【Hackathon No57】add_fp16_bf16_for_dot & bf16_for_cross

paddle/phi/kernels/gpu/cross_grad_kernel.cu

@@ -191,6 +191,7 @@ PD_REGISTER_KERNEL(cross_grad,
                    ALL_LAYOUT,
                    phi::CrossGradKernel,
                    phi::dtype::float16,
+                   phi::dtype::bfloat16,
                    float,
                    double,
                    int,

paddle/phi/kernels/gpu/cross_kernel.cu

@@ -168,6 +168,7 @@ PD_REGISTER_KERNEL(cross,
                    ALL_LAYOUT,
                    phi::CrossKernel,
                    phi::dtype::float16,
+                   phi::dtype::bfloat16,
                    float,
                    double,
                    int,

paddle/phi/kernels/gpu/dot_grad_kernel.cu

@@ -15,7 +15,9 @@ limitations under the License. */
 #include "paddle/phi/kernels/dot_grad_kernel.h"
 
 #include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/common/bfloat16.h"
 #include "paddle/phi/common/complex.h"
+#include "paddle/phi/common/float16.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/impl/dot_grad_kernel_impl.h"
 
@@ -28,4 +30,6 @@ PD_REGISTER_KERNEL(dot_grad,
                    int,
                    int64_t,
                    phi::dtype::complex<float>,
-                   phi::dtype::complex<double>) {}
+                   phi::dtype::complex<double>,
+                   phi::dtype::float16,
+                   phi::dtype::bfloat16) {}

paddle/phi/kernels/gpu/dot_kernel.cu

@@ -15,6 +15,8 @@
 #include "paddle/phi/kernels/dot_kernel.h"
 
 #include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/common/bfloat16.h"
+#include "paddle/phi/common/float16.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/funcs/eigen/common.h"
 
@@ -61,4 +63,6 @@ PD_REGISTER_KERNEL(dot,
                    int,
                    int64_t,
                    complex64,
-                   complex128) {}
+                   complex128,
+                   phi::dtype::float16,
+                   phi::dtype::bfloat16) {}

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

@@ -15,11 +15,11 @@
 import unittest
 
 import numpy as np
-from eager_op_test import OpTest
+from eager_op_test import OpTest, convert_float_to_uint16
 
 import paddle
 from paddle import fluid
-from paddle.fluid import Program, program_guard
+from paddle.fluid import Program, core, program_guard
 
 
 class TestCrossOp(OpTest):
@@ -65,6 +65,9 @@ def init_output(self):
         self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
 
 
+@unittest.skipIf(
+    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
+)
 class TestCrossFP16Op(TestCrossOp):
     def initTestCase(self):
         self.shape = (2048, 3)
@@ -77,6 +80,51 @@ def init_output(self):
         self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
 
 
+@unittest.skipIf(
+    not core.is_compiled_with_cuda()
+    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
+    "core is not compiled with CUDA and not support the bfloat16",
+)
+class TestCrossBF16Op(OpTest):
+    def setUp(self):
+        self.op_type = "cross"
+        self.python_api = paddle.cross
+        self.initTestCase()
+        self.x = np.random.random(self.shape).astype(np.float32)
+        self.y = np.random.random(self.shape).astype(np.float32)
+        self.inputs = {
+            'X': convert_float_to_uint16(self.x),
+            'Y': convert_float_to_uint16(self.y),
+        }
+        self.init_output()
+
+    def initTestCase(self):
+        self.attrs = {'dim': -2}
+        self.dtype = np.uint16
+        self.shape = (1024, 3, 1)
+
+    def init_output(self):
+        x = np.squeeze(self.x, 2)
+        y = np.squeeze(self.y, 2)
+        z_list = []
+        for i in range(1024):
+            z_list.append(np.cross(x[i], y[i]))
+        out = np.array(z_list).astype(np.float32).reshape(self.shape)
+        self.outputs = {'Out': convert_float_to_uint16(out)}
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_bfloat16_supported(place):
+                self.check_output_with_place(place)
+
+    def test_check_grad_normal(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_bfloat16_supported(place):
+                self.check_grad_with_place(place, ['X', 'Y'], 'Out')
+
+
 class TestCrossAPI(unittest.TestCase):
     def input_data(self):
         self.data_x = np.array(

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

@@ -15,7 +15,7 @@
 import unittest
 
 import numpy as np
-from eager_op_test import OpTest
+from eager_op_test import OpTest, convert_float_to_uint16
 
 import paddle
 from paddle import fluid
@@ -85,7 +85,7 @@ def test_check_grad_ingore_y(self):
     def init_input_output(self):
         self.x = np.random.uniform(0.1, 1, [121]).astype(self.dtype)
         self.y = np.random.uniform(1, 3, [121]).astype(self.dtype)
-        self.out = np.dot(self.x, self.y)
+        self.out = np.dot(self.x, self.y).astype(self.dtype)
 
     def init_dtype(self):
         self.dtype = np.float64
@@ -314,6 +314,201 @@ def test_check_grad_ingore_y(self):
         )
 
 
+@unittest.skipIf(
+    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
+)
+class TestDotFP16Op(OpTest):
+    def setUp(self):
+        self.op_type = "dot"
+        self.python_api = paddle.dot
+        self.init_dtype()
+        self.init_input_output()
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y),
+        }
+        self.outputs = {'Out': self.out}
+        self.attrs = {}
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_output_with_place(place, atol=0.125)
+
+    def test_check_grad_normal(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_grad_with_place(place, ['X', 'Y'], 'Out')
+
+    def test_check_grad_ingore_x(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_grad_with_place(
+                    place, ['Y'], 'Out', no_grad_set=set("X")
+                )
+
+    def test_check_grad_ingore_y(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_grad_with_place(
+                    place, ['X'], 'Out', no_grad_set=set("Y")
+                )
+
+    def init_input_output(self):
+        self.x = np.random.uniform(0.1, 1, [121]).astype(self.dtype)
+        self.y = np.random.uniform(1, 3, [121]).astype(self.dtype)
+        self.out = np.dot(self.x, self.y)
+
+
+@unittest.skipIf(
+    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
+)
+class DotFP16OpBatch(TestDotFP16Op):
+    def init_input_output(self):
+        self.x = (
+            np.random.uniform(0.1, 1, [132])
+            .astype(self.dtype)
+            .reshape([11, 12])
+        )
+        self.y = (
+            np.random.uniform(1, 3, [132]).astype(self.dtype).reshape([11, 12])
+        )
+        self.out = np.sum(self.x * self.y, axis=1).reshape([11, 1])
+
+
+@unittest.skipIf(
+    not core.is_compiled_with_cuda()
+    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
+    "core is not compiled with CUDA and not support the bfloat16",
+)
+class TestDotBF16Op(OpTest):
+    def setUp(self):
+        self.op_type = "dot"
+        self.python_api = paddle.dot
+        self.init_dtype()
+        self.init_input_output()
+
+        self.inputs = {
+            'X': convert_float_to_uint16(self.x),
+            'Y': convert_float_to_uint16(self.y),
+        }
+        self.outputs = {'Out': convert_float_to_uint16(self.out)}
+        self.attrs = {}
+
+    def init_dtype(self):
+        self.dtype = np.uint16
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_bfloat16_supported(place):
+                self.check_output_with_place(place, atol=0.5)
+
+    def test_check_grad_normal(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_bfloat16_supported(place):
+                self.check_grad_with_place(
+                    place,
+                    ['X', 'Y'],
+                    'Out',
+                    user_defined_grads=[self.inputs['Y'], self.inputs['X']],
+                )
+
+    def test_check_grad_ingore_x(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_bfloat16_supported(place):
+                self.check_grad_with_place(
+                    place,
+                    ['Y'],
+                    'Out',
+                    no_grad_set=set("X"),
+                    user_defined_grads=[self.inputs['X']],
+                )
+
+    def test_check_grad_ingore_y(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_bfloat16_supported(place):
+                self.check_grad_with_place(
+                    place,
+                    ['X'],
+                    'Out',
+                    no_grad_set=set("Y"),
+                    user_defined_grads=[self.inputs['Y']],
+                )
+
+    def init_input_output(self):
+        self.x = np.random.uniform(0.1, 1, [121]).astype(np.float32)
+        self.y = np.random.uniform(1, 3, [121]).astype(np.float32)
+        self.out = np.dot(self.x, self.y)
+
+
+@unittest.skipIf(
+    not core.is_compiled_with_cuda()
+    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
+    "core is not compiled with CUDA and not support the bfloat16",
+)
+class DotBF16OpBatch(TestDotBF16Op):
+    def init_input_output(self):
+        self.x = (
+            np.random.uniform(0.1, 1, [132])
+            .astype(np.float32)
+            .reshape([11, 12])
+        )
+        self.y = (
+            np.random.uniform(1, 3, [132]).astype(np.float32).reshape([11, 12])
+        )
+        self.out = np.sum(self.x * self.y, axis=1).reshape([11, 1])
+
+    def test_check_grad_normal(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_bfloat16_supported(place):
+                self.check_grad_with_place(
+                    place,
+                    ['X', 'Y'],
+                    'Out',
+                    user_defined_grads=[
+                        self.y / self.y.shape[0],
+                        self.x / self.x.shape[0],
+                    ],
+                )
+
+    def test_check_grad_ingore_x(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_bfloat16_supported(place):
+                self.check_grad_with_place(
+                    place,
+                    ['Y'],
+                    'Out',
+                    no_grad_set=set("X"),
+                    user_defined_grads=[self.x / self.x.shape[0]],
+                )
+
+    def test_check_grad_ingore_y(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_bfloat16_supported(place):
+                self.check_grad_with_place(
+                    place,
+                    ['X'],
+                    'Out',
+                    no_grad_set=set("Y"),
+                    user_defined_grads=[self.y / self.y.shape[0]],
+                )
+
+
 if __name__ == '__main__':
     paddle.enable_static()
     unittest.main()