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

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


# kernel path: runs/run_shard_4/inductor_cache/gx/cgxraajlq2wp5cxw5sfa7jlhdy6cs5ctq4gcigpy4z6jg33sjblu.py
# Topologically Sorted Source Nodes: [x, x_2], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => relu
#   x_2 => relu_2
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*i1', 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_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, '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_0(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, 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)
    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
    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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xu/cxuokj3dvl4qdhy5uebutpnhdphnu4roqpfcjq7w7rsg4iwxl32x.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_4 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%relu_1, %relu_3], 2), 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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = (xindex // 64)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((32*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4 & xmask, 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], 64, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((32*x1) + ((-32) + x0)), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + ((-32) + x0), tmp12 & xmask, 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 + (x2), tmp21, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hg/chg3iq6bscxmmxv5f7tuzgwycb4mgrimwfhv2nauw5rj4tt5cmv2.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_9, [2], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_9, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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__softmax_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__softmax_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_4/inductor_cache/zu/czuvep3dmpmqmhiiliwubh4ghdt2qr27va67sszkua7trziinwov.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_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__softmax_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__softmax_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')


# kernel path: runs/run_shard_4/inductor_cache/b5/cb5fa7yeighdghdh7bpl3qbtzerhee5qcv2q6lmyrgoofjeovin5.py
# Topologically Sorted Source Nodes: [x_1, x_3], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_1 => relu_1
#   x_3 => relu_3
# 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_7,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_4 = async_compile.triton('triton_poi_fused_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: '*i1', 4: '*i1', 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_threshold_backward_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (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
    tl.store(out_ptr0 + (x2), tmp6, xmask)
    tl.store(out_ptr1 + (x2), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
    args.clear()
    assert_size_stride(primals_1, (32, 4), (4, 1))
    assert_size_stride(primals_2, (32, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (32, 32), (32, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 64), (64, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 32), (1, 4), 0), out=buf0)
        buf3 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 32), (1, 4), 0), out=buf3)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 32), (128, 32, 1), 0); del buf0  # reuse
        buf13 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf4 = reinterpret_tensor(buf3, (4, 4, 32), (128, 32, 1), 0); del buf3  # reuse
        buf11 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x, x_2], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, buf4, primals_2, buf13, buf11, 512, grid=grid(512), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 32), (32, 1), 0), reinterpret_tensor(primals_4, (32, 32), (1, 32), 0), out=buf2)
        buf5 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf4, (16, 32), (32, 1), 0), reinterpret_tensor(primals_4, (32, 32), (1, 32), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf2, primals_5, buf5, buf6, 1024, grid=grid(1024), stream=stream0)
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf6, (16, 64), (64, 1), 0), reinterpret_tensor(primals_7, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf7)
        del primals_8
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf7, buf8, 64, grid=grid(64), stream=stream0)
        buf9 = reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(buf8, buf9, 64, grid=grid(64), stream=stream0)
        del buf8
        buf10 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf12 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1, x_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_4.run(buf5, primals_5, buf2, buf10, buf12, 512, grid=grid(512), stream=stream0)
        del buf2
        del buf5
        del primals_5
    return (buf9, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(buf1, (16, 32), (32, 1), 0), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(buf4, (16, 32), (32, 1), 0), reinterpret_tensor(buf6, (16, 64), (64, 1), 0), buf9, primals_7, buf10, primals_4, buf11, buf12, buf13, )


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((32, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
    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.functional as F
from torch import nn
from torch import optim


class EmbeddingModel(nn.Module):

    def __init__(self, obs_size, num_outputs):
        super(EmbeddingModel, self).__init__()
        self.obs_size = obs_size
        self.num_outputs = num_outputs
        self.fc1 = nn.Linear(obs_size, 32)
        self.fc2 = nn.Linear(32, 32)
        self.last = nn.Linear(32 * 2, num_outputs)
        self.optimizer = optim.Adam(self.parameters(), lr=1e-05)

    def forward(self, x1, x2):
        x1 = self.embedding(x1)
        x2 = self.embedding(x2)
        x = torch.cat([x1, x2], dim=2)
        x = self.last(x)
        return nn.Softmax(dim=2)(x)

    def embedding(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        return x

    def train_model(self, batch):
        batch_size = torch.stack(batch.state).size()[0]
        states = torch.stack(batch.state).view(batch_size, config.
            sequence_length, self.obs_size)[:, -5:, :]
        next_states = torch.stack(batch.next_state).view(batch_size, config
            .sequence_length, self.obs_size)[:, -5:, :]
        actions = torch.stack(batch.action).view(batch_size, config.
            sequence_length, -1).long()[:, -5:, :]
        self.optimizer.zero_grad()
        net_out = self.forward(states, next_states)
        actions_one_hot = torch.squeeze(F.one_hot(actions, self.num_outputs)
            ).float()
        loss = nn.MSELoss()(net_out, actions_one_hot)
        loss.backward()
        self.optimizer.step()
        return loss.item()


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


def get_init_inputs():
    return [[], {'obs_size': 4, 'num_outputs': 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.functional as F
from torch import nn
from torch import optim
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_threshold_backward_0(in_out_ptr0, in_out_ptr1,
    in_ptr0, out_ptr0, out_ptr1, 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)
    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
    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)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (32 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, 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], 64, tl.int64)
    tmp15 = tl.load(in_ptr2 + (32 * x1 + (-32 + x0)), tmp12 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + (-32 + x0), tmp12 & xmask, 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 + x2, tmp21, xmask)


@triton.jit
def triton_poi_fused__softmax_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__softmax_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)


@triton.jit
def triton_poi_fused_relu_threshold_backward_4(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, 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_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + 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
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (32, 4), (4, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (32, 32), (32, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 64), (64, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 32), (1, 4), 0), out=buf0)
        buf3 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 32), (1, 4), 0), out=buf3)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 32), (128, 32, 1), 0)
        del buf0
        buf13 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf4 = reinterpret_tensor(buf3, (4, 4, 32), (128, 32, 1), 0)
        del buf3
        buf11 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(512)](buf1, buf4,
            primals_2, buf13, buf11, 512, XBLOCK=256, num_warps=4, num_stages=1
            )
        del primals_2
        buf2 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 32), (1, 32), 0), out=buf2)
        buf5 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 32), (1, 32), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.float32)
        triton_poi_fused_cat_1[grid(1024)](buf2, primals_5, buf5, buf6, 
            1024, XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf6, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_7, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf7)
        del primals_8
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0)
        del buf7
        triton_poi_fused__softmax_3[grid(64)](buf8, buf9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf8
        buf10 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf12 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_4[grid(512)](buf5,
            primals_5, buf2, buf10, buf12, 512, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf2
        del buf5
        del primals_5
    return buf9, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (16, 32), (32, 1), 0), reinterpret_tensor(
        primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(buf4, (16, 32),
        (32, 1), 0), reinterpret_tensor(buf6, (16, 64), (64, 1), 0
        ), buf9, primals_7, buf10, primals_4, buf11, buf12, buf13


class EmbeddingModelNew(nn.Module):

    def __init__(self, obs_size, num_outputs):
        super(EmbeddingModelNew, self).__init__()
        self.obs_size = obs_size
        self.num_outputs = num_outputs
        self.fc1 = nn.Linear(obs_size, 32)
        self.fc2 = nn.Linear(32, 32)
        self.last = nn.Linear(32 * 2, num_outputs)
        self.optimizer = optim.Adam(self.parameters(), lr=1e-05)

    def embedding(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        return x

    def train_model(self, batch):
        batch_size = torch.stack(batch.state).size()[0]
        states = torch.stack(batch.state).view(batch_size, config.
            sequence_length, self.obs_size)[:, -5:, :]
        next_states = torch.stack(batch.next_state).view(batch_size, config
            .sequence_length, self.obs_size)[:, -5:, :]
        actions = torch.stack(batch.action).view(batch_size, config.
            sequence_length, -1).long()[:, -5:, :]
        self.optimizer.zero_grad()
        net_out = self.forward(states, next_states)
        actions_one_hot = torch.squeeze(F.one_hot(actions, self.num_outputs)
            ).float()
        loss = nn.MSELoss()(net_out, actions_one_hot)
        loss.backward()
        self.optimizer.step()
        return loss.item()

    def forward(self, input_0, input_1):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_7 = self.last.weight
        primals_8 = self.last.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])
        return output[0]
