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.triton_helpers import libdevice, math as tl_math
import torch.utils.data
import torch.distributed
import torch.nn as nn
import torch.optim
import torch.optim.lr_scheduler
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__softmax_mul_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp14 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp16 / tmp16
    tl.store(in_out_ptr0 + x0, tmp17, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tl.load(in_ptr1 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp5 * tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp13 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp10 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused_tanh_2(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)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = 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, 4), (4, 1))
    assert_size_stride(primals_4, (4, 8), (8, 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, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 1), (1, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_poi_fused__softmax_mul_sum_0[grid(4)](buf2, primals_3, buf0,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(32)](buf2, primals_3, primals_2, buf3, 
            32, XBLOCK=32, num_warps=1, num_stages=1)
        buf4 = buf0
        del buf0
        extern_kernels.mm(buf3, reinterpret_tensor(primals_4, (8, 4), (1, 8
            ), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_tanh_2[grid(16)](buf5, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
    return buf5, reinterpret_tensor(buf2, (1, 4), (1, 1), 0
        ), primals_2, primals_3, buf2, buf3, buf5, primals_4


def Linear(in_features, out_features, bias=True, dropout=0):
    """Weight-normalized Linear layer (input: N x T x C)"""
    m = nn.Linear(in_features, out_features, bias=bias)
    m.weight.data.uniform_(-0.1, 0.1)
    if bias:
        m.bias.data.uniform_(-0.1, 0.1)
    return m


class AttentionLayerNew(nn.Module):

    def __init__(self, input_embed_dim, output_embed_dim):
        super().__init__()
        self.input_proj = Linear(input_embed_dim, output_embed_dim, bias=False)
        self.output_proj = Linear(2 * output_embed_dim, output_embed_dim,
            bias=False)

    def forward(self, input_0, input_1):
        primals_1 = self.input_proj.weight
        primals_4 = self.output_proj.weight
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]


Measured latency_ms: 0.053963