# 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/fi/cfikixpq5lzim334pqb7w54534lmf5lfdtbdalwqxjl5ayanhjuj.py
# Topologically Sorted Source Nodes: [x, x_1, x_2], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => add_1, mul_1, mul_2, sub
#   x_2 => relu
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2], [0], [1], True, [0], 1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %unsqueeze), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %unsqueeze_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %unsqueeze_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %unsqueeze_3), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_0 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_no_training_convolution_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.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, '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__native_batch_norm_legit_no_training_convolution_relu_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 9) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ev/cevlcyjbyu7ypnpanaqqcfffagrvagef5jim25hubq7nac2beppv.py
# Topologically Sorted Source Nodes: [x_3, x_4, x_5], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
# Source node to ATen node mapping:
#   x_3 => convolution_1
#   x_4 => add_3, mul_4, mul_5, sub_1
#   x_5 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_1, %primals_2, [2], [0], [1], True, [0], 1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_1, %unsqueeze), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %unsqueeze_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %unsqueeze_2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %unsqueeze_3), kwargs = {})
#   %relu_1 : [num_users=4] = call_function[target=torch.ops.aten.relu.default](args = (%add_3,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_1 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_no_training_convolution_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.pointwise(
    size_hints=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, '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__native_batch_norm_legit_no_training_convolution_relu_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 19) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/b3/cb377caka5tiptsr6mysifpyaevbhdouaus6tdxgr4dyzxgbtlbw.py
# Topologically Sorted Source Nodes: [x_hop1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_hop1 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_8, %primals_9, [1], [1], [1], True, [0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_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=[512], 
    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_convolution_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_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 19) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ue/cueee7jwpk7sjya3cpk5i5z5vasxviuifpkd5nai4ijsqryqqjqw.py
# Topologically Sorted Source Nodes: [x_hop1_attention, x_hop1_attention_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_hop1_attention => convolution_3
#   x_hop1_attention_1 => relu_2
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_10, %primals_11, [1], [0], [1], True, [0], 1), kwargs = {})
#   %relu_2 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_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=[512], 
    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_convolution_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_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 19) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, 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 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3l/c3ljoysaogzfh2quip2uh6mv5fdokkq75vtnorq6kzzaazs367yz.py
# Topologically Sorted Source Nodes: [x_6, x_7, x_8], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
# Source node to ATen node mapping:
#   x_6 => convolution_4
#   x_7 => add_5, mul_7, mul_8, sub_2
#   x_8 => relu_3
# Graph fragment:
#   %convolution_4 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_1, %primals_2, [2], [0], [1], True, [0], 1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_4, %unsqueeze), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %unsqueeze_1), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_7, %unsqueeze_2), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, %unsqueeze_3), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_5,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_4 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_4', '''
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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, '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__native_batch_norm_legit_no_training_convolution_relu_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 624
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 39) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/j2/cj2p7sbedalvoqh3yffiqucooezaag5cxkxe2aptv5p7vke2yems.py
# Topologically Sorted Source Nodes: [x_9, x_10, x_11], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
# Source node to ATen node mapping:
#   x_10 => add_7, mul_10, mul_11, sub_3
#   x_11 => relu_4
#   x_9 => convolution_5
# Graph fragment:
#   %convolution_5 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_1, %primals_2, [2], [0], [1], True, [0], 1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_5, %unsqueeze), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %unsqueeze_1), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_10, %unsqueeze_2), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_11, %unsqueeze_3), kwargs = {})
#   %relu_4 : [num_users=4] = call_function[target=torch.ops.aten.relu.default](args = (%add_7,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_5 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_5', '''
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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, '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__native_batch_norm_legit_no_training_convolution_relu_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 79) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/af/cafjz24563i2t5ljhszsoovt2oepld2mzg7i6uq6mzjjgqc5oylz.py
# Topologically Sorted Source Nodes: [x_hop2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_hop2 => convolution_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_4, %primals_8, %primals_9, [1], [1], [1], True, [0], 1), kwargs = {})
triton_poi_fused_convolution_6 = async_compile.triton('triton_poi_fused_convolution_6', '''
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=[2048], 
    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_convolution_6', '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_convolution_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 79) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3x/c3xdgqs5vqwmpq2ig2yaxbz66aioe6zcm3nzug2jmeinhhxntiya.py
# Topologically Sorted Source Nodes: [x_hop2_attention, x_hop2_attention_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_hop2_attention => convolution_7
#   x_hop2_attention_1 => relu_5
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_4, %primals_10, %primals_11, [1], [0], [1], True, [0], 1), kwargs = {})
#   %relu_5 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
triton_poi_fused_convolution_relu_7 = async_compile.triton('triton_poi_fused_convolution_relu_7', '''
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=[2048], 
    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_convolution_relu_7', '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_convolution_relu_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 79) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, 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 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ht/chtcdkar63vmwnrz4utggqe6jq5qb32mcu6h2qahbdcm3nrak35f.py
# Topologically Sorted Source Nodes: [x_12, x_13, x_14], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
# Source node to ATen node mapping:
#   x_12 => convolution_8
#   x_13 => add_9, mul_13, mul_14, sub_4
#   x_14 => relu_6
# Graph fragment:
#   %convolution_8 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_4, %primals_1, %primals_2, [2], [0], [1], True, [0], 1), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_8, %unsqueeze), kwargs = {})
#   %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %unsqueeze_1), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_13, %unsqueeze_2), kwargs = {})
#   %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_14, %unsqueeze_3), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_9,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_8 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_8', '''
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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, '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__native_batch_norm_legit_no_training_convolution_relu_8(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2544
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 159) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7z/c7zzvdftphdqo2ntrx7ys2fub5rlu65voxtz5duqwrvetee4qf2q.py
# Topologically Sorted Source Nodes: [x_15, x_16, x_17], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
# Source node to ATen node mapping:
#   x_15 => convolution_9
#   x_16 => add_11, mul_16, mul_17, sub_5
#   x_17 => relu_7
# Graph fragment:
#   %convolution_9 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_6, %primals_1, %primals_2, [2], [0], [1], True, [0], 1), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_9, %unsqueeze), kwargs = {})
#   %mul_16 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_5, %unsqueeze_1), kwargs = {})
#   %mul_17 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_16, %unsqueeze_2), kwargs = {})
#   %add_11 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_17, %unsqueeze_3), kwargs = {})
#   %relu_7 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%add_11,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_9 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_9', '''
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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_9', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, '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__native_batch_norm_legit_no_training_convolution_relu_9(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5104
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 319) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4h/c4hkwy6nrngr73ptzptlklzczknh5prh337xiqhh7llpmojmstyj.py
# Topologically Sorted Source Nodes: [x_hop3], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_hop3 => convolution_10
# Graph fragment:
#   %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_7, %primals_8, %primals_9, [1], [1], [1], True, [0], 1), kwargs = {})
triton_poi_fused_convolution_10 = async_compile.triton('triton_poi_fused_convolution_10', '''
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_convolution_10', '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_convolution_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5104
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 319) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2r/c2r7iwbgkwvnsfzroiemirkarm6gxxkut6zx5xsskeuk6ylrkjy6.py
# Topologically Sorted Source Nodes: [x_hop3_attention, x_hop3_attention_1, x_hop3_attention_2], Original ATen: [aten.convolution, aten.relu, aten.bmm]
# Source node to ATen node mapping:
#   x_hop3_attention => convolution_11
#   x_hop3_attention_1 => relu_8
#   x_hop3_attention_2 => bmm_2
# Graph fragment:
#   %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_7, %primals_10, %primals_11, [1], [0], [1], True, [0], 1), kwargs = {})
#   %relu_8 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {})
#   %bmm_2 : [num_users=1] = call_function[target=torch.ops.aten.bmm.default](args = (%expand_4, %expand_5), kwargs = {})
triton_poi_fused_bmm_convolution_relu_11 = async_compile.triton('triton_poi_fused_bmm_convolution_relu_11', '''
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: '*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_bmm_convolution_relu_11', '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_bmm_convolution_relu_11(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5104
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = (xindex // 319) % 4
    x2 = (xindex // 1276)
    x3 = xindex % 1276
    tmp0 = tl.load(in_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x3 + (1280*x2)), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + (1280*x2)), tmp4, 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, 3), (12, 3, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 3), (12, 3, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2,), padding=(0,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 9), (36, 9, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 9), (36, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_1, x_2], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_0.run(buf1, primals_2, primals_4, primals_5, primals_6, primals_7, buf2, 144, grid=grid(144), stream=stream0)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_1, stride=(2,), padding=(0,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 19), (76, 19, 1))
        buf4 = buf3; del buf3  # reuse
        buf5 = empty_strided_cuda((4, 4, 19), (76, 19, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3, x_4, x_5], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_1.run(buf4, primals_2, primals_4, primals_5, primals_6, primals_7, buf5, 304, grid=grid(304), stream=stream0)
        # Topologically Sorted Source Nodes: [x_hop1], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1,), padding=(1,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 19), (76, 19, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [x_hop1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf7, primals_9, 304, grid=grid(304), stream=stream0)
        # Topologically Sorted Source Nodes: [x_hop1_attention], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf5, primals_10, stride=(1,), padding=(0,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 19), (76, 19, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [x_hop1_attention, x_hop1_attention_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf9, primals_11, 304, grid=grid(304), stream=stream0)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_hop1_attention_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf9, reinterpret_tensor(buf9, (4, 19, 4), (76, 4, 1), 0), out=buf10)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf5, primals_1, stride=(2,), padding=(0,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 39), (156, 39, 1))
        buf12 = buf11; del buf11  # reuse
        buf13 = empty_strided_cuda((4, 4, 39), (156, 39, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6, x_7, x_8], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_4.run(buf12, primals_2, primals_4, primals_5, primals_6, primals_7, buf13, 624, grid=grid(624), stream=stream0)
        # Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, primals_1, stride=(2,), padding=(0,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf14, (4, 4, 79), (316, 79, 1))
        buf15 = buf14; del buf14  # reuse
        buf16 = empty_strided_cuda((4, 4, 79), (316, 79, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_9, x_10, x_11], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_5.run(buf15, primals_2, primals_4, primals_5, primals_6, primals_7, buf16, 1264, grid=grid(1264), stream=stream0)
        # Topologically Sorted Source Nodes: [x_hop2], Original ATen: [aten.convolution]
        buf17 = extern_kernels.convolution(buf16, primals_8, stride=(1,), padding=(1,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf17, (4, 4, 79), (316, 79, 1))
        buf18 = buf17; del buf17  # reuse
        # Topologically Sorted Source Nodes: [x_hop2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_6.run(buf18, primals_9, 1264, grid=grid(1264), stream=stream0)
        # Topologically Sorted Source Nodes: [x_hop2_attention], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf16, primals_10, stride=(1,), padding=(0,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf19, (4, 4, 79), (316, 79, 1))
        buf20 = buf19; del buf19  # reuse
        # Topologically Sorted Source Nodes: [x_hop2_attention, x_hop2_attention_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_7.run(buf20, primals_11, 1264, grid=grid(1264), stream=stream0)
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_hop2_attention_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf20, reinterpret_tensor(buf20, (4, 79, 4), (316, 4, 1), 0), out=buf21)
        # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf16, primals_1, stride=(2,), padding=(0,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf22, (4, 4, 159), (636, 159, 1))
        buf23 = buf22; del buf22  # reuse
        buf24 = empty_strided_cuda((4, 4, 159), (636, 159, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_12, x_13, x_14], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_8.run(buf23, primals_2, primals_4, primals_5, primals_6, primals_7, buf24, 2544, grid=grid(2544), stream=stream0)
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.convolution]
        buf25 = extern_kernels.convolution(buf24, primals_1, stride=(2,), padding=(0,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf25, (4, 4, 319), (1276, 319, 1))
        buf26 = buf25; del buf25  # reuse
        buf27 = empty_strided_cuda((4, 4, 319), (1276, 319, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_15, x_16, x_17], Original ATen: [aten.convolution, aten._native_batch_norm_legit_no_training, aten.relu]
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_9.run(buf26, primals_2, primals_4, primals_5, primals_6, primals_7, buf27, 5104, grid=grid(5104), stream=stream0)
        del primals_2
        del primals_7
        # Topologically Sorted Source Nodes: [x_hop3], Original ATen: [aten.convolution]
        buf28 = extern_kernels.convolution(buf27, primals_8, stride=(1,), padding=(1,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf28, (4, 4, 319), (1276, 319, 1))
        buf29 = buf28; del buf28  # reuse
        # Topologically Sorted Source Nodes: [x_hop3], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_10.run(buf29, primals_9, 5104, grid=grid(5104), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [x_hop3_attention], Original ATen: [aten.convolution]
        buf30 = extern_kernels.convolution(buf27, primals_10, stride=(1,), padding=(0,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf30, (4, 4, 319), (1276, 319, 1))
        buf31 = empty_strided_cuda((4, 4, 319), (1280, 319, 1), torch.float32)
        buf32 = empty_strided_cuda((4, 319, 4), (1280, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_hop3_attention, x_hop3_attention_1, x_hop3_attention_2], Original ATen: [aten.convolution, aten.relu, aten.bmm]
        triton_poi_fused_bmm_convolution_relu_11.run(buf30, primals_11, buf31, buf32, 5104, grid=grid(5104), stream=stream0)
        del buf30
        del primals_11
        buf33 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_hop3_attention_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf31, buf32, out=buf33)
        del buf32
    return (buf7, buf18, buf29, buf10, buf21, buf33, primals_1, primals_3, primals_4, primals_5, primals_6, primals_8, primals_10, buf1, buf2, buf4, buf5, buf9, buf12, buf13, buf15, buf16, buf20, buf23, buf24, buf26, buf27, buf31, )


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, 3), (12, 3, 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), (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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 3), (12, 3, 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, 1), (4, 1, 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
import torch.nn as nn
import torch.nn.init as init


class CNN_decoder_attention(nn.Module):

    def __init__(self, input_size, output_size, stride=2):
        super(CNN_decoder_attention, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.relu = nn.ReLU()
        self.deconv = nn.ConvTranspose1d(in_channels=int(self.input_size),
            out_channels=int(self.input_size), kernel_size=3, stride=stride)
        self.bn = nn.BatchNorm1d(int(self.input_size))
        self.deconv_out = nn.ConvTranspose1d(in_channels=int(self.
            input_size), out_channels=int(self.output_size), kernel_size=3,
            stride=1, padding=1)
        self.deconv_attention = nn.ConvTranspose1d(in_channels=int(self.
            input_size), out_channels=int(self.input_size), kernel_size=1,
            stride=1, padding=0)
        self.bn_attention = nn.BatchNorm1d(int(self.input_size))
        self.relu_leaky = nn.LeakyReLU(0.2)
        for m in self.modules():
            if isinstance(m, nn.ConvTranspose1d):
                m.weight.data = init.xavier_uniform(m.weight.data, gain=nn.
                    init.calculate_gain('relu'))
            elif isinstance(m, nn.BatchNorm1d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def forward(self, x):
        """

        :param
        x: batch * channel * length
        :return:
        """
        x = self.deconv(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.deconv(x)
        x = self.bn(x)
        x = self.relu(x)
        x_hop1 = self.deconv_out(x)
        x_hop1_attention = self.deconv_attention(x)
        x_hop1_attention = self.relu(x_hop1_attention)
        x_hop1_attention = torch.matmul(x_hop1_attention, x_hop1_attention.
            view(-1, x_hop1_attention.size(2), x_hop1_attention.size(1)))
        x = self.deconv(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.deconv(x)
        x = self.bn(x)
        x = self.relu(x)
        x_hop2 = self.deconv_out(x)
        x_hop2_attention = self.deconv_attention(x)
        x_hop2_attention = self.relu(x_hop2_attention)
        x_hop2_attention = torch.matmul(x_hop2_attention, x_hop2_attention.
            view(-1, x_hop2_attention.size(2), x_hop2_attention.size(1)))
        x = self.deconv(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.deconv(x)
        x = self.bn(x)
        x = self.relu(x)
        x_hop3 = self.deconv_out(x)
        x_hop3_attention = self.deconv_attention(x)
        x_hop3_attention = self.relu(x_hop3_attention)
        x_hop3_attention = torch.matmul(x_hop3_attention, x_hop3_attention.
            view(-1, x_hop3_attention.size(2), x_hop3_attention.size(1)))
        return (x_hop1, x_hop2, x_hop3, x_hop1_attention, x_hop2_attention,
            x_hop3_attention)


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


def get_init_inputs():
    return [[], {'input_size': 4, 'output_size': 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
import torch.nn.init as init
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__native_batch_norm_legit_no_training_convolution_relu_0(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 9 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 19 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 19 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 19 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_4(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 624
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 39 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_5(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 79 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused_convolution_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 79 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_7(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 79 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_8(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 2544
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 159 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_9(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 5104
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 319 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp4 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused_convolution_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 5104
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 319 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_bmm_convolution_relu_11(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 5104
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 319 % 4
    x2 = xindex // 1276
    x3 = xindex % 1276
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x3 + 1280 * x2), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + 1280 * x2), tmp4, 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, 3), (12, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 3), (12, 3, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2,),
            padding=(0,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 9), (36, 9, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 9), (36, 9, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_0[
            grid(144)](buf1, primals_2, primals_4, primals_5, primals_6,
            primals_7, buf2, 144, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, primals_1, stride=(2,),
            padding=(0,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 19), (76, 19, 1))
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 19), (76, 19, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_1[
            grid(304)](buf4, primals_2, primals_4, primals_5, primals_6,
            primals_7, buf5, 304, XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1,),
            padding=(1,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 19), (76, 19, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_2[grid(304)](buf7, primals_9, 304,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf5, primals_10, stride=(1,),
            padding=(0,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 19), (76, 19, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_3[grid(304)](buf9, primals_11, 
            304, XBLOCK=256, num_warps=4, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf9, reinterpret_tensor(buf9, (4, 19, 4), (76, 
            4, 1), 0), out=buf10)
        buf11 = extern_kernels.convolution(buf5, primals_1, stride=(2,),
            padding=(0,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 39), (156, 39, 1))
        buf12 = buf11
        del buf11
        buf13 = empty_strided_cuda((4, 4, 39), (156, 39, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_4[
            grid(624)](buf12, primals_2, primals_4, primals_5, primals_6,
            primals_7, buf13, 624, XBLOCK=128, num_warps=4, num_stages=1)
        buf14 = extern_kernels.convolution(buf13, primals_1, stride=(2,),
            padding=(0,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf14, (4, 4, 79), (316, 79, 1))
        buf15 = buf14
        del buf14
        buf16 = empty_strided_cuda((4, 4, 79), (316, 79, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_5[
            grid(1264)](buf15, primals_2, primals_4, primals_5, primals_6,
            primals_7, buf16, 1264, XBLOCK=256, num_warps=4, num_stages=1)
        buf17 = extern_kernels.convolution(buf16, primals_8, stride=(1,),
            padding=(1,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf17, (4, 4, 79), (316, 79, 1))
        buf18 = buf17
        del buf17
        triton_poi_fused_convolution_6[grid(1264)](buf18, primals_9, 1264,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf19 = extern_kernels.convolution(buf16, primals_10, stride=(1,),
            padding=(0,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf19, (4, 4, 79), (316, 79, 1))
        buf20 = buf19
        del buf19
        triton_poi_fused_convolution_relu_7[grid(1264)](buf20, primals_11, 
            1264, XBLOCK=256, num_warps=4, num_stages=1)
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf20, reinterpret_tensor(buf20, (4, 79, 4), (
            316, 4, 1), 0), out=buf21)
        buf22 = extern_kernels.convolution(buf16, primals_1, stride=(2,),
            padding=(0,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf22, (4, 4, 159), (636, 159, 1))
        buf23 = buf22
        del buf22
        buf24 = empty_strided_cuda((4, 4, 159), (636, 159, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_8[
            grid(2544)](buf23, primals_2, primals_4, primals_5, primals_6,
            primals_7, buf24, 2544, XBLOCK=256, num_warps=4, num_stages=1)
        buf25 = extern_kernels.convolution(buf24, primals_1, stride=(2,),
            padding=(0,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf25, (4, 4, 319), (1276, 319, 1))
        buf26 = buf25
        del buf25
        buf27 = empty_strided_cuda((4, 4, 319), (1276, 319, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_no_training_convolution_relu_9[
            grid(5104)](buf26, primals_2, primals_4, primals_5, primals_6,
            primals_7, buf27, 5104, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        del primals_7
        buf28 = extern_kernels.convolution(buf27, primals_8, stride=(1,),
            padding=(1,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf28, (4, 4, 319), (1276, 319, 1))
        buf29 = buf28
        del buf28
        triton_poi_fused_convolution_10[grid(5104)](buf29, primals_9, 5104,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf30 = extern_kernels.convolution(buf27, primals_10, stride=(1,),
            padding=(0,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf30, (4, 4, 319), (1276, 319, 1))
        buf31 = empty_strided_cuda((4, 4, 319), (1280, 319, 1), torch.float32)
        buf32 = empty_strided_cuda((4, 319, 4), (1280, 4, 1), torch.float32)
        triton_poi_fused_bmm_convolution_relu_11[grid(5104)](buf30,
            primals_11, buf31, buf32, 5104, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf30
        del primals_11
        buf33 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf31, buf32, out=buf33)
        del buf32
    return (buf7, buf18, buf29, buf10, buf21, buf33, primals_1, primals_3,
        primals_4, primals_5, primals_6, primals_8, primals_10, buf1, buf2,
        buf4, buf5, buf9, buf12, buf13, buf15, buf16, buf20, buf23, buf24,
        buf26, buf27, buf31)


class CNN_decoder_attentionNew(nn.Module):

    def __init__(self, input_size, output_size, stride=2):
        super(CNN_decoder_attentionNew, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.relu = nn.ReLU()
        self.deconv = nn.ConvTranspose1d(in_channels=int(self.input_size),
            out_channels=int(self.input_size), kernel_size=3, stride=stride)
        self.bn = nn.BatchNorm1d(int(self.input_size))
        self.deconv_out = nn.ConvTranspose1d(in_channels=int(self.
            input_size), out_channels=int(self.output_size), kernel_size=3,
            stride=1, padding=1)
        self.deconv_attention = nn.ConvTranspose1d(in_channels=int(self.
            input_size), out_channels=int(self.input_size), kernel_size=1,
            stride=1, padding=0)
        self.bn_attention = nn.BatchNorm1d(int(self.input_size))
        self.relu_leaky = nn.LeakyReLU(0.2)
        for m in self.modules():
            if isinstance(m, nn.ConvTranspose1d):
                m.weight.data = init.xavier_uniform(m.weight.data, gain=nn.
                    init.calculate_gain('relu'))
            elif isinstance(m, nn.BatchNorm1d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def forward(self, input_0):
        primals_1 = self.deconv.weight
        primals_2 = self.deconv.bias
        primals_4 = self.bn.weight
        primals_5 = self.bn.bias
        primals_8 = self.deconv_out.weight
        primals_6 = self.deconv_out.bias
        primals_10 = self.deconv_attention.weight
        primals_7 = self.deconv_attention.bias
        primals_9 = self.bn_attention.weight
        primals_11 = self.bn_attention.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], output[1], output[2], output[3], output[4], output[5]
