# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/kj/ckjt7otcndomrxrrcxufztdiirrwnu4fbyih2bbgvbowwzgi4kft.py
# Topologically Sorted Source Nodes: [upper_indices], Original ATen: [aten.triu_indices]
# Source node to ATen node mapping:
#   upper_indices => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%add_1, %add_2],), kwargs = {})
triton_poi_fused_triu_indices_0 = async_compile.triton('triton_poi_fused_triu_indices_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_triu_indices_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
    tmp2 = tmp0 >= tmp1
    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
    tmp19 = tl.full([1], 20, tl.int64)
    tmp20 = tmp0 < tmp19
    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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/3u/c3u527yjixokxirt6uopj7rbnm52377p7p3ekxs2ui6oenscoxw4.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.index]
# Source node to ATen node mapping:
#   x => index
# Graph fragment:
#   %index : [num_users=2] = call_function[target=torch.ops.aten.index.Tensor](args = (%primals_1, [None, %select, %select_1]), kwargs = {})
triton_poi_fused_index_1 = async_compile.triton('triton_poi_fused_index_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/v4/cv4npzde5hmtlzg5ytildbsefgwc3vph732gnrjzhpw5tmlcgyui.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_1 => relu
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_3), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_2 = async_compile.triton('triton_poi_fused_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = 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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kf/ckf4ymtqer5mpqdh6uye5letk5ma76sey2j7l3ryhz4wmztc7iq5.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_2 => relu_1
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_5), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_relu_3 = async_compile.triton('triton_poi_fused_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = 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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [upper_indices], Original ATen: [aten.triu_indices]
        stream0 = get_raw_stream(0)
        triton_poi_fused_triu_indices_0.run(buf0, 20, grid=grid(20), stream=stream0)
        buf1 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.index]
        triton_poi_fused_index_1.run(buf0, primals_1, buf1, 40, grid=grid(40), stream=stream0)
        del buf0
        del primals_1
        buf2 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf1, reinterpret_tensor(primals_2, (10, 2048), (1, 10), 0), out=buf2)
        del primals_2
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu]
        triton_poi_fused_relu_2.run(buf3, primals_3, 8192, grid=grid(8192), stream=stream0)
        del primals_3
        buf4 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf3, reinterpret_tensor(primals_4, (2048, 1024), (1, 2048), 0), out=buf4)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu]
        triton_poi_fused_relu_3.run(buf5, primals_5, 4096, grid=grid(4096), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        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, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2048, 10), (10, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2048, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1024, 2048), (2048, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 1024), (1024, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.nn.functional as F


class DQFFN(nn.Module):

    def __init__(self, n):
        """
        Create Feed-forward Network with n dim input and n dim output
        """
        super(DQFFN, 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, x):
        """
        input is of shape (batch_size, n, n)
        """
        upper_indices = torch.triu_indices(self.n, self.n)
        x = x[:, upper_indices[0], upper_indices[1]]
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        return self.l3(x)


def get_inputs():
    return [torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'n': 4}]

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]
