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, 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)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x2, tmp17, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 12), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (48, 12, 1),
            0), reinterpret_tensor(buf0, (4, 4, 4), (48, 1, 12), 4), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        triton_poi_fused__softmax_1[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = buf2
        del buf2
        extern_kernels.bmm(buf3, reinterpret_tensor(buf0, (4, 4, 4), (48, 
            12, 1), 8), out=buf4)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf4, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        del primals_4
    return reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf3, reinterpret_tensor(buf4, (16, 4), (4, 1), 0
        ), primals_3, reinterpret_tensor(buf0, (4, 4, 4), (48, 1, 12), 8
        ), reinterpret_tensor(buf0, (4, 4, 4), (48, 1, 12), 0
        ), reinterpret_tensor(buf0, (4, 4, 4), (48, 12, 1), 4)


class AttentionNew(nn.Module):

    def __init__(self, dim_in, dim_out, dim_inner, causal=False):
        super().__init__()
        self.scale = dim_inner ** -0.5
        self.causal = causal
        self.to_qkv = nn.Linear(dim_in, dim_inner * 3, bias=False)
        self.to_out = nn.Linear(dim_inner, dim_out)

    def forward(self, input_0):
        primals_2 = self.to_qkv.weight
        primals_3 = self.to_out.weight
        primals_4 = self.to_out.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.05661