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.onnx
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_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, 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 = 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_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
    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_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_4(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = 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, (12, 4), (4, 1))
    assert_size_stride(primals_6, (12,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (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_2, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, 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_2, reinterpret_tensor(primals_4, (4, 4),
            (1, 4), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_5, (4, 4), (1, 4
            ), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_6, (4,), (1,), 4),
            buf1, reinterpret_tensor(primals_5, (4, 4), (1, 4), 16), alpha=
            1, beta=1, out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_6, (4,), (1,), 8),
            buf2, reinterpret_tensor(primals_5, (4, 4), (1, 4), 32), alpha=
            1, beta=1, out=buf5)
        buf6 = reinterpret_tensor(buf3, (4, 4, 1), (1, 4, 16), 0)
        del buf3
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(16)](buf6, primals_6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_6
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf6, reinterpret_tensor(buf4, (4, 1, 4), (1, 1,
            4), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf9 = buf7
        del buf7
        triton_poi_fused__softmax_2[grid(64)](buf8, buf9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf8
        buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf9, reinterpret_tensor(buf5, (4, 4, 1), (1, 4,
            1), 0), out=buf10)
        buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(4, 4)](buf10, buf11, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf12 = reinterpret_tensor(buf10, (4, 4), (4, 1), 0)
        del buf10
        extern_kernels.mm(reinterpret_tensor(buf11, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf12)
        buf13 = buf12
        del buf12
        triton_poi_fused_add_4[grid(16)](buf13, primals_8, primals_2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_8
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf13, reinterpret_tensor(primals_9, (4, 4), (1, 
            4), 0), out=buf14)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf13, buf14, reinterpret_tensor(primals_10, (
            4, 4), (1, 4), 0), alpha=1, beta=1, out=buf15)
    return buf15, primals_2, buf0, buf1, buf2, buf9, reinterpret_tensor(buf11,
        (4, 4), (4, 1), 0
        ), buf13, buf14, primals_10, primals_9, primals_7, reinterpret_tensor(
        buf5, (4, 1, 4), (1, 1, 4), 0), reinterpret_tensor(buf6, (4, 1, 4),
        (1, 1, 4), 0), reinterpret_tensor(buf4, (4, 4, 1), (1, 4, 1), 0
        ), reinterpret_tensor(primals_5, (4, 4), (4, 1), 32
        ), reinterpret_tensor(primals_5, (4, 4), (4, 1), 16
        ), reinterpret_tensor(primals_5, (4, 4), (4, 1), 0)


class TransformerLayerNew(nn.Module):

    def __init__(self, channels, num_heads):
        super().__init__()
        self.q = nn.Linear(channels, channels, bias=False)
        self.k = nn.Linear(channels, channels, bias=False)
        self.v = nn.Linear(channels, channels, bias=False)
        self.ma = nn.MultiheadAttention(embed_dim=channels, num_heads=num_heads
            )
        self.fc1 = nn.Linear(channels, channels, bias=False)
        self.fc2 = nn.Linear(channels, channels, bias=False)

    def forward(self, input_0):
        primals_1 = self.q.weight
        primals_2 = self.k.weight
        primals_3 = self.v.weight
        primals_5 = self.ma.in_proj_weight
        primals_6 = self.ma.in_proj_bias
        primals_4 = self.ma.out_proj.weight
        primals_8 = self.ma.out_proj.bias
        primals_7 = self.fc1.weight
        primals_9 = self.fc2.weight
        primals_10 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.015508