# 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_6/inductor_cache/gj/cgjvtebls4tlyx7vrq7femvjnasdisgzq2dhtos5qfetig5jihmc.py
# Topologically Sorted Source Nodes: [conv1d, x], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv1d => convolution
#   x => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1], [0], [1], False, [0], 1), kwargs = {})
#   %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_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=[16384], 
    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_0', '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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/g2/cg2nw6ecspq7a3naj7mf2x6difdt6rg4piybxw2z4ajnllncw56b.py
# Topologically Sorted Source Nodes: [pointfeat_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   pointfeat_2 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=3] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_2, %relu_3], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 64) % 256
    x0 = xindex % 64
    x2 = (xindex // 16384)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (64*x1) + (8192*x2)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 256, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + (64*((-128) + x1)) + (8192*x2)), tmp12, other=0.0)
    tmp16 = tl.load(in_ptr3 + ((-128) + x1), tmp12, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + (x3), tmp21, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/an/canhpnb43drx25h7zse5diln5yhkabraygooa4ndxm44eud6u5dl.py
# Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%cat, %cat_1, %relu_4], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 163840
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 64) % 640
    x0 = xindex % 64
    x2 = (xindex // 40960)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 64, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.load(in_ptr0 + (x0 + (64*x1) + (4096*x2)), tmp7, other=0.0)
    tmp9 = tmp0 >= tmp5
    tmp10 = tmp9 & tmp4
    tmp11 = tl.load(in_ptr1 + (x0 + (64*((-64) + x1)) + (4096*x2)), tmp10, other=0.0)
    tmp12 = tl.where(tmp6, tmp8, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 384, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr2 + (x0 + (64*((-128) + x1)) + (16384*x2)), tmp18, other=0.0)
    tmp20 = tmp0 >= tmp16
    tmp21 = tl.full([1], 640, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tl.load(in_ptr3 + (x0 + (64*((-384) + x1)) + (16384*x2)), tmp20, other=0.0)
    tmp24 = tl.load(in_ptr4 + ((-384) + x1), tmp20, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.full([1], 0, tl.int32)
    tmp27 = triton_helpers.maximum(tmp26, tmp25)
    tmp28 = tl.full(tmp27.shape, 0.0, tmp27.dtype)
    tmp29 = tl.where(tmp20, tmp27, tmp28)
    tmp30 = tl.where(tmp18, tmp19, tmp29)
    tmp31 = tl.where(tmp4, tmp14, tmp30)
    tl.store(out_ptr0 + (x3), tmp31, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/vo/cvohlke4xnfgzejz5kvozs5rcwgr4vxsqfc4mjjsdfo43lpeufna.py
# Topologically Sorted Source Nodes: [conv1d_5, x_4], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   conv1d_5 => convolution_5
#   x_4 => gt, mul, where
# Graph fragment:
#   %convolution_5 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_2, %primals_13, %primals_14, [1], [0], [1], False, [0], 1), kwargs = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_5, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_5, 0.01), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution_5, %mul), kwargs = {})
triton_poi_fused_convolution_leaky_relu_3 = async_compile.triton('triton_poi_fused_convolution_leaky_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_3', '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_convolution_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 81920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 320
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/gf/cgfraiihnqesk5wbpwygpjlbm4yn3jflohmpeu3fitfqezchskef.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_5 => convolution_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_15, %primals_16, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_4 = async_compile.triton('triton_poi_fused_convolution_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=[65536], 
    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_4', '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_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 40960
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 160
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ij/cij356nxgzjxr37734cyp7zp3rxxxpzv3bzotmc5e5bqayalpyqb.py
# Topologically Sorted Source Nodes: [conv1d_4, x_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv1d_4 => convolution_4
#   x_2 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_1, %primals_11, %primals_12, [1], [0], [1], False, [0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_5 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_5', '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_convolution_relu_threshold_backward_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 256
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ho/choewu5xusgcnc4x2qdqs3iu2nxek254eb3qotcljnxxh32ofi24.py
# Topologically Sorted Source Nodes: [conv1d_3, emb_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv1d_3 => convolution_3
#   emb_1 => relu_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_9, %primals_10, [1], [0], [1], False, [0], 1), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_6 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_6', '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_convolution_relu_threshold_backward_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 128
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16 = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 1), (3, 1, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64), (192, 64, 1))
    assert_size_stride(primals_4, (64, 32, 1), (32, 1, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (4, 32, 64), (2048, 64, 1))
    assert_size_stride(primals_7, (128, 64, 1), (64, 1, 1))
    assert_size_stride(primals_8, (128, ), (1, ))
    assert_size_stride(primals_9, (128, 64, 1), (64, 1, 1))
    assert_size_stride(primals_10, (128, ), (1, ))
    assert_size_stride(primals_11, (256, 256, 1), (256, 1, 1))
    assert_size_stride(primals_12, (256, ), (1, ))
    assert_size_stride(primals_13, (320, 640, 1), (640, 1, 1))
    assert_size_stride(primals_14, (320, ), (1, ))
    assert_size_stride(primals_15, (160, 320, 1), (320, 1, 1))
    assert_size_stride(primals_16, (160, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64), (4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv1d, x], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 16384, grid=grid(16384), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_6, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 64), (4096, 64, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv1d_1, emb], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 16384, grid=grid(16384), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv1d_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf1, primals_7, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 128, 64), (8192, 64, 1))
        # Topologically Sorted Source Nodes: [conv1d_3], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf3, primals_9, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 128, 64), (8192, 64, 1))
        buf6 = empty_strided_cuda((4, 256, 64), (16384, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pointfeat_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf4, primals_8, buf5, primals_10, buf6, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [conv1d_4], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_11, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 256, 64), (16384, 64, 1))
        buf8 = empty_strided_cuda((4, 640, 64), (40960, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf1, buf3, buf6, buf7, primals_12, buf8, 163840, grid=grid(163840), stream=stream0)
        # Topologically Sorted Source Nodes: [conv1d_5], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_13, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf9, (4, 320, 64), (20480, 64, 1))
        buf10 = empty_strided_cuda((4, 320, 64), (20480, 64, 1), torch.bool)
        buf11 = empty_strided_cuda((4, 320, 64), (20480, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1d_5, x_4], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_3.run(buf9, primals_14, buf10, buf11, 81920, grid=grid(81920), stream=stream0)
        del buf9
        del primals_14
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_15, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf12, (4, 160, 64), (10240, 64, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf13, primals_16, 40960, grid=grid(40960), stream=stream0)
        del primals_16
        buf14 = empty_strided_cuda((4, 256, 64), (16384, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv1d_4, x_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_5.run(buf7, primals_12, buf14, 65536, grid=grid(65536), stream=stream0)
        del buf7
        del primals_12
        buf15 = empty_strided_cuda((4, 128, 64), (8192, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv1d_3, emb_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_6.run(buf5, primals_10, buf15, 32768, grid=grid(32768), stream=stream0)
        del buf5
        del primals_10
        buf16 = empty_strided_cuda((4, 128, 64), (8192, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv1d_2, x_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_6.run(buf4, primals_8, buf16, 32768, grid=grid(32768), stream=stream0)
        del buf4
        del primals_8
    return (buf13, primals_1, primals_3, primals_4, primals_6, primals_7, primals_9, primals_11, primals_13, primals_15, buf1, buf3, buf6, buf8, buf10, buf11, buf14, buf15, buf16, )


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((64, 3, 1), (3, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64), (192, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 32, 1), (32, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 32, 64), (2048, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, 64, 1), (64, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, 64, 1), (64, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((256, 256, 1), (256, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((320, 640, 1), (640, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((320, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((160, 320, 1), (320, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((160, ), (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, primals_12, primals_13, primals_14, primals_15, primals_16])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.utils.cpp_extension


class PoseNetFeat(nn.Module):

    def __init__(self, num_points):
        super(PoseNetFeat, self).__init__()
        self.conv1 = torch.nn.Conv1d(3, 64, 1)
        self.conv2 = torch.nn.Conv1d(64, 128, 1)
        self.e_conv1 = torch.nn.Conv1d(32, 64, 1)
        self.e_conv2 = torch.nn.Conv1d(64, 128, 1)
        self.conv5 = torch.nn.Conv1d(256, 256, 1)
        self.all_conv1 = torch.nn.Conv1d(640, 320, 1)
        self.all_conv2 = torch.nn.Conv1d(320, 160, 1)
        self.num_points = num_points

    def forward(self, x, emb):
        x = F.relu(self.conv1(x))
        emb = F.relu(self.e_conv1(emb))
        pointfeat_1 = torch.cat((x, emb), dim=1)
        x = F.relu(self.conv2(x))
        emb = F.relu(self.e_conv2(emb))
        pointfeat_2 = torch.cat((x, emb), dim=1)
        x = F.relu(self.conv5(pointfeat_2))
        x = torch.cat([pointfeat_1, pointfeat_2, x], dim=1).contiguous()
        x = F.leaky_relu(self.all_conv1(x))
        x = self.all_conv2(x)
        return x


def get_inputs():
    return [torch.rand([4, 3, 64]), torch.rand([4, 32, 64])]


def get_init_inputs():
    return [[], {'num_points': 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
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 64 % 256
    x0 = xindex % 64
    x2 = xindex // 16384
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x1 + 8192 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp15 = tl.load(in_ptr2 + (x0 + 64 * (-128 + x1) + 8192 * x2), tmp12,
        other=0.0)
    tmp16 = tl.load(in_ptr3 + (-128 + x1), tmp12, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x3, tmp21, None)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 64 % 640
    x0 = xindex % 64
    x2 = xindex // 40960
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 64, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.load(in_ptr0 + (x0 + 64 * x1 + 4096 * x2), tmp7, other=0.0)
    tmp9 = tmp0 >= tmp5
    tmp10 = tmp9 & tmp4
    tmp11 = tl.load(in_ptr1 + (x0 + 64 * (-64 + x1) + 4096 * x2), tmp10,
        other=0.0)
    tmp12 = tl.where(tmp6, tmp8, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 384, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr2 + (x0 + 64 * (-128 + x1) + 16384 * x2), tmp18,
        other=0.0)
    tmp20 = tmp0 >= tmp16
    tl.full([1], 640, tl.int64)
    tmp23 = tl.load(in_ptr3 + (x0 + 64 * (-384 + x1) + 16384 * x2), tmp20,
        other=0.0)
    tmp24 = tl.load(in_ptr4 + (-384 + x1), tmp20, eviction_policy=
        'evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.full([1], 0, tl.int32)
    tmp27 = triton_helpers.maximum(tmp26, tmp25)
    tmp28 = tl.full(tmp27.shape, 0.0, tmp27.dtype)
    tmp29 = tl.where(tmp20, tmp27, tmp28)
    tmp30 = tl.where(tmp18, tmp19, tmp29)
    tmp31 = tl.where(tmp4, tmp14, tmp30)
    tl.store(out_ptr0 + x3, tmp31, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 320
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 160
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_5(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 256
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_6(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 128
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


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, primals_12,
        primals_13, primals_14, primals_15, primals_16) = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 1), (3, 1, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 3, 64), (192, 64, 1))
    assert_size_stride(primals_4, (64, 32, 1), (32, 1, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (4, 32, 64), (2048, 64, 1))
    assert_size_stride(primals_7, (128, 64, 1), (64, 1, 1))
    assert_size_stride(primals_8, (128,), (1,))
    assert_size_stride(primals_9, (128, 64, 1), (64, 1, 1))
    assert_size_stride(primals_10, (128,), (1,))
    assert_size_stride(primals_11, (256, 256, 1), (256, 1, 1))
    assert_size_stride(primals_12, (256,), (1,))
    assert_size_stride(primals_13, (320, 640, 1), (640, 1, 1))
    assert_size_stride(primals_14, (320,), (1,))
    assert_size_stride(primals_15, (160, 320, 1), (320, 1, 1))
    assert_size_stride(primals_16, (160,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64), (4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(16384)](buf1, primals_2, 
            16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(primals_6, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 64), (4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(16384)](buf3, primals_5, 
            16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf1, primals_7, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 128, 64), (8192, 64, 1))
        buf5 = extern_kernels.convolution(buf3, primals_9, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 128, 64), (8192, 64, 1))
        buf6 = empty_strided_cuda((4, 256, 64), (16384, 64, 1), torch.float32)
        triton_poi_fused_cat_1[grid(65536)](buf4, primals_8, buf5,
            primals_10, buf6, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        buf7 = extern_kernels.convolution(buf6, primals_11, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 256, 64), (16384, 64, 1))
        buf8 = empty_strided_cuda((4, 640, 64), (40960, 64, 1), torch.float32)
        triton_poi_fused_cat_2[grid(163840)](buf1, buf3, buf6, buf7,
            primals_12, buf8, 163840, XBLOCK=512, num_warps=8, num_stages=1)
        buf9 = extern_kernels.convolution(buf8, primals_13, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf9, (4, 320, 64), (20480, 64, 1))
        buf10 = empty_strided_cuda((4, 320, 64), (20480, 64, 1), torch.bool)
        buf11 = empty_strided_cuda((4, 320, 64), (20480, 64, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_3[grid(81920)](buf9,
            primals_14, buf10, buf11, 81920, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf9
        del primals_14
        buf12 = extern_kernels.convolution(buf11, primals_15, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf12, (4, 160, 64), (10240, 64, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_4[grid(40960)](buf13, primals_16, 
            40960, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_16
        buf14 = empty_strided_cuda((4, 256, 64), (16384, 64, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_5[grid(65536)](
            buf7, primals_12, buf14, 65536, XBLOCK=512, num_warps=4,
            num_stages=1)
        del buf7
        del primals_12
        buf15 = empty_strided_cuda((4, 128, 64), (8192, 64, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(32768)](
            buf5, primals_10, buf15, 32768, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf5
        del primals_10
        buf16 = empty_strided_cuda((4, 128, 64), (8192, 64, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(32768)](
            buf4, primals_8, buf16, 32768, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf4
        del primals_8
    return (buf13, primals_1, primals_3, primals_4, primals_6, primals_7,
        primals_9, primals_11, primals_13, primals_15, buf1, buf3, buf6,
        buf8, buf10, buf11, buf14, buf15, buf16)


class PoseNetFeatNew(nn.Module):

    def __init__(self, num_points):
        super(PoseNetFeatNew, self).__init__()
        self.conv1 = torch.nn.Conv1d(3, 64, 1)
        self.conv2 = torch.nn.Conv1d(64, 128, 1)
        self.e_conv1 = torch.nn.Conv1d(32, 64, 1)
        self.e_conv2 = torch.nn.Conv1d(64, 128, 1)
        self.conv5 = torch.nn.Conv1d(256, 256, 1)
        self.all_conv1 = torch.nn.Conv1d(640, 320, 1)
        self.all_conv2 = torch.nn.Conv1d(320, 160, 1)
        self.num_points = num_points

    def forward(self, input_0, input_1):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_7 = self.conv2.weight
        primals_8 = self.conv2.bias
        primals_4 = self.e_conv1.weight
        primals_5 = self.e_conv1.bias
        primals_9 = self.e_conv2.weight
        primals_10 = self.e_conv2.bias
        primals_11 = self.conv5.weight
        primals_12 = self.conv5.bias
        primals_13 = self.all_conv1.weight
        primals_14 = self.all_conv1.bias
        primals_15 = self.all_conv2.weight
        primals_16 = self.all_conv2.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16])
        return output[0]
