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
import torch.nn as 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_triu_indices_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 10, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp0.to(tl.float64)
    tmp6 = tl.full([1], 2.0, tl.float64)
    tmp7 = tmp5 * tmp6
    tmp8 = tl.full([1], 20.25, tl.float64)
    tmp9 = tmp8 - tmp7
    tmp10 = libdevice.sqrt(tmp9)
    tmp11 = tl.full([1], 4.5, tl.float64)
    tmp12 = tmp11 - tmp10
    tmp13 = libdevice.floor(tmp12)
    tmp14 = tmp13.to(tl.int64)
    tmp15 = tmp14 + tmp1
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp4, tmp15, tmp16)
    tmp18 = tmp0 >= tmp3
    tl.full([1], 20, tl.int64)
    tmp21 = -10 + x0
    tmp22 = tmp21.to(tl.float64)
    tmp23 = tmp22 * tmp6
    tmp24 = tmp8 - tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = tmp11 - tmp25
    tmp27 = libdevice.floor(tmp26)
    tmp28 = tl.full([1], 7.0, tl.float64)
    tmp29 = tmp28 - tmp27
    tmp30 = tmp29 * tmp27
    tmp31 = tl.full([1], 0.5, tl.float64)
    tmp32 = tmp30 * tmp31
    tmp33 = tmp22 - tmp32
    tmp34 = libdevice.floor(tmp33)
    tmp35 = tmp34.to(tl.int64)
    tmp36 = tmp35 + tmp1
    tmp37 = tl.full(tmp36.shape, 0.0, tmp36.dtype)
    tmp38 = tl.where(tmp18, tmp36, tmp37)
    tmp39 = tl.where(tmp4, tmp17, tmp38)
    tl.store(out_ptr0 + x0, tmp39, xmask)


@triton.jit
def triton_poi_fused_index_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 40
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = xindex // 10
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (10 + x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp7 = tmp6 + tmp1
    tmp8 = tmp6 < 0
    tmp9 = tl.where(tmp8, tmp7, tmp6)
    tl.device_assert((0 <= tmp9) & (tmp9 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp9 < 4')
    tmp11 = tl.load(in_ptr1 + (tmp9 + 4 * tmp4 + 16 * x1), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp11, xmask)


@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 2048
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


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), (16, 4, 1))
    assert_size_stride(primals_2, (2048, 10), (10, 1))
    assert_size_stride(primals_3, (2048,), (1,))
    assert_size_stride(primals_4, (1024, 2048), (2048, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    assert_size_stride(primals_6, (4, 1024), (1024, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((20,), (1,), torch.int64)
        get_raw_stream(0)
        triton_poi_fused_triu_indices_0[grid(20)](buf0, 20, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        triton_poi_fused_index_1[grid(40)](buf0, primals_1, buf1, 40,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del primals_1
        buf2 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_2, (10, 2048), (
            1, 10), 0), out=buf2)
        del primals_2
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_2[grid(8192)](buf3, primals_3, 8192, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_4, (2048, 1024),
            (1, 2048), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_3[grid(4096)](buf5, primals_5, 4096, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf5, reinterpret_tensor(primals_6,
            (1024, 4), (1, 1024), 0), alpha=1, beta=1, out=buf6)
        del primals_7
    return buf6, buf1, buf3, buf5, primals_6, primals_4


class DQFFNNew(nn.Module):

    def __init__(self, n):
        """
        Create Feed-forward Network with n dim input and n dim output
        """
        super(DQFFNNew, self).__init__()
        self.n = n
        self.l1 = nn.Linear(n * (n + 1) // 2, 2048)
        self.l2 = nn.Linear(2048, 1024)
        self.l3 = nn.Linear(1024, n)

    def forward(self, input_0):
        primals_2 = self.l1.weight
        primals_3 = self.l1.bias
        primals_4 = self.l2.weight
        primals_5 = self.l2.bias
        primals_6 = self.l3.weight
        primals_7 = self.l3.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: 0.075059