GPUCONCAT64);
REGISTER_GPUCONCAT64(bfloat16); REGISTER_GPUCONCAT64(bfloat16);
REGISTER_GPUCONCAT64(bool);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU32); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU32);
TF_CALL_complex64(REGISTER_GPU32); TF_CALL_complex64(REGISTER_GPU32);
TF_CALL_complex128(REGISTER_GPU32); TF_CALL_complex128(REGISTER_GPU32);
TF_CALL_int64(REGISTER_GPU32); TF_CALL_int64(REGISTER_GPU32);
REGISTER_GPU32(bfloat16); REGISTER_GPU32(bfloat16);
REGISTER_GPU32(bool);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU64); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU64);
TF_CALL_complex64(REGISTER_GPU64); TF_CALL_complex64(REGISTER_GPU64);
TF_CALL_complex128(REGISTER_GPU64); TF_CALL_complex128(REGISTER_GPU64);
TF_CALL_int64(REGISTER_GPU64); TF_CALL_int64(REGISTER_GPU64);
REGISTER_GPU64(bfloat16); REGISTER_GPU64(bfloat16);
REGISTER_GPU64(bool);
#undef REGISTER_GPUCONCAT32 #undef REGISTER_GPUCONCAT32
#undef REGISTER_GPUCONCAT64 #undef REGISTER_GPUCONCAT64

1
tensorflow/core/kernels/concat_op.cc
View File

@ -196,6 +196,7 @@ REGISTER_GPU(bfloat16);
TF_CALL_complex64(REGISTER_GPU); TF_CALL_complex64(REGISTER_GPU);
TF_CALL_complex128(REGISTER_GPU); TF_CALL_complex128(REGISTER_GPU);
TF_CALL_int64(REGISTER_GPU); TF_CALL_int64(REGISTER_GPU);
REGISTER_GPU(bool);
#undef REGISTER_GPU #undef REGISTER_GPU
// A special GPU kernel for int32. // A special GPU kernel for int32.

1
tensorflow/core/kernels/pack_op.cc
View File

@ -158,6 +158,7 @@ REGISTER_PACK(string);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU);
TF_CALL_int64(REGISTER_GPU); TF_CALL_int64(REGISTER_GPU);
REGISTER_GPU(bool);
#undef REGISTER_GPU #undef REGISTER_GPU
// A special GPU kernel for int32. // A special GPU kernel for int32.

1
tensorflow/core/kernels/reshape_op.cc
View File

@ -32,6 +32,7 @@ REGISTER_KERNEL_BUILDER(Name("Reshape")
.TypeConstraint<int32>("Tshape"), \ .TypeConstraint<int32>("Tshape"), \
ReshapeOp); ReshapeOp);
TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL); TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
REGISTER_GPU_KERNEL(bool);
#undef REGISTER_GPU_KERNEL #undef REGISTER_GPU_KERNEL
#ifdef TENSORFLOW_USE_SYCL #ifdef TENSORFLOW_USE_SYCL

1
tensorflow/python/kernel_tests/concat_op_test.py
View File

@ -138,6 +138,7 @@ class ConcatOpTest(test.TestCase):
self.assertAllClose(result[ind], params[p[i]], 0.01) self.assertAllClose(result[ind], params[p[i]], 0.01)
def testRandom(self): def testRandom(self):
self._testRandom(dtypes.bool)
self._testRandom(dtypes.float32) self._testRandom(dtypes.float32)
self._testRandom(dtypes.int16) self._testRandom(dtypes.int16)
self._testRandom(dtypes.int32) self._testRandom(dtypes.int32)

4
tensorflow/python/kernel_tests/reshape_op_test.py
View File

@ -41,6 +41,10 @@ class ReshapeTest(test.TestCase):
self._testReshape(x, y, False) self._testReshape(x, y, False)
self._testReshape(x, y, True) self._testReshape(x, y, True)
def testBoolBasic(self):
x = np.arange(1., 7.).reshape([1, 6]) > 3
self._testBothReshape(x, [2, 3])
def testFloatBasic(self): def testFloatBasic(self):
x = np.arange(1., 7.).reshape([1, 6]).astype(np.float32) x = np.arange(1., 7.).reshape([1, 6]).astype(np.float32)
self._testBothReshape(x, [2, 3]) self._testBothReshape(x, [2, 3])

4
tensorflow/python/kernel_tests/stack_op_test.py
View File

@ -45,7 +45,7 @@ class StackOpTest(test.TestCase):
np.random.seed(7) np.random.seed(7)
with self.test_session(use_gpu=True): with self.test_session(use_gpu=True):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2): for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
for dtype in [np.float32, np.int32, np.int64]: for dtype in [np.bool, np.float32, np.int32, np.int64]:
data = np.random.randn(*shape).astype(dtype) data = np.random.randn(*shape).astype(dtype)
# Convert [data[0], data[1], ...] separately to tensorflow # Convert [data[0], data[1], ...] separately to tensorflow
# TODO(irving): Remove list() once we handle maps correctly # TODO(irving): Remove list() once we handle maps correctly
@ -67,7 +67,7 @@ class StackOpTest(test.TestCase):
np.random.seed(7) np.random.seed(7)
with self.test_session(use_gpu=True): with self.test_session(use_gpu=True):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2): for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
for dtype in [np.float32, np.int32, np.int64]: for dtype in [np.bool, np.float32, np.int32, np.int64]:
data = np.random.randn(*shape).astype(dtype) data = np.random.randn(*shape).astype(dtype)
# Pack back into a single tensorflow tensor directly using np array # Pack back into a single tensorflow tensor directly using np array
c = array_ops.stack(data) c = array_ops.stack(data)