import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_tanh_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tmp9 = tmp5 / tmp8
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp9, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(16)](buf1, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf1, primals_3, out=buf2)
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused__softmax_1[grid(1)](buf2, buf5, 1, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf6 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (1, 4), (0, 1), 0),
            primals_2, out=buf6)
        del buf5
    return reinterpret_tensor(buf6, (4,), (1,), 0
        ), buf1, buf2, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_3, (1, 4), (1, 1), 0)


class SelfAttentionBatchNew(nn.Module):

    def __init__(self, dim, da, alpha=0.2, dropout=0.5):
        super(SelfAttentionBatchNew, self).__init__()
        self.dim = dim
        self.da = da
        self.alpha = alpha
        self.dropout = dropout
        self.a = nn.Parameter(torch.zeros(size=(self.dim, self.da)),
            requires_grad=True)
        self.b = nn.Parameter(torch.zeros(size=(self.da, 1)), requires_grad
            =True)
        nn.init.xavier_uniform_(self.a.data, gain=1.414)
        nn.init.xavier_uniform_(self.b.data, gain=1.414)

    def forward(self, input_0):
        primals_1 = self.a
        primals_3 = self.b
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.050812