# 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/2y/c2yvnrh4qw7iobnj2y55zkgm4a2aygygbngwrmj3tjttme3bq6s4.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.elu, aten.native_layer_norm]
# Source node to ATen node mapping:
#   out => expm1, gt, mul, mul_2, where
#   out_1 => add, add_1, mul_3, mul_4, rsqrt, sub, var_mean
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%where, [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 = (%where, %getitem_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %primals_4), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %primals_5), kwargs = {})
triton_per_fused_elu_native_layer_norm_0 = async_compile.triton('triton_per_fused_elu_native_layer_norm_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, 512],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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_elu_native_layer_norm_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, '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_elu_native_layer_norm_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 64
    XBLOCK: tl.constexpr = 1
    rnumel = 400
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (400*x0)), rmask, other=0.0)
    tmp31 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp33 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = tl.where(rmask, tmp8, 0)
    tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp13 = tl.where(rmask, tmp11, 0)
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp15 = tl.full([1], 400, tl.int32)
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp14 / tmp16
    tmp18 = tmp8 - tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
    tmp22 = tl.where(rmask, tmp20, 0)
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp24 = 400.0
    tmp25 = tmp23 / tmp24
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tmp29 = tmp7 - tmp17
    tmp30 = tmp29 * tmp28
    tmp32 = tmp30 * tmp31
    tmp34 = tmp32 + tmp33
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp28, None)
    tl.store(out_ptr1 + (r1 + (400*x0)), tmp34, rmask)
    tl.store(out_ptr0 + (x0), tmp17, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/re/crea4iehptuussfvrmrbjdmyltojnwuyigbeqo625h7lt23zdpgr.py
# Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.elu, aten.native_layer_norm]
# Source node to ATen node mapping:
#   out_2 => expm1_1, gt_1, mul_5, mul_7, where_1
#   out_3 => add_2, add_3, mul_8, mul_9, rsqrt_1, sub_1, var_mean_1
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_3, 0), kwargs = {})
#   %mul_5 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_5,), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_5, %mul_7), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%where_1, [3]), kwargs = {correction: 0, keepdim: True})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_2,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %getitem_3), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt_1), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_8, %primals_8), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_9, %primals_9), kwargs = {})
triton_per_fused_elu_native_layer_norm_1 = async_compile.triton('triton_per_fused_elu_native_layer_norm_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.persistent_reduction(
    size_hints=[64, 512],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_elu_native_layer_norm_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, '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_elu_native_layer_norm_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 64
    XBLOCK: tl.constexpr = 1
    rnumel = 300
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    x3 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (r1 + (300*x0)), rmask, other=0.0)
    tmp31 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp33 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = tl.where(rmask, tmp8, 0)
    tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp13 = tl.where(rmask, tmp11, 0)
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp15 = tl.full([1], 300, tl.int32)
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp14 / tmp16
    tmp18 = tmp8 - tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
    tmp22 = tl.where(rmask, tmp20, 0)
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp24 = 300.0
    tmp25 = tmp23 / tmp24
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tmp29 = tmp7 - tmp17
    tmp30 = tmp29 * tmp28
    tmp32 = tmp30 * tmp31
    tmp34 = tmp32 + tmp33
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp28, None)
    tl.store(out_ptr1 + (r1 + (300*x2) + (1216*x3)), tmp34, rmask)
    tl.store(out_ptr0 + (x0), tmp17, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2r/c2rmyrj7dnfukudojvinjco53soc7nl752rwnsqyli5fxai3eqid.py
# Topologically Sorted Source Nodes: [out_2, out_3, linear_2], Original ATen: [aten.elu, aten.native_layer_norm, aten.view]
# Source node to ATen node mapping:
#   linear_2 => view_4
#   out_2 => expm1_1, gt_1, mul_5, mul_7, where_1
#   out_3 => add_3, mul_8, mul_9, sub_1
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_3, 0), kwargs = {})
#   %mul_5 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_5,), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_5, %mul_7), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %getitem_3), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt_1), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_8, %primals_8), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_9, %primals_9), kwargs = {})
#   %view_4 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%add_3, [64, 300]), kwargs = {})
triton_poi_fused_elu_native_layer_norm_view_2 = async_compile.triton('triton_poi_fused_elu_native_layer_norm_view_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=[32768], 
    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_elu_native_layer_norm_view_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_native_layer_norm_view_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 19200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 300
    x1 = (xindex // 300)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (300*(x1 % 4)) + (1216*(x1 // 4))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xp/cxpqywcqam7evubfwwa5zmt733w2zov6otomgqgpramgjdsnjg5k.py
# Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   sigmoid => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_5,), kwargs = {})
triton_poi_fused_sigmoid_3 = async_compile.triton('triton_poi_fused_sigmoid_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_sigmoid_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_sigmoid_3(in_out_ptr0, 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 % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, 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, (400, 4), (4, 1))
    assert_size_stride(primals_2, (400, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (400, ), (1, ))
    assert_size_stride(primals_5, (400, ), (1, ))
    assert_size_stride(primals_6, (300, 400), (400, 1))
    assert_size_stride(primals_7, (300, ), (1, ))
    assert_size_stride(primals_8, (300, ), (1, ))
    assert_size_stride(primals_9, (300, ), (1, ))
    assert_size_stride(primals_10, (4, 300), (300, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 400), (400, 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, 400), (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, 400), (6400, 1600, 400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.elu, aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_per_fused_elu_native_layer_norm_0.run(buf4, buf0, primals_4, primals_5, buf1, buf5, 64, 400, grid=grid(64), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf5, (64, 400), (400, 1), 0), reinterpret_tensor(primals_6, (400, 300), (1, 400), 0), alpha=1, beta=1, out=buf6)
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf8  # reuse
        buf11 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.elu, aten.native_layer_norm]
        triton_per_fused_elu_native_layer_norm_1.run(buf10, buf6, primals_8, primals_9, buf7, buf11, 64, 300, grid=grid(64), stream=stream0)
        del primals_9
        buf12 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_2, out_3, linear_2], Original ATen: [aten.elu, aten.native_layer_norm, aten.view]
        triton_poi_fused_elu_native_layer_norm_view_2.run(buf11, buf12, 19200, grid=grid(19200), stream=stream0)
        del buf11
        buf13 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf12, reinterpret_tensor(primals_10, (300, 4), (1, 300), 0), out=buf13)
        buf14 = reinterpret_tensor(buf13, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf13  # reuse
        # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_3.run(buf14, primals_11, 256, grid=grid(256), stream=stream0)
        del primals_11
    return (buf14, primals_4, primals_8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf1, buf4, reinterpret_tensor(buf5, (64, 400), (400, 1), 0), buf6, buf7, buf10, buf12, buf14, primals_10, primals_6, )


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((400, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((400, ), (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((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((300, 400), (400, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 300), (300, 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
from torch import nn
import torch.nn.functional as F


class Actor(nn.Module):
    """Actor model

        Parameters:
              args (object): Parameter class
    """

    def __init__(self, state_dim, action_dim, wwid):
        super(Actor, self).__init__()
        self.wwid = torch.Tensor([wwid])
        l1 = 400
        l2 = 300
        self.f1 = nn.Linear(state_dim, l1)
        self.ln1 = nn.LayerNorm(l1)
        self.f2 = nn.Linear(l1, l2)
        self.ln2 = nn.LayerNorm(l2)
        self.w_out = nn.Linear(l2, action_dim)

    def forward(self, input):
        """Method to forward propagate through the actor's graph

            Parameters:
                  input (tensor): states

            Returns:
                  action (tensor): actions


        """
        out = F.elu(self.f1(input))
        out = self.ln1(out)
        out = F.elu(self.f2(out))
        out = self.ln2(out)
        return torch.sigmoid(self.w_out(out))


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


def get_init_inputs():
    return [[], {'state_dim': 4, 'action_dim': 4, 'wwid': 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
from torch import 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_elu_native_layer_norm_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    rnumel = 400
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 400 * x0), rmask, other=0.0)
    tmp31 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp33 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tl.where(rmask, tmp8, 0)
    tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp13 = tl.where(rmask, tmp11, 0)
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp15 = tl.full([1], 400, tl.int32)
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp14 / tmp16
    tmp18 = tmp8 - tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
    tmp22 = tl.where(rmask, tmp20, 0)
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp24 = 400.0
    tmp25 = tmp23 / tmp24
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tmp29 = tmp7 - tmp17
    tmp30 = tmp29 * tmp28
    tmp32 = tmp30 * tmp31
    tmp34 = tmp32 + tmp33
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp28, None)
    tl.store(out_ptr1 + (r1 + 400 * x0), tmp34, rmask)
    tl.store(out_ptr0 + x0, tmp17, None)


@triton.jit
def triton_per_fused_elu_native_layer_norm_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    rnumel = 300
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    x3 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (r1 + 300 * x0), rmask, other=0.0)
    tmp31 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp33 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tl.where(rmask, tmp8, 0)
    tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp13 = tl.where(rmask, tmp11, 0)
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp15 = tl.full([1], 300, tl.int32)
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp14 / tmp16
    tmp18 = tmp8 - tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
    tmp22 = tl.where(rmask, tmp20, 0)
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp24 = 300.0
    tmp25 = tmp23 / tmp24
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tmp29 = tmp7 - tmp17
    tmp30 = tmp29 * tmp28
    tmp32 = tmp30 * tmp31
    tmp34 = tmp32 + tmp33
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp28, None)
    tl.store(out_ptr1 + (r1 + 300 * x2 + 1216 * x3), tmp34, rmask)
    tl.store(out_ptr0 + x0, tmp17, None)


@triton.jit
def triton_poi_fused_elu_native_layer_norm_view_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 19200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 300
    x1 = xindex // 300
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 300 * (x1 % 4) + 1216 * (x1 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_sigmoid_3(in_out_ptr0, 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 % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, 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, (400, 4), (4, 1))
    assert_size_stride(primals_2, (400,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (400,), (1,))
    assert_size_stride(primals_5, (400,), (1,))
    assert_size_stride(primals_6, (300, 400), (400, 1))
    assert_size_stride(primals_7, (300,), (1,))
    assert_size_stride(primals_8, (300,), (1,))
    assert_size_stride(primals_9, (300,), (1,))
    assert_size_stride(primals_10, (4, 300), (300, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 400), (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, 400), (6400, 1600, 400, 1),
            torch.float32)
        get_raw_stream(0)
        triton_per_fused_elu_native_layer_norm_0[grid(64)](buf4, buf0,
            primals_4, primals_5, buf1, buf5, 64, 400, num_warps=4,
            num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf5, (64, 400),
            (400, 1), 0), reinterpret_tensor(primals_6, (400, 300), (1, 400
            ), 0), alpha=1, beta=1, out=buf6)
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf8
        buf11 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1),
            torch.float32)
        triton_per_fused_elu_native_layer_norm_1[grid(64)](buf10, buf6,
            primals_8, primals_9, buf7, buf11, 64, 300, num_warps=4,
            num_stages=1)
        del primals_9
        buf12 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        triton_poi_fused_elu_native_layer_norm_view_2[grid(19200)](buf11,
            buf12, 19200, XBLOCK=256, num_warps=4, num_stages=1)
        del buf11
        buf13 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf12, reinterpret_tensor(primals_10, (300, 4), (
            1, 300), 0), out=buf13)
        buf14 = reinterpret_tensor(buf13, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf13
        triton_poi_fused_sigmoid_3[grid(256)](buf14, primals_11, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
    return buf14, primals_4, primals_8, reinterpret_tensor(primals_3, (64, 
        4), (4, 1), 0), buf0, buf1, buf4, reinterpret_tensor(buf5, (64, 400
        ), (400, 1), 0), buf6, buf7, buf10, buf12, buf14, primals_10, primals_6


class ActorNew(nn.Module):
    """Actor model

        Parameters:
              args (object): Parameter class
    """

    def __init__(self, state_dim, action_dim, wwid):
        super(ActorNew, self).__init__()
        self.wwid = torch.Tensor([wwid])
        l1 = 400
        l2 = 300
        self.f1 = nn.Linear(state_dim, l1)
        self.ln1 = nn.LayerNorm(l1)
        self.f2 = nn.Linear(l1, l2)
        self.ln2 = nn.LayerNorm(l2)
        self.w_out = nn.Linear(l2, action_dim)

    def forward(self, input_0):
        primals_1 = self.f1.weight
        primals_2 = self.f1.bias
        primals_4 = self.ln1.weight
        primals_5 = self.ln1.bias
        primals_6 = self.f2.weight
        primals_7 = self.f2.bias
        primals_8 = self.ln2.weight
        primals_9 = self.ln2.bias
        primals_10 = self.w_out.weight
        primals_11 = self.w_out.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]
