# 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/r4/cr4qs7g66krlhqldbwy7bilbdelqowjzjr3hpo2vmrnuio6ze3t6.py
# Topologically Sorted Source Nodes: [layer_norm, x], Original ATen: [aten.native_layer_norm, aten.leaky_relu]
# Source node to ATen node mapping:
#   layer_norm => add, add_1, mul, mul_1, rsqrt, sub, var_mean
#   x => gt, mul_2, where
# 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=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_5), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_1, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.01), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add_1, %mul_2), kwargs = {})
triton_per_fused_leaky_relu_native_layer_norm_0 = async_compile.triton('triton_per_fused_leaky_relu_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, 32],
    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_leaky_relu_native_layer_norm_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], '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_leaky_relu_native_layer_norm_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 32
    RBLOCK: tl.constexpr = 32
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (32*x0)), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + (r1), None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + (r1), None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 32, 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(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 32.0
    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 = 0.0
    tmp29 = tmp27 > tmp28
    tmp30 = 0.01
    tmp31 = tmp27 * tmp30
    tmp32 = tl.where(tmp29, tmp27, tmp31)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp21, xmask)
    tl.store(in_out_ptr1 + (r1 + (32*x0)), tmp32, xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/aw/cawcaeotbueu5g5ifjssgghczfxd3vmnzhgjbqqmgopew6u6jp6t.py
# Topologically Sorted Source Nodes: [layer_norm_1, x_1], Original ATen: [aten.native_layer_norm, aten.leaky_relu]
# Source node to ATen node mapping:
#   layer_norm_1 => add_2, add_3, mul_3, mul_4, rsqrt_1, sub_1, var_mean_1
#   x_1 => gt_1, mul_5, where_1
# Graph fragment:
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_3, [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 = (%view_3, %getitem_3), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %primals_8), kwargs = {})
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %primals_9), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_3, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, 0.01), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %add_3, %mul_5), kwargs = {})
triton_per_fused_leaky_relu_native_layer_norm_1 = async_compile.triton('triton_per_fused_leaky_relu_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, 64],
    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_leaky_relu_native_layer_norm_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], '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_leaky_relu_native_layer_norm_1(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + (r1), None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + (r1), None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 64, 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(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 64.0
    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 = 0.0
    tmp29 = tmp27 > tmp28
    tmp30 = 0.01
    tmp31 = tmp27 * tmp30
    tmp32 = tl.where(tmp29, tmp27, tmp31)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp21, xmask)
    tl.store(in_out_ptr1 + (r1 + (64*x0)), tmp32, xmask)
    tl.store(out_ptr0 + (x0), 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, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19 = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (32, ), (1, ))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (64, 32), (32, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, ), (1, ))
    assert_size_stride(primals_9, (64, ), (1, ))
    assert_size_stride(primals_10, (64, 64), (64, 1))
    assert_size_stride(primals_11, (64, ), (1, ))
    assert_size_stride(primals_12, (64, ), (1, ))
    assert_size_stride(primals_13, (64, ), (1, ))
    assert_size_stride(primals_14, (32, 64), (64, 1))
    assert_size_stride(primals_15, (32, ), (1, ))
    assert_size_stride(primals_16, (32, ), (1, ))
    assert_size_stride(primals_17, (32, ), (1, ))
    assert_size_stride(primals_18, (4, 32), (32, 1))
    assert_size_stride(primals_19, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 32), (32, 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, 32), (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, 32), (512, 128, 32, 1), torch.float32)
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [layer_norm, x], Original ATen: [aten.native_layer_norm, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_per_fused_leaky_relu_native_layer_norm_0.run(buf4, buf6, buf0, primals_4, primals_5, buf1, 64, 32, grid=grid(64), stream=stream0)
        buf7 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf6, (64, 32), (32, 1), 0), reinterpret_tensor(primals_6, (32, 64), (1, 32), 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 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.float32)
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [layer_norm_1, x_1], Original ATen: [aten.native_layer_norm, aten.leaky_relu]
        triton_per_fused_leaky_relu_native_layer_norm_1.run(buf11, buf13, buf7, primals_8, primals_9, buf8, 64, 64, grid=grid(64), stream=stream0)
        buf14 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf13, (64, 64), (64, 1), 0), reinterpret_tensor(primals_10, (64, 64), (1, 64), 0), alpha=1, beta=1, out=buf14)
        del primals_11
        buf15 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf16 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf18 = reinterpret_tensor(buf16, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf16  # reuse
        buf19 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.float32)
        buf20 = buf19; del buf19  # reuse
        # Topologically Sorted Source Nodes: [layer_norm_2, x_2], Original ATen: [aten.native_layer_norm, aten.leaky_relu]
        triton_per_fused_leaky_relu_native_layer_norm_1.run(buf18, buf20, buf14, primals_12, primals_13, buf15, 64, 64, grid=grid(64), stream=stream0)
        buf21 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_15, reinterpret_tensor(buf20, (64, 64), (64, 1), 0), reinterpret_tensor(primals_14, (64, 32), (1, 64), 0), alpha=1, beta=1, out=buf21)
        del primals_15
        buf22 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf25 = reinterpret_tensor(buf23, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf23  # reuse
        buf26 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.float32)
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [layer_norm_3, x_3], Original ATen: [aten.native_layer_norm, aten.leaky_relu]
        triton_per_fused_leaky_relu_native_layer_norm_0.run(buf25, buf27, buf21, primals_16, primals_17, buf22, 64, 32, grid=grid(64), stream=stream0)
        buf28 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_19, reinterpret_tensor(buf27, (64, 32), (32, 1), 0), reinterpret_tensor(primals_18, (32, 4), (1, 32), 0), alpha=1, beta=1, out=buf28)
        del primals_19
    return (reinterpret_tensor(buf28, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_4, primals_5, primals_8, primals_9, primals_12, primals_13, primals_16, primals_17, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf1, buf4, reinterpret_tensor(buf6, (64, 32), (32, 1), 0), buf7, buf8, buf11, reinterpret_tensor(buf13, (64, 64), (64, 1), 0), buf14, buf15, buf18, reinterpret_tensor(buf20, (64, 64), (64, 1), 0), buf21, buf22, buf25, reinterpret_tensor(buf27, (64, 32), (32, 1), 0), primals_18, primals_14, 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((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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((32, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((4, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = 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, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19])
    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 DQN(nn.Module):

    def __init__(self, num_in_features, num_out_features):
        super(DQN, self).__init__()
        self.linear1 = nn.Linear(num_in_features, 32)
        self.ln1 = nn.LayerNorm(32)
        self.linear2 = nn.Linear(32, 64)
        self.ln2 = nn.LayerNorm(64)
        self.linear3 = nn.Linear(64, 64)
        self.ln3 = nn.LayerNorm(64)
        self.linear4 = nn.Linear(64, 32)
        self.ln4 = nn.LayerNorm(32)
        self.out_layer = nn.Linear(32, num_out_features)

    def forward(self, x):
        x = F.leaky_relu(self.ln1(self.linear1(x)))
        x = F.leaky_relu(self.ln2(self.linear2(x)))
        x = F.leaky_relu(self.ln3(self.linear3(x)))
        x = F.leaky_relu(self.ln4(self.linear4(x)))
        return self.out_layer(x)


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


def get_init_inputs():
    return [[], {'num_in_features': 4, 'num_out_features': 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.triton_helpers import libdevice
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_leaky_relu_native_layer_norm_0(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 32 * x0), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 32, 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(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 32.0
    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 = 0.0
    tmp29 = tmp27 > tmp28
    tmp30 = 0.01
    tmp31 = tmp27 * tmp30
    tmp32 = tl.where(tmp29, tmp27, tmp31)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(in_out_ptr1 + (r1 + 32 * x0), tmp32, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_per_fused_leaky_relu_native_layer_norm_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 64, 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(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 64.0
    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 = 0.0
    tmp29 = tmp27 > tmp28
    tmp30 = 0.01
    tmp31 = tmp27 * tmp30
    tmp32 = tl.where(tmp29, tmp27, tmp31)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(in_out_ptr1 + (r1 + 64 * x0), tmp32, xmask)
    tl.store(out_ptr0 + x0, tmp10, 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, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19) = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32), (32, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64,), (1,))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (64, 64), (64, 1))
    assert_size_stride(primals_11, (64,), (1,))
    assert_size_stride(primals_12, (64,), (1,))
    assert_size_stride(primals_13, (64,), (1,))
    assert_size_stride(primals_14, (32, 64), (64, 1))
    assert_size_stride(primals_15, (32,), (1,))
    assert_size_stride(primals_16, (32,), (1,))
    assert_size_stride(primals_17, (32,), (1,))
    assert_size_stride(primals_18, (4, 32), (32, 1))
    assert_size_stride(primals_19, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 32), (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, 32), (512, 128, 32, 1), torch.
            float32)
        buf6 = buf5
        del buf5
        get_raw_stream(0)
        triton_per_fused_leaky_relu_native_layer_norm_0[grid(64)](buf4,
            buf6, buf0, primals_4, primals_5, buf1, 64, 32, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf7 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf6, (64, 32),
            (32, 1), 0), reinterpret_tensor(primals_6, (32, 64), (1, 32), 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 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .float32)
        buf13 = buf12
        del buf12
        triton_per_fused_leaky_relu_native_layer_norm_1[grid(64)](buf11,
            buf13, buf7, primals_8, primals_9, buf8, 64, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf14 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf13, (64, 64),
            (64, 1), 0), reinterpret_tensor(primals_10, (64, 64), (1, 64), 
            0), alpha=1, beta=1, out=buf14)
        del primals_11
        buf15 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf16 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf18 = reinterpret_tensor(buf16, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf16
        buf19 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .float32)
        buf20 = buf19
        del buf19
        triton_per_fused_leaky_relu_native_layer_norm_1[grid(64)](buf18,
            buf20, buf14, primals_12, primals_13, buf15, 64, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf21 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
        extern_kernels.addmm(primals_15, reinterpret_tensor(buf20, (64, 64),
            (64, 1), 0), reinterpret_tensor(primals_14, (64, 32), (1, 64), 
            0), alpha=1, beta=1, out=buf21)
        del primals_15
        buf22 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf25 = reinterpret_tensor(buf23, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf23
        buf26 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.
            float32)
        buf27 = buf26
        del buf26
        triton_per_fused_leaky_relu_native_layer_norm_0[grid(64)](buf25,
            buf27, buf21, primals_16, primals_17, buf22, 64, 32, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf28 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_19, reinterpret_tensor(buf27, (64, 32),
            (32, 1), 0), reinterpret_tensor(primals_18, (32, 4), (1, 32), 0
            ), alpha=1, beta=1, out=buf28)
        del primals_19
    return (reinterpret_tensor(buf28, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        primals_4, primals_5, primals_8, primals_9, primals_12, primals_13,
        primals_16, primals_17, reinterpret_tensor(primals_3, (64, 4), (4, 
        1), 0), buf0, buf1, buf4, reinterpret_tensor(buf6, (64, 32), (32, 1
        ), 0), buf7, buf8, buf11, reinterpret_tensor(buf13, (64, 64), (64, 
        1), 0), buf14, buf15, buf18, reinterpret_tensor(buf20, (64, 64), (
        64, 1), 0), buf21, buf22, buf25, reinterpret_tensor(buf27, (64, 32),
        (32, 1), 0), primals_18, primals_14, primals_10, primals_6)


class DQNNew(nn.Module):

    def __init__(self, num_in_features, num_out_features):
        super(DQNNew, self).__init__()
        self.linear1 = nn.Linear(num_in_features, 32)
        self.ln1 = nn.LayerNorm(32)
        self.linear2 = nn.Linear(32, 64)
        self.ln2 = nn.LayerNorm(64)
        self.linear3 = nn.Linear(64, 64)
        self.ln3 = nn.LayerNorm(64)
        self.linear4 = nn.Linear(64, 32)
        self.ln4 = nn.LayerNorm(32)
        self.out_layer = nn.Linear(32, num_out_features)

    def forward(self, input_0):
        primals_1 = self.linear1.weight
        primals_2 = self.linear1.bias
        primals_4 = self.ln1.weight
        primals_5 = self.ln1.bias
        primals_6 = self.linear2.weight
        primals_7 = self.linear2.bias
        primals_8 = self.ln2.weight
        primals_9 = self.ln2.bias
        primals_10 = self.linear3.weight
        primals_11 = self.linear3.bias
        primals_12 = self.ln3.weight
        primals_13 = self.ln3.bias
        primals_14 = self.linear4.weight
        primals_15 = self.linear4.bias
        primals_16 = self.ln4.weight
        primals_17 = self.ln4.bias
        primals_18 = self.out_layer.weight
        primals_19 = self.out_layer.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, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19])
        return output[0]
