gpu Gelu kernel support fp16

mindspore/ccsrc/kernel/gpu/cuda_impl/gelu_impl.cu

@@ -14,32 +14,62 @@
  * limitations under the License.
  */
 
-
 #include "kernel/gpu/cuda_impl/gelu_impl.cuh"
 #include "device/gpu/cuda_common.h"
 
-template<typename T>
-__global__ void GeluKernel(size_t size, T* input_addr, T* output_addr) {
+template <typename T>
+__global__ void GeluKernel(size_t size, T *input_addr, T *output_addr) {
   // formula:
   // gelu(x) = 0.5 * x * (1.0 + tanh(y))
   // tanh(y) = 2 / (1 + exp(-2y)) - 1)
   // y = sqrt(2/pi) * (x + 0.044715 * x^3)
-  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
     float x = input_addr[pos];
     float tanh_res = tanh(0.7978845608 * (x + 0.044715 * x * x * x));
     output_addr[pos] = 0.5 * x * (1.0 + tanh_res);
   }
 }
 
-template<typename T>
-void Gelu(size_t size, T* input_addr, T* output_addr, cudaStream_t cuda_stream) {
+template <>
+__global__ void GeluKernel(size_t size, half *input_addr, half *output_addr) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
+    half x = input_addr[pos];
+    float tanh_res = tanh(__half2float(half(0.7978845608) * (x + half(0.044715) * x * x * x)));
+    output_addr[pos] = half(0.5) * x * (half(1.0) + __float2half(tanh_res));
+  }
+}
+
+template <>
+__global__ void GeluKernel(size_t size, half2 *input_addr, half2 *output_addr) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
+    half2 x = input_addr[pos];
+    float2 tanh_param = __half22float2(half2(0.7978845608, 0.7978845608) * (x + half2(0.044715, 0.044715) * x * x * x));
+    float2 tanh_res;
+    tanh_res.x = tanh(tanh_param.x);
+    tanh_res.y = tanh(tanh_param.y);
+    output_addr[pos] = half2(0.5, 0.5) * x * (half2(1.0, 1.0) + __float22half2_rn(tanh_res));
+  }
+}
+
+template <typename T>
+void Gelu(size_t size, T *input_addr, T *output_addr, cudaStream_t cuda_stream) {
   GeluKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input_addr, output_addr);
   return;
 }
 
+template <>
+void Gelu(size_t size, half *input_addr, half *output_addr, cudaStream_t cuda_stream) {
+  if (size % 2 == 0) {
+    GeluKernel<half2><<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(
+      size / 2, reinterpret_cast<half2 *>(input_addr), reinterpret_cast<half2 *>(output_addr));
+  } else {
+    GeluKernel<half><<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input_addr, output_addr);
+  }
+  return;
+}
 
-template<typename T>
-__global__ void GeluGradKernel(size_t size, T* dy_addr, T* x_addr, T* dx_addr) {
+template <typename T>
+__global__ void GeluGradKernel(size_t size, T *dy_addr, T *x_addr, T *dx_addr) {
   // formula:
   // dx = dy * y'
   // y' = 0.5 * (1 + tanh(tanh_para)) +
@@ -48,18 +78,59 @@ __global__ void GeluGradKernel(size_t size, T* dy_addr, T* x_addr, T* dx_addr) {
   // mul_right = sqrt(2/pi) * (1 + 3 * 0.044715 * x^2))
   for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
     T x = x_addr[pos];
-    T tanh_res = tanh(0.7978845608  * (x + 0.044715 * x * x * x));
-    T mul_right = 0.7978845608  + 0.1070322244 * x * x;
-    T y_res = 0.5 * (1 + tanh_res) + 0.5 * x * (1 - tanh_res * tanh_res) * mul_right;
+    T tanh_res = tanh(0.7978845608 * (x + 0.044715 * x * x * x));
+    T mul_right = 0.7978845608 + 0.1070322244 * x * x;
+    T y_res = 0.5 * (1.0 + tanh_res) + 0.5 * x * (1.0 - tanh_res * tanh_res) * mul_right;
+    dx_addr[pos] = dy_addr[pos] * y_res;
+  }
+}
+
+template <typename T>
+__global__ void GeluGradKernel(size_t size, half2 *dy_addr, half2 *x_addr, half2 *dx_addr) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
+    half2 x = x_addr[pos];
+    float2 tanh_param = __half22float2(half2(0.7978845608, 0.7978845608) * (x + half2(0.044715, 0.044715) * x * x * x));
+    float2 tanh_res;
+    tanh_res.x = tanh(tanh_param.x);
+    tanh_res.y = tanh(tanh_param.y);
+    half2 tanh_res_half = __float22half2_rn(tanh_res);
+    half2 mul_right = half2(0.7978845608, 0.7978845608) + half2(0.1070322244, 0.1070322244) * x * x;
+    half2 y_res = half2(0.5, 0.5) * (half2(1.0, 1.0) + tanh_res_half) +
+                  half2(0.5, 0.5) * x * (half2(1.0, 1.0) - tanh_res_half * tanh_res_half) * mul_right;
+    dx_addr[pos] = dy_addr[pos] * y_res;
+  }
+}
+
+template <typename T>
+__global__ void GeluGradKernel(size_t size, half *dy_addr, half *x_addr, half *dx_addr) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
+    half x = x_addr[pos];
+    half tanh_param = half(0.7978845608) * (x + half(0.044715) * x * x * x);
+    half tanh_res = __float2half_rn(tanh(__half2float(tanh_param)));
+    half mul_right = half(0.7978845608) + half(0.1070322244) * x * x;
+    half y_res = half(0.5) * (half(1.0) + tanh_res) + half(0.5) * x * (half(1.0) - tanh_res * tanh_res) * mul_right;
     dx_addr[pos] = dy_addr[pos] * y_res;
   }
 }
 
-template<typename T>
-void GeluGradKernel(size_t size, T* dy_addr, T* x_addr, T* dx_addr, cudaStream_t cuda_stream) {
+template <typename T>
+void GeluGradKernel(size_t size, T *dy_addr, T *x_addr, T *dx_addr, cudaStream_t cuda_stream) {
   GeluGradKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, dy_addr, x_addr, dx_addr);
 }
 
+template <>
+void GeluGradKernel(size_t size, half *dy_addr, half *x_addr, half *dx_addr, cudaStream_t cuda_stream) {
+  if (size % 2 == 0) {
+    GeluGradKernel<half2><<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(
+      size / 2, reinterpret_cast<half2 *>(dy_addr), reinterpret_cast<half2 *>(x_addr),
+      reinterpret_cast<half2 *>(dx_addr));
+  } else {
+    GeluGradKernel<half><<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, dy_addr, x_addr, dx_addr);
+  }
+  return;
+}
 
-template void Gelu(size_t size, float* input_addr, float* output_addr, cudaStream_t cuda_stream);
-template void GeluGradKernel(size_t size, float* dy_addr, float* x_addr, float* dx_addr, cudaStream_t cuda_stream);
+template void Gelu(size_t size, float *input_addr, float *output_addr, cudaStream_t cuda_stream);
+template void Gelu(size_t size, half *input_addr, half *output_addr, cudaStream_t cuda_stream);
+template void GeluGradKernel(size_t size, float *dy_addr, float *x_addr, float *dx_addr, cudaStream_t cuda_stream);
+template void GeluGradKernel(size_t size, half *dy_addr, half *x_addr, half *dx_addr, cudaStream_t cuda_stream);

mindspore/ccsrc/kernel/gpu/nn/gelu_grad_kernel.cc

@@ -25,5 +25,12 @@ MS_REG_GPU_KERNEL_ONE(GeluGrad,
                         .AddInputAttr(kNumberTypeFloat32)
                         .AddOutputAttr(kNumberTypeFloat32),
                       GeLUGpuGradKernel, float)
+MS_REG_GPU_KERNEL_ONE(GeluGrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16),
+                      GeLUGpuGradKernel, half)
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/kernel/gpu/nn/gelu_kernel.cc

@@ -20,5 +20,7 @@ namespace mindspore {
 namespace kernel {
 MS_REG_GPU_KERNEL_ONE(Gelu, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                       GeluGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(Gelu, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+                      GeluGpuKernel, half)
 }  // namespace kernel
 }  // namespace mindspore

tests/st/ops/gpu/test_gelu_grad_op.py

@@ -58,7 +58,37 @@ def test_gelugrad():
     grad = Grad(net)
 
     output = grad(x_ms, dy_ms)
-    print(output)
     expect = [0.50963277, 0.9414753, 0.2667653, 0.21358444, 0.25243032, 0.0352667,
               0.34266686, 0.57757664, 0.04707306, 0.51536125]
     assert np.allclose(output[0].asnumpy(), expect)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_gelugrad_fp16():
+    np.random.seed(42)
+    x_np = np.random.randn(5, 3, 6).astype(np.float16)
+    dy_np = np.random.randn(5, 3, 6).astype(np.float16)
+    net = GeluNet()
+    grad = Grad(net)
+    output = grad(Tensor(x_np), Tensor(dy_np))
+    expect = [[[8.4045e-02, 3.7817e-01, -6.6748e-01, -3.6914e-01, -1.2415e-01, -4.6362e-01],
+               [3.3301e-01, 2.6270e-01, 7.7534e-04, -2.0947e-01, -2.2021e-01, -6.4880e-02],
+               [-2.3633e-01, 7.6538e-02, 1.8280e-02, 3.8635e-02, -1.6235e-01, 1.2964e-01]],
+
+              [[-1.4801e-02, 9.6130e-03, -2.1660e+00, -8.5602e-03, 3.3356e-02, -3.1885e-01],
+               [-2.0355e-02, 1.7737e-01, 3.8719e-03, -9.1895e-01, 8.4717e-02, 2.0593e-01],
+               [5.8350e-02, -1.0020e+00, 6.8652e-01, 1.3428e-01, 6.0352e-01, -2.6270e-01]],
+
+              [[-6.5820e-01, 5.1147e-02, -1.2650e-02, -3.2983e-01, -1.5410e+00, 4.3518e-02],
+               [-4.3359e-01, 1.2659e-01, 1.1792e-01, 2.2705e-02, -1.2329e-01, -3.5278e-01],
+               [6.2109e-01, 1.3611e-01, 1.7041e-01, 2.7124e-01, -5.5908e-02, 1.7212e-01]],
+
+              [[2.8320e-01, 8.3252e-01, 4.2480e-02, -3.4473e-01, 3.9429e-01, 3.1958e-01],
+               [3.6499e-02, 1.2250e-01, 7.1350e-02, -2.7267e-02, 3.0029e-01, -8.0566e-01],
+               [8.2617e-01, 5.1367e-01, -9.2480e-01, 3.3203e-02, -7.5684e-01, 8.8623e-01]],
+
+              [[5.4590e-01, -9.2383e-01, -2.8107e-02, 4.2432e-01, 4.6826e-01, 5.0879e-01],
+               [-1.4062e-01, 6.6284e-02, -2.9126e-01, -6.3086e-01, -8.6975e-02, 4.1504e-02],
+               [-6.3171e-03, 1.0852e-01, 1.3779e-02, 1.0947e+00, -3.0334e-02, 2.3828e+00]]]
+    assert np.allclose(output[0].asnumpy(), expect, rtol=1e-2)

tests/st/ops/gpu/test_gelu_op.py

@@ -91,3 +91,16 @@ def test_gelu_neg():
     y_ms = net(x_ms)
 
     assert np.allclose(y_np, y_ms.asnumpy())
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_gelu_4d_fp16():
+    x_np = np.random.random((32, 3, 224, 224)).astype(np.float16)
+    y_np = GeluCompute(x_np)
+
+    x_ms = Tensor(x_np)
+    net = GeluNet()
+    y_ms = net(x_ms)
+
+    assert np.allclose(y_np, y_ms.asnumpy(), rtol=1e-3)