# 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/bt/cbta3cyp725e55zoqfanbs6mpgotirenv6gkyacrb7nurqpqh4mu.py
# Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   add => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4], 1), kwargs = {dtype: torch.int64, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_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: '*i64', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_0(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.full([1], 1, tl.int64)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vc/cvcqz4yv2btxlyovncok4t3xycchzzcwnsgq5cguozxs2i6crn6d.py
# Topologically Sorted Source Nodes: [floordiv], Original ATen: [aten.floor_divide]
# Source node to ATen node mapping:
#   floordiv => full_default_1
# Graph fragment:
#   %full_default_1 : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4], 0), kwargs = {dtype: torch.int64, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_floor_divide_1 = async_compile.triton('triton_poi_fused_floor_divide_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: '*i64', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_floor_divide_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_floor_divide_1(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.full([1], 0, tl.int64)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wa/cwavcpy3ybzmlh6wf3bzvj7crv6tqwzuhggjotqslbw4g6lnqoc6.py
# Topologically Sorted Source Nodes: [direction_embeddings, length_embeddings, add_1, embeddings, position_embeddings, embeddings_1], Original ATen: [aten.embedding, aten.add]
# Source node to ATen node mapping:
#   add_1 => add_1
#   direction_embeddings => embedding
#   embeddings => add_2
#   embeddings_1 => add_3
#   length_embeddings => embedding_1
#   position_embeddings => embedding_2
# Graph fragment:
#   %embedding : [num_users=1] = call_function[target=torch.ops.aten.embedding.default](args = (%primals_4, %full_default), kwargs = {})
#   %embedding_1 : [num_users=1] = call_function[target=torch.ops.aten.embedding.default](args = (%primals_5, %full_default_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %embedding), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %embedding_1), kwargs = {})
#   %embedding_2 : [num_users=1] = call_function[target=torch.ops.aten.embedding.default](args = (%primals_6, %expand), kwargs = {})
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %embedding_2), kwargs = {})
triton_poi_fused_add_embedding_2 = async_compile.triton('triton_poi_fused_add_embedding_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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_embedding_2', 'mutated_arg_names': ['in_out_ptr0'], '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_embedding_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x5 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x5), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tl.store(in_out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vp/cvp2dnzklxlaylmiwf7hk7no2nhcy225mtekakdvnvn3bauwxx44.py
# Topologically Sorted Source Nodes: [embeddings_2], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   embeddings_2 => add_4, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_3, [3]), kwargs = {correction: 0, keepdim: True})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1.0), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
triton_poi_fused_native_layer_norm_3 = async_compile.triton('triton_poi_fused_native_layer_norm_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=[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_native_layer_norm_3', '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_3(in_ptr0, out_ptr0, out_ptr1, 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 + (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 = 1.0
    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/yd/cydu4qjvgfymhhpyhhqxb3snu5mgfygbkhn2nemqx5nozidar6fc.py
# Topologically Sorted Source Nodes: [embeddings_2], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   embeddings_2 => add_4, add_5, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_3, [3]), kwargs = {correction: 0, keepdim: True})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1.0), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_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_7), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_8), kwargs = {})
triton_poi_fused_native_layer_norm_4 = async_compile.triton('triton_poi_fused_native_layer_norm_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=[256], 
    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_4', '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_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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')


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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (3, 4), (4, 1))
    assert_size_stride(primals_5, (2, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_0.run(buf1, 64, grid=grid(64), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [floordiv], Original ATen: [aten.floor_divide]
        triton_poi_fused_floor_divide_1.run(buf2, 64, grid=grid(64), stream=stream0)
        buf3 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [direction_embeddings, length_embeddings, add_1, embeddings, position_embeddings, embeddings_1], Original ATen: [aten.embedding, aten.add]
        triton_poi_fused_add_embedding_2.run(buf3, primals_3, primals_4, primals_5, primals_6, 256, grid=grid(256), stream=stream0)
        del primals_3
        del primals_4
        del primals_5
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [embeddings_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_3.run(buf3, buf4, buf5, 64, grid=grid(64), stream=stream0)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embeddings_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_4.run(buf3, buf4, buf5, primals_7, primals_8, buf6, 256, grid=grid(256), stream=stream0)
        del buf4
        del buf5
        del primals_8
    return (buf6, primals_7, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf1, buf2, buf3, )


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, 4), (64, 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((3, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2, 4), (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, ), (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])
    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 typing
from torch import nn


def create_sinusoidal_embeddings(n_pos, dim, out):
    out.requires_grad = False
    positions = torch.arange(0, n_pos)[:, None]
    dimensions = torch.arange(0, dim)
    position_enc = positions / torch.pow(10000, 2 * (dimensions // 2) / dim)
    out[:, 0::2] = torch.sin(position_enc[:, 0::2])
    out[:, 1::2] = torch.cos(position_enc[:, 1::2])


class GeCEmbeddings(nn.Module):
    """Construct the embeddings from gene, (strand and spacing embeddings).
    """

    def __init__(self, config: 'BioConfig', position_embeddings: 'bool'=True):
        super().__init__()
        self.generep_embeddings = nn.Linear(config.input_rep_size, config.
            hidden_size)
        if position_embeddings:
            self.position_embeddings: 'nn.Embedding' = nn.Embedding(config.
                max_position_embeddings, config.hidden_size)
            if config.sinusoidal_pos_embds:
                create_sinusoidal_embeddings(n_pos=config.
                    max_position_embeddings, dim=config.hidden_size, out=
                    self.position_embeddings.weight)
        self.direction_embeddings: 'nn.Embedding' = nn.Embedding(3, config.
            hidden_size)
        self.length_embeddings: 'nn.Embedding' = nn.Embedding(config.
            gene_max_length // config.gene_length_bin_size + 1, config.
            hidden_size)
        self.gene_length_bin_size = config.gene_length_bin_size
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.
            layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, gene_reps: 'torch.Tensor', strands:
        'typing.Optional[torch.Tensor]'=None, lengths:
        'typing.Optional[torch.Tensor]'=None, **kwargs) ->torch.Tensor:
        if strands is None:
            strands = torch.zeros_like(gene_reps[:, :, 0], dtype=torch.long)
        else:
            strands = strands.long()
        if lengths is None:
            lengths = torch.ones_like(gene_reps[:, :, 0], dtype=torch.long)
        else:
            lengths = strands.long()
        generep_embeddings = self.generep_embeddings(gene_reps)
        direction_embeddings = self.direction_embeddings(strands + 1)
        length_embeddings = self.length_embeddings(torch.clamp(lengths, 1,
            self.length_embeddings.num_embeddings) // self.gene_length_bin_size
            )
        embeddings = (generep_embeddings + direction_embeddings +
            length_embeddings)
        if hasattr(self, 'position_embeddings'):
            position_ids = torch.arange(gene_reps.size()[1], dtype=torch.
                long, device=gene_reps.device)
            position_ids = position_ids.unsqueeze(0).expand(gene_reps.shape
                [:-1])
            position_embeddings = self.position_embeddings(position_ids)
            embeddings = embeddings + position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


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


def get_init_inputs():
    return [[], {'config': _mock_config(input_rep_size=4, hidden_size=4,
        max_position_embeddings=4, sinusoidal_pos_embds=4, gene_max_length=
        4, gene_length_bin_size=4, layer_norm_eps=1, hidden_dropout_prob=0.5)}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_0(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.full([1], 1, tl.int64)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_floor_divide_1(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.full([1], 0, tl.int64)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_embedding_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x5 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x5, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tl.store(in_out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_3(in_ptr0, out_ptr0, out_ptr1,
    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 + 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 = 1.0
    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_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (3, 4), (4, 1))
    assert_size_stride(primals_5, (2, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(64)](buf1, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        triton_poi_fused_floor_divide_1[grid(64)](buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_add_embedding_2[grid(256)](buf3, primals_3,
            primals_4, primals_5, primals_6, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_3
        del primals_4
        del primals_5
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused_native_layer_norm_3[grid(64)](buf3, buf4, buf5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_4[grid(256)](buf3, buf4, buf5,
            primals_7, primals_8, buf6, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf4
        del buf5
        del primals_8
    return buf6, primals_7, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf1, buf2, buf3


def create_sinusoidal_embeddings(n_pos, dim, out):
    out.requires_grad = False
    positions = torch.arange(0, n_pos)[:, None]
    dimensions = torch.arange(0, dim)
    position_enc = positions / torch.pow(10000, 2 * (dimensions // 2) / dim)
    out[:, 0::2] = torch.sin(position_enc[:, 0::2])
    out[:, 1::2] = torch.cos(position_enc[:, 1::2])


class GeCEmbeddingsNew(nn.Module):
    """Construct the embeddings from gene, (strand and spacing embeddings).
    """

    def __init__(self, config: 'BioConfig', position_embeddings: 'bool'=True):
        super().__init__()
        self.generep_embeddings = nn.Linear(config.input_rep_size, config.
            hidden_size)
        if position_embeddings:
            self.position_embeddings: 'nn.Embedding' = nn.Embedding(config.
                max_position_embeddings, config.hidden_size)
            if config.sinusoidal_pos_embds:
                create_sinusoidal_embeddings(n_pos=config.
                    max_position_embeddings, dim=config.hidden_size, out=
                    self.position_embeddings.weight)
        self.direction_embeddings: 'nn.Embedding' = nn.Embedding(3, config.
            hidden_size)
        self.length_embeddings: 'nn.Embedding' = nn.Embedding(config.
            gene_max_length // config.gene_length_bin_size + 1, config.
            hidden_size)
        self.gene_length_bin_size = config.gene_length_bin_size
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.
            layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, input_0):
        primals_2 = self.generep_embeddings.weight
        primals_3 = self.generep_embeddings.bias
        primals_6 = self.position_embeddings.weight
        primals_4 = self.direction_embeddings.weight
        primals_5 = self.length_embeddings.weight
        primals_7 = self.LayerNorm.weight
        primals_8 = self.LayerNorm.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
