[bf16] add bf16 kernel: gaussian_random fill_constant fill_any_like

paddle/fluid/operators/gaussian_random_op.cu

@@ -45,7 +45,8 @@ struct GaussianGenerator {
     thrust::minstd_rand rng;
     rng.seed(seed_);
     using MT = typename details::MPTypeTrait<T>::Type;
-    thrust::normal_distribution<MT> dist(mean_, std_);
+    thrust::normal_distribution<MT> dist(static_cast<MT>(mean_),
+                                         static_cast<MT>(std_));
     unsigned int new_n = n + offset_;
     rng.discard(new_n);
     MT out = dist(rng);

paddle/phi/kernels/funcs/distribution_helper.h

@@ -23,6 +23,7 @@ limitations under the License. */
 
 #include "paddle/phi/backends/gpu/gpu_context.h"
 #include "paddle/phi/backends/gpu/gpu_info.h"
+#include "paddle/phi/common/amp_type_traits.h"
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/generator.h"
 #include "paddle/phi/core/hostdevice.h"
@@ -255,11 +256,13 @@ __global__ void DistributionKernel(size_t size,
   using SType = hiprandStatePhilox4_32_10_t;
 #endif
   size_t total_thread = GRID_NUM_X * BLOCK_NUM_X;
-  T args[kCount];
+  using MT = typename phi::dtype::MPTypeTrait<T>::Type;
+  MT args[kCount];
   T result[kCount];
   for (size_t i = idx; i < size; i += total_thread * kCount) {
-    kps::ElementwiseRandom<SType, T, kCount, 1, DistOp>(&args[0], dist, &state);
-    kps::ElementwiseUnary<T, T, kCount, 1, 1, TransformOp>(
+    kps::ElementwiseRandom<SType, MT, kCount, 1, DistOp>(
+        &args[0], dist, &state);
+    kps::ElementwiseUnary<MT, T, kCount, 1, 1, TransformOp>(
         &result[0], &args[0], trans);
     kps::WriteData<T, T, kCount, 1, 1, true>(
         out_data + i, &result[0], size - i, 1, stride, 1);

paddle/phi/kernels/gpu/full_kernel.cu

@@ -63,9 +63,11 @@ void FullLikeKernel(const Context& dev_ctx,
   auto value = val.to<float>();
   using CommonType = typename std::common_type<
       float,
-      typename std::conditional<std::is_same<T, phi::dtype::float16>::value,
-                                float,
-                                T>::type>::type;
+      typename std::conditional<
+          std::is_same<T, phi::dtype::float16>::value ||
+              std::is_same<T, phi::dtype::bfloat16>::value,
+          float,
+          T>::type>::type;
 
   auto common_type_value = static_cast<CommonType>(value);
 
@@ -110,6 +112,7 @@ PD_REGISTER_KERNEL(full,
                    int64_t,
                    bool,
                    phi::dtype::float16,
+                   phi::dtype::bfloat16,
                    phi::dtype::complex<float>,
                    phi::dtype::complex<double>) {}
 
@@ -123,6 +126,7 @@ PD_REGISTER_KERNEL(full_like,
                    int,
                    int64_t,
                    bool,
+                   phi::dtype::bfloat16,
                    phi::dtype::float16) {
   kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
 }

paddle/phi/kernels/gpu/gaussian_random_kernel.cu

@@ -18,8 +18,8 @@
 #include <thrust/host_vector.h>
 #include <thrust/random.h>
 #include <thrust/transform.h>
-
 #include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/common/amp_type_traits.h"
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/funcs/distribution_helper.h"
@@ -46,8 +46,9 @@ struct GaussianGenerator {
   __host__ __device__ T operator()(const unsigned int n) const {
     thrust::minstd_rand rng;
     rng.seed(seed_);
-    using MT = typename phi::kps::details::MPTypeTrait<T>::Type;
-    thrust::normal_distribution<MT> dist(mean_, std_);
+    using MT = typename phi::dtype::MPTypeTrait<T>::Type;
+    thrust::normal_distribution<MT> dist(static_cast<MT>(mean_),
+                                         static_cast<MT>(std_));
     unsigned int new_n = n + offset_;
     rng.discard(new_n);
     MT out = dist(rng);
@@ -83,9 +84,10 @@ void GaussianRandomKernel(const Context& dev_ctx,
 
   if (gen_cuda->GetIsInitPy() && seed_flag) {
     if (FLAGS_use_curand) {
-      using MT = typename phi::kps::details::MPTypeTrait<T>::Type;
+      using MT = typename phi::dtype::MPTypeTrait<T>::Type;
       funcs::normal_distribution<MT> dist;
-      funcs::normal_transform<MT> trans(mean, std);
+      funcs::normal_transform<MT> trans(static_cast<MT>(mean),
+                                        static_cast<MT>(std));
       funcs::distribution_and_transform<T>(dev_ctx, tensor, dist, trans);
     } else {
       auto seed_offset = gen_cuda->IncrementOffset(1);
@@ -110,5 +112,6 @@ PD_REGISTER_KERNEL(gaussian_random,
                    ALL_LAYOUT,
                    phi::GaussianRandomKernel,
                    phi::dtype::float16,
+                   phi::dtype::bfloat16,
                    float,
                    double) {}

paddle/phi/kernels/primitive/compute_primitives.h

@@ -22,6 +22,7 @@
 #endif
 
 #include "paddle/fluid/platform/device/gpu/gpu_device_function.h"
+// #include "paddle/phi/common/bfloat16.h"
 #include "paddle/phi/common/float16.h"
 
 namespace phi {

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

@@ -21,7 +21,7 @@
 import paddle.compat as cpt
 import unittest
 import numpy as np
-from op_test import OpTest
+from op_test import OpTest, convert_float_to_uint16
 
 
 class TestFillAnyLikeOp(OpTest):
@@ -47,6 +47,25 @@ def init(self):
         self.value = 0.0
 
 
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFillAnyLikeOpBfloat16(OpTest):
+    def setUp(self):
+        self.op_type = "fill_any_like"
+        self.dtype = np.uint16
+        self.value = 0.0
+        self.inputs = {'X': np.random.random((219, 232)).astype(np.float32)}
+        self.attrs = {'value': self.value, 'dtype': core.VarDesc.VarType.BF16}
+        self.outputs = {
+            'Out':
+            convert_float_to_uint16(self.value * np.ones_like(self.inputs["X"]))
+        }
+
+    def test_check_output(self):
+        place = core.CUDAPlace(0)
+        self.check_output_with_place(place)
+
+
 class TestFillAnyLikeOpValue1(TestFillAnyLikeOp):
     def init(self):
         self.value = 1.0

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

@@ -83,6 +83,27 @@ def test_check_output(self):
         self.check_output()
 
 
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestFillConstantBF16Op(OpTest):
+    def setUp(self):
+        '''Test fill_constant op with specified value
+        '''
+        self.op_type = "fill_constant"
+        self.dtype = np.uint16
+        self.inputs = {}
+        self.attrs = {
+            'shape': [123, 92],
+            'value': 3.8,
+            'dtype': core.VarDesc.VarType.BF16
+        }
+        self.outputs = {'Out': convert_float_to_uint16(np.full((123, 92), 3.8))}
+
+    def test_check_output(self):
+        place = core.CUDAPlace(0)
+        self.check_output_with_place(place)
+
+
 class TestFillConstantOpWithSelectedRows(unittest.TestCase):
     def check_with_place(self, place):
         scope = core.Scope()

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

@@ -22,7 +22,7 @@
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
 from paddle.fluid.executor import Executor
-from paddle.fluid.tests.unittests.op_test import OpTest
+from paddle.fluid.tests.unittests.op_test import OpTest, convert_uint16_to_float
 import paddle
 
 
@@ -65,6 +65,50 @@ def verify_output(self, outs):
             "hist: " + str(hist) + " hist2: " + str(hist2))
 
 
+@unittest.skipIf(not core.is_compiled_with_cuda(),
+                 "core is not compiled with CUDA")
+class TestGaussianRandomBF16Op(OpTest):
+    def setUp(self):
+        self.op_type = "gaussian_random"
+        self.set_attrs()
+        self.inputs = {}
+        self.use_mkldnn = False
+        self.attrs = {
+            "shape": [123, 92],
+            "mean": self.mean,
+            "std": self.std,
+            "seed": 10,
+            "dtype": paddle.fluid.core.VarDesc.VarType.BF16,
+            "use_mkldnn": self.use_mkldnn
+        }
+        paddle.seed(10)
+
+        self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}
+
+    def set_attrs(self):
+        self.mean = 1.0
+        self.std = 2.
+
+    def test_check_output(self):
+        self.check_output_with_place_customized(
+            self.verify_output, place=core.CUDAPlace(0))
+
+    def verify_output(self, outs):
+        outs = convert_uint16_to_float(outs)
+        self.assertEqual(outs[0].shape, (123, 92))
+        hist, _ = np.histogram(outs[0], range=(-3, 5))
+        hist = hist.astype("float32")
+        hist /= float(outs[0].size)
+        data = np.random.normal(size=(123, 92), loc=1, scale=2)
+        hist2, _ = np.histogram(data, range=(-3, 5))
+        hist2 = hist2.astype("float32")
+        hist2 /= float(outs[0].size)
+        self.assertTrue(
+            np.allclose(
+                hist, hist2, rtol=0, atol=0.05),
+            "hist: " + str(hist) + " hist2: " + str(hist2))
+
+
 class TestMeanStdAreInt(TestGaussianRandomOp):
     def set_attrs(self):
         self.mean = 1