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

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


# kernel path: runs/run_shard_7/inductor_cache/yy/cyya3js6wt64vdji3sfisvrqyfvqxwkwqq5mzg5bqjl2crzjs4t3.py
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%select,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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=[64], 
    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_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tg/ctgicrga7tyksujbsb6u2lffvcpwo3edehi6erwefgwwhq6mp7sw.py
# Topologically Sorted Source Nodes: [linear_6], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear_6 => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%select_2,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*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_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/le/cle6agray5z5y3bvf2ioratctytzbz3iy5xinmz6xcvb6beeucxc.py
# Topologically Sorted Source Nodes: [linear_9], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear_9 => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%select_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_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=[64], 
    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_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fo/cfoulu6dfinyiro3cxnwee7kom7kdz3kaw67llxpvrizh5alvmb7.py
# Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear_3 => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%select_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_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=[64], 
    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_clone_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/q2/cq2qlv2phdaezq3l67gtbb63gqfdn3op5rmnchrmzve3lzfwi7ea.py
# Topologically Sorted Source Nodes: [linear, hidden_trace, linear_3, hidden_trace_3, linear_6, hidden_trace_6, linear_9, hidden_trace_9], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   hidden_trace => relu
#   hidden_trace_3 => relu_2
#   hidden_trace_6 => relu_4
#   hidden_trace_9 => relu_6
#   linear => add
#   linear_3 => add_1
#   linear_6 => add_2
#   linear_9 => add_3
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_3), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_7, %primals_3), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_13, %primals_3), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_2,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_19, %primals_3), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_3,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
#   %le_5 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
#   %le_7 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_relu_threshold_backward_4 = async_compile.triton('triton_poi_fused_add_relu_threshold_backward_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: '*i1', 7: '*i1', 8: '*i1', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_relu_threshold_backward_4', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1', 'in_out_ptr2', 'in_out_ptr3'], '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_add_relu_threshold_backward_4(in_out_ptr0, in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp11 = tl.load(in_out_ptr2 + (x2), xmask)
    tmp15 = tl.load(in_out_ptr3 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = tmp13 <= tmp5
    tmp16 = tmp15 + tmp1
    tmp17 = triton_helpers.maximum(tmp3, tmp16)
    tmp18 = tmp17 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
    tl.store(in_out_ptr1 + (x2), tmp9, xmask)
    tl.store(out_ptr1 + (x2), tmp10, xmask)
    tl.store(in_out_ptr2 + (x2), tmp13, xmask)
    tl.store(out_ptr2 + (x2), tmp14, xmask)
    tl.store(in_out_ptr3 + (x2), tmp17, xmask)
    tl.store(out_ptr3 + (x2), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/h6/ch6w3tzs4kqel4ucmf56p4p6nionfp7jp44slx7u3jotzb6khrf5.py
# Topologically Sorted Source Nodes: [hidden_trace_1, hidden_trace_4, hidden_trace_7, hidden_trace_10], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   hidden_trace_1 => relu_1
#   hidden_trace_10 => relu_7
#   hidden_trace_4 => relu_3
#   hidden_trace_7 => relu_5
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_9,), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_15,), kwargs = {})
#   %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_21,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_7, 0), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_5, 0), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
#   %le_6 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_5 = async_compile.triton('triton_poi_fused_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: '*i1', 7: '*i1', 8: '*i1', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_5', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1', 'in_out_ptr2', 'in_out_ptr3'], '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_relu_threshold_backward_5(in_out_ptr0, in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp11 = tl.load(in_out_ptr2 + (x2), xmask)
    tmp15 = tl.load(in_out_ptr3 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = tmp13 <= tmp5
    tmp16 = tmp15 + tmp1
    tmp17 = triton_helpers.maximum(tmp3, tmp16)
    tmp18 = tmp17 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
    tl.store(in_out_ptr1 + (x2), tmp9, xmask)
    tl.store(out_ptr1 + (x2), tmp10, xmask)
    tl.store(in_out_ptr2 + (x2), tmp13, xmask)
    tl.store(out_ptr2 + (x2), tmp14, xmask)
    tl.store(in_out_ptr3 + (x2), tmp17, xmask)
    tl.store(out_ptr3 + (x2), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zu/czua5znvuyo2wgzh7xs43k37nlmar4eu4cp46spj3syorclwqiuv.py
# Topologically Sorted Source Nodes: [hidden_traces], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   hidden_traces => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_5, %view_11, %view_17, %view_23], 1), kwargs = {})
triton_poi_fused_stack_6 = async_compile.triton('triton_poi_fused_stack_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_6', '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_stack_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 16
    x0 = xindex % 4
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (4*((-4) + x1)) + (16*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + (4*((-8) + x1)) + (16*x2)), tmp14 & xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tl.load(in_ptr3 + (x0 + (4*((-12) + x1)) + (16*x2)), tmp16 & xmask, other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + (x3), tmp22, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (32, 4), (4, 1))
    assert_size_stride(primals_3, (32, ), (1, ))
    assert_size_stride(primals_4, (64, 32), (32, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (4, 64), (64, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf1)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_6], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(primals_1, buf12, 64, grid=grid(64), stream=stream0)
        buf13 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_6], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf12, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf13)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_9], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(primals_1, buf18, 64, grid=grid(64), stream=stream0)
        buf19 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_9], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf18, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf19)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(primals_1, buf6, 64, grid=grid(64), stream=stream0)
        del primals_1
        buf7 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf7)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 32), (128, 32, 1), 0); del buf1  # reuse
        buf32 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf8 = reinterpret_tensor(buf7, (4, 4, 32), (128, 32, 1), 0); del buf7  # reuse
        buf30 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf14 = reinterpret_tensor(buf13, (4, 4, 32), (128, 32, 1), 0); del buf13  # reuse
        buf28 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf20 = reinterpret_tensor(buf19, (4, 4, 32), (128, 32, 1), 0); del buf19  # reuse
        buf26 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [linear, hidden_trace, linear_3, hidden_trace_3, linear_6, hidden_trace_6, linear_9, hidden_trace_9], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
        triton_poi_fused_add_relu_threshold_backward_4.run(buf2, buf8, buf14, buf20, primals_3, buf32, buf30, buf28, buf26, 512, grid=grid(512), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 32), (32, 1), 0), reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf3)
        buf15 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 32), (32, 1), 0), reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf15)
        buf21 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf20, (16, 32), (32, 1), 0), reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf21)
        buf9 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 32), (32, 1), 0), reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf9)
        buf4 = reinterpret_tensor(buf3, (4, 4, 64), (256, 64, 1), 0); del buf3  # reuse
        buf31 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        buf10 = reinterpret_tensor(buf9, (4, 4, 64), (256, 64, 1), 0); del buf9  # reuse
        buf29 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        buf16 = reinterpret_tensor(buf15, (4, 4, 64), (256, 64, 1), 0); del buf15  # reuse
        buf27 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        buf22 = reinterpret_tensor(buf21, (4, 4, 64), (256, 64, 1), 0); del buf21  # reuse
        buf25 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [hidden_trace_1, hidden_trace_4, hidden_trace_7, hidden_trace_10], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_5.run(buf4, buf10, buf16, buf22, primals_5, buf31, buf29, buf27, buf25, 1024, grid=grid(1024), stream=stream0)
        del primals_5
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hidden_trace_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (16, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf5)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hidden_trace_5], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf10, (16, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf11)
        buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hidden_trace_8], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf16, (16, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf17)
        buf23 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hidden_trace_11], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf22, (16, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf23)
        del primals_7
        buf24 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hidden_traces], Original ATen: [aten.stack]
        triton_poi_fused_stack_6.run(buf5, buf11, buf17, buf23, buf24, 256, grid=grid(256), stream=stream0)
        del buf11
        del buf17
        del buf23
        del buf5
    return (reinterpret_tensor(buf24, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(buf2, (16, 32), (32, 1), 0), reinterpret_tensor(buf4, (16, 64), (64, 1), 0), reinterpret_tensor(buf6, (16, 4), (4, 1), 0), reinterpret_tensor(buf8, (16, 32), (32, 1), 0), reinterpret_tensor(buf10, (16, 64), (64, 1), 0), reinterpret_tensor(buf12, (16, 4), (4, 1), 0), reinterpret_tensor(buf14, (16, 32), (32, 1), 0), reinterpret_tensor(buf16, (16, 64), (64, 1), 0), reinterpret_tensor(buf18, (16, 4), (4, 1), 0), reinterpret_tensor(buf20, (16, 32), (32, 1), 0), reinterpret_tensor(buf22, (16, 64), (64, 1), 0), primals_6, buf25, primals_4, buf26, buf27, buf28, buf29, buf30, buf31, buf32, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    return print_performance(fn, times=times, repeat=repeat)


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

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


class EncoderNet(nn.Module):

    def __init__(self, pedestrian_num, input_size, hidden_size):
        super(EncoderNet, self).__init__()
        self.pedestrian_num = pedestrian_num
        self.input_size = input_size
        self.hidden_size = hidden_size
        hidden1_size = 32
        hidden2_size = 64
        self.fc1 = torch.nn.Linear(input_size, hidden1_size)
        self.fc2 = torch.nn.Linear(hidden1_size, hidden2_size)
        self.fc3 = torch.nn.Linear(hidden2_size, hidden_size)

    def forward(self, input_traces):
        hidden_list = []
        for i in range(self.pedestrian_num):
            input_trace = input_traces[:, i, :]
            hidden_trace = F.relu(self.fc1(input_trace))
            hidden_trace = F.relu(self.fc2(hidden_trace))
            hidden_trace = self.fc3(hidden_trace)
            hidden_list.append(hidden_trace)
        hidden_traces = torch.stack(hidden_list, 1)
        return hidden_traces


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


def get_init_inputs():
    return [[], {'pedestrian_num': 4, 'input_size': 4, 'hidden_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
import torch.nn as nn
import torch.utils.data
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_4(in_out_ptr0, in_out_ptr1,
    in_out_ptr2, in_out_ptr3, in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr1 + x2, xmask)
    tmp11 = tl.load(in_out_ptr2 + x2, xmask)
    tmp15 = tl.load(in_out_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = tmp13 <= tmp5
    tmp16 = tmp15 + tmp1
    tmp17 = triton_helpers.maximum(tmp3, tmp16)
    tmp18 = tmp17 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(in_out_ptr1 + x2, tmp9, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)
    tl.store(in_out_ptr2 + x2, tmp13, xmask)
    tl.store(out_ptr2 + x2, tmp14, xmask)
    tl.store(in_out_ptr3 + x2, tmp17, xmask)
    tl.store(out_ptr3 + x2, tmp18, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_5(in_out_ptr0, in_out_ptr1,
    in_out_ptr2, in_out_ptr3, in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr1 + x2, xmask)
    tmp11 = tl.load(in_out_ptr2 + x2, xmask)
    tmp15 = tl.load(in_out_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = tmp13 <= tmp5
    tmp16 = tmp15 + tmp1
    tmp17 = triton_helpers.maximum(tmp3, tmp16)
    tmp18 = tmp17 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(in_out_ptr1 + x2, tmp9, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)
    tl.store(in_out_ptr2 + x2, tmp13, xmask)
    tl.store(out_ptr2 + x2, tmp14, xmask)
    tl.store(in_out_ptr3 + x2, tmp17, xmask)
    tl.store(out_ptr3 + x2, tmp18, xmask)


@triton.jit
def triton_poi_fused_stack_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 16
    x0 = xindex % 4
    x2 = xindex // 64
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + 4 * (-8 + x1) + 16 * x2), tmp14 & xmask,
        other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1) + 16 * x2), tmp16 &
        xmask, other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x3, tmp22, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (32, 4), (4, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (64, 32), (32, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (4, 64), (64, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf1)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(64)](primals_1, buf12, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf13 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf12, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf13)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(64)](primals_1, buf18, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf19 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf18, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf19)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_3[grid(64)](primals_1, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf7 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf6, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf7)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 32), (128, 32, 1), 0)
        del buf1
        buf32 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf8 = reinterpret_tensor(buf7, (4, 4, 32), (128, 32, 1), 0)
        del buf7
        buf30 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf14 = reinterpret_tensor(buf13, (4, 4, 32), (128, 32, 1), 0)
        del buf13
        buf28 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf20 = reinterpret_tensor(buf19, (4, 4, 32), (128, 32, 1), 0)
        del buf19
        buf26 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_4[grid(512)](buf2,
            buf8, buf14, buf20, primals_3, buf32, buf30, buf28, buf26, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf3)
        buf15 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf15)
        buf21 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf20, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf21)
        buf9 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf9)
        buf4 = reinterpret_tensor(buf3, (4, 4, 64), (256, 64, 1), 0)
        del buf3
        buf31 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        buf10 = reinterpret_tensor(buf9, (4, 4, 64), (256, 64, 1), 0)
        del buf9
        buf29 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        buf16 = reinterpret_tensor(buf15, (4, 4, 64), (256, 64, 1), 0)
        del buf15
        buf27 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        buf22 = reinterpret_tensor(buf21, (4, 4, 64), (256, 64, 1), 0)
        del buf21
        buf25 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_5[grid(1024)](buf4, buf10,
            buf16, buf22, primals_5, buf31, buf29, buf27, buf25, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf5)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf10, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf11)
        buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf16, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf17)
        buf23 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf22, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf23)
        del primals_7
        buf24 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        triton_poi_fused_stack_6[grid(256)](buf5, buf11, buf17, buf23,
            buf24, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf11
        del buf17
        del buf23
        del buf5
    return (reinterpret_tensor(buf24, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf2, (16, 32), (32, 1), 0), reinterpret_tensor(buf4, (16, 64), (64,
        1), 0), reinterpret_tensor(buf6, (16, 4), (4, 1), 0),
        reinterpret_tensor(buf8, (16, 32), (32, 1), 0), reinterpret_tensor(
        buf10, (16, 64), (64, 1), 0), reinterpret_tensor(buf12, (16, 4), (4,
        1), 0), reinterpret_tensor(buf14, (16, 32), (32, 1), 0),
        reinterpret_tensor(buf16, (16, 64), (64, 1), 0), reinterpret_tensor
        (buf18, (16, 4), (4, 1), 0), reinterpret_tensor(buf20, (16, 32), (
        32, 1), 0), reinterpret_tensor(buf22, (16, 64), (64, 1), 0),
        primals_6, buf25, primals_4, buf26, buf27, buf28, buf29, buf30,
        buf31, buf32)


class EncoderNetNew(nn.Module):

    def __init__(self, pedestrian_num, input_size, hidden_size):
        super(EncoderNetNew, self).__init__()
        self.pedestrian_num = pedestrian_num
        self.input_size = input_size
        self.hidden_size = hidden_size
        hidden1_size = 32
        hidden2_size = 64
        self.fc1 = torch.nn.Linear(input_size, hidden1_size)
        self.fc2 = torch.nn.Linear(hidden1_size, hidden2_size)
        self.fc3 = torch.nn.Linear(hidden2_size, hidden_size)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
