# 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/ke/ckev4oib3cv2oksvgsnixm2ir66mykxwkb2phwtnkc7jitdkle52.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.native_group_norm, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => add, add_1, mul_1, rsqrt, var_mean
#   x_1 => relu
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %unsqueeze_6), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_3), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_per_fused_native_group_norm_relu_threshold_backward_0 = async_compile.triton('triton_per_fused_native_group_norm_relu_threshold_backward_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: '*fp32', 7: 'i32', 8: '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, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_native_group_norm_relu_threshold_backward_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_group_norm_relu_threshold_backward_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3, out_ptr4, 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
    x2 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    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
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tmp28 = tl.full([1, 1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = 0.0
    tmp31 = tmp29 <= tmp30
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp29, xmask)
    tl.store(out_ptr3 + (r1 + (16*x0)), tmp31, xmask)
    tl.store(out_ptr4 + (x0), tmp22, xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ex/cexccvjnx76cxpye4rdgovum364hqpnhyw7qtgoxiqjkkplj5quo.py
# Topologically Sorted Source Nodes: [x_2, x_3, x_4], Original ATen: [aten.convolution, aten.native_group_norm, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_2 => convolution_1
#   x_3 => add_2, add_3, mul_3, rsqrt_1, var_mean_1
#   x_4 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%unsqueeze_7, %primals_6, %primals_7, [1, 1, 1], [1, 1, 1], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_2, [2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_2 : [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_2,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, %unsqueeze_13), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %unsqueeze_10), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_3,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_per_fused_convolution_native_group_norm_relu_threshold_backward_1 = async_compile.triton('triton_per_fused_convolution_native_group_norm_relu_threshold_backward_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=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: '*fp32', 8: 'i32', 9: '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, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_convolution_native_group_norm_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_convolution_native_group_norm_relu_threshold_backward_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3, out_ptr4, 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)
    r2 = rindex
    x3 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + (16*x3)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = tmp2 - tmp12
    tmp20 = 16.0
    tmp21 = tmp18 / tmp20
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.rsqrt(tmp23)
    tmp25 = tmp19 * tmp24
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tl.full([1, 1], 0, tl.int32)
    tmp31 = triton_helpers.maximum(tmp30, tmp29)
    tmp32 = 0.0
    tmp33 = tmp31 <= tmp32
    tl.store(in_out_ptr0 + (r2 + (16*x3)), tmp2, xmask)
    tl.store(out_ptr2 + (r2 + (16*x3)), tmp31, xmask)
    tl.store(out_ptr3 + (r2 + (16*x3)), tmp33, xmask)
    tl.store(out_ptr4 + (x3), tmp24, xmask)
    tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hi/chi5de6wkhiuhrbwler5y57mp3zifkrpug7qo5wbq74vf32ceipa.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   out => add_4
# Graph fragment:
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%squeeze, %squeeze_6), kwargs = {})
triton_poi_fused_add_2 = async_compile.triton('triton_poi_fused_add_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_2', 'mutated_arg_names': ['in_out_ptr0'], '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_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 64)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', 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, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [inp_res], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 1), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf16 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.native_group_norm, aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_per_fused_native_group_norm_relu_threshold_backward_0.run(primals_3, primals_4, primals_5, buf1, buf4, buf15, buf16, 16, 16, grid=grid(16), stream=stream0)
        del primals_4
        del primals_5
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf4, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_6, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf6 = buf5; del buf5  # reuse
        buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf10 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [x_2, x_3, x_4], Original ATen: [aten.convolution, aten.native_group_norm, aten.relu, aten.threshold_backward]
        triton_per_fused_convolution_native_group_norm_relu_threshold_backward_1.run(buf6, primals_7, primals_8, primals_9, buf7, buf11, buf14, buf10, 16, 16, grid=grid(16), stream=stream0)
        del primals_7
        del primals_9
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(reinterpret_tensor(buf11, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_10, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf13 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.add]
        triton_poi_fused_add_2.run(buf13, primals_2, buf12, primals_11, 256, grid=grid(256), stream=stream0)
        del buf12
        del primals_11
        del primals_2
    return (buf13, primals_1, primals_3, primals_6, primals_8, primals_10, reinterpret_tensor(buf4, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf6, reinterpret_tensor(buf7, (4, 4), (4, 1), 0), reinterpret_tensor(buf10, (4, 4), (4, 1), 0), reinterpret_tensor(buf11, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf14, buf15, reinterpret_tensor(buf1, (4, 4, 1), (4, 1, 1), 0), reinterpret_tensor(buf16, (4, 4, 1), (4, 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, 1, 1, 1), (4, 1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 3, 3, 3), (108, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4, 3, 3, 3), (108, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = 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, primals_8, primals_9, primals_10, primals_11])
    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
from torch import nn


def conv3d(in_channels, out_channels, kernel_size, bias, padding=1, stride=1):
    return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
        padding, bias=bias, stride=stride)


class GreenBlock(nn.Module):
    """
    green_block(inp, filters, name=None)
    ------------------------------------
    Implementation of the special residual block used in the paper. The block
    consists of two (GroupNorm --> ReLu --> 3x3x3 non-strided Convolution)
    units, with a residual connection from the input `inp` to the output. Used
    internally in the model. Can be used independently as well.

    Parameters
    ----------
    `inp`: An keras.layers.layer instance, required
        The keras layer just preceding the green block.
    `filters`: integer, required
        No. of filters to use in the 3D convolutional block. The output
        layer of this green block will have this many no. of channels.
    `data_format`: string, optional
        The format of the input data. Must be either 'chanels_first' or
        'channels_last'. Defaults to `channels_first`, as used in the paper.
    `name`: string, optional
        The name to be given to this green block. Defaults to None, in which
        case, keras uses generated names for the involved layers. If a string
        is provided, the names of individual layers are generated by attaching
        a relevant prefix from [GroupNorm_, Res_, Conv3D_, Relu_, ], followed
        by _1 or _2.

    Returns
    -------
    `out`: A keras.layers.Layer instance
        The output of the green block. Has no. of channels equal to `filters`.
        The size of the rest of the dimensions remains same as in `inp`.
    """

    def __init__(self, input_channels, output_channels):
        super(GreenBlock, self).__init__()
        self.conv3d_1 = conv3d(input_channels, output_channels, padding=0,
            kernel_size=1, bias=True)
        self.gn1 = nn.GroupNorm(input_channels, input_channels)
        self.act_1 = nn.ReLU()
        self.conv3d_2 = conv3d(input_channels, output_channels, kernel_size
            =3, bias=True)
        self.gn2 = nn.GroupNorm(output_channels, output_channels)
        self.act_2 = nn.ReLU()
        self.conv3d_3 = conv3d(output_channels, output_channels,
            kernel_size=3, bias=True)

    def forward(self, x):
        inp_res = self.conv3d_1(x)
        x = self.gn1(x)
        x = self.act_1(x)
        x = self.conv3d_2(x)
        x = self.gn2(x)
        x = self.act_2(x)
        x = self.conv3d_3(x)
        out = inp_res + x
        return out


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


def get_init_inputs():
    return [[], {'input_channels': 4, 'output_channels': 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
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_relu_threshold_backward_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3, out_ptr4, 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
    x2 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + x2, xmask, 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], 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
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tmp28 = tl.full([1, 1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = 0.0
    tmp31 = tmp29 <= tmp30
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp29, xmask)
    tl.store(out_ptr3 + (r1 + 16 * x0), tmp31, xmask)
    tl.store(out_ptr4 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_per_fused_convolution_native_group_norm_relu_threshold_backward_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3,
    out_ptr4, 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)
    r2 = rindex
    x3 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = tmp2 - tmp12
    tmp20 = 16.0
    tmp21 = tmp18 / tmp20
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.rsqrt(tmp23)
    tmp25 = tmp19 * tmp24
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tl.full([1, 1], 0, tl.int32)
    tmp31 = triton_helpers.maximum(tmp30, tmp29)
    tmp32 = 0.0
    tmp33 = tmp31 <= tmp32
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
    tl.store(out_ptr2 + (r2 + 16 * x3), tmp31, xmask)
    tl.store(out_ptr3 + (r2 + 16 * x3), tmp33, xmask)
    tl.store(out_ptr4 + x3, tmp24, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 
            1), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf16 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        get_raw_stream(0)
        triton_per_fused_native_group_norm_relu_threshold_backward_0[grid(16)](
            primals_3, primals_4, primals_5, buf1, buf4, buf15, buf16, 16, 
            16, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_4
        del primals_5
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf4, (1, 4, 4,
            4, 4), (256, 64, 16, 4, 1), 0), primals_6, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf10 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        triton_per_fused_convolution_native_group_norm_relu_threshold_backward_1[
            grid(16)](buf6, primals_7, primals_8, primals_9, buf7, buf11,
            buf14, buf10, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_7
        del primals_9
        buf12 = extern_kernels.convolution(reinterpret_tensor(buf11, (1, 4,
            4, 4, 4), (256, 64, 16, 4, 1), 0), primals_10, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf13 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_add_2[grid(256)](buf13, primals_2, buf12,
            primals_11, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf12
        del primals_11
        del primals_2
    return (buf13, primals_1, primals_3, primals_6, primals_8, primals_10,
        reinterpret_tensor(buf4, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0),
        buf6, reinterpret_tensor(buf7, (4, 4), (4, 1), 0),
        reinterpret_tensor(buf10, (4, 4), (4, 1), 0), reinterpret_tensor(
        buf11, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf14, buf15,
        reinterpret_tensor(buf1, (4, 4, 1), (4, 1, 1), 0),
        reinterpret_tensor(buf16, (4, 4, 1), (4, 1, 1), 0))


def conv3d(in_channels, out_channels, kernel_size, bias, padding=1, stride=1):
    return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
        padding, bias=bias, stride=stride)


class GreenBlockNew(nn.Module):
    """
    green_block(inp, filters, name=None)
    ------------------------------------
    Implementation of the special residual block used in the paper. The block
    consists of two (GroupNorm --> ReLu --> 3x3x3 non-strided Convolution)
    units, with a residual connection from the input `inp` to the output. Used
    internally in the model. Can be used independently as well.

    Parameters
    ----------
    `inp`: An keras.layers.layer instance, required
        The keras layer just preceding the green block.
    `filters`: integer, required
        No. of filters to use in the 3D convolutional block. The output
        layer of this green block will have this many no. of channels.
    `data_format`: string, optional
        The format of the input data. Must be either 'chanels_first' or
        'channels_last'. Defaults to `channels_first`, as used in the paper.
    `name`: string, optional
        The name to be given to this green block. Defaults to None, in which
        case, keras uses generated names for the involved layers. If a string
        is provided, the names of individual layers are generated by attaching
        a relevant prefix from [GroupNorm_, Res_, Conv3D_, Relu_, ], followed
        by _1 or _2.

    Returns
    -------
    `out`: A keras.layers.Layer instance
        The output of the green block. Has no. of channels equal to `filters`.
        The size of the rest of the dimensions remains same as in `inp`.
    """

    def __init__(self, input_channels, output_channels):
        super(GreenBlockNew, self).__init__()
        self.conv3d_1 = conv3d(input_channels, output_channels, padding=0,
            kernel_size=1, bias=True)
        self.gn1 = nn.GroupNorm(input_channels, input_channels)
        self.act_1 = nn.ReLU()
        self.conv3d_2 = conv3d(input_channels, output_channels, kernel_size
            =3, bias=True)
        self.gn2 = nn.GroupNorm(output_channels, output_channels)
        self.act_2 = nn.ReLU()
        self.conv3d_3 = conv3d(output_channels, output_channels,
            kernel_size=3, bias=True)

    def forward(self, input_0):
        primals_1 = self.conv3d_1.weight
        primals_2 = self.conv3d_1.bias
        primals_4 = self.gn1.weight
        primals_5 = self.gn1.bias
        primals_6 = self.conv3d_2.weight
        primals_7 = self.conv3d_2.bias
        primals_8 = self.gn2.weight
        primals_9 = self.gn2.bias
        primals_10 = self.conv3d_3.weight
        primals_11 = self.conv3d_3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
