（beta）torch_npu.npu_geglu

接口原型

torch_npu.npu_geglu(Tensor self, int dim=-1, int approximate=1) -> (Tensor, Tensor)

功能描述

对输入Tensor完成GeGlu运算。

其中，A和B是对输入self沿指定轴平均切分，dim默认值为-1。

参数说明

• Tensor self：待进行GeGlu计算的入参，npu device侧的aclTensor，数据类型支持FLOAT32、FLOAT16、BFLOAT16（Atlas A2 训练系列产品支持），支持非连续的Tensor，数据格式支持ND。
• int dim：可选入参，设定的slice轴，数据类型支持INT64。
• int approximate：可选入参，GeGlu计算使用的激活函数索引，0表示使用none，1表示使用tanh，数据类型支持INT64。
• out：GeGlu计算的出参，npu device侧的aclTensor，数据类型必须和self一致，支持非连续的Tensor，数据格式支持ND。
• outGelu：GeGlu计算的出参，npu device侧的aclTensor，数据类型必须和self一致，支持非连续的Tensor，数据格式支持ND。

约束说明

out、outGelu在dim维的size等于self在dim维size的1/2。

当self.dim()==0时，dim的取值在[-1, 0]范围内；当self.dim()>0时，取值在[-self.dim(), self.dim()-1]范围内。

调用示例

data_x = np.random.uniform(-2, 2, [24,9216,2560]).astype(np.float16)
x_npu = torch.from_numpy(data_x).npu()
 
x_npu:
tensor([[[ 0.8750,  0.4766, -0.3535,  ..., -1.4619,  0.3542, -1.8389],
         [ 0.9424, -0.0291,  0.9482,  ...,  0.5640, -1.2959,  1.7666],
         [-0.4958, -0.6787,  0.0179,  ...,  0.4365, -0.8311, -1.7676],
         ...,
         [-1.1611,  1.4766, -1.1934,  ..., -0.5913,  1.1553, -0.4626],
         [ 0.4873, -1.8105,  0.5723,  ...,  1.3193, -0.1558, -1.6191],
         [ 1.6816, -1.2080, -1.6953,  ..., -1.3096,  0.4158, -1.2168]],
 
        [[ 1.4287, -1.9863,  1.4053,  ..., -1.7676, -1.6709, -1.1582],
         [-1.3281, -1.9043,  0.7725,  ..., -1.5596,  0.1632, -1.0732],
         [ 1.0254, -1.6650,  0.1318,  ..., -0.8159, -0.7134, -0.4536],
         ...,
         [ 0.0327, -0.6206, -0.1492,  ..., -1.2559,  0.3777, -1.2822],
         [-1.1904,  1.1260, -1.3369,  ..., -1.4814,  0.4463,  1.0205],
         [-0.1192,  1.7783,  0.1040,  ...,  1.0010,  1.5342, -0.5728]],
 
        [[-0.3296,  0.5703,  0.6338,  ...,  0.2131,  1.1113,  0.9854],
         [ 1.4336, -1.7568,  1.8164,  ..., -1.2012, -1.8721,  0.6904],
         [ 0.6934,  0.3743, -0.9448,  ..., -0.9946, -1.6494, -1.3564],
         ...,
         [ 1.1855, -0.9663, -0.8252,  ...,  0.2285, -1.5684, -0.4277],
         [ 1.1260,  1.2871,  1.2754,  ..., -0.5171, -1.1064,  0.9624],
         [-1.4639, -0.0661, -1.7178,  ...,  1.2656, -1.9023, -1.1641]],
 
        ...,
 
        [[-1.8350,  1.0625,  1.6172,  ...,  1.4160,  1.2490,  1.9775],
         [-0.5615, -1.9990, -0.5996,  ..., -1.9404,  0.5068, -0.9829],
         [-1.0771, -1.5537, -1.5654,  ...,  0.4678, -1.5215, -1.7920],
         ...,
         [-1.3389, -0.3228, -1.1514,  ...,  0.8882, -1.9971,  1.2432],
         [-1.5439, -1.8154, -1.9238,  ...,  0.2556,  0.2131, -1.7471],
         [-1.1074,  1.0391,  0.1556,  ...,  1.1689,  0.6470,  0.2463]],
 
        [[ 1.2617, -0.8911,  1.9160,  ..., -0.3027,  1.7764,  0.3381],
         [-1.4160,  1.6201, -0.5396,  ...,  1.8271,  1.3086, -1.8770],
         [ 1.8252,  1.3779, -0.3535,  ..., -1.5215, -1.4727, -1.0420],
         ...,
         [-1.4600, -1.7617, -0.7754,  ...,  0.4697, -0.4734, -0.3838],
         [ 1.8506, -0.3945, -0.0142,  ..., -1.3447, -0.6587,  0.5728],
         [ 1.1523, -1.8027,  0.4731,  ...,  0.5464,  1.4014, -1.8594]],
 
        [[-0.1467, -0.5752,  0.3298,  ..., -1.9902, -1.8281,  1.8506],
         [ 0.2473,  1.0693, -1.8184,  ...,  1.9277,  1.6543,  1.0088],
         [ 0.0804, -0.7939,  1.3486,  ..., -1.1543, -0.4053, -0.0055],
         ...,
         [ 0.3672,  0.3274, -0.3369,  ...,  1.4951, -1.9580, -0.7847],
         [ 1.3525, -0.4780, -0.5000,  ..., -0.1610, -1.9209,  1.5498],
         [ 0.4905, -1.7832,  0.4243,  ...,  0.9492,  0.3335,  0.9565]]],
       device='npu:0', dtype=torch.float16)
 
y_npu, y_gelu_npu = torch_npu.npu_geglu(x_npu, dim=-1, approximate=1)
 
y_npu:
tensor([[[-9.2590e-02, -1.2054e-01,  1.6980e-01,  ..., -6.8542e-02,
          -2.5254e+00, -6.9519e-02],
         [ 1.2405e-02, -1.4902e+00,  8.0750e-02,  ...,  3.4570e-01,
          -1.5029e+00,  2.8442e-01],
         [-9.0271e-02,  4.3335e-01, -1.7402e+00,  ...,  1.3574e-01,
          -5.5762e-01, -1.3123e-01],
         ...,
         [ 1.0004e-01,  1.5312e+00,  1.4189e+00,  ..., -2.6172e-01,
           1.6113e-01, -1.1887e-02],
         [-5.9845e-02,  2.0911e-01, -6.4735e-03,  ...,  5.1422e-02,
           2.6289e+00,  2.5977e-01],
         [ 1.3649e-02, -1.3329e-02, -6.9031e-02,  ...,  3.5977e+00,
          -1.2178e+00, -2.3242e+00]],
 
        [[-3.1816e+00, -2.6719e+00,  1.4038e-01,  ...,  2.6660e+00,
           7.7820e-02,  2.3999e-01],
         [ 2.9297e+00, -1.7754e+00,  2.6703e-02,  ..., -1.3318e-01,
          -6.2109e-01, -1.9072e+00],
         [ 1.1316e-01,  5.8887e-01,  8.2959e-01,  ...,  1.1273e-01,
           1.1481e-01,  4.2419e-02],
         ...,
         [-2.6831e-01, -1.7288e-02,  2.6343e-01,  ...,  9.3750e-02,
          -2.2324e+00,  1.2894e-02],
         [-2.0630e-01,  5.9619e-01, -1.4210e-03,  ..., -1.2598e-01,
          -6.5552e-02,  1.1115e-01],
         [-1.6143e+00, -1.6150e-01, -4.9774e-02,  ...,  8.6426e-02,
           1.1879e-02, -1.9795e+00]],
 
        [[ 4.3152e-02,  1.9250e-01, -4.7485e-02,  ..., -5.8632e-03,
           1.4551e-01, -2.1289e+00],
         [ 4.7951e-03,  2.0691e-01,  4.4458e-01,  ...,  4.7485e-02,
          -4.8889e-02,  1.5684e+00],
         [-8.9404e-01, -8.0420e-01, -2.9248e-01,  ...,  1.6205e-02,
           3.5449e+00,  8.2397e-02],
         ...,
         [-1.9385e+00, -1.8838e+00,  6.0010e-01,  ..., -8.5059e-01,
           6.1829e-02,  1.0547e-01],
         [-5.1086e-02, -1.0760e-01, -7.1228e-02,  ..., -9.2468e-02,
           4.7900e-01, -3.5278e-01],
         [ 1.7078e-01,  1.6846e-01,  2.5528e-02,  ...,  1.3708e-01,
           1.4954e-01, -2.8418e-01]],
 
        ...,
 
        [[-6.3574e-01, -2.0156e+00,  9.3994e-02,  ...,  2.2402e+00,
          -6.2218e-03,  8.7402e-01],
         [ 1.5010e+00, -1.8518e-01, -3.0930e-02,  ...,  1.1511e-01,
          -3.8300e-02, -1.6150e-01],
         [-2.8442e-01,  4.4373e-02, -1.0022e-01,  ...,  9.2468e-02,
          -1.2524e-01, -1.2115e-01],
         ...,
         [ 3.4760e-02,  1.9812e-01, -9.1431e-02,  ..., -1.1650e+00,
           2.4011e-01, -1.0919e-01],
         [-1.5283e-01,  1.8535e+00,  4.4360e-01,  ...,  6.4844e-01,
          -2.8784e-01, -2.5938e+00],
         [-9.9915e-02,  4.6436e-01,  6.6528e-02,  ..., -1.2817e-01,
          -1.5686e-01, -5.4962e-02]],
 
        [[-2.3279e-01,  4.5630e-01, -5.4834e-01,  ...,  5.9013e-03,
          -4.7974e-02, -2.7617e+00],
         [-1.0760e-01, -2.0371e+00,  3.7915e-01,  ...,  6.4551e-01,
           2.6953e-01, -1.0910e-03],
         [ 4.9683e-01,  1.2402e+00, -1.0429e-02,  ...,  3.4294e-03,
          -8.2959e-01,  1.2012e-01],
         ...,
         [ 1.6956e-01,  5.3027e-01, -1.6418e-01,  ..., -2.1094e-01,
          -9.8267e-02,  2.3364e-01],
         [ 4.1687e-02, -1.1365e-01,  1.2598e+00,  ..., -5.6299e-01,
           1.5967e+00,  9.3445e-02],
         [ 9.7656e-02, -4.5410e-01, -2.9395e-01,  ..., -1.6565e-01,
          -8.2153e-02, -7.0068e-01]],
 
        [[ 1.6345e-01,  2.5806e-01, -6.1951e-02,  ..., -6.5857e-02,
          -6.0303e-02, -1.9080e-01],
         [ 1.9666e-01,  1.8262e+00, -1.1951e-01,  ...,  1.0138e-01,
          -2.0911e-01, -6.0638e-02],
         [-6.9141e-01, -2.5234e+00, -1.2734e+00,  ...,  1.0510e-01,
          -1.6504e+00, -9.7070e-01],
         ...,
         [-2.5406e-03, -3.1342e-02, -7.0862e-02,  ...,  9.2041e-02,
           7.7271e-02,  8.0518e-01],
         [-1.5161e-01, -6.8848e-02,  7.0801e-01,  ...,  7.0166e-01,
          -3.3661e-02, -1.4319e-01],
         [-3.0899e-02,  1.4490e-01,  1.9763e-01,  ..., -8.1116e-02,
           7.8955e-01,  1.8347e-01]]], device='npu:0', dtype=torch.float16)
 
y_gelu_npu:
tensor([[[-1.5771e-01, -1.4331e-01, -1.0846e-01,  ..., -1.1133e-01,
           1.3818e+00, -1.5076e-01],
         [-1.8600e-02,  1.6904e+00, -6.9336e-02,  ...,  3.6890e-01,
           1.6768e+00,  2.5146e-01],
         [ 7.5342e-01,  6.0742e-01,  1.0820e+00,  ...,  1.5063e-01,
           1.1572e+00, -9.4482e-02],
         ...,
         [-1.5796e-01,  8.4082e-01,  9.2627e-01,  ..., -1.6064e-01,
          -1.1096e-01, -1.6370e-01],
         [ 3.4814e-01, -1.6418e-01, -3.1982e-02,  ..., -1.5186e-01,
           1.3330e+00, -1.4111e-01],
         [-8.4778e-02, -1.1023e-01, -1.0669e-01,  ...,  1.9521e+00,
           9.5654e-01,  1.5635e+00]],
 
        [[ 1.7881e+00,  1.8359e+00, -1.6663e-01,  ...,  1.4609e+00,
          -1.6760e-01, -1.6528e-01],
         [ 1.9434e+00,  1.7168e+00, -1.1615e-01,  ..., -9.8816e-02,
           9.4043e-01,  1.2344e+00],
         [-1.6064e-01,  5.7031e-01,  1.6475e+00,  ..., -1.0809e-01,
          -1.6785e-01, -1.6345e-01],
         ...,
         [-1.6797e-01, -4.6326e-02,  2.6904e-01,  ...,  6.9458e-02,
           1.3174e+00,  1.3486e+00],
         [-1.0645e-01,  3.0249e-01, -9.9411e-03,  ..., -1.3928e-01,
          -1.0974e-01, -7.1533e-02],
         [ 1.7012e+00, -1.0254e-01, -8.2825e-02,  ..., -4.8492e-02,
          -1.1926e-01,  1.7490e+00]],
 
        [[-6.6650e-02, -1.0370e-01, -2.3788e-02,  ..., -1.0706e-01,
          -1.6980e-01,  1.4209e+00],
         [-5.2986e-03, -1.1133e-01,  2.5439e-01,  ..., -3.9459e-02,
          -6.8909e-02,  1.2119e+00],
         [ 6.1035e-01,  6.8506e-01, -1.5039e-01,  ...,  5.8136e-02,
           1.8232e+00, -6.7383e-02],
         ...,
         [ 1.4434e+00,  1.6787e+00,  1.2422e+00,  ...,  7.5488e-01,
          -5.0720e-02, -6.8787e-02],
         [-1.4600e-01, -1.2213e-01, -1.6711e-01,  ...,  3.7280e-01,
           1.3125e+00,  2.2375e-01],
         [ 3.4985e-01, -1.2659e-01, -4.6722e-02,  ..., -1.4685e-01,
           1.4856e-01, -1.6406e-01]],
 
        ...,
 
        [[ 4.8730e-01,  1.6680e+00, -5.7098e-02,  ...,  1.4189e+00,
           7.1983e-03,  7.8857e-01],
         [ 1.1328e+00, -1.6931e-01, -1.1163e-01,  ..., -1.6467e-01,
           3.5309e-02, -1.5173e-01],
         [-1.6858e-01, -8.9111e-02, -1.4709e-01,  ..., -8.1970e-02,
           5.4248e-01,  5.0830e-01],
         ...,
         [ 2.1936e-01,  7.7197e-01,  4.8737e-02,  ...,  8.7842e-01,
          -1.6406e-01, -7.1716e-02],
         [-1.2720e-01,  1.9404e+00,  1.0391e+00,  ...,  7.3877e-01,
          -1.6199e-01,  1.5781e+00],
         [-1.6968e-01,  1.0664e+00, -1.6431e-01,  ..., -7.5439e-02,
          -1.5332e-01,  2.1790e-01]],
 
        [[ 3.0981e-01,  6.0010e-01,  7.9346e-01,  ...,  4.0169e-03,
           5.8447e-01,  1.7109e+00],
         [-1.6699e-01,  1.7646e+00,  5.9326e-01,  ...,  3.3813e-01,
          -1.5845e-01, -4.7699e-02],
         [ 3.7573e-01,  9.4580e-01, -9.5276e-02,  ...,  2.4805e-01,
           8.3350e-01,  1.2573e-01],
         ...,
         [-1.5369e-01,  1.2021e+00, -1.6626e-01,  ..., -1.1108e-01,
           1.6084e+00, -1.4807e-01],
         [-4.6234e-02, -6.4331e-02,  8.9844e-01,  ...,  9.2871e-01,
           7.9834e-01, -1.6992e-01],
         [-6.4941e-02,  1.1465e+00, -1.5161e-01,  ..., -1.5076e-01,
          -8.6487e-02,  1.0137e+00]],
 
        [[-1.1731e-01, -1.4404e-01, -8.9050e-02,  ..., -1.2128e-01,
          -1.0919e-01, -1.6943e-01],
         [ 1.5186e-01,  1.1396e+00, -6.5735e-02,  ..., -7.4829e-02,
          -1.6455e-01, -8.9355e-02],
         [ 6.4404e-01,  1.5625e+00,  1.7725e+00,  ..., -5.5176e-02,
           1.7920e+00,  6.6504e-01],
         ...,
         [ 1.9083e-03,  3.8452e-01, -4.9011e-02,  ..., -1.5405e-01,
          -1.6003e-01,  1.3975e+00],
         [ 1.0437e-01, -8.6182e-02,  5.5713e-01,  ...,  1.0645e+00,
          -1.3818e-01,  5.1562e-01],
         [-1.0229e-01, -1.0529e-01,  2.6562e-01,  ..., -5.6702e-02,
           1.0830e+00, -1.6833e-01]]], device='npu:0', dtype=torch.float16)