# 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_4/inductor_cache/4m/c4msm3gcnvqm52kgfglaipw57py7fgpg6ss2ephzs6eadc3ycwd6.py
# Topologically Sorted Source Nodes: [out1, out1_1], Original ATen: [aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   out1 => var_mean
#   out1_1 => relu
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
triton_per_fused__native_batch_norm_legit_relu_0 = async_compile.triton('triton_per_fused__native_batch_norm_legit_relu_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_relu_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    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, :]
    roffset = 0
    rmask = 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, 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], 16, 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 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tl.full([1, 1], 0, tl.int32)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/s7/cs7bn5vk6z77xfaow55ofvrgwbti6m3wecvgag3dz7w6xz3pjuyc.py
# Topologically Sorted Source Nodes: [out2, out2_1], Original ATen: [aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   out2 => add_1, rsqrt_1, var_mean_1
#   out2_1 => relu_1
# Graph fragment:
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_4, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
#   %relu_1 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {})
triton_per_fused__native_batch_norm_legit_relu_1 = async_compile.triton('triton_per_fused__native_batch_norm_legit_relu_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.persistent_reduction(
    size_hints=[8, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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, 3, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_relu_1(in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    rnumel = 16
    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, :]
    roffset = 0
    rmask = 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, 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], 16, 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 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tl.full([1, 1], 0, tl.int32)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp25, xmask)
    tl.store(out_ptr3 + (x0), tmp22, xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/lt/cltsrsxjtguuc4qfizqdocxzrpyopl6e6rupmtti5ryt2idude3y.py
# Topologically Sorted Source Nodes: [out3, out3_1], Original ATen: [aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   out3 => add_2, rsqrt_2, var_mean_2
#   out3_1 => relu_2
# Graph fragment:
#   %var_mean_2 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_9, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, 1e-05), kwargs = {})
#   %rsqrt_2 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_2,), kwargs = {})
#   %relu_2 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_10,), kwargs = {})
triton_per_fused__native_batch_norm_legit_relu_2 = async_compile.triton('triton_per_fused__native_batch_norm_legit_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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, 3, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_relu_2(in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 16
    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, :]
    roffset = 0
    rmask = 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, 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], 16, 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 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tl.full([1, 1], 0, tl.int32)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp25, xmask)
    tl.store(out_ptr3 + (x0), tmp22, xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4m/c4mjawqnzpr6o4o442ynjxsffhu5gxk44hpqmwnacvrkkldqyh5n.py
# Topologically Sorted Source Nodes: [out3_3, out3_4], Original ATen: [aten.cat, aten.add]
# Source node to ATen node mapping:
#   out3_3 => cat
#   out3_4 => add_3
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %convolution_1, %convolution_2], 1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%cat, %primals_1), kwargs = {})
triton_poi_fused_add_cat_3 = async_compile.triton('triton_poi_fused_add_cat_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_add_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 4
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp17 = tl.load(in_ptr3 + (x3), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (32*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*x2)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 4, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr2 + (x0 + (16*x2)), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tmp18 = tmp16 + tmp17
    tl.store(out_ptr0 + (x3), tmp18, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (1, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_4, (1, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out1, out1_1], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        stream0 = get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_relu_0.run(primals_1, buf3, 16, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [out1_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 2, 4, 4), (32, 16, 4, 1))
        buf5 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        buf9 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf8 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        # Topologically Sorted Source Nodes: [out2, out2_1], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_per_fused__native_batch_norm_legit_relu_1.run(buf4, buf5, buf9, buf8, 8, 16, grid=grid(8), stream=stream0)
        # Topologically Sorted Source Nodes: [out2_2], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 1, 4, 4), (16, 16, 4, 1))
        buf11 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        buf15 = empty_strided_cuda((4, 1, 4, 4), (16, 1, 4, 1), torch.float32)
        buf14 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [out3, out3_1], Original ATen: [aten._native_batch_norm_legit, aten.relu]
        triton_per_fused__native_batch_norm_legit_relu_2.run(buf10, buf11, buf15, buf14, 4, 16, grid=grid(4), stream=stream0)
        # Topologically Sorted Source Nodes: [out3_2], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(reinterpret_tensor(buf15, (4, 1, 4, 4), (16, 0, 4, 1), 0), primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 1, 4, 4), (16, 16, 4, 1))
        buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out3_3, out3_4], Original ATen: [aten.cat, aten.add]
        triton_poi_fused_add_cat_3.run(buf4, buf10, buf16, primals_1, buf17, 256, grid=grid(256), stream=stream0)
        del buf16
        del primals_1
    return (buf17, primals_2, primals_3, primals_4, buf3, buf4, reinterpret_tensor(buf8, (8, ), (1, ), 0), buf9, buf10, reinterpret_tensor(buf14, (4, ), (1, ), 0), reinterpret_tensor(buf15, (4, 1, 4, 4), (16, 16, 4, 1), 0), reinterpret_tensor(buf11, (1, 4, 1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(buf5, (1, 8, 1, 1), (8, 1, 1, 1), 0), )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 2, 3, 3), (18, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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


def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd,
        padding=padding, bias=bias)


class ConvBlock(nn.Module):

    def __init__(self, in_planes, out_planes, use_instance_norm):
        super(ConvBlock, self).__init__()
        self.bn1 = nn.InstanceNorm2d(in_planes
            ) if use_instance_norm else nn.BatchNorm2d(in_planes)
        self.conv1 = conv3x3(in_planes, int(out_planes // 2))
        self.bn2 = nn.InstanceNorm2d(int(out_planes / 2)
            ) if use_instance_norm else nn.BatchNorm2d(int(out_planes / 2))
        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
        self.bn3 = nn.InstanceNorm2d(int(out_planes / 4)
            ) if use_instance_norm else nn.BatchNorm2d(int(out_planes / 4))
        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
        if in_planes != out_planes:
            self.downsample = nn.Sequential(nn.InstanceNorm2d(in_planes) if
                use_instance_norm else nn.BatchNorm2d(in_planes), nn.ReLU(
                True), nn.Conv2d(in_planes, out_planes, kernel_size=1,
                stride=1, bias=False))
        else:
            self.downsample = None

    def forward(self, x):
        residual = x
        out1 = self.bn1(x)
        out1 = F.relu(out1, True)
        out1 = self.conv1(out1)
        out2 = self.bn2(out1)
        out2 = F.relu(out2, True)
        out2 = self.conv2(out2)
        out3 = self.bn3(out2)
        out3 = F.relu(out3, True)
        out3 = self.conv3(out3)
        out3 = torch.cat((out1, out2, out3), 1)
        if self.downsample is not None:
            residual = self.downsample(residual)
        out3 += residual
        return out3


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


def get_init_inputs():
    return [[], {'in_planes': 4, 'out_planes': 4, 'use_instance_norm': 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_per_fused__native_batch_norm_legit_relu_0(in_ptr0, out_ptr2,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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])
    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], 16, 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 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tl.full([1, 1], 0, tl.int32)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp25, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_relu_1(in_ptr0, out_ptr0,
    out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    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])
    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], 16, 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 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tl.full([1, 1], 0, tl.int32)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp25, xmask)
    tl.store(out_ptr3 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_relu_2(in_ptr0, out_ptr0,
    out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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])
    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], 16, 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 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tl.full([1, 1], 0, tl.int32)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp25, xmask)
    tl.store(out_ptr3 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp17 = tl.load(in_ptr3 + x3, xmask)
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 32 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * x2), tmp9 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 4, tl.int64)
    tmp14 = tl.load(in_ptr2 + (x0 + 16 * x2), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tmp18 = tmp16 + tmp17
    tl.store(out_ptr0 + x3, tmp18, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (1, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_4, (1, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_relu_0[grid(16)](primals_1,
            buf3, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 2, 4, 4), (32, 16, 4, 1))
        buf5 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        buf9 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf8 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        triton_per_fused__native_batch_norm_legit_relu_1[grid(8)](buf4,
            buf5, buf9, buf8, 8, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 1, 4, 4), (16, 16, 4, 1))
        buf11 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        buf15 = empty_strided_cuda((4, 1, 4, 4), (16, 1, 4, 1), torch.float32)
        buf14 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        triton_per_fused__native_batch_norm_legit_relu_2[grid(4)](buf10,
            buf11, buf15, buf14, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf16 = extern_kernels.convolution(reinterpret_tensor(buf15, (4, 1,
            4, 4), (16, 0, 4, 1), 0), primals_4, stride=(1, 1), padding=(1,
            1), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf16, (4, 1, 4, 4), (16, 16, 4, 1))
        buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_cat_3[grid(256)](buf4, buf10, buf16, primals_1,
            buf17, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf16
        del primals_1
    return (buf17, primals_2, primals_3, primals_4, buf3, buf4,
        reinterpret_tensor(buf8, (8,), (1,), 0), buf9, buf10,
        reinterpret_tensor(buf14, (4,), (1,), 0), reinterpret_tensor(buf15,
        (4, 1, 4, 4), (16, 16, 4, 1), 0), reinterpret_tensor(buf11, (1, 4, 
        1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(buf5, (1, 8, 1, 1), (8,
        1, 1, 1), 0))


def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd,
        padding=padding, bias=bias)


class ConvBlockNew(nn.Module):

    def __init__(self, in_planes, out_planes, use_instance_norm):
        super(ConvBlockNew, self).__init__()
        self.bn1 = nn.InstanceNorm2d(in_planes
            ) if use_instance_norm else nn.BatchNorm2d(in_planes)
        self.conv1 = conv3x3(in_planes, int(out_planes // 2))
        self.bn2 = nn.InstanceNorm2d(int(out_planes / 2)
            ) if use_instance_norm else nn.BatchNorm2d(int(out_planes / 2))
        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
        self.bn3 = nn.InstanceNorm2d(int(out_planes / 4)
            ) if use_instance_norm else nn.BatchNorm2d(int(out_planes / 4))
        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
        if in_planes != out_planes:
            self.downsample = nn.Sequential(nn.InstanceNorm2d(in_planes) if
                use_instance_norm else nn.BatchNorm2d(in_planes), nn.ReLU(
                True), nn.Conv2d(in_planes, out_planes, kernel_size=1,
                stride=1, bias=False))
        else:
            self.downsample = None

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_4 = self.conv3.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
