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

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


# kernel path: runs/run_shard_4/inductor_cache/wd/cwdz7kqs3uwyg53zsyekt77eye7yjl6v7vulow2q6ni534mkf6zw.py
# Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm => add, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [2]), 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=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_native_layer_norm_0 = async_compile.triton('triton_poi_fused_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vs/cvsfvbs4wlaqvwxm3svg65dnhcq336ptudvn6xetnbnrtzj7xssn.py
# Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [2]), 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=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_3, %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_1), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_2), kwargs = {})
triton_poi_fused_native_layer_norm_1 = async_compile.triton('triton_poi_fused_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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_poi_fused_native_layer_norm_1', '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_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nd/cndylbi2istcpkocwhsm4jestn2apwwzoefwqaf2ic3dyc2y5ike.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%constant_pad_nd, %slice_6], -1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], '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_cat_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x3 = (xindex // 4)
    x4 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 4, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tmp9 & tmp4
    tmp11 = tl.load(in_ptr0 + ((-4) + (4*x3) + x0), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tmp0 < tmp7
    tmp16 = tl.load(in_ptr0 + (2 + (4*x3) + ((-2) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tl.where(tmp4, tmp13, tmp16)
    tl.store(out_ptr0 + (x4), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/sp/cspl2t5rdvmswt3sg2z7q5earrolbjdav3yqy6kfgrecaaqjanfr.py
# Topologically Sorted Source Nodes: [k_1, k_2, sum_k], Original ATen: [aten.clamp, aten.exp, aten.cumsum]
# Source node to ATen node mapping:
#   k_1 => clamp_max, clamp_min
#   k_2 => exp
#   sum_k => cumsum
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%view_1, -60), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 30), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%clamp_max,), kwargs = {})
#   %cumsum : [num_users=2] = call_function[target=torch.ops.aten.cumsum.default](args = (%exp, 1), kwargs = {})
triton_per_fused_clamp_cumsum_exp_3 = async_compile.triton('triton_per_fused_clamp_cumsum_exp_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.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0

@triton_heuristics.persistent_reduction(
    size_hints=[16, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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_per_fused_clamp_cumsum_exp_3', '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}
)
@triton.jit
def triton_per_fused_clamp_cumsum_exp_3(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    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)
    r2 = rindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x0 + (4*r2) + (16*x1)), xmask, other=0.0)
    tmp1 = -60.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 30.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp8, = tl.associative_scan((tmp7,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (x0 + (4*r2) + (16*x1)), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/zg/czg55migd4lu6agk2bfujmhzv4qu2uki7jmxf6l3ny6zonkigb2e.py
# Topologically Sorted Source Nodes: [k_1, k_2, mul], Original ATen: [aten.clamp, aten.exp, aten.mul]
# Source node to ATen node mapping:
#   k_1 => clamp_max, clamp_min
#   k_2 => exp
#   mul => mul_2
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%view_1, -60), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 30), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%clamp_max,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %view_3), kwargs = {})
triton_poi_fused_clamp_exp_mul_4 = async_compile.triton('triton_poi_fused_clamp_exp_mul_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_exp_mul_4', 'mutated_arg_names': [], '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_clamp_exp_mul_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp6 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = -60.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 30.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 * tmp6
    tl.store(out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nw/cnw3palrt3zfokrf6rjqwg55f4evb5xugb3x3xwzjvytyhaswlvt.py
# Topologically Sorted Source Nodes: [sigmoid, mul_1, rwkv], Original ATen: [aten.sigmoid, aten.mul, aten.div]
# Source node to ATen node mapping:
#   mul_1 => mul_3
#   rwkv => div
#   sigmoid => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_5,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %view_11), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_3, %cumsum), kwargs = {})
triton_poi_fused_div_mul_sigmoid_5 = async_compile.triton('triton_poi_fused_div_mul_sigmoid_5', '''
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=[4, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mul_sigmoid_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_mul_sigmoid_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x3 = xindex
    y0 = yindex
    x1 = xindex % 4
    x2 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x3 + (16*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + (y0 + (4*x2) + (16*x1)), xmask & ymask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x3 + (16*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 / tmp4
    tl.store(out_ptr0 + (x3 + (16*y0)), tmp5, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/od/codkrgmakntrux3jaqxnbzn67buygb6k5a5u2qdnlgtysamky3c7.py
# Topologically Sorted Source Nodes: [mul_2, x_1, layer_norm_1], Original ATen: [aten.mul, aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm_1 => var_mean_1
#   mul_2 => mul_4
#   x_1 => add_2
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_13, %primals_13), kwargs = {})
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %mul_4), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_2, [2]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_mul_native_layer_norm_6 = async_compile.triton('triton_poi_fused_add_mul_native_layer_norm_6', '''
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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_native_layer_norm_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_add_mul_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x2), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tmp5 + tmp7
    tmp9 = tmp4 + tmp8
    tmp12 = tmp11 * tmp2
    tmp13 = tmp10 + tmp12
    tmp14 = tmp9 + tmp13
    tmp17 = tmp16 * tmp2
    tmp18 = tmp15 + tmp17
    tmp19 = tmp14 + tmp18
    tmp20 = 4.0
    tmp21 = tmp19 / tmp20
    tmp22 = tmp4 - tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp8 - tmp21
    tmp25 = tmp24 * tmp24
    tmp26 = tmp23 + tmp25
    tmp27 = tmp13 - tmp21
    tmp28 = tmp27 * tmp27
    tmp29 = tmp26 + tmp28
    tmp30 = tmp18 - tmp21
    tmp31 = tmp30 * tmp30
    tmp32 = tmp29 + tmp31
    tmp33 = tmp32 / tmp20
    tl.store(out_ptr0 + (x2), tmp21, xmask)
    tl.store(out_ptr1 + (x2), tmp33, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ni/cnidjfe7dgay4yiqbs7a4feyalqrzs7xffy5hiwutgy7ps6coiif.py
# Topologically Sorted Source Nodes: [mul_2, x_1, layer_norm_1], Original ATen: [aten.mul, aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm_1 => add_3, add_4, mul_5, mul_6, rsqrt_1, sub_1
#   mul_2 => mul_4
#   x_1 => add_2
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_13, %primals_13), kwargs = {})
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %mul_4), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_3,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %getitem_3), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt_1), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_5, %primals_14), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_6, %primals_15), kwargs = {})
triton_poi_fused_add_mul_native_layer_norm_7 = async_compile.triton('triton_poi_fused_add_mul_native_layer_norm_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 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, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_native_layer_norm_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_add_mul_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    x4 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x3), xmask)
    tmp2 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x4), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x4), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + (x3), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qn/cqnq5ydr4yvzog6hlxpgvawzjl6gaiuscgdgvzgpltmhzetqshs2.py
# Topologically Sorted Source Nodes: [mish, mul_3], Original ATen: [aten.mish, aten.mul]
# Source node to ATen node mapping:
#   mish => exp_1, gt, log1p, mul_7, tanh, where
#   mul_3 => mul_8
# Graph fragment:
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%view_15,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp_1,), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_15, 20), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_15, %log1p), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%where,), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_15, %tanh), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_7, %view_17), kwargs = {})
triton_poi_fused_mish_mul_8 = async_compile.triton('triton_poi_fused_mish_mul_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mish_mul_8', 'mutated_arg_names': [], '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_mish_mul_8(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp8 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = 20.0
    tmp2 = tmp0 > tmp1
    tmp3 = tl_math.exp(tmp0)
    tmp4 = libdevice.log1p(tmp3)
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = libdevice.tanh(tmp5)
    tmp7 = tmp0 * tmp6
    tmp9 = tmp7 * tmp8
    tl.store(out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pr/cprjeaqvtkr3qwwz2dl4nm5lhzrfsri4uijoskmpsca5eqhdcisy.py
# Topologically Sorted Source Nodes: [mul_2, x_1, sigmoid_1, rwkv_2, x_3], Original ATen: [aten.mul, aten.add, aten.sigmoid]
# Source node to ATen node mapping:
#   mul_2 => mul_4
#   rwkv_2 => mul_9
#   sigmoid_1 => sigmoid_1
#   x_1 => add_2
#   x_3 => add_5
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_13, %primals_13), kwargs = {})
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %mul_4), kwargs = {})
#   %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_19,), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %view_21), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_9), kwargs = {})
triton_poi_fused_add_mul_sigmoid_9 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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_poi_fused_add_mul_sigmoid_9', '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_add_mul_sigmoid_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x3), xmask)
    tmp2 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x3), xmask)
    tmp7 = tl.load(in_ptr4 + (x3), xmask)
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tl.sigmoid(tmp5)
    tmp8 = tmp6 * tmp7
    tmp9 = tmp4 + tmp8
    tl.store(out_ptr0 + (x3), tmp9, 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, primals_20, primals_21, primals_22, primals_23 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    assert_size_stride(primals_13, (4, 1), (1, 1))
    assert_size_stride(primals_14, (4, ), (1, ))
    assert_size_stride(primals_15, (4, ), (1, ))
    assert_size_stride(primals_16, (10, 4), (4, 1))
    assert_size_stride(primals_17, (10, ), (1, ))
    assert_size_stride(primals_18, (10, 4), (4, 1))
    assert_size_stride(primals_19, (10, ), (1, ))
    assert_size_stride(primals_20, (4, 4), (4, 1))
    assert_size_stride(primals_21, (4, ), (1, ))
    assert_size_stride(primals_22, (4, 10), (10, 1))
    assert_size_stride(primals_23, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0.run(primals_3, buf0, buf1, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_1.run(primals_3, buf0, buf1, primals_1, primals_2, buf2, 64, grid=grid(64), stream=stream0)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf2, buf3, 64, grid=grid(64), stream=stream0)
        buf4 = reinterpret_tensor(buf2, (16, 4), (4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_5
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [r], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6)
        del primals_9
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k_1, k_2, sum_k], Original ATen: [aten.clamp, aten.exp, aten.cumsum]
        triton_per_fused_clamp_cumsum_exp_3.run(buf4, buf7, 16, 4, grid=grid(16), stream=stream0)
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k_1, k_2, mul], Original ATen: [aten.clamp, aten.exp, aten.mul]
        triton_poi_fused_clamp_exp_mul_4.run(buf4, buf5, buf8, 64, grid=grid(64), stream=stream0)
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [einsum], Original ATen: [aten.bmm]
        extern_kernels.bmm(primals_10, reinterpret_tensor(buf8, (4, 4, 4), (1, 4, 16), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, mul_1, rwkv], Original ATen: [aten.sigmoid, aten.mul, aten.div]
        triton_poi_fused_div_mul_sigmoid_5.run(buf6, buf9, buf7, buf10, 4, 16, grid=grid(4, 16), stream=stream0)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [rwkv_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf10, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf11)
        del primals_12
        buf12 = buf1; del buf1  # reuse
        buf13 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [mul_2, x_1, layer_norm_1], Original ATen: [aten.mul, aten.add, aten.native_layer_norm]
        triton_poi_fused_add_mul_native_layer_norm_6.run(primals_3, buf11, primals_13, buf12, buf13, 16, grid=grid(16), stream=stream0)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_2, x_1, layer_norm_1], Original ATen: [aten.mul, aten.add, aten.native_layer_norm]
        triton_poi_fused_add_mul_native_layer_norm_7.run(primals_3, buf11, primals_13, buf12, buf13, primals_14, primals_15, buf14, 64, grid=grid(64), stream=stream0)
        del buf12
        del buf13
        del primals_15
        buf15 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf14, buf15, 64, grid=grid(64), stream=stream0)
        buf16 = empty_strided_cuda((16, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf15, (16, 4), (4, 1), 0), reinterpret_tensor(primals_16, (4, 10), (1, 4), 0), alpha=1, beta=1, out=buf16)
        del primals_17
        buf17 = empty_strided_cuda((16, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_19, reinterpret_tensor(buf15, (16, 4), (4, 1), 0), reinterpret_tensor(primals_18, (4, 10), (1, 4), 0), alpha=1, beta=1, out=buf17)
        del primals_19
        buf18 = reinterpret_tensor(buf14, (16, 4), (4, 1), 0); del buf14  # reuse
        # Topologically Sorted Source Nodes: [r_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_21, reinterpret_tensor(buf15, (16, 4), (4, 1), 0), reinterpret_tensor(primals_20, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf18)
        del primals_21
        buf19 = empty_strided_cuda((4, 4, 10), (40, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mish, mul_3], Original ATen: [aten.mish, aten.mul]
        triton_poi_fused_mish_mul_8.run(buf16, buf17, buf19, 160, grid=grid(160), stream=stream0)
        buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [wkv_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_23, reinterpret_tensor(buf19, (16, 10), (10, 1), 0), reinterpret_tensor(primals_22, (10, 4), (1, 10), 0), alpha=1, beta=1, out=buf20)
        del primals_23
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_2, x_1, sigmoid_1, rwkv_2, x_3], Original ATen: [aten.mul, aten.add, aten.sigmoid]
        triton_poi_fused_add_mul_sigmoid_9.run(primals_3, buf11, primals_13, buf18, buf20, buf21, 64, grid=grid(64), stream=stream0)
    return (buf21, primals_3, primals_13, primals_14, reinterpret_tensor(buf3, (16, 4), (4, 1), 0), buf4, buf5, buf6, buf7, buf9, buf10, buf11, reinterpret_tensor(buf15, (16, 4), (4, 1), 0), buf16, buf17, buf18, reinterpret_tensor(buf19, (16, 10), (10, 1), 0), buf20, primals_22, primals_20, primals_18, primals_16, primals_11, reinterpret_tensor(primals_10, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf8, (4, 4, 4), (1, 16, 4), 0), primals_8, primals_6, primals_4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((10, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((10, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((4, 10), (10, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = 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, primals_20, primals_21, primals_22, primals_23])
    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)

from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.nn import functional as F


class RWKV_TimeMix(nn.Module):

    def __init__(self, config, layer_id):
        super().__init__()
        assert config.n_attn % config.n_head == 0
        self.layer_id = layer_id
        self.ctx_len = config.ctx_len
        self.n_head = config.n_head
        self.head_size = config.n_attn // config.n_head
        self.time_ww = nn.Parameter(torch.ones(config.n_head, config.
            ctx_len, config.ctx_len))
        self.time_gamma = nn.Parameter(torch.ones(config.ctx_len, 1))
        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        self.key = nn.Linear(config.n_embd, config.n_attn)
        self.value = nn.Linear(config.n_embd, config.n_attn)
        self.receptance = nn.Linear(config.n_embd, config.n_attn)
        self.output = nn.Linear(config.n_attn, config.n_embd)
        self.key.scale_init = 0
        self.receptance.scale_init = 0
        self.output.scale_init = 0

    def forward(self, x):
        B, T, C = x.size()
        x = torch.cat([self.time_shift(x[:, :, :C // 2]), x[:, :, C // 2:]],
            dim=-1)
        k = self.key(x)
        v = self.value(x)
        r = self.receptance(x)
        k = torch.clamp(k, max=30, min=-60)
        k = torch.exp(k)
        sum_k = torch.cumsum(k, dim=1)
        kv = (k * v).view(B, T, self.n_head, self.head_size)
        wkv = torch.einsum('htu,buhc->bthc', self.time_ww[:, :T, :T], kv
            ).contiguous().view(B, T, -1)
        rwkv = torch.sigmoid(r) * wkv / sum_k
        rwkv = self.output(rwkv)
        return rwkv * self.time_gamma[:T, :]


class RWKV_ChannelMix(nn.Module):

    def __init__(self, config, layer_id):
        super().__init__()
        self.layer_id = layer_id
        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        hidden_sz = 5 * config.n_ffn // 2
        self.key = nn.Linear(config.n_embd, hidden_sz)
        self.value = nn.Linear(config.n_embd, hidden_sz)
        self.weight = nn.Linear(hidden_sz, config.n_embd)
        self.receptance = nn.Linear(config.n_embd, config.n_embd)
        self.receptance.scale_init = 0
        self.weight.scale_init = 0

    def forward(self, x):
        _B, _T, C = x.size()
        x = torch.cat([self.time_shift(x[:, :, :C // 2]), x[:, :, C // 2:]],
            dim=-1)
        k = self.key(x)
        v = self.value(x)
        r = self.receptance(x)
        wkv = self.weight(F.mish(k) * v)
        rwkv = torch.sigmoid(r) * wkv
        return rwkv


class Block(nn.Module):

    def __init__(self, config, layer_id):
        super().__init__()
        self.config = config
        self.ln1 = nn.LayerNorm(config.n_embd)
        self.ln2 = nn.LayerNorm(config.n_embd)
        self.attn = RWKV_TimeMix(config, layer_id)
        self.mlp = RWKV_ChannelMix(config, layer_id)

    def forward(self, x):
        x = x + self.attn(self.ln1(x))
        x = x + self.mlp(self.ln2(x))
        return x


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


def get_init_inputs():
    return [[], {'config': _mock_config(n_embd=4, n_attn=4, n_head=4,
        ctx_len=4, n_ffn=4), 'layer_id': 1}]

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
from torch.nn import functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x3 = xindex // 4
    x4 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 4, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tmp9 & tmp4
    tmp11 = tl.load(in_ptr0 + (-4 + 4 * x3 + x0), tmp10 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp16 = tl.load(in_ptr0 + (2 + 4 * x3 + (-2 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp17 = tl.where(tmp4, tmp13, tmp16)
    tl.store(out_ptr0 + x4, tmp17, xmask)


@triton.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0


@triton.jit
def triton_per_fused_clamp_cumsum_exp_3(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 4
    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)
    r2 = rindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * r2 + 16 * x1), xmask, other=0.0)
    tmp1 = -60.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 30.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp8, = tl.associative_scan((tmp7,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (x0 + 4 * r2 + 16 * x1), tmp8, xmask)


@triton.jit
def triton_poi_fused_clamp_exp_mul_4(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp6 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = -60.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 30.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 * tmp6
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_div_mul_sigmoid_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x3 = xindex
    y0 = yindex
    x1 = xindex % 4
    x2 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x3 + 16 * y0), xmask & ymask, eviction_policy
        ='evict_last')
    tmp2 = tl.load(in_ptr1 + (y0 + 4 * x2 + 16 * x1), xmask & ymask,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x3 + 16 * y0), xmask & ymask, eviction_policy
        ='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 / tmp4
    tl.store(out_ptr0 + (x3 + 16 * y0), tmp5, xmask & ymask)


@triton.jit
def triton_poi_fused_add_mul_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tmp5 + tmp7
    tmp9 = tmp4 + tmp8
    tmp12 = tmp11 * tmp2
    tmp13 = tmp10 + tmp12
    tmp14 = tmp9 + tmp13
    tmp17 = tmp16 * tmp2
    tmp18 = tmp15 + tmp17
    tmp19 = tmp14 + tmp18
    tmp20 = 4.0
    tmp21 = tmp19 / tmp20
    tmp22 = tmp4 - tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp8 - tmp21
    tmp25 = tmp24 * tmp24
    tmp26 = tmp23 + tmp25
    tmp27 = tmp13 - tmp21
    tmp28 = tmp27 * tmp27
    tmp29 = tmp26 + tmp28
    tmp30 = tmp18 - tmp21
    tmp31 = tmp30 * tmp30
    tmp32 = tmp29 + tmp31
    tmp33 = tmp32 / tmp20
    tl.store(out_ptr0 + x2, tmp21, xmask)
    tl.store(out_ptr1 + x2, tmp33, xmask)


@triton.jit
def triton_poi_fused_add_mul_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    x4 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x4, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_poi_fused_mish_mul_8(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp8 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = 20.0
    tmp2 = tmp0 > tmp1
    tmp3 = tl_math.exp(tmp0)
    tmp4 = libdevice.log1p(tmp3)
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = libdevice.tanh(tmp5)
    tmp7 = tmp0 * tmp6
    tmp9 = tmp7 * tmp8
    tl.store(out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x3, xmask)
    tmp7 = tl.load(in_ptr4 + x3, xmask)
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tl.sigmoid(tmp5)
    tmp8 = tmp6 * tmp7
    tmp9 = tmp4 + tmp8
    tl.store(out_ptr0 + x3, tmp9, 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, primals_20, primals_21, primals_22, primals_23
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4, 1), (1, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (10, 4), (4, 1))
    assert_size_stride(primals_17, (10,), (1,))
    assert_size_stride(primals_18, (10, 4), (4, 1))
    assert_size_stride(primals_19, (10,), (1,))
    assert_size_stride(primals_20, (4, 4), (4, 1))
    assert_size_stride(primals_21, (4,), (1,))
    assert_size_stride(primals_22, (4, 10), (10, 1))
    assert_size_stride(primals_23, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](primals_3, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](primals_3, buf0,
            buf1, primals_1, primals_2, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_cat_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (16, 4), (4, 1), 0)
        del buf2
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf3, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_5
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf6)
        del primals_9
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_per_fused_clamp_cumsum_exp_3[grid(16)](buf4, buf7, 16, 4,
            XBLOCK=8, num_warps=2, num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clamp_exp_mul_4[grid(64)](buf4, buf5, buf8, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_10, reinterpret_tensor(buf8, (4, 4, 4),
            (1, 4, 16), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_div_mul_sigmoid_5[grid(4, 16)](buf6, buf9, buf7,
            buf10, 4, 16, XBLOCK=8, YBLOCK=4, num_warps=1, num_stages=1)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf10, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf11)
        del primals_12
        buf12 = buf1
        del buf1
        buf13 = buf0
        del buf0
        triton_poi_fused_add_mul_native_layer_norm_6[grid(16)](primals_3,
            buf11, primals_13, buf12, buf13, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_native_layer_norm_7[grid(64)](primals_3,
            buf11, primals_13, buf12, buf13, primals_14, primals_15, buf14,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf12
        del buf13
        del primals_15
        buf15 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_cat_2[grid(64)](buf14, buf15, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf16 = empty_strided_cuda((16, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf15, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_16, (4, 10), (1, 4), 0),
            alpha=1, beta=1, out=buf16)
        del primals_17
        buf17 = empty_strided_cuda((16, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_19, reinterpret_tensor(buf15, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_18, (4, 10), (1, 4), 0),
            alpha=1, beta=1, out=buf17)
        del primals_19
        buf18 = reinterpret_tensor(buf14, (16, 4), (4, 1), 0)
        del buf14
        extern_kernels.addmm(primals_21, reinterpret_tensor(buf15, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_20, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf18)
        del primals_21
        buf19 = empty_strided_cuda((4, 4, 10), (40, 10, 1), torch.float32)
        triton_poi_fused_mish_mul_8[grid(160)](buf16, buf17, buf19, 160,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_23, reinterpret_tensor(buf19, (16, 10),
            (10, 1), 0), reinterpret_tensor(primals_22, (10, 4), (1, 10), 0
            ), alpha=1, beta=1, out=buf20)
        del primals_23
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_9[grid(64)](primals_3, buf11,
            primals_13, buf18, buf20, buf21, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
    return (buf21, primals_3, primals_13, primals_14, reinterpret_tensor(
        buf3, (16, 4), (4, 1), 0), buf4, buf5, buf6, buf7, buf9, buf10,
        buf11, reinterpret_tensor(buf15, (16, 4), (4, 1), 0), buf16, buf17,
        buf18, reinterpret_tensor(buf19, (16, 10), (10, 1), 0), buf20,
        primals_22, primals_20, primals_18, primals_16, primals_11,
        reinterpret_tensor(primals_10, (4, 4, 4), (16, 1, 4), 0),
        reinterpret_tensor(buf8, (4, 4, 4), (1, 16, 4), 0), primals_8,
        primals_6, primals_4)


class RWKV_TimeMix(nn.Module):

    def __init__(self, config, layer_id):
        super().__init__()
        assert config.n_attn % config.n_head == 0
        self.layer_id = layer_id
        self.ctx_len = config.ctx_len
        self.n_head = config.n_head
        self.head_size = config.n_attn // config.n_head
        self.time_ww = nn.Parameter(torch.ones(config.n_head, config.
            ctx_len, config.ctx_len))
        self.time_gamma = nn.Parameter(torch.ones(config.ctx_len, 1))
        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        self.key = nn.Linear(config.n_embd, config.n_attn)
        self.value = nn.Linear(config.n_embd, config.n_attn)
        self.receptance = nn.Linear(config.n_embd, config.n_attn)
        self.output = nn.Linear(config.n_attn, config.n_embd)
        self.key.scale_init = 0
        self.receptance.scale_init = 0
        self.output.scale_init = 0

    def forward(self, x):
        B, T, C = x.size()
        x = torch.cat([self.time_shift(x[:, :, :C // 2]), x[:, :, C // 2:]],
            dim=-1)
        k = self.key(x)
        v = self.value(x)
        r = self.receptance(x)
        k = torch.clamp(k, max=30, min=-60)
        k = torch.exp(k)
        sum_k = torch.cumsum(k, dim=1)
        kv = (k * v).view(B, T, self.n_head, self.head_size)
        wkv = torch.einsum('htu,buhc->bthc', self.time_ww[:, :T, :T], kv
            ).contiguous().view(B, T, -1)
        rwkv = torch.sigmoid(r) * wkv / sum_k
        rwkv = self.output(rwkv)
        return rwkv * self.time_gamma[:T, :]


class RWKV_ChannelMix(nn.Module):

    def __init__(self, config, layer_id):
        super().__init__()
        self.layer_id = layer_id
        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        hidden_sz = 5 * config.n_ffn // 2
        self.key = nn.Linear(config.n_embd, hidden_sz)
        self.value = nn.Linear(config.n_embd, hidden_sz)
        self.weight = nn.Linear(hidden_sz, config.n_embd)
        self.receptance = nn.Linear(config.n_embd, config.n_embd)
        self.receptance.scale_init = 0
        self.weight.scale_init = 0

    def forward(self, x):
        _B, _T, C = x.size()
        x = torch.cat([self.time_shift(x[:, :, :C // 2]), x[:, :, C // 2:]],
            dim=-1)
        k = self.key(x)
        v = self.value(x)
        r = self.receptance(x)
        wkv = self.weight(F.mish(k) * v)
        rwkv = torch.sigmoid(r) * wkv
        return rwkv


class BlockNew(nn.Module):

    def __init__(self, config, layer_id):
        super().__init__()
        self.config = config
        self.ln1 = nn.LayerNorm(config.n_embd)
        self.ln2 = nn.LayerNorm(config.n_embd)
        self.attn = RWKV_TimeMix(config, layer_id)
        self.mlp = RWKV_ChannelMix(config, layer_id)

    def forward(self, input_0):
        primals_1 = self.ln1.weight
        primals_2 = self.ln1.bias
        primals_5 = self.ln2.weight
        primals_7 = self.ln2.bias
        primals_3 = self.attn.time_ww
        primals_13 = self.attn.time_gamma
        primals_4 = self.attn.key.weight
        primals_9 = self.attn.key.bias
        primals_6 = self.attn.value.weight
        primals_12 = self.attn.value.bias
        primals_8 = self.attn.receptance.weight
        primals_14 = self.attn.receptance.bias
        primals_11 = self.attn.output.weight
        primals_15 = self.attn.output.bias
        primals_16 = self.mlp.key.weight
        primals_17 = self.mlp.key.bias
        primals_18 = self.mlp.value.weight
        primals_19 = self.mlp.value.bias
        primals_22 = self.mlp.weight.weight
        primals_21 = self.mlp.weight.bias
        primals_20 = self.mlp.receptance.weight
        primals_23 = self.mlp.receptance.bias
        primals_10 = 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,
            primals_20, primals_21, primals_22, primals_23])
        return output[0]
