# 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/nx/cnxhzcqzdmx72ijihpvoxpjgx5zd4hb67sbygcb53l3urrorezrj.py
# Topologically Sorted Source Nodes: [output, output_3, output_6, output_9], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   output => relu
#   output_3 => relu_2
#   output_6 => relu_4
#   output_9 => relu_6
# Graph fragment:
#   %add_tensor_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_7, %primals_3), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_7,), kwargs = {})
#   %add_tensor_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_5, %primals_3), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_5,), kwargs = {})
#   %add_tensor_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_3, %primals_3), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_3,), kwargs = {})
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_3), kwargs = {})
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_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=[128], 
    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_relu_0', '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_0(in_out_ptr0, in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    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')
    tmp5 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp8 = tl.load(in_out_ptr2 + (x2), xmask)
    tmp11 = tl.load(in_out_ptr3 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp9 = tmp8 + tmp1
    tmp10 = triton_helpers.maximum(tmp3, tmp9)
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(in_out_ptr1 + (x2), tmp7, xmask)
    tl.store(in_out_ptr2 + (x2), tmp10, xmask)
    tl.store(in_out_ptr3 + (x2), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/af/cafhlregx27h5d2u24jstgmsau6wtq5knpvfotqbt5pm6fmdptu5.py
# Topologically Sorted Source Nodes: [output_1, output_4, output_7, output_10], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   output_1 => relu_1
#   output_10 => relu_7
#   output_4 => relu_3
#   output_7 => relu_5
# Graph fragment:
#   %add_tensor_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_6, %primals_5), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_6,), kwargs = {})
#   %add_tensor_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_4, %primals_5), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_4,), kwargs = {})
#   %add_tensor_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_5), kwargs = {})
#   %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_2,), kwargs = {})
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_5), kwargs = {})
#   %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_relu_1', '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_1(in_out_ptr0, in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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')
    tmp5 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp8 = tl.load(in_out_ptr2 + (x2), xmask)
    tmp11 = tl.load(in_out_ptr3 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp9 = tmp8 + tmp1
    tmp10 = triton_helpers.maximum(tmp3, tmp9)
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(in_out_ptr1 + (x2), tmp7, xmask)
    tl.store(in_out_ptr2 + (x2), tmp10, xmask)
    tl.store(in_out_ptr3 + (x2), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ip/cipelptzqm3qbvewnrnl7fzwwxrf7gpvkzztjdbnqcqfotsmeqn3.py
# Topologically Sorted Source Nodes: [Attn_1, exp_Attn], Original ATen: [aten.sub, aten.exp]
# Source node to ATen node mapping:
#   Attn_1 => sub
#   exp_Attn => exp
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %expand), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused_exp_sub_2 = async_compile.triton('triton_poi_fused_exp_sub_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_exp_sub_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_exp_sub_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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/sy/csy7lxjevlfgxsiarxivbogsfltediompwwbqh22aspy6lk4lxvj.py
# Topologically Sorted Source Nodes: [Attn_2], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   Attn_2 => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %expand_1), kwargs = {})
triton_poi_fused_div_3 = async_compile.triton('triton_poi_fused_div_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_div_3', '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_div_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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, 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), (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, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 0), reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf0)
        buf12 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 12), reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf12)
        buf4 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 4), reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf4)
        buf8 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 8), reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf8)
        del primals_2
        buf1 = buf0; del buf0  # reuse
        buf5 = buf4; del buf4  # reuse
        buf9 = buf8; del buf8  # reuse
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [output, output_3, output_6, output_9], Original ATen: [aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_0.run(buf1, buf5, buf9, buf13, primals_3, 128, grid=grid(128), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf2)
        buf10 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf9, reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf10)
        buf14 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf13, reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf14)
        buf6 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf5, reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf6)
        buf3 = buf2; del buf2  # reuse
        buf7 = buf6; del buf6  # reuse
        buf11 = buf10; del buf10  # reuse
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [output_1, output_4, output_7, output_10], Original ATen: [aten.relu]
        triton_poi_fused_relu_1.run(buf3, buf7, buf11, buf15, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf20 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        buf16 = reinterpret_tensor(buf20, (4, 4), (16, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [outputs], Original ATen: [aten.stack]
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf16)
        buf17 = reinterpret_tensor(buf20, (4, 4), (16, 1), 4)  # alias
        # Topologically Sorted Source Nodes: [outputs], Original ATen: [aten.stack]
        extern_kernels.addmm(primals_7, buf7, reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf17)
        buf18 = reinterpret_tensor(buf20, (4, 4), (16, 1), 8)  # alias
        # Topologically Sorted Source Nodes: [outputs], Original ATen: [aten.stack]
        extern_kernels.addmm(primals_7, buf11, reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf18)
        buf19 = reinterpret_tensor(buf20, (4, 4), (16, 1), 12)  # alias
        # Topologically Sorted Source Nodes: [outputs], Original ATen: [aten.stack]
        extern_kernels.addmm(primals_7, buf15, reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf19)
        del primals_7
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [Attn], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf20, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf20, (4, 4, 4), (16, 1, 4), 0), out=buf21)
        buf22 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [Attn_1, exp_Attn], Original ATen: [aten.sub, aten.exp]
        triton_poi_fused_exp_sub_2.run(buf21, buf22, 64, grid=grid(64), stream=stream0)
        buf23 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [Attn_2], Original ATen: [aten.div]
        triton_poi_fused_div_3.run(buf22, buf23, 64, grid=grid(64), stream=stream0)
        del buf22
    return (buf23, reinterpret_tensor(primals_1, (4, 4), (16, 1), 0), buf1, buf3, reinterpret_tensor(primals_1, (4, 4), (16, 1), 4), buf5, buf7, reinterpret_tensor(primals_1, (4, 4), (16, 1), 8), buf9, buf11, reinterpret_tensor(primals_1, (4, 4), (16, 1), 12), buf13, buf15, reinterpret_tensor(buf20, (4, 4, 4), (16, 1, 4), 0), buf21, primals_6, primals_4, )


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), (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 LocationEncoder(nn.Module):

    def __init__(self, pedestrian_num, input_size, hidden_size, batch_size):
        super(LocationEncoder, self).__init__()
        self.pedestrian_num = pedestrian_num
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.batch_size = batch_size
        self.fc1 = nn.Linear(input_size, 32)
        self.fc2 = nn.Linear(32, 64)
        self.fc3 = nn.Linear(64, self.hidden_size)
        self.soft = nn.Softmax(dim=1)
        pass

    def forward(self, data):
        outputs = self.get_hidden_output(data)
        output = self.Attention(outputs, outputs)
        return output

    def get_hidden_output(self, data):
        output_list = []
        for idx in range(0, self.pedestrian_num):
            output = F.relu(self.fc1(data[:, idx]))
            output = F.relu(self.fc2(output))
            output = self.fc3(output)
            output_list.append(output)
        outputs = torch.stack(output_list, 1)
        return outputs

    def Attention(self, input_data, target_data):
        Attn = torch.bmm(target_data, input_data.transpose(1, 2))
        Attn_size = Attn.size()
        Attn = Attn - Attn.max(2)[0].unsqueeze(2).expand(Attn_size)
        exp_Attn = torch.exp(Attn)
        Attn = exp_Attn / exp_Attn.sum(2).unsqueeze(2).expand(Attn_size)
        return Attn

    def get_spatial_affinity(self, data):
        output = torch.zeros(self.batch_size, self.pedestrian_num, self.
            pedestrian_num)
        for batch in range(0, self.batch_size):
            for i in range(0, self.pedestrian_num):
                row_data = torch.Tensor([])
                for j in range(0, i + 1):
                    row_data = torch.cat([row_data, torch.dot(data[batch][i
                        ], data[batch][j]).unsqueeze(0)], dim=0)
                output[batch, i, 0:i + 1] = row_data
            for i in range(0, self.pedestrian_num):
                col_data = output[batch, :, i].view(1, -1)
                output[batch, i, :] = col_data
            output[batch] = self.soft(output[batch])
        """
        outputs will be like this :
        <h1, h1>, <h2, h1>, <h3, h1> ...
        <h2, h1>, <h2, h2>, <h3, h2> ...
        <h3, h1>, <h3, h2>, <h3, h3> ...
        ......
        """
        return output

    def softmax(self, data):
        output = torch.zeros(self.batch_size, self.pedestrian_num, self.
            pedestrian_num)
        exp_data = torch.exp(data)
        for batch in range(0, self.batch_size):
            for i in range(0, self.pedestrian_num):
                count = 0
                for j in range(0, self.pedestrian_num):
                    count += exp_data[batch][max(i, j)][min(i, j)].item()
                for j in range(0, self.pedestrian_num):
                    output[batch][i][j] = exp_data[batch][max(i, j)][min(i, j)
                        ].item() / count
        return output


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


def get_init_inputs():
    return [[], {'pedestrian_num': 4, 'input_size': 4, 'hidden_size': 4,
        'batch_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as F
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_relu_0(in_out_ptr0, in_out_ptr1, in_out_ptr2,
    in_out_ptr3, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    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')
    tmp5 = tl.load(in_out_ptr1 + x2, xmask)
    tmp8 = tl.load(in_out_ptr2 + x2, xmask)
    tmp11 = tl.load(in_out_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp9 = tmp8 + tmp1
    tmp10 = triton_helpers.maximum(tmp3, tmp9)
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)
    tl.store(in_out_ptr2 + x2, tmp10, xmask)
    tl.store(in_out_ptr3 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_out_ptr1, in_out_ptr2,
    in_out_ptr3, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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')
    tmp5 = tl.load(in_out_ptr1 + x2, xmask)
    tmp8 = tl.load(in_out_ptr2 + x2, xmask)
    tmp11 = tl.load(in_out_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp9 = tmp8 + tmp1
    tmp10 = triton_helpers.maximum(tmp3, tmp9)
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)
    tl.store(in_out_ptr2 + x2, tmp10, xmask)
    tl.store(in_out_ptr3 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_exp_sub_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_div_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, 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), (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, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf0)
        buf12 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 12
            ), reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf12)
        buf4 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 4),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf4)
        buf8 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 8),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf8)
        del primals_2
        buf1 = buf0
        del buf0
        buf5 = buf4
        del buf4
        buf9 = buf8
        del buf8
        buf13 = buf12
        del buf12
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(128)](buf1, buf5, buf9, buf13,
            primals_3, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (32, 64), (1,
            32), 0), out=buf2)
        buf10 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_4, (32, 64), (1,
            32), 0), out=buf10)
        buf14 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(buf13, reinterpret_tensor(primals_4, (32, 64), (1,
            32), 0), out=buf14)
        buf6 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_4, (32, 64), (1,
            32), 0), out=buf6)
        buf3 = buf2
        del buf2
        buf7 = buf6
        del buf6
        buf11 = buf10
        del buf10
        buf15 = buf14
        del buf14
        triton_poi_fused_relu_1[grid(256)](buf3, buf7, buf11, buf15,
            primals_5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf20 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        buf16 = reinterpret_tensor(buf20, (4, 4), (16, 1), 0)
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6,
            (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf16)
        buf17 = reinterpret_tensor(buf20, (4, 4), (16, 1), 4)
        extern_kernels.addmm(primals_7, buf7, reinterpret_tensor(primals_6,
            (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf17)
        buf18 = reinterpret_tensor(buf20, (4, 4), (16, 1), 8)
        extern_kernels.addmm(primals_7, buf11, reinterpret_tensor(primals_6,
            (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf18)
        buf19 = reinterpret_tensor(buf20, (4, 4), (16, 1), 12)
        extern_kernels.addmm(primals_7, buf15, reinterpret_tensor(primals_6,
            (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf19)
        del primals_7
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf20, (4, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf20, (4, 4, 4), (16, 1, 4), 0), out=buf21)
        buf22 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_exp_sub_2[grid(64)](buf21, buf22, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf23 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_div_3[grid(64)](buf22, buf23, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf22
    return buf23, reinterpret_tensor(primals_1, (4, 4), (16, 1), 0
        ), buf1, buf3, reinterpret_tensor(primals_1, (4, 4), (16, 1), 4
        ), buf5, buf7, reinterpret_tensor(primals_1, (4, 4), (16, 1), 8
        ), buf9, buf11, reinterpret_tensor(primals_1, (4, 4), (16, 1), 12
        ), buf13, buf15, reinterpret_tensor(buf20, (4, 4, 4), (16, 1, 4), 0
        ), buf21, primals_6, primals_4


class LocationEncoderNew(nn.Module):

    def __init__(self, pedestrian_num, input_size, hidden_size, batch_size):
        super(LocationEncoderNew, self).__init__()
        self.pedestrian_num = pedestrian_num
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.batch_size = batch_size
        self.fc1 = nn.Linear(input_size, 32)
        self.fc2 = nn.Linear(32, 64)
        self.fc3 = nn.Linear(64, self.hidden_size)
        self.soft = nn.Softmax(dim=1)
        pass

    def get_hidden_output(self, data):
        output_list = []
        for idx in range(0, self.pedestrian_num):
            output = F.relu(self.fc1(data[:, idx]))
            output = F.relu(self.fc2(output))
            output = self.fc3(output)
            output_list.append(output)
        outputs = torch.stack(output_list, 1)
        return outputs

    def Attention(self, input_data, target_data):
        Attn = torch.bmm(target_data, input_data.transpose(1, 2))
        Attn_size = Attn.size()
        Attn = Attn - Attn.max(2)[0].unsqueeze(2).expand(Attn_size)
        exp_Attn = torch.exp(Attn)
        Attn = exp_Attn / exp_Attn.sum(2).unsqueeze(2).expand(Attn_size)
        return Attn

    def get_spatial_affinity(self, data):
        output = torch.zeros(self.batch_size, self.pedestrian_num, self.
            pedestrian_num)
        for batch in range(0, self.batch_size):
            for i in range(0, self.pedestrian_num):
                row_data = torch.Tensor([])
                for j in range(0, i + 1):
                    row_data = torch.cat([row_data, torch.dot(data[batch][i
                        ], data[batch][j]).unsqueeze(0)], dim=0)
                output[batch, i, 0:i + 1] = row_data
            for i in range(0, self.pedestrian_num):
                col_data = output[batch, :, i].view(1, -1)
                output[batch, i, :] = col_data
            output[batch] = self.soft(output[batch])
        """
        outputs will be like this :
        <h1, h1>, <h2, h1>, <h3, h1> ...
        <h2, h1>, <h2, h2>, <h3, h2> ...
        <h3, h1>, <h3, h2>, <h3, h3> ...
        ......
        """
        return output

    def softmax(self, data):
        output = torch.zeros(self.batch_size, self.pedestrian_num, self.
            pedestrian_num)
        exp_data = torch.exp(data)
        for batch in range(0, self.batch_size):
            for i in range(0, self.pedestrian_num):
                count = 0
                for j in range(0, self.pedestrian_num):
                    count += exp_data[batch][max(i, j)][min(i, j)].item()
                for j in range(0, self.pedestrian_num):
                    output[batch][i][j] = exp_data[batch][max(i, j)][min(i, j)
                        ].item() / count
        return output

    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]
