Use gpugemm for gemv in float16. fix gh-4341

theano/gpuarray/opt.py

@@ -1171,6 +1171,14 @@ def local_gpua_careduce(op, context_name, inputs, outputs):
 @op_lifter([tensor.blas.Gemv, tensor.blas_c.CGemv])
 @register_opt2([tensor.blas.Gemv], 'fast_compile')
 def local_gpua_gemv(op, context_name, inputs, outputs):
+    if inputs[0].dtype == 'float16':
+        # Use gemm implementation as cublas gemv don't support float16
+        return gpugemm_no_inplace(inputs[0][:, None],
+                                  inputs[1],
+                                  inputs[2],
+                                  inputs[3][:, None],
+                                  inputs[4]).dimshuffle(0)
+
     if inputs[0].dtype not in ['float32', 'float64']:
         return
     if op.inplace:

theano/gpuarray/tests/test_blas.py

@@ -18,7 +18,7 @@ from ..blas import (gpugemv_inplace, gpugemv_no_inplace,
                     gpugemm_inplace, gpugemm_no_inplace,
                     gpugemmbatch_no_inplace,
                     gpuger_inplace, gpuger_no_inplace,
-                    GpuGer, gpu_dot22)
+                    GpuGer, GpuGemm, gpu_dot22)
 
 
 GpuGemvTester = makeTester(
@@ -42,6 +42,22 @@ GpuGemvTester = makeTester(
 
 
 def test_float16():
+    # gemv (gemm called)
+    float16_data = [rand(3).astype('float16'),
+                    np.asarray(1, dtype=np.float32),
+                    rand(3, 3).astype('float16'),
+                    rand(3).astype('float16'),
+                    np.asarray(0.5, dtype=np.float32)]
+    float16_shared = [gpuarray_shared_constructor(val, target=test_ctx_name)
+                      for val in float16_data]
+    o = gemm(*float16_shared)
+    f = theano.function([], o, mode=mode_with_gpu)
+    y, alpha, A, x, beta = float16_data
+    out = f()
+    utt.assert_allclose(np.asarray(out), alpha * np.dot(A, x) + beta * y)
+    topo = f.maker.fgraph.toposort()
+    assert any([isinstance(n.op, GpuGemm) for n in topo])
+
     # gemm
     float16_data = [rand(3, 3).astype('float16'),
                     np.asarray(1, dtype=np.float32),