Repository: pytorch
Issue: inductor: support horizontal reduction with vec_transpose to improve TIMM swin_base_patch4_window7_224  dynamic shape performance 
Labels: feature,triaged,oncall: pt2,module: inductor,oncall: cpu inductor

### 🚀 The feature, motivation and pitch

For TIMM swin_base_patch4_window7_224 dynamic shape, there has a horizontal reduction with vec_transpose:
```
extern "C" void kernel(float* in_out_ptr0,
                       const float* in_ptr0,
                       const float* in_ptr1,
                       const long ks0)
{
    auto out_ptr0 = in_out_ptr0;
    #pragma omp parallel num_threads(20)
    {
        {
            #pragma omp for
            for(long i0=static_cast<long>(0L); i0<static_cast<long>(ks0); i0+=static_cast<long>(1L))
            {
                #pragma GCC ivdep
                for(long i1=static_cast<long>(0L); i1<static_cast<long>(3136L); i1+=static_cast<long>(16L))
                {
                    {
                        #pragma omp declare reduction(+:at::vec::Vectorized<float>:omp_out = omp_out + omp_in) initializer(omp_priv={{0}})
                        float tmp_acc0 = 0;
                        auto tmp_acc0_vec = at::vec::Vectorized<float>(tmp_acc0);
                        for(long i2=static_cast<long>(0L); i2<static_cast<long>(128L); i2+=static_cast<long>(16L))
                        {
                            for (long i2_inner = 0; i2_inner < 16; i2_inner++)
                            {
                                auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + static_cast<long>(i1 + (3136L*i2) + (3136L*i2_inner) + (401408L*i0)));
                                auto tmp1 = ([&]() { __at_align__ float tmpbuf[16]; for (long i1_inner = 0; i1_inner < 16; i1_inner++) tmpbuf[i1_inner] = in_ptr1[static_cast<long>(i2 + i2_inner + (128L*(static_cast<long>((static_cast<long>((i1 + i1_inner)) % static_cast<long>(56L))) % static_cast<long>(7L))) + (896L*(static_cast<long>(at::native::div_floor_integer((i1 + i1_inner), 56L)) % static_cast<long>(7L))) + (6272L*(at::native::div_floor_integer((static_cast<long>((i1 + i1_inner)) % static_cast<long>(56L)), 7L))) + (50176L*(at::native::div_floor_integer((i1 + i1_inner), 392L))) + (401408L*i0))]; return at::vec::Vectorized<float>::loadu(tmpbuf); })();
                                auto tmp2 = tmp0 + tmp1;
                                tmp_acc0_vec = tmp_acc0_vec + tmp2;
                            }
                        }
                        tmp_acc0_vec.store(out_ptr0 + static_cast<long>(i1 + (3136L*i0)));
                    }
                }
            }
        }
        #pragma omp single
        {
            {
                for(long i0=static_cast<long>(0L); i0<static_cast<long>(3136L*ks0); i0+=static_cast<long>(16L))
                {
                    auto tmp0 = at::vec::Vectorized<float>::loadu(out_ptr0 + static_cast<long>(i0));
                    auto tmp1 = at::vec::Vectorized<float>(static_cast<float>(128.0));
                    auto tmp2 = tmp0 / tmp1;
                    tmp2.store(in_out_ptr0 + static_cast<long>(i0));
                }
            }
        }
    }
}
``` 

https://github.com/pytorch/pytorch/pull/103651 fix the accuracy issue, but the performance can be further improved.

### Alternatives

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### Additional context

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