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 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__softmax_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    x3 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (4 + x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (8 + x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (12 + x0 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = tmp0 - tmp16
    tmp18 = tl_math.exp(tmp17)
    tl.store(out_ptr0 + x4, tmp9, xmask)
    tl.store(out_ptr1 + x4, tmp18, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    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 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_4(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_1, reinterpret_tensor(primals_2, (4, 4, 
            4), (16, 1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf0, buf1, buf2, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf3 = buf0
        del buf0
        triton_poi_fused__softmax_1[grid(64)](buf1, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = buf1
        del buf1
        extern_kernels.bmm(buf3, primals_2, out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](buf2, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)
        del buf2
        extern_kernels.bmm(buf5, primals_1, out=buf6)
        buf7 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_3[grid(128)](buf4, primals_1, buf7, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        buf8 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        extern_kernels.bmm(buf5, buf7, out=buf8)
        del buf5
        buf9 = buf7
        del buf7
        triton_poi_fused_cat_3[grid(128)](buf6, primals_2, buf9, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        extern_kernels.bmm(buf3, buf9, out=buf10)
        del buf9
        buf11 = reinterpret_tensor(buf3, (16, 4), (4, 1), 0)
        del buf3
        extern_kernels.mm(reinterpret_tensor(buf10, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf11)
        del primals_3
        buf12 = reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0)
        del buf11
        triton_poi_fused_add_4[grid(64)](buf12, primals_1, primals_4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        del primals_4
        buf13 = reinterpret_tensor(buf6, (16, 4), (4, 1), 0)
        del buf6
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf13)
        del primals_5
        buf14 = reinterpret_tensor(buf13, (4, 4, 4), (16, 4, 1), 0)
        del buf13
        triton_poi_fused_add_4[grid(64)](buf14, primals_2, primals_6, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        del primals_6
    return buf12, buf14, reinterpret_tensor(buf10, (16, 8), (8, 1), 0
        ), reinterpret_tensor(buf8, (16, 8), (8, 1), 0)


class DPDALayearNew(nn.Module):

    def __init__(self, dim):
        super(DPDALayearNew, self).__init__()
        self.W_p = nn.Linear(2 * dim, dim)
        self.W_q = nn.Linear(2 * dim, dim)

    def forward(self, input_0, input_1):
        primals_3 = self.W_p.weight
        primals_4 = self.W_p.bias
        primals_5 = self.W_q.weight
        primals_6 = self.W_q.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]


Measured latency_ms: 0.140196