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
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, 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 = 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)
    tl.store(out_ptr0 + x2, tmp9, 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)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 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_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_3, (4, 4),
            (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_4, (4, 4),
            (1, 4), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (1, 4, 1), 0
            ), reinterpret_tensor(buf1, (4, 1, 4), (1, 1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = buf3
        del buf3
        triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf4
        buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf5, reinterpret_tensor(buf2, (4, 4, 1), (1, 4,
            1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf6, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf7)
    return buf7, primals_1, buf5, reinterpret_tensor(buf6, (4, 4), (4, 1), 0
        ), primals_5, reinterpret_tensor(buf2, (4, 1, 4), (1, 1, 4), 0
        ), reinterpret_tensor(buf0, (4, 1, 4), (1, 1, 4), 0
        ), reinterpret_tensor(buf1, (4, 4, 1), (1, 4, 1), 0)


class AttentionNew(torch.nn.Module):

    def __init__(self, d_model, heads):
        super().__init__()
        self.d_model = d_model
        self.heads = heads
        self.query = torch.nn.Linear(in_features=d_model, out_features=
            d_model, bias=False)
        self.key = torch.nn.Linear(in_features=d_model, out_features=
            d_model, bias=False)
        self.value = torch.nn.Linear(in_features=d_model, out_features=
            d_model, bias=False)
        self.dense = torch.nn.Linear(in_features=d_model, out_features=
            d_model, bias=False)

    def forward(self, input_0):
        primals_1 = self.query.weight
        primals_2 = self.key.weight
        primals_3 = self.value.weight
        primals_4 = self.dense.weight
        primals_5 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.067203