matmul support fp16

mindspore/ccsrc/kernel/gpu/math/matmul_gpu_kernel.h

@@ -67,7 +67,7 @@ class MatMulGpuKernel : public GpuKernel {
     CHECK_CUBLAS_RET_WITH_EXCEPT(
       cublasGemmStridedBatchedEx(handle_, transpose_x2_, transpose_x1_, SizeToInt(n_), SizeToInt(m_), SizeToInt(k_),
                                  &alpha, input2_addr, dtype_b_, ldb, stride_b, input1_addr, dtype_a_, lda, stride_a,
-                                 &beta, output_addr, dtype_c_, ldc, stride_c, batch_, dtype_c_, algo_),
+                                 &beta, output_addr, dtype_c_, ldc, stride_c, batch_, CUDA_R_32F, algo_),
       "cublasSgemm Call Fail");
     return true;
   }

tests/st/ops/gpu/test_batch_matmul.py

@@ -60,7 +60,7 @@ def test_4D():
 def test_4D_transpose_a():
     input_x = Tensor(np.arange(2*4*3*1).reshape(2,4,3,1), mstype.float32)
     input_y = Tensor(np.arange(2*4*3*4).reshape(2,4,3,4), mstype.float32)
-    
+
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     net = BatchMatMulNet(transpose_a=True)
     output = net(input_x, input_y)
@@ -82,7 +82,7 @@ def test_4D_transpose_a():
 def test_4D_transpose_b():
     input_x = Tensor(np.arange(2*4*1*3).reshape(2,4,1,3), mstype.float32)
     input_y = Tensor(np.arange(2*4*4*3).reshape(2,4,4,3), mstype.float32)
-    
+
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     net = BatchMatMulNet(transpose_b=True)
     output = net(input_x, input_y)
@@ -104,7 +104,7 @@ def test_4D_transpose_b():
 def test_4D_transpose_ab():
     input_x = Tensor(np.arange(2*4*3*1).reshape(2,4,3,1), mstype.float32)
     input_y = Tensor(np.arange(2*4*4*3).reshape(2,4,4,3), mstype.float32)
-    
+
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     net = BatchMatMulNet(transpose_a=True, transpose_b=True)
     output = net(input_x, input_y)
@@ -118,3 +118,29 @@ def test_4D_transpose_ab():
               [[4163, 4334, 4505, 4676]],
               [[5612, 5810, 6008, 6206]]]]
     assert (output.asnumpy() == expect).all()
+
+class BatchMatMulNet(nn.Cell):
+    def __init__(self, transpose_a=False, transpose_b=False):
+        super(BatchMatMulNet, self).__init__()
+        self.batch_matmul = P.BatchMatMul(transpose_a, transpose_b)
+
+    def construct(self, x, y):
+        return self.batch_matmul(x, y)
+
+def test_4D_fp16():
+    input_x = Tensor(np.arange(2 * 4 * 1 * 3).reshape(2, 4, 1, 3), mstype.float16)
+    input_y = Tensor(np.arange(2 * 4 * 3 * 4).reshape(2, 4, 3, 4), mstype.float16)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    net = BatchMatMulNet()
+    output = net(input_x, input_y)
+    expect = [[[[  20,   23,   26,   29]],
+              [[ 200,  212,  224,  236]],
+              [[ 596,  617,  638,  659]],
+              [[1208, 1238, 1268, 1298]]],
+
+              [[[2036, 2075, 2114, 2153]],
+              [[3080, 3128, 3176, 3224]],
+              [[4340, 4397, 4454, 4511]],
+              [[5816, 5882, 5948, 6014]]]]
+    assert (output.asnumpy() == expect).all()