# 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_1/inductor_cache/n6/cn6hukg5yul6wxhyp26kisvsmjurlyhbellokstfdiym7avvkvlm.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_0 = async_compile.triton('triton_poi_fused_cat_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_0(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_1/inductor_cache/4b/c4bcxddxivimelahulv46twjs2qx62d7uzvmztnhdb4ytty42vxe.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_1 = async_compile.triton('triton_per_fused_clamp_cumsum_exp_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.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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_1(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_1/inductor_cache/b2/cb27iv4r6fghoxc2k6unsg4wkcogshlzrrd26rgnkmihl3mdyhq7.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
# 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 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %view_3), kwargs = {})
triton_poi_fused_clamp_exp_mul_2 = async_compile.triton('triton_poi_fused_clamp_exp_mul_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: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_2(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_1/inductor_cache/zv/czv5yudoza6xheax5e7plmzbhdaconi75vvtbljyeblf4iebogri.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_1
#   rwkv => div
#   sigmoid => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_5,), kwargs = {})
#   %mul_1 : [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_1, %cumsum), kwargs = {})
triton_poi_fused_div_mul_sigmoid_3 = async_compile.triton('triton_poi_fused_div_mul_sigmoid_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_3(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_1/inductor_cache/ds/cdsyk25id2yevrz3ttr6huknedlirbvg5gvfuhpb7mgoaikegj7y.py
# Topologically Sorted Source Nodes: [mul_2], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul_2 => mul_2
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_13, %primals_11), kwargs = {})
triton_poi_fused_mul_4 = async_compile.triton('triton_poi_fused_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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_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
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (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), (16, 4, 1))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_4
        del primals_5
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [r], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_6
        del primals_7
        buf4 = 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_1.run(buf1, buf4, 16, 4, grid=grid(16), stream=stream0)
        buf5 = 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_2.run(buf1, buf2, buf5, 64, grid=grid(64), stream=stream0)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [einsum], Original ATen: [aten.bmm]
        extern_kernels.bmm(primals_8, reinterpret_tensor(buf5, (4, 4, 4), (1, 4, 16), 0), out=buf6)
        buf7 = 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_3.run(buf3, buf6, buf4, buf7, 4, 16, grid=grid(4, 16), stream=stream0)
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [rwkv_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf7, (16, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf8)
        del primals_10
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_2], Original ATen: [aten.mul]
        triton_poi_fused_mul_4.run(buf8, primals_11, buf9, 64, grid=grid(64), stream=stream0)
    return (buf9, primals_11, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf1, buf2, buf3, buf4, buf6, buf7, buf8, primals_9, reinterpret_tensor(primals_8, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf5, (4, 4, 4), (1, 16, 4), 0), )


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


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

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


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, :]


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


def get_init_inputs():
    return [[], {'config': _mock_config(n_attn=4, n_head=4, ctx_len=4,
        n_embd=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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(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_1(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_2(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_3(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_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
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, tmp2, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (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), (16, 4, 1))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf0, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_4
        del primals_5
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf0, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_6
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_per_fused_clamp_cumsum_exp_1[grid(16)](buf1, buf4, 16, 4,
            XBLOCK=8, num_warps=2, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clamp_exp_mul_2[grid(64)](buf1, buf2, buf5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_8, reinterpret_tensor(buf5, (4, 4, 4), (
            1, 4, 16), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_div_mul_sigmoid_3[grid(4, 16)](buf3, buf6, buf4,
            buf7, 4, 16, XBLOCK=16, YBLOCK=2, num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf7, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf8)
        del primals_10
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_mul_4[grid(64)](buf8, primals_11, buf9, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
    return buf9, primals_11, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
        ), buf1, buf2, buf3, buf4, buf6, buf7, buf8, primals_9, reinterpret_tensor(
        primals_8, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf5, (4, 
        4, 4), (1, 16, 4), 0)


class RWKV_TimeMixNew(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, input_0):
        primals_1 = self.time_ww
        primals_11 = self.time_gamma
        primals_2 = self.key.weight
        primals_3 = self.key.bias
        primals_4 = self.value.weight
        primals_5 = self.value.bias
        primals_6 = self.receptance.weight
        primals_7 = self.receptance.bias
        primals_9 = self.output.weight
        primals_10 = self.output.bias
        primals_8 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
