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 libdevice, 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_per_fused_native_group_norm_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    r3 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = tmp0 - tmp10
    tmp18 = 64.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tl.store(out_ptr2 + (r1 + 64 * x0), tmp27, xmask)
    tl.store(out_ptr3 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_mul_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    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
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 192 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_mul_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 64
    x3 = xindex % 64
    x1 = xindex // 16 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (64 + x3 + 192 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + x4, tmp4, xmask)


@triton.jit
def triton_per_fused__softmax_3(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 12
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_convolution_6(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (12, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (12,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf17 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        get_raw_stream(0)
        triton_per_fused_native_group_norm_0[grid(4)](primals_1, primals_2,
            primals_3, buf0, buf3, buf17, 4, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_2
        del primals_3
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 12, 4, 4), (192, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 1, 16, 4), (64, 64, 4, 1), torch.float32)
        triton_poi_fused_mul_1[grid(64, 4)](buf4, primals_5, buf5, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((4, 1, 4, 16), (64, 1, 16, 1), torch.float32)
        triton_poi_fused_mul_2[grid(256)](buf4, primals_5, buf6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (4, 16, 4), (64, 4, 1),
            0), reinterpret_tensor(buf6, (4, 4, 16), (64, 16, 1), 0), out=buf7)
        buf10 = empty_strided_cuda((4, 1, 16, 16), (256, 256, 16, 1), torch
            .float32)
        triton_per_fused__softmax_3[grid(64)](buf7, buf10, 64, 16, XBLOCK=
            32, num_warps=4, num_stages=1)
        del buf7
        buf11 = buf4
        del buf4
        triton_poi_fused_convolution_4[grid(768)](buf11, primals_5, 768,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf12 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf10, (4, 16, 16), (256, 16,
            1), 0), reinterpret_tensor(buf11, (4, 16, 4), (192, 1, 16), 128
            ), out=buf12)
        buf13 = empty_strided_cuda((4, 1, 4, 16), (64, 1, 16, 1), torch.float32
            )
        triton_poi_fused_clone_5[grid(16, 16)](buf12, buf13, 16, 16, XBLOCK
            =16, YBLOCK=16, num_warps=4, num_stages=1)
        buf14 = extern_kernels.convolution(reinterpret_tensor(buf13, (4, 4,
            4, 4), (64, 16, 4, 1), 0), primals_6, stride=(1, 1), padding=(0,
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf14, (4, 4, 4, 4), (64, 16, 4, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_add_convolution_6[grid(256)](buf15, primals_1,
            primals_7, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf16 = reinterpret_tensor(buf12, (4, 4, 16), (64, 16, 1), 0)
        del buf12
        triton_poi_fused_clone_5[grid(16, 16)](buf5, buf16, 16, 16, XBLOCK=
            16, YBLOCK=16, num_warps=4, num_stages=1)
        del buf5
    return (buf15, primals_1, primals_4, primals_6, buf3, buf10,
        reinterpret_tensor(buf13, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        reinterpret_tensor(buf11, (4, 4, 16), (192, 16, 1), 128), buf16,
        reinterpret_tensor(buf6, (4, 16, 4), (64, 1, 16), 0),
        reinterpret_tensor(buf0, (4, 1, 1), (1, 1, 1), 0),
        reinterpret_tensor(buf17, (4, 1, 1), (1, 1, 1), 0))


class SelfAttention2dNew(nn.Module):

    def __init__(self, c_in, n_head=1, dropout_rate=0.1):
        super().__init__()
        assert c_in % n_head == 0
        self.norm = nn.GroupNorm(1, c_in)
        self.n_head = n_head
        self.qkv_proj = nn.Conv2d(c_in, c_in * 3, 1)
        self.out_proj = nn.Conv2d(c_in, c_in, 1)
        self.dropout = nn.Identity()

    def forward(self, input_0):
        primals_2 = self.norm.weight
        primals_3 = self.norm.bias
        primals_4 = self.qkv_proj.weight
        primals_5 = self.qkv_proj.bias
        primals_6 = self.out_proj.weight
        primals_7 = self.out_proj.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.034586