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
import torch.nn as nn
import torch._utils
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_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    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)
    tmp18 = tmp0 * tmp15
    tl.store(out_ptr0 + x2, tmp17, xmask)
    tl.store(out_ptr1 + x2, tmp18, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 1, 1, 4), (4, 4, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (8, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 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, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 8), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0
            ), reinterpret_tensor(buf1, (4, 4, 4), (32, 1, 8), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 1, 1, 4), (4, 16, 16, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 1, 1, 4), (4, 1, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_mul_0[grid(16)](buf2, buf3, buf7, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (4, 1, 1, 4), (4, 4, 4, 1), 0)
        del buf2
        triton_poi_fused__softmax_1[grid(16)](buf3, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 1, 4), (4, 4, 1), 0)
        del buf3
        extern_kernels.bmm(reinterpret_tensor(buf4, (4, 1, 4), (4, 4, 1), 0
            ), reinterpret_tensor(buf1, (4, 4, 4), (32, 8, 1), 4), out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf5, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf6)
        del primals_6
    return reinterpret_tensor(buf7, (4, 1, 1, 4), (4, 4, 4, 1), 0
        ), reinterpret_tensor(buf6, (4, 1, 4), (4, 4, 1), 0
        ), reinterpret_tensor(primals_2, (4, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf4, reinterpret_tensor(buf5, (4, 4), (4, 1), 0
        ), primals_5, reinterpret_tensor(buf1, (4, 4, 4), (32, 1, 8), 4
        ), reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 4), 0
        ), reinterpret_tensor(buf1, (4, 4, 4), (32, 8, 1), 0)


class Attention_DecoderNew(nn.Module):

    def __init__(self, dim, num_heads=1, qkv_bias=False, qk_scale=None,
        attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        self.fc_q = nn.Linear(dim, dim * 1, bias=qkv_bias)
        self.fc_kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

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


Measured latency_ms: 0.065743