# 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_8/inductor_cache/lu/cluxqdroskxl5peg47xkpkwyiodyuyiuf2pewzefxrncawuvcuw6.py
# Topologically Sorted Source Nodes: [layer_norm, x], Original ATen: [aten.native_layer_norm, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   layer_norm => add, add_1, mul, mul_1, rsqrt, sub, var_mean
#   x => relu
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_1, [3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_4), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_5), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_per_fused_native_layer_norm_relu_threshold_backward_0 = async_compile.triton('triton_per_fused_native_layer_norm_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.persistent_reduction(
    size_hints=[64, 128],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_native_layer_norm_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_native_layer_norm_relu_threshold_backward_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 125
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    x2 = xindex % 16
    x3 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (r1 + (125*x0)), rmask & xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 125, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(rmask & xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 125.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp0 - tmp10
    tmp23 = tmp22 * tmp21
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tmp28 = tl.full([1, 1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = 0.0
    tmp31 = tmp29 <= tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp21, xmask)
    tl.store(out_ptr1 + (r1 + (125*x2) + (2016*x3)), tmp29, rmask & xmask)
    tl.store(out_ptr2 + (r1 + (125*x2) + (2048*x3)), tmp31, rmask & xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/lu/cluoryozjyqkqykn3jhp3vosjfo2p7zkguljzqzxxvxnnl2yimqr.py
# Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   linear_1 => view_2
# Graph fragment:
#   %view_2 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%relu, [64, 125]), kwargs = {})
triton_poi_fused_view_1 = async_compile.triton('triton_poi_fused_view_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_view_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_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 125
    x1 = (xindex // 125)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (125*(x1 % 16)) + (2016*(x1 // 16))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ld/cldtk5skh6gtzdd62vyilgjgd55ch7o62ebbhqgbpau5cmhd5sca.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   x_2 => amax, exp, sub_2
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_5, [1], True), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_5, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), 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=[256], 
    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 = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), 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 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/7v/c7vy54xspuettz5pgulxporznj2yqlyufnh2o2cvg7er4bnu4zox.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   x_2 => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], 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=[256], 
    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 = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x3), 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, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (125, 4), (4, 1))
    assert_size_stride(primals_2, (125, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (125, ), (1, ))
    assert_size_stride(primals_5, (125, ), (1, ))
    assert_size_stride(primals_6, (125, 125), (125, 1))
    assert_size_stride(primals_7, (125, ), (1, ))
    assert_size_stride(primals_8, (125, ), (1, ))
    assert_size_stride(primals_9, (125, ), (1, ))
    assert_size_stride(primals_10, (4, 125), (125, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 125), (125, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 125), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf2  # reuse
        buf5 = empty_strided_cuda((4, 4, 4, 125), (2016, 500, 125, 1), torch.float32)
        buf18 = empty_strided_cuda((4, 4, 4, 125), (2048, 500, 125, 1), torch.bool)
        # Topologically Sorted Source Nodes: [layer_norm, x], Original ATen: [aten.native_layer_norm, aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_per_fused_native_layer_norm_relu_threshold_backward_0.run(buf4, buf0, primals_4, primals_5, buf1, buf5, buf18, 64, 125, grid=grid(64), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((64, 125), (125, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf5, buf6, 8000, grid=grid(8000), stream=stream0)
        buf7 = empty_strided_cuda((64, 125), (125, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf6, reinterpret_tensor(primals_6, (125, 125), (1, 125), 0), alpha=1, beta=1, out=buf7)
        del primals_7
        buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf11 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf9  # reuse
        buf12 = buf5; del buf5  # reuse
        buf17 = empty_strided_cuda((4, 4, 4, 125), (2048, 500, 125, 1), torch.bool)
        # Topologically Sorted Source Nodes: [layer_norm_1, x_1], Original ATen: [aten.native_layer_norm, aten.relu, aten.threshold_backward]
        triton_per_fused_native_layer_norm_relu_threshold_backward_0.run(buf11, buf7, primals_8, primals_9, buf8, buf12, buf17, 64, 125, grid=grid(64), stream=stream0)
        del primals_9
        buf13 = empty_strided_cuda((64, 125), (125, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf12, buf13, 8000, grid=grid(8000), stream=stream0)
        del buf12
        buf14 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, buf13, reinterpret_tensor(primals_10, (125, 4), (1, 125), 0), alpha=1, beta=1, out=buf14)
        del primals_11
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf14, buf15, 256, grid=grid(256), stream=stream0)
        buf16 = reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf14  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(buf15, buf16, 256, grid=grid(256), stream=stream0)
        del buf15
    return (buf16, primals_4, primals_8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf1, buf4, buf6, buf7, buf8, buf11, buf13, buf16, primals_10, buf17, primals_6, buf18, )


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((125, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((125, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((125, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((125, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((125, 125), (125, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((125, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((125, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((125, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 125), (125, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F


class Policy(nn.Module):

    def __init__(self, state_size, action_size):
        super(Policy, self).__init__()
        self.state_size = state_size
        self.action_size = action_size
        self.fc1 = nn.Linear(state_size, 125)
        self.fc_norm = nn.LayerNorm(125)
        self.fc2 = nn.Linear(125, 125)
        self.fc2_norm = nn.LayerNorm(125)
        self.action_prob = nn.Linear(125, action_size)

    def forward(self, x):
        x = F.relu(self.fc_norm(self.fc1(x)))
        x = F.relu(self.fc2_norm(self.fc2(x)))
        x = F.softmax(self.action_prob(x), dim=1)
        return x


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


def get_init_inputs():
    return [[], {'state_size': 4, 'action_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
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_per_fused_native_layer_norm_relu_threshold_backward_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    rnumel = 125
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    x2 = xindex % 16
    x3 = xindex // 16
    tmp0 = tl.load(in_ptr0 + (r1 + 125 * x0), rmask & xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp26 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 125, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(rmask & xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 125.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp0 - tmp10
    tmp23 = tmp22 * tmp21
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tmp28 = tl.full([1, 1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = 0.0
    tmp31 = tmp29 <= tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr1 + (r1 + 125 * x2 + 2016 * x3), tmp29, rmask & xmask)
    tl.store(out_ptr2 + (r1 + 125 * x2 + 2048 * x3), tmp31, rmask & xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 125
    x1 = xindex // 125
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 125 * (x1 % 16) + 2016 * (x1 // 16)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), 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 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (125, 4), (4, 1))
    assert_size_stride(primals_2, (125,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (125,), (1,))
    assert_size_stride(primals_5, (125,), (1,))
    assert_size_stride(primals_6, (125, 125), (125, 1))
    assert_size_stride(primals_7, (125,), (1,))
    assert_size_stride(primals_8, (125,), (1,))
    assert_size_stride(primals_9, (125,), (1,))
    assert_size_stride(primals_10, (4, 125), (125, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 125), (125, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 125), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf2
        buf5 = empty_strided_cuda((4, 4, 4, 125), (2016, 500, 125, 1),
            torch.float32)
        buf18 = empty_strided_cuda((4, 4, 4, 125), (2048, 500, 125, 1),
            torch.bool)
        get_raw_stream(0)
        triton_per_fused_native_layer_norm_relu_threshold_backward_0[grid(64)](
            buf4, buf0, primals_4, primals_5, buf1, buf5, buf18, 64, 125,
            XBLOCK=1, num_warps=2, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((64, 125), (125, 1), torch.float32)
        triton_poi_fused_view_1[grid(8000)](buf5, buf6, 8000, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((64, 125), (125, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf6, reinterpret_tensor(primals_6,
            (125, 125), (1, 125), 0), alpha=1, beta=1, out=buf7)
        del primals_7
        buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf11 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf9
        buf12 = buf5
        del buf5
        buf17 = empty_strided_cuda((4, 4, 4, 125), (2048, 500, 125, 1),
            torch.bool)
        triton_per_fused_native_layer_norm_relu_threshold_backward_0[grid(64)](
            buf11, buf7, primals_8, primals_9, buf8, buf12, buf17, 64, 125,
            XBLOCK=1, num_warps=2, num_stages=1)
        del primals_9
        buf13 = empty_strided_cuda((64, 125), (125, 1), torch.float32)
        triton_poi_fused_view_1[grid(8000)](buf12, buf13, 8000, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf12
        buf14 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf13, reinterpret_tensor(
            primals_10, (125, 4), (1, 125), 0), alpha=1, beta=1, out=buf14)
        del primals_11
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf14, buf15, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf16 = reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf14
        triton_poi_fused__softmax_3[grid(256)](buf15, buf16, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf15
    return (buf16, primals_4, primals_8, reinterpret_tensor(primals_3, (64,
        4), (4, 1), 0), buf0, buf1, buf4, buf6, buf7, buf8, buf11, buf13,
        buf16, primals_10, buf17, primals_6, buf18)


class PolicyNew(nn.Module):

    def __init__(self, state_size, action_size):
        super(PolicyNew, self).__init__()
        self.state_size = state_size
        self.action_size = action_size
        self.fc1 = nn.Linear(state_size, 125)
        self.fc_norm = nn.LayerNorm(125)
        self.fc2 = nn.Linear(125, 125)
        self.fc2_norm = nn.LayerNorm(125)
        self.action_prob = nn.Linear(125, action_size)

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc_norm.weight
        primals_5 = self.fc_norm.bias
        primals_6 = self.fc2.weight
        primals_7 = self.fc2.bias
        primals_8 = self.fc2_norm.weight
        primals_9 = self.fc2_norm.bias
        primals_10 = self.action_prob.weight
        primals_11 = self.action_prob.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
